diff --git a/docs/_downloads/066a96b3e7333f9a789eb7e687c45875/plot_histogram_2d.py b/docs/_downloads/066a96b3e7333f9a789eb7e687c45875/plot_histogram_2d.py
new file mode 100644
index 00000000..3cc6baeb
--- /dev/null
+++ b/docs/_downloads/066a96b3e7333f9a789eb7e687c45875/plot_histogram_2d.py
@@ -0,0 +1,305 @@
+"""
+Histogram 2D
+------------
+
+This 2D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.
+
+"""
+
+#%%
+import h5py
+import geobipy
+from geobipy import StatArray
+from geobipy import Histogram
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from geobipy import RectilinearMesh2D
+import numpy as np
+
+
+#%%
+# Create some histogram bins in x and y
+x = StatArray(np.linspace(-4.0, 4.0, 100), 'Variable 1')
+y = StatArray(np.linspace(-4.0, 4.0, 105), 'Variable 2')
+
+mesh = RectilinearMesh2D(x_edges=x, y_edges=y)
+#%%
+# Instantiate
+H = Histogram(mesh)
+
+#%%
+# Generate some random numbers
+a = np.random.randn(1000000)
+b = np.random.randn(1000000)
+
+#%%
+# Update the histogram counts
+H.update(a, b)
+
+#%%
+plt.figure()
+plt.subplot(131)
+plt.title("2D Histogram")
+_ = H.plot(cmap='gray_r')
+plt.subplot(132)
+H.pdf.plot(cmap='gray_r')
+plt.subplot(133)
+H.pmf.plot(cmap='gray_r')
+
+
+plt.figure()
+plt.subplot(131)
+H.cdf(axis=0).plot()
+plt.subplot(132)
+H.cdf(axis=1).plot()
+plt.subplot(133)
+H.cdf().plot()
+
+#%%
+# We can overlay the histogram with its credible intervals
+plt.figure()
+plt.title("90% credible intervals overlain")
+H.pcolor(cmap='gray_r')
+H.plotCredibleIntervals(axis=0, percent=95.0)
+_ = H.plotCredibleIntervals(axis=1, percent=95.0)
+
+#%%
+# Generate marginal histograms along an axis
+h1 = H.marginalize(axis=0)
+h2 = H.marginalize(axis=1)
+
+#%%
+# Note that the names of the variables are automatically displayed
+plt.figure()
+plt.suptitle("Marginals along each axis")
+plt.subplot(121)
+h1.plot()
+plt.subplot(122)
+_ = h2.plot()
+
+#%%
+# Create a combination plot with marginal histograms.
+# sphinx_gallery_thumbnail_number = 3
+plt.figure()
+gs = gridspec.GridSpec(5, 5)
+gs.update(wspace=0.3, hspace=0.3)
+ax = [plt.subplot(gs[1:, :4])]
+H.pcolor(colorbar = False)
+
+ax.append(plt.subplot(gs[:1, :4]))
+h = H.marginalize(axis=0).plot()
+plt.xlabel(''); plt.ylabel('')
+plt.xticks([]); plt.yticks([])
+ax[-1].spines["left"].set_visible(False)
+
+ax.append(plt.subplot(gs[1:, 4:]))
+h = H.marginalize(axis=1).plot(transpose=True)
+plt.ylabel(''); plt.xlabel('')
+plt.yticks([]); plt.xticks([])
+ax[-1].spines["bottom"].set_visible(False)
+
+#%%
+# Take the mean or median estimates from the histogram
+mean = H.mean()
+median = H.median()
+
+#%%
+plt.figure(figsize=(9.5, 5))
+plt.suptitle("Mean, median, and credible interval overlain")
+ax = plt.subplot(121)
+H.pcolor(cmap='gray_r', colorbar=False)
+H.plotCredibleIntervals(axis=0)
+H.plotMedian(axis=0, color='g')
+H.plotMean(axis=0, color='y')
+plt.legend()
+
+plt.subplot(122, sharex=ax, sharey=ax)
+H.pcolor(cmap='gray_r', colorbar=False)
+H.plotCredibleIntervals(axis=1)
+H.plotMedian(axis=1, color='g')
+H.plotMean(axis=1, color='y')
+plt.legend()
+
+#%%
+# Get the range between credible intervals
+H.credible_range(percent=95.0)
+
+#%%
+# We can map the credible range to an opacity or transparency
+H.opacity()
+H.transparency()
+
+# H.animate(0, 'test.mp4')
+
+import h5py
+with h5py.File('h2d.h5', 'w') as f:
+    H.toHdf(f, 'h2d')
+
+with h5py.File('h2d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h2d'])
+
+plt.close('all')
+
+x = StatArray(5.0 + np.linspace(-4.0, 4.0, 100), 'Variable 1')
+y = StatArray(10.0 + np.linspace(-4.0, 4.0, 105), 'Variable 2')
+
+mesh = RectilinearMesh2D(x_edges=x, x_relative_to=5.0, y_edges=y, y_relative_to=10.0)
+#%%
+# Instantiate
+H = Histogram(mesh)
+
+#%%
+# Generate some random numbers
+a = np.random.randn(1000000) + 5.0
+b = np.random.randn(1000000) + 10.0
+
+#%%
+# Update the histogram counts
+H.update(a, b)
+
+#%%
+plt.figure()
+plt.subplot(131)
+plt.title("2D Histogram")
+_ = H.plot(cmap='gray_r')
+plt.subplot(132)
+H.pdf.plot(cmap='gray_r')
+plt.subplot(133)
+H.pmf.plot(cmap='gray_r')
+
+plt.figure()
+plt.subplot(131)
+H.cdf(axis=0).plot()
+plt.subplot(132)
+H.cdf(axis=1).plot()
+plt.subplot(133)
+H.cdf().plot()
+
+#%%
+# We can overlay the histogram with its credible intervals
+plt.figure()
+plt.title("90% credible intervals overlain")
+H.pcolor(cmap='gray_r')
+H.plotCredibleIntervals(axis=0, percent=95.0)
+_ = H.plotCredibleIntervals(axis=1, percent=95.0)
+
+# Generate marginal histograms along an axis
+h1 = H.marginalize(axis=0)
+h2 = H.marginalize(axis=1)
+
+#%%
+# Note that the names of the variables are automatically displayed
+plt.figure()
+plt.suptitle("Marginals along each axis")
+plt.subplot(121)
+h1.plot()
+plt.subplot(122)
+_ = h2.plot()
+
+#%%
+# Create a combination plot with marginal histograms.
+# sphinx_gallery_thumbnail_number = 3
+plt.figure()
+gs = gridspec.GridSpec(5, 5)
+gs.update(wspace=0.3, hspace=0.3)
+ax = [plt.subplot(gs[1:, :4])]
+H.pcolor(colorbar = False)
+
+ax.append(plt.subplot(gs[:1, :4]))
+h = H.marginalize(axis=0).plot()
+plt.xlabel(''); plt.ylabel('')
+plt.xticks([]); plt.yticks([])
+ax[-1].spines["left"].set_visible(False)
+
+ax.append(plt.subplot(gs[1:, 4:]))
+h = H.marginalize(axis=1).plot(transpose=True)
+plt.ylabel(''); plt.xlabel('')
+plt.yticks([]); plt.xticks([])
+ax[-1].spines["bottom"].set_visible(False)
+
+#%%
+# Take the mean or median estimates from the histogram
+mean = H.mean()
+median = H.median()
+
+#%%
+plt.figure(figsize=(9.5, 5))
+plt.suptitle("Mean, median, and credible interval overlain")
+ax = plt.subplot(121)
+H.pcolor(cmap='gray_r', colorbar=False)
+H.plotCredibleIntervals(axis=0)
+H.plotMedian(axis=0, color='g')
+H.plotMean(axis=0, color='y')
+plt.legend()
+
+plt.subplot(122, sharex=ax, sharey=ax)
+H.pcolor(cmap='gray_r', colorbar=False)
+H.plotCredibleIntervals(axis=1)
+H.plotMedian(axis=1, color='g')
+H.plotMean(axis=1, color='y')
+plt.legend()
+
+#%%
+# Get the range between credible intervals
+H.credible_range(percent=95.0)
+
+#%%
+# We can map the credible range to an opacity or transparency
+H.opacity()
+H.transparency()
+
+# # H.animate(0, 'test.mp4')
+
+with h5py.File('h2d.h5', 'w') as f:
+    H.toHdf(f, 'h2d')
+
+with h5py.File('h2d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h2d'])
+
+plt.figure(figsize=(9.5, 5))
+plt.suptitle("Mean, median, and credible interval overlain")
+ax = plt.subplot(121)
+H1.pcolor(cmap='gray_r', colorbar=False)
+H1.plotCredibleIntervals(axis=0)
+H1.plotMedian(axis=0, color='g')
+H1.plotMean(axis=0, color='y')
+plt.legend()
+
+plt.subplot(122, sharex=ax, sharey=ax)
+H1.pcolor(cmap='gray_r', colorbar=False)
+H1.plotCredibleIntervals(axis=1)
+H1.plotMedian(axis=1, color='g')
+H1.plotMean(axis=1, color='y')
+plt.legend()
+
+with h5py.File('h2d.h5', 'w') as f:
+    H.createHdf(f, 'h2d', add_axis=StatArray(np.arange(3.0), name='Easting', units="m"))
+    for i in range(3):
+        H.writeHdf(f, 'h2d', index=i)
+
+with h5py.File('h2d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h2d'], index=0)
+
+plt.figure(figsize=(9.5, 5))
+plt.suptitle("Mean, median, and credible interval overlain")
+ax = plt.subplot(121)
+H1.pcolor(cmap='gray_r', colorbar=False)
+H1.plotCredibleIntervals(axis=0)
+H1.plotMedian(axis=0, color='g')
+H1.plotMean(axis=0, color='y')
+plt.legend()
+
+plt.subplot(122, sharex=ax, sharey=ax)
+H1.pcolor(cmap='gray_r', colorbar=False)
+H1.plotCredibleIntervals(axis=1)
+H1.plotMedian(axis=1, color='g')
+H1.plotMean(axis=1, color='y')
+plt.legend()
+
+with h5py.File('h2d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h2d'])
+
+# H1.pyvista_mesh().save('h3d_read.vtk')
+
+plt.show()
diff --git a/docs/_downloads/09f048ba9debdfb740a9a828a370954b/plot_inference_1d_resolve.py b/docs/_downloads/09f048ba9debdfb740a9a828a370954b/plot_inference_1d_resolve.py
new file mode 100644
index 00000000..50ae8ebc
--- /dev/null
+++ b/docs/_downloads/09f048ba9debdfb740a9a828a370954b/plot_inference_1d_resolve.py
@@ -0,0 +1,89 @@
+"""
+Running GeoBIPy to invert Resolve data
+++++++++++++++++++++++++++++++++++++++
+"""
+
+import os
+import sys
+import pathlib
+from datetime import timedelta
+import time
+import numpy as np
+from geobipy import Inference3D
+from geobipy import user_parameters
+from geobipy import get_prng
+
+def checkCommandArguments():
+    """Check the users command line arguments. """
+    import argparse
+    # warnings.filterwarnings('error')
+
+    Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+    Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem_512', 'tempest', 'resolve']")
+    Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+    return Parser.parse_args()
+
+#%%
+np.random.seed(0)
+
+args = checkCommandArguments()
+sys.path.append(os.getcwd())
+
+models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+data_type = "Resolve"
+model_type = models[args.index]
+
+#%%
+# The directory where HDF files will be stored
+#%%
+file_path = os.path.join(data_type, model_type)
+pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+for filename in os.listdir(file_path):
+    try:
+        if os.path.isfile(file_path) or os.path.islink(file_path):
+            os.unlink(file_path)
+    except Exception as e:
+        print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+output_directory = file_path
+
+data_filename = data_type + '_' + model_type
+
+supplementary = "..//..//supplementary//"
+
+parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+inputFile = pathlib.Path(parameter_file)
+assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+output_directory = pathlib.Path(output_directory)
+assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+print('Using user input file {}'.format(parameter_file))
+print('Output files will be produced at {}'.format(output_directory))
+
+kwargs = user_parameters.read(inputFile)
+
+kwargs['n_markov_chains'] = 5000
+
+kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+kwargs['system_filename'] = supplementary + "//data//" + kwargs['system_filename']
+
+# Everyone needs the system classes read in early.
+data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+# Start keeping track of time.
+t0 = time.time()
+
+seed = 146100583096709124601953385843316024947
+prng = get_prng(seed=seed)
+
+inference3d = Inference3D(data, prng=prng)
+inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+inference3d.infer(index=30, **kwargs)
\ No newline at end of file
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diff --git a/docs/_downloads/2144e9773886d1394652515ba8d8dc41/plot_histogram_1d.zip b/docs/_downloads/2144e9773886d1394652515ba8d8dc41/plot_histogram_1d.zip
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diff --git a/docs/_downloads/2b313676e6ab4e7fef5cb85211b43803/plot_model_3d.py b/docs/_downloads/2b313676e6ab4e7fef5cb85211b43803/plot_model_3d.py
new file mode 100644
index 00000000..0d0aadaa
--- /dev/null
+++ b/docs/_downloads/2b313676e6ab4e7fef5cb85211b43803/plot_model_3d.py
@@ -0,0 +1,171 @@
+"""
+3D Rectilinear Model
+--------------------
+This 3D rectilinear model defines a grid with straight cell boundaries.
+
+"""
+
+#%%
+from geobipy import StatArray
+from geobipy import RectilinearMesh3D
+from geobipy import Model
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+
+
+"""
+3D Rectilinear Mesh
+-------------------
+This 3D rectilinear mesh defines a grid with straight cell boundaries.
+
+"""
+
+#%%
+from geobipy import StatArray
+from geobipy import RectilinearMesh3D
+from geobipy import Model
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+
+
+#%%
+# Specify some cell centres in x and y
+x = StatArray(np.arange(10.0), 'Easting', 'm')
+y = StatArray(np.arange(15.0), 'Northing', 'm')
+z = StatArray(np.arange(20.0), 'Depth', 'm')
+
+mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+values = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Height")
+values = np.repeat(values[:, :, None], mesh.z.nCells, 2)
+
+model = Model(mesh=mesh, values=values)
+
+model1 = model[:5, :5, :5]
+model2 = model[:, :, 5]
+model3 = model[:, 5, :]
+model4 = model[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+model2.pcolor()
+plt.subplot(232)
+model3.pcolor()
+plt.subplot(233)
+model4.pcolor()
+
+#%%
+model2 = model[:, 5, 5]
+model3 = model[5, :, 5]
+model4 = model[5, 5, :]
+
+plt.subplot(234)
+model2.pcolor()
+plt.subplot(235)
+model3.pcolor()
+plt.subplot(236)
+model4.pcolor()
+
+#%%
+with h5py.File('model3d.h5', 'w') as f:
+    model.createHdf(f, 'test')
+    model.writeHdf(f, 'test')
+
+with h5py.File('model3d.h5', 'r') as f:
+    model2 = Model.fromHdf(f['test'])
+
+model.pyvista_mesh().save('model3d.vtk')
+
+
+xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z, z_relative_to=z_re)
+model = Model(mesh=mesh, values=values)
+
+model1 = model[:5, :5, :5]
+model2 = model[:, :, 5]
+model3 = model[:, 5, :]
+model4 = model[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+model2.pcolor()
+plt.subplot(232)
+model3.pcolor()
+plt.subplot(233)
+model4.pcolor()
+
+#%%
+# We can plot the mesh in 3D!
+pv = model.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+model.pyvista_mesh().save('model3d_re1.vtk')
+
+
+x_re = StatArray(np.sin(np.repeat(mesh.y.centres[:, None], mesh.z.nCells, 1)), "x_re")
+mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, z_edges=z, z_relative_to=z_re)
+model = Model(mesh=mesh, values=values)
+
+model1 = model[:5, :5, :5]
+model2 = model[:, :, 5]
+model3 = model[:, 5, :]
+model4 = model[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+model2.pcolor()
+plt.subplot(232)
+model3.pcolor()
+plt.subplot(233)
+model4.pcolor()
+
+#%%
+# We can plot the mesh in 3D!
+pv = model.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+model.pyvista_mesh().save('model3d_re2.vtk')
+
+
+xx, yy = np.meshgrid(mesh.z.centres, mesh.x.centres)
+y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+model = Model(mesh=mesh, values=values)
+
+model1 = model[:5, :5, :5]
+model2 = model[:, :, 5]
+model3 = model[:, 5, :]
+model4 = model[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+model2.pcolor()
+plt.subplot(232)
+model3.pcolor()
+plt.subplot(233)
+model4.pcolor()
+
+#%%
+# We can plot the mesh in 3D!
+pv = model.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+model.pyvista_mesh().save('model3d_re3.vtk')
+
+# with h5py.File('mesh3d.h5', 'w') as f:
+#     mesh.toHdf(f, 'test')
+
+# with h5py.File('mesh3d.h5', 'r') as f:
+#     mesh2 = RectilinearMesh3D.fromHdf(f['test'])
+
+# mesh2.pyvista_mesh().save('mesh3d_read.vtk')
+
+plt.show()
diff --git a/docs/_downloads/2dd62cc642303de6b0d2af1a789eccaa/plot_skytem_datapoint.py b/docs/_downloads/2dd62cc642303de6b0d2af1a789eccaa/plot_skytem_datapoint.py
new file mode 100644
index 00000000..e2412647
--- /dev/null
+++ b/docs/_downloads/2dd62cc642303de6b0d2af1a789eccaa/plot_skytem_datapoint.py
@@ -0,0 +1,244 @@
+"""
+Skytem Datapoint Class
+----------------------
+"""
+
+#%%
+# Credits:
+# We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+# https://github.com/GeoscienceAustralia/ga-aem
+#
+# For ground-based time domain data, we are using Dieter Werthmuller's python package Empymod
+# https://empymod.github.io/
+#
+# Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy
+
+#%%
+from os.path import join
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+from geobipy import Waveform
+from geobipy import SquareLoop, CircularLoop
+from geobipy import butterworth
+from geobipy import TdemSystem
+from geobipy import TdemData
+from geobipy import TdemDataPoint
+from geobipy import RectilinearMesh1D
+from geobipy import Model
+from geobipy import StatArray
+from geobipy import Distribution
+
+dataFolder = "..//..//supplementary//data//"
+
+# Obtaining a datapoint from a dataset
+# ++++++++++++++++++++++++++++++++++++
+# More often than not, our observed data is stored in a file on disk.
+# We can read in a dataset and pull datapoints from it.
+#
+# For more information about the time domain data set, see :ref:`Time domain dataset`
+
+# The data file name
+dataFile=dataFolder + 'skytem_saline_clay.csv'
+# The EM system file name
+systemFile=[dataFolder + 'SkytemHM.stm', dataFolder + 'SkytemLM.stm']
+
+#%%
+# Initialize and read an EM data set
+# Prepare the dataset so that we can read a point at a time.
+Dataset = TdemData._initialize_sequential_reading(dataFile, systemFile)
+# Get a datapoint from the file.
+tdp = Dataset._read_record()
+
+Dataset._file.close()
+
+#%%
+# Using a time domain datapoint
+# +++++++++++++++++++++++++++++
+
+#%%
+# We can define a 1D layered earth model, and use it to predict some data
+par = StatArray(np.r_[500.0, 20.0], "Conductivity", "$\frac{S}{m}$")
+mod = Model(RectilinearMesh1D(edges=np.r_[0, 75.0, np.inf]), values=par)
+
+#%%
+# Forward model the data
+tdp.forward(mod)
+
+#%%
+plt.figure()
+plt.subplot(121)
+_ = mod.pcolor()
+plt.subplot(122)
+_ = tdp.plot()
+_ = tdp.plot_predicted()
+plt.tight_layout()
+
+#%%
+plt.figure()
+tdp.plotDataResidual(yscale='log', xscale='log')
+plt.title('new')
+
+#%%
+# Compute the sensitivity matrix for a given model
+J = tdp.sensitivity(mod)
+plt.figure()
+_ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+#%%
+# Attaching statistical descriptors to the skytem datapoint
+# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+# Set values of relative and additive error for both systems.
+tdp.relative_error = np.r_[0.05, 0.05]
+tdp.additive_error = np.r_[1e-14, 1e-13]
+# Define a multivariate normal distribution as the prior on the predicted data.
+data_prior = Distribution('MvNormal', tdp.data[tdp.active], tdp.std[tdp.active]**2.0, prng=prng)
+
+tdp.set_priors(data_prior=data_prior)
+
+
+#%%
+# This allows us to evaluate the likelihood of the predicted data
+print(tdp.likelihood(log=True))
+# Or the misfit
+print(tdp.data_misfit())
+
+#%%
+# Plot the misfits for a range of half space conductivities
+plt.figure()
+_ = tdp.plot_halfspace_responses(-6.0, 4.0, 200)
+plt.title("Halfspace responses")
+
+#%%
+# We can perform a quick search for the best fitting half space
+halfspace = tdp.find_best_halfspace()
+
+print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+plt.figure()
+_ = tdp.plot()
+_ = tdp.plot_predicted()
+
+#%%
+# Compute the misfit between observed and predicted data
+print(tdp.data_misfit())
+
+#%%
+# We can attach priors to the height of the datapoint,
+# the relative error multiplier, and the additive error noise floor
+
+# Define the distributions used as priors.
+z_prior = Distribution('Uniform', min=np.float64(tdp.z) - 2.0, max=np.float64(tdp.z) + 2.0, prng=prng)
+relativePrior = Distribution('Uniform', min=np.r_[0.01, 0.01], max=np.r_[0.5, 0.5], prng=prng)
+additivePrior = Distribution('Uniform', min=np.r_[1e-16, 1e-16], max=np.r_[1e-10, 1e-10], log=True, prng=prng)
+tdp.set_priors(relative_error_prior=relativePrior, additive_error_prior=additivePrior, z_prior=z_prior, prng=prng)
+
+#%%
+# In order to perturb our solvable parameters, we need to attach proposal distributions
+z_proposal = Distribution('Normal', mean=tdp.z, variance = 0.01, prng=prng)
+relativeProposal = Distribution('MvNormal', mean=tdp.relative_error, variance=2.5e-7, prng=prng)
+additiveProposal = Distribution('MvLogNormal', mean=tdp.additive_error, variance=2.5e-3, linearSpace=True, prng=prng)
+tdp.set_proposals(relativeProposal, additiveProposal, z_proposal=z_proposal, prng=prng)
+
+#%%
+# With priorss set we can auto generate the posteriors
+tdp.set_posteriors()
+
+#%%
+# Perturb the datapoint and record the perturbations
+# Note we are not using the priors to accept or reject perturbations.
+for i in range(10):
+    tdp.perturb()
+    tdp.update_posteriors()
+
+
+#%%
+# Plot the posterior distributions
+plt.figure()
+tdp.plot_posteriors(overlay=tdp)
+
+plt.show()
+
+#%%
+# File Format for a time domain datapoint
+# +++++++++++++++++++++++++++++++++++++++
+# Here we describe the file format for a time domain datapoint.
+#
+# For individual datapoints we are using the AarhusInv data format.
+#
+# Here we take the description for the AarhusInv TEM data file, modified to reflect what we can
+# currently handle in GeoBIPy.
+#
+# Line 1 :: string
+#   User-defined label describing the TEM datapoint.
+#   This line must contain the following, separated by semicolons.
+#   XUTM=
+#   YUTM=
+#   Elevation=
+#   StationNumber=
+#   LineNumber=
+#   Current=
+#
+# Line 2 :: first integer, sourceType
+#   7 = Rectangular loop source parallel to the x - y plane
+# Line 2 :: second integer, polarization
+#   3 = Vertical magnetic field
+#
+# Line 3 :: 6 floats, transmitter and receiver offsets relative to X/Y UTM location.
+#   If sourceType = 7, Position of the center loop sounding.
+#
+# Line 4 :: Transmitter loop dimensions
+#   If sourceType = 7, 2 floats.  Loop side length in the x and y directions
+#
+# Line 5 :: Fixed
+#   3 3 3
+#
+# Line 6 :: first integer, transmitter waveform type. Fixed
+#   3 = User defined waveform.
+#
+# Line 6 :: second integer, number of transmitter waveforms. Fixed
+#   1
+#
+# Line 7 :: transmitter waveform definition
+#   A user-defined waveform with piecewise linear segments.
+#   A full transmitter waveform definition consists of a number of linear segments
+#   This line contains an integer as the first entry, which specifies the number of
+#   segments, followed by each segment with 4 floats each. The 4 floats per segment
+#   are the start and end times, and start and end amplitudes of the waveform. e.g.
+#   3  -8.333e-03 -8.033e-03 0.0 1.0 -8.033e-03 0.0 1.0 1.0 0.0 5.4e-06 1.0 0.0
+#
+# Line 8 :: On time information. Not used but needs specifying.
+#   1 1 1
+#
+# Line 9 :: On time low-pass filters.  Not used but need specifying.
+#   0
+#
+# Line 10 :: On time high-pass filters. Not used but need specifying.
+#   0
+#
+# Line 11 :: Front-gate time. Not used but need specifying.
+#   0.0
+#
+# Line 12 :: first integer, Number of off time filters
+#   Number of filters
+#
+# Line 12 :: second integer, Order of the butterworth filter
+#   1 or 2
+#
+# Line 12 :: cutoff frequencies Hz, one per the number of filters
+#   e.g. 4.5e5
+#
+# Line 13 :: Off time high pass filters.
+#   See Line 12
+#
+# Lines after 13 contain 3 columns that pertain to
+# Measurement Time, Data Value, Estimated Standard Deviation
+#
+# Example data files are contained in
+# `the supplementary folder`_ in this repository
+#
+# .. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
\ No newline at end of file
diff --git a/docs/_downloads/315f11f9fe5f088152f7ee4638bd2209/plot_model_1d.zip b/docs/_downloads/315f11f9fe5f088152f7ee4638bd2209/plot_model_1d.zip
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diff --git a/docs/_downloads/3bf606a1e1bcade69bb1094393e978cd/plot_histogram_3d.py b/docs/_downloads/3bf606a1e1bcade69bb1094393e978cd/plot_histogram_3d.py
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--- /dev/null
+++ b/docs/_downloads/3bf606a1e1bcade69bb1094393e978cd/plot_histogram_3d.py
@@ -0,0 +1,157 @@
+"""
+Histogram 3D
+------------
+
+This 3D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.
+
+"""
+
+#%%
+import geobipy
+from geobipy import StatArray
+from geobipy import Histogram
+import matplotlib.pyplot as plt
+from geobipy import RectilinearMesh3D
+import numpy as np
+
+
+#%%
+# Create some histogram bins in x and y
+x = StatArray(np.linspace(-4.0, 4.0, 11), 'Variable 1')
+y = StatArray(np.linspace(-4.0, 4.0, 21), 'Variable 2')
+z = StatArray(np.linspace(-4.0, 4.0, 31), 'Variable 3')
+
+mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+#%%
+# Instantiate
+H = Histogram(mesh=mesh)
+
+#%%
+# Generate some random numbers
+a = np.random.randn(100000)
+b = np.random.randn(100000)
+c = np.random.randn(100000)
+# x = np.asarray([a, b, c])
+
+
+#%%
+# Update the histogram counts
+H.update(a, b, c)
+
+#%%
+plt.figure()
+plt.suptitle("Slice half way along each dimension")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    s = [5 if i  == axis else np.s_[:] for i in range(3)]
+    _ = H[tuple(s)].pcolor(cmap='gray_r')
+
+#%%
+# Generate marginal histograms along an axis
+plt.figure()
+plt.suptitle("Marginals along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.marginalize(axis=axis).plot()
+
+
+#%%
+# Take the mean estimate from the histogram
+plt.figure()
+plt.suptitle("Mean along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.mean(axis=axis).pcolor()
+
+#%%
+# Take the median estimate from the histogram
+plt.figure()
+plt.suptitle("Median along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.median(axis=axis).pcolor()
+
+# #%%
+# # We can map the credible range to an opacity or transparency
+# H.opacity()
+# H.transparency()
+
+H.animate(0, 'test.mp4')
+
+H.to_vtk('h3d.vtk')
+
+
+
+
+# Create some histogram bins in x and y
+xx, yy = np.meshgrid(mesh.z.centres, mesh.y.centres)
+x_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "x_re")
+
+xx, yy = np.meshgrid(mesh.z.centres, mesh.x.centres)
+y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+
+mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+
+#%%
+# Instantiate
+H = Histogram(mesh=mesh)
+
+#%%
+# Generate some random numbers
+a = np.random.randn(100000)
+b = np.random.randn(100000)
+c = np.random.randn(100000)
+# x = np.asarray([a, b, c])
+
+#%%
+# Update the histogram counts
+H.update(a, b, c)
+
+#%%
+plt.figure()
+plt.suptitle("Slice half way along each dimension")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    s = [5 if i  == axis else np.s_[:] for i in range(3)]
+    _ = H[tuple(s)].pcolor(cmap='gray_r')
+
+#%%
+# Generate marginal histograms along an axis
+plt.figure()
+plt.suptitle("Marginals along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.marginalize(axis=axis).plot()
+
+
+#%%
+# Take the mean estimate from the histogram
+plt.figure()
+plt.suptitle("Mean along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.mean(axis=axis).pcolor()
+
+#%%
+# Take the median estimate from the histogram
+plt.figure()
+plt.suptitle("Median along each axis")
+for axis in range(3):
+    plt.subplot(1, 3, axis+1)
+    _ = H.median(axis=axis).pcolor()
+
+# #%%
+# # We can map the credible range to an opacity or transparency
+# H.opacity()
+# H.transparency()
+
+H.animate(0, 'test.mp4')
+
+plt.show()
+
+# H.to_vtk('h3d.vtk')
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diff --git a/docs/_downloads/4299735e0ab2a252322239b0f8491c1a/plot_DataArray.zip b/docs/_downloads/4299735e0ab2a252322239b0f8491c1a/plot_DataArray.zip
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diff --git a/docs/_downloads/439c06574d36795305525eb492c7861b/plot_tempest_datapoint.py b/docs/_downloads/439c06574d36795305525eb492c7861b/plot_tempest_datapoint.py
new file mode 100644
index 00000000..95b559c4
--- /dev/null
+++ b/docs/_downloads/439c06574d36795305525eb492c7861b/plot_tempest_datapoint.py
@@ -0,0 +1,184 @@
+"""
+Tempest Datapoint Class
+-----------------------
+"""
+
+#%%
+# Credits:
+# We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+# https://github.com/GeoscienceAustralia/ga-aem
+#
+# For ground-based time domain data, we are using Dieter Werthmuller's python package Empymod
+# https://empymod.github.io/
+#
+# Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy
+
+#%%
+from os.path import join
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+from geobipy import TempestData
+# from geobipy import TemDataPoint
+from geobipy import RectilinearMesh1D
+from geobipy import Model
+from geobipy import StatArray
+from geobipy import Distribution
+from geobipy import get_prng
+
+dataFolder = "..//..//supplementary//data//"
+# dataFolder = "source//examples//supplementary//Data"
+
+# Obtaining a tempest datapoint from a dataset
+# ++++++++++++++++++++++++++++++++++++++++++++
+# More often than not, our observed data is stored in a file on disk.
+# We can read in a dataset and pull datapoints from it.
+#
+# For more information about the time domain data set, see :ref:`Time domain dataset`
+
+# The data file name
+dataFile = dataFolder + 'tempest_saline_clay.csv'
+# The EM system file name
+systemFile = dataFolder + 'Tempest.stm'
+
+# Prepare the dataset so that we can read a point at a time.
+Dataset = TempestData._initialize_sequential_reading(dataFile, systemFile)
+# Get a datapoint from the file.
+tdp = Dataset._read_record(0)
+
+plt.figure()
+tdp.plot()
+
+prng = get_prng(seed=146100583096709124601953385843316024947)
+
+#%%
+# Using a tempest domain datapoint
+# ++++++++++++++++++++++++++++++++
+
+#%%
+# We can define a 1D layered earth model, and use it to predict some data
+par = StatArray(np.r_[0.01, 0.1, 1.], "Conductivity", "$\frac{S}{m}$")
+mod = Model(mesh=RectilinearMesh1D(edges=np.r_[0.0, 50.0, 75.0, np.inf]), values=par)
+
+par = StatArray(np.logspace(-3, 3, 30), "Conductivity", "$\frac{S}{m}$")
+e = np.linspace(0, 350, 31); e[-1] = np.inf
+mod = Model(mesh=RectilinearMesh1D(edges=e), values=par)
+
+#%%
+# Forward model the data
+tdp.forward(mod)
+
+print('primary', tdp.primary_field)
+print('sx', tdp.secondary_field[:15])
+print('sz', tdp.secondary_field[15:])
+
+# #%%
+# plt.figure()
+# plt.subplot(121)
+# _ = mod.pcolor(transpose=True)
+# plt.subplot(122)
+# _ = tdp.plot()
+# _ = tdp.plot_predicted()
+# plt.tight_layout()
+# plt.suptitle('Model and response')
+
+# #%%
+# # plt.figure()
+# # tdp.plotDataResidual(xscale='log')
+# # plt.title('data residual')
+
+# #%%
+# # Compute the sensitivity matrix for a given model
+J = tdp.sensitivity(mod)
+# plt.figure()
+# _ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+print('J', J)
+# print('J shape', J.shape)
+# print('sx 0', J[:16, 0])
+
+tdp.fm_dlogc(mod)
+
+print('new primary', tdp.primary_field)
+print('sx', tdp.secondary_field[:15])
+print('sz', tdp.secondary_field[15:])
+
+print('new J', tdp.sensitivity_matrix)
+
+#%%
+# Attaching statistical descriptors to the tempest datapoint
+# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+# Set relative errors for the primary fields, and secondary fields.
+tdp.relative_error = np.r_[0.001, 0.001]
+
+# Set the additive errors for
+tdp.additive_error = np.hstack([[0.011474, 0.012810, 0.008507, 0.005154, 0.004742, 0.004477, 0.004168, 0.003539, 0.003352, 0.003213, 0.003161, 0.003122, 0.002587, 0.002038, 0.002201],
+                                [0.007383, 0.005693, 0.005178, 0.003659, 0.003426, 0.003046, 0.003095, 0.003247, 0.002775, 0.002627, 0.002460, 0.002178, 0.001754, 0.001405, 0.001283]])
+# Define a multivariate log normal distribution as the prior on the predicted data.
+tdp.predictedData.prior = Distribution('MvLogNormal', tdp.data[tdp.active], tdp.std[tdp.active]**2.0, prng=prng)
+
+#%%
+# This allows us to evaluate the likelihood of the predicted data
+print(tdp.likelihood(log=True))
+# Or the misfit
+print(tdp.data_misfit())
+
+#%%
+# Plot the misfits for a range of half space conductivities
+plt.figure()
+plt.subplot(1, 2, 1)
+_ = tdp.plot_halfspace_responses(-6.0, 4.0, 200)
+plt.title("Halfspace responses")
+
+#%%
+# We can perform a quick search for the best fitting half space
+halfspace = tdp.find_best_halfspace()
+print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+plt.subplot(1, 2, 2)
+_ = tdp.plot()
+_ = tdp.plot_predicted()
+
+plt.figure()
+tdp.plot_secondary_field()
+tdp.plot_predicted_secondary_field()
+
+# #%%
+# # We can attach priors to the height of the datapoint,
+# # the relative error multiplier, and the additive error noise floor
+
+# Define the distributions used as priors.
+relative_prior = Distribution('Uniform', min=np.r_[0.01, 0.01], max=np.r_[0.5, 0.5], prng=prng)
+receiver_x_prior = Distribution('Uniform', min=np.float64(tdp.receiver.x) - 1.0, max=np.float64(tdp.receiver.x) + 1.0, prng=prng)
+receiver_z_prior = Distribution('Uniform', min=np.float64(tdp.receiver.z) - 1.0, max=np.float64(tdp.receiver.z) + 1.0, prng=prng)
+receiver_pitch_prior = Distribution('Uniform', min=tdp.receiver.pitch - 5.0, max=tdp.receiver.pitch + 5.0, prng=prng)
+tdp.set_priors(relative_error_prior=relative_prior, receiver_x_prior=receiver_x_prior, receiver_z_prior=receiver_z_prior, receiver_pitch_prior=receiver_pitch_prior, prng=prng)
+
+#%%
+# In order to perturb our solvable parameters, we need to attach proposal distributions
+relative_proposal = Distribution('MvNormal', mean=tdp.relative_error, variance=2.5e-4, prng=prng)
+receiver_x_proposal = Distribution('Normal', mean=tdp.receiver.x, variance = 0.01, prng=prng)
+receiver_z_proposal = Distribution('Normal', mean=tdp.receiver.z, variance = 0.01, prng=prng)
+receiver_pitch_proposal = Distribution('Normal', mean=tdp.receiver.pitch, variance = 0.01, prng=prng)
+tdp.set_proposals(relative_error_proposal=relative_proposal,
+                  receiver_x_proposal=receiver_x_proposal,
+                  receiver_z_proposal=receiver_z_proposal,
+                  receiver_pitch_proposal=receiver_pitch_proposal,
+                  solve_additive_error=True, additive_error_proposal_variance=1e-4, prng=prng)
+
+#%%
+# With priors set we can auto generate the posteriors
+tdp.set_posteriors()
+
+#%%
+# Perturb the datapoint and record the perturbations
+# Note we are not using the priors to accept or reject perturbations.
+for i in range(10):
+    tdp.perturb()
+    tdp.update_posteriors()
+
+plt.show()
\ No newline at end of file
diff --git a/docs/_downloads/48c7722b252e7ddf33d95322115dcaf5/plot_rectilinear_mesh_2d.py b/docs/_downloads/48c7722b252e7ddf33d95322115dcaf5/plot_rectilinear_mesh_2d.py
new file mode 100644
index 00000000..d580939b
--- /dev/null
+++ b/docs/_downloads/48c7722b252e7ddf33d95322115dcaf5/plot_rectilinear_mesh_2d.py
@@ -0,0 +1,210 @@
+"""
+2D Rectilinear Mesh
+-------------------
+This 2D rectilinear mesh defines a grid with straight cell boundaries.
+
+It can be instantiated in two ways.
+
+The first is by providing the cell centres or
+cell edges in two dimensions.
+
+The second embeds the 2D mesh in 3D by providing the cell centres or edges in three dimensions.
+The first two dimensions specify the mesh coordinates in the horiztontal cartesian plane
+while the third discretizes in depth. This allows us to characterize a mesh whose horizontal coordinates
+do not follow a line that is parallel to either the "x" or "y" axis.
+
+"""
+
+#%%
+import h5py
+from geobipy import StatArray
+from geobipy import RectilinearMesh1D, RectilinearMesh2D, RectilinearMesh3D
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+#%%
+# Specify some cell centres in x and y
+x = StatArray(np.arange(10.0), 'Easting', 'm')
+y = StatArray(np.arange(20.0), 'Depth', 'm')
+rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+#%%
+# We can plot the grid lines of the mesh.
+p=0;
+plt.figure(p)
+_  = rm.plot_grid(flipY=True, linewidth=0.5)
+
+# Intersecting multisegment lines with a mesh
+arr = np.zeros(rm.shape)
+i = rm.line_indices([0.0, 3.0, 6.0, 9], [2.0, 6.0, 0.0, 10])
+arr[i[:, 0], i[:, 1]] = 1
+p += 1; plt.figure(p)
+rm.pcolor(values = arr)
+
+#%%
+# We can pcolor the mesh by providing cell values.
+xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+arr = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Values")
+
+rm2, values2 = rm.resample(0.5, 0.5, arr, method='linear')
+
+
+p += 1; plt.figure(p)
+_ = rm.pcolor(arr, grid=True, flipY=True, linewidth=0.5)
+
+
+
+#%%
+# Mask the x axis cells by a distance
+rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(x_distance=0.4, values=arr)
+p += 1; plt.figure(p)
+_ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+#%%
+# Mask the z axis cells by a distance
+rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(y_distance=0.2, values=arr)
+p += 1; plt.figure(p)
+_ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+#%%
+# Mask axes by a distance
+rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(x_distance=0.4, y_distance=0.2, values=arr)
+p += 1; plt.figure(p)
+_ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+x = StatArray(np.arange(10.0), 'Easting', 'm')
+y = StatArray(np.cumsum(np.arange(15.0)), 'Depth', 'm')
+rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+#%%
+# We can perform some interval statistics on the cell values of the mesh
+# Generate some values
+a = np.repeat(np.arange(1.0, np.float64(rm.x.nCells+1))[:, np.newaxis], rm.y.nCells, 1)
+
+#%%
+# Compute the mean over an interval for the mesh.
+rm.intervalStatistic(a, intervals=[6.8, 12.4], axis=0, statistic='mean')
+
+#%%
+# Compute the mean over multiple intervals for the mesh.
+rm.intervalStatistic(a, intervals=[6.8, 12.4, 20.0, 40.0], axis=0, statistic='mean')
+
+#%%
+# We can specify either axis
+rm.intervalStatistic(a, intervals=[2.8, 4.2], axis=1, statistic='mean')
+
+#%%
+rm.intervalStatistic(a, intervals=[2.8, 4.2, 5.1, 8.4], axis=1, statistic='mean')
+
+#%%
+# Slice the 2D mesh to retrieve either a 2D mesh or 1D mesh
+rm2 = rm[:5, :5]
+rm3 = rm[:5, 5]
+rm4 = rm[5, :5]
+
+p += 1; plt.figure(p)
+plt.subplot(131)
+rm2.plot_grid()
+plt.subplot(132)
+rm3.plot_grid()
+plt.subplot(133)
+rm4.plot_grid(transpose=True)
+
+#%%
+# Resample a grid
+values = StatArray(np.random.randn(*rm.shape))
+rm2, values2 = rm.resample(0.5, 0.5, values)
+
+p += 1; plt.figure(p)
+plt.subplot(121)
+rm.pcolor(values)
+plt.subplot(122)
+rm2.pcolor(values2)
+
+#%%
+# Axes in log space
+# +++++++++++++++++
+x = StatArray(np.logspace(-1, 4, 10), 'x')
+y = StatArray(np.logspace(0, 3, 10), 'y')
+rm = RectilinearMesh2D(x_edges=x, x_log=10, y_edges=y, y_log=10)
+
+# We can plot the grid lines of the mesh.
+p += 1; plt.figure(p)
+_  = rm.plot_grid(linewidth=0.5)
+
+#%%
+with h5py.File('rm2d.h5', 'w') as f:
+    rm.toHdf(f, 'test')
+
+with h5py.File('rm2d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['test'])
+
+arr = np.random.randn(*rm.shape)
+p += 1; plt.figure(p)
+plt.subplot(211)
+rm.pcolor(arr)
+plt.subplot(212)
+rm2.pcolor(arr)
+
+#%%
+# relative_to
+# ++++++++++
+x = StatArray(np.arange(10.0), 'Northing', 'm')
+y = StatArray(np.arange(20.0), 'Depth', 'm')
+
+rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+p += 1; plt.figure(p)
+plt.subplot(121)
+_  = rm.plot_grid(linewidth=0.5, flipY=True)
+rm = RectilinearMesh2D(x_centres=x, x_relative_to=0.2*np.random.randn(y.size), y_centres=y, y_relative_to=0.2*np.random.randn(x.size))
+plt.subplot(122)
+_  = rm.plot_grid(linewidth=0.5, flipY=True)
+
+# relative_to single
+with h5py.File('rm2d.h5', 'w') as f:
+    rm.toHdf(f, 'test')
+
+with h5py.File('rm2d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['test'])
+
+arr = np.random.randn(*rm.shape)
+p += 1; plt.figure(p)
+plt.subplot(211)
+rm.pcolor(arr, flipY=True)
+plt.subplot(212)
+rm2.pcolor(arr, flipY=True)
+
+# relative_to expanded
+with h5py.File('rm2d.h5', 'w') as f:
+    rm.createHdf(f, 'test', add_axis=RectilinearMesh1D(centres=StatArray(np.arange(3.0), name='Easting', units="m"), relative_to = 0.2*np.random.randn(x.size, y.size)))
+    for i in range(3):
+        rm.x.relative_to += 0.5
+        rm.y.relative_to += 0.5
+        rm.writeHdf(f, 'test', index=i)
+
+with h5py.File('rm2d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['test'], index=0)
+
+with h5py.File('rm2d.h5', 'r') as f:
+    rm3 = RectilinearMesh3D.fromHdf(f['test'])
+
+p += 1; plt.figure(p)
+plt.subplot(311)
+rm.pcolor(arr, flipY=True)
+plt.subplot(312)
+rm2.pcolor(arr, flipY=True)
+
+p += 1; plt.figure(p)
+arr = np.random.randn(*rm3.shape)
+plt.subplot(311)
+mesh = rm3[0, :, :]
+mesh.pcolor(arr[0, :, :], flipY=True)
+plt.subplot(312)
+mesh = rm3[:, 0, :]
+mesh.pcolor(arr[:, 0, :], flipY=True)
+plt.subplot(313)
+rm3[:, :, 0].pcolor(arr[:, :, 0])
+
+plt.show()
diff --git a/docs/_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py b/docs/_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py
new file mode 100644
index 00000000..b0f4d8a7
--- /dev/null
+++ b/docs/_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py
@@ -0,0 +1,150 @@
+"""
+2D Posterior analysis of Resolve inference
+------------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from geobipy import Model
+from geobipy import Inference2D
+
+def plot_2d_summary(folder, data_type, model_type):
+   #%%
+   # Inference for a line of inferences
+   # ++++++++++++++++++++++++++++++++++
+   #
+   # We can instantiate the inference handler by providing a path to the directory containing
+   # HDF5 files generated by GeoBIPy.
+   #
+   # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+   # as and when it is needed.
+   #
+   # The first time you use these classes to create plots, expect longer initial processing times.
+   # I precompute expensive properties and store them in the HDF5 files for later use.
+
+   from numpy.random import Generator
+   from numpy.random import PCG64DXSM
+   generator = PCG64DXSM(seed=0)
+   prng = Generator(generator)
+
+   #%%
+   results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+   kwargs = {
+         "log" : 10,
+         "cmap" : 'jet'
+         }
+
+   fig = plt.figure(figsize=(16, 8))
+   plt.suptitle("{} {}".format(data_type, model_type))
+   gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+   true_model = Model.create_synthetic_model(model_type)
+   true_model.mesh.y_edges = true_model.mesh.y_edges / 10.0
+
+   kwargs['vmin'] = np.log10(np.min(true_model.values))
+   kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+   ax = fig.add_subplot(gs0[0, 0])
+   true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+   plt.ylim([-160, 60])
+
+   ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+   ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # # # # # We can also choose to keep parameters above the DOI opaque.
+   # # # # results_2d.compute_doi()
+   # # # # plt.subplot(313)
+   # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+   # # # # results_2d.plot_data_elevation(linewidth=0.3);
+   # # # # results_2d.plot_elevation(linewidth=0.3);
+
+   ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+   results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+   ax1.set_title('Best model')
+
+   del kwargs['vmin']
+   del kwargs['vmax']
+
+   ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+   results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+   results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+   results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   #%%
+   # We can plot the parameter values that produced the highest posterior
+   ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+   results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+   ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+   ll, bb, ww, hh = ax1.get_position().bounds
+   ax1.set_position([ll, bb, ww*0.8, hh])
+
+   results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+   results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+   #%%
+   # Now we can start plotting some more interesting posterior properties.
+   # How about the confidence?
+   ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+   results_2d.plot_confidence(ax=ax1);
+   results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+   results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+   #%%
+   # We can take the interface depth posterior for each data point,
+   # and display an interface probability cross section
+   # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+   # the top and bottom 0.5% of the colour range
+   ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+   ax1.set_title('P(Interface)')
+   results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+   results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+
+   plt.show()
+   # plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+if __name__ == '__main__':
+   models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+   for model in models:
+      try:
+         plot_2d_summary("../../../Parallel_Inference/", "resolve", model)
+      except Exception as e:
+         print(model)
+         print(e)
+         pass
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diff --git a/docs/_downloads/53dc65a717762d4693b891c0caabbde0/plot_StatArray.py b/docs/_downloads/53dc65a717762d4693b891c0caabbde0/plot_StatArray.py
new file mode 100644
index 00000000..c6db6e60
--- /dev/null
+++ b/docs/_downloads/53dc65a717762d4693b891c0caabbde0/plot_StatArray.py
@@ -0,0 +1,636 @@
+"""
+StatArray Class
+----------------
+
+Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.
+
+"""
+#%%
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from geobipy import DataArray, StatArray, Histogram, Distribution, RectilinearMesh1D
+
+
+# plt.style.use('seaborn-pastel')
+
+#%%
+# Instantiating a new StatArray class
+# +++++++++++++++++++++++++++++++++++
+#
+
+# Integer
+test = StatArray(1, name='1')
+assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 0.0, TypeError("da 0")
+print(test.summary)
+test = StatArray(10, name='10')
+assert isinstance(test, StatArray) and test.size ==  10 and np.all(test == 0.0), TypeError("da 1")
+print(test.summary)
+# tuple/Shape
+test = StatArray((2, 10), name='(2, 10)')
+assert isinstance(test, StatArray) and np.all(test.shape ==  (2, 10)) and np.all(test == 0.0), TypeError("da 2")
+print(test.summary)
+
+test = StatArray([2, 10], name='(2, 10)')
+assert isinstance(test, StatArray) and np.all(test ==  [2, 10]), TypeError("da 2")
+print(test.summary)
+
+# float
+test = StatArray(45.454, name='45.454')
+assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 3")
+print(test.summary)
+test = StatArray(np.float64(45.454), name='45.454')
+assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 4")
+print(test.summary)
+
+# array
+test = StatArray(np.random.randn(1), name="test", units="$\frac{g}{cc}$")
+assert isinstance(test, StatArray) and test.size ==  1, TypeError("da 5")
+print(test.summary)
+
+test = StatArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+assert isinstance(test, StatArray) and test.size ==  10, TypeError("da 6")
+print(test.summary)
+
+
+test = DataArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+test = StatArray(test)
+assert isinstance(test, StatArray) and test.size ==  10, TypeError("da 6")
+print(test.summary)
+
+
+
+
+# The StatArray can take any numpy function that returns an array as an input.
+# The name and units of the variable can be assigned to the StatArray.
+
+#%%
+# Attaching Prior and Proposal Distributions to a StatArray
+# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#
+# The StatArray class has been built so that we may easily
+# attach not only names and units, but statistical distributions too.
+# We won't go into too much detail about the different distribution
+#
+# Two types of distributions can be attached to the StatArray.
+#
+# * Prior Distribution
+#     The prior represents how the user believes the variable should
+#     behave from a statistical standpoint.
+#     The values of the variable can be evaluated against the attached prior,
+#     to determine how likely they are to have occured https://en.wikipedia.org/wiki/Prior_probability
+#
+# * Proposal Distribution
+#     The proposal describes a probability distribution from which to
+#     sample when we wish to perturb the variable
+#     https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm
+
+# Obtain an instantiation of a random number generator.
+# This is optional, but is an important consideration for parallel programming.
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+Density = StatArray(10.0, name="test", units="$\frac{g}{cc}$")
+
+Density.prior = Distribution('Uniform', -2.0, 2.0, prng=prng)
+
+#%%
+# We can also attach a proposal distribution
+Density.proposal = Distribution('Normal', 0.0, 1.0, prng=prng)
+print(Density.summary)
+print("Class type of the prior: ",type(Density.prior))
+print("Class type of the proposal: ",type(Density.proposal))
+
+
+#%%
+# The values in the variable can be evaluated against the prior.
+# In this case, we have 3 elements in the variable, and a univariate Normal for the prior.
+# Therefore each element is evaluated to get 3 probabilities, one for each element.
+print(Density.probability(log=False))
+
+#%%
+# The univariate proposal distribution can generate random samples from itself.
+print(Density.propose())
+
+#%%
+# From a sampling stand point we can either sample using only the proposal
+# Or we can only generate samples that simultaneously satisfy the prior.
+print(Density.propose(relative=True))
+
+#%%
+# We can perturb the variable by drawing from the attached proposal distribution.
+
+Density.perturb()
+print(Density.summary)
+
+#%%
+# Attaching a Histogram to capture the posterior distribution
+# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+# The StatArray can perturb itself, evaluate its current probability given its priors
+# and a histogram can be attached to capture its posterior distribution.
+# As an example, lets create a Histogram class with bins generated from the prior.
+bins = Density.prior.bins()
+#%%
+# Attach the histogram
+Density.posterior = Histogram(mesh = RectilinearMesh1D(edges=bins))
+
+#%%
+# In an iterative sense, we can propose and evaluate new values, and update the posterior
+for i in range(1000):
+    Density.perturb()
+    p = Density.probability(log=False)
+
+    if p > 0.0: # This is a simple example!
+        Density.update_posterior()
+
+#%%
+plt.figure()
+Density.summaryPlot()
+
+#%%
+# Attach a multivariate normal distribution as the prior and proposal
+# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#
+# Attach the multivariate prior
+
+mean = np.random.randn(Density.size)
+variance = np.ones(Density.size)
+Density.prior = Distribution('MvNormal', mean, variance, prng=prng)
+
+
+#%%
+# Since the prior is multivariate, the appropriate equations are used to
+# evaluate the probability for all elements in the StatArray.
+# This produces a single probability.
+
+print(Density.probability(log=False))
+
+#%%
+# Attach the multivariate proposal
+
+mean = np.random.randn(Density.size)
+variance = np.ones(Density.size)
+Density.proposal = Distribution('MvNormal', mean, variance, prng=prng)
+
+
+#%%
+# Perturb the variables using the multivariate proposal.
+
+Density.perturb()
+Density.summary
+
+with h5py.File('statarray.h5', 'w') as f:
+    Density.createHdf(f, 'statarray', withPosterior=True, add_axis=3)
+    Density.writeHdf(f, 'statarray', withPosterior=True, index=0)
+
+with h5py.File('statarray.h5', 'r') as f:
+    tmp = StatArray.fromHdf(f, 'statarray', index=0, skip_posterior=False)
+
+with h5py.File('statarray.h5', 'r') as f:
+    tmp = StatArray.fromHdf(f, 'statarray', skip_posterior=False)
+
+
+#%%
+# Basic manipulation
+# ++++++++++++++++++
+#
+# The StatArray contains other functions to perform basic array manipulations
+#
+# These routines essentially wrap around numpy functions,
+# but the result will have the same name and units,
+# and if any prior or proposal are set, those will be carried through too.
+#
+# 1D example
+# __________
+
+x = StatArray(-np.cumsum(np.arange(10.0)))
+print(x)
+
+#%%
+
+
+print(x.insert(i=[0, 9], values=[999.0, 999.0]))
+
+
+#%%
+
+
+print(x.prepend(999.0))
+
+
+#%%
+
+
+print(x.prepend([998.0, 999.0]))
+
+
+#%%
+
+
+print(x.append([998.0, 999.0]))
+
+
+#%%
+
+
+print(x.resize(14))
+
+
+#%%
+
+
+print(x.delete([5,8]))
+
+
+#%%
+
+
+print(x.edges())
+
+
+#%%
+
+
+print(x.internalEdges())
+
+
+#%%
+
+
+print(x.firstNonZero())
+
+
+#%%
+
+
+print(x.lastNonZero())
+
+
+#%%
+
+
+print(x.abs())
+
+
+#%%
+# 2D example
+# __________
+
+x = StatArray(np.asarray([[0, -2, 3],[3, 0, -1],[1, 2, 0]]))
+print(x)
+
+
+#%%
+
+
+print(x.insert(i=0, values=4))
+
+
+#%%
+
+
+print(x.insert(i=[2, 3], values=5, axis=1))
+
+
+#%%
+
+
+print(x.insert(i=2, values=[10, 11, 12], axis=1))
+
+
+#%%
+
+
+print(x.prepend(999))
+
+
+#%%
+
+
+print(x.prepend([999, 998, 997], axis=1))
+
+
+#%%
+
+
+print(x.append([[999, 998, 997]]))
+
+
+#%%
+
+
+print(x.resize([5,5]))
+
+
+#%%
+
+
+print(x.delete(5))
+
+
+#%%
+
+
+print(x.delete(2, axis=0))
+
+
+#%%
+
+
+print(x.firstNonZero(axis=0))
+
+
+#%%
+
+
+print(x.lastNonZero(axis=0))
+
+
+#%%
+
+
+print(x.firstNonZero(axis=1))
+
+
+#%%
+
+
+print(x.lastNonZero(axis=1))
+
+
+#%%
+
+
+print(x.abs())
+
+
+#%%
+# Plotting
+# ++++++++
+#
+# We can easily plot the StatArray with its built in plotting functions.
+# All plotting functions can take matplotlib keywords
+
+# The simplest is to just plot the array
+
+Density = StatArray(np.random.randn(100),name="Density",units="$\frac{g}{cc}$")
+Time = StatArray(np.linspace(0, 100, Density.size), name='Time', units='s')
+Depth = StatArray(np.random.exponential(size=Density.size), name='Depth', units='m')
+
+
+#%%
+
+
+plt.figure()
+_ = Density.plot(linewidth=0.5, marker='x', markersize=1.0)
+
+#%%
+# We can quickly plot a bar graph.
+
+plt.figure()
+_ = Density.bar()
+
+
+#%%
+# We can scatter the contents of the StatArray if it is 1D
+
+plt.figure()
+_ = Density.scatter(alpha=0.7)
+
+
+#%%
+# Histogram Equalization
+# ______________________
+#
+# A neat trick with colourmaps is histogram equalization.
+# This approach forces all colours in the images to have an equal weight.
+# This distorts the colour bar, but can really highlight the lower and higher
+# ends of whatever you are plotting. Just add the equalize keyword!
+
+plt.figure()
+_ = Density.scatter(alpha=0.7, equalize=True)
+
+
+#%%
+# Take the log base(x) of the data
+#
+# We can also take the data to a log, log10, log2, or a custom number!
+
+plt.figure()
+_ = Density.scatter(alpha=0.7,edgecolor='k',log='e') # could also use log='e', log=2, log=x) where x is the base you require
+
+#%%
+# X and Y axes
+#
+# We can specify the x axis of the scatter plot.
+
+
+plt.figure()
+_ = Density.scatter(x=Time, alpha=0.7, edgecolor='k')
+
+
+#%%
+# Notice that I never specified the y axis, so the y axis defaulted to the values in the StatArray.
+# In this case, any operations applied to the colours, are also applied to the y axis, e.g. log=10.
+# When I take the values of Density to log base 10, because I do not specify the y plotting locations, those locations are similarly affected.
+#
+# I can however force the y co-ordinates by specifying it as input.
+# In the second subplot I explicitly plot distance on the y axis.
+# In the first subplot, the y axis is the same as the colourbar.
+
+
+plt.figure()
+ax1 = plt.subplot(211)
+Density.scatter(x=Time, alpha=0.7, edgecolor='k', log=10)
+plt.subplot(212, sharex=ax1)
+_ = Density.scatter(x=Time, y=Depth, alpha=0.7, edgecolor='k', log=10)
+
+
+#%%
+# Point sizes
+#
+# Since the plotting functions take matplotlib keywords, I can also specify the size of each points.
+
+#%%
+
+
+s = np.ceil(100*(np.abs(np.random.randn(Density.size))))
+plt.figure()
+plt.tight_layout()
+ax1 = plt.subplot(211)
+Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', legend_size=2)
+plt.subplot(212, sharex=ax1)
+#Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', sizeLegend=[1.0, 100, 200, 300])
+v = np.abs(Density)+1.0
+_ = Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', legend_size=[1.0, 100, 200, 300], log=10)
+
+
+
+
+#%%
+# Of course we can still take the log, or equalize the colour histogram
+
+plt.figure()
+_ = Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k',equalize=True,log=10)
+
+
+#%%
+# Typically pcolor only works with 2D arrays. The StatArray has a pcolor method that will pcolor a 1D array
+
+plt.figure()
+plt.subplot(221)
+Density.pcolor()
+plt.subplot(222)
+Density.pcolor(y=Time)
+plt.subplot(223)
+Density.pcolor(y=Time, flip=True)
+plt.subplot(224)
+_ = Density.pcolor(y=Time, log=10, equalize=True)
+
+
+#%%
+# We can add grid lines, and add opacity to each element in the pcolor image
+#
+# This is useful if the colour values need to be scaled by another variable e.g. variance.
+
+
+plt.figure()
+plt.subplot(121)
+Density.pcolor(grid=True, cmap='jet')
+plt.subplot(122)
+a = np.linspace(1.0, 0.0, Density.size)
+_ = Density.pcolor(grid=True, alpha=a, cmap='jet')
+
+
+#%%
+# We can plot a histogram of the StatArray
+
+plt.figure()
+_ = Density.hist(100)
+
+
+#%%
+# We can write the StatArray to a HDF5 file.  HDF5 files are binary files that can include compression.  They allow quick and easy access to parts of the file, and can also be written to and read from in parallel!
+
+with h5py.File('1Dtest.h5','w') as f:
+    Density.toHdf(f,'test')
+
+
+#%%
+# We can then read the StatArray from the file
+# Here x is a new variable, that is read in from the hdf5 file we just wrote.
+
+x = StatArray.fromHdf('1Dtest.h5', 'test')
+print('x has the same values as Density? ',np.all(x == Density))
+x[2] = 5.0 # Change one of the values in x
+print('x has its own memory allocated (not a reference/pointer)? ', id(x) != id(Density))
+
+
+#%%
+# We can also define a 2D array
+
+Density = StatArray(np.random.randn(50,100),"Density","$\frac{g}{cc}$")
+Density.summary
+
+
+#%%
+# The StatArray Class's functions work whether it is 1D or 2D
+#
+# We can still do a histogram
+
+plt.figure()
+_ = Density.hist()
+
+
+#%%
+# And we can use pcolor to plot the 2D array
+
+plt.figure()
+_ = Density.pcolor()
+
+
+#%%
+# The StatArray comes with extra plotting options
+#
+# Here we specify the x and y axes for the 2D array using two other 1D StatArrays
+
+plt.figure()
+x = StatArray(np.arange(101),name='x Axis',units = 'mm')
+y = StatArray(np.arange(51),name='y Axis',units = 'elephants')
+_ = Density.pcolor(x=x, y=y)
+
+
+#%%
+# We can plot using a log10 scale, in this case, we have values that are less
+# than or equal to 0.0.  Plotting with the log option will by default mask any
+# of those values, and will let you know that it has done so!
+
+plt.figure()
+_ = Density.pcolor(x=x,y=y,log=2)
+
+
+#%%
+# A neat trick with colourmaps is histogram equalization.
+# This approach forces all colours in the image to have an equal amount.
+# This distorts the colours, but can really highlight the lower and higher
+# ends of whatever you are plotting
+
+plt.figure()
+_ = Density.pcolor(x=x, y=y, equalize=True)
+
+
+#%%
+# We can equalize the log10 plot too :)
+
+plt.figure()
+_ = Density.pcolor(x=x,y=y,equalize=True, log=10)
+
+
+#%%
+# We can add opacity to each pixel in the image
+
+a = StatArray(np.random.random(Density.shape), 'Opacity from 0.0 to 1.0')
+
+
+#%%
+
+
+plt.figure()
+ax1 = plt.subplot(131)
+ax = Density.pcolor(x=x, y=y, flipY=True, linewidth=0.1, colorbar=False)
+plt.subplot(132, sharex=ax1, sharey=ax1)
+ax = Density.pcolor(x=x, y=y, alpha=a, flipY=True, linewidth=0.1, colorbar=False)
+plt.subplot(133, sharex=ax1, sharey=ax1)
+_ = a.pcolor(x=x, y=y, flipY=True)
+
+
+#%%
+# If the array potentially has a lot of white space around the edges, we can trim the image
+
+Density[:10, :] = 0.0
+Density[-10:, :] = 0.0
+Density[:, :10] = 0.0
+Density[:, -10:] = 0.0
+plt.figure()
+plt.subplot(121)
+Density.pcolor()
+plt.subplot(122)
+_ = Density.pcolor(trim=0.0)
+
+
+#%%
+# Create a stacked area plot of a 2D StatArray
+
+A = StatArray(np.abs(np.random.randn(13,100)), name='Variable', units="units")
+x = StatArray(np.arange(100),name='x Axis',units = 'mm')
+plt.figure()
+ax1 = plt.subplot(211)
+A.stackedAreaPlot(x=x, axis=1)
+plt.subplot(212, sharex=ax1)
+_ = A.stackedAreaPlot(x=x, i=np.s_[[1,3,4],:], axis=1, labels=['a','b','c'])
+
+plt.show()
diff --git a/docs/_downloads/54ce350904e65ae0e6717397118ed43f/plot_inference_2d_tempest.py b/docs/_downloads/54ce350904e65ae0e6717397118ed43f/plot_inference_2d_tempest.py
new file mode 100644
index 00000000..3bff5d1c
--- /dev/null
+++ b/docs/_downloads/54ce350904e65ae0e6717397118ed43f/plot_inference_2d_tempest.py
@@ -0,0 +1,152 @@
+"""
+2D Posterior analysis of Tempest inference
+------------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+"""
+
+import argparse
+import matplotlib.pyplot as plt
+import numpy as np
+from geobipy import Model
+from geobipy import Inference2D
+
+def plot_2d_summary(folder, data_type, model_type):
+   #%%
+   # Inference for a line of inferences
+   # ++++++++++++++++++++++++++++++++++
+   #
+   # We can instantiate the inference handler by providing a path to the directory containing
+   # HDF5 files generated by GeoBIPy.
+   #
+   # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+   # as and when it is needed.
+   #
+   # The first time you use these classes to create plots, expect longer initial processing times.
+   # I precompute expensive properties and store them in the HDF5 files for later use.
+
+   from numpy.random import Generator
+   from numpy.random import PCG64DXSM
+   generator = PCG64DXSM(seed=0)
+   prng = Generator(generator)
+
+   #%%
+   results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+   kwargs = {
+         "log" : 10,
+         "cmap" : 'jet'
+         }
+
+   fig = plt.figure(figsize=(16, 8))
+   plt.suptitle("{} {}".format(data_type, model_type))
+   gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+   true_model = Model.create_synthetic_model(model_type)
+
+   kwargs['vmin'] = np.log10(np.min(true_model.values))
+   kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+   ax = fig.add_subplot(gs0[0, 0])
+   true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+   plt.ylim([-550, 60])
+
+   ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+   ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # # # # # We can also choose to keep parameters above the DOI opaque.
+   # # # # results_2d.compute_doi()
+   # # # # plt.subplot(313)
+   # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+   # # # # results_2d.plot_data_elevation(linewidth=0.3);
+   # # # # results_2d.plot_elevation(linewidth=0.3);
+
+   ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+   results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+   ax1.set_title('Best model')
+
+   del kwargs['vmin']
+   del kwargs['vmax']
+
+   ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+   results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+   results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+   results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   #%%
+   # We can plot the parameter values that produced the highest posterior
+   ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+   results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+   ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+   ll, bb, ww, hh = ax1.get_position().bounds
+   ax1.set_position([ll, bb, ww*0.8, hh])
+
+   results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+   results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+   #%%
+   # Now we can start plotting some more interesting posterior properties.
+   # How about the confidence?
+   ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+   results_2d.plot_confidence(ax=ax1);
+   results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+   results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+   #%%
+   # We can take the interface depth posterior for each data point,
+   # and display an interface probability cross section
+   # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+   # the top and bottom 0.5% of the colour range
+   ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+   ax1.set_title('P(Interface)')
+   results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+   results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # plt.show()
+   plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+
+if __name__ == '__main__':
+   models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+   # import warnings
+   # warnings.filterwarnings('error')
+   for model in models:
+      try:
+         plot_2d_summary('../../../Parallel_Inference/', "tempest", model)
+      except Exception as e:
+         print(model)
+         print(e)
+         pass
diff --git a/docs/_downloads/54daa4e7c10c4176fd98102617f46076/plot_pointcloud3d.zip b/docs/_downloads/54daa4e7c10c4176fd98102617f46076/plot_pointcloud3d.zip
new file mode 100644
index 00000000..4d29820c
Binary files /dev/null and b/docs/_downloads/54daa4e7c10c4176fd98102617f46076/plot_pointcloud3d.zip differ
diff --git a/docs/_downloads/553c44c8f0a927032d89154ad011db0e/plot_rectilinear_mesh_1d.py b/docs/_downloads/553c44c8f0a927032d89154ad011db0e/plot_rectilinear_mesh_1d.py
new file mode 100644
index 00000000..43972362
--- /dev/null
+++ b/docs/_downloads/553c44c8f0a927032d89154ad011db0e/plot_rectilinear_mesh_1d.py
@@ -0,0 +1,289 @@
+"""
+1D Rectilinear Mesh
+-------------------
+"""
+#%%
+from copy import deepcopy
+from geobipy import DataArray, StatArray
+from geobipy import RectilinearMesh1D, RectilinearMesh2D, RectilinearMesh2D_stitched
+import matplotlib.gridspec as gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+
+#%%
+# The basics
+# ++++++++++
+# Instantiate a new 1D rectilinear mesh by specifying cell centres, edges, or widths.
+x = StatArray(np.cumsum(np.arange(0.0, 10.0)), 'Depth', 'm')
+
+#%%
+# Cell edges
+rm = RectilinearMesh1D(edges=x, centres=None, widths=None)
+
+#%%
+# We can plot the grid of the mesh
+# Or Pcolor the mesh showing. An array of cell values is used as the colour.
+arr = StatArray(np.random.randn(*rm.shape), "Name", "Units")
+p=0; plt.figure(p)
+plt.subplot(121)
+_ = rm.plot_grid(transpose=True, flip=True)
+plt.subplot(122)
+_ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+# Mask the mesh cells by a distance
+rm_masked, indices, arr2 = rm.mask_cells(2.0, values=arr)
+p+=1; plt.figure(p)
+_ = rm_masked.pcolor(StatArray(arr2), grid=True, transpose=True, flip=True)
+
+# Writing and reading to/from HDF5
+# ++++++++++++++++++++++++++++++++
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.toHdf(f, 'rm1d')
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(122)
+_ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.createHdf(f, 'rm1d', add_axis=10)
+    for i in range(10):
+        rm.writeHdf(f, 'rm1d', index=i)
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+with h5py.File('rm1d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(131)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(132)
+_ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+plt.subplot(133)
+_ = rm2.pcolor(np.repeat(arr[None, :], 10, 0), grid=True, flipY=True)
+
+
+#%%
+# Log-space rectilinear mesh
+# ++++++++++++++++++++++++++
+# Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+# Here we use edges
+x = StatArray(np.logspace(-3, 3, 10), 'Depth', 'm')
+
+#%%
+rm = RectilinearMesh1D(edges=x, log=10)
+
+# We can plot the grid of the mesh
+# Or Pcolor the mesh showing. An array of cell values is used as the colour.
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.plot_grid(transpose=True, flip=True)
+plt.subplot(122)
+arr = StatArray(np.random.randn(rm.nCells), "Name", "Units")
+_ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+# Writing and reading to/from HDF5
+# ++++++++++++++++++++++++++++++++
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.toHdf(f, 'rm1d')
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(122)
+_ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.createHdf(f, 'rm1d', add_axis=10)
+    for i in range(10):
+        rm.writeHdf(f, 'rm1d', index=i)
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+with h5py.File('rm1d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(131)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(132)
+_ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+plt.subplot(133)
+_ = rm2.pcolor(np.repeat(arr[None, :], 10, 0), grid=True, flipY=True)
+
+#%%
+# relative_to
+# ++++++++++
+# Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+# Here we use edges
+x = StatArray(np.arange(11.0), 'Deviation', 'm')
+
+#%%
+rm = RectilinearMesh1D(edges=x, relative_to=5.0)
+
+#%%
+# We can plot the grid of the mesh
+# Or Pcolor the mesh showing. An array of cell values is used as the colour.
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.plot_grid(transpose=True, flip=True)
+plt.subplot(122)
+arr = StatArray(np.random.randn(rm.nCells), "Name", "Units")
+_ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+# Writing and reading to/from HDF5
+# ++++++++++++++++++++++++++++++++
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.createHdf(f, 'rm1d')
+    rm.writeHdf(f, 'rm1d')
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(122)
+_ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.createHdf(f, 'rm1d', add_axis=3)
+    for i in range(3):
+        rm.relative_to += 0.5
+        rm.writeHdf(f, 'rm1d', index=i)
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+with h5py.File('rm1d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(131)
+_ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+plt.subplot(132)
+_ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+plt.subplot(133)
+_ = rm2.pcolor(np.repeat(arr[None, :], 3, 0), grid=True, flipY=True)
+
+
+# Making a mesh perturbable
+# +++++++++++++++++++++++++
+n_cells = 2
+widths = DataArray(np.full(n_cells, fill_value=10.0), 'test')
+rm = RectilinearMesh1D(widths=widths, relative_to=0.0)
+
+#%%
+# Randomness and Model Perturbations
+# ++++++++++++++++++++++++++++++++++
+# We can set the priors on the 1D model by assigning minimum and maximum layer
+# depths and a maximum number of layers.  These are used to create priors on
+# the number of cells in the model, a new depth interface, new parameter values
+# and the vertical gradient of those parameters.
+# The halfSpaceValue is used as a reference value for the parameter prior.
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+# Set the priors
+rm.set_priors(min_edge = 1.0,
+              max_edge = 150.0,
+              max_cells = 30,
+              prng = prng)
+
+#%%
+# We can evaluate the prior of the model using depths only
+print('Log probability of the Mesh given its priors: ', rm.probability)
+
+#%%
+# To propose new meshes, we specify the probabilities of creating, removing, perturbing, and not changing
+# an edge interface
+# Here we force the creation of a layer.
+rm.set_proposals(probabilities = [0.25, 0.25, 0.25, 0.25], prng=prng)
+rm.set_posteriors()
+
+rm0 = deepcopy(rm)
+
+#%%
+# We can then perturb the layers of the model
+for i in range(1000):
+    rm = rm.perturb()
+    rm.update_posteriors()
+
+#%%
+p+=1; fig = plt.figure(p)
+ax = rm._init_posterior_plots(fig)
+
+rm.plot_posteriors(axes=ax)
+
+with h5py.File('rm1d.h5', 'w') as f:
+    rm.createHdf(f, 'rm1d', withPosterior = True)
+    rm.writeHdf(f, 'rm1d', withPosterior = True)
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(121)
+_ = rm.pcolor(StatArray(rm.shape), grid=True, transpose=True, flip=True)
+plt.subplot(122)
+_ = rm1.pcolor(StatArray(rm1.shape), grid=True, transpose=True, flip=True)
+
+p+=1; fig = plt.figure(p)
+ax = rm1._init_posterior_plots(fig)
+rm1.plot_posteriors(axes=ax)
+
+#%%
+# Expanded
+with h5py.File('rm1d.h5', 'w') as f:
+    tmp = rm.pad(rm.max_cells)
+    tmp.createHdf(f, 'rm1d', withPosterior=True, add_axis=DataArray(np.arange(3.0), name='Easting', units="m"))
+
+    print(list(f['rm1d'].keys()))
+
+    rm.relative_to = 5.0
+    print(rm.summary)
+    rm.writeHdf(f, 'rm1d', withPosterior = True, index=0)
+
+    rm = deepcopy(rm0)
+    for i in range(1000):
+        rm = rm.perturb(); rm.update_posteriors()
+    rm.relative_to = 10.0
+    rm.writeHdf(f, 'rm1d', withPosterior = True, index=1)
+
+    rm = deepcopy(rm0)
+    for i in range(1000):
+        rm = rm.perturb(); rm.update_posteriors()
+    rm.relative_to = 25.0
+    rm.writeHdf(f, 'rm1d', withPosterior = True, index=2)
+
+with h5py.File('rm1d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+p+=1; plt.figure(p)
+plt.subplot(121)
+arr = np.random.randn(3, rm.max_cells) * 10
+_ = rm0.pcolor(arr[0, :rm0.nCells.item()], grid=True, transpose=True, flip=True)
+plt.subplot(122)
+_ = rm2.pcolor(arr, grid=True, flipY=True, equalize=True)
+
+from geobipy import RectilinearMesh2D
+with h5py.File('rm1d.h5', 'r') as f:
+    rm2 = RectilinearMesh2D.fromHdf(f['rm1d'], index=0)
+
+plt.figure()
+plt.subplot(121)
+rm2.plot_grid(transpose=True, flip=True)
+plt.subplot(122)
+rm2.edges.posterior.pcolor(transpose=True, flip=True)
+
+plt.show()
\ No newline at end of file
diff --git a/docs/_downloads/5b5d77f6a7b150a24e5d195babd5a68d/plot_DataArray.py b/docs/_downloads/5b5d77f6a7b150a24e5d195babd5a68d/plot_DataArray.py
new file mode 100644
index 00000000..1d25796a
--- /dev/null
+++ b/docs/_downloads/5b5d77f6a7b150a24e5d195babd5a68d/plot_DataArray.py
@@ -0,0 +1,48 @@
+"""
+DataArray Class
+----------------
+
+Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.
+
+"""
+import  numpy as np
+from geobipy import DataArray, StatArray
+
+# Integer
+test = DataArray(1, name='1')
+assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 0.0, TypeError("da 0")
+print(test.summary)
+test = DataArray(10, name='10')
+assert isinstance(test, DataArray) and test.size ==  10 and np.all(test == 0.0), TypeError("da 1")
+print(test.summary)
+# tuple/Shape
+test = DataArray((2, 10), name='(2, 10)')
+assert isinstance(test, DataArray) and np.all(test.shape ==  (2, 10)) and np.all(test == 0.0), TypeError("da 2")
+print(test.summary)
+
+test = DataArray([2, 10], name='(2, 10)')
+assert isinstance(test, DataArray) and np.all(test ==  [2, 10]), TypeError("da 2")
+print(test.summary)
+
+# float
+test = DataArray(45.454, name='45.454')
+assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 3")
+print(test.summary)
+test = DataArray(np.float64(45.454), name='45.454')
+assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 4")
+print(test.summary)
+
+# array
+test = DataArray(np.random.randn(1), name="test", units="$\frac{g}{cc}$")
+assert isinstance(test, DataArray) and test.size ==  1, TypeError("da 5")
+print(test.summary)
+
+test = DataArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+assert isinstance(test, DataArray) and test.size ==  10, TypeError("da 6")
+print(test.summary)
+
+test = DataArray(test)
+assert isinstance(test, DataArray) and test.size ==  10, TypeError("da 6")
+print(test.summary)
\ No newline at end of file
diff --git a/docs/_downloads/5c85bfce6697082c125824937647a640/plot_histogram_2d.zip b/docs/_downloads/5c85bfce6697082c125824937647a640/plot_histogram_2d.zip
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diff --git a/docs/_downloads/5cf20ebc694f8382ff502459c2eae019/plot_inference_1d_skytem.zip b/docs/_downloads/5cf20ebc694f8382ff502459c2eae019/plot_inference_1d_skytem.zip
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diff --git a/docs/_downloads/6c96ba056672a7e415f479abafdaf81d/plot_pointcloud3d.py b/docs/_downloads/6c96ba056672a7e415f479abafdaf81d/plot_pointcloud3d.py
new file mode 100644
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--- /dev/null
+++ b/docs/_downloads/6c96ba056672a7e415f479abafdaf81d/plot_pointcloud3d.py
@@ -0,0 +1,137 @@
+"""
+3D Point Cloud class
+--------------------
+"""
+
+#%%
+
+from geobipy import Point
+from os.path import join
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+
+nPoints = 200
+
+#%%
+# Create a quick test example using random points
+# $z=x(1-x)cos(4\pi x)sin(4\pi y^{2})^{2}$
+x = -np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+y = -np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+z = x * (1.0 - x) * np.cos(np.pi * x) * np.sin(np.pi * y)
+
+PC3D = Point(x=x, y=y, z=z)
+
+#%%
+# Append pointclouds together
+x = np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+y = np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+z = x * (1.0 - x) * np.cos(np.pi * x) * np.sin(np.pi * y)
+
+Other_PC = Point(x=x, y=y, z=z)
+PC3D.append(Other_PC)
+
+#%%
+# Write a summary of the contents of the point cloud
+
+print(PC3D.summary)
+
+#%%
+# Get a single location from the point as a 3x1 vector
+
+Point = PC3D[50]
+# Print the point to the screen
+
+#%%
+# Plot the locations with Height as colour
+
+plt.figure()
+PC3D.scatter2D(edgecolor='k')
+
+#%%
+# Plotting routines take matplotlib arguments for customization
+#
+# For example, plotting the size of the points according to the absolute value of height
+plt.figure()
+ax = PC3D.scatter2D(s=100*np.abs(PC3D.z), edgecolor='k')
+
+#%%
+# Interpolate the points to a 2D rectilinear mesh
+mesh, dum = PC3D.interpolate(0.01, 0.01, values=PC3D.z, method='sibson', mask=0.03)
+
+# We can save that mesh to VTK
+PC3D.to_vtk('pc3d.vtk')
+mesh.to_vtk('interpolated_pc3d.vtk')
+
+#%%
+# Grid the points using a triangulated CloughTocher, or minimum curvature interpolation
+
+plt.figure()
+plt.subplot(331)
+PC3D.map(dx=0.01, dy=0.01, method='ct')
+plt.subplot(332)
+PC3D.map(dx=0.01, dy=0.01, method='mc')
+plt.subplot(333)
+PC3D.map(dx=0.01, dy=0.01, method='sibson')
+
+plt.subplot(334)
+PC3D.map(dx=0.01, dy=0.01, method='ct', mask=0.03)
+plt.subplot(335)
+PC3D.map(dx=0.01, dy=0.01, method='mc', mask=0.3)
+plt.subplot(336)
+PC3D.map(dx=0.01, dy=0.01, method='sibson', mask=0.03)
+#%%
+# For lots of points, these surfaces can look noisy. Using a block filter will help
+PCsub = PC3D.block_median(0.05, 0.05)
+plt.subplot(337)
+PCsub.map(dx=0.01, dy=0.01, method='ct', mask=0.03)
+plt.subplot(338)
+PCsub.map(dx=0.01, dy=0.01, method='mc', mask=0.03)
+plt.subplot(339)
+PCsub.map(dx=0.01, dy=0.01, method='sibson', mask=0.03)
+
+
+#%%
+# We can perform spatial searches on the 3D point cloud
+
+PC3D.set_kdtree(ndim=2)
+p = PC3D.nearest((0.0,0.0), k=200, p=2, radius=0.3)
+
+#%%
+# .nearest returns the distances and indices into the point cloud of the nearest points.
+# We can then obtain those points as another point cloud
+
+# pNear = PC3D[p[1]]
+# plt.figure()
+# ax1 = plt.subplot(1,2,1)
+# pNear.scatter2D()
+# plt.plot(0.0, 0.0, 'x')
+# plt.subplot(1,2,2, sharex=ax1, sharey=ax1)
+# ax, sc, cb = PC3D.scatter2D(edgecolor='k')
+# searchRadius = plt.Circle((0.0, 0.0), 0.3, color='b', fill=False)
+# ax.add_artist(searchRadius)
+# plt.plot(0.0, 0.0, 'x')
+
+#%%
+# Read in the xyz co-ordinates in columns 2,3,4 from a file. Skip 1 header line.
+
+dataFolder = "..//..//supplementary//Data//"
+
+PC3D.read_csv(filename=dataFolder + 'Resolve1.txt')
+
+
+#%%
+plt.figure()
+f = PC3D.scatter2D(s=10)
+
+with h5py.File('test.h5', 'w') as f:
+    PC3D.createHdf(f, 'test')
+    PC3D.writeHdf(f, 'test')
+
+with h5py.File('test.h5', 'r') as f:
+    PC3D1 = Point.fromHdf(f['test'])
+
+with h5py.File('test.h5', 'r') as f:
+    point = Point.fromHdf(f['test'], index=0)
+
+plt.show()
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diff --git a/docs/_downloads/6ea40b011244de987af10f1723709b1e/plot_StatArray.zip b/docs/_downloads/6ea40b011244de987af10f1723709b1e/plot_StatArray.zip
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diff --git a/docs/_downloads/762d4f393eb8c7294ca843748ae360e3/plot_skytem_dataset.py b/docs/_downloads/762d4f393eb8c7294ca843748ae360e3/plot_skytem_dataset.py
new file mode 100644
index 00000000..0426d5e4
--- /dev/null
+++ b/docs/_downloads/762d4f393eb8c7294ca843748ae360e3/plot_skytem_dataset.py
@@ -0,0 +1,159 @@
+"""
+Skytem dataset
+--------------
+"""
+#%%
+from geobipy import plotting as cP
+from os.path import join
+import matplotlib.pyplot as plt
+import numpy as np
+from geobipy import StatArray
+from geobipy import TdemData
+import h5py
+
+#%%
+# Reading in the Data
+# +++++++++++++++++++
+
+#%%
+dataFolder = "..//..//supplementary//data//"
+# The data file name
+dataFiles=dataFolder + 'skytem_saline_clay.csv'
+# dataFiles = dataFolder + 'Skytem.csv'
+# The EM system file name
+systemFiles=[dataFolder + 'SkytemHM.stm', dataFolder + 'SkytemLM.stm']
+
+from pathlib import Path
+for f in systemFiles[:1]:
+    txt = Path(f).read_text()
+    print(txt)
+
+#%%
+# Read in the data from file
+TD = TdemData.read_csv(dataFiles, systemFiles)
+
+#%%
+# Plot the locations of the data points
+plt.figure(1, figsize=(8,6))
+_ = TD.scatter2D()
+
+#%%
+# Plot all the data along the specified line
+plt.figure(2, figsize=(8,6))
+_ = TD.plotLine(0.0, log=10)
+
+#%%
+# Or, plot specific channels in the data
+plt.figure(3, figsize=(8,6))
+_ = TD.plot_data(system=0, channels=[1, 3, 5], log=10)
+
+#%%
+plt.figure(4)
+plt.subplot(211)
+_ = TD.pcolor(system=0, xscale='log', log=10)
+plt.subplot(212)
+_ = TD.pcolor(system=1, xscale='log', log=10)
+
+#%%
+plt.figure(5)
+ax = TD.scatter2D(c=TD.secondary_field[:, TD.channel_index(system=0, channel=6)], log=10)
+plt.axis('equal')
+
+
+# with h5py.File('tdem.h5', 'w') as f:
+#     TD.createHdf(f, 'tdem')
+#     TD.writeHdf(f, 'tdem')
+
+# with h5py.File('tdem.h5', 'r') as f:
+#     TD3 = TdemData.fromHdf(f['tdem'])
+
+# with h5py.File('tdem.h5', 'r') as f:
+#     tdp = TdemData.fromHdf(f['tdem'], index=0)
+
+
+# #%%
+# # Obtain a line from the data set
+# # +++++++++++++++++++++++++++++++
+# line = TD.line(0.0)
+
+# #%%
+# plt.figure(6)
+# _ = line.scatter2D(c=line.secondary_field[:, line.channel_index(system=0, channel=6)], log=10)
+
+# #%%
+# plt.figure(7)
+# _ = line.plot(xAxis='index', log=10)
+
+# Prepare the dataset so that we can read a point at a time.
+Dataset = TdemData._initialize_sequential_reading(dataFiles, systemFiles)
+# Get a datapoint from the file.
+DataPoint = Dataset._read_record()
+
+plt.show()
+
+#%%
+# File Format for time domain data
+# ++++++++++++++++++++++++++++++++
+# Here we describe the file format for time domain data.
+#
+# The data columns are read in according to the column names in the first line
+#
+# In this description, the column name or its alternatives are given followed by what the name represents
+# Optional columns are also described.
+#
+# Required columns
+# ________________
+# line
+#     Line number for the data point
+# fid
+#     Unique identification number of the data point
+# x or northing or n
+#     Northing co-ordinate of the data point, (m)
+# y or easting or e
+#     Easting co-ordinate of the data point, (m)
+# z or alt
+#     Altitude of the transmitter coil above ground level (m)
+# elevation
+#     Elevation of the ground at the data point (m)
+# txrx_dx
+#     Distance in x between transmitter and reciever (m)
+# txrx_dy
+#     Distance in y between transmitter and reciever (m)
+# txrx_dz
+#     Distance in z between transmitter and reciever (m)
+# Tx_Pitch
+#     Pitch of the transmitter loop
+# Tx_Roll
+#     Roll of the transmitter loop
+# Tx_Yaw
+#     Yaw of the transmitter loop
+# Rx_Pitch
+#     Pitch of the receiver loop
+# Rx_Roll
+#     Roll of the receiver loop
+# Rx_Yaw
+#     Yaw of the receiver loop
+# Off_time[0] Off_time[1] ... Off_time[last]  - with the number and square brackets
+#     The measurements for each time gate specified in the accompanying system file under Receiver Window Times
+#     The total number of off_time columns should equal the sum of the receiver windows in all system files.
+# Optional columns
+# ________________
+# Off_time_Error[0] Off_time_Error[1] ... Off_time_Error[last]
+#     Estimates of standard deviation for each off time measurement
+# Example Header
+# ______________
+# Line fid easting northing elevation height txrx_dx txrx_dy txrx_dz TxPitch TxRoll TxYaw RxPitch RxRoll RxYaw Off[0] Off[1]
+
+#%%
+# File Format for a time domain system
+# ++++++++++++++++++++++++++++++++++++
+# Please see Page 13 of Ross Brodie's `instructions`_
+#
+# .. _instructions: https://github.com/GeoscienceAustralia/ga-aem/blob/master/docs/GA%20AEM%20Programs%20User%20Manual.pdf
+#
+# We use GA-AEM for our airborne time domain forward modeller.
+#
+# Example system files are contained in
+# `the supplementary folder`_ in this repository
+#
+# .. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
diff --git a/docs/_downloads/76ee0e4ad4daaf5556108538842b1772/hdf5.py b/docs/_downloads/76ee0e4ad4daaf5556108538842b1772/hdf5.py
new file mode 100644
index 00000000..df6a170a
--- /dev/null
+++ b/docs/_downloads/76ee0e4ad4daaf5556108538842b1772/hdf5.py
@@ -0,0 +1,100 @@
+"""
+Using HDF5 within GeoBIPy
+-------------------------
+
+Inference for large scale datasets in GeoBIPy is handled using MPI and distributed memory systems.
+A common bottleneck with large parallel algorithms is the input output of information to disk.
+We use HDF5 to read and write data in order to leverage the parallel capabililties of the HDF5 API.
+
+Each object within GeoBIPy has a create_hdf, write_hdf, and read_hdf routine.
+
+"""
+import numpy as np
+import h5py
+from geobipy import StatArray
+
+#%%
+# StatArray
+
+# Instantiate a StatArray
+x = StatArray(np.arange(10.0), name = 'an Array', units = 'some units')
+
+# Write the StatArray to a HDF file.
+with h5py.File("x.h5", 'w') as f:
+    x.toHdf(f, "x")
+
+# Read the StatArray back in.
+with h5py.File("x.h5", 'r') as f:
+    y = StatArray.fromHdf(f, 'x')
+
+print('x', x)
+print('y', y)
+
+#%%
+# There are actually steps within the "toHdf" function.
+# First, space is created within the HDF file and second, the data is written to that space
+# These functions are split because during the execution of a parallel enabled program,
+# all the space within the HDF file needs to be allocated before we can write to the file
+# using multiple cores.
+
+# Write the StatArray to a HDF file.
+with h5py.File("x.h5", 'w') as f:
+    x.createHdf(f, "x")
+    x.writeHdf(f, "x")
+
+# Read the StatArray back in.
+with h5py.File("x.h5", 'r') as f:
+    y = StatArray.fromHdf(f, 'x')
+
+print('x', x)
+print('y', y)
+
+#%%
+# The create and write HDF methods also allow extra space to be allocated so that
+# the extra memory can be written later, perhaps by multiple cores.
+# Here we specify space for 2 arrays, the memory is stored contiguously as a numpy array.
+# We then write to only the first index.
+
+# Write the StatArray to a HDF file.
+with h5py.File("x.h5", 'w') as f:
+    x.createHdf(f, "x", nRepeats=2)
+    x.writeHdf(f, "x", index=0)
+
+# Read the StatArray back in.
+with h5py.File("x.h5", 'r') as f:
+    y = StatArray.fromHdf(f, 'x', index=0)
+
+print('x', x)
+print('y', y)
+
+
+#%%
+# The duplication can also be a shape.
+
+# Write the StatArray to a HDF file.
+with h5py.File("x.h5", 'w') as f:
+    x.createHdf(f, "x", nRepeats=(2, 2))
+    x.writeHdf(f, "x", index=(0, 0))
+
+# Read the StatArray back in.
+with h5py.File("x.h5", 'r') as f:
+    y = StatArray.fromHdf(f, 'x', index=(0, 0))
+
+print('x', x)
+print('y', y)
+
+#%%
+# Similarly, we can duplicate a 2D array with an extra 2D duplication
+
+x = StatArray(np.random.randn(2, 2), name = 'an Array', units = 'some units')
+# Write the StatArray to a HDF file.
+with h5py.File("x.h5", 'w') as f:
+    x.createHdf(f, "x", nRepeats=(2, 2))
+    x.writeHdf(f, "x", index=(0, 0))
+
+# Read the StatArray back in.
+with h5py.File("x.h5", 'r') as f:
+    y = StatArray.fromHdf(f, 'x', index=(0, 0))
+
+print('x', x)
+print('y', y)
\ No newline at end of file
diff --git a/docs/_downloads/7f0310ad46a1dedad4717ad1e7d657a4/plot_tempest_datapoint.zip b/docs/_downloads/7f0310ad46a1dedad4717ad1e7d657a4/plot_tempest_datapoint.zip
new file mode 100644
index 00000000..af7f94da
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diff --git a/docs/_downloads/7f3bb43ebb9eba21697953fbfc04f6d2/plot_rectilinear_mesh_3d.py b/docs/_downloads/7f3bb43ebb9eba21697953fbfc04f6d2/plot_rectilinear_mesh_3d.py
new file mode 100644
index 00000000..d07cbb5c
--- /dev/null
+++ b/docs/_downloads/7f3bb43ebb9eba21697953fbfc04f6d2/plot_rectilinear_mesh_3d.py
@@ -0,0 +1,152 @@
+"""
+3D Rectilinear Mesh
+-------------------
+This 3D rectilinear mesh defines a grid with straight cell boundaries.
+
+"""
+
+#%%
+from geobipy import StatArray
+from geobipy import RectilinearMesh3D
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+
+
+#%%
+# Specify some cell centres in x and y
+x = StatArray(np.arange(10.0), 'Easting', 'm')
+y = StatArray(np.arange(15.0), 'Northing', 'm')
+z = StatArray(np.arange(20.0), 'Depth', 'm')
+
+rm = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+rm1 = rm[:5, :5, :5]
+rm2 = rm[:, :, 5]
+rm3 = rm[:, 5, :]
+rm4 = rm[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+rm2.plot_grid()
+plt.subplot(232)
+rm3.plot_grid()
+plt.subplot(233)
+rm4.plot_grid()
+
+#%%
+rm2 = rm[:, 5, 5]
+rm3 = rm[5, :, 5]
+rm4 = rm[5, 5, :]
+
+plt.subplot(234)
+rm2.plot_grid()
+plt.subplot(235)
+rm3.plot_grid()
+plt.subplot(236)
+rm4.plot_grid()
+
+#%%
+with h5py.File('rm3d.h5', 'w') as f:
+    rm.createHdf(f, 'test')
+    rm.writeHdf(f, 'test')
+
+with h5py.File('rm3d.h5', 'r') as f:
+    rm2 = RectilinearMesh3D.fromHdf(f['test'])
+
+rm.pyvista_mesh().save('rm3d.vtk')
+
+
+xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+rm = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z, z_relative_to=z_re)
+
+rm1 = rm[:5, :5, :5]
+rm2 = rm[:, :, 5]
+rm3 = rm[:, 5, :]
+rm4 = rm[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+rm2.plot_grid()
+plt.subplot(232)
+rm3.plot_grid()
+plt.subplot(233)
+rm4.plot_grid()
+
+#%%
+# We can plot the mesh in 3D!
+pv = rm.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+mesh = rm.pyvista_mesh().save('rm3d_re1.vtk')
+
+x_re = StatArray(np.sin(np.repeat(rm.y.centres[:, None], rm.z.nCells, 1)), "x_re")
+
+xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+rm = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, z_edges=z, z_relative_to=z_re)
+
+rm1 = rm[:5, :5, :5]
+rm2 = rm[:, :, 5]
+rm3 = rm[:, 5, :]
+rm4 = rm[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+rm2.plot_grid()
+plt.subplot(232)
+rm3.plot_grid()
+plt.subplot(233)
+rm4.plot_grid()
+
+#%%
+# We can plot the mesh in 3D!
+pv = rm.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+mesh = rm.pyvista_mesh().save('rm3d_re2.vtk')
+
+
+xx, yy = np.meshgrid(rm.z.centres, rm.y.centres)
+x_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "x_re")
+
+xx, yy = np.meshgrid(rm.z.centres, rm.x.centres)
+y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+rm = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+
+rm1 = rm[:5, :5, :5]
+rm2 = rm[:, :, 5]
+rm3 = rm[:, 5, :]
+rm4 = rm[5, :, :]
+
+plt.figure()
+plt.subplot(231)
+rm2.plot_grid()
+plt.subplot(232)
+rm3.plot_grid()
+plt.subplot(233)
+rm4.plot_grid()
+
+#%%
+# We can plot the mesh in 3D!
+pv = rm.pyvista_plotter()
+
+#%%
+# We can plot the mesh in 3D!
+mesh = rm.pyvista_mesh().save('rm3d_re3.vtk')
+
+with h5py.File('rm3d.h5', 'w') as f:
+    rm.toHdf(f, 'test')
+
+with h5py.File('rm3d.h5', 'r') as f:
+    rm2 = RectilinearMesh3D.fromHdf(f['test'])
+
+rm2.pyvista_mesh().save('rm3d_read.vtk')
+
+plt.show()
diff --git a/docs/_downloads/82b91681ed5787c4d84b792aeddadc44/plot_skytem_datapoint.zip b/docs/_downloads/82b91681ed5787c4d84b792aeddadc44/plot_skytem_datapoint.zip
new file mode 100644
index 00000000..da975f9b
Binary files /dev/null and b/docs/_downloads/82b91681ed5787c4d84b792aeddadc44/plot_skytem_datapoint.zip differ
diff --git a/docs/_downloads/8a8596f0390404145eca225a8f310218/plot_frequency_dataset.py b/docs/_downloads/8a8596f0390404145eca225a8f310218/plot_frequency_dataset.py
new file mode 100644
index 00000000..74a873f8
--- /dev/null
+++ b/docs/_downloads/8a8596f0390404145eca225a8f310218/plot_frequency_dataset.py
@@ -0,0 +1,266 @@
+"""
+Frequency domain dataset
+------------------------
+"""
+#%%
+import matplotlib.pyplot as plt
+from geobipy import CircularLoop
+from geobipy import FdemSystem
+from geobipy import FdemData
+import h5py
+import numpy as np
+
+
+#%%
+# Defining data using a frequency domain system
+# +++++++++++++++++++++++++++++++++++++++++++++
+
+#%%
+# We can start by defining the frequencies, transmitter loops, and receiver loops
+# For each frequency we need to define a pair of loops
+frequencies = np.asarray([395.0, 822.0, 3263.0, 8199.0, 38760.0, 128755.0])
+
+#%%
+# Transmitter positions are defined relative to the observation locations in the data
+# This is usually a constant offset for all data points.
+transmitters = CircularLoop(orientation=['z','z','x','z','z','z'],
+                             moment=np.r_[1, 1, -1, 1, 1, 1],
+                             x = np.r_[0,0,0,0,0,0],
+                             y = np.r_[0,0,0,0,0,0],
+                             z = np.r_[0,0,0,0,0,0],
+                             pitch = np.r_[0,0,0,0,0,0],
+                             roll = np.r_[0,0,0,0,0,0],
+                             yaw = np.r_[0,0,0,0,0,0],
+                             radius = np.r_[1,1,1,1,1,1])
+
+#%%
+# Receiver positions are defined relative to the transmitter
+receivers = CircularLoop(orientation=['z','z','x','z','z','z'],
+                             moment=np.r_[1, 1, -1, 1, 1, 1],
+                             x = np.r_[7.91, 7.91, 9.03, 7.91, 7.91, 7.89],
+                             y = np.r_[0,0,0,0,0,0],
+                             z = np.r_[0,0,0,0,0,0],
+                             pitch = np.r_[0,0,0,0,0,0],
+                             roll = np.r_[0,0,0,0,0,0],
+                             yaw = np.r_[0,0,0,0,0,0],
+                             radius = np.r_[1,1,1,1,1,1])
+
+# Instantiate the system for the data
+system = FdemSystem(frequencies=frequencies, transmitter=transmitters, receiver=receivers)
+
+# Create some data with random co-ordinates
+x = np.random.randn(100)
+y = np.random.randn(100)
+z = np.random.randn(100)
+
+data = FdemData(x=x, y=-y, z=z, system = system)
+
+#%%
+# Reading in the Data
+# +++++++++++++++++++
+# Of course measured field data is stored on disk. So instead we can read data from file.
+
+#%%
+dataFolder = "..//..//supplementary//data//"
+# The data file name
+dataFile = dataFolder + 'Resolve2.txt'
+# The EM system file name
+systemFile = dataFolder + 'FdemSystem2.stm'
+
+#%%
+# Read in a data set from file.
+FD1 = FdemData.read_csv(dataFile, systemFile)
+
+#%%
+# Take a look at the channel names
+for name in FD1.channel_names:
+    print(name)
+
+# #%%
+# # Get data points by slicing
+# FDa = FD1[10:]
+# FD1 = FD1[:10]
+
+# #%%
+# # Append data sets together
+# FD1.append(FDa)
+
+
+# #%%
+# # Plot the locations of the data points
+# plt.figure(figsize=(8,6))
+# _ = FD1.scatter2D();
+
+# #%%
+# # Plot all the data along the specified line
+# plt.figure(figsize=(8,6))
+# _ = FD1.plotLine(30010.0, log=10);
+
+# #%%
+# # Or, plot specific channels in the data
+# plt.figure(figsize=(8,6))
+# _ = FD1.plot(channels=[0,11,8], log=10, linewidth=0.5);
+
+#%%
+# Read in a second data set
+FD2 = FdemData.read_csv(dataFilename=dataFolder + 'Resolve1.txt', system=dataFolder + 'FdemSystem1.stm')
+
+#%%
+# We can create maps of the elevations in two separate figures
+plt.figure(figsize=(8,6))
+_ = FD1.map(dx=50.0, dy=50.0, mask = 200.0)
+plt.axis('equal');
+
+#%%
+
+plt.figure(figsize=(8,6))
+_ = FD2.map(dx=50.0, dy=50.0, mask = 200.0)
+plt.axis('equal');
+
+#%%
+# Or, we can plot both data sets in one figure to see their positions relative
+# to each other.
+#
+# In this case, I use a 2D scatter plot of the data point co-ordinates, and pass
+# one of the channels as the colour.
+
+plt.figure(figsize=(8,6))
+_ = FD1.scatter2D(s=1.0, c=FD1.data[:, 0])
+_ = FD2.scatter2D(s=1.0, c=FD2.data[:, 0], cmap='jet');
+
+#%%
+# Or, interpolate the values to create a gridded "map". mapChannel will
+# interpolate the specified channel number.
+
+plt.figure(figsize=(8,6))
+_ = FD1.mapData(channel=3, system=0, dx=200, dy=200, mask=250)
+plt.axis('equal');
+
+#%%
+# Export the data to VTK
+FD1.to_vtk('FD_one.vtk')
+# FD2.to_vtk('FD_two.vtk')
+
+#%%
+# Obtain a line from the data set
+# +++++++++++++++++++++++++++++++
+
+#%%
+# Take a look at the line numbers in the dataset
+print(np.unique(FD1.lineNumber))
+
+#%%
+L = FD1.line(30010.0)
+
+#%%
+# A summary will now show the properties of the line.
+
+print(L.summary)
+
+#%%
+# And we can scatter2D the points in the line.
+
+plt.figure(figsize=(8,6))
+_ = L.scatter2D();
+
+#%%
+# We can specify the axis along which to plot.
+# xAxis can be index, x, y, z, r2d, r3d
+plt.figure(figsize=(8,6))
+_ = FD1.plot_data(channels=np.r_[0, 11, 8], log=10, linewidth=0.5);
+
+with h5py.File('fdem.h5', 'w') as f:
+    FD1.createHdf(f, 'fdem')
+    FD1.writeHdf(f, 'fdem')
+
+with h5py.File('fdem.h5', 'r') as f:
+    FD3 = FdemData.fromHdf(f['fdem'])
+
+with h5py.File('fdem.h5', 'r') as f:
+    fdp = FdemData.fromHdf(f['fdem'], index=0)
+
+
+# #%%
+# # Obtain a single datapoint from the data set
+# # +++++++++++++++++++++++++++++++++++++++++++
+# #
+# # Checkout :ref:`Frequency domain datapoint` for an example
+# # about how to use a datapoint once it is instantiated.
+# dp = FD1.datapoint(0)
+
+# # Prepare the dataset so that we can read a point at a time.
+# Dataset = FdemData._initialize_sequential_reading(dataFile, systemFile)
+# # Get a datapoint from the file.
+# DataPoint = Dataset._read_record()
+
+plt.show()
+
+#%%
+# File Format for frequency domain data
+# +++++++++++++++++++++++++++++++++++++
+# Here we describe the file format for frequency domain data.
+#
+# The data columns are read in according to the column names in the first line.
+#
+# In this description, the column name or its alternatives are given followed by what the name represents.
+# Optional columns are also described.
+#
+# Required columns
+# ________________
+# line
+#     Line number for the data point
+# fid
+#     Unique identification number of the data point
+# x or northing or n
+#     Northing co-ordinate of the data point, (m)
+# y or easting or e
+#     Easting co-ordinate of the data point, (m)
+# z or alt
+#     Altitude of the transmitter coil above ground level (m)
+# elevation
+#     Elevation of the ground at the data point (m)
+# I_<frequency[0]> Q_<frequency[0]> ... I_<frequency[last]> Q_<frequency[last]>  - with the number and square brackets
+#     The measurements for each frequency specified in the accompanying system file.
+#     I is the real inphase measurement in (ppm)
+#     Q is the imaginary quadrature measurement in (ppm)
+# Optional columns
+# ________________
+# InphaseErr[0] QuadratureErr[0] ... InphaseErr[nFrequencies] QuadratureErr[nFrequencies]
+#     Estimates of standard deviation for each inphase and quadrature measurement.
+#     These must appear after the data colums.
+#
+# Example Header
+# ______________
+# Line fid easting northing elevation height I_380 Q_380 ... ... I_129550 Q_129550
+
+#%%
+# File Format for a frequency domain system
+# +++++++++++++++++++++++++++++++++++++++++
+# .. role:: raw-html(raw)
+#    :format: html
+#
+# The system file is structured using columns with the first line containing header information
+#
+# Each subsequent row contains the information for each measurement frequency
+#
+# freq
+#     Frequency of the channel
+# tor
+#     Orientation of the transmitter loop 'x', or 'z'
+# tmom
+#     Transmitter moment
+# tx, ty, tx
+#     Offset of the transmitter with respect to the observation locations
+# ror
+#     Orientation of the receiver loop 'x', or 'z'
+# rmom
+#     Receiver moment
+# rx, ry, rz
+#     Offset of the receiver with respect to the transmitter location
+#
+# Example system files are contained in
+# `the supplementary folder`_ in this repository
+#
+# .. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
+#
+# See the Resolve.stm files.
\ No newline at end of file
diff --git a/docs/_downloads/950360871c2acdf8ad1b1627e54009da/plot_resolve_datapoint.py b/docs/_downloads/950360871c2acdf8ad1b1627e54009da/plot_resolve_datapoint.py
new file mode 100644
index 00000000..1db92427
--- /dev/null
+++ b/docs/_downloads/950360871c2acdf8ad1b1627e54009da/plot_resolve_datapoint.py
@@ -0,0 +1,227 @@
+"""
+Frequency domain datapoint
+--------------------------
+"""
+#%%
+from os.path import join
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+from geobipy import CircularLoop
+from geobipy import FdemSystem
+from geobipy import FdemData
+from geobipy import FdemDataPoint
+from geobipy import RectilinearMesh1D
+from geobipy import Model
+from geobipy import StatArray
+from geobipy import Distribution
+
+# Instantiating a frequency domain data point
+# +++++++++++++++++++++++++++++++++++++++++++
+#
+# To instantiate a frequency domain datapoint we need to define some
+# characteristics of the acquisition system.
+#
+# We need to define the frequencies in Hz of the transmitter,
+# and the geometery of the loops used for each frequency.
+
+frequencies = np.asarray([380.0, 1776.0, 3345.0, 8171.0, 41020.0, 129550.0])
+
+# Transmitter positions are defined relative to the observation locations in the data
+# This is usually a constant offset for all data points.
+transmitters = CircularLoop(orientation=['z','z','x','z','z','z'],
+                             moment=np.r_[1, 1, -1, 1, 1, 1],
+                             x = np.r_[0,0,0,0,0,0],
+                             y = np.r_[0,0,0,0,0,0],
+                             z = np.r_[0,0,0,0,0,0],
+                             pitch = np.r_[0,0,0,0,0,0],
+                             roll = np.r_[0,0,0,0,0,0],
+                             yaw = np.r_[0,0,0,0,0,0],
+                             radius = np.r_[1,1,1,1,1,1])
+
+# Receiver positions are defined relative to the transmitter
+receivers = CircularLoop(orientation=['z','z','x','z','z','z'],
+                             moment=np.r_[1, 1, -1, 1, 1, 1],
+                             x = np.r_[7.91, 7.91, 9.03, 7.91, 7.91, 7.89],
+                             y = np.r_[0,0,0,0,0,0],
+                             z = np.r_[0,0,0,0,0,0],
+                             pitch = np.r_[0,0,0,0,0,0],
+                             roll = np.r_[0,0,0,0,0,0],
+                             yaw = np.r_[0,0,0,0,0,0],
+                             radius = np.r_[1,1,1,1,1,1])
+
+# Now we can instantiate the system.
+fds = FdemSystem(frequencies, transmitters, receivers)
+
+# And use the system to instantiate a datapoint
+#
+# Note the extra arguments that can be used to create the data point.
+# data is for any observed data one might have, while std are the estimated standard
+# deviations of those observed data.
+#
+# Define some in-phase then quadrature data for each frequency.
+data = np.r_[145.3, 435.8, 260.6, 875.1, 1502.7, 1516.9,
+             217.9, 412.5, 178.7, 516.5, 405.7, 255.7]
+
+fdp = FdemDataPoint(x=0.0, y=0.0, z=30.0, elevation=0.0,
+                    data=data, std=None, predictedData=None,
+                    system=fds, lineNumber=0.0, fiducial=0.0)
+
+# plt.figure()
+# _ = fdp.plot()
+
+# Obtaining a datapoint from a dataset
+# ++++++++++++++++++++++++++++++++++++
+#
+# More often than not, our observed data is stored in a file on disk.
+# We can read in a dataset and pull datapoints from it.
+#
+# For more information about the frequency domain data set see :ref:`Frequency domain dataset`
+
+# Set some paths and file names
+dataFolder = "..//..//supplementary//Data//"
+# The data file name
+dataFile = dataFolder + 'Resolve2.txt'
+# The EM system file name
+systemFile = dataFolder + 'FdemSystem2.stm'
+
+#%%
+# Initialize and read an EM data set
+# Prepare the dataset so that we can read a point at a time.
+Dataset = FdemData._initialize_sequential_reading(dataFile, systemFile)
+# Get a datapoint from the file.
+fdp = Dataset._read_record()
+#%%
+
+# # Initialize and read an EM data set
+# D = FdemData.read_csv(dataFile,systemFile)
+
+# # Get a data point from the dataset
+# fdp = D.datapoint(0)
+# plt.figure()
+# _ = fdp.plot()
+
+# Using a resolve datapoint
+# +++++++++++++++++++++++++
+
+# We can define a 1D layered earth model, and use it to predict some data
+nCells = 19
+par = StatArray(np.linspace(0.01, 0.1, nCells), "Conductivity", "$\frac{S}{m}$")
+depth = StatArray(np.arange(nCells+1) * 10.0, "Depth", 'm')
+depth[-1] = np.inf
+mod = Model(mesh=RectilinearMesh1D(edges=depth), values=par)
+
+# Forward model the data
+fdp.forward(mod)
+
+plt.figure()
+plt.subplot(121)
+_ = mod.pcolor(transpose=True)
+plt.subplot(122)
+_ = fdp.plot_predicted()
+plt.tight_layout()
+
+# Compute the sensitivity matrix for a given model
+J = fdp.sensitivity(mod)
+
+plt.figure()
+_ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+# Attaching statistical descriptors to the resolve datapoint
+# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+# Set values of relative and additive error for both systems.
+fdp.relative_error = 0.05
+fdp.additive_error = 10.0
+# Define a multivariate log normal distribution as the prior on the predicted data.
+fdp.predictedData.prior = Distribution('MvLogNormal', fdp.data[fdp.active], fdp.std[fdp.active]**2.0, prng=prng)
+
+# This allows us to evaluate the likelihood of the predicted data
+print(fdp.likelihood(log=True))
+# Or the misfit
+print(fdp.data_misfit())
+
+# Plot the misfits for a range of half space conductivities
+plt.figure()
+_ = fdp.plot_halfspace_responses(-6.0, 4.0, 200)
+
+plt.title("Halfspace responses");
+
+# We can perform a quick search for the best fitting half space
+halfspace = fdp.find_best_halfspace()
+print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+plt.figure()
+_ = fdp.plot()
+_ = fdp.plot_predicted()
+
+# Compute the misfit between observed and predicted data
+print(fdp.data_misfit())
+
+# We can attach priors to the height of the datapoint,
+# the relative error multiplier, and the additive error noise floor
+
+
+# Define the distributions used as priors.
+zPrior = Distribution('Uniform', min=fdp.z - 2.0, max=fdp.z + 2.0, prng=prng)
+relativePrior = Distribution('Uniform', min=0.01, max=0.5, prng=prng)
+additivePrior = Distribution('Uniform', min=5, max=15, prng=prng)
+fdp.set_priors(z_prior=zPrior, relative_error_prior=relativePrior, additive_error_prior=additivePrior, prng=prng)
+
+
+# In order to perturb our solvable parameters, we need to attach proposal distributions
+z_proposal = Distribution('Normal', mean=fdp.z, variance = 0.01, prng=prng)
+relativeProposal = Distribution('MvNormal', mean=fdp.relative_error, variance=2.5e-7, prng=prng)
+additiveProposal = Distribution('MvLogNormal', mean=fdp.additive_error, variance=1e-4, prng=prng)
+fdp.set_proposals(relativeProposal, additiveProposal, z_proposal=z_proposal)
+
+# With priors set we can auto generate the posteriors
+fdp.set_posteriors()
+
+nCells = 19
+par = StatArray(np.linspace(0.01, 0.1, nCells), "Conductivity", "$\frac{S}{m}$")
+depth = StatArray(np.arange(nCells+1) * 10.0, "Depth", 'm')
+depth[-1] = np.inf
+mod = Model(mesh=RectilinearMesh1D(edges=depth), values=par)
+fdp.forward(mod)
+
+# Perturb the datapoint and record the perturbations
+for i in range(10):
+    fdp.perturb()
+    fdp.update_posteriors()
+
+
+# Plot the posterior distributions
+fig = plt.figure()
+fdp.plot_posteriors(overlay=fdp)
+
+import h5py
+with h5py.File('fdp.h5', 'w') as f:
+    fdp.createHdf(f, 'fdp', withPosterior=True)
+    fdp.writeHdf(f, 'fdp', withPosterior=True)
+
+with h5py.File('fdp.h5', 'r') as f:
+    fdp1 = FdemDataPoint.fromHdf(f['fdp'])
+
+plt.figure()
+fdp1.plot_posteriors(overlay=fdp1)
+
+import h5py
+with h5py.File('fdp.h5', 'w') as f:
+    fdp.createHdf(f, 'fdp', withPosterior=True, add_axis=np.arange(10.0))
+
+    for i in range(10):
+        fdp.writeHdf(f, 'fdp', withPosterior=True, index=i)
+
+from geobipy import FdemData
+with h5py.File('fdp.h5', 'r') as f:
+    fdp1 = FdemDataPoint.fromHdf(f['fdp'], index=0)
+    fdp2 = FdemData.fromHdf(f['fdp'])
+
+fdp1.plot_posteriors(overlay=fdp1)
+
+plt.show()
+# %%
\ No newline at end of file
diff --git a/docs/_downloads/9570534d71f4f6a00b8f20360000c3ff/plot_histogram_1d.py b/docs/_downloads/9570534d71f4f6a00b8f20360000c3ff/plot_histogram_1d.py
new file mode 100644
index 00000000..10a7fc18
--- /dev/null
+++ b/docs/_downloads/9570534d71f4f6a00b8f20360000c3ff/plot_histogram_1d.py
@@ -0,0 +1,156 @@
+"""
+Histogram 1D
+------------
+
+This histogram class allows efficient updating of histograms, plotting and
+saving as HDF5
+"""
+
+#%%
+from geobipy.src.classes.mesh.RectilinearMesh1D import RectilinearMesh1D
+import h5py
+from geobipy import StatArray
+from geobipy import Histogram
+import numpy as np
+import matplotlib.pyplot as plt
+
+#%%
+# Histogram with regular bins
+# +++++++++++++++++++++++++++
+
+# Create regularly spaced bins
+mesh = RectilinearMesh1D(edges=StatArray(np.linspace(-3.0, 3.0, 101), 'bins', 'm'))
+
+#%%
+# Set the histogram using the bins, and update
+H = Histogram(mesh=mesh)
+
+#%%
+# We can update the histogram with some new values
+H.update(np.random.randn(1000), trim=True)
+
+# Plot the histogram
+plt.figure()
+plt.subplot(221)
+_ = H.plot()
+plt.subplot(222)
+_ = H.pdf.bar()
+plt.subplot(223)
+H.pmf.bar()
+plt.subplot(224)
+H.cdf().bar()
+
+#%%
+# Get the median, and 95% confidence values
+print(H.credible_intervals(percent=95.0))
+
+plt.figure()
+H.plot()
+H.plotCredibleIntervals()
+H.plotMean()
+H.plotMedian()
+
+#%%
+# Histogram with irregular bins
+# +++++++++++++++++++++++++++++
+
+# Create irregularly spaced bins
+x = np.cumsum(np.arange(10, dtype=np.float64))
+irregularBins = np.hstack([-x[::-1], x[1:]])
+
+#%%
+# Create a named StatArray
+edges = StatArray(irregularBins, 'irregular bins')
+mesh = RectilinearMesh1D(edges = edges)
+
+#%%
+# Instantiate the histogram with bin edges
+H = Histogram(mesh=mesh)
+
+# Update the histogram
+H.update((np.random.randn(10000)*20.0) - 10.0)
+
+#%%
+# Plot the histogram
+plt.figure()
+plt.subplot(211)
+_ = H.plot()
+plt.subplot(212)
+_ = H.plot(normalize=True)
+
+plt.figure()
+H.plot()
+H.plotCredibleIntervals()
+H.plotMean()
+H.plotMedian()
+
+#%%
+# We can plot the histogram as a pcolor plot
+plt.figure()
+_ = H.pcolor(grid=True, transpose=True)
+
+#%%
+# Histogram with linear space entries that are logged internally
+# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+# Create some bins spaced logarithmically
+mesh = RectilinearMesh1D(edges = StatArray(np.logspace(-5, 3), 'positive bins'), log=10)
+
+#%%
+# Instantiate the Histogram with log=10
+H = Histogram(mesh)
+
+#%%
+# The update takes in the numbers in linear space and takes their log=10
+H.update(10.0**(np.random.randn(1000)*2.0), trim=True)
+
+#%%
+plt.figure()
+plt.subplot(211)
+_ = H.plot()
+
+import h5py
+with h5py.File('h1d.h5', 'w') as f:
+    H.toHdf(f, 'h1d')
+
+with h5py.File('h1d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h1d'])
+
+plt.subplot(212)
+_ = H1.plot()
+
+
+#%%
+mesh = RectilinearMesh1D(edges=StatArray(np.linspace(-3.0, 3.0, 101), 'bins', 'm'))
+#%%
+# Set the histogram using the bins, and update
+H = Histogram(mesh=mesh)
+
+#%%
+# We can update the histogram with some new values
+H.update(np.random.randn(1000), trim=True)
+
+import h5py
+with h5py.File('h1d.h5', 'w') as f:
+    H.createHdf(f, 'h1d', add_axis=StatArray(np.arange(3.0), "Name", "Units"))
+    H.writeHdf(f, 'h1d', index=0)
+    H.update(np.random.randn(1000), trim=True)
+    H.writeHdf(f, 'h1d', index=1)
+    H.update(np.random.randn(1000), trim=True)
+    H.writeHdf(f, 'h1d', index=2)
+
+with h5py.File('h1d.h5', 'r') as f:
+    H1 = Histogram.fromHdf(f['h1d'])
+    H2 = Histogram.fromHdf(f['h1d'], index=0)
+    H3 = Histogram.fromHdf(f['h1d'], index=1)
+    H4 = Histogram.fromHdf(f['h1d'], index=2)
+
+
+print(H4.summary)
+
+# plt.figure()
+# plt.subplot(211)
+# _ = H1.plot()
+# plt.subplot(212)
+# _ = H4.plot()
+
+plt.show()
\ No newline at end of file
diff --git a/docs/_downloads/a209de4dae1a6f3f3daeab70fe6f557a/plot_inference_1d_skytem.py b/docs/_downloads/a209de4dae1a6f3f3daeab70fe6f557a/plot_inference_1d_skytem.py
new file mode 100644
index 00000000..14a3f440
--- /dev/null
+++ b/docs/_downloads/a209de4dae1a6f3f3daeab70fe6f557a/plot_inference_1d_skytem.py
@@ -0,0 +1,89 @@
+"""
+Running GeoBIPy to invert Skytem data
+++++++++++++++++++++++++++++++++++++++
+"""
+
+import os
+import sys
+import pathlib
+from datetime import timedelta
+import time
+import numpy as np
+from geobipy import Inference3D
+from geobipy import user_parameters
+from geobipy import get_prng
+
+def checkCommandArguments():
+    """Check the users command line arguments. """
+    import argparse
+    # warnings.filterwarnings('error')
+
+    Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+    Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem', 'tempest', 'resolve']")
+    Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+    return Parser.parse_args()
+
+#%%
+np.random.seed(0)
+
+args = checkCommandArguments()
+sys.path.append(os.getcwd())
+
+models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+data_type = "Skytem"
+model_type = models[args.index]
+
+#%%
+# The directory where HDF files will be stored
+#%%
+file_path = os.path.join(data_type, model_type)
+pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+for filename in os.listdir(file_path):
+    try:
+        if os.path.isfile(file_path) or os.path.islink(file_path):
+            os.unlink(file_path)
+    except Exception as e:
+        print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+output_directory = file_path
+
+data_filename = data_type + '_' + model_type
+
+supplementary = "..//..//supplementary//"
+parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+inputFile = pathlib.Path(parameter_file)
+assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+output_directory = pathlib.Path(output_directory)
+assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+print('Using user input file {}'.format(parameter_file))
+print('Output files will be produced at {}'.format(output_directory))
+
+kwargs = user_parameters.read(inputFile)
+
+kwargs['n_markov_chains'] = 5000
+
+kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+kwargs['system_filename'] = [supplementary + "//data//" + x for x in kwargs['system_filename']]
+
+# Everyone needs the system classes read in early.
+data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+# Start keeping track of time.
+t0 = time.time()
+
+seed = 146100583096709124601953385843316024947
+prng = get_prng(seed=seed)
+
+inference3d = Inference3D(data, prng=prng)
+inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+inference3d.infer(index=2, **kwargs)
\ No newline at end of file
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diff --git a/docs/_downloads/b65392b576618a906ad35b58f026489b/plot_model_2d.py b/docs/_downloads/b65392b576618a906ad35b58f026489b/plot_model_2d.py
new file mode 100644
index 00000000..bf428207
--- /dev/null
+++ b/docs/_downloads/b65392b576618a906ad35b58f026489b/plot_model_2d.py
@@ -0,0 +1,56 @@
+"""
+2D Rectilinear Model
+--------------------
+This 2D rectilinear model defines a grid with straight cell boundaries.
+
+"""
+
+#%%
+from geobipy import StatArray
+from geobipy import RectilinearMesh2D
+from geobipy import Model
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+#%%
+# Specify some cell centres in x and y
+x = StatArray(np.arange(11.0), 'Easting', 'm')
+y = StatArray(np.arange(11.0), 'Northing', 'm')
+mesh = RectilinearMesh2D(x_edges=x, y_edges=y)
+
+xx, yy = np.meshgrid(mesh.x.centres, mesh.y.centres)
+values = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Values")
+
+mod = Model(mesh=mesh, values = values)
+
+plt.figure()
+mod.pcolor()
+
+mod2 = mod.resample(0.5, 0.5)
+mod3 = mod.resample(1.5, 1.5)
+plt.figure()
+plt.subplot(121)
+mod2.pcolor()
+plt.axis('equal')
+plt.subplot(122)
+mod3.pcolor()
+plt.axis('equal')
+
+
+# #%%
+# # We can plot the mesh in 3D!
+# pv = rm.pyvista_plotter()
+# pv.show()
+
+# rm.to_vtk('Model3D.vtk')
+
+with h5py.File('Model2D.h5', 'w') as f:
+    mod.toHdf(f, 'model')
+
+with h5py.File('Model2D.h5', 'r') as f:
+    mod2 = Model.fromHdf(f['model'])
+
+
+plt.show()
\ No newline at end of file
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diff --git a/docs/_downloads/cad36827134302329c83d6bd810a1239/plot_tempest_dataset.py b/docs/_downloads/cad36827134302329c83d6bd810a1239/plot_tempest_dataset.py
new file mode 100644
index 00000000..8fda1079
--- /dev/null
+++ b/docs/_downloads/cad36827134302329c83d6bd810a1239/plot_tempest_dataset.py
@@ -0,0 +1,88 @@
+"""
+Tempest dataset
+--------------------
+"""
+#%%
+import h5py
+from geobipy import plotting as cP
+from os.path import join
+import matplotlib.pyplot as plt
+import numpy as np
+from geobipy import TempestData
+
+#%%
+# Reading in the Data
+# +++++++++++++++++++
+
+#%%
+dataFolder = "..//..//supplementary//data//"
+
+# # The data file name
+# dataFiles = dataFolder + 'Tempest.nc'
+# # The EM system file name
+# systemFiles = dataFolder + 'Tempest.stm'
+
+# #%%
+# # Read in the data from file
+# TD = TempestData.read_netcdf(dataFiles, systemFiles)
+
+# TD.write_csv(dataFolder + 'Tempest.csv')
+TD = TempestData.read_csv(dataFolder + 'tempest_saline_clay.csv', system_filename=dataFolder + 'Tempest.stm')
+
+
+#%%
+# Plot the locations of the data points
+plt.figure(figsize=(8,6))
+_ = TD.scatter2D()
+plt.title("Scatter plot")
+
+#%%
+# Plot all the data along the specified line
+plt.figure(figsize=(8,6))
+_ = TD.plotLine(0.0)
+plt.title('Line {}'.format(225401.0))
+
+#%%
+# Or, plot specific channels in the data
+plt.figure(figsize=(8,6))
+_ = TD.plot_data(system=0, channels=[0, 6, 18])
+plt.title("3 channels of data")
+
+#%%
+plt.figure()
+_ = TD.pcolor(system=0)
+plt.title('Data as an array')
+
+#%%
+plt.figure()
+ax = TD.scatter2D(c=TD.data[:, TD.channel_index(system=0, channel=10)], equalize=True)
+plt.axis('equal')
+plt.title(f"scatter plot of channel {TD.channel_index(system=0, channel=10)}")
+
+with h5py.File('tdem.h5', 'w') as f:
+    TD.createHdf(f, 'tdem')
+    TD.writeHdf(f, 'tdem')
+
+with h5py.File('tdem.h5', 'r') as f:
+    TD3 = TempestData.fromHdf(f['tdem'])
+
+with h5py.File('tdem.h5', 'r') as f:
+    tdp = TempestData.fromHdf(f['tdem'], index=0)
+
+
+# #%%
+# # Obtain a line from the data set
+# # +++++++++++++++++++++++++++++++
+# line = TD.line(0.0)
+
+# #%%
+# plt.figure()
+# _ = line.scatter2D()
+# plt.title('Channel')
+
+# #%%
+# plt.figure()
+# _ = line.plot_data(xAxis='index', log=10)
+# plt.title("All data along line")
+
+plt.show()
diff --git a/docs/_downloads/d11ca9540d0abc72d005f3af4dbab908/plot_tempest_dataset.zip b/docs/_downloads/d11ca9540d0abc72d005f3af4dbab908/plot_tempest_dataset.zip
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diff --git a/docs/_downloads/e7cc18385c0a8722277c7651cb3e9fdb/plot_inference_1d_tempest.py b/docs/_downloads/e7cc18385c0a8722277c7651cb3e9fdb/plot_inference_1d_tempest.py
new file mode 100644
index 00000000..87823af0
--- /dev/null
+++ b/docs/_downloads/e7cc18385c0a8722277c7651cb3e9fdb/plot_inference_1d_tempest.py
@@ -0,0 +1,90 @@
+"""
+Running GeoBIPy to invert Tempest data
+++++++++++++++++++++++++++++++++++++++
+"""
+
+import os
+import sys
+import pathlib
+from datetime import timedelta
+import time
+import numpy as np
+from geobipy import Inference3D
+from geobipy import user_parameters
+from geobipy import get_prng
+
+def checkCommandArguments():
+    """Check the users command line arguments. """
+    import argparse
+    # warnings.filterwarnings('error')
+
+    Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+    Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem_512', 'tempest', 'resolve']")
+    Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+    return Parser.parse_args()
+
+#%%
+np.random.seed(0)
+
+args = checkCommandArguments()
+sys.path.append(os.getcwd())
+
+models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+data_type = "Tempest"
+model_type = models[args.index]
+
+#%%
+# The directory where HDF files will be stored
+#%%
+file_path = os.path.join(data_type, model_type)
+pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+for filename in os.listdir(file_path):
+    try:
+        if os.path.isfile(file_path) or os.path.islink(file_path):
+            os.unlink(file_path)
+    except Exception as e:
+        print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+output_directory = file_path
+
+data_filename = data_type + '_' + model_type
+
+supplementary = "..//..//supplementary//"
+
+parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+inputFile = pathlib.Path(parameter_file)
+assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+output_directory = pathlib.Path(output_directory)
+assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+print('Using user input file {}'.format(parameter_file))
+print('Output files will be produced at {}'.format(output_directory))
+
+kwargs = user_parameters.read(inputFile)
+
+kwargs['n_markov_chains'] = 5000
+
+kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+kwargs['system_filename'] = supplementary + "//data//" + kwargs['system_filename']
+
+# Everyone needs the system classes read in early.
+data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+# Start keeping track of time.
+t0 = time.time()
+
+seed = 146100583096709124601953385843316024947
+prng = get_prng(seed=seed)
+
+inference3d = Inference3D(data, prng=prng)
+inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+inference3d.infer(index=2, **kwargs)
diff --git a/docs/_downloads/ec453d00c4b1863c6db7fb665ba67ecb/plot_distributions.py b/docs/_downloads/ec453d00c4b1863c6db7fb665ba67ecb/plot_distributions.py
new file mode 100644
index 00000000..9fc321db
--- /dev/null
+++ b/docs/_downloads/ec453d00c4b1863c6db7fb665ba67ecb/plot_distributions.py
@@ -0,0 +1,48 @@
+"""
+Distribution Class
+++++++++++++++++++
+
+Handles the initialization of different statistical distribution
+"""
+
+#%%
+from geobipy import Distribution
+from geobipy import plotting as cP
+import matplotlib.pyplot as plt
+import numpy as np
+
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+#%%
+# Univariate Normal Distribution
+# ++++++++++++++++++++++++++++++
+D = Distribution('Normal', 0.0, 1.0, prng=prng)
+
+# Get the bins of the Distribution from +- 4 standard deviations of the mean
+bins = D.bins()
+
+# Grab random samples from the distribution
+D.rng(10)
+
+# We can then get the Probability Density Function for those bins
+pdf = D.probability(bins, log=False)
+
+# And we can plot that PDF
+# sphinx_gallery_thumbnail_number = 1
+plt.figure()
+plt.plot(bins, pdf)
+
+#%%
+# Multivariate Normal Distribution
+# ++++++++++++++++++++++++++++++++
+D = Distribution('MvNormal',[0.0,1.0,2.0],[1.0,1.0,1.0], prng=prng)
+D.rng()
+
+
+#%%
+# Uniform Distribution
+D = Distribution('Uniform', 0.0, 1.0, prng=prng)
+D.bins()
diff --git a/docs/_downloads/f48e5c05cfcaf1411cc06bb7a0ed1eb0/plot_model_1d.py b/docs/_downloads/f48e5c05cfcaf1411cc06bb7a0ed1eb0/plot_model_1d.py
new file mode 100644
index 00000000..0076ce37
--- /dev/null
+++ b/docs/_downloads/f48e5c05cfcaf1411cc06bb7a0ed1eb0/plot_model_1d.py
@@ -0,0 +1,160 @@
+"""
+1D Model with an infinite halfspace
+-----------------------------------
+"""
+
+# %%
+from copy import deepcopy
+from geobipy import StatArray
+from geobipy import RectilinearMesh1D
+from geobipy import Model
+from geobipy import Distribution
+import matplotlib.pyplot as plt
+import numpy as np
+
+# %%
+# Instantiate the 1D Model with a Half Space
+# ++++++++++++++++++++++++++++++++++++++++++
+
+# Make a test model with 10 layers, and increasing parameter values
+nLayers = 2
+par = StatArray(np.linspace(0.001, 0.02, nLayers), "Conductivity", "$\\frac{S}{m}$")
+thk = StatArray(np.full(nLayers, fill_value=10.0))
+thk[-1] = np.inf
+mesh = RectilinearMesh1D(widths = thk)
+
+mod = Model(mesh = mesh, values=par)
+
+plt.figure()
+mod.plot_grid(transpose=True, flip=True)
+
+#%%
+# Randomness and Model Perturbations
+# ++++++++++++++++++++++++++++++++++
+# We can set the priors on the 1D model by assigning minimum and maximum layer
+# depths and a maximum number of layers.  These are used to create priors on
+# the number of cells in the model, a new depth interface, new parameter values
+# and the vertical gradient of those parameters.
+# The halfSpaceValue is used as a reference value for the parameter prior.
+from numpy.random import Generator
+from numpy.random import PCG64DXSM
+generator = PCG64DXSM(seed=0)
+prng = Generator(generator)
+
+# Set the priors
+mod.set_priors(value_mean=0.01,
+              min_edge=1.0,
+              max_edge=150.0,
+              max_cells=30,
+              solve_value=True,
+              solve_gradient=True,
+              prng=prng)
+
+#%%
+# We can evaluate the prior of the model using depths only
+print('Log probability of the Model given its priors: ', mod.probability(False, False))
+# Or with priors on its parameters, and parameter gradient with depth.
+print('Log probability of the Model given its priors: ', mod.probability(True, True))
+
+#%%
+# To propose new models, we specify the probabilities of creating, removing, perturbing, and not changing
+# a layer interface
+pProposal = Distribution('LogNormal', 0.01, np.log(2.0)**2.0, linearSpace=True, prng=prng)
+mod.set_proposals(probabilities=[0.25, 0.25, 0.5, 0.25], proposal=pProposal, prng=prng)
+
+#%%
+# We can then perturb the layers of the model
+remapped, perturbed = mod.perturb()
+
+#%%
+fig = plt.figure(figsize=(8, 6))
+ax = plt.subplot(121)
+mod.pcolor(transpose=True, flip=True, log=10)  # , grid=True)
+ax = plt.subplot(122)
+perturbed.pcolor(transpose=True, flip=True, log=10)  # , grid=True)
+
+#%%
+# We can evaluate the prior of the model using depths only
+print('Log probability of the Model given its priors: ',perturbed.probability(False, False))
+# Or with priors on its parameters, and parameter gradient with depth.
+print('Log probability of the Model given its priors: ',perturbed.probability(True, True))
+
+
+# %%
+# Perturbing a model multiple times
+# +++++++++++++++++++++++++++++++++
+# In the stochasitic inference process, we perturb the model structure,
+# and parameter values, multiple times.
+# Each time the model is perturbed, we can record its state
+# in a posterior distribution.
+#
+# For a 1D model, the parameter posterior is a 2D hitmap with depth in one dimension
+# and the parameter value in the other.
+# We also attach a 1D histogram for the number of layers,
+# and a 1D histogram for the locations of interfaces.
+#
+# Since we have already set the priors on the Model, we can set the posteriors
+# based on bins from from the priors.
+
+mod.set_posteriors()
+
+mod0 = deepcopy(mod)
+
+#%%
+# Now we randomly perturb the model, and update its posteriors.
+mod.update_posteriors()
+for i in range(1001):
+    remapped, perturbed = mod.perturb()
+
+    # And update the model posteriors
+    perturbed.update_posteriors()
+
+    mod = perturbed
+
+#%%
+# We can now plot the posteriors of the model.
+#
+# Remember in this case, we are simply perturbing the model structure and parameter values
+# The proposal for the parameter values is fixed and centred around a single value.
+# fig = plt.figure(figsize=(8, 6))
+
+# plt.subplot(131)
+# mod.nCells.posterior.plot()
+# ax = plt.subplot(132)
+# mod.values.posterior.pcolor(cmap='gray_r', colorbar=False, flipY=True, logX=10)
+# plt.subplot(133, sharey=ax)
+# mod.mesh.edges.posterior.plot(transpose=True, flipY=True)
+
+# plt.figure()
+# mod.plot_posteriors(**{"cmap": 'gray_r',
+#                   "xscale": 'log',
+#                   "noColorbar": True,
+#                   "flipY": True,
+#                   'credible_interval_kwargs':{'axis': 1,
+#                                           'reciprocate': True,
+#                                           'xscale': 'log'}})
+# mod.par.posterior.plotCredibleIntervals(xscale='log', axis=1)
+
+
+fig = plt.figure(figsize=(8, 6))
+# gs = fig.add_gridspec(nrows=1, ncols=1)
+mod.plot_posteriors(axes=fig,
+                    edges_kwargs = {
+                        "transpose":True,
+                        "flipY":True
+                    },
+                    parameter_kwargs = {
+                        "cmap": 'gray_r',
+                        "xscale": 'log',
+                        "colorbar": False,
+                        "flipY": True,
+                        'credible_interval_kwargs':{
+                              'reciprocate':True,
+                            #   'axis': 1,
+                              'xscale': 'log'
+                        }
+                    },
+                    best = mod)
+
+
+plt.show()
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new file mode 100644
index 00000000..92da3c30
--- /dev/null
+++ b/docs/_downloads/fd0d450029ed4426f6b6d51ec196e9de/plot_inference_2d_skytem.py
@@ -0,0 +1,152 @@
+"""
+2D Posterior analysis of Skytem inference
+-----------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+"""
+
+import argparse
+import matplotlib.pyplot as plt
+import numpy as np
+from geobipy import Model
+from geobipy import Inference2D
+
+def plot_2d_summary(folder, data_type, model_type):
+   #%%
+   # Inference for a line of inferences
+   # ++++++++++++++++++++++++++++++++++
+   #
+   # We can instantiate the inference handler by providing a path to the directory containing
+   # HDF5 files generated by GeoBIPy.
+   #
+   # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+   # as and when it is needed.
+   #
+   # The first time you use these classes to create plots, expect longer initial processing times.
+   # I precompute expensive properties and store them in the HDF5 files for later use.
+
+   from numpy.random import Generator
+   from numpy.random import PCG64DXSM
+   generator = PCG64DXSM(seed=0)
+   prng = Generator(generator)
+
+   #%%
+   results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+   kwargs = {
+         "log" : 10,
+         "cmap" : 'jet'
+         }
+
+   fig = plt.figure(figsize=(16, 8))
+   plt.suptitle("{} {}".format(data_type, model_type))
+   gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+   true_model = Model.create_synthetic_model(model_type)
+
+   kwargs['vmin'] = np.log10(np.min(true_model.values))
+   kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+   ax = fig.add_subplot(gs0[0, 0])
+   true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+   plt.ylim([-550, 60])
+
+   print(results_2d.mean_parameters().summary)
+
+   ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+   ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+   results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # # # # # We can also choose to keep parameters above the DOI opaque.
+   # # # # results_2d.compute_doi()
+   # # # # plt.subplot(313)
+   # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+   # # # # results_2d.plot_data_elevation(linewidth=0.3);
+   # # # # results_2d.plot_elevation(linewidth=0.3);
+
+   ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+   results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+   ax1.set_title('Best model')
+
+   del kwargs['vmin']
+   del kwargs['vmax']
+
+   ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+   results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+   results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+   results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   #%%
+   # We can plot the parameter values that produced the highest posterior
+   ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+   results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+   ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+   ll, bb, ww, hh = ax1.get_position().bounds
+   ax1.set_position([ll, bb, ww*0.8, hh])
+
+   results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+   results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+   #%%
+   # Now we can start plotting some more interesting posterior properties.
+   # How about the confidence?
+   ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+   results_2d.plot_confidence(ax=ax1);
+   results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+   results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+   #%%
+   # We can take the interface depth posterior for each data point,
+   # and display an interface probability cross section
+   # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+   # the top and bottom 0.5% of the colour range
+   ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+   ax1.set_title('P(Interface)')
+   results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+   results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+   results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+   results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+   # plt.show()
+   plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+
+if __name__ == '__main__':
+   types = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+   for model in types:
+      # try:
+         plot_2d_summary('../../../Parallel_Inference/', "skytem", model)
+      # except Exception as e:
+      #    print(model)
+      #    print(e)
+      #    pass
\ No newline at end of file
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diff --git a/docs/_sources/content/api/api.rst.txt b/docs/_sources/content/api/api.rst.txt
new file mode 100644
index 00000000..af707cb2
--- /dev/null
+++ b/docs/_sources/content/api/api.rst.txt
@@ -0,0 +1,11 @@
+***
+API
+***
+
+The source code is split into two types of files.  Those that contain a set of python functions, and those that contain classes.
+
+.. toctree::
+   :maxdepth: 1
+
+   base/base
+   classes/classes
diff --git a/docs/_sources/content/api/base/HDF.rst.txt b/docs/_sources/content/api/base/HDF.rst.txt
new file mode 100644
index 00000000..622d0e4e
--- /dev/null
+++ b/docs/_sources/content/api/base/HDF.rst.txt
@@ -0,0 +1,7 @@
+Heirarchical Data Format (HDF)
+==============================
+
+.. automodule:: geobipy.src.base.HDF.hdfRead
+   :members:
+.. automodule:: geobipy.src.base.HDF.hdfWrite
+   :members:
diff --git a/docs/_sources/content/api/base/MPI.rst.txt b/docs/_sources/content/api/base/MPI.rst.txt
new file mode 100644
index 00000000..7e1f75dd
--- /dev/null
+++ b/docs/_sources/content/api/base/MPI.rst.txt
@@ -0,0 +1,5 @@
+MPI wrapper functions
+=====================
+
+.. automodule:: geobipy.src.base.MPI
+   :members:
diff --git a/docs/_sources/content/api/base/base.rst.txt b/docs/_sources/content/api/base/base.rst.txt
new file mode 100644
index 00000000..1feb68bc
--- /dev/null
+++ b/docs/_sources/content/api/base/base.rst.txt
@@ -0,0 +1,14 @@
+********************************
+Core routines needed for GeoBIPy
+********************************
+
+.. toctree::
+   :maxdepth: 1
+
+   utilities
+   plotting
+   fileIO
+   interpolation
+   HDF
+   MPI
+
diff --git a/docs/_sources/content/api/base/fileIO.rst.txt b/docs/_sources/content/api/base/fileIO.rst.txt
new file mode 100644
index 00000000..5cbf82e2
--- /dev/null
+++ b/docs/_sources/content/api/base/fileIO.rst.txt
@@ -0,0 +1,5 @@
+fileIO
+======
+
+.. automodule:: geobipy.src.base.fileIO
+   :members:
diff --git a/docs/_sources/content/api/base/interpolation.rst.txt b/docs/_sources/content/api/base/interpolation.rst.txt
new file mode 100644
index 00000000..be572b21
--- /dev/null
+++ b/docs/_sources/content/api/base/interpolation.rst.txt
@@ -0,0 +1,5 @@
+Interpolation
+=============
+
+.. automodule:: geobipy.src.base.interpolation
+   :members:
diff --git a/docs/_sources/content/api/base/plotting.rst.txt b/docs/_sources/content/api/base/plotting.rst.txt
new file mode 100644
index 00000000..817f9cbc
--- /dev/null
+++ b/docs/_sources/content/api/base/plotting.rst.txt
@@ -0,0 +1,5 @@
+plotting
+========
+
+.. automodule:: geobipy.src.base.plotting
+   :members:
diff --git a/docs/_sources/content/api/base/utilities.rst.txt b/docs/_sources/content/api/base/utilities.rst.txt
new file mode 100644
index 00000000..98345120
--- /dev/null
+++ b/docs/_sources/content/api/base/utilities.rst.txt
@@ -0,0 +1,5 @@
+utilities
+=========
+
+.. automodule:: geobipy.src.base.utilities
+   :members:
diff --git a/docs/_sources/content/api/classes/classes.rst.txt b/docs/_sources/content/api/classes/classes.rst.txt
new file mode 100644
index 00000000..3c29c371
--- /dev/null
+++ b/docs/_sources/content/api/classes/classes.rst.txt
@@ -0,0 +1,15 @@
+***********************
+Classes used in GeoBIPy
+***********************
+
+
+.. toctree::
+   :maxdepth: 1
+
+   core/core
+   data/data
+   mesh/mesh
+   model/model
+   pointcloud/pointcloud
+   statistics/statistics
+   system/system
diff --git a/docs/_sources/content/api/classes/core/StatArray.rst.txt b/docs/_sources/content/api/classes/core/StatArray.rst.txt
new file mode 100644
index 00000000..8ef3b391
--- /dev/null
+++ b/docs/_sources/content/api/classes/core/StatArray.rst.txt
@@ -0,0 +1,10 @@
+StatArray
+---------
+
+.. inheritance-diagram:: geobipy.src.classes.core.StatArray.StatArray 
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.core.StatArray
+   :members:
+   :undoc-members:
+
diff --git a/docs/_sources/content/api/classes/core/core.rst.txt b/docs/_sources/content/api/classes/core/core.rst.txt
new file mode 100644
index 00000000..18fffe5a
--- /dev/null
+++ b/docs/_sources/content/api/classes/core/core.rst.txt
@@ -0,0 +1,9 @@
+Core classes
+============
+
+.. toctree::
+   :maxdepth: 1
+
+   myObject
+   StatArray
+
diff --git a/docs/_sources/content/api/classes/core/myObject.rst.txt b/docs/_sources/content/api/classes/core/myObject.rst.txt
new file mode 100644
index 00000000..dce4f231
--- /dev/null
+++ b/docs/_sources/content/api/classes/core/myObject.rst.txt
@@ -0,0 +1,5 @@
+Core object class
+-----------------
+
+.. automodule:: geobipy.src.classes.core.myObject
+   :members:
diff --git a/docs/_sources/content/api/classes/data/data.rst.txt b/docs/_sources/content/api/classes/data/data.rst.txt
new file mode 100644
index 00000000..994b8e24
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/data.rst.txt
@@ -0,0 +1,8 @@
+Data classes
+============
+
+.. toctree::
+   :maxdepth: 1
+
+   dataset/dataset
+   datapoint/datapointrst
diff --git a/docs/_sources/content/api/classes/data/datapoint/EmDataPoint.rst.txt b/docs/_sources/content/api/classes/data/datapoint/EmDataPoint.rst.txt
new file mode 100644
index 00000000..5c02be96
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/EmDataPoint.rst.txt
@@ -0,0 +1,8 @@
+EmDataPoint
+^^^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint
+    :parts: 1
+
+.. automodule:: geobipy.src.classes.data.datapoint.EmDataPoint
+    :members:
diff --git a/docs/_sources/content/api/classes/data/datapoint/FdemDataPoint.rst.txt b/docs/_sources/content/api/classes/data/datapoint/FdemDataPoint.rst.txt
new file mode 100644
index 00000000..b521cdd2
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/FdemDataPoint.rst.txt
@@ -0,0 +1,8 @@
+FdemDataPoint
+^^^^^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.datapoint.FdemDataPoint
+   :members:
diff --git a/docs/_sources/content/api/classes/data/datapoint/TdemDataPoint.rst.txt b/docs/_sources/content/api/classes/data/datapoint/TdemDataPoint.rst.txt
new file mode 100644
index 00000000..45ca6a07
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/TdemDataPoint.rst.txt
@@ -0,0 +1,8 @@
+TdemDataPoint
+^^^^^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.datapoint.TdemDataPoint
+   :members:
diff --git a/docs/_sources/content/api/classes/data/datapoint/Tempest_dataPoint.rst.txt b/docs/_sources/content/api/classes/data/datapoint/Tempest_dataPoint.rst.txt
new file mode 100644
index 00000000..e8a6288e
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/Tempest_dataPoint.rst.txt
@@ -0,0 +1,8 @@
+Tempest_datapoint
+^^^^^^^^^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.datapoint.Tempest_datapoint
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.datapoint.Tempest_datapoint
+   :members:
diff --git a/docs/_sources/content/api/classes/data/datapoint/datapoint.rst.txt b/docs/_sources/content/api/classes/data/datapoint/datapoint.rst.txt
new file mode 100644
index 00000000..e1e4f6f1
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/datapoint.rst.txt
@@ -0,0 +1,8 @@
+DataPoint
+^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.datapoint.DataPoint.DataPoint
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.datapoint.DataPoint
+   :members:
diff --git a/docs/_sources/content/api/classes/data/datapoint/datapointrst.rst.txt b/docs/_sources/content/api/classes/data/datapoint/datapointrst.rst.txt
new file mode 100644
index 00000000..b5a34a1d
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/datapoint/datapointrst.rst.txt
@@ -0,0 +1,10 @@
+Datapoint classes
+-----------------
+
+.. toctree::
+   :maxdepth: 1
+
+   datapoint
+   EmDataPoint
+   FdemDataPoint
+   TdemDataPoint
diff --git a/docs/_sources/content/api/classes/data/dataset/Data.rst.txt b/docs/_sources/content/api/classes/data/dataset/Data.rst.txt
new file mode 100644
index 00000000..192a83cd
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/dataset/Data.rst.txt
@@ -0,0 +1,8 @@
+Data
+^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.dataset.Data.Data
+    :parts: 1
+
+.. automodule:: geobipy.src.classes.data.dataset.Data
+    :members:
diff --git a/docs/_sources/content/api/classes/data/dataset/FdemData.rst.txt b/docs/_sources/content/api/classes/data/dataset/FdemData.rst.txt
new file mode 100644
index 00000000..7256f21f
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/dataset/FdemData.rst.txt
@@ -0,0 +1,7 @@
+FdemData
+^^^^^^^^
+.. inheritance-diagram:: geobipy.src.classes.data.dataset.FdemData
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.dataset.FdemData
+   :members:
diff --git a/docs/_sources/content/api/classes/data/dataset/TdemData.rst.txt b/docs/_sources/content/api/classes/data/dataset/TdemData.rst.txt
new file mode 100644
index 00000000..5786ddc2
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/dataset/TdemData.rst.txt
@@ -0,0 +1,8 @@
+TdemData
+^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.dataset.TdemData
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.dataset.TdemData
+   :members:
diff --git a/docs/_sources/content/api/classes/data/dataset/TempestData.rst.txt b/docs/_sources/content/api/classes/data/dataset/TempestData.rst.txt
new file mode 100644
index 00000000..8a08b82f
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/dataset/TempestData.rst.txt
@@ -0,0 +1,8 @@
+TempestData
+^^^^^^^^^^^
+
+.. inheritance-diagram:: geobipy.src.classes.data.dataset.TempestData
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.data.dataset.TempestData
+   :members:
diff --git a/docs/_sources/content/api/classes/data/dataset/dataset.rst.txt b/docs/_sources/content/api/classes/data/dataset/dataset.rst.txt
new file mode 100644
index 00000000..c829cb99
--- /dev/null
+++ b/docs/_sources/content/api/classes/data/dataset/dataset.rst.txt
@@ -0,0 +1,10 @@
+Dataset classes
+---------------
+
+.. toctree::
+   :maxdepth: 1
+
+   Data
+   FdemData
+   TdemData
+   TempestData
diff --git a/docs/_sources/content/api/classes/mesh/RectilinearMesh1D.rst.txt b/docs/_sources/content/api/classes/mesh/RectilinearMesh1D.rst.txt
new file mode 100644
index 00000000..9104f467
--- /dev/null
+++ b/docs/_sources/content/api/classes/mesh/RectilinearMesh1D.rst.txt
@@ -0,0 +1,8 @@
+RectilinearMesh1D
+=================
+
+.. inheritance-diagram:: geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.mesh.RectilinearMesh1D
+   :members:
diff --git a/docs/_sources/content/api/classes/mesh/RectilinearMesh2D.rst.txt b/docs/_sources/content/api/classes/mesh/RectilinearMesh2D.rst.txt
new file mode 100644
index 00000000..a2f2fa64
--- /dev/null
+++ b/docs/_sources/content/api/classes/mesh/RectilinearMesh2D.rst.txt
@@ -0,0 +1,8 @@
+RectilinearMesh2D
+=================
+
+.. inheritance-diagram:: geobipy.src.classes.mesh.RectilinearMesh2D
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.mesh.RectilinearMesh2D
+   :members:
diff --git a/docs/_sources/content/api/classes/mesh/RectilinearMesh2D_stitched.rst.txt b/docs/_sources/content/api/classes/mesh/RectilinearMesh2D_stitched.rst.txt
new file mode 100644
index 00000000..af033881
--- /dev/null
+++ b/docs/_sources/content/api/classes/mesh/RectilinearMesh2D_stitched.rst.txt
@@ -0,0 +1,8 @@
+RectilinearMesh2D_stitched
+==========================
+
+.. inheritance-diagram:: geobipy.src.classes.mesh.RectilinearMesh2D_stitched
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.mesh.RectilinearMesh2D_stitched
+   :members:
diff --git a/docs/_sources/content/api/classes/mesh/RectilinearMesh3D.rst.txt b/docs/_sources/content/api/classes/mesh/RectilinearMesh3D.rst.txt
new file mode 100644
index 00000000..940cca60
--- /dev/null
+++ b/docs/_sources/content/api/classes/mesh/RectilinearMesh3D.rst.txt
@@ -0,0 +1,8 @@
+RectilinearMesh3D
+=================
+
+.. inheritance-diagram:: geobipy.src.classes.mesh.RectilinearMesh3D
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.mesh.RectilinearMesh3D
+   :members:
diff --git a/docs/_sources/content/api/classes/mesh/mesh.rst.txt b/docs/_sources/content/api/classes/mesh/mesh.rst.txt
new file mode 100644
index 00000000..95210c6f
--- /dev/null
+++ b/docs/_sources/content/api/classes/mesh/mesh.rst.txt
@@ -0,0 +1,10 @@
+Mesh classes
+=================================
+
+.. toctree::
+   :maxdepth: 1
+
+   RectilinearMesh1D
+   RectilinearMesh2D
+   RectilinearMesh2D_stitched
+   RectilinearMesh3D
diff --git a/docs/_sources/content/api/classes/model/Model_.rst.txt b/docs/_sources/content/api/classes/model/Model_.rst.txt
new file mode 100644
index 00000000..0f0f9341
--- /dev/null
+++ b/docs/_sources/content/api/classes/model/Model_.rst.txt
@@ -0,0 +1,8 @@
+Model
+=========================
+
+.. inheritance-diagram:: geobipy.src.classes.model.Model
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.model.Model
+   :members:
diff --git a/docs/_sources/content/api/classes/model/model.rst.txt b/docs/_sources/content/api/classes/model/model.rst.txt
new file mode 100644
index 00000000..87acf8e0
--- /dev/null
+++ b/docs/_sources/content/api/classes/model/model.rst.txt
@@ -0,0 +1,7 @@
+Model classes
+=================================
+
+.. toctree::
+   :maxdepth: 1
+
+   Model_
diff --git a/docs/_sources/content/api/classes/pointcloud/Point.rst.txt b/docs/_sources/content/api/classes/pointcloud/Point.rst.txt
new file mode 100644
index 00000000..4b67dff8
--- /dev/null
+++ b/docs/_sources/content/api/classes/pointcloud/Point.rst.txt
@@ -0,0 +1,7 @@
+Point
+============
+.. inheritance-diagram:: geobipy.src.classes.pointcloud.Point
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.pointcloud.Point
+   :members:
diff --git a/docs/_sources/content/api/classes/pointcloud/pointcloud.rst.txt b/docs/_sources/content/api/classes/pointcloud/pointcloud.rst.txt
new file mode 100644
index 00000000..b7c05794
--- /dev/null
+++ b/docs/_sources/content/api/classes/pointcloud/pointcloud.rst.txt
@@ -0,0 +1,7 @@
+Pointcloud classes
+=================================
+
+.. toctree::
+   :maxdepth: 1
+
+   Point
\ No newline at end of file
diff --git a/docs/_sources/content/api/classes/statistics/Distribution.rst.txt b/docs/_sources/content/api/classes/statistics/Distribution.rst.txt
new file mode 100644
index 00000000..4dc7d624
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/Distribution.rst.txt
@@ -0,0 +1,8 @@
+Distribution Wrapper
+=================================
+
+.. .. inheritance-diagram:: geobipy.src.classes.statistics.Distribution
+..    :parts: 1
+
+.. automodule:: geobipy.src.classes.statistics.Distribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/GammaDistribution.rst.txt b/docs/_sources/content/api/classes/statistics/GammaDistribution.rst.txt
new file mode 100644
index 00000000..432c3e32
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/GammaDistribution.rst.txt
@@ -0,0 +1,9 @@
+Gamma Distribution
+=================================
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.GammaDistribution
+   :parts: 1
+
+
+.. automodule:: geobipy.src.classes.statistics.GammaDistribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/Histogram.rst.txt b/docs/_sources/content/api/classes/statistics/Histogram.rst.txt
new file mode 100644
index 00000000..3b894888
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/Histogram.rst.txt
@@ -0,0 +1,9 @@
+Histogram
+=========
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.Histogram
+   :parts: 1
+
+
+.. automodule:: geobipy.src.classes.statistics.Histogram
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/MvNormalDistribution.rst.txt b/docs/_sources/content/api/classes/statistics/MvNormalDistribution.rst.txt
new file mode 100644
index 00000000..6210866f
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/MvNormalDistribution.rst.txt
@@ -0,0 +1,8 @@
+MvNormal
+========
+.. inheritance-diagram:: geobipy.src.classes.statistics.MvNormalDistribution
+   :parts: 1
+
+
+.. automodule:: geobipy.src.classes.statistics.MvNormalDistribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/NormalDistribution.rst.txt b/docs/_sources/content/api/classes/statistics/NormalDistribution.rst.txt
new file mode 100644
index 00000000..ef806bb9
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/NormalDistribution.rst.txt
@@ -0,0 +1,8 @@
+Normal distribution
+=================================
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.NormalDistribution
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.statistics.NormalDistribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/OrderStatistics.rst.txt b/docs/_sources/content/api/classes/statistics/OrderStatistics.rst.txt
new file mode 100644
index 00000000..4f0293ee
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/OrderStatistics.rst.txt
@@ -0,0 +1,9 @@
+Order Statistics
+=================================
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.OrderStatistics
+   :parts: 1
+
+
+.. automodule:: geobipy.src.classes.statistics.OrderStatistics
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/UniformDistribution.rst.txt b/docs/_sources/content/api/classes/statistics/UniformDistribution.rst.txt
new file mode 100644
index 00000000..95e11d53
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/UniformDistribution.rst.txt
@@ -0,0 +1,8 @@
+Uniform distribution
+=================================
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.UniformDistribution
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.statistics.UniformDistribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/baseDistribution.rst.txt b/docs/_sources/content/api/classes/statistics/baseDistribution.rst.txt
new file mode 100644
index 00000000..1977a063
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/baseDistribution.rst.txt
@@ -0,0 +1,8 @@
+baseDistribution
+=================================
+
+.. inheritance-diagram:: geobipy.src.classes.statistics.baseDistribution
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.statistics.baseDistribution
+   :members:
diff --git a/docs/_sources/content/api/classes/statistics/statistics.rst.txt b/docs/_sources/content/api/classes/statistics/statistics.rst.txt
new file mode 100644
index 00000000..0966ed51
--- /dev/null
+++ b/docs/_sources/content/api/classes/statistics/statistics.rst.txt
@@ -0,0 +1,14 @@
+Statistics classes
+==================
+
+.. toctree::
+   :maxdepth: 1
+
+   Distribution
+   Histogram
+   baseDistribution
+   NormalDistribution
+   MvNormalDistribution
+   UniformDistribution
+   GammaDistribution
+   OrderStatistics
diff --git a/docs/_sources/content/api/classes/system/CircularLoop.rst.txt b/docs/_sources/content/api/classes/system/CircularLoop.rst.txt
new file mode 100644
index 00000000..3a1ef55c
--- /dev/null
+++ b/docs/_sources/content/api/classes/system/CircularLoop.rst.txt
@@ -0,0 +1,8 @@
+Circular Loop
+=============
+
+.. inheritance-diagram:: geobipy.src.classes.system.CircularLoop
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.system.CircularLoop
+   :members:
diff --git a/docs/_sources/content/api/classes/system/EmLoop.rst.txt b/docs/_sources/content/api/classes/system/EmLoop.rst.txt
new file mode 100644
index 00000000..b6d69075
--- /dev/null
+++ b/docs/_sources/content/api/classes/system/EmLoop.rst.txt
@@ -0,0 +1,8 @@
+EmLoop
+======
+
+.. inheritance-diagram:: geobipy.src.classes.system.EmLoop
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.system.EmLoop
+   :members:
diff --git a/docs/_sources/content/api/classes/system/FdemSystem.rst.txt b/docs/_sources/content/api/classes/system/FdemSystem.rst.txt
new file mode 100644
index 00000000..8de0ceeb
--- /dev/null
+++ b/docs/_sources/content/api/classes/system/FdemSystem.rst.txt
@@ -0,0 +1,8 @@
+Frequency domain system
+=======================
+
+.. inheritance-diagram:: geobipy.src.classes.system.FdemSystem
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.system.FdemSystem
+   :members:
diff --git a/docs/_sources/content/api/classes/system/TdemSystem.rst.txt b/docs/_sources/content/api/classes/system/TdemSystem.rst.txt
new file mode 100644
index 00000000..a2b39a53
--- /dev/null
+++ b/docs/_sources/content/api/classes/system/TdemSystem.rst.txt
@@ -0,0 +1,8 @@
+Time domain system
+=======================
+
+.. inheritance-diagram:: geobipy.src.classes.system.TdemSystem
+   :parts: 1
+
+.. automodule:: geobipy.src.classes.system.TdemSystem
+   :members:
diff --git a/docs/_sources/content/api/classes/system/system.rst.txt b/docs/_sources/content/api/classes/system/system.rst.txt
new file mode 100644
index 00000000..3921db3d
--- /dev/null
+++ b/docs/_sources/content/api/classes/system/system.rst.txt
@@ -0,0 +1,10 @@
+System classes
+==============
+
+.. toctree::
+   :maxdepth: 1
+
+   EmLoop
+   CircularLoop
+   FdemSystem
+   TdemSystem
diff --git a/docs/_sources/content/getting_started/getting_started.rst.txt b/docs/_sources/content/getting_started/getting_started.rst.txt
new file mode 100644
index 00000000..edd40256
--- /dev/null
+++ b/docs/_sources/content/getting_started/getting_started.rst.txt
@@ -0,0 +1,8 @@
+***************
+Getting Started
+***************
+
+.. toctree::
+   :maxdepth: 1
+
+   installation
\ No newline at end of file
diff --git a/docs/_sources/content/getting_started/installation.rst.txt b/docs/_sources/content/getting_started/installation.rst.txt
new file mode 100644
index 00000000..6a580968
--- /dev/null
+++ b/docs/_sources/content/getting_started/installation.rst.txt
@@ -0,0 +1,194 @@
+******************
+Installing GeoBIPy
+******************
+
+First things first, install a Python 3.5+ distribution.  This is the minimum version that we have tested with.
+You will also need to install Numpy and a Fortran compiler.
+
+This package has a few requirements depending on what you wish to do with it.
+
+If you require a serial version of the code, see `Installing a serial version of GeoBIPy`_.
+
+If you require an parallel implementation, you will need to install an MPI library, and Python's mpi4py module. See `Installing MPI and mpi4py`_.
+
+If you require parallel file reading and writing, you will also need to install an MPI enabled HDF5 library, as well as Python's h5py wrapper to that library. It is important to read the notes below on installing h5py on top of a parallel HDF library.  The traditional "pip install h5py" will not work correctly. See `Installing parallel HDF5 and h5py`_ to do this correctly.
+
+If you need to install the parallel IO version of the code, we would recommend that you start with a clean install of Python. This makes it easier to determine whether you have installed and linked the correct version of the parallel HDF5 library.
+
+
+There are two versions when installing GeoBIPy, a serial version, and a parallel version. Since GeoBIPy uses a Fortran backend for forward modelling frequency domain data, you will need to have a Fortran compiler installed. Make sure that the compiler can handle derived data types since I make use of object oriented programming in Fortran.
+
+Installing a serial version of GeoBIPy
+======================================
+This is the easiest installation and provides access to a serial implementation of the code.
+
+Simply clone the git repository, navigate to the package folder that contains the setup.py file, and type "pip install ."
+
+You should then be able to import modules from geobipy.  For this type of installation mpi will not need to be installed, and the serial version of h5py will suffice i.e. the standard "pip install h5py" is fine.  h5py will automatically be installed during the install of GeoBIPy since it is a dependency.
+
+**Side note:**  Let's say you ran a production run on a parallel machine with MPI and parallel HDF capabilities. You generated all the results, copied them back to your local machine, and wish to make plots and images.  You will only need to install the serial version of the code on your local machine to do this.
+
+Installing a parallel version of GeoBIPy
+========================================
+Installing the parallel version of the code is a little trickier due to the dependencies necessary between the OpenMPI and/or HDF libraries, and how Python's mpi4py and h5py wrap around those.
+
+
+Installing MPI and mpi4py
+-------------------------
+To run this code in parallel you will need both an MPI library and the python wrapper, mpi4py.  You must install MPI first before mpi4py.
+
+MPI
+^^^
+
+If you are installing GeoBIPy on a parallel machine, I would think that you have access to prebuilt MPI libraries.
+If you are on a local laptop, you will need to install one.
+
+mpi4py
+^^^^^^
+
+At this point, if you have an mpi4py module already installed, please remove it (you can check with "pip list").
+If you started with a clean installation you should not have to worry about this.
+To test whether a new install of mpi4py will see the mpi library you have, just type "which mpicc".
+The path that you see should point to the implementation that you want mpi4py to link to.
+Make sure you are about to install mpi4py to the correct python installation.
+If you type 'which python' it should return the path to the correct python distribution.
+If you are using environments, make sure you have activated the correct one.
+
+Next, use "env MPICC=<Your mpicc compiler> python -m pip install mpi4py".  This last option is very important, without it, pip might install its own MPI library called MPICH2.
+I would try to avoid this because if you need to install the HDF5 library you will need know which directories to link to (see `Installing parallel HDF5 and h5py`_).
+
+At the end of the day,  h5py needs to communicate with both the correct HDF5 library and mpi4py, and both of those need to communicate with the same MPI library.
+
+Installing parallel HDF5 and h5py
+---------------------------------
+If a parallel HDF5 library is not available, you will need to install one. First make sure you follow `Installing MPI and mpi4py`_ so that an MPI library is available to you. You must install a HDF5 library first before h5py.
+
+HDF5
+^^^^
+When you install HDF5, make sure that the correct MPI library can be seen by typing "which mpicc".  When you configure the HDF5 library, be sure to use the --enable-parallel option.
+
+h5py
+^^^^
+Once the HDF5 library is installed you will need to install a parallel enabled `h5py package`_
+
+.. _`h5py package`: https://github.com/h5py/h5py
+
+Make sure you are about to install h5py to the correct python installation.  If you type 'which python' it should return the path to the correct python installation.
+
+First check the following
+
+- HDF5_DIR = Get the path to the HDF5 installation.
+- Check that 'which mpicc' returns the correct version of an mpi enabled compiler. This needs to point to the same MPI library that mpi4py was installed on top of.
+
+- Do the following, replacing items in < > with your mpicc compiler and you HDF5 install directory.
+
+This will install h5py and compile the source.
+
+.. code:: bash
+
+    CC=<Your mpicc compiler> HDF5_MPI="ON" HDF5_DIR=<Your HDF5_DIR> pip install --no-binary=h5py h5py
+
+.. _Installing_time_domain_forward_modeller:
+
+Installing the time domain forward modeller
+===========================================
+Ross Brodie at Geoscience Australia has written a great forward modeller, gatdaem1D,  in C++ with a python interface.
+You can obtain that code here at the `GA repository`_
+
+.. _`GA repository`: https://github.com/GeoscienceAustralia/ga-aem
+
+Go ahead and "git clone" that repository.
+
+These instructions only describe how to install Ross' forward modeller, but it is part of a larger code base for inversion.
+If you wish to install his entire package, please follow his instructions.
+
+Prerequisites
+-------------
+
+To compile his forward modeller, you will need a c++ compiler, and `FFTW`_
+
+.. _`FFTW`: http://www.fftw.org/
+
+On a Mac, installing these two items is easy if you use a package manager such as `homebrew`_
+
+.. _`homebrew`: https://brew.sh/
+
+If you use brew, simply do the following
+
+.. code:: bash
+
+   brew install gcc
+   brew install fftw
+
+If you do not have brew, or use a package manager, you can install fftw from source instead.
+
+Download fftw-3.3.7.tar.gz from the `FFTW downloads`_ .
+
+.. _`FFTW downloads`: http://www.fftw.org/download.html
+
+Untar the folder and install fftw using the following.
+
+.. code:: bash
+
+  tar -zxvf fftw-3.3.7.tar.gz
+  cd fftw-3.3.7
+  mkdir build
+  cd build
+  ../configure --prefix=path-to-install-to/fftw-3.3.7 --enable-threads
+  make
+  make install
+
+where, path-to-install-to is the location where you want fftw to be installed.
+
+
+Compile the gatdaem1d shared library
+------------------------------------
+Next, within the gatdaem1d folder, navigate to the makefiles folder and modify the top part of the file "gatdaem1d_python.make" to the following
+
+.. code:: bash
+
+  SHELL = /bin/sh
+  .SUFFIXES:
+  .SUFFIXES: .cpp .o
+  cxx = g++
+  cxxflags = -std=c++11 -O3 -Wall -fPIC
+  FFTW_DIR = path-to-fftw
+
+  ldflags    += -shared
+  bindir     = ../python/gatdaem1d
+
+  srcdir     = ../src
+  objdir     = ./obj
+  includes   = -I$(srcdir) -I$(FFTW_DIR)/include
+  libs       = -L$(FFTW_DIR)/lib -lfftw3
+  library    = $(bindir)/gatdaem1d.so
+
+You can find out where brew installed fftw by typing
+
+.. code:: bash
+
+  brew info fftw
+
+Which may return something like "/usr/local/Cellar/fftw/3.3.5"
+
+In this case, path-to-fftw is "/usr/local/Cellar/fftw/3.3.5"
+
+If you installed fftw from source, then path-to-fftw is that install path.
+
+Next, type the following to compile the gatdaem1d c++ code.
+
+.. code:: bash
+
+  make -f gatdaem1d_python.make
+
+Installing the Python Bindings
+------------------------------
+
+Finally, to install the python wrapper to gatdaem1d, navigate to the python folder of the gatdaem1d repository.
+Type,
+
+.. code:: bash
+
+  pip install .
+
+You should now have access to the time domain forward modeller within geobipy.
diff --git a/docs/_sources/examples/Data/plot_frequency_dataset.rst.txt b/docs/_sources/examples/Data/plot_frequency_dataset.rst.txt
new file mode 100644
index 00000000..68f9f6d7
--- /dev/null
+++ b/docs/_sources/examples/Data/plot_frequency_dataset.rst.txt
@@ -0,0 +1,758 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Data/plot_frequency_dataset.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Data_plot_frequency_dataset.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Data_plot_frequency_dataset.py:
+
+
+Frequency domain dataset
+------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 6-14
+
+.. code-block:: Python
+
+    import matplotlib.pyplot as plt
+    from geobipy import CircularLoop
+    from geobipy import FdemSystem
+    from geobipy import FdemData
+    import h5py
+    import numpy as np
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+Defining data using a frequency domain system
++++++++++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-21
+
+We can start by defining the frequencies, transmitter loops, and receiver loops
+For each frequency we need to define a pair of loops
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-23
+
+.. code-block:: Python
+
+    frequencies = np.asarray([395.0, 822.0, 3263.0, 8199.0, 38760.0, 128755.0])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 24-26
+
+Transmitter positions are defined relative to the observation locations in the data
+This is usually a constant offset for all data points.
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-36
+
+.. code-block:: Python
+
+    transmitters = CircularLoop(orientation=['z','z','x','z','z','z'],
+                                 moment=np.r_[1, 1, -1, 1, 1, 1],
+                                 x = np.r_[0,0,0,0,0,0],
+                                 y = np.r_[0,0,0,0,0,0],
+                                 z = np.r_[0,0,0,0,0,0],
+                                 pitch = np.r_[0,0,0,0,0,0],
+                                 roll = np.r_[0,0,0,0,0,0],
+                                 yaw = np.r_[0,0,0,0,0,0],
+                                 radius = np.r_[1,1,1,1,1,1])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-38
+
+Receiver positions are defined relative to the transmitter
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-58
+
+.. code-block:: Python
+
+    receivers = CircularLoop(orientation=['z','z','x','z','z','z'],
+                                 moment=np.r_[1, 1, -1, 1, 1, 1],
+                                 x = np.r_[7.91, 7.91, 9.03, 7.91, 7.91, 7.89],
+                                 y = np.r_[0,0,0,0,0,0],
+                                 z = np.r_[0,0,0,0,0,0],
+                                 pitch = np.r_[0,0,0,0,0,0],
+                                 roll = np.r_[0,0,0,0,0,0],
+                                 yaw = np.r_[0,0,0,0,0,0],
+                                 radius = np.r_[1,1,1,1,1,1])
+
+    # Instantiate the system for the data
+    system = FdemSystem(frequencies=frequencies, transmitter=transmitters, receiver=receivers)
+
+    # Create some data with random co-ordinates
+    x = np.random.randn(100)
+    y = np.random.randn(100)
+    z = np.random.randn(100)
+
+    data = FdemData(x=x, y=-y, z=z, system = system)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-62
+
+Reading in the Data
++++++++++++++++++++
+Of course measured field data is stored on disk. So instead we can read data from file.
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-70
+
+.. code-block:: Python
+
+    dataFolder = "..//..//supplementary//data//"
+    # The data file name
+    dataFile = dataFolder + 'Resolve2.txt'
+    # The EM system file name
+    systemFile = dataFolder + 'FdemSystem2.stm'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 71-72
+
+Read in a data set from file.
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-74
+
+.. code-block:: Python
+
+    FD1 = FdemData.read_csv(dataFile, systemFile)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-76
+
+Take a look at the channel names
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-104
+
+.. code-block:: Python
+
+    for name in FD1.channel_names:
+        print(name)
+
+    # #%%
+    # # Get data points by slicing
+    # FDa = FD1[10:]
+    # FD1 = FD1[:10]
+
+    # #%%
+    # # Append data sets together
+    # FD1.append(FDa)
+
+
+    # #%%
+    # # Plot the locations of the data points
+    # plt.figure(figsize=(8,6))
+    # _ = FD1.scatter2D();
+
+    # #%%
+    # # Plot all the data along the specified line
+    # plt.figure(figsize=(8,6))
+    # _ = FD1.plotLine(30010.0, log=10);
+
+    # #%%
+    # # Or, plot specific channels in the data
+    # plt.figure(figsize=(8,6))
+    # _ = FD1.plot(channels=[0,11,8], log=10, linewidth=0.5);
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    In_Phase 380.0
+    In_Phase 1776.0
+    In_Phase 3345.0
+    In_Phase 8171.0
+    In_Phase 41020.0
+    In_Phase 129550.0
+    Quadrature 380.0
+    Quadrature 1776.0
+    Quadrature 3345.0
+    Quadrature 8171.0
+    Quadrature 41020.0
+    Quadrature 129550.0
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 105-106
+
+Read in a second data set
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-108
+
+.. code-block:: Python
+
+    FD2 = FdemData.read_csv(dataFilename=dataFolder + 'Resolve1.txt', system=dataFolder + 'FdemSystem1.stm')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Warning: Your data contains values that are <= 0.0
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-110
+
+We can create maps of the elevations in two separate figures
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-114
+
+.. code-block:: Python
+
+    plt.figure(figsize=(8,6))
+    _ = FD1.map(dx=50.0, dy=50.0, mask = 200.0)
+    plt.axis('equal');
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_001.png
+   :alt: plot frequency dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    surface [WARNING]: 66659 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+    (np.float64(584494.28), np.float64(590194.28), np.float64(4639054.24), np.float64(4661854.24))
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 115-120
+
+.. code-block:: Python
+
+
+    plt.figure(figsize=(8,6))
+    _ = FD2.map(dx=50.0, dy=50.0, mask = 200.0)
+    plt.axis('equal');
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_002.png
+   :alt: plot frequency dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    surface [WARNING]: 123487 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+    (np.float64(662822.398), np.float64(668372.398), np.float64(4560028.655), np.float64(4600678.655))
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 121-126
+
+Or, we can plot both data sets in one figure to see their positions relative
+to each other.
+
+In this case, I use a 2D scatter plot of the data point co-ordinates, and pass
+one of the channels as the colour.
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-131
+
+.. code-block:: Python
+
+
+    plt.figure(figsize=(8,6))
+    _ = FD1.scatter2D(s=1.0, c=FD1.data[:, 0])
+    _ = FD2.scatter2D(s=1.0, c=FD2.data[:, 0], cmap='jet');
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_003.png
+   :alt: plot frequency dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 132-134
+
+Or, interpolate the values to create a gridded "map". mapChannel will
+interpolate the specified channel number.
+
+.. GENERATED FROM PYTHON SOURCE LINES 134-139
+
+.. code-block:: Python
+
+
+    plt.figure(figsize=(8,6))
+    _ = FD1.mapData(channel=3, system=0, dx=200, dy=200, mask=250)
+    plt.axis('equal');
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_004.png
+   :alt: In_Phase 8171.0
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    surface [WARNING]: 70336 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+    (np.float64(584419.28), np.float64(590219.28), np.float64(4638979.24), np.float64(4661979.24))
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 140-141
+
+Export the data to VTK
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-144
+
+.. code-block:: Python
+
+    FD1.to_vtk('FD_one.vtk')
+    # FD2.to_vtk('FD_two.vtk')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 145-147
+
+Obtain a line from the data set
++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 149-150
+
+Take a look at the line numbers in the dataset
+
+.. GENERATED FROM PYTHON SOURCE LINES 150-152
+
+.. code-block:: Python
+
+    print(np.unique(FD1.lineNumber))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [30010 30020 30030 ... 30100 39010 39020]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 153-155
+
+.. code-block:: Python
+
+    L = FD1.line(30010.0)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 156-157
+
+A summary will now show the properties of the line.
+
+.. GENERATED FROM PYTHON SOURCE LINES 157-160
+
+.. code-block:: Python
+
+
+    print(L.summary)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    FdemData
+    x:
+    |   StatArray
+    |   Name:   Easting (m)
+    |   Address:['0x156368350']
+    |   Shape:  (6710,)
+    |   Values: [586852.29 586852.23 586852.17 ... 586123.57 586123.2  586122.82]
+    |   Min:    586122.82
+    |   Max:    586852.29
+    |   has_posterior: False
+
+    y:
+    |   StatArray
+    |   Name:   Northing (m)
+    |   Address:['0x156369ed0']
+    |   Shape:  (6710,)
+    |   Values: [4639119.38 4639122.68 4639125.98 ... 4661765.26 4661768.84 4661772.42]
+    |   Min:    4639119.38
+    |   Max:    4661772.42
+    |   has_posterior: False
+
+    z:
+    |   StatArray
+    |   Name:   Height (m)
+    |   Address:['0x1563681d0']
+    |   Shape:  (6710,)
+    |   Values: [36.115 36.498 36.835 ... 27.799 27.704 27.601]
+    |   Min:    23.830000000000002
+    |   Max:    50.567
+    |   has_posterior: False
+
+    elevation:
+    |   StatArray
+    |   Name:   Elevation (m)
+    |   Address:['0x15468a450']
+    |   Shape:  (6710,)
+    |   Values: [1246.84 1246.71 1246.61 ... 1337.94 1337.96 1338.02]
+    |   Min:    1213.18
+    |   Max:    1338.02
+    |   has_posterior: False
+
+    channel names:
+    |   In_Phase 380.0, In_Phase 1776.0, In_Phase 3345.0, In_Phase 8171.0, In_Phase 41020.0, 
+    |   In_Phase 129550.0, Quadrature 380.0, Quadrature 1776.0, Quadrature 3345.0, Quadrature 8171.0, 
+    |   Quadrature 41020.0, Quadrature 129550.0
+    data:
+    |   DataArray
+    |   Name:   Data (ppm)
+    |   Address:['0x17eb61950']
+    |   Shape:  (80520,)
+    |   Values: [145.3 435.8 260.6 ... 749.2 976.5 928.3]
+    |   Min:    37.7
+    |   Max:    3726.9
+
+    predicted data:
+    |   DataArray
+    |   Name:   Predicted Data (ppm)
+    |   Address:['0x17eb61950']
+    |   Shape:  (80520,)
+    |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   Min:    0.0
+    |   Max:    0.0
+
+    std:
+    |   DataArray
+    |   Name:   std (ppm)
+    |   Address:['0x17eb604d0']
+    |   Shape:  (80520,)
+    |   Values: [1.453 4.358 2.606 ... 7.492 9.765 9.283]
+    |   Min:    0.37700000000000006
+    |   Max:    37.269
+
+    line number:
+    |   DataArray
+    |   Name:   Line number
+    |   Address:['0x17ec01150']
+    |   Shape:  (6710,)
+    |   Values: [30010. 30010. 30010. ... 30010. 30010. 30010.]
+    |   Min:    30010.0
+    |   Max:    30010.0
+
+    fiducial:
+    |   DataArray
+    |   Name:   Fiducial
+    |   Address:['0x156368250']
+    |   Shape:  (6710,)
+    |   Values: [30000 30000 30000 ... 30670 30670 30670]
+    |   Min:    30000
+    |   Max:    30670
+
+    relative error:
+    |   DataArray
+    |   Name:   Relative error (%)
+    |   Address:['0x15468ad50']
+    |   Shape:  (6710, 1)
+    |   Values: [[0.01]
+    |    [0.01]
+    |    [0.01]
+    |    ...
+    |    [0.01]
+    |    [0.01]
+    |    [0.01]]
+    |   Min:    0.01
+    |   Max:    0.01
+
+    additive error:
+    |   DataArray
+    |   Name:   Additive error (ppm)
+    |   Address:['0x15468ac50']
+    |   Shape:  (6710, 1)
+    |   Values: [[0.]
+    |    [0.]
+    |    [0.]
+    |    ...
+    |    [0.]
+    |    [0.]
+    |    [0.]]
+    |   Min:    0.0
+    |   Max:    0.0
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 161-162
+
+And we can scatter2D the points in the line.
+
+.. GENERATED FROM PYTHON SOURCE LINES 162-166
+
+.. code-block:: Python
+
+
+    plt.figure(figsize=(8,6))
+    _ = L.scatter2D();
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_005.png
+   :alt: plot frequency dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 167-169
+
+We can specify the axis along which to plot.
+xAxis can be index, x, y, z, r2d, r3d
+
+.. GENERATED FROM PYTHON SOURCE LINES 169-198
+
+.. code-block:: Python
+
+    plt.figure(figsize=(8,6))
+    _ = FD1.plot_data(channels=np.r_[0, 11, 8], log=10, linewidth=0.5);
+
+    with h5py.File('fdem.h5', 'w') as f:
+        FD1.createHdf(f, 'fdem')
+        FD1.writeHdf(f, 'fdem')
+
+    with h5py.File('fdem.h5', 'r') as f:
+        FD3 = FdemData.fromHdf(f['fdem'])
+
+    with h5py.File('fdem.h5', 'r') as f:
+        fdp = FdemData.fromHdf(f['fdem'], index=0)
+
+
+    # #%%
+    # # Obtain a single datapoint from the data set
+    # # +++++++++++++++++++++++++++++++++++++++++++
+    # #
+    # # Checkout :ref:`Frequency domain datapoint` for an example
+    # # about how to use a datapoint once it is instantiated.
+    # dp = FD1.datapoint(0)
+
+    # # Prepare the dataset so that we can read a point at a time.
+    # Dataset = FdemData._initialize_sequential_reading(dataFile, systemFile)
+    # # Get a datapoint from the file.
+    # DataPoint = Dataset._read_record()
+
+    plt.show()
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_frequency_dataset_006.png
+   :alt: plot frequency dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_frequency_dataset_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 199-235
+
+File Format for frequency domain data
++++++++++++++++++++++++++++++++++++++
+Here we describe the file format for frequency domain data.
+
+The data columns are read in according to the column names in the first line.
+
+In this description, the column name or its alternatives are given followed by what the name represents.
+Optional columns are also described.
+
+Required columns
+________________
+line
+    Line number for the data point
+fid
+    Unique identification number of the data point
+x or northing or n
+    Northing co-ordinate of the data point, (m)
+y or easting or e
+    Easting co-ordinate of the data point, (m)
+z or alt
+    Altitude of the transmitter coil above ground level (m)
+elevation
+    Elevation of the ground at the data point (m)
+I_<frequency[0]> Q_<frequency[0]> ... I_<frequency[last]> Q_<frequency[last]>  - with the number and square brackets
+    The measurements for each frequency specified in the accompanying system file.
+    I is the real inphase measurement in (ppm)
+    Q is the imaginary quadrature measurement in (ppm)
+Optional columns
+________________
+InphaseErr[0] QuadratureErr[0] ... InphaseErr[nFrequencies] QuadratureErr[nFrequencies]
+    Estimates of standard deviation for each inphase and quadrature measurement.
+    These must appear after the data colums.
+
+Example Header
+______________
+Line fid easting northing elevation height I_380 Q_380 ... ... I_129550 Q_129550
+
+.. GENERATED FROM PYTHON SOURCE LINES 237-266
+
+File Format for a frequency domain system
++++++++++++++++++++++++++++++++++++++++++
+.. role:: raw-html(raw)
+   :format: html
+
+The system file is structured using columns with the first line containing header information
+
+Each subsequent row contains the information for each measurement frequency
+
+freq
+    Frequency of the channel
+tor
+    Orientation of the transmitter loop 'x', or 'z'
+tmom
+    Transmitter moment
+tx, ty, tx
+    Offset of the transmitter with respect to the observation locations
+ror
+    Orientation of the receiver loop 'x', or 'z'
+rmom
+    Receiver moment
+rx, ry, rz
+    Offset of the receiver with respect to the transmitter location
+
+Example system files are contained in
+`the supplementary folder`_ in this repository
+
+.. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
+
+See the Resolve.stm files.
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 3.257 seconds)
+
+
+.. _sphx_glr_download_examples_Data_plot_frequency_dataset.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_frequency_dataset.ipynb <plot_frequency_dataset.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_frequency_dataset.py <plot_frequency_dataset.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_frequency_dataset.zip <plot_frequency_dataset.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Data/plot_pointcloud3d.rst.txt b/docs/_sources/examples/Data/plot_pointcloud3d.rst.txt
new file mode 100644
index 00000000..a0424ded
--- /dev/null
+++ b/docs/_sources/examples/Data/plot_pointcloud3d.rst.txt
@@ -0,0 +1,478 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Data/plot_pointcloud3d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Data_plot_pointcloud3d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Data_plot_pointcloud3d.py:
+
+
+3D Point Cloud class
+--------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-16
+
+.. code-block:: Python
+
+
+    from geobipy import Point
+    from os.path import join
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import h5py
+
+    nPoints = 200
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-19
+
+Create a quick test example using random points
+$z=x(1-x)cos(4\pi x)sin(4\pi y^{2})^{2}$
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-25
+
+.. code-block:: Python
+
+    x = -np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+    y = -np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+    z = x * (1.0 - x) * np.cos(np.pi * x) * np.sin(np.pi * y)
+
+    PC3D = Point(x=x, y=y, z=z)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-27
+
+Append pointclouds together
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-34
+
+.. code-block:: Python
+
+    x = np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+    y = np.abs((2.0 * np.random.rand(nPoints)) - 1.0)
+    z = x * (1.0 - x) * np.cos(np.pi * x) * np.sin(np.pi * y)
+
+    Other_PC = Point(x=x, y=y, z=z)
+    PC3D.append(Other_PC)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <geobipy.src.classes.pointcloud.Point.Point object at 0x10efdda80>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-36
+
+Write a summary of the contents of the point cloud
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-39
+
+.. code-block:: Python
+
+
+    print(PC3D.summary)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Point
+    x:
+    |   StatArray
+    |   Name:   Easting (m)
+    |   Address:['0x1462b7250']
+    |   Shape:  (400,)
+    |   Values: [-0.75575613 -0.37227056 -0.5082486  ...  0.91062364  0.56073342
+    |     0.52761544]
+    |   Min:    -0.9805553827696698
+    |   Max:    0.9916814356778278
+    |   has_posterior: False
+
+    y:
+    |   StatArray
+    |   Name:   Northing (m)
+    |   Address:['0x1462b72d0']
+    |   Shape:  (400,)
+    |   Values: [-0.15201495 -0.55717559 -0.53823673 ...  0.30290861  0.17254113
+    |     0.23398822]
+    |   Min:    -0.9899649834962019
+    |   Max:    0.9979698382486921
+    |   has_posterior: False
+
+    z:
+    |   StatArray
+    |   Name:   Height (m)
+    |   Address:['0x1462b7350']
+    |   Shape:  (400,)
+    |   Values: [-0.43897973  0.19632561 -0.01971922 ... -0.06368326 -0.02409831
+    |    -0.01448342]
+    |   Min:    -1.9063110648103332
+    |   Max:    0.22889687797869024
+    |   has_posterior: False
+
+    elevation:
+    |   StatArray
+    |   Name:   Elevation (m)
+    |   Address:['0x1462b73d0']
+    |   Shape:  (400,)
+    |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   Min:    0.0
+    |   Max:    0.0
+    |   has_posterior: False
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-41
+
+Get a single location from the point as a 3x1 vector
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-45
+
+.. code-block:: Python
+
+
+    Point = PC3D[50]
+    # Print the point to the screen
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-47
+
+Plot the locations with Height as colour
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-51
+
+.. code-block:: Python
+
+
+    plt.figure()
+    PC3D.scatter2D(edgecolor='k')
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_pointcloud3d_001.png
+   :alt: plot pointcloud3d
+   :srcset: /examples/Data/images/sphx_glr_plot_pointcloud3d_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Easting (m)', ylabel='Northing (m)'>, <matplotlib.collections.PathCollection object at 0x143a91b20>, <matplotlib.colorbar.Colorbar object at 0x144cb0c50>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 52-55
+
+Plotting routines take matplotlib arguments for customization
+
+For example, plotting the size of the points according to the absolute value of height
+
+.. GENERATED FROM PYTHON SOURCE LINES 55-58
+
+.. code-block:: Python
+
+    plt.figure()
+    ax = PC3D.scatter2D(s=100*np.abs(PC3D.z), edgecolor='k')
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_pointcloud3d_002.png
+   :alt: plot pointcloud3d
+   :srcset: /examples/Data/images/sphx_glr_plot_pointcloud3d_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-60
+
+Interpolate the points to a 2D rectilinear mesh
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-66
+
+.. code-block:: Python
+
+    mesh, dum = PC3D.interpolate(0.01, 0.01, values=PC3D.z, method='sibson', mask=0.03)
+
+    # We can save that mesh to VTK
+    PC3D.to_vtk('pc3d.vtk')
+    mesh.to_vtk('interpolated_pc3d.vtk')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Grid the points using a triangulated CloughTocher, or minimum curvature interpolation
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-83
+
+.. code-block:: Python
+
+
+    plt.figure()
+    plt.subplot(331)
+    PC3D.map(dx=0.01, dy=0.01, method='ct')
+    plt.subplot(332)
+    PC3D.map(dx=0.01, dy=0.01, method='mc')
+    plt.subplot(333)
+    PC3D.map(dx=0.01, dy=0.01, method='sibson')
+
+    plt.subplot(334)
+    PC3D.map(dx=0.01, dy=0.01, method='ct', mask=0.03)
+    plt.subplot(335)
+    PC3D.map(dx=0.01, dy=0.01, method='mc', mask=0.3)
+    plt.subplot(336)
+    PC3D.map(dx=0.01, dy=0.01, method='sibson', mask=0.03)
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_pointcloud3d_003.png
+   :alt: plot pointcloud3d
+   :srcset: /examples/Data/images/sphx_glr_plot_pointcloud3d_003.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    surface [WARNING]: 5 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+    surface [WARNING]: 5 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+    (<Axes: >, <matplotlib.collections.QuadMesh object at 0x17eb0c470>, <matplotlib.colorbar.Colorbar object at 0x17eca7650>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-85
+
+For lots of points, these surfaces can look noisy. Using a block filter will help
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-94
+
+.. code-block:: Python
+
+    PCsub = PC3D.block_median(0.05, 0.05)
+    plt.subplot(337)
+    PCsub.map(dx=0.01, dy=0.01, method='ct', mask=0.03)
+    plt.subplot(338)
+    PCsub.map(dx=0.01, dy=0.01, method='mc', mask=0.03)
+    plt.subplot(339)
+    PCsub.map(dx=0.01, dy=0.01, method='sibson', mask=0.03)
+
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_pointcloud3d_004.png
+   :alt: plot pointcloud3d
+   :srcset: /examples/Data/images/sphx_glr_plot_pointcloud3d_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    surface [WARNING]: 2 unusable points were supplied; these will be ignored.
+    surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+    surface [WARNING]: Check that previous processing steps write results with enough decimals.
+    surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+    (<Axes: >, <matplotlib.collections.QuadMesh object at 0x17ec7aea0>, <matplotlib.colorbar.Colorbar object at 0x17eeb34d0>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-96
+
+We can perform spatial searches on the 3D point cloud
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-100
+
+.. code-block:: Python
+
+
+    PC3D.set_kdtree(ndim=2)
+    p = PC3D.nearest((0.0,0.0), k=200, p=2, radius=0.3)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-103
+
+.nearest returns the distances and indices into the point cloud of the nearest points.
+We can then obtain those points as another point cloud
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-115
+
+.. code-block:: Python
+
+
+    # pNear = PC3D[p[1]]
+    # plt.figure()
+    # ax1 = plt.subplot(1,2,1)
+    # pNear.scatter2D()
+    # plt.plot(0.0, 0.0, 'x')
+    # plt.subplot(1,2,2, sharex=ax1, sharey=ax1)
+    # ax, sc, cb = PC3D.scatter2D(edgecolor='k')
+    # searchRadius = plt.Circle((0.0, 0.0), 0.3, color='b', fill=False)
+    # ax.add_artist(searchRadius)
+    # plt.plot(0.0, 0.0, 'x')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-117
+
+Read in the xyz co-ordinates in columns 2,3,4 from a file. Skip 1 header line.
+
+.. GENERATED FROM PYTHON SOURCE LINES 117-123
+
+.. code-block:: Python
+
+
+    dataFolder = "..//..//supplementary//Data//"
+
+    PC3D.read_csv(filename=dataFolder + 'Resolve1.txt')
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <geobipy.src.classes.pointcloud.Point.Point object at 0x17ed3c0e0>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 124-138
+
+.. code-block:: Python
+
+    plt.figure()
+    f = PC3D.scatter2D(s=10)
+
+    with h5py.File('test.h5', 'w') as f:
+        PC3D.createHdf(f, 'test')
+        PC3D.writeHdf(f, 'test')
+
+    with h5py.File('test.h5', 'r') as f:
+        PC3D1 = Point.fromHdf(f['test'])
+
+    with h5py.File('test.h5', 'r') as f:
+        point = Point.fromHdf(f['test'], index=0)
+
+    plt.show()
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_pointcloud3d_005.png
+   :alt: plot pointcloud3d
+   :srcset: /examples/Data/images/sphx_glr_plot_pointcloud3d_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 19.063 seconds)
+
+
+.. _sphx_glr_download_examples_Data_plot_pointcloud3d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_pointcloud3d.ipynb <plot_pointcloud3d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_pointcloud3d.py <plot_pointcloud3d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_pointcloud3d.zip <plot_pointcloud3d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Data/plot_skytem_dataset.rst.txt b/docs/_sources/examples/Data/plot_skytem_dataset.rst.txt
new file mode 100644
index 00000000..4aecaba6
--- /dev/null
+++ b/docs/_sources/examples/Data/plot_skytem_dataset.rst.txt
@@ -0,0 +1,545 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Data/plot_skytem_dataset.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Data_plot_skytem_dataset.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Data_plot_skytem_dataset.py:
+
+
+Skytem dataset
+--------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 6-14
+
+.. code-block:: Python
+
+    from geobipy import plotting as cP
+    from os.path import join
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from geobipy import StatArray
+    from geobipy import TdemData
+    import h5py
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-17
+
+Reading in the Data
++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-31
+
+.. code-block:: Python
+
+    dataFolder = "..//..//supplementary//data//"
+    # The data file name
+    dataFiles=dataFolder + 'skytem_saline_clay.csv'
+    # dataFiles = dataFolder + 'Skytem.csv'
+    # The EM system file name
+    systemFiles=[dataFolder + 'SkytemHM.stm', dataFolder + 'SkytemLM.stm']
+
+    from pathlib import Path
+    for f in systemFiles[:1]:
+        txt = Path(f).read_text()
+        print(txt)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    System Begin
+            Name = SkyTemHighMoment-ElkHills
+            Type = Time Domain
+            Transmitter Begin
+                    NumberOfTurns = 1
+                    PeakCurrent   = 1
+                    LoopArea      = 1
+                    BaseFrequency = 30.0
+                    WaveformDigitisingFrequency = 491520
+                    WaveFormCurrent Begin
+    -4.00E-03       0.00E+00
+    -3.91E-03       3.17E-01
+    -3.81E-03       6.30E-01
+    -3.72E-03       8.79E-01
+    -3.68E-03       9.61E-01
+    -2.30E-03       9.74E-01
+    -1.01E-03       9.88E-01
+    0.00E+00        1.00E+00
+    3.25E-06        9.91E-01
+    1.00E-04        7.02E-01
+    2.02E-04        3.78E-01
+    2.82E-04        1.16E-01
+    3.08E-04        2.79E-02
+    3.13E-04        1.21E-02
+    3.15E-04        6.61E-03
+    3.17E-04        3.03E-03
+    3.19E-04        0.00E+00
+    0.012666667     0.00E+00
+
+                    WaveFormCurrent End
+            Transmitter End
+    Receiver Begin
+            NumberOfWindows = 26
+            WindowWeightingScheme = AreaUnderCurve
+            WindowTimes Begin
+    3.796E-04       3.872E-04
+    3.876E-04       3.972E-04
+    3.976E-04       4.102E-04
+    4.106E-04       4.262E-04
+    4.266E-04       4.462E-04
+    4.466E-04       4.712E-04
+    4.716E-04       5.022E-04
+    5.026E-04       5.422E-04
+    5.426E-04       5.932E-04
+    5.936E-04       6.562E-04
+    6.566E-04       7.372E-04
+    7.376E-04       8.382E-04
+    8.386E-04       9.652E-04
+    9.656E-04       1.126E-03
+    1.127E-03       1.328E-03
+    1.329E-03       1.583E-03
+    1.584E-03       1.905E-03
+    1.906E-03       2.311E-03
+    2.312E-03       2.822E-03
+    2.823E-03       3.468E-03
+    3.469E-03       4.260E-03
+    4.261E-03       5.228E-03
+    5.229E-03       6.413E-03
+    6.414E-03       7.865E-03
+    7.866E-03       9.641E-03
+    9.642E-03       1.182E-02
+
+                    WindowTimes End
+                    LowPassFilter Begin
+                            CutOffFrequency = 300000 210000
+                            Order           = 1       2
+                    LowPassFilter End
+            Receiver End
+    ForwardModelling Begin
+                    //TX loop area is was 340.82 m^2 -> r = sqrt(340.82/pi)
+                    ModellingLoopRadius = 10.416
+                    OutputType = dB/dt
+                    XOutputScaling = 0
+                    YOutputScaling = 0
+                    ZOutputScaling = 1
+                    SecondaryFieldNormalisation  =  none
+                    FrequenciesPerDecade = 5
+                    NumberOfAbsiccaInHankelTransformEvaluation = 21
+            ForwardModelling End
+
+    System End
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-33
+
+Read in the data from file
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-35
+
+.. code-block:: Python
+
+    TD = TdemData.read_csv(dataFiles, systemFiles)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-37
+
+Plot the locations of the data points
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-40
+
+.. code-block:: Python
+
+    plt.figure(1, figsize=(8,6))
+    _ = TD.scatter2D()
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_skytem_dataset_001.png
+   :alt: plot skytem dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_skytem_dataset_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-42
+
+Plot all the data along the specified line
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-45
+
+.. code-block:: Python
+
+    plt.figure(2, figsize=(8,6))
+    _ = TD.plotLine(0.0, log=10)
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_skytem_dataset_002.png
+   :alt: plot skytem dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_skytem_dataset_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-47
+
+Or, plot specific channels in the data
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-50
+
+.. code-block:: Python
+
+    plt.figure(3, figsize=(8,6))
+    _ = TD.plot_data(system=0, channels=[1, 3, 5], log=10)
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_skytem_dataset_003.png
+   :alt: plot skytem dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_skytem_dataset_003.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-57
+
+.. code-block:: Python
+
+    plt.figure(4)
+    plt.subplot(211)
+    _ = TD.pcolor(system=0, xscale='log', log=10)
+    plt.subplot(212)
+    _ = TD.pcolor(system=1, xscale='log', log=10)
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_skytem_dataset_004.png
+   :alt: plot skytem dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_skytem_dataset_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 58-94
+
+.. code-block:: Python
+
+    plt.figure(5)
+    ax = TD.scatter2D(c=TD.secondary_field[:, TD.channel_index(system=0, channel=6)], log=10)
+    plt.axis('equal')
+
+
+    # with h5py.File('tdem.h5', 'w') as f:
+    #     TD.createHdf(f, 'tdem')
+    #     TD.writeHdf(f, 'tdem')
+
+    # with h5py.File('tdem.h5', 'r') as f:
+    #     TD3 = TdemData.fromHdf(f['tdem'])
+
+    # with h5py.File('tdem.h5', 'r') as f:
+    #     tdp = TdemData.fromHdf(f['tdem'], index=0)
+
+
+    # #%%
+    # # Obtain a line from the data set
+    # # +++++++++++++++++++++++++++++++
+    # line = TD.line(0.0)
+
+    # #%%
+    # plt.figure(6)
+    # _ = line.scatter2D(c=line.secondary_field[:, line.channel_index(system=0, channel=6)], log=10)
+
+    # #%%
+    # plt.figure(7)
+    # _ = line.plot(xAxis='index', log=10)
+
+    # Prepare the dataset so that we can read a point at a time.
+    Dataset = TdemData._initialize_sequential_reading(dataFiles, systemFiles)
+    # Get a datapoint from the file.
+    DataPoint = Dataset._read_record()
+
+    plt.show()
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_skytem_dataset_005.png
+   :alt: plot skytem dataset
+   :srcset: /examples/Data/images/sphx_glr_plot_skytem_dataset_005.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-146
+
+File Format for time domain data
+++++++++++++++++++++++++++++++++
+Here we describe the file format for time domain data.
+
+The data columns are read in according to the column names in the first line
+
+In this description, the column name or its alternatives are given followed by what the name represents
+Optional columns are also described.
+
+Required columns
+________________
+line
+    Line number for the data point
+fid
+    Unique identification number of the data point
+x or northing or n
+    Northing co-ordinate of the data point, (m)
+y or easting or e
+    Easting co-ordinate of the data point, (m)
+z or alt
+    Altitude of the transmitter coil above ground level (m)
+elevation
+    Elevation of the ground at the data point (m)
+txrx_dx
+    Distance in x between transmitter and reciever (m)
+txrx_dy
+    Distance in y between transmitter and reciever (m)
+txrx_dz
+    Distance in z between transmitter and reciever (m)
+Tx_Pitch
+    Pitch of the transmitter loop
+Tx_Roll
+    Roll of the transmitter loop
+Tx_Yaw
+    Yaw of the transmitter loop
+Rx_Pitch
+    Pitch of the receiver loop
+Rx_Roll
+    Roll of the receiver loop
+Rx_Yaw
+    Yaw of the receiver loop
+Off_time[0] Off_time[1] ... Off_time[last]  - with the number and square brackets
+    The measurements for each time gate specified in the accompanying system file under Receiver Window Times
+    The total number of off_time columns should equal the sum of the receiver windows in all system files.
+Optional columns
+________________
+Off_time_Error[0] Off_time_Error[1] ... Off_time_Error[last]
+    Estimates of standard deviation for each off time measurement
+Example Header
+______________
+Line fid easting northing elevation height txrx_dx txrx_dy txrx_dz TxPitch TxRoll TxYaw RxPitch RxRoll RxYaw Off[0] Off[1]
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-160
+
+File Format for a time domain system
+++++++++++++++++++++++++++++++++++++
+Please see Page 13 of Ross Brodie's `instructions`_
+
+.. _instructions: https://github.com/GeoscienceAustralia/ga-aem/blob/master/docs/GA%20AEM%20Programs%20User%20Manual.pdf
+
+We use GA-AEM for our airborne time domain forward modeller.
+
+Example system files are contained in
+`the supplementary folder`_ in this repository
+
+.. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.295 seconds)
+
+
+.. _sphx_glr_download_examples_Data_plot_skytem_dataset.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_skytem_dataset.ipynb <plot_skytem_dataset.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_skytem_dataset.py <plot_skytem_dataset.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_skytem_dataset.zip <plot_skytem_dataset.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Data/plot_tempest_dataset.rst.txt b/docs/_sources/examples/Data/plot_tempest_dataset.rst.txt
new file mode 100644
index 00000000..446c64a2
--- /dev/null
+++ b/docs/_sources/examples/Data/plot_tempest_dataset.rst.txt
@@ -0,0 +1,385 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Data/plot_tempest_dataset.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Data_plot_tempest_dataset.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Data_plot_tempest_dataset.py:
+
+
+Tempest dataset
+--------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 6-13
+
+.. code-block:: Python
+
+    import h5py
+    from geobipy import plotting as cP
+    from os.path import join
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from geobipy import TempestData
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 14-16
+
+Reading in the Data
++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-33
+
+.. code-block:: Python
+
+    dataFolder = "..//..//supplementary//data//"
+
+    # # The data file name
+    # dataFiles = dataFolder + 'Tempest.nc'
+    # # The EM system file name
+    # systemFiles = dataFolder + 'Tempest.stm'
+
+    # #%%
+    # # Read in the data from file
+    # TD = TempestData.read_netcdf(dataFiles, systemFiles)
+
+    # TD.write_csv(dataFolder + 'Tempest.csv')
+    TD = TempestData.read_csv(dataFolder + 'tempest_saline_clay.csv', system_filename=dataFolder + 'Tempest.stm')
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-35
+
+Plot the locations of the data points
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-39
+
+.. code-block:: Python
+
+    plt.figure(figsize=(8,6))
+    _ = TD.scatter2D()
+    plt.title("Scatter plot")
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_tempest_dataset_001.png
+   :alt: Scatter plot
+   :srcset: /examples/Data/images/sphx_glr_plot_tempest_dataset_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    Text(0.5, 1.0, 'Scatter plot')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-41
+
+Plot all the data along the specified line
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-45
+
+.. code-block:: Python
+
+    plt.figure(figsize=(8,6))
+    _ = TD.plotLine(0.0)
+    plt.title('Line {}'.format(225401.0))
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_tempest_dataset_002.png
+   :alt: Line 225401.0
+   :srcset: /examples/Data/images/sphx_glr_plot_tempest_dataset_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+    Text(0.5, 1.0, 'Line 225401.0')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-47
+
+Or, plot specific channels in the data
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-51
+
+.. code-block:: Python
+
+    plt.figure(figsize=(8,6))
+    _ = TD.plot_data(system=0, channels=[0, 6, 18])
+    plt.title("3 channels of data")
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_tempest_dataset_003.png
+   :alt: 3 channels of data
+   :srcset: /examples/Data/images/sphx_glr_plot_tempest_dataset_003.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+    Text(0.5, 1.0, '3 channels of data')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 52-56
+
+.. code-block:: Python
+
+    plt.figure()
+    _ = TD.pcolor(system=0)
+    plt.title('Data as an array')
+
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_tempest_dataset_004.png
+   :alt: Data as an array
+   :srcset: /examples/Data/images/sphx_glr_plot_tempest_dataset_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+    Text(0.5, 1.0, 'Data as an array')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 57-89
+
+.. code-block:: Python
+
+    plt.figure()
+    ax = TD.scatter2D(c=TD.data[:, TD.channel_index(system=0, channel=10)], equalize=True)
+    plt.axis('equal')
+    plt.title(f"scatter plot of channel {TD.channel_index(system=0, channel=10)}")
+
+    with h5py.File('tdem.h5', 'w') as f:
+        TD.createHdf(f, 'tdem')
+        TD.writeHdf(f, 'tdem')
+
+    with h5py.File('tdem.h5', 'r') as f:
+        TD3 = TempestData.fromHdf(f['tdem'])
+
+    with h5py.File('tdem.h5', 'r') as f:
+        tdp = TempestData.fromHdf(f['tdem'], index=0)
+
+
+    # #%%
+    # # Obtain a line from the data set
+    # # +++++++++++++++++++++++++++++++
+    # line = TD.line(0.0)
+
+    # #%%
+    # plt.figure()
+    # _ = line.scatter2D()
+    # plt.title('Channel')
+
+    # #%%
+    # plt.figure()
+    # _ = line.plot_data(xAxis='index', log=10)
+    # plt.title("All data along line")
+
+    plt.show()
+
+
+
+.. image-sg:: /examples/Data/images/sphx_glr_plot_tempest_dataset_005.png
+   :alt: scatter plot of channel 10
+   :srcset: /examples/Data/images/sphx_glr_plot_tempest_dataset_005.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 4.125 seconds)
+
+
+.. _sphx_glr_download_examples_Data_plot_tempest_dataset.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_tempest_dataset.ipynb <plot_tempest_dataset.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_tempest_dataset.py <plot_tempest_dataset.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_tempest_dataset.zip <plot_tempest_dataset.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Data/readme.rst.txt b/docs/_sources/examples/Data/readme.rst.txt
new file mode 100644
index 00000000..f18be396
--- /dev/null
+++ b/docs/_sources/examples/Data/readme.rst.txt
@@ -0,0 +1,2 @@
+Data
+====
\ No newline at end of file
diff --git a/docs/_sources/examples/Data/sg_execution_times.rst.txt b/docs/_sources/examples/Data/sg_execution_times.rst.txt
new file mode 100644
index 00000000..fdadb173
--- /dev/null
+++ b/docs/_sources/examples/Data/sg_execution_times.rst.txt
@@ -0,0 +1,46 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Data_sg_execution_times:
+
+
+Computation times
+=================
+**00:28.741** total execution time for 4 files **from examples/Data**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Data_plot_pointcloud3d.py` (``plot_pointcloud3d.py``)
+     - 00:19.063
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_tempest_dataset.py` (``plot_tempest_dataset.py``)
+     - 00:04.125
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_frequency_dataset.py` (``plot_frequency_dataset.py``)
+     - 00:03.257
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_skytem_dataset.py` (``plot_skytem_dataset.py``)
+     - 00:02.295
+     - 0.0
diff --git a/docs/_sources/examples/Datapoints/plot_resolve_datapoint.rst.txt b/docs/_sources/examples/Datapoints/plot_resolve_datapoint.rst.txt
new file mode 100644
index 00000000..df0df104
--- /dev/null
+++ b/docs/_sources/examples/Datapoints/plot_resolve_datapoint.rst.txt
@@ -0,0 +1,362 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Datapoints/plot_resolve_datapoint.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Datapoints_plot_resolve_datapoint.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Datapoints_plot_resolve_datapoint.py:
+
+
+Frequency domain datapoint
+--------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 6-88
+
+.. code-block:: Python
+
+    from os.path import join
+    import numpy as np
+    import h5py
+    import matplotlib.pyplot as plt
+    from geobipy import CircularLoop
+    from geobipy import FdemSystem
+    from geobipy import FdemData
+    from geobipy import FdemDataPoint
+    from geobipy import RectilinearMesh1D
+    from geobipy import Model
+    from geobipy import StatArray
+    from geobipy import Distribution
+
+    # Instantiating a frequency domain data point
+    # +++++++++++++++++++++++++++++++++++++++++++
+    #
+    # To instantiate a frequency domain datapoint we need to define some
+    # characteristics of the acquisition system.
+    #
+    # We need to define the frequencies in Hz of the transmitter,
+    # and the geometery of the loops used for each frequency.
+
+    frequencies = np.asarray([380.0, 1776.0, 3345.0, 8171.0, 41020.0, 129550.0])
+
+    # Transmitter positions are defined relative to the observation locations in the data
+    # This is usually a constant offset for all data points.
+    transmitters = CircularLoop(orientation=['z','z','x','z','z','z'],
+                                 moment=np.r_[1, 1, -1, 1, 1, 1],
+                                 x = np.r_[0,0,0,0,0,0],
+                                 y = np.r_[0,0,0,0,0,0],
+                                 z = np.r_[0,0,0,0,0,0],
+                                 pitch = np.r_[0,0,0,0,0,0],
+                                 roll = np.r_[0,0,0,0,0,0],
+                                 yaw = np.r_[0,0,0,0,0,0],
+                                 radius = np.r_[1,1,1,1,1,1])
+
+    # Receiver positions are defined relative to the transmitter
+    receivers = CircularLoop(orientation=['z','z','x','z','z','z'],
+                                 moment=np.r_[1, 1, -1, 1, 1, 1],
+                                 x = np.r_[7.91, 7.91, 9.03, 7.91, 7.91, 7.89],
+                                 y = np.r_[0,0,0,0,0,0],
+                                 z = np.r_[0,0,0,0,0,0],
+                                 pitch = np.r_[0,0,0,0,0,0],
+                                 roll = np.r_[0,0,0,0,0,0],
+                                 yaw = np.r_[0,0,0,0,0,0],
+                                 radius = np.r_[1,1,1,1,1,1])
+
+    # Now we can instantiate the system.
+    fds = FdemSystem(frequencies, transmitters, receivers)
+
+    # And use the system to instantiate a datapoint
+    #
+    # Note the extra arguments that can be used to create the data point.
+    # data is for any observed data one might have, while std are the estimated standard
+    # deviations of those observed data.
+    #
+    # Define some in-phase then quadrature data for each frequency.
+    data = np.r_[145.3, 435.8, 260.6, 875.1, 1502.7, 1516.9,
+                 217.9, 412.5, 178.7, 516.5, 405.7, 255.7]
+
+    fdp = FdemDataPoint(x=0.0, y=0.0, z=30.0, elevation=0.0,
+                        data=data, std=None, predictedData=None,
+                        system=fds, lineNumber=0.0, fiducial=0.0)
+
+    # plt.figure()
+    # _ = fdp.plot()
+
+    # Obtaining a datapoint from a dataset
+    # ++++++++++++++++++++++++++++++++++++
+    #
+    # More often than not, our observed data is stored in a file on disk.
+    # We can read in a dataset and pull datapoints from it.
+    #
+    # For more information about the frequency domain data set see :ref:`Frequency domain dataset`
+
+    # Set some paths and file names
+    dataFolder = "..//..//supplementary//Data//"
+    # The data file name
+    dataFile = dataFolder + 'Resolve2.txt'
+    # The EM system file name
+    systemFile = dataFolder + 'FdemSystem2.stm'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-91
+
+Initialize and read an EM data set
+Prepare the dataset so that we can read a point at a time.
+
+.. GENERATED FROM PYTHON SOURCE LINES 91-94
+
+.. code-block:: Python
+
+    Dataset = FdemData._initialize_sequential_reading(dataFile, systemFile)
+    # Get a datapoint from the file.
+    fdp = Dataset._read_record()
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-227
+
+.. code-block:: Python
+
+
+    # # Initialize and read an EM data set
+    # D = FdemData.read_csv(dataFile,systemFile)
+
+    # # Get a data point from the dataset
+    # fdp = D.datapoint(0)
+    # plt.figure()
+    # _ = fdp.plot()
+
+    # Using a resolve datapoint
+    # +++++++++++++++++++++++++
+
+    # We can define a 1D layered earth model, and use it to predict some data
+    nCells = 19
+    par = StatArray(np.linspace(0.01, 0.1, nCells), "Conductivity", "$\frac{S}{m}$")
+    depth = StatArray(np.arange(nCells+1) * 10.0, "Depth", 'm')
+    depth[-1] = np.inf
+    mod = Model(mesh=RectilinearMesh1D(edges=depth), values=par)
+
+    # Forward model the data
+    fdp.forward(mod)
+
+    plt.figure()
+    plt.subplot(121)
+    _ = mod.pcolor(transpose=True)
+    plt.subplot(122)
+    _ = fdp.plot_predicted()
+    plt.tight_layout()
+
+    # Compute the sensitivity matrix for a given model
+    J = fdp.sensitivity(mod)
+
+    plt.figure()
+    _ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+    # Attaching statistical descriptors to the resolve datapoint
+    # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    # Set values of relative and additive error for both systems.
+    fdp.relative_error = 0.05
+    fdp.additive_error = 10.0
+    # Define a multivariate log normal distribution as the prior on the predicted data.
+    fdp.predictedData.prior = Distribution('MvLogNormal', fdp.data[fdp.active], fdp.std[fdp.active]**2.0, prng=prng)
+
+    # This allows us to evaluate the likelihood of the predicted data
+    print(fdp.likelihood(log=True))
+    # Or the misfit
+    print(fdp.data_misfit())
+
+    # Plot the misfits for a range of half space conductivities
+    plt.figure()
+    _ = fdp.plot_halfspace_responses(-6.0, 4.0, 200)
+
+    plt.title("Halfspace responses");
+
+    # We can perform a quick search for the best fitting half space
+    halfspace = fdp.find_best_halfspace()
+    print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+    plt.figure()
+    _ = fdp.plot()
+    _ = fdp.plot_predicted()
+
+    # Compute the misfit between observed and predicted data
+    print(fdp.data_misfit())
+
+    # We can attach priors to the height of the datapoint,
+    # the relative error multiplier, and the additive error noise floor
+
+
+    # Define the distributions used as priors.
+    zPrior = Distribution('Uniform', min=fdp.z - 2.0, max=fdp.z + 2.0, prng=prng)
+    relativePrior = Distribution('Uniform', min=0.01, max=0.5, prng=prng)
+    additivePrior = Distribution('Uniform', min=5, max=15, prng=prng)
+    fdp.set_priors(z_prior=zPrior, relative_error_prior=relativePrior, additive_error_prior=additivePrior, prng=prng)
+
+
+    # In order to perturb our solvable parameters, we need to attach proposal distributions
+    z_proposal = Distribution('Normal', mean=fdp.z, variance = 0.01, prng=prng)
+    relativeProposal = Distribution('MvNormal', mean=fdp.relative_error, variance=2.5e-7, prng=prng)
+    additiveProposal = Distribution('MvLogNormal', mean=fdp.additive_error, variance=1e-4, prng=prng)
+    fdp.set_proposals(relativeProposal, additiveProposal, z_proposal=z_proposal)
+
+    # With priors set we can auto generate the posteriors
+    fdp.set_posteriors()
+
+    nCells = 19
+    par = StatArray(np.linspace(0.01, 0.1, nCells), "Conductivity", "$\frac{S}{m}$")
+    depth = StatArray(np.arange(nCells+1) * 10.0, "Depth", 'm')
+    depth[-1] = np.inf
+    mod = Model(mesh=RectilinearMesh1D(edges=depth), values=par)
+    fdp.forward(mod)
+
+    # Perturb the datapoint and record the perturbations
+    for i in range(10):
+        fdp.perturb()
+        fdp.update_posteriors()
+
+
+    # Plot the posterior distributions
+    fig = plt.figure()
+    fdp.plot_posteriors(overlay=fdp)
+
+    import h5py
+    with h5py.File('fdp.h5', 'w') as f:
+        fdp.createHdf(f, 'fdp', withPosterior=True)
+        fdp.writeHdf(f, 'fdp', withPosterior=True)
+
+    with h5py.File('fdp.h5', 'r') as f:
+        fdp1 = FdemDataPoint.fromHdf(f['fdp'])
+
+    plt.figure()
+    fdp1.plot_posteriors(overlay=fdp1)
+
+    import h5py
+    with h5py.File('fdp.h5', 'w') as f:
+        fdp.createHdf(f, 'fdp', withPosterior=True, add_axis=np.arange(10.0))
+
+        for i in range(10):
+            fdp.writeHdf(f, 'fdp', withPosterior=True, index=i)
+
+    from geobipy import FdemData
+    with h5py.File('fdp.h5', 'r') as f:
+        fdp1 = FdemDataPoint.fromHdf(f['fdp'], index=0)
+        fdp2 = FdemData.fromHdf(f['fdp'])
+
+    fdp1.plot_posteriors(overlay=fdp1)
+
+    plt.show()
+    # %%
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_001.png
+         :alt: Frequency Domain EM Data
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_002.png
+         :alt: plot resolve datapoint
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_002.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_003.png
+         :alt: Halfspace responses
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_004.png
+         :alt: Frequency Domain EM Data
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_004.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_005.png
+         :alt: Frequency Domain EM Data
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_005.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_006.png
+         :alt: Frequency Domain EM Data, Frequency Domain EM Data
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_resolve_datapoint_006.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    -733.5454886696688
+    1367.81945885548
+    Best half space conductivity is [0.097701] $S/m$
+    45286.828623928755
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 7.271 seconds)
+
+
+.. _sphx_glr_download_examples_Datapoints_plot_resolve_datapoint.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_resolve_datapoint.ipynb <plot_resolve_datapoint.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_resolve_datapoint.py <plot_resolve_datapoint.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_resolve_datapoint.zip <plot_resolve_datapoint.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Datapoints/plot_skytem_datapoint.rst.txt b/docs/_sources/examples/Datapoints/plot_skytem_datapoint.rst.txt
new file mode 100644
index 00000000..a85ab087
--- /dev/null
+++ b/docs/_sources/examples/Datapoints/plot_skytem_datapoint.rst.txt
@@ -0,0 +1,596 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Datapoints/plot_skytem_datapoint.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Datapoints_plot_skytem_datapoint.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Datapoints_plot_skytem_datapoint.py:
+
+
+Skytem Datapoint Class
+----------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-15
+
+Credits:
+We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+https://github.com/GeoscienceAustralia/ga-aem
+
+For ground-based time domain data, we are using Dieter Werthmuller's python package Empymod
+https://empymod.github.io/
+
+Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-46
+
+.. code-block:: Python
+
+    from os.path import join
+    import numpy as np
+    import h5py
+    import matplotlib.pyplot as plt
+    from geobipy import Waveform
+    from geobipy import SquareLoop, CircularLoop
+    from geobipy import butterworth
+    from geobipy import TdemSystem
+    from geobipy import TdemData
+    from geobipy import TdemDataPoint
+    from geobipy import RectilinearMesh1D
+    from geobipy import Model
+    from geobipy import StatArray
+    from geobipy import Distribution
+
+    dataFolder = "..//..//supplementary//data//"
+
+    # Obtaining a datapoint from a dataset
+    # ++++++++++++++++++++++++++++++++++++
+    # More often than not, our observed data is stored in a file on disk.
+    # We can read in a dataset and pull datapoints from it.
+    #
+    # For more information about the time domain data set, see :ref:`Time domain dataset`
+
+    # The data file name
+    dataFile=dataFolder + 'skytem_saline_clay.csv'
+    # The EM system file name
+    systemFile=[dataFolder + 'SkytemHM.stm', dataFolder + 'SkytemLM.stm']
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-49
+
+Initialize and read an EM data set
+Prepare the dataset so that we can read a point at a time.
+
+.. GENERATED FROM PYTHON SOURCE LINES 49-55
+
+.. code-block:: Python
+
+    Dataset = TdemData._initialize_sequential_reading(dataFile, systemFile)
+    # Get a datapoint from the file.
+    tdp = Dataset._read_record()
+
+    Dataset._file.close()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 56-58
+
+Using a time domain datapoint
++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-61
+
+We can define a 1D layered earth model, and use it to predict some data
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-64
+
+.. code-block:: Python
+
+    par = StatArray(np.r_[500.0, 20.0], "Conductivity", "$\frac{S}{m}$")
+    mod = Model(RectilinearMesh1D(edges=np.r_[0, 75.0, np.inf]), values=par)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-66
+
+Forward model the data
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-68
+
+.. code-block:: Python
+
+    tdp.forward(mod)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-77
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(121)
+    _ = mod.pcolor()
+    plt.subplot(122)
+    _ = tdp.plot()
+    _ = tdp.plot_predicted()
+    plt.tight_layout()
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_001.png
+   :alt: Time Domain EM Data
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    /Users/nfoks/codes/repositories/geobipy_docs/geobipy/src/classes/data/datapoint/TdemDataPoint.py:363: RuntimeWarning: divide by zero encountered in log
+      additive_error = exp(log(self.additive_error[i]) - 0.5 * (log(off_times) - log(1e-3)))
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-82
+
+.. code-block:: Python
+
+    plt.figure()
+    tdp.plotDataResidual(yscale='log', xscale='log')
+    plt.title('new')
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_002.png
+   :alt: new
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    Text(0.5, 1.0, 'new')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-84
+
+Compute the sensitivity matrix for a given model
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-88
+
+.. code-block:: Python
+
+    J = tdp.sensitivity(mod)
+    plt.figure()
+    _ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_003.png
+   :alt: plot skytem datapoint
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-91
+
+Attaching statistical descriptors to the skytem datapoint
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 91-105
+
+.. code-block:: Python
+
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    # Set values of relative and additive error for both systems.
+    tdp.relative_error = np.r_[0.05, 0.05]
+    tdp.additive_error = np.r_[1e-14, 1e-13]
+    # Define a multivariate normal distribution as the prior on the predicted data.
+    data_prior = Distribution('MvNormal', tdp.data[tdp.active], tdp.std[tdp.active]**2.0, prng=prng)
+
+    tdp.set_priors(data_prior=data_prior)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-107
+
+This allows us to evaluate the likelihood of the predicted data
+
+.. GENERATED FROM PYTHON SOURCE LINES 107-111
+
+.. code-block:: Python
+
+    print(tdp.likelihood(log=True))
+    # Or the misfit
+    print(tdp.data_misfit())
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    -320327.7331520327
+    643134.8665682999
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 112-113
+
+Plot the misfits for a range of half space conductivities
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-117
+
+.. code-block:: Python
+
+    plt.figure()
+    _ = tdp.plot_halfspace_responses(-6.0, 4.0, 200)
+    plt.title("Halfspace responses")
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_004.png
+   :alt: Halfspace responses
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    Text(0.5, 1.0, 'Halfspace responses')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 118-119
+
+We can perform a quick search for the best fitting half space
+
+.. GENERATED FROM PYTHON SOURCE LINES 119-126
+
+.. code-block:: Python
+
+    halfspace = tdp.find_best_halfspace()
+
+    print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+    plt.figure()
+    _ = tdp.plot()
+    _ = tdp.plot_predicted()
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_005.png
+   :alt: Time Domain EM Data
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_005.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Best half space conductivity is [0.01047616] $S/m$
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 127-128
+
+Compute the misfit between observed and predicted data
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-130
+
+.. code-block:: Python
+
+    print(tdp.data_misfit())
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    19656.31514467744
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-133
+
+We can attach priors to the height of the datapoint,
+the relative error multiplier, and the additive error noise floor
+
+.. GENERATED FROM PYTHON SOURCE LINES 133-140
+
+.. code-block:: Python
+
+
+    # Define the distributions used as priors.
+    z_prior = Distribution('Uniform', min=np.float64(tdp.z) - 2.0, max=np.float64(tdp.z) + 2.0, prng=prng)
+    relativePrior = Distribution('Uniform', min=np.r_[0.01, 0.01], max=np.r_[0.5, 0.5], prng=prng)
+    additivePrior = Distribution('Uniform', min=np.r_[1e-16, 1e-16], max=np.r_[1e-10, 1e-10], log=True, prng=prng)
+    tdp.set_priors(relative_error_prior=relativePrior, additive_error_prior=additivePrior, z_prior=z_prior, prng=prng)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    /Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_skytem_datapoint.py:135: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+      z_prior = Distribution('Uniform', min=np.float64(tdp.z) - 2.0, max=np.float64(tdp.z) + 2.0, prng=prng)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-142
+
+In order to perturb our solvable parameters, we need to attach proposal distributions
+
+.. GENERATED FROM PYTHON SOURCE LINES 142-147
+
+.. code-block:: Python
+
+    z_proposal = Distribution('Normal', mean=tdp.z, variance = 0.01, prng=prng)
+    relativeProposal = Distribution('MvNormal', mean=tdp.relative_error, variance=2.5e-7, prng=prng)
+    additiveProposal = Distribution('MvLogNormal', mean=tdp.additive_error, variance=2.5e-3, linearSpace=True, prng=prng)
+    tdp.set_proposals(relativeProposal, additiveProposal, z_proposal=z_proposal, prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-149
+
+With priorss set we can auto generate the posteriors
+
+.. GENERATED FROM PYTHON SOURCE LINES 149-151
+
+.. code-block:: Python
+
+    tdp.set_posteriors()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 152-154
+
+Perturb the datapoint and record the perturbations
+Note we are not using the priors to accept or reject perturbations.
+
+.. GENERATED FROM PYTHON SOURCE LINES 154-159
+
+.. code-block:: Python
+
+    for i in range(10):
+        tdp.perturb()
+        tdp.update_posteriors()
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 160-161
+
+Plot the posterior distributions
+
+.. GENERATED FROM PYTHON SOURCE LINES 161-166
+
+.. code-block:: Python
+
+    plt.figure()
+    tdp.plot_posteriors(overlay=tdp)
+
+    plt.show()
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_006.png
+   :alt: Time Domain EM Data
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_skytem_datapoint_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 167-244
+
+File Format for a time domain datapoint
++++++++++++++++++++++++++++++++++++++++
+Here we describe the file format for a time domain datapoint.
+
+For individual datapoints we are using the AarhusInv data format.
+
+Here we take the description for the AarhusInv TEM data file, modified to reflect what we can
+currently handle in GeoBIPy.
+
+Line 1 :: string
+  User-defined label describing the TEM datapoint.
+  This line must contain the following, separated by semicolons.
+  XUTM=
+  YUTM=
+  Elevation=
+  StationNumber=
+  LineNumber=
+  Current=
+
+Line 2 :: first integer, sourceType
+  7 = Rectangular loop source parallel to the x - y plane
+Line 2 :: second integer, polarization
+  3 = Vertical magnetic field
+
+Line 3 :: 6 floats, transmitter and receiver offsets relative to X/Y UTM location.
+  If sourceType = 7, Position of the center loop sounding.
+
+Line 4 :: Transmitter loop dimensions
+  If sourceType = 7, 2 floats.  Loop side length in the x and y directions
+
+Line 5 :: Fixed
+  3 3 3
+
+Line 6 :: first integer, transmitter waveform type. Fixed
+  3 = User defined waveform.
+
+Line 6 :: second integer, number of transmitter waveforms. Fixed
+  1
+
+Line 7 :: transmitter waveform definition
+  A user-defined waveform with piecewise linear segments.
+  A full transmitter waveform definition consists of a number of linear segments
+  This line contains an integer as the first entry, which specifies the number of
+  segments, followed by each segment with 4 floats each. The 4 floats per segment
+  are the start and end times, and start and end amplitudes of the waveform. e.g.
+  3  -8.333e-03 -8.033e-03 0.0 1.0 -8.033e-03 0.0 1.0 1.0 0.0 5.4e-06 1.0 0.0
+
+Line 8 :: On time information. Not used but needs specifying.
+  1 1 1
+
+Line 9 :: On time low-pass filters.  Not used but need specifying.
+  0
+
+Line 10 :: On time high-pass filters. Not used but need specifying.
+  0
+
+Line 11 :: Front-gate time. Not used but need specifying.
+  0.0
+
+Line 12 :: first integer, Number of off time filters
+  Number of filters
+
+Line 12 :: second integer, Order of the butterworth filter
+  1 or 2
+
+Line 12 :: cutoff frequencies Hz, one per the number of filters
+  e.g. 4.5e5
+
+Line 13 :: Off time high pass filters.
+  See Line 12
+
+Lines after 13 contain 3 columns that pertain to
+Measurement Time, Data Value, Estimated Standard Deviation
+
+Example data files are contained in
+`the supplementary folder`_ in this repository
+
+.. _the supplementary folder: https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.306 seconds)
+
+
+.. _sphx_glr_download_examples_Datapoints_plot_skytem_datapoint.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_skytem_datapoint.ipynb <plot_skytem_datapoint.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_skytem_datapoint.py <plot_skytem_datapoint.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_skytem_datapoint.zip <plot_skytem_datapoint.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Datapoints/plot_tempest_datapoint.rst.txt b/docs/_sources/examples/Datapoints/plot_tempest_datapoint.rst.txt
new file mode 100644
index 00000000..4a96ba66
--- /dev/null
+++ b/docs/_sources/examples/Datapoints/plot_tempest_datapoint.rst.txt
@@ -0,0 +1,463 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Datapoints/plot_tempest_datapoint.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Datapoints_plot_tempest_datapoint.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Datapoints_plot_tempest_datapoint.py:
+
+
+Tempest Datapoint Class
+-----------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-15
+
+Credits:
+We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+https://github.com/GeoscienceAustralia/ga-aem
+
+For ground-based time domain data, we are using Dieter Werthmuller's python package Empymod
+https://empymod.github.io/
+
+Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-54
+
+.. code-block:: Python
+
+    from os.path import join
+    import numpy as np
+    import h5py
+    import matplotlib.pyplot as plt
+    from geobipy import TempestData
+    # from geobipy import TemDataPoint
+    from geobipy import RectilinearMesh1D
+    from geobipy import Model
+    from geobipy import StatArray
+    from geobipy import Distribution
+    from geobipy import get_prng
+
+    dataFolder = "..//..//supplementary//data//"
+    # dataFolder = "source//examples//supplementary//Data"
+
+    # Obtaining a tempest datapoint from a dataset
+    # ++++++++++++++++++++++++++++++++++++++++++++
+    # More often than not, our observed data is stored in a file on disk.
+    # We can read in a dataset and pull datapoints from it.
+    #
+    # For more information about the time domain data set, see :ref:`Time domain dataset`
+
+    # The data file name
+    dataFile = dataFolder + 'tempest_saline_clay.csv'
+    # The EM system file name
+    systemFile = dataFolder + 'Tempest.stm'
+
+    # Prepare the dataset so that we can read a point at a time.
+    Dataset = TempestData._initialize_sequential_reading(dataFile, systemFile)
+    # Get a datapoint from the file.
+    tdp = Dataset._read_record(0)
+
+    plt.figure()
+    tdp.plot()
+
+    prng = get_prng(seed=146100583096709124601953385843316024947)
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_001.png
+   :alt: Time Domain EM Data
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 55-57
+
+Using a tempest domain datapoint
+++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-60
+
+We can define a 1D layered earth model, and use it to predict some data
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-67
+
+.. code-block:: Python
+
+    par = StatArray(np.r_[0.01, 0.1, 1.], "Conductivity", "$\frac{S}{m}$")
+    mod = Model(mesh=RectilinearMesh1D(edges=np.r_[0.0, 50.0, 75.0, np.inf]), values=par)
+
+    par = StatArray(np.logspace(-3, 3, 30), "Conductivity", "$\frac{S}{m}$")
+    e = np.linspace(0, 350, 31); e[-1] = np.inf
+    mod = Model(mesh=RectilinearMesh1D(edges=e), values=par)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Forward model the data
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-108
+
+.. code-block:: Python
+
+    tdp.forward(mod)
+
+    print('primary', tdp.primary_field)
+    print('sx', tdp.secondary_field[:15])
+    print('sz', tdp.secondary_field[15:])
+
+    # #%%
+    # plt.figure()
+    # plt.subplot(121)
+    # _ = mod.pcolor(transpose=True)
+    # plt.subplot(122)
+    # _ = tdp.plot()
+    # _ = tdp.plot_predicted()
+    # plt.tight_layout()
+    # plt.suptitle('Model and response')
+
+    # #%%
+    # # plt.figure()
+    # # tdp.plotDataResidual(xscale='log')
+    # # plt.title('data residual')
+
+    # #%%
+    # # Compute the sensitivity matrix for a given model
+    J = tdp.sensitivity(mod)
+    # plt.figure()
+    # _ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)
+
+    print('J', J)
+    # print('J shape', J.shape)
+    # print('sx 0', J[:16, 0])
+
+    tdp.fm_dlogc(mod)
+
+    print('new primary', tdp.primary_field)
+    print('sx', tdp.secondary_field[:15])
+    print('sz', tdp.secondary_field[15:])
+
+    print('new J', tdp.sensitivity_matrix)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    primary [34.27253219 17.55503397]
+    sx [4.46362582 2.52720951 2.10544857 ... 0.34346631 0.27359586 0.19875285]
+    sz [6.47100177 4.53101158 3.87594468 ... 1.05345525 0.79969548 0.56994112]
+    J [[ 1.13463137e-01  1.49920887e-01  1.76789170e-01 ... -1.01809840e-09
+       1.13341751e-11  7.27489718e-13]
+     [ 2.09383016e-02  3.20412212e-02  4.74815387e-02 ... -1.02489023e-09
+       1.15994185e-11  7.25910166e-13]
+     [ 1.04188675e-02  1.61552555e-02  2.45575508e-02 ... -1.03167228e-09
+       1.18645190e-11  7.24296662e-13]
+     ...
+     [ 7.20880061e-05  1.13758034e-04  1.79138645e-04 ... -6.62044639e-09
+       1.91310127e-10  4.83737910e-13]
+     [ 3.95655826e-05  6.25753935e-05  9.87713313e-05 ... -6.33418159e-09
+       2.26727066e-10 -1.05451995e-12]
+     [ 1.60007270e-05  3.08385747e-05  5.05626410e-05 ... -1.28316523e-09
+       1.11041966e-10 -2.41585978e-12]]
+    new primary [34.27253219 17.55503397]
+    sx [4.46362582 2.52720951 2.10544857 ... 0.34346631 0.27359586 0.19875285]
+    sz [6.47100177 4.53101158 3.87594468 ... 1.05345525 0.79969548 0.56994112]
+    new J [[ 1.13463137e-01  1.49920887e-01  1.76789170e-01 ... -1.01809840e-09
+       1.13341751e-11  7.27489718e-13]
+     [ 2.09383016e-02  3.20412212e-02  4.74815387e-02 ... -1.02489023e-09
+       1.15994185e-11  7.25910166e-13]
+     [ 1.04188675e-02  1.61552555e-02  2.45575508e-02 ... -1.03167228e-09
+       1.18645190e-11  7.24296662e-13]
+     ...
+     [ 7.20880061e-05  1.13758034e-04  1.79138645e-04 ... -6.62044639e-09
+       1.91310127e-10  4.83737910e-13]
+     [ 3.95655826e-05  6.25753935e-05  9.87713313e-05 ... -6.33418159e-09
+       2.26727066e-10 -1.05451995e-12]
+     [ 1.60007270e-05  3.08385747e-05  5.05626410e-05 ... -1.28316523e-09
+       1.11041966e-10 -2.41585978e-12]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-111
+
+Attaching statistical descriptors to the tempest datapoint
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 111-125
+
+.. code-block:: Python
+
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    # Set relative errors for the primary fields, and secondary fields.
+    tdp.relative_error = np.r_[0.001, 0.001]
+
+    # Set the additive errors for
+    tdp.additive_error = np.hstack([[0.011474, 0.012810, 0.008507, 0.005154, 0.004742, 0.004477, 0.004168, 0.003539, 0.003352, 0.003213, 0.003161, 0.003122, 0.002587, 0.002038, 0.002201],
+                                    [0.007383, 0.005693, 0.005178, 0.003659, 0.003426, 0.003046, 0.003095, 0.003247, 0.002775, 0.002627, 0.002460, 0.002178, 0.001754, 0.001405, 0.001283]])
+    # Define a multivariate log normal distribution as the prior on the predicted data.
+    tdp.predictedData.prior = Distribution('MvLogNormal', tdp.data[tdp.active], tdp.std[tdp.active]**2.0, prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-127
+
+This allows us to evaluate the likelihood of the predicted data
+
+.. GENERATED FROM PYTHON SOURCE LINES 127-131
+
+.. code-block:: Python
+
+    print(tdp.likelihood(log=True))
+    # Or the misfit
+    print(tdp.data_misfit())
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    -36389.6500813217
+    72940.71365767403
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 132-133
+
+Plot the misfits for a range of half space conductivities
+
+.. GENERATED FROM PYTHON SOURCE LINES 133-138
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(1, 2, 1)
+    _ = tdp.plot_halfspace_responses(-6.0, 4.0, 200)
+    plt.title("Halfspace responses")
+
+
+
+
+.. image-sg:: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_002.png
+   :alt: Halfspace responses
+   :srcset: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    Text(0.5, 1.0, 'Halfspace responses')
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 139-140
+
+We can perform a quick search for the best fitting half space
+
+.. GENERATED FROM PYTHON SOURCE LINES 140-161
+
+.. code-block:: Python
+
+    halfspace = tdp.find_best_halfspace()
+    print('Best half space conductivity is {} $S/m$'.format(halfspace.values))
+    plt.subplot(1, 2, 2)
+    _ = tdp.plot()
+    _ = tdp.plot_predicted()
+
+    plt.figure()
+    tdp.plot_secondary_field()
+    tdp.plot_predicted_secondary_field()
+
+    # #%%
+    # # We can attach priors to the height of the datapoint,
+    # # the relative error multiplier, and the additive error noise floor
+
+    # Define the distributions used as priors.
+    relative_prior = Distribution('Uniform', min=np.r_[0.01, 0.01], max=np.r_[0.5, 0.5], prng=prng)
+    receiver_x_prior = Distribution('Uniform', min=np.float64(tdp.receiver.x) - 1.0, max=np.float64(tdp.receiver.x) + 1.0, prng=prng)
+    receiver_z_prior = Distribution('Uniform', min=np.float64(tdp.receiver.z) - 1.0, max=np.float64(tdp.receiver.z) + 1.0, prng=prng)
+    receiver_pitch_prior = Distribution('Uniform', min=tdp.receiver.pitch - 5.0, max=tdp.receiver.pitch + 5.0, prng=prng)
+    tdp.set_priors(relative_error_prior=relative_prior, receiver_x_prior=receiver_x_prior, receiver_z_prior=receiver_z_prior, receiver_pitch_prior=receiver_pitch_prior, prng=prng)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_003.png
+         :alt: Time Domain EM Data
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_004.png
+         :alt: plot tempest datapoint
+         :srcset: /examples/Datapoints/images/sphx_glr_plot_tempest_datapoint_004.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Best half space conductivity is [0.01830738] $S/m$
+    /Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_tempest_datapoint.py:156: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+      receiver_x_prior = Distribution('Uniform', min=np.float64(tdp.receiver.x) - 1.0, max=np.float64(tdp.receiver.x) + 1.0, prng=prng)
+    /Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_tempest_datapoint.py:157: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+      receiver_z_prior = Distribution('Uniform', min=np.float64(tdp.receiver.z) - 1.0, max=np.float64(tdp.receiver.z) + 1.0, prng=prng)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 162-163
+
+In order to perturb our solvable parameters, we need to attach proposal distributions
+
+.. GENERATED FROM PYTHON SOURCE LINES 163-173
+
+.. code-block:: Python
+
+    relative_proposal = Distribution('MvNormal', mean=tdp.relative_error, variance=2.5e-4, prng=prng)
+    receiver_x_proposal = Distribution('Normal', mean=tdp.receiver.x, variance = 0.01, prng=prng)
+    receiver_z_proposal = Distribution('Normal', mean=tdp.receiver.z, variance = 0.01, prng=prng)
+    receiver_pitch_proposal = Distribution('Normal', mean=tdp.receiver.pitch, variance = 0.01, prng=prng)
+    tdp.set_proposals(relative_error_proposal=relative_proposal,
+                      receiver_x_proposal=receiver_x_proposal,
+                      receiver_z_proposal=receiver_z_proposal,
+                      receiver_pitch_proposal=receiver_pitch_proposal,
+                      solve_additive_error=True, additive_error_proposal_variance=1e-4, prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 174-175
+
+With priors set we can auto generate the posteriors
+
+.. GENERATED FROM PYTHON SOURCE LINES 175-177
+
+.. code-block:: Python
+
+    tdp.set_posteriors()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 178-180
+
+Perturb the datapoint and record the perturbations
+Note we are not using the priors to accept or reject perturbations.
+
+.. GENERATED FROM PYTHON SOURCE LINES 180-184
+
+.. code-block:: Python
+
+    for i in range(10):
+        tdp.perturb()
+        tdp.update_posteriors()
+
+    plt.show()
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.653 seconds)
+
+
+.. _sphx_glr_download_examples_Datapoints_plot_tempest_datapoint.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_tempest_datapoint.ipynb <plot_tempest_datapoint.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_tempest_datapoint.py <plot_tempest_datapoint.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_tempest_datapoint.zip <plot_tempest_datapoint.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Datapoints/readme.rst.txt b/docs/_sources/examples/Datapoints/readme.rst.txt
new file mode 100644
index 00000000..4b1753c1
--- /dev/null
+++ b/docs/_sources/examples/Datapoints/readme.rst.txt
@@ -0,0 +1,2 @@
+Datapoints
+==========
\ No newline at end of file
diff --git a/docs/_sources/examples/Datapoints/sg_execution_times.rst.txt b/docs/_sources/examples/Datapoints/sg_execution_times.rst.txt
new file mode 100644
index 00000000..4415abaf
--- /dev/null
+++ b/docs/_sources/examples/Datapoints/sg_execution_times.rst.txt
@@ -0,0 +1,43 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Datapoints_sg_execution_times:
+
+
+Computation times
+=================
+**00:11.231** total execution time for 3 files **from examples/Datapoints**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Datapoints_plot_resolve_datapoint.py` (``plot_resolve_datapoint.py``)
+     - 00:07.271
+     - 0.0
+   * - :ref:`sphx_glr_examples_Datapoints_plot_skytem_datapoint.py` (``plot_skytem_datapoint.py``)
+     - 00:02.306
+     - 0.0
+   * - :ref:`sphx_glr_examples_Datapoints_plot_tempest_datapoint.py` (``plot_tempest_datapoint.py``)
+     - 00:01.653
+     - 0.0
diff --git a/docs/_sources/examples/Distributions/plot_distributions.rst.txt b/docs/_sources/examples/Distributions/plot_distributions.rst.txt
new file mode 100644
index 00000000..6ea3575f
--- /dev/null
+++ b/docs/_sources/examples/Distributions/plot_distributions.rst.txt
@@ -0,0 +1,168 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Distributions/plot_distributions.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Distributions_plot_distributions.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Distributions_plot_distributions.py:
+
+
+Distribution Class
+++++++++++++++++++
+
+Handles the initialization of different statistical distribution
+
+.. GENERATED FROM PYTHON SOURCE LINES 9-19
+
+.. code-block:: Python
+
+    from geobipy import Distribution
+    from geobipy import plotting as cP
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-22
+
+Univariate Normal Distribution
+++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-37
+
+.. code-block:: Python
+
+    D = Distribution('Normal', 0.0, 1.0, prng=prng)
+
+    # Get the bins of the Distribution from +- 4 standard deviations of the mean
+    bins = D.bins()
+
+    # Grab random samples from the distribution
+    D.rng(10)
+
+    # We can then get the Probability Density Function for those bins
+    pdf = D.probability(bins, log=False)
+
+    # And we can plot that PDF
+    plt.figure()
+    plt.plot(bins, pdf)
+
+
+
+
+.. image-sg:: /examples/Distributions/images/sphx_glr_plot_distributions_001.png
+   :alt: plot distributions
+   :srcset: /examples/Distributions/images/sphx_glr_plot_distributions_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    [<matplotlib.lines.Line2D object at 0x17f679190>]
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-41
+
+Multivariate Normal Distribution
+++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-45
+
+.. code-block:: Python
+
+    D = Distribution('MvNormal',[0.0,1.0,2.0],[1.0,1.0,1.0], prng=prng)
+    D.rng()
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    array([ 0.64050649,  1.77177243, -0.34500474])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-47
+
+Uniform Distribution
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-49
+
+.. code-block:: Python
+
+    D = Distribution('Uniform', 0.0, 1.0, prng=prng)
+    D.bins()
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    DataArray([0.        , 0.01010101, 0.02020202, ..., 0.97979798,
+               0.98989899, 1.        ])
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.050 seconds)
+
+
+.. _sphx_glr_download_examples_Distributions_plot_distributions.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_distributions.ipynb <plot_distributions.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_distributions.py <plot_distributions.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_distributions.zip <plot_distributions.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Distributions/readme.rst.txt b/docs/_sources/examples/Distributions/readme.rst.txt
new file mode 100644
index 00000000..c9d04d99
--- /dev/null
+++ b/docs/_sources/examples/Distributions/readme.rst.txt
@@ -0,0 +1,2 @@
+Distributions
+=============
\ No newline at end of file
diff --git a/docs/_sources/examples/Distributions/sg_execution_times.rst.txt b/docs/_sources/examples/Distributions/sg_execution_times.rst.txt
new file mode 100644
index 00000000..47ec0e63
--- /dev/null
+++ b/docs/_sources/examples/Distributions/sg_execution_times.rst.txt
@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Distributions_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.050** total execution time for 1 file **from examples/Distributions**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Distributions_plot_distributions.py` (``plot_distributions.py``)
+     - 00:00.050
+     - 0.0
diff --git a/docs/_sources/examples/HDF5/hdf5.rst.txt b/docs/_sources/examples/HDF5/hdf5.rst.txt
new file mode 100644
index 00000000..047f1bde
--- /dev/null
+++ b/docs/_sources/examples/HDF5/hdf5.rst.txt
@@ -0,0 +1,182 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/HDF5/hdf5.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_HDF5_hdf5.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_HDF5_hdf5.py:
+
+
+Using HDF5 within GeoBIPy
+-------------------------
+
+Inference for large scale datasets in GeoBIPy is handled using MPI and distributed memory systems.
+A common bottleneck with large parallel algorithms is the input output of information to disk.
+We use HDF5 to read and write data in order to leverage the parallel capabililties of the HDF5 API.
+
+Each object within GeoBIPy has a create_hdf, write_hdf, and read_hdf routine.
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-16
+
+.. code-block:: Python
+
+    import numpy as np
+    import h5py
+    from geobipy import StatArray
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-18
+
+StatArray
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-33
+
+.. code-block:: Python
+
+
+    # Instantiate a StatArray
+    x = StatArray(np.arange(10.0), name = 'an Array', units = 'some units')
+
+    # Write the StatArray to a HDF file.
+    with h5py.File("x.h5", 'w') as f:
+        x.toHdf(f, "x")
+
+    # Read the StatArray back in.
+    with h5py.File("x.h5", 'r') as f:
+        y = StatArray.fromHdf(f, 'x')
+
+    print('x', x)
+    print('y', y)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-39
+
+There are actually steps within the "toHdf" function.
+First, space is created within the HDF file and second, the data is written to that space
+These functions are split because during the execution of a parallel enabled program,
+all the space within the HDF file needs to be allocated before we can write to the file
+using multiple cores.
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-52
+
+.. code-block:: Python
+
+
+    # Write the StatArray to a HDF file.
+    with h5py.File("x.h5", 'w') as f:
+        x.createHdf(f, "x")
+        x.writeHdf(f, "x")
+
+    # Read the StatArray back in.
+    with h5py.File("x.h5", 'r') as f:
+        y = StatArray.fromHdf(f, 'x')
+
+    print('x', x)
+    print('y', y)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 53-57
+
+The create and write HDF methods also allow extra space to be allocated so that
+the extra memory can be written later, perhaps by multiple cores.
+Here we specify space for 2 arrays, the memory is stored contiguously as a numpy array.
+We then write to only the first index.
+
+.. GENERATED FROM PYTHON SOURCE LINES 57-71
+
+.. code-block:: Python
+
+
+    # Write the StatArray to a HDF file.
+    with h5py.File("x.h5", 'w') as f:
+        x.createHdf(f, "x", nRepeats=2)
+        x.writeHdf(f, "x", index=0)
+
+    # Read the StatArray back in.
+    with h5py.File("x.h5", 'r') as f:
+        y = StatArray.fromHdf(f, 'x', index=0)
+
+    print('x', x)
+    print('y', y)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-73
+
+The duplication can also be a shape.
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-86
+
+.. code-block:: Python
+
+
+    # Write the StatArray to a HDF file.
+    with h5py.File("x.h5", 'w') as f:
+        x.createHdf(f, "x", nRepeats=(2, 2))
+        x.writeHdf(f, "x", index=(0, 0))
+
+    # Read the StatArray back in.
+    with h5py.File("x.h5", 'r') as f:
+        y = StatArray.fromHdf(f, 'x', index=(0, 0))
+
+    print('x', x)
+    print('y', y)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-88
+
+Similarly, we can duplicate a 2D array with an extra 2D duplication
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-100
+
+.. code-block:: Python
+
+
+    x = StatArray(np.random.randn(2, 2), name = 'an Array', units = 'some units')
+    # Write the StatArray to a HDF file.
+    with h5py.File("x.h5", 'w') as f:
+        x.createHdf(f, "x", nRepeats=(2, 2))
+        x.writeHdf(f, "x", index=(0, 0))
+
+    # Read the StatArray back in.
+    with h5py.File("x.h5", 'r') as f:
+        y = StatArray.fromHdf(f, 'x', index=(0, 0))
+
+    print('x', x)
+    print('y', y)
+
+.. _sphx_glr_download_examples_HDF5_hdf5.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: hdf5.ipynb <hdf5.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: hdf5.py <hdf5.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: hdf5.zip <hdf5.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/HDF5/readme.rst.txt b/docs/_sources/examples/HDF5/readme.rst.txt
new file mode 100644
index 00000000..7582962c
--- /dev/null
+++ b/docs/_sources/examples/HDF5/readme.rst.txt
@@ -0,0 +1,2 @@
+HDF 5
+=====
\ No newline at end of file
diff --git a/docs/_sources/examples/HDF5/sg_execution_times.rst.txt b/docs/_sources/examples/HDF5/sg_execution_times.rst.txt
new file mode 100644
index 00000000..c7859313
--- /dev/null
+++ b/docs/_sources/examples/HDF5/sg_execution_times.rst.txt
@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_examples_HDF5_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 1 file **from examples/HDF5**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_HDF5_hdf5.py` (``hdf5.py``)
+     - 00:00.000
+     - 0.0
diff --git a/docs/_sources/examples/Inference_1D/plot_inference_1d_resolve.rst.txt b/docs/_sources/examples/Inference_1D/plot_inference_1d_resolve.rst.txt
new file mode 100644
index 00000000..18c47b34
--- /dev/null
+++ b/docs/_sources/examples/Inference_1D/plot_inference_1d_resolve.rst.txt
@@ -0,0 +1,191 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_1D/plot_inference_1d_resolve.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_1D_plot_inference_1d_resolve.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_1D_plot_inference_1d_resolve.py:
+
+
+Running GeoBIPy to invert Resolve data
+++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 5-29
+
+.. code-block:: Python
+
+
+    import os
+    import sys
+    import pathlib
+    from datetime import timedelta
+    import time
+    import numpy as np
+    from geobipy import Inference3D
+    from geobipy import user_parameters
+    from geobipy import get_prng
+
+    def checkCommandArguments():
+        """Check the users command line arguments. """
+        import argparse
+        # warnings.filterwarnings('error')
+
+        Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+        Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+        Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem_512', 'tempest', 'resolve']")
+        Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+        return Parser.parse_args()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-39
+
+.. code-block:: Python
+
+    np.random.seed(0)
+
+    args = checkCommandArguments()
+    sys.path.append(os.getcwd())
+
+    models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+    data_type = "Resolve"
+    model_type = models[args.index]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-42
+
+The directory where HDF files will be stored
+%%
+
+.. GENERATED FROM PYTHON SOURCE LINES 42-89
+
+.. code-block:: Python
+
+    file_path = os.path.join(data_type, model_type)
+    pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+    for filename in os.listdir(file_path):
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+    output_directory = file_path
+
+    data_filename = data_type + '_' + model_type
+
+    supplementary = "..//..//supplementary//"
+
+    parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+    inputFile = pathlib.Path(parameter_file)
+    assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+    output_directory = pathlib.Path(output_directory)
+    assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+    print('Using user input file {}'.format(parameter_file))
+    print('Output files will be produced at {}'.format(output_directory))
+
+    kwargs = user_parameters.read(inputFile)
+
+    kwargs['n_markov_chains'] = 5000
+
+    kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+    kwargs['system_filename'] = supplementary + "//data//" + kwargs['system_filename']
+
+    # Everyone needs the system classes read in early.
+    data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+    # Start keeping track of time.
+    t0 = time.time()
+
+    seed = 146100583096709124601953385843316024947
+    prng = get_prng(seed=seed)
+
+    inference3d = Inference3D(data, prng=prng)
+    inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+    print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+    inference3d.infer(index=30, **kwargs)
+
+
+.. image-sg:: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_resolve_001.png
+   :alt: Fiducial [30], Frequency Domain EM Data
+   :srcset: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_resolve_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Using user input file ..//..//supplementary////options_files//Resolve_options
+    Output files will be produced at Resolve/glacial
+    Creating HDF5 files, this may take a few minutes...
+    Files are being created for data files ..//..//supplementary////data//Resolve_glacial.csv and system files ..//..//supplementary////data//..//data/FdemSystem2.stm
+    Created hdf5 file for line 0.0 with 79 data points
+    Created hdf5 files 79 total data points
+    Created hdf5 files in 0:00:00.164874 h:m:s
+    i=5000, k=1, acc=*49.200, 0.006 s/Model, 28.026 s Elapsed, eta=--:--:-- h:m:s
+
+    Remaining Points -30/1 || Elapsed Time: 0:00:28.993323 h:m:s || ETA 0:00:00.935268 h:m:s
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 29.449 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_1D_plot_inference_1d_resolve.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_1d_resolve.ipynb <plot_inference_1d_resolve.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_1d_resolve.py <plot_inference_1d_resolve.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_1d_resolve.zip <plot_inference_1d_resolve.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_1D/plot_inference_1d_skytem.rst.txt b/docs/_sources/examples/Inference_1D/plot_inference_1d_skytem.rst.txt
new file mode 100644
index 00000000..c2df5c3a
--- /dev/null
+++ b/docs/_sources/examples/Inference_1D/plot_inference_1d_skytem.rst.txt
@@ -0,0 +1,264 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_1D/plot_inference_1d_skytem.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_1D_plot_inference_1d_skytem.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_1D_plot_inference_1d_skytem.py:
+
+
+Running GeoBIPy to invert Skytem data
+++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 5-29
+
+.. code-block:: Python
+
+
+    import os
+    import sys
+    import pathlib
+    from datetime import timedelta
+    import time
+    import numpy as np
+    from geobipy import Inference3D
+    from geobipy import user_parameters
+    from geobipy import get_prng
+
+    def checkCommandArguments():
+        """Check the users command line arguments. """
+        import argparse
+        # warnings.filterwarnings('error')
+
+        Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+        Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+        Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem', 'tempest', 'resolve']")
+        Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+        return Parser.parse_args()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-40
+
+.. code-block:: Python
+
+    np.random.seed(0)
+
+    args = checkCommandArguments()
+    sys.path.append(os.getcwd())
+
+    models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+    data_type = "Skytem"
+    model_type = models[args.index]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-43
+
+The directory where HDF files will be stored
+%%
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-89
+
+.. code-block:: Python
+
+    file_path = os.path.join(data_type, model_type)
+    pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+    for filename in os.listdir(file_path):
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+    output_directory = file_path
+
+    data_filename = data_type + '_' + model_type
+
+    supplementary = "..//..//supplementary//"
+    parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+    inputFile = pathlib.Path(parameter_file)
+    assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+    output_directory = pathlib.Path(output_directory)
+    assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+    print('Using user input file {}'.format(parameter_file))
+    print('Output files will be produced at {}'.format(output_directory))
+
+    kwargs = user_parameters.read(inputFile)
+
+    kwargs['n_markov_chains'] = 5000
+
+    kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+    kwargs['system_filename'] = [supplementary + "//data//" + x for x in kwargs['system_filename']]
+
+    # Everyone needs the system classes read in early.
+    data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+    # Start keeping track of time.
+    t0 = time.time()
+
+    seed = 146100583096709124601953385843316024947
+    prng = get_prng(seed=seed)
+
+    inference3d = Inference3D(data, prng=prng)
+    inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+    print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+    inference3d.infer(index=2, **kwargs)
+
+
+.. image-sg:: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_skytem_001.png
+   :alt: Fiducial [2.], Time Domain EM Data
+   :srcset: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_skytem_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Using user input file ..//..//supplementary////options_files//Skytem_options
+    Output files will be produced at Skytem/glacial
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Creating HDF5 files, this may take a few minutes...
+    Files are being created for data files ..//..//supplementary////data//Skytem_glacial.csv and system files ['..//..//supplementary////data//..//data//SkytemHM.stm', '..//..//supplementary////data//..//data//SkytemLM.stm']
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Created hdf5 file for line 0.0 with 79 data points
+    Created hdf5 files 79 total data points
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Created hdf5 files in 0:00:00.368412 h:m:s
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    i=5000, k=6, acc=*25.760, 0.019 s/Model, 94.498 s Elapsed, eta=--:--:-- h:m:s
+
+    Remaining Points -2/1 || Elapsed Time: 0:01:36.342864 h:m:s || ETA 0:00:32.114288 h:m:s
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (1 minutes 37.169 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_1D_plot_inference_1d_skytem.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_1d_skytem.ipynb <plot_inference_1d_skytem.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_1d_skytem.py <plot_inference_1d_skytem.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_1d_skytem.zip <plot_inference_1d_skytem.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_1D/plot_inference_1d_tempest.rst.txt b/docs/_sources/examples/Inference_1D/plot_inference_1d_tempest.rst.txt
new file mode 100644
index 00000000..6be4c2ee
--- /dev/null
+++ b/docs/_sources/examples/Inference_1D/plot_inference_1d_tempest.rst.txt
@@ -0,0 +1,266 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_1D/plot_inference_1d_tempest.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_1D_plot_inference_1d_tempest.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_1D_plot_inference_1d_tempest.py:
+
+
+Running GeoBIPy to invert Tempest data
+++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 5-29
+
+.. code-block:: Python
+
+
+    import os
+    import sys
+    import pathlib
+    from datetime import timedelta
+    import time
+    import numpy as np
+    from geobipy import Inference3D
+    from geobipy import user_parameters
+    from geobipy import get_prng
+
+    def checkCommandArguments():
+        """Check the users command line arguments. """
+        import argparse
+        # warnings.filterwarnings('error')
+
+        Parser = argparse.ArgumentParser(description="GeoBIPy",
+                                         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+        Parser.add_argument('--index', default=0, type=int, help='job array index 0-18')
+        Parser.add_argument('--data', default=None, help="Data type. Choose from ['skytem_512', 'tempest', 'resolve']")
+        Parser.add_argument('--model', default=None, help="Model type. Choose from ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']")
+
+        return Parser.parse_args()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-40
+
+.. code-block:: Python
+
+    np.random.seed(0)
+
+    args = checkCommandArguments()
+    sys.path.append(os.getcwd())
+
+    models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+    data_type = "Tempest"
+    model_type = models[args.index]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-43
+
+The directory where HDF files will be stored
+%%
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-91
+
+.. code-block:: Python
+
+    file_path = os.path.join(data_type, model_type)
+    pathlib.Path(file_path).mkdir(parents=True, exist_ok=True)
+
+    for filename in os.listdir(file_path):
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+    output_directory = file_path
+
+    data_filename = data_type + '_' + model_type
+
+    supplementary = "..//..//supplementary//"
+
+    parameter_file = supplementary + "//options_files//{}_options".format(data_type)
+    inputFile = pathlib.Path(parameter_file)
+    assert inputFile.exists(), Exception("Cannot find input file {}".format(inputFile))
+
+    output_directory = pathlib.Path(output_directory)
+    assert output_directory.exists(), Exception("Make sure the output directory exists {}".format(output_directory))
+
+    print('Using user input file {}'.format(parameter_file))
+    print('Output files will be produced at {}'.format(output_directory))
+
+    kwargs = user_parameters.read(inputFile)
+
+    kwargs['n_markov_chains'] = 5000
+
+    kwargs['data_filename'] = supplementary + '//data//' + data_filename + '.csv'
+    kwargs['system_filename'] = supplementary + "//data//" + kwargs['system_filename']
+
+    # Everyone needs the system classes read in early.
+    data = kwargs['data_type']._initialize_sequential_reading(kwargs['data_filename'], kwargs['system_filename'])
+
+    # Start keeping track of time.
+    t0 = time.time()
+
+    seed = 146100583096709124601953385843316024947
+    prng = get_prng(seed=seed)
+
+    inference3d = Inference3D(data, prng=prng)
+    inference3d.create_hdf5(directory=output_directory, **kwargs)
+
+    print("Created hdf5 files in {} h:m:s".format(str(timedelta(seconds=time.time()-t0))))
+
+    inference3d.infer(index=2, **kwargs)
+
+
+
+.. image-sg:: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_tempest_001.png
+   :alt: Fiducial [2.], Time Domain EM Data
+   :srcset: /examples/Inference_1D/images/sphx_glr_plot_inference_1d_tempest_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Using user input file ..//..//supplementary////options_files//Tempest_options
+    Output files will be produced at Tempest/glacial
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    Creating HDF5 files, this may take a few minutes...
+    Files are being created for data files ..//..//supplementary////data//Tempest_glacial.csv and system files ..//..//supplementary////data//..//data/tempest.stm
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    Created hdf5 file for line 0.0 with 79 data points
+    Created hdf5 files 79 total data points
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    Created hdf5 files in 0:00:00.544066 h:m:s
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    i=5000, k=5, acc=*24.800, 0.023 s/Model, 113.655 s Elapsed, eta=--:--:-- h:m:s
+
+    Remaining Points -2/1 || Elapsed Time: 0:01:55.563057 h:m:s || ETA 0:00:38.521019 h:m:s
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (1 minutes 56.531 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_1D_plot_inference_1d_tempest.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_1d_tempest.ipynb <plot_inference_1d_tempest.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_1d_tempest.py <plot_inference_1d_tempest.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_1d_tempest.zip <plot_inference_1d_tempest.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_1D/readme.rst.txt b/docs/_sources/examples/Inference_1D/readme.rst.txt
new file mode 100644
index 00000000..9c93f798
--- /dev/null
+++ b/docs/_sources/examples/Inference_1D/readme.rst.txt
@@ -0,0 +1,11 @@
+1D Inference
+============
+
+There are a couple of ways to run an inference using geobipy, the first is via command line using
+
+.. code-block:: bash
+
+    geobipy skytem_options.py <output folder>
+
+The other is with a python script similar to the examples in this folder.
+In both cases, you will need to write an options file (also shown in these examples)
diff --git a/docs/_sources/examples/Inference_1D/sg_execution_times.rst.txt b/docs/_sources/examples/Inference_1D/sg_execution_times.rst.txt
new file mode 100644
index 00000000..ceebf7c0
--- /dev/null
+++ b/docs/_sources/examples/Inference_1D/sg_execution_times.rst.txt
@@ -0,0 +1,43 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Inference_1D_sg_execution_times:
+
+
+Computation times
+=================
+**04:03.149** total execution time for 3 files **from examples/Inference_1D**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_tempest.py` (``plot_inference_1d_tempest.py``)
+     - 01:56.531
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_skytem.py` (``plot_inference_1d_skytem.py``)
+     - 01:37.169
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_resolve.py` (``plot_inference_1d_resolve.py``)
+     - 00:29.449
+     - 0.0
diff --git a/docs/_sources/examples/Inference_2D/plot_inference_2d_resolve.rst.txt b/docs/_sources/examples/Inference_2D/plot_inference_2d_resolve.rst.txt
new file mode 100644
index 00000000..d446af63
--- /dev/null
+++ b/docs/_sources/examples/Inference_2D/plot_inference_2d_resolve.rst.txt
@@ -0,0 +1,254 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_2D/plot_inference_2d_resolve.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_2D_plot_inference_2d_resolve.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_2D_plot_inference_2d_resolve.py:
+
+
+2D Posterior analysis of Resolve inference
+------------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+.. GENERATED FROM PYTHON SOURCE LINES 8-151
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_001.png
+         :alt: resolve glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_002.png
+         :alt: resolve saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_002.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_003.png
+         :alt: resolve resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_004.png
+         :alt: resolve resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_004.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_005.png
+         :alt: resolve coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_005.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_006.png
+         :alt: resolve ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_resolve_006.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. code-block:: Python
+
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from geobipy import Model
+    from geobipy import Inference2D
+
+    def plot_2d_summary(folder, data_type, model_type):
+       #%%
+       # Inference for a line of inferences
+       # ++++++++++++++++++++++++++++++++++
+       #
+       # We can instantiate the inference handler by providing a path to the directory containing
+       # HDF5 files generated by GeoBIPy.
+       #
+       # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+       # as and when it is needed.
+       #
+       # The first time you use these classes to create plots, expect longer initial processing times.
+       # I precompute expensive properties and store them in the HDF5 files for later use.
+
+       from numpy.random import Generator
+       from numpy.random import PCG64DXSM
+       generator = PCG64DXSM(seed=0)
+       prng = Generator(generator)
+
+       #%%
+       results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+       kwargs = {
+             "log" : 10,
+             "cmap" : 'jet'
+             }
+
+       fig = plt.figure(figsize=(16, 8))
+       plt.suptitle("{} {}".format(data_type, model_type))
+       gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+       true_model = Model.create_synthetic_model(model_type)
+       true_model.mesh.y_edges = true_model.mesh.y_edges / 10.0
+
+       kwargs['vmin'] = np.log10(np.min(true_model.values))
+       kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+       ax = fig.add_subplot(gs0[0, 0])
+       true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+       plt.ylim([-160, 60])
+
+       ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+       ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # # # # # We can also choose to keep parameters above the DOI opaque.
+       # # # # results_2d.compute_doi()
+       # # # # plt.subplot(313)
+       # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+       # # # # results_2d.plot_data_elevation(linewidth=0.3);
+       # # # # results_2d.plot_elevation(linewidth=0.3);
+
+       ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+       results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+       ax1.set_title('Best model')
+
+       del kwargs['vmin']
+       del kwargs['vmax']
+
+       ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+       results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+       results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+       results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       #%%
+       # We can plot the parameter values that produced the highest posterior
+       ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+       results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+       ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+       ll, bb, ww, hh = ax1.get_position().bounds
+       ax1.set_position([ll, bb, ww*0.8, hh])
+
+       results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+       results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+       #%%
+       # Now we can start plotting some more interesting posterior properties.
+       # How about the confidence?
+       ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+       results_2d.plot_confidence(ax=ax1);
+       results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+       results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+       #%%
+       # We can take the interface depth posterior for each data point,
+       # and display an interface probability cross section
+       # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+       # the top and bottom 0.5% of the colour range
+       ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+       ax1.set_title('P(Interface)')
+       results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+       results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+
+       plt.show()
+       # plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+    if __name__ == '__main__':
+       models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+       for model in models:
+          try:
+             plot_2d_summary("../../../Parallel_Inference/", "resolve", model)
+          except Exception as e:
+             print(model)
+             print(e)
+             pass
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 21.524 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_2D_plot_inference_2d_resolve.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_2d_resolve.ipynb <plot_inference_2d_resolve.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_2d_resolve.py <plot_inference_2d_resolve.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_2d_resolve.zip <plot_inference_2d_resolve.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_2D/plot_inference_2d_skytem.rst.txt b/docs/_sources/examples/Inference_2D/plot_inference_2d_skytem.rst.txt
new file mode 100644
index 00000000..c43df09b
--- /dev/null
+++ b/docs/_sources/examples/Inference_2D/plot_inference_2d_skytem.rst.txt
@@ -0,0 +1,972 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_2D/plot_inference_2d_skytem.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_2D_plot_inference_2d_skytem.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_2D_plot_inference_2d_skytem.py:
+
+
+2D Posterior analysis of Skytem inference
+-----------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+.. GENERATED FROM PYTHON SOURCE LINES 8-152
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_001.png
+         :alt: skytem glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_002.png
+         :alt: skytem saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_002.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_003.png
+         :alt: skytem resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_004.png
+         :alt: skytem resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_004.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_005.png
+         :alt: skytem coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_005.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_006.png
+         :alt: skytem ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_skytem_006.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17f00d0d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17f00ebd0']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x182cba8d0']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17f00fa50']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17f00e9d0']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x17ec00950']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x17f00d650']
+    |   Shape:  (79, 132)
+    |   Values: [[0.01120778 0.01120778 0.01089737 ... 0.01842322 0.01842322 0.01842322]
+    |    [0.01004315 0.01004315 0.01001518 ... 0.01627435 0.01627435 0.01627435]
+    |    [0.0098209  0.0098209  0.00983342 ... 0.01667789 0.01667789 0.01667789]
+    |    ...
+    |    [0.06755182 0.06755182 0.11932536 ... 0.0915802  0.0915802  0.0915802 ]
+    |    [0.08104576 0.08104576 0.09688295 ... 0.09217019 0.09217019 0.09217019]
+    |    [0.09121028 0.09121028 0.09943253 ... 0.11213815 0.11213815 0.11213815]]
+    |   Min:    0.007485002271267226
+    |   Max:    0.21717666705189165
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x182cb9c50']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17f114d50']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x182c2d4d0']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17f115b50']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17ec01c50']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x17ec036d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x17ec006d0']
+    |   Shape:  (79, 132)
+    |   Values: [[0.01022279 0.01022279 0.01016321 ... 0.0112021  0.0112021  0.0112021 ]
+    |    [0.01121804 0.01121804 0.01062572 ... 0.0110357  0.0110357  0.0110357 ]
+    |    [0.01049917 0.01049917 0.01007536 ... 0.01354095 0.01354095 0.01354095]
+    |    ...
+    |    [0.06896877 0.06896877 0.082946   ... 0.07112381 0.07112381 0.07112381]
+    |    [0.08055856 0.08055856 0.0985627  ... 0.07903887 0.07903887 0.07903887]
+    |    [0.09166633 0.09166633 0.09878492 ... 0.11154601 0.11154601 0.11154601]]
+    |   Min:    0.006775009348709323
+    |   Max:    0.8077378262817626
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x18229f350']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17f21de50']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x182600c50']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x18229d5d0']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x18229dbd0']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x18229e6d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x18229cb50']
+    |   Shape:  (79, 132)
+    |   Values: [[0.02104953 0.02104953 0.02099504 ... 0.01952426 0.01952426 0.01952426]
+    |    [0.02008789 0.02008789 0.01997577 ... 0.02017019 0.02017019 0.02017019]
+    |    [0.0198301  0.0198301  0.01986773 ... 0.0145253  0.0145253  0.0145253 ]
+    |    ...
+    |    [0.00511314 0.00511314 0.0029654  ... 0.01258484 0.01258484 0.01258484]
+    |    [0.00392961 0.00392961 0.00385047 ... 0.00870505 0.00870505 0.00870505]
+    |    [0.00279718 0.00279718 0.00269725 ... 0.00924302 0.00924302 0.00924302]]
+    |   Min:    0.0008600086897131668
+    |   Max:    9.774547726888374
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17eab3d50']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x17eab07d0']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x17f2d60d0']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17eab31d0']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x17eab0bd0']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x182fd6bd0']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x182fd5650']
+    |   Shape:  (79, 132)
+    |   Values: [[0.01147797 0.01147797 0.01033699 ... 0.00131732 0.00131732 0.00131732]
+    |    [0.00999299 0.00999299 0.01000167 ... 0.00120333 0.00120333 0.00120333]
+    |    [0.01007022 0.01007022 0.01005285 ... 0.00844038 0.00844038 0.00844038]
+    |    ...
+    |    [0.06763163 0.06763163 0.11942951 ... 0.09924038 0.09924038 0.09924038]
+    |    [0.08122468 0.08122468 0.09651811 ... 0.09608009 0.09608009 0.09608009]
+    |    [0.09162505 0.09162505 0.09883382 ... 0.11272764 0.11272764 0.11272764]]
+    |   Min:    0.0007661982574834428
+    |   Max:    0.21045876958147633
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x181fa95d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x181fa88d0']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x181f49a50']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x181fa8450']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x181fa8150']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x181fa9150']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x181fa87d0']
+    |   Shape:  (79, 132)
+    |   Values: [[1.02310541 1.02310541 1.02438661 ... 1.05490484 1.05490484 1.05490484]
+    |    [1.02229478 1.02229478 1.02483553 ... 0.83077926 0.83077926 0.83077926]
+    |    [1.02256309 1.02256309 1.02298923 ... 1.16206135 1.16206135 1.16206135]
+    |    ...
+    |    [0.18317234 0.18317234 0.18317234 ... 0.04611609 0.04611609 0.04611609]
+    |    [0.13296225 0.13296225 0.13296225 ... 0.02326468 0.02326468 0.02326468]
+    |    [0.08897776 0.08897776 0.08897776 ... 0.02807133 0.02807133 0.02807133]]
+    |   Min:    0.005922638381206899
+    |   Max:    15.778868401978407
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    Model:
+    mesh:
+    |   RectilinearMesh2D: 
+    |   Shape: : (79, 132) 
+    |   x
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   79
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x180d2a5d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+    |   |   Min:    0.0
+    |   |   Max:    78.0
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   Easting (m)
+    |   |   Address:['0x180d2aad0']
+    |   |   Shape:  (80,)
+    |   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+    |   |   Min:    -0.5
+    |   |   Max:    78.5
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x17f866cd0']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    |   y
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   132
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x180d2a750']
+    |   |   Shape:  (132,)
+    |   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+    |   |   Min:    -602.5125
+    |   |   Max:    -3.1875
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   elevation (m)
+    |   |   Address:['0x180d2ac50']
+    |   |   Shape:  (133,)
+    |   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+    |   |   Min:    -604.8
+    |   |   Max:    -0.9
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   Elevation (m)
+    |   |   Address:['0x180d216d0']
+    |   |   Shape:  (79,)
+    |   |   Values: [0. 0. 0. ... 0. 0. 0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   StatArray
+    |   Name:   Mean Conductivity ($\frac{S}{m}$)
+    |   Address:['0x180d21350']
+    |   Shape:  (79, 132)
+    |   Values: [[0.00056777 0.00056777 0.00056795 ... 0.00059446 0.00059446 0.00059446]
+    |    [0.00052266 0.00052266 0.00052266 ... 0.00071041 0.00071041 0.00071041]
+    |    [0.00080071 0.00080071 0.00080071 ... 0.00104502 0.00104502 0.00104502]
+    |    ...
+    |    [0.00854703 0.00854703 0.00857136 ... 0.00167267 0.00167267 0.00167267]
+    |    [0.00894549 0.00894549 0.00908742 ... 0.00255047 0.00255047 0.00255047]
+    |    [0.00925526 0.00925526 0.00948505 ... 0.00507188 0.00507188 0.00507188]]
+    |   Min:    0.0004901610032814771
+    |   Max:    0.7309893437585234
+    |   has_posterior: False
+
+
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+    self.n_components=1, self.nTimes=array([26, 19])
+
+
+
+
+
+
+|
+
+.. code-block:: Python
+
+
+    import argparse
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from geobipy import Model
+    from geobipy import Inference2D
+
+    def plot_2d_summary(folder, data_type, model_type):
+       #%%
+       # Inference for a line of inferences
+       # ++++++++++++++++++++++++++++++++++
+       #
+       # We can instantiate the inference handler by providing a path to the directory containing
+       # HDF5 files generated by GeoBIPy.
+       #
+       # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+       # as and when it is needed.
+       #
+       # The first time you use these classes to create plots, expect longer initial processing times.
+       # I precompute expensive properties and store them in the HDF5 files for later use.
+
+       from numpy.random import Generator
+       from numpy.random import PCG64DXSM
+       generator = PCG64DXSM(seed=0)
+       prng = Generator(generator)
+
+       #%%
+       results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+       kwargs = {
+             "log" : 10,
+             "cmap" : 'jet'
+             }
+
+       fig = plt.figure(figsize=(16, 8))
+       plt.suptitle("{} {}".format(data_type, model_type))
+       gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+       true_model = Model.create_synthetic_model(model_type)
+
+       kwargs['vmin'] = np.log10(np.min(true_model.values))
+       kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+       ax = fig.add_subplot(gs0[0, 0])
+       true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+       plt.ylim([-550, 60])
+
+       print(results_2d.mean_parameters().summary)
+
+       ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+       ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # # # # # We can also choose to keep parameters above the DOI opaque.
+       # # # # results_2d.compute_doi()
+       # # # # plt.subplot(313)
+       # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+       # # # # results_2d.plot_data_elevation(linewidth=0.3);
+       # # # # results_2d.plot_elevation(linewidth=0.3);
+
+       ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+       results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+       ax1.set_title('Best model')
+
+       del kwargs['vmin']
+       del kwargs['vmax']
+
+       ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+       results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+       results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+       results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       #%%
+       # We can plot the parameter values that produced the highest posterior
+       ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+       results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+       ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+       ll, bb, ww, hh = ax1.get_position().bounds
+       ax1.set_position([ll, bb, ww*0.8, hh])
+
+       results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+       results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+       #%%
+       # Now we can start plotting some more interesting posterior properties.
+       # How about the confidence?
+       ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+       results_2d.plot_confidence(ax=ax1);
+       results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+       results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+       #%%
+       # We can take the interface depth posterior for each data point,
+       # and display an interface probability cross section
+       # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+       # the top and bottom 0.5% of the colour range
+       ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+       ax1.set_title('P(Interface)')
+       results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+       results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # plt.show()
+       plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+
+    if __name__ == '__main__':
+       types = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+       for model in types:
+          # try:
+             plot_2d_summary('../../../Parallel_Inference/', "skytem", model)
+          # except Exception as e:
+          #    print(model)
+          #    print(e)
+          #    pass
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 18.965 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_2D_plot_inference_2d_skytem.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_2d_skytem.ipynb <plot_inference_2d_skytem.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_2d_skytem.py <plot_inference_2d_skytem.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_2d_skytem.zip <plot_inference_2d_skytem.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_2D/plot_inference_2d_tempest.rst.txt b/docs/_sources/examples/Inference_2D/plot_inference_2d_tempest.rst.txt
new file mode 100644
index 00000000..a8127abf
--- /dev/null
+++ b/docs/_sources/examples/Inference_2D/plot_inference_2d_tempest.rst.txt
@@ -0,0 +1,445 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Inference_2D/plot_inference_2d_tempest.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Inference_2D_plot_inference_2d_tempest.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Inference_2D_plot_inference_2d_tempest.py:
+
+
+2D Posterior analysis of Tempest inference
+------------------------------------------
+
+All plotting in GeoBIPy can be carried out using the 3D inference class
+
+.. GENERATED FROM PYTHON SOURCE LINES 8-153
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_001.png
+         :alt: tempest glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_002.png
+         :alt: tempest saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_002.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_003.png
+         :alt: tempest resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_004.png
+         :alt: tempest resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_004.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_005.png
+         :alt: tempest coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_005.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_006.png
+         :alt: tempest ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)
+         :srcset: /examples/Inference_2D/images/sphx_glr_plot_inference_2d_tempest_006.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+    self.n_components=2, self.nTimes=array([15])
+
+
+
+
+
+
+|
+
+.. code-block:: Python
+
+
+    import argparse
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from geobipy import Model
+    from geobipy import Inference2D
+
+    def plot_2d_summary(folder, data_type, model_type):
+       #%%
+       # Inference for a line of inferences
+       # ++++++++++++++++++++++++++++++++++
+       #
+       # We can instantiate the inference handler by providing a path to the directory containing
+       # HDF5 files generated by GeoBIPy.
+       #
+       # The InfereceXD classes are low memory.  They only read information from the HDF5 files
+       # as and when it is needed.
+       #
+       # The first time you use these classes to create plots, expect longer initial processing times.
+       # I precompute expensive properties and store them in the HDF5 files for later use.
+
+       from numpy.random import Generator
+       from numpy.random import PCG64DXSM
+       generator = PCG64DXSM(seed=0)
+       prng = Generator(generator)
+
+       #%%
+       results_2d = Inference2D.fromHdf('{}/{}/{}/0.0.h5'.format(folder, data_type, model_type), prng=prng)
+
+       kwargs = {
+             "log" : 10,
+             "cmap" : 'jet'
+             }
+
+       fig = plt.figure(figsize=(16, 8))
+       plt.suptitle("{} {}".format(data_type, model_type))
+       gs0 = fig.add_gridspec(6, 2, hspace=1.0)
+
+       true_model = Model.create_synthetic_model(model_type)
+
+       kwargs['vmin'] = np.log10(np.min(true_model.values))
+       kwargs['vmax'] = np.log10(np.max(true_model.values))
+
+       ax = fig.add_subplot(gs0[0, 0])
+       true_model.pcolor(flipY=True, ax=ax, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax, xlabel=False, ylabel=False);
+
+       plt.ylim([-550, 60])
+
+       ax1 = fig.add_subplot(gs0[0, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mean_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # By adding the useVariance keyword, we can make regions of lower confidence more transparent
+       ax1 = fig.add_subplot(gs0[1, 1], sharex=ax, sharey=ax)
+       results_2d.plot_mode_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # # # # # We can also choose to keep parameters above the DOI opaque.
+       # # # # results_2d.compute_doi()
+       # # # # plt.subplot(313)
+       # # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);
+       # # # # results_2d.plot_data_elevation(linewidth=0.3);
+       # # # # results_2d.plot_elevation(linewidth=0.3);
+
+       ax1 = fig.add_subplot(gs0[2, 1], sharex=ax, sharey=ax)
+       results_2d.plot_best_model(ax=ax1, wrap_clabel=True, **kwargs);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+       ax1.set_title('Best model')
+
+       del kwargs['vmin']
+       del kwargs['vmax']
+
+       ax1 = fig.add_subplot(gs0[3, 1], sharex=ax, sharey=ax); ax1.set_title('5%')
+       results_2d.plot_percentile(ax=ax1, percent=0.05, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[4, 1], sharex=ax, sharey=ax); ax1.set_title('50%')
+       results_2d.plot_percentile(ax=ax1, percent=0.5, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 1], sharex=ax, sharey=ax); ax1.set_title('95%')
+       results_2d.plot_percentile(ax=ax1, percent=0.95, wrap_clabel=True, **kwargs)
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       #%%
+       # We can plot the parameter values that produced the highest posterior
+       ax1 = fig.add_subplot(gs0[2, 0], sharex=ax)
+       results_2d.plot_k_layers(ax=ax1, wrap_ylabel=True)
+
+       ax1 = fig.add_subplot(gs0[1, 0], sharex=ax)
+
+       ll, bb, ww, hh = ax1.get_position().bounds
+       ax1.set_position([ll, bb, ww*0.8, hh])
+
+       results_2d.plot_channel_saturation(ax=ax1, wrap_ylabel=True)
+       results_2d.plot_burned_in(ax=ax1, underlay=True)
+
+       #%%
+       # Now we can start plotting some more interesting posterior properties.
+       # How about the confidence?
+       ax1 = fig.add_subplot(gs0[3, 0], sharex=ax, sharey=ax)
+       results_2d.plot_confidence(ax=ax1);
+       results_2d.plot_data_elevation(ax=ax1, linewidth=0.3);
+       results_2d.plot_elevation(ax=ax1, linewidth=0.3);
+
+       #%%
+       # We can take the interface depth posterior for each data point,
+       # and display an interface probability cross section
+       # This posterior can be washed out, so the clim_scaling keyword lets me saturate
+       # the top and bottom 0.5% of the colour range
+       ax1 = fig.add_subplot(gs0[4, 0], sharex=ax, sharey=ax)
+       ax1.set_title('P(Interface)')
+       results_2d.plot_interfaces(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       ax1 = fig.add_subplot(gs0[5, 0], sharex=ax, sharey=ax)
+       results_2d.plot_entropy(cmap='Greys', clim_scaling=0.5, ax=ax1);
+       results_2d.plot_data_elevation(linewidth=0.3, ax=ax1);
+       results_2d.plot_elevation(linewidth=0.3, ax=ax1);
+
+       # plt.show()
+       plt.savefig('{}_{}.png'.format(data_type, model_type), dpi=300)
+
+
+    if __name__ == '__main__':
+       models = ['glacial', 'saline_clay', 'resistive_dolomites', 'resistive_basement', 'coastal_salt_water', 'ice_over_salt_water']
+
+       # import warnings
+       # warnings.filterwarnings('error')
+       for model in models:
+          try:
+             plot_2d_summary('../../../Parallel_Inference/', "tempest", model)
+          except Exception as e:
+             print(model)
+             print(e)
+             pass
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 19.999 seconds)
+
+
+.. _sphx_glr_download_examples_Inference_2D_plot_inference_2d_tempest.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_inference_2d_tempest.ipynb <plot_inference_2d_tempest.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_inference_2d_tempest.py <plot_inference_2d_tempest.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_inference_2d_tempest.zip <plot_inference_2d_tempest.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Inference_2D/readme.rst.txt b/docs/_sources/examples/Inference_2D/readme.rst.txt
new file mode 100644
index 00000000..669f32ab
--- /dev/null
+++ b/docs/_sources/examples/Inference_2D/readme.rst.txt
@@ -0,0 +1,2 @@
+2D Inference
+============
\ No newline at end of file
diff --git a/docs/_sources/examples/Inference_2D/sg_execution_times.rst.txt b/docs/_sources/examples/Inference_2D/sg_execution_times.rst.txt
new file mode 100644
index 00000000..ff3edf14
--- /dev/null
+++ b/docs/_sources/examples/Inference_2D/sg_execution_times.rst.txt
@@ -0,0 +1,43 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Inference_2D_sg_execution_times:
+
+
+Computation times
+=================
+**01:00.488** total execution time for 3 files **from examples/Inference_2D**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_resolve.py` (``plot_inference_2d_resolve.py``)
+     - 00:21.524
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_tempest.py` (``plot_inference_2d_tempest.py``)
+     - 00:19.999
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_skytem.py` (``plot_inference_2d_skytem.py``)
+     - 00:18.965
+     - 0.0
diff --git a/docs/_sources/examples/Meshes/plot_rectilinear_mesh_1d.rst.txt b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_1d.rst.txt
new file mode 100644
index 00000000..c3682320
--- /dev/null
+++ b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_1d.rst.txt
@@ -0,0 +1,716 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Meshes/plot_rectilinear_mesh_1d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_1d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Meshes_plot_rectilinear_mesh_1d.py:
+
+
+1D Rectilinear Mesh
+-------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 6-14
+
+.. code-block:: Python
+
+    from copy import deepcopy
+    from geobipy import DataArray, StatArray
+    from geobipy import RectilinearMesh1D, RectilinearMesh2D, RectilinearMesh2D_stitched
+    import matplotlib.gridspec as gridspec
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import h5py
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 15-18
+
+The basics
+++++++++++
+Instantiate a new 1D rectilinear mesh by specifying cell centres, edges, or widths.
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-20
+
+.. code-block:: Python
+
+    x = StatArray(np.cumsum(np.arange(0.0, 10.0)), 'Depth', 'm')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-22
+
+Cell edges
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-24
+
+.. code-block:: Python
+
+    rm = RectilinearMesh1D(edges=x, centres=None, widths=None)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-27
+
+We can plot the grid of the mesh
+Or Pcolor the mesh showing. An array of cell values is used as the colour.
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-72
+
+.. code-block:: Python
+
+    arr = StatArray(np.random.randn(*rm.shape), "Name", "Units")
+    p=0; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.plot_grid(transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+    # Mask the mesh cells by a distance
+    rm_masked, indices, arr2 = rm.mask_cells(2.0, values=arr)
+    p+=1; plt.figure(p)
+    _ = rm_masked.pcolor(StatArray(arr2), grid=True, transpose=True, flip=True)
+
+    # Writing and reading to/from HDF5
+    # ++++++++++++++++++++++++++++++++
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.toHdf(f, 'rm1d')
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.createHdf(f, 'rm1d', add_axis=10)
+        for i in range(10):
+            rm.writeHdf(f, 'rm1d', index=i)
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(131)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(132)
+    _ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+    plt.subplot(133)
+    _ = rm2.pcolor(np.repeat(arr[None, :], 10, 0), grid=True, flipY=True)
+
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_001.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_002.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_002.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_003.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_004.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_004.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-77
+
+Log-space rectilinear mesh
+++++++++++++++++++++++++++
+Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+Here we use edges
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-79
+
+.. code-block:: Python
+
+    x = StatArray(np.logspace(-3, 3, 10), 'Depth', 'm')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-123
+
+.. code-block:: Python
+
+    rm = RectilinearMesh1D(edges=x, log=10)
+
+    # We can plot the grid of the mesh
+    # Or Pcolor the mesh showing. An array of cell values is used as the colour.
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.plot_grid(transpose=True, flip=True)
+    plt.subplot(122)
+    arr = StatArray(np.random.randn(rm.nCells), "Name", "Units")
+    _ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+    # Writing and reading to/from HDF5
+    # ++++++++++++++++++++++++++++++++
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.toHdf(f, 'rm1d')
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.createHdf(f, 'rm1d', add_axis=10)
+        for i in range(10):
+            rm.writeHdf(f, 'rm1d', index=i)
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(131)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(132)
+    _ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+    plt.subplot(133)
+    _ = rm2.pcolor(np.repeat(arr[None, :], 10, 0), grid=True, flipY=True)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_005.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_005.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_006.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_006.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_007.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_007.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 124-128
+
+relative_to
+++++++++++
+Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+Here we use edges
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-130
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(11.0), 'Deviation', 'm')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-133
+
+.. code-block:: Python
+
+    rm = RectilinearMesh1D(edges=x, relative_to=5.0)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 134-136
+
+We can plot the grid of the mesh
+Or Pcolor the mesh showing. An array of cell values is used as the colour.
+
+.. GENERATED FROM PYTHON SOURCE LINES 136-184
+
+.. code-block:: Python
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.plot_grid(transpose=True, flip=True)
+    plt.subplot(122)
+    arr = StatArray(np.random.randn(rm.nCells), "Name", "Units")
+    _ = rm.pcolor(arr, grid=True, transpose=True, flip=True)
+
+    # Writing and reading to/from HDF5
+    # ++++++++++++++++++++++++++++++++
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.createHdf(f, 'rm1d')
+        rm.writeHdf(f, 'rm1d')
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm1.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.createHdf(f, 'rm1d', add_axis=3)
+        for i in range(3):
+            rm.relative_to += 0.5
+            rm.writeHdf(f, 'rm1d', index=i)
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'], index=0)
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(131)
+    _ = rm.pcolor(StatArray(arr), grid=True, transpose=True, flip=True)
+    plt.subplot(132)
+    _ = rm1.pcolor(arr, grid=True, transpose=True, flip=True)
+    plt.subplot(133)
+    _ = rm2.pcolor(np.repeat(arr[None, :], 3, 0), grid=True, flipY=True)
+
+
+    # Making a mesh perturbable
+    # +++++++++++++++++++++++++
+    n_cells = 2
+    widths = DataArray(np.full(n_cells, fill_value=10.0), 'test')
+    rm = RectilinearMesh1D(widths=widths, relative_to=0.0)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_008.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_008.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_009.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_009.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_010.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_010.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 185-192
+
+Randomness and Model Perturbations
+++++++++++++++++++++++++++++++++++
+We can set the priors on the 1D model by assigning minimum and maximum layer
+depths and a maximum number of layers.  These are used to create priors on
+the number of cells in the model, a new depth interface, new parameter values
+and the vertical gradient of those parameters.
+The halfSpaceValue is used as a reference value for the parameter prior.
+
+.. GENERATED FROM PYTHON SOURCE LINES 192-203
+
+.. code-block:: Python
+
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    # Set the priors
+    rm.set_priors(min_edge = 1.0,
+                  max_edge = 150.0,
+                  max_cells = 30,
+                  prng = prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 204-205
+
+We can evaluate the prior of the model using depths only
+
+.. GENERATED FROM PYTHON SOURCE LINES 205-207
+
+.. code-block:: Python
+
+    print('Log probability of the Mesh given its priors: ', rm.probability)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Log probability of the Mesh given its priors:  -3.367295829986474
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 208-211
+
+To propose new meshes, we specify the probabilities of creating, removing, perturbing, and not changing
+an edge interface
+Here we force the creation of a layer.
+
+.. GENERATED FROM PYTHON SOURCE LINES 211-216
+
+.. code-block:: Python
+
+    rm.set_proposals(probabilities = [0.25, 0.25, 0.25, 0.25], prng=prng)
+    rm.set_posteriors()
+
+    rm0 = deepcopy(rm)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 217-218
+
+We can then perturb the layers of the model
+
+.. GENERATED FROM PYTHON SOURCE LINES 218-222
+
+.. code-block:: Python
+
+    for i in range(1000):
+        rm = rm.perturb()
+        rm.update_posteriors()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 223-245
+
+.. code-block:: Python
+
+    p+=1; fig = plt.figure(p)
+    ax = rm._init_posterior_plots(fig)
+
+    rm.plot_posteriors(axes=ax)
+
+    with h5py.File('rm1d.h5', 'w') as f:
+        rm.createHdf(f, 'rm1d', withPosterior = True)
+        rm.writeHdf(f, 'rm1d', withPosterior = True)
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm1 = RectilinearMesh1D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    _ = rm.pcolor(StatArray(rm.shape), grid=True, transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm1.pcolor(StatArray(rm1.shape), grid=True, transpose=True, flip=True)
+
+    p+=1; fig = plt.figure(p)
+    ax = rm1._init_posterior_plots(fig)
+    rm1.plot_posteriors(axes=ax)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_011.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_011.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_012.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_012.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_013.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_013.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    [<Axes: xlabel='# of Layers', ylabel='Density'>, <Axes: xlabel='test', ylabel='Density'>]
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 246-247
+
+Expanded
+
+.. GENERATED FROM PYTHON SOURCE LINES 247-289
+
+.. code-block:: Python
+
+    with h5py.File('rm1d.h5', 'w') as f:
+        tmp = rm.pad(rm.max_cells)
+        tmp.createHdf(f, 'rm1d', withPosterior=True, add_axis=DataArray(np.arange(3.0), name='Easting', units="m"))
+
+        print(list(f['rm1d'].keys()))
+
+        rm.relative_to = 5.0
+        print(rm.summary)
+        rm.writeHdf(f, 'rm1d', withPosterior = True, index=0)
+
+        rm = deepcopy(rm0)
+        for i in range(1000):
+            rm = rm.perturb(); rm.update_posteriors()
+        rm.relative_to = 10.0
+        rm.writeHdf(f, 'rm1d', withPosterior = True, index=1)
+
+        rm = deepcopy(rm0)
+        for i in range(1000):
+            rm = rm.perturb(); rm.update_posteriors()
+        rm.relative_to = 25.0
+        rm.writeHdf(f, 'rm1d', withPosterior = True, index=2)
+
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['rm1d'])
+
+    p+=1; plt.figure(p)
+    plt.subplot(121)
+    arr = np.random.randn(3, rm.max_cells) * 10
+    _ = rm0.pcolor(arr[0, :rm0.nCells.item()], grid=True, transpose=True, flip=True)
+    plt.subplot(122)
+    _ = rm2.pcolor(arr, grid=True, flipY=True, equalize=True)
+
+    from geobipy import RectilinearMesh2D
+    with h5py.File('rm1d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['rm1d'], index=0)
+
+    plt.figure()
+    plt.subplot(121)
+    rm2.plot_grid(transpose=True, flip=True)
+    plt.subplot(122)
+    rm2.edges.posterior.pcolor(transpose=True, flip=True)
+
+    plt.show()
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_014.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_014.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_015.png
+         :alt: plot rectilinear mesh 1d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_1d_015.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    ['nCells', 'x', 'y']
+    RectilinearMesh1D
+    Number of Cells:
+    |   StatArray
+    |   Name:   Number of cells
+    |   Address:['0x154b0fd50' '0x182c1a750' '0x180970e10' '0x1809733f0']
+    |   Shape:  (1,)
+    |   Values: [15]
+    |   Min:    15
+    |   Max:    15
+    |   Prior:
+    |   |   Uniform Distribution: 
+    |   |   Min: 1
+    |   |   Max: 30
+    |   has_posterior: True
+
+    Cell Centres:
+    |   StatArray
+    |   Name:   test
+    |   Address:['0x17f3b4850']
+    |   Shape:  (15,)
+    |   Values: [  1.22331766   3.28906551   6.36608642 ...  66.85590133 108.99206549
+    |    148.449825  ]
+    |   Min:    1.2233176602046558
+    |   Max:    148.4498250024555
+    |   has_posterior: False
+
+    Cell Edges:
+    |   StatArray
+    |   Name:   test
+    |   Address:['0x1834e3950' '0x17ebbc1a0' '0x182174450' '0x17f387aa0' '0x1808cd750'
+    |    '0x1808ced70']
+    |   Shape:  (16,)
+    |   Values: [  0.           2.44663532   4.13149571 ...  70.19008745 147.79404353
+    |    149.10560647]
+    |   Min:    0.0
+    |   Max:    149.1056064716983
+    |   Prior:
+    |   |   Order Statistics: 
+    |   |   None 
+    |   Proposal:
+    |   |   Uniform Distribution: 
+    |   |   Min: 1.0
+    |   |   Max: 149.99999999999997
+    |   has_posterior: True
+
+    log:
+    |   None
+    relative_to:
+    |   StatArray
+    |   Name:   
+    |   Address:['0x17ef83e50']
+    |   Shape:  (1,)
+    |   Values: [5.]
+    |   Min:    5.0
+    |   Max:    5.0
+    |   has_posterior: False
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 4.585 seconds)
+
+
+.. _sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_1d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_rectilinear_mesh_1d.ipynb <plot_rectilinear_mesh_1d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_rectilinear_mesh_1d.py <plot_rectilinear_mesh_1d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_rectilinear_mesh_1d.zip <plot_rectilinear_mesh_1d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Meshes/plot_rectilinear_mesh_2d.rst.txt b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_2d.rst.txt
new file mode 100644
index 00000000..b96baffa
--- /dev/null
+++ b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_2d.rst.txt
@@ -0,0 +1,613 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Meshes/plot_rectilinear_mesh_2d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_2d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Meshes_plot_rectilinear_mesh_2d.py:
+
+
+2D Rectilinear Mesh
+-------------------
+This 2D rectilinear mesh defines a grid with straight cell boundaries.
+
+It can be instantiated in two ways.
+
+The first is by providing the cell centres or
+cell edges in two dimensions.
+
+The second embeds the 2D mesh in 3D by providing the cell centres or edges in three dimensions.
+The first two dimensions specify the mesh coordinates in the horiztontal cartesian plane
+while the third discretizes in depth. This allows us to characterize a mesh whose horizontal coordinates
+do not follow a line that is parallel to either the "x" or "y" axis.
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-26
+
+.. code-block:: Python
+
+    import h5py
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh1D, RectilinearMesh2D, RectilinearMesh3D
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-28
+
+Specify some cell centres in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 28-32
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(10.0), 'Easting', 'm')
+    y = StatArray(np.arange(20.0), 'Depth', 'm')
+    rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-34
+
+We can plot the grid lines of the mesh.
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-45
+
+.. code-block:: Python
+
+    p=0;
+    plt.figure(p)
+    _  = rm.plot_grid(flipY=True, linewidth=0.5)
+
+    # Intersecting multisegment lines with a mesh
+    arr = np.zeros(rm.shape)
+    i = rm.line_indices([0.0, 3.0, 6.0, 9], [2.0, 6.0, 0.0, 10])
+    arr[i[:, 0], i[:, 1]] = 1
+    p += 1; plt.figure(p)
+    rm.pcolor(values = arr)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_001.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_002.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_002.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Easting (m)', ylabel='Depth (m)'>, <matplotlib.collections.QuadMesh object at 0x180d19bb0>, <matplotlib.colorbar.Colorbar object at 0x1824970b0>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-47
+
+We can pcolor the mesh by providing cell values.
+
+.. GENERATED FROM PYTHON SOURCE LINES 47-58
+
+.. code-block:: Python
+
+    xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+    arr = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Values")
+
+    rm2, values2 = rm.resample(0.5, 0.5, arr, method='linear')
+
+
+    p += 1; plt.figure(p)
+    _ = rm.pcolor(arr, grid=True, flipY=True, linewidth=0.5)
+
+
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_003.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-60
+
+Mask the x axis cells by a distance
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-64
+
+.. code-block:: Python
+
+    rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(x_distance=0.4, values=arr)
+    p += 1; plt.figure(p)
+    _ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_004.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_004.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-66
+
+Mask the z axis cells by a distance
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-70
+
+.. code-block:: Python
+
+    rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(y_distance=0.2, values=arr)
+    p += 1; plt.figure(p)
+    _ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_005.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 71-72
+
+Mask axes by a distance
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-80
+
+.. code-block:: Python
+
+    rm_masked, x_indices, z_indices, arr2 = rm.mask_cells(x_distance=0.4, y_distance=0.2, values=arr)
+    p += 1; plt.figure(p)
+    _ = rm_masked.pcolor(StatArray(arr2), grid=True, flipY=True)
+
+    x = StatArray(np.arange(10.0), 'Easting', 'm')
+    y = StatArray(np.cumsum(np.arange(15.0)), 'Depth', 'm')
+    rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_006.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-83
+
+We can perform some interval statistics on the cell values of the mesh
+Generate some values
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-85
+
+.. code-block:: Python
+
+    a = np.repeat(np.arange(1.0, np.float64(rm.x.nCells+1))[:, np.newaxis], rm.y.nCells, 1)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 86-87
+
+Compute the mean over an interval for the mesh.
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-89
+
+.. code-block:: Python
+
+    rm.intervalStatistic(a, intervals=[6.8, 12.4], axis=0, statistic='mean')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (array([[9., 9., 9., ..., 9., 9., 9.]]), [6.8, 12.4])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 90-91
+
+Compute the mean over multiple intervals for the mesh.
+
+.. GENERATED FROM PYTHON SOURCE LINES 91-93
+
+.. code-block:: Python
+
+    rm.intervalStatistic(a, intervals=[6.8, 12.4, 20.0, 40.0], axis=0, statistic='mean')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (array([[ 9.,  9.,  9., ...,  9.,  9.,  9.],
+           [nan, nan, nan, ..., nan, nan, nan],
+           [nan, nan, nan, ..., nan, nan, nan]]), [6.8, 12.4, 20.0, 40.0])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-95
+
+We can specify either axis
+
+.. GENERATED FROM PYTHON SOURCE LINES 95-97
+
+.. code-block:: Python
+
+    rm.intervalStatistic(a, intervals=[2.8, 4.2], axis=1, statistic='mean')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (array([[ 1.],
+           [ 2.],
+           [ 3.],
+           ...,
+           [ 8.],
+           [ 9.],
+           [10.]]), [2.8, 4.2])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-100
+
+.. code-block:: Python
+
+    rm.intervalStatistic(a, intervals=[2.8, 4.2, 5.1, 8.4], axis=1, statistic='mean')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (array([[ 1., nan,  1.],
+           [ 2., nan,  2.],
+           [ 3., nan,  3.],
+           ...,
+           [ 8., nan,  8.],
+           [ 9., nan,  9.],
+           [10., nan, 10.]]), [2.8, 4.2, 5.1, 8.4])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-102
+
+Slice the 2D mesh to retrieve either a 2D mesh or 1D mesh
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-114
+
+.. code-block:: Python
+
+    rm2 = rm[:5, :5]
+    rm3 = rm[:5, 5]
+    rm4 = rm[5, :5]
+
+    p += 1; plt.figure(p)
+    plt.subplot(131)
+    rm2.plot_grid()
+    plt.subplot(132)
+    rm3.plot_grid()
+    plt.subplot(133)
+    rm4.plot_grid(transpose=True)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_007.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_007.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 115-116
+
+Resample a grid
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-125
+
+.. code-block:: Python
+
+    values = StatArray(np.random.randn(*rm.shape))
+    rm2, values2 = rm.resample(0.5, 0.5, values)
+
+    p += 1; plt.figure(p)
+    plt.subplot(121)
+    rm.pcolor(values)
+    plt.subplot(122)
+    rm2.pcolor(values2)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_008.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_008.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: >, <matplotlib.collections.QuadMesh object at 0x17faebb00>, <matplotlib.colorbar.Colorbar object at 0x17f487b60>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-128
+
+Axes in log space
++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-136
+
+.. code-block:: Python
+
+    x = StatArray(np.logspace(-1, 4, 10), 'x')
+    y = StatArray(np.logspace(0, 3, 10), 'y')
+    rm = RectilinearMesh2D(x_edges=x, x_log=10, y_edges=y, y_log=10)
+
+    # We can plot the grid lines of the mesh.
+    p += 1; plt.figure(p)
+    _  = rm.plot_grid(linewidth=0.5)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_009.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_009.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 137-150
+
+.. code-block:: Python
+
+    with h5py.File('rm2d.h5', 'w') as f:
+        rm.toHdf(f, 'test')
+
+    with h5py.File('rm2d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['test'])
+
+    arr = np.random.randn(*rm.shape)
+    p += 1; plt.figure(p)
+    plt.subplot(211)
+    rm.pcolor(arr)
+    plt.subplot(212)
+    rm2.pcolor(arr)
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_010.png
+   :alt: plot rectilinear mesh 2d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_010.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='x', ylabel='y'>, <matplotlib.collections.QuadMesh object at 0x182aca390>, <matplotlib.colorbar.Colorbar object at 0x17f6df0b0>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 151-153
+
+relative_to
+++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 153-211
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(10.0), 'Northing', 'm')
+    y = StatArray(np.arange(20.0), 'Depth', 'm')
+
+    rm = RectilinearMesh2D(x_centres=x, y_centres=y)
+
+    p += 1; plt.figure(p)
+    plt.subplot(121)
+    _  = rm.plot_grid(linewidth=0.5, flipY=True)
+    rm = RectilinearMesh2D(x_centres=x, x_relative_to=0.2*np.random.randn(y.size), y_centres=y, y_relative_to=0.2*np.random.randn(x.size))
+    plt.subplot(122)
+    _  = rm.plot_grid(linewidth=0.5, flipY=True)
+
+    # relative_to single
+    with h5py.File('rm2d.h5', 'w') as f:
+        rm.toHdf(f, 'test')
+
+    with h5py.File('rm2d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['test'])
+
+    arr = np.random.randn(*rm.shape)
+    p += 1; plt.figure(p)
+    plt.subplot(211)
+    rm.pcolor(arr, flipY=True)
+    plt.subplot(212)
+    rm2.pcolor(arr, flipY=True)
+
+    # relative_to expanded
+    with h5py.File('rm2d.h5', 'w') as f:
+        rm.createHdf(f, 'test', add_axis=RectilinearMesh1D(centres=StatArray(np.arange(3.0), name='Easting', units="m"), relative_to = 0.2*np.random.randn(x.size, y.size)))
+        for i in range(3):
+            rm.x.relative_to += 0.5
+            rm.y.relative_to += 0.5
+            rm.writeHdf(f, 'test', index=i)
+
+    with h5py.File('rm2d.h5', 'r') as f:
+        rm2 = RectilinearMesh2D.fromHdf(f['test'], index=0)
+
+    with h5py.File('rm2d.h5', 'r') as f:
+        rm3 = RectilinearMesh3D.fromHdf(f['test'])
+
+    p += 1; plt.figure(p)
+    plt.subplot(311)
+    rm.pcolor(arr, flipY=True)
+    plt.subplot(312)
+    rm2.pcolor(arr, flipY=True)
+
+    p += 1; plt.figure(p)
+    arr = np.random.randn(*rm3.shape)
+    plt.subplot(311)
+    mesh = rm3[0, :, :]
+    mesh.pcolor(arr[0, :, :], flipY=True)
+    plt.subplot(312)
+    mesh = rm3[:, 0, :]
+    mesh.pcolor(arr[:, 0, :], flipY=True)
+    plt.subplot(313)
+    rm3[:, :, 0].pcolor(arr[:, :, 0])
+
+    plt.show()
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_011.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_011.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_012.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_012.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_013.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_013.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_014.png
+         :alt: plot rectilinear mesh 2d
+         :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_2d_014.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.747 seconds)
+
+
+.. _sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_2d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_rectilinear_mesh_2d.ipynb <plot_rectilinear_mesh_2d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_rectilinear_mesh_2d.py <plot_rectilinear_mesh_2d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_rectilinear_mesh_2d.zip <plot_rectilinear_mesh_2d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Meshes/plot_rectilinear_mesh_3d.rst.txt b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_3d.rst.txt
new file mode 100644
index 00000000..f6597fde
--- /dev/null
+++ b/docs/_sources/examples/Meshes/plot_rectilinear_mesh_3d.rst.txt
@@ -0,0 +1,345 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Meshes/plot_rectilinear_mesh_3d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_3d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Meshes_plot_rectilinear_mesh_3d.py:
+
+
+3D Rectilinear Mesh
+-------------------
+This 3D rectilinear mesh defines a grid with straight cell boundaries.
+
+.. GENERATED FROM PYTHON SOURCE LINES 9-16
+
+.. code-block:: Python
+
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh3D
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import h5py
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-18
+
+Specify some cell centres in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-37
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(10.0), 'Easting', 'm')
+    y = StatArray(np.arange(15.0), 'Northing', 'm')
+    z = StatArray(np.arange(20.0), 'Depth', 'm')
+
+    rm = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+    rm1 = rm[:5, :5, :5]
+    rm2 = rm[:, :, 5]
+    rm3 = rm[:, 5, :]
+    rm4 = rm[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    rm2.plot_grid()
+    plt.subplot(232)
+    rm3.plot_grid()
+    plt.subplot(233)
+    rm4.plot_grid()
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_001.png
+   :alt: plot rectilinear mesh 3d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-49
+
+.. code-block:: Python
+
+    rm2 = rm[:, 5, 5]
+    rm3 = rm[5, :, 5]
+    rm4 = rm[5, 5, :]
+
+    plt.subplot(234)
+    rm2.plot_grid()
+    plt.subplot(235)
+    rm3.plot_grid()
+    plt.subplot(236)
+    rm4.plot_grid()
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_002.png
+   :alt: plot rectilinear mesh 3d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-77
+
+.. code-block:: Python
+
+    with h5py.File('rm3d.h5', 'w') as f:
+        rm.createHdf(f, 'test')
+        rm.writeHdf(f, 'test')
+
+    with h5py.File('rm3d.h5', 'r') as f:
+        rm2 = RectilinearMesh3D.fromHdf(f['test'])
+
+    rm.pyvista_mesh().save('rm3d.vtk')
+
+
+    xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+    z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+    rm = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z, z_relative_to=z_re)
+
+    rm1 = rm[:5, :5, :5]
+    rm2 = rm[:, :, 5]
+    rm3 = rm[:, 5, :]
+    rm4 = rm[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    rm2.plot_grid()
+    plt.subplot(232)
+    rm3.plot_grid()
+    plt.subplot(233)
+    rm4.plot_grid()
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_003.png
+   :alt: plot rectilinear mesh 3d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-79
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-81
+
+.. code-block:: Python
+
+    pv = rm.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-83
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-104
+
+.. code-block:: Python
+
+    mesh = rm.pyvista_mesh().save('rm3d_re1.vtk')
+
+    x_re = StatArray(np.sin(np.repeat(rm.y.centres[:, None], rm.z.nCells, 1)), "x_re")
+
+    xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+    z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+    rm = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, z_edges=z, z_relative_to=z_re)
+
+    rm1 = rm[:5, :5, :5]
+    rm2 = rm[:, :, 5]
+    rm3 = rm[:, 5, :]
+    rm4 = rm[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    rm2.plot_grid()
+    plt.subplot(232)
+    rm3.plot_grid()
+    plt.subplot(233)
+    rm4.plot_grid()
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_004.png
+   :alt: plot rectilinear mesh 3d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_004.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 105-106
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-108
+
+.. code-block:: Python
+
+    pv = rm.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-110
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-136
+
+.. code-block:: Python
+
+    mesh = rm.pyvista_mesh().save('rm3d_re2.vtk')
+
+
+    xx, yy = np.meshgrid(rm.z.centres, rm.y.centres)
+    x_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "x_re")
+
+    xx, yy = np.meshgrid(rm.z.centres, rm.x.centres)
+    y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+    xx, yy = np.meshgrid(rm.y.centres, rm.x.centres)
+    z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+    rm = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+
+    rm1 = rm[:5, :5, :5]
+    rm2 = rm[:, :, 5]
+    rm3 = rm[:, 5, :]
+    rm4 = rm[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    rm2.plot_grid()
+    plt.subplot(232)
+    rm3.plot_grid()
+    plt.subplot(233)
+    rm4.plot_grid()
+
+
+
+
+.. image-sg:: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_005.png
+   :alt: plot rectilinear mesh 3d
+   :srcset: /examples/Meshes/images/sphx_glr_plot_rectilinear_mesh_3d_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 137-138
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 138-140
+
+.. code-block:: Python
+
+    pv = rm.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-142
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 142-153
+
+.. code-block:: Python
+
+    mesh = rm.pyvista_mesh().save('rm3d_re3.vtk')
+
+    with h5py.File('rm3d.h5', 'w') as f:
+        rm.toHdf(f, 'test')
+
+    with h5py.File('rm3d.h5', 'r') as f:
+        rm2 = RectilinearMesh3D.fromHdf(f['test'])
+
+    rm2.pyvista_mesh().save('rm3d_read.vtk')
+
+    plt.show()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 1.378 seconds)
+
+
+.. _sphx_glr_download_examples_Meshes_plot_rectilinear_mesh_3d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_rectilinear_mesh_3d.ipynb <plot_rectilinear_mesh_3d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_rectilinear_mesh_3d.py <plot_rectilinear_mesh_3d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_rectilinear_mesh_3d.zip <plot_rectilinear_mesh_3d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Meshes/readme.rst.txt b/docs/_sources/examples/Meshes/readme.rst.txt
new file mode 100644
index 00000000..781066d7
--- /dev/null
+++ b/docs/_sources/examples/Meshes/readme.rst.txt
@@ -0,0 +1,2 @@
+Meshes
+======
\ No newline at end of file
diff --git a/docs/_sources/examples/Meshes/sg_execution_times.rst.txt b/docs/_sources/examples/Meshes/sg_execution_times.rst.txt
new file mode 100644
index 00000000..c05fc45b
--- /dev/null
+++ b/docs/_sources/examples/Meshes/sg_execution_times.rst.txt
@@ -0,0 +1,43 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Meshes_sg_execution_times:
+
+
+Computation times
+=================
+**00:07.710** total execution time for 3 files **from examples/Meshes**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_1d.py` (``plot_rectilinear_mesh_1d.py``)
+     - 00:04.585
+     - 0.0
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_2d.py` (``plot_rectilinear_mesh_2d.py``)
+     - 00:01.747
+     - 0.0
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_3d.py` (``plot_rectilinear_mesh_3d.py``)
+     - 00:01.378
+     - 0.0
diff --git a/docs/_sources/examples/Models/plot_model_1d.rst.txt b/docs/_sources/examples/Models/plot_model_1d.rst.txt
new file mode 100644
index 00000000..5526b3fd
--- /dev/null
+++ b/docs/_sources/examples/Models/plot_model_1d.rst.txt
@@ -0,0 +1,380 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Models/plot_model_1d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Models_plot_model_1d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Models_plot_model_1d.py:
+
+
+1D Model with an infinite halfspace
+-----------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 7-15
+
+.. code-block:: Python
+
+    from copy import deepcopy
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh1D
+    from geobipy import Model
+    from geobipy import Distribution
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-18
+
+Instantiate the 1D Model with a Half Space
+++++++++++++++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-31
+
+.. code-block:: Python
+
+
+    # Make a test model with 10 layers, and increasing parameter values
+    nLayers = 2
+    par = StatArray(np.linspace(0.001, 0.02, nLayers), "Conductivity", "$\\frac{S}{m}$")
+    thk = StatArray(np.full(nLayers, fill_value=10.0))
+    thk[-1] = np.inf
+    mesh = RectilinearMesh1D(widths = thk)
+
+    mod = Model(mesh = mesh, values=par)
+
+    plt.figure()
+    mod.plot_grid(transpose=True, flip=True)
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_1d_001.png
+   :alt: plot model 1d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_1d_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-39
+
+Randomness and Model Perturbations
+++++++++++++++++++++++++++++++++++
+We can set the priors on the 1D model by assigning minimum and maximum layer
+depths and a maximum number of layers.  These are used to create priors on
+the number of cells in the model, a new depth interface, new parameter values
+and the vertical gradient of those parameters.
+The halfSpaceValue is used as a reference value for the parameter prior.
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-53
+
+.. code-block:: Python
+
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    # Set the priors
+    mod.set_priors(value_mean=0.01,
+                  min_edge=1.0,
+                  max_edge=150.0,
+                  max_cells=30,
+                  solve_value=True,
+                  solve_gradient=True,
+                  prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 54-55
+
+We can evaluate the prior of the model using depths only
+
+.. GENERATED FROM PYTHON SOURCE LINES 55-59
+
+.. code-block:: Python
+
+    print('Log probability of the Model given its priors: ', mod.probability(False, False))
+    # Or with priors on its parameters, and parameter gradient with depth.
+    print('Log probability of the Model given its priors: ', mod.probability(True, True))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Log probability of the Model given its priors:  -3.367295829986474
+    Log probability of the Model given its priors:  -9.157731937350919
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-62
+
+To propose new models, we specify the probabilities of creating, removing, perturbing, and not changing
+a layer interface
+
+.. GENERATED FROM PYTHON SOURCE LINES 62-65
+
+.. code-block:: Python
+
+    pProposal = Distribution('LogNormal', 0.01, np.log(2.0)**2.0, linearSpace=True, prng=prng)
+    mod.set_proposals(probabilities=[0.25, 0.25, 0.5, 0.25], proposal=pProposal, prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-67
+
+We can then perturb the layers of the model
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-69
+
+.. code-block:: Python
+
+    remapped, perturbed = mod.perturb()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-76
+
+.. code-block:: Python
+
+    fig = plt.figure(figsize=(8, 6))
+    ax = plt.subplot(121)
+    mod.pcolor(transpose=True, flip=True, log=10)  # , grid=True)
+    ax = plt.subplot(122)
+    perturbed.pcolor(transpose=True, flip=True, log=10)  # , grid=True)
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_1d_002.png
+   :alt: plot model 1d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_1d_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <Axes: >
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-78
+
+We can evaluate the prior of the model using depths only
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-83
+
+.. code-block:: Python
+
+    print('Log probability of the Model given its priors: ',perturbed.probability(False, False))
+    # Or with priors on its parameters, and parameter gradient with depth.
+    print('Log probability of the Model given its priors: ',perturbed.probability(True, True))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Log probability of the Model given its priors:  -3.367295829986474
+    Log probability of the Model given its priors:  -8.559817917094882
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-98
+
+Perturbing a model multiple times
++++++++++++++++++++++++++++++++++
+In the stochasitic inference process, we perturb the model structure,
+and parameter values, multiple times.
+Each time the model is perturbed, we can record its state
+in a posterior distribution.
+
+For a 1D model, the parameter posterior is a 2D hitmap with depth in one dimension
+and the parameter value in the other.
+We also attach a 1D histogram for the number of layers,
+and a 1D histogram for the locations of interfaces.
+
+Since we have already set the priors on the Model, we can set the posteriors
+based on bins from from the priors.
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-103
+
+.. code-block:: Python
+
+
+    mod.set_posteriors()
+
+    mod0 = deepcopy(mod)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-105
+
+Now we randomly perturb the model, and update its posteriors.
+
+.. GENERATED FROM PYTHON SOURCE LINES 105-114
+
+.. code-block:: Python
+
+    mod.update_posteriors()
+    for i in range(1001):
+        remapped, perturbed = mod.perturb()
+
+        # And update the model posteriors
+        perturbed.update_posteriors()
+
+        mod = perturbed
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 115-120
+
+We can now plot the posteriors of the model.
+
+Remember in this case, we are simply perturbing the model structure and parameter values
+The proposal for the parameter values is fixed and centred around a single value.
+fig = plt.figure(figsize=(8, 6))
+
+.. GENERATED FROM PYTHON SOURCE LINES 120-161
+
+.. code-block:: Python
+
+
+    # plt.subplot(131)
+    # mod.nCells.posterior.plot()
+    # ax = plt.subplot(132)
+    # mod.values.posterior.pcolor(cmap='gray_r', colorbar=False, flipY=True, logX=10)
+    # plt.subplot(133, sharey=ax)
+    # mod.mesh.edges.posterior.plot(transpose=True, flipY=True)
+
+    # plt.figure()
+    # mod.plot_posteriors(**{"cmap": 'gray_r',
+    #                   "xscale": 'log',
+    #                   "noColorbar": True,
+    #                   "flipY": True,
+    #                   'credible_interval_kwargs':{'axis': 1,
+    #                                           'reciprocate': True,
+    #                                           'xscale': 'log'}})
+    # mod.par.posterior.plotCredibleIntervals(xscale='log', axis=1)
+
+
+    fig = plt.figure(figsize=(8, 6))
+    # gs = fig.add_gridspec(nrows=1, ncols=1)
+    mod.plot_posteriors(axes=fig,
+                        edges_kwargs = {
+                            "transpose":True,
+                            "flipY":True
+                        },
+                        parameter_kwargs = {
+                            "cmap": 'gray_r',
+                            "xscale": 'log',
+                            "colorbar": False,
+                            "flipY": True,
+                            'credible_interval_kwargs':{
+                                  'reciprocate':True,
+                                #   'axis': 1,
+                                  'xscale': 'log'
+                            }
+                        },
+                        best = mod)
+
+
+    plt.show()
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_1d_003.png
+   :alt: plot model 1d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_1d_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 3.051 seconds)
+
+
+.. _sphx_glr_download_examples_Models_plot_model_1d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_model_1d.ipynb <plot_model_1d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_model_1d.py <plot_model_1d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_model_1d.zip <plot_model_1d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Models/plot_model_2d.rst.txt b/docs/_sources/examples/Models/plot_model_2d.rst.txt
new file mode 100644
index 00000000..85a85b61
--- /dev/null
+++ b/docs/_sources/examples/Models/plot_model_2d.rst.txt
@@ -0,0 +1,142 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Models/plot_model_2d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Models_plot_model_2d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Models_plot_model_2d.py:
+
+
+2D Rectilinear Model
+--------------------
+This 2D rectilinear model defines a grid with straight cell boundaries.
+
+.. GENERATED FROM PYTHON SOURCE LINES 9-17
+
+.. code-block:: Python
+
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh2D
+    from geobipy import Model
+    import h5py
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-19
+
+Specify some cell centres in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-56
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(11.0), 'Easting', 'm')
+    y = StatArray(np.arange(11.0), 'Northing', 'm')
+    mesh = RectilinearMesh2D(x_edges=x, y_edges=y)
+
+    xx, yy = np.meshgrid(mesh.x.centres, mesh.y.centres)
+    values = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Values")
+
+    mod = Model(mesh=mesh, values = values)
+
+    plt.figure()
+    mod.pcolor()
+
+    mod2 = mod.resample(0.5, 0.5)
+    mod3 = mod.resample(1.5, 1.5)
+    plt.figure()
+    plt.subplot(121)
+    mod2.pcolor()
+    plt.axis('equal')
+    plt.subplot(122)
+    mod3.pcolor()
+    plt.axis('equal')
+
+
+    # #%%
+    # # We can plot the mesh in 3D!
+    # pv = rm.pyvista_plotter()
+    # pv.show()
+
+    # rm.to_vtk('Model3D.vtk')
+
+    with h5py.File('Model2D.h5', 'w') as f:
+        mod.toHdf(f, 'model')
+
+    with h5py.File('Model2D.h5', 'r') as f:
+        mod2 = Model.fromHdf(f['model'])
+
+
+    plt.show()
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Models/images/sphx_glr_plot_model_2d_001.png
+         :alt: plot model 2d
+         :srcset: /examples/Models/images/sphx_glr_plot_model_2d_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Models/images/sphx_glr_plot_model_2d_002.png
+         :alt: plot model 2d
+         :srcset: /examples/Models/images/sphx_glr_plot_model_2d_002.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.212 seconds)
+
+
+.. _sphx_glr_download_examples_Models_plot_model_2d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_model_2d.ipynb <plot_model_2d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_model_2d.py <plot_model_2d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_model_2d.zip <plot_model_2d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Models/plot_model_3d.rst.txt b/docs/_sources/examples/Models/plot_model_3d.rst.txt
new file mode 100644
index 00000000..4cebc3ff
--- /dev/null
+++ b/docs/_sources/examples/Models/plot_model_3d.rst.txt
@@ -0,0 +1,410 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Models/plot_model_3d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Models_plot_model_3d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Models_plot_model_3d.py:
+
+
+3D Rectilinear Model
+--------------------
+This 3D rectilinear model defines a grid with straight cell boundaries.
+
+.. GENERATED FROM PYTHON SOURCE LINES 9-24
+
+.. code-block:: Python
+
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh3D
+    from geobipy import Model
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import h5py
+
+
+    """
+    3D Rectilinear Mesh
+    -------------------
+    This 3D rectilinear mesh defines a grid with straight cell boundaries.
+
+    """
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    '\n3D Rectilinear Mesh\n-------------------\nThis 3D rectilinear mesh defines a grid with straight cell boundaries.\n\n'
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-33
+
+.. code-block:: Python
+
+    from geobipy import StatArray
+    from geobipy import RectilinearMesh3D
+    from geobipy import Model
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import h5py
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 34-35
+
+Specify some cell centres in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-60
+
+.. code-block:: Python
+
+    x = StatArray(np.arange(10.0), 'Easting', 'm')
+    y = StatArray(np.arange(15.0), 'Northing', 'm')
+    z = StatArray(np.arange(20.0), 'Depth', 'm')
+
+    mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+    xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+    values = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "Height")
+    values = np.repeat(values[:, :, None], mesh.z.nCells, 2)
+
+    model = Model(mesh=mesh, values=values)
+
+    model1 = model[:5, :5, :5]
+    model2 = model[:, :, 5]
+    model3 = model[:, 5, :]
+    model4 = model[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    model2.pcolor()
+    plt.subplot(232)
+    model3.pcolor()
+    plt.subplot(233)
+    model4.pcolor()
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_3d_001.png
+   :alt: plot model 3d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_3d_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Northing (m)', ylabel='Depth (m)'>, <matplotlib.collections.QuadMesh object at 0x1828e1340>, <matplotlib.colorbar.Colorbar object at 0x182d6cb30>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-72
+
+.. code-block:: Python
+
+    model2 = model[:, 5, 5]
+    model3 = model[5, :, 5]
+    model4 = model[5, 5, :]
+
+    plt.subplot(234)
+    model2.pcolor()
+    plt.subplot(235)
+    model3.pcolor()
+    plt.subplot(236)
+    model4.pcolor()
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_3d_002.png
+   :alt: plot model 3d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_3d_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <Axes: xlabel='Depth (m)'>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-101
+
+.. code-block:: Python
+
+    with h5py.File('model3d.h5', 'w') as f:
+        model.createHdf(f, 'test')
+        model.writeHdf(f, 'test')
+
+    with h5py.File('model3d.h5', 'r') as f:
+        model2 = Model.fromHdf(f['test'])
+
+    model.pyvista_mesh().save('model3d.vtk')
+
+
+    xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+    z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+    mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z, z_relative_to=z_re)
+    model = Model(mesh=mesh, values=values)
+
+    model1 = model[:5, :5, :5]
+    model2 = model[:, :, 5]
+    model3 = model[:, 5, :]
+    model4 = model[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    model2.pcolor()
+    plt.subplot(232)
+    model3.pcolor()
+    plt.subplot(233)
+    model4.pcolor()
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_3d_003.png
+   :alt: plot model 3d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_3d_003.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Northing (m)', ylabel='z_re (m)'>, <matplotlib.collections.QuadMesh object at 0x17f18acc0>, <matplotlib.colorbar.Colorbar object at 0x182510410>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-103
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-105
+
+.. code-block:: Python
+
+    pv = model.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-107
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 107-127
+
+.. code-block:: Python
+
+    model.pyvista_mesh().save('model3d_re1.vtk')
+
+
+    x_re = StatArray(np.sin(np.repeat(mesh.y.centres[:, None], mesh.z.nCells, 1)), "x_re")
+    mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, z_edges=z, z_relative_to=z_re)
+    model = Model(mesh=mesh, values=values)
+
+    model1 = model[:5, :5, :5]
+    model2 = model[:, :, 5]
+    model3 = model[:, 5, :]
+    model4 = model[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    model2.pcolor()
+    plt.subplot(232)
+    model3.pcolor()
+    plt.subplot(233)
+    model4.pcolor()
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_3d_004.png
+   :alt: plot model 3d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_3d_004.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Northing (m)', ylabel='z_re (m)'>, <matplotlib.collections.QuadMesh object at 0x17ee3a0f0>, <matplotlib.colorbar.Colorbar object at 0x154bb9a30>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-129
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 129-131
+
+.. code-block:: Python
+
+    pv = model.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 132-133
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 133-155
+
+.. code-block:: Python
+
+    model.pyvista_mesh().save('model3d_re2.vtk')
+
+
+    xx, yy = np.meshgrid(mesh.z.centres, mesh.x.centres)
+    y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+    mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+    model = Model(mesh=mesh, values=values)
+
+    model1 = model[:5, :5, :5]
+    model2 = model[:, :, 5]
+    model3 = model[:, 5, :]
+    model4 = model[5, :, :]
+
+    plt.figure()
+    plt.subplot(231)
+    model2.pcolor()
+    plt.subplot(232)
+    model3.pcolor()
+    plt.subplot(233)
+    model4.pcolor()
+
+
+
+
+.. image-sg:: /examples/Models/images/sphx_glr_plot_model_3d_005.png
+   :alt: plot model 3d
+   :srcset: /examples/Models/images/sphx_glr_plot_model_3d_005.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='y_re (m)', ylabel='z_re (m)'>, <matplotlib.collections.QuadMesh object at 0x17faea990>, <matplotlib.colorbar.Colorbar object at 0x15e95dc10>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 156-157
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 157-159
+
+.. code-block:: Python
+
+    pv = model.pyvista_plotter()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 160-161
+
+We can plot the mesh in 3D!
+
+.. GENERATED FROM PYTHON SOURCE LINES 161-172
+
+.. code-block:: Python
+
+    model.pyvista_mesh().save('model3d_re3.vtk')
+
+    # with h5py.File('mesh3d.h5', 'w') as f:
+    #     mesh.toHdf(f, 'test')
+
+    # with h5py.File('mesh3d.h5', 'r') as f:
+    #     mesh2 = RectilinearMesh3D.fromHdf(f['test'])
+
+    # mesh2.pyvista_mesh().save('mesh3d_read.vtk')
+
+    plt.show()
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.788 seconds)
+
+
+.. _sphx_glr_download_examples_Models_plot_model_3d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_model_3d.ipynb <plot_model_3d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_model_3d.py <plot_model_3d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_model_3d.zip <plot_model_3d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Models/readme.rst.txt b/docs/_sources/examples/Models/readme.rst.txt
new file mode 100644
index 00000000..f6155583
--- /dev/null
+++ b/docs/_sources/examples/Models/readme.rst.txt
@@ -0,0 +1,2 @@
+Models
+======
\ No newline at end of file
diff --git a/docs/_sources/examples/Models/sg_execution_times.rst.txt b/docs/_sources/examples/Models/sg_execution_times.rst.txt
new file mode 100644
index 00000000..02ca942f
--- /dev/null
+++ b/docs/_sources/examples/Models/sg_execution_times.rst.txt
@@ -0,0 +1,43 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Models_sg_execution_times:
+
+
+Computation times
+=================
+**00:04.051** total execution time for 3 files **from examples/Models**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Models_plot_model_1d.py` (``plot_model_1d.py``)
+     - 00:03.051
+     - 0.0
+   * - :ref:`sphx_glr_examples_Models_plot_model_3d.py` (``plot_model_3d.py``)
+     - 00:00.788
+     - 0.0
+   * - :ref:`sphx_glr_examples_Models_plot_model_2d.py` (``plot_model_2d.py``)
+     - 00:00.212
+     - 0.0
diff --git a/docs/_sources/examples/Statistics/plot_DataArray.rst.txt b/docs/_sources/examples/Statistics/plot_DataArray.rst.txt
new file mode 100644
index 00000000..d739d765
--- /dev/null
+++ b/docs/_sources/examples/Statistics/plot_DataArray.rst.txt
@@ -0,0 +1,188 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Statistics/plot_DataArray.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Statistics_plot_DataArray.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Statistics_plot_DataArray.py:
+
+
+DataArray Class
+----------------
+
+Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-48
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    DataArray
+    Name:   1
+    Address:['0x17f629d50']
+    Shape:  (1,)
+    Values: [0.]
+    Min:    0.0
+    Max:    0.0
+
+    DataArray
+    Name:   10
+    Address:['0x17f629450']
+    Shape:  (10,)
+    Values: [0. 0. 0. ... 0. 0. 0.]
+    Min:    0.0
+    Max:    0.0
+
+    DataArray
+    Name:   (2, 10)
+    Address:['0x17f62aad0']
+    Shape:  (2, 10)
+    Values: [[0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]]
+    Min:    0.0
+    Max:    0.0
+
+    DataArray
+    Name:   (2, 10)
+    Address:['0x154ab7a50']
+    Shape:  (2,)
+    Values: [ 2 10]
+    Min:    2
+    Max:    10
+
+    DataArray
+    Name:   45.454
+    Address:['0x154ab6dd0']
+    Shape:  (1,)
+    Values: [45.454]
+    Min:    45.454
+    Max:    45.454
+
+    DataArray
+    Name:   45.454
+    Address:['0x17f62aad0']
+    Shape:  (1,)
+    Values: [45.454]
+    Min:    45.454
+    Max:    45.454
+
+    DataArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x154ab6dd0']
+    Shape:  (1,)
+    Values: [0.00257118]
+    Min:    0.002571182431510025
+    Max:    0.002571182431510025
+
+    DataArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x17f62aad0']
+    Shape:  (10,)
+    Values: [0. 1. 2. ... 7. 8. 9.]
+    Min:    0.0
+    Max:    9.0
+
+    DataArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x154ab7a50']
+    Shape:  (10,)
+    Values: [0. 1. 2. ... 7. 8. 9.]
+    Min:    0.0
+    Max:    9.0
+
+
+
+
+
+
+
+|
+
+.. code-block:: Python
+
+    import  numpy as np
+    from geobipy import DataArray, StatArray
+
+    # Integer
+    test = DataArray(1, name='1')
+    assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 0.0, TypeError("da 0")
+    print(test.summary)
+    test = DataArray(10, name='10')
+    assert isinstance(test, DataArray) and test.size ==  10 and np.all(test == 0.0), TypeError("da 1")
+    print(test.summary)
+    # tuple/Shape
+    test = DataArray((2, 10), name='(2, 10)')
+    assert isinstance(test, DataArray) and np.all(test.shape ==  (2, 10)) and np.all(test == 0.0), TypeError("da 2")
+    print(test.summary)
+
+    test = DataArray([2, 10], name='(2, 10)')
+    assert isinstance(test, DataArray) and np.all(test ==  [2, 10]), TypeError("da 2")
+    print(test.summary)
+
+    # float
+    test = DataArray(45.454, name='45.454')
+    assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 3")
+    print(test.summary)
+    test = DataArray(np.float64(45.454), name='45.454')
+    assert isinstance(test, DataArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 4")
+    print(test.summary)
+
+    # array
+    test = DataArray(np.random.randn(1), name="test", units="$\frac{g}{cc}$")
+    assert isinstance(test, DataArray) and test.size ==  1, TypeError("da 5")
+    print(test.summary)
+
+    test = DataArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+    assert isinstance(test, DataArray) and test.size ==  10, TypeError("da 6")
+    print(test.summary)
+
+    test = DataArray(test)
+    assert isinstance(test, DataArray) and test.size ==  10, TypeError("da 6")
+    print(test.summary)
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.003 seconds)
+
+
+.. _sphx_glr_download_examples_Statistics_plot_DataArray.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_DataArray.ipynb <plot_DataArray.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_DataArray.py <plot_DataArray.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_DataArray.zip <plot_DataArray.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Statistics/plot_StatArray.rst.txt b/docs/_sources/examples/Statistics/plot_StatArray.rst.txt
new file mode 100644
index 00000000..30e50ab1
--- /dev/null
+++ b/docs/_sources/examples/Statistics/plot_StatArray.rst.txt
@@ -0,0 +1,1960 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Statistics/plot_StatArray.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Statistics_plot_StatArray.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Statistics_plot_StatArray.py:
+
+
+StatArray Class
+----------------
+
+Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-19
+
+.. code-block:: Python
+
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import h5py
+    from geobipy import DataArray, StatArray, Histogram, Distribution, RectilinearMesh1D
+
+
+    # plt.style.use('seaborn-pastel')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-23
+
+Instantiating a new StatArray class
++++++++++++++++++++++++++++++++++++
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-69
+
+.. code-block:: Python
+
+
+    # Integer
+    test = StatArray(1, name='1')
+    assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 0.0, TypeError("da 0")
+    print(test.summary)
+    test = StatArray(10, name='10')
+    assert isinstance(test, StatArray) and test.size ==  10 and np.all(test == 0.0), TypeError("da 1")
+    print(test.summary)
+    # tuple/Shape
+    test = StatArray((2, 10), name='(2, 10)')
+    assert isinstance(test, StatArray) and np.all(test.shape ==  (2, 10)) and np.all(test == 0.0), TypeError("da 2")
+    print(test.summary)
+
+    test = StatArray([2, 10], name='(2, 10)')
+    assert isinstance(test, StatArray) and np.all(test ==  [2, 10]), TypeError("da 2")
+    print(test.summary)
+
+    # float
+    test = StatArray(45.454, name='45.454')
+    assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 3")
+    print(test.summary)
+    test = StatArray(np.float64(45.454), name='45.454')
+    assert isinstance(test, StatArray) and test.size ==  1 and test.item() == 45.454, TypeError("da 4")
+    print(test.summary)
+
+    # array
+    test = StatArray(np.random.randn(1), name="test", units="$\frac{g}{cc}$")
+    assert isinstance(test, StatArray) and test.size ==  1, TypeError("da 5")
+    print(test.summary)
+
+    test = StatArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+    assert isinstance(test, StatArray) and test.size ==  10, TypeError("da 6")
+    print(test.summary)
+
+
+    test = DataArray(np.arange(10.0), name="test", units="$\frac{g}{cc}$")
+    test = StatArray(test)
+    assert isinstance(test, StatArray) and test.size ==  10, TypeError("da 6")
+    print(test.summary)
+
+
+
+
+    # The StatArray can take any numpy function that returns an array as an input.
+    # The name and units of the variable can be assigned to the StatArray.
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    StatArray
+    Name:   1
+    Address:['0x154bfe950']
+    Shape:  (1,)
+    Values: [0.]
+    Min:    0.0
+    Max:    0.0
+    has_posterior: False
+
+    StatArray
+    Name:   10
+    Address:['0x17f089a50']
+    Shape:  (10,)
+    Values: [0. 0. 0. ... 0. 0. 0.]
+    Min:    0.0
+    Max:    0.0
+    has_posterior: False
+
+    StatArray
+    Name:   (2, 10)
+    Address:['0x17f08a7d0']
+    Shape:  (2, 10)
+    Values: [[0. 0. 0. ... 0. 0. 0.]
+     [0. 0. 0. ... 0. 0. 0.]]
+    Min:    0.0
+    Max:    0.0
+    has_posterior: False
+
+    StatArray
+    Name:   (2, 10)
+    Address:['0x17f6825d0']
+    Shape:  (2,)
+    Values: [ 2 10]
+    Min:    2
+    Max:    10
+    has_posterior: False
+
+    StatArray
+    Name:   45.454
+    Address:['0x17f08a7d0']
+    Shape:  (1,)
+    Values: [45.454]
+    Min:    45.454
+    Max:    45.454
+    has_posterior: False
+
+    StatArray
+    Name:   45.454
+    Address:['0x17f680a50']
+    Shape:  (1,)
+    Values: [45.454]
+    Min:    45.454
+    Max:    45.454
+    has_posterior: False
+
+    StatArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x17f08a7d0']
+    Shape:  (1,)
+    Values: [-0.70419088]
+    Min:    -0.7041908829549965
+    Max:    -0.7041908829549965
+    has_posterior: False
+
+    StatArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x17f6819d0']
+    Shape:  (10,)
+    Values: [0. 1. 2. ... 7. 8. 9.]
+    Min:    0.0
+    Max:    9.0
+    has_posterior: False
+
+    StatArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x17f680a50']
+    Shape:  (10,)
+    Values: [0. 1. 2. ... 7. 8. 9.]
+    Min:    0.0
+    Max:    9.0
+    has_posterior: False
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-89
+
+Attaching Prior and Proposal Distributions to a StatArray
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+The StatArray class has been built so that we may easily
+attach not only names and units, but statistical distributions too.
+We won't go into too much detail about the different distribution
+
+Two types of distributions can be attached to the StatArray.
+
+* Prior Distribution
+    The prior represents how the user believes the variable should
+    behave from a statistical standpoint.
+    The values of the variable can be evaluated against the attached prior,
+    to determine how likely they are to have occured https://en.wikipedia.org/wiki/Prior_probability
+
+* Proposal Distribution
+    The proposal describes a probability distribution from which to
+    sample when we wish to perturb the variable
+    https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-101
+
+.. code-block:: Python
+
+
+    # Obtain an instantiation of a random number generator.
+    # This is optional, but is an important consideration for parallel programming.
+    from numpy.random import Generator
+    from numpy.random import PCG64DXSM
+    generator = PCG64DXSM(seed=0)
+    prng = Generator(generator)
+
+    Density = StatArray(10.0, name="test", units="$\frac{g}{cc}$")
+
+    Density.prior = Distribution('Uniform', -2.0, 2.0, prng=prng)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-103
+
+We can also attach a proposal distribution
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-109
+
+.. code-block:: Python
+
+    Density.proposal = Distribution('Normal', 0.0, 1.0, prng=prng)
+    print(Density.summary)
+    print("Class type of the prior: ",type(Density.prior))
+    print("Class type of the proposal: ",type(Density.proposal))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    StatArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x154bfd7d0' '0x17f652360' '0x181310930' ... '0x17f652180' '0x181313b10'
+     '0x1813134b0']
+    Shape:  (1,)
+    Values: [10.]
+    Min:    10.0
+    Max:    10.0
+    Prior:
+    |   Uniform Distribution: 
+    |   Min: -2.0
+    |   Max: 2.0
+    Proposal:
+    |   Normal
+    |       Mean:0.0
+    |   Variance:1.0
+    has_posterior: False
+
+    Class type of the prior:  <class 'geobipy.src.classes.statistics.UniformDistribution.Uniform'>
+    Class type of the proposal:  <class 'geobipy.src.classes.statistics.NormalDistribution.Normal'>
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-113
+
+The values in the variable can be evaluated against the prior.
+In this case, we have 3 elements in the variable, and a univariate Normal for the prior.
+Therefore each element is evaluated to get 3 probabilities, one for each element.
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-115
+
+.. code-block:: Python
+
+    print(Density.probability(log=False))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    0.0
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-117
+
+The univariate proposal distribution can generate random samples from itself.
+
+.. GENERATED FROM PYTHON SOURCE LINES 117-119
+
+.. code-block:: Python
+
+    print(Density.propose())
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    1.1375024404290368
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 120-122
+
+From a sampling stand point we can either sample using only the proposal
+Or we can only generate samples that simultaneously satisfy the prior.
+
+.. GENERATED FROM PYTHON SOURCE LINES 122-124
+
+.. code-block:: Python
+
+    print(Density.propose(relative=True))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [10.53816627]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 125-126
+
+We can perturb the variable by drawing from the attached proposal distribution.
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-130
+
+.. code-block:: Python
+
+
+    Density.perturb()
+    print(Density.summary)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    StatArray
+    Name:   test ($\frac{g}{cc}$)
+    Address:['0x154bfd7d0' '0x17f652360' '0x181310930' ... '0x17f652180' '0x181313b10'
+     '0x1813134b0']
+    Shape:  (1,)
+    Values: [0.38188467]
+    Min:    0.38188466718060166
+    Max:    0.38188466718060166
+    Prior:
+    |   Uniform Distribution: 
+    |   Min: -2.0
+    |   Max: 2.0
+    Proposal:
+    |   Normal
+    |       Mean:0.0
+    |   Variance:1.0
+    has_posterior: False
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-136
+
+Attaching a Histogram to capture the posterior distribution
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+The StatArray can perturb itself, evaluate its current probability given its priors
+and a histogram can be attached to capture its posterior distribution.
+As an example, lets create a Histogram class with bins generated from the prior.
+
+.. GENERATED FROM PYTHON SOURCE LINES 136-137
+
+.. code-block:: Python
+
+    bins = Density.prior.bins()
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 138-139
+
+Attach the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 139-141
+
+.. code-block:: Python
+
+    Density.posterior = Histogram(mesh = RectilinearMesh1D(edges=bins))
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 142-143
+
+In an iterative sense, we can propose and evaluate new values, and update the posterior
+
+.. GENERATED FROM PYTHON SOURCE LINES 143-150
+
+.. code-block:: Python
+
+    for i in range(1000):
+        Density.perturb()
+        p = Density.probability(log=False)
+
+        if p > 0.0: # This is a simple example!
+            Density.update_posterior()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 151-154
+
+.. code-block:: Python
+
+    plt.figure()
+    Density.summaryPlot()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_001.png
+   :alt: Prior, Proposal, Posterior
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 155-159
+
+Attach a multivariate normal distribution as the prior and proposal
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Attach the multivariate prior
+
+.. GENERATED FROM PYTHON SOURCE LINES 159-165
+
+.. code-block:: Python
+
+
+    mean = np.random.randn(Density.size)
+    variance = np.ones(Density.size)
+    Density.prior = Distribution('MvNormal', mean, variance, prng=prng)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 166-169
+
+Since the prior is multivariate, the appropriate equations are used to
+evaluate the probability for all elements in the StatArray.
+This produces a single probability.
+
+.. GENERATED FROM PYTHON SOURCE LINES 169-172
+
+.. code-block:: Python
+
+
+    print(Density.probability(log=False))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    0.16541198455442488
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 173-174
+
+Attach the multivariate proposal
+
+.. GENERATED FROM PYTHON SOURCE LINES 174-180
+
+.. code-block:: Python
+
+
+    mean = np.random.randn(Density.size)
+    variance = np.ones(Density.size)
+    Density.proposal = Distribution('MvNormal', mean, variance, prng=prng)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 181-182
+
+Perturb the variables using the multivariate proposal.
+
+.. GENERATED FROM PYTHON SOURCE LINES 182-197
+
+.. code-block:: Python
+
+
+    Density.perturb()
+    Density.summary
+
+    with h5py.File('statarray.h5', 'w') as f:
+        Density.createHdf(f, 'statarray', withPosterior=True, add_axis=3)
+        Density.writeHdf(f, 'statarray', withPosterior=True, index=0)
+
+    with h5py.File('statarray.h5', 'r') as f:
+        tmp = StatArray.fromHdf(f, 'statarray', index=0, skip_posterior=False)
+
+    with h5py.File('statarray.h5', 'r') as f:
+        tmp = StatArray.fromHdf(f, 'statarray', skip_posterior=False)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 198-209
+
+Basic manipulation
+++++++++++++++++++
+
+The StatArray contains other functions to perform basic array manipulations
+
+These routines essentially wrap around numpy functions,
+but the result will have the same name and units,
+and if any prior or proposal are set, those will be carried through too.
+
+1D example
+__________
+
+.. GENERATED FROM PYTHON SOURCE LINES 209-213
+
+.. code-block:: Python
+
+
+    x = StatArray(-np.cumsum(np.arange(10.0)))
+    print(x)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ -0.  -1.  -3. ... -28. -36. -45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 214-219
+
+.. code-block:: Python
+
+
+
+    print(x.insert(i=[0, 9], values=[999.0, 999.0]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [999.  -0.  -1. ... -36. 999. -45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 220-225
+
+.. code-block:: Python
+
+
+
+    print(x.prepend(999.0))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [999.  -0.  -1. ... -28. -36. -45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 226-231
+
+.. code-block:: Python
+
+
+
+    print(x.prepend([998.0, 999.0]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [998. 999.  -0. ... -28. -36. -45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 232-237
+
+.. code-block:: Python
+
+
+
+    print(x.append([998.0, 999.0]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ -0.  -1.  -3. ... -45. 998. 999.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 238-243
+
+.. code-block:: Python
+
+
+
+    print(x.resize(14))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [-0. -1. -3. ... -1. -3. -6.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 244-249
+
+.. code-block:: Python
+
+
+
+    print(x.delete([5,8]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ -0.  -1.  -3. ... -21. -28. -45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 250-255
+
+.. code-block:: Python
+
+
+
+    print(x.edges())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [  0.5  -0.5  -2.  ... -32.  -40.5 -49.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 256-261
+
+.. code-block:: Python
+
+
+
+    print(x.internalEdges())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ -0.5  -2.   -4.5 ... -24.5 -32.  -40.5]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 262-267
+
+.. code-block:: Python
+
+
+
+    print(x.firstNonZero())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    1
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 268-273
+
+.. code-block:: Python
+
+
+
+    print(x.lastNonZero())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    10
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 274-279
+
+.. code-block:: Python
+
+
+
+    print(x.abs())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ 0.  1.  3. ... 28. 36. 45.]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 280-282
+
+2D example
+__________
+
+.. GENERATED FROM PYTHON SOURCE LINES 282-287
+
+.. code-block:: Python
+
+
+    x = StatArray(np.asarray([[0, -2, 3],[3, 0, -1],[1, 2, 0]]))
+    print(x)
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 0 -2  3]
+     [ 3  0 -1]
+     [ 1  2  0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 288-293
+
+.. code-block:: Python
+
+
+
+    print(x.insert(i=0, values=4))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 4  4  4]
+     [ 0 -2  3]
+     [ 3  0 -1]
+     [ 1  2  0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 294-299
+
+.. code-block:: Python
+
+
+
+    print(x.insert(i=[2, 3], values=5, axis=1))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 0 -2  5  3  5]
+     [ 3  0  5 -1  5]
+     [ 1  2  5  0  5]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 300-305
+
+.. code-block:: Python
+
+
+
+    print(x.insert(i=2, values=[10, 11, 12], axis=1))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 0 -2 10  3]
+     [ 3  0 11 -1]
+     [ 1  2 12  0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 306-311
+
+.. code-block:: Python
+
+
+
+    print(x.prepend(999))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[999 999 999]
+     [  0  -2   3]
+     [  3   0  -1]
+     [  1   2   0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 312-317
+
+.. code-block:: Python
+
+
+
+    print(x.prepend([999, 998, 997], axis=1))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[999 998 997   0  -2   3]
+     [999 998 997   3   0  -1]
+     [999 998 997   1   2   0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 318-323
+
+.. code-block:: Python
+
+
+
+    print(x.append([[999, 998, 997]]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[  0  -2   3]
+     [  3   0  -1]
+     [  1   2   0]
+     [999 998 997]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 324-329
+
+.. code-block:: Python
+
+
+
+    print(x.resize([5,5]))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 0 -2  3  3  0]
+     [-1  1  2  0  0]
+     [-2  3  3  0 -1]
+     [ 1  2  0  0 -2]
+     [ 3  3  0 -1  1]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 330-335
+
+.. code-block:: Python
+
+
+
+    print(x.delete(5))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [ 0 -2  3 ...  1  2  0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 336-341
+
+.. code-block:: Python
+
+
+
+    print(x.delete(2, axis=0))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[ 0 -2  3]
+     [ 3  0 -1]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 342-347
+
+.. code-block:: Python
+
+
+
+    print(x.firstNonZero(axis=0))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [1 0 0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 348-353
+
+.. code-block:: Python
+
+
+
+    print(x.lastNonZero(axis=0))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [3 3 2]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 354-359
+
+.. code-block:: Python
+
+
+
+    print(x.firstNonZero(axis=1))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [1 0 0]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 360-365
+
+.. code-block:: Python
+
+
+
+    print(x.lastNonZero(axis=1))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [3 3 2]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 366-371
+
+.. code-block:: Python
+
+
+
+    print(x.abs())
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    [[0 2 3]
+     [3 0 1]
+     [1 2 0]]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 372-377
+
+Plotting
+++++++++
+
+We can easily plot the StatArray with its built in plotting functions.
+All plotting functions can take matplotlib keywords
+
+.. GENERATED FROM PYTHON SOURCE LINES 377-385
+
+.. code-block:: Python
+
+
+    # The simplest is to just plot the array
+
+    Density = StatArray(np.random.randn(100),name="Density",units="$\frac{g}{cc}$")
+    Time = StatArray(np.linspace(0, 100, Density.size), name='Time', units='s')
+    Depth = StatArray(np.random.exponential(size=Density.size), name='Depth', units='m')
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 386-391
+
+.. code-block:: Python
+
+
+
+    plt.figure()
+    _ = Density.plot(linewidth=0.5, marker='x', markersize=1.0)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_002.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 392-393
+
+We can quickly plot a bar graph.
+
+.. GENERATED FROM PYTHON SOURCE LINES 393-398
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.bar()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_003.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 399-400
+
+We can scatter the contents of the StatArray if it is 1D
+
+.. GENERATED FROM PYTHON SOURCE LINES 400-405
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.scatter(alpha=0.7)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_004.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_004.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 406-413
+
+Histogram Equalization
+______________________
+
+A neat trick with colourmaps is histogram equalization.
+This approach forces all colours in the images to have an equal weight.
+This distorts the colour bar, but can really highlight the lower and higher
+ends of whatever you are plotting. Just add the equalize keyword!
+
+.. GENERATED FROM PYTHON SOURCE LINES 413-418
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.scatter(alpha=0.7, equalize=True)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_005.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 419-422
+
+Take the log base(x) of the data
+
+We can also take the data to a log, log10, log2, or a custom number!
+
+.. GENERATED FROM PYTHON SOURCE LINES 422-426
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.scatter(alpha=0.7,edgecolor='k',log='e') # could also use log='e', log=2, log=x) where x is the base you require
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_006.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 427-430
+
+X and Y axes
+
+We can specify the x axis of the scatter plot.
+
+.. GENERATED FROM PYTHON SOURCE LINES 430-436
+
+.. code-block:: Python
+
+
+
+    plt.figure()
+    _ = Density.scatter(x=Time, alpha=0.7, edgecolor='k')
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_007.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_007.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 437-444
+
+Notice that I never specified the y axis, so the y axis defaulted to the values in the StatArray.
+In this case, any operations applied to the colours, are also applied to the y axis, e.g. log=10.
+When I take the values of Density to log base 10, because I do not specify the y plotting locations, those locations are similarly affected.
+
+I can however force the y co-ordinates by specifying it as input.
+In the second subplot I explicitly plot distance on the y axis.
+In the first subplot, the y axis is the same as the colourbar.
+
+.. GENERATED FROM PYTHON SOURCE LINES 444-453
+
+.. code-block:: Python
+
+
+
+    plt.figure()
+    ax1 = plt.subplot(211)
+    Density.scatter(x=Time, alpha=0.7, edgecolor='k', log=10)
+    plt.subplot(212, sharex=ax1)
+    _ = Density.scatter(x=Time, y=Depth, alpha=0.7, edgecolor='k', log=10)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_008.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_008.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 454-457
+
+Point sizes
+
+Since the plotting functions take matplotlib keywords, I can also specify the size of each points.
+
+.. GENERATED FROM PYTHON SOURCE LINES 459-474
+
+.. code-block:: Python
+
+
+
+    s = np.ceil(100*(np.abs(np.random.randn(Density.size))))
+    plt.figure()
+    plt.tight_layout()
+    ax1 = plt.subplot(211)
+    Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', legend_size=2)
+    plt.subplot(212, sharex=ax1)
+    #Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', sizeLegend=[1.0, 100, 200, 300])
+    v = np.abs(Density)+1.0
+    _ = Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k', legend_size=[1.0, 100, 200, 300], log=10)
+
+
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_009.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_009.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 475-476
+
+Of course we can still take the log, or equalize the colour histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 476-481
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor='k',equalize=True,log=10)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_010.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_010.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 482-483
+
+Typically pcolor only works with 2D arrays. The StatArray has a pcolor method that will pcolor a 1D array
+
+.. GENERATED FROM PYTHON SOURCE LINES 483-495
+
+.. code-block:: Python
+
+
+    plt.figure()
+    plt.subplot(221)
+    Density.pcolor()
+    plt.subplot(222)
+    Density.pcolor(y=Time)
+    plt.subplot(223)
+    Density.pcolor(y=Time, flip=True)
+    plt.subplot(224)
+    _ = Density.pcolor(y=Time, log=10, equalize=True)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_011.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_011.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 496-499
+
+We can add grid lines, and add opacity to each element in the pcolor image
+
+This is useful if the colour values need to be scaled by another variable e.g. variance.
+
+.. GENERATED FROM PYTHON SOURCE LINES 499-509
+
+.. code-block:: Python
+
+
+
+    plt.figure()
+    plt.subplot(121)
+    Density.pcolor(grid=True, cmap='jet')
+    plt.subplot(122)
+    a = np.linspace(1.0, 0.0, Density.size)
+    _ = Density.pcolor(grid=True, alpha=a, cmap='jet')
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_012.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_012.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 510-511
+
+We can plot a histogram of the StatArray
+
+.. GENERATED FROM PYTHON SOURCE LINES 511-516
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.hist(100)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_013.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_013.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 517-518
+
+We can write the StatArray to a HDF5 file.  HDF5 files are binary files that can include compression.  They allow quick and easy access to parts of the file, and can also be written to and read from in parallel!
+
+.. GENERATED FROM PYTHON SOURCE LINES 518-523
+
+.. code-block:: Python
+
+
+    with h5py.File('1Dtest.h5','w') as f:
+        Density.toHdf(f,'test')
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 524-526
+
+We can then read the StatArray from the file
+Here x is a new variable, that is read in from the hdf5 file we just wrote.
+
+.. GENERATED FROM PYTHON SOURCE LINES 526-533
+
+.. code-block:: Python
+
+
+    x = StatArray.fromHdf('1Dtest.h5', 'test')
+    print('x has the same values as Density? ',np.all(x == Density))
+    x[2] = 5.0 # Change one of the values in x
+    print('x has its own memory allocated (not a reference/pointer)? ', id(x) != id(Density))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    x has the same values as Density?  True
+    x has its own memory allocated (not a reference/pointer)?  True
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 534-535
+
+We can also define a 2D array
+
+.. GENERATED FROM PYTHON SOURCE LINES 535-540
+
+.. code-block:: Python
+
+
+    Density = StatArray(np.random.randn(50,100),"Density","$\frac{g}{cc}$")
+    Density.summary
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    "StatArray\nName:   Density ($\\frac{g}{cc}$)\nAddress:['0x17f4e05d0']\nShape:  (50, 100)\nValues: [[ 0.14104158  0.47155933  1.95502179 ...  1.76214533  1.24396239\n   0.1215391 ]\n [-1.85725407 -1.83481902  1.04987135 ...  0.85985878 -0.34529634\n  -0.85558253]\n [ 0.76292177  1.13113089 -1.35816931 ...  1.31159774  0.46698778\n   1.68630769]\n ...\n [ 0.48384681  0.22774246 -0.51285518 ... -0.24617681  0.81498656\n   1.57095942]\n [-0.13056782  1.72508382 -1.33506224 ... -1.17319374  0.27482725\n   0.39378825]\n [ 1.36799972  0.26714623 -0.02588039 ... -0.28979426 -0.8747471\n  -0.98512505]]\nMin:    -3.2750329304041186\nMax:    3.445969826454679\nhas_posterior: False\n"
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 541-544
+
+The StatArray Class's functions work whether it is 1D or 2D
+
+We can still do a histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 544-549
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.hist()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_014.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_014.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 550-551
+
+And we can use pcolor to plot the 2D array
+
+.. GENERATED FROM PYTHON SOURCE LINES 551-556
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.pcolor()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_015.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_015.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 557-560
+
+The StatArray comes with extra plotting options
+
+Here we specify the x and y axes for the 2D array using two other 1D StatArrays
+
+.. GENERATED FROM PYTHON SOURCE LINES 560-567
+
+.. code-block:: Python
+
+
+    plt.figure()
+    x = StatArray(np.arange(101),name='x Axis',units = 'mm')
+    y = StatArray(np.arange(51),name='y Axis',units = 'elephants')
+    _ = Density.pcolor(x=x, y=y)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_016.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_016.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 568-571
+
+We can plot using a log10 scale, in this case, we have values that are less
+than or equal to 0.0.  Plotting with the log option will by default mask any
+of those values, and will let you know that it has done so!
+
+.. GENERATED FROM PYTHON SOURCE LINES 571-576
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.pcolor(x=x,y=y,log=2)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_017.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_017.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 577-581
+
+A neat trick with colourmaps is histogram equalization.
+This approach forces all colours in the image to have an equal amount.
+This distorts the colours, but can really highlight the lower and higher
+ends of whatever you are plotting
+
+.. GENERATED FROM PYTHON SOURCE LINES 581-586
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.pcolor(x=x, y=y, equalize=True)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_018.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_018.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 587-588
+
+We can equalize the log10 plot too :)
+
+.. GENERATED FROM PYTHON SOURCE LINES 588-593
+
+.. code-block:: Python
+
+
+    plt.figure()
+    _ = Density.pcolor(x=x,y=y,equalize=True, log=10)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_019.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_019.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 594-595
+
+We can add opacity to each pixel in the image
+
+.. GENERATED FROM PYTHON SOURCE LINES 595-599
+
+.. code-block:: Python
+
+
+    a = StatArray(np.random.random(Density.shape), 'Opacity from 0.0 to 1.0')
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 600-611
+
+.. code-block:: Python
+
+
+
+    plt.figure()
+    ax1 = plt.subplot(131)
+    ax = Density.pcolor(x=x, y=y, flipY=True, linewidth=0.1, colorbar=False)
+    plt.subplot(132, sharex=ax1, sharey=ax1)
+    ax = Density.pcolor(x=x, y=y, alpha=a, flipY=True, linewidth=0.1, colorbar=False)
+    plt.subplot(133, sharex=ax1, sharey=ax1)
+    _ = a.pcolor(x=x, y=y, flipY=True)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_020.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_020.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 612-613
+
+If the array potentially has a lot of white space around the edges, we can trim the image
+
+.. GENERATED FROM PYTHON SOURCE LINES 613-625
+
+.. code-block:: Python
+
+
+    Density[:10, :] = 0.0
+    Density[-10:, :] = 0.0
+    Density[:, :10] = 0.0
+    Density[:, -10:] = 0.0
+    plt.figure()
+    plt.subplot(121)
+    Density.pcolor()
+    plt.subplot(122)
+    _ = Density.pcolor(trim=0.0)
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_021.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_021.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 626-627
+
+Create a stacked area plot of a 2D StatArray
+
+.. GENERATED FROM PYTHON SOURCE LINES 627-637
+
+.. code-block:: Python
+
+
+    A = StatArray(np.abs(np.random.randn(13,100)), name='Variable', units="units")
+    x = StatArray(np.arange(100),name='x Axis',units = 'mm')
+    plt.figure()
+    ax1 = plt.subplot(211)
+    A.stackedAreaPlot(x=x, axis=1)
+    plt.subplot(212, sharex=ax1)
+    _ = A.stackedAreaPlot(x=x, i=np.s_[[1,3,4],:], axis=1, labels=['a','b','c'])
+
+    plt.show()
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_StatArray_022.png
+   :alt: plot StatArray
+   :srcset: /examples/Statistics/images/sphx_glr_plot_StatArray_022.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 2.767 seconds)
+
+
+.. _sphx_glr_download_examples_Statistics_plot_StatArray.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_StatArray.ipynb <plot_StatArray.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_StatArray.py <plot_StatArray.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_StatArray.zip <plot_StatArray.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Statistics/plot_histogram_1d.rst.txt b/docs/_sources/examples/Statistics/plot_histogram_1d.rst.txt
new file mode 100644
index 00000000..2cca20da
--- /dev/null
+++ b/docs/_sources/examples/Statistics/plot_histogram_1d.rst.txt
@@ -0,0 +1,516 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Statistics/plot_histogram_1d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Statistics_plot_histogram_1d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Statistics_plot_histogram_1d.py:
+
+
+Histogram 1D
+------------
+
+This histogram class allows efficient updating of histograms, plotting and
+saving as HDF5
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-17
+
+.. code-block:: Python
+
+    from geobipy.src.classes.mesh.RectilinearMesh1D import RectilinearMesh1D
+    import h5py
+    from geobipy import StatArray
+    from geobipy import Histogram
+    import numpy as np
+    import matplotlib.pyplot as plt
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-20
+
+Histogram with regular bins
++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-24
+
+.. code-block:: Python
+
+
+    # Create regularly spaced bins
+    mesh = RectilinearMesh1D(edges=StatArray(np.linspace(-3.0, 3.0, 101), 'bins', 'm'))
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 25-26
+
+Set the histogram using the bins, and update
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-28
+
+.. code-block:: Python
+
+    H = Histogram(mesh=mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-30
+
+We can update the histogram with some new values
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-43
+
+.. code-block:: Python
+
+    H.update(np.random.randn(1000), trim=True)
+
+    # Plot the histogram
+    plt.figure()
+    plt.subplot(221)
+    _ = H.plot()
+    plt.subplot(222)
+    _ = H.pdf.bar()
+    plt.subplot(223)
+    H.pmf.bar()
+    plt.subplot(224)
+    H.cdf().bar()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_001.png
+   :alt: plot histogram 1d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_001.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <Axes: xlabel='bins (m)', ylabel='Cumulative Density Function'>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 44-45
+
+Get the median, and 95% confidence values
+
+.. GENERATED FROM PYTHON SOURCE LINES 45-53
+
+.. code-block:: Python
+
+    print(H.credible_intervals(percent=95.0))
+
+    plt.figure()
+    H.plot()
+    H.plotCredibleIntervals()
+    H.plotMean()
+    H.plotMedian()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_002.png
+   :alt: plot histogram 1d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_002.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    (np.float64(-0.030000000000000027), np.float64(-1.83), np.float64(1.8299999999999996))
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 54-56
+
+Histogram with irregular bins
++++++++++++++++++++++++++++++
+
+.. GENERATED FROM PYTHON SOURCE LINES 56-61
+
+.. code-block:: Python
+
+
+    # Create irregularly spaced bins
+    x = np.cumsum(np.arange(10, dtype=np.float64))
+    irregularBins = np.hstack([-x[::-1], x[1:]])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 62-63
+
+Create a named StatArray
+
+.. GENERATED FROM PYTHON SOURCE LINES 63-66
+
+.. code-block:: Python
+
+    edges = StatArray(irregularBins, 'irregular bins')
+    mesh = RectilinearMesh1D(edges = edges)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Instantiate the histogram with bin edges
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
+
+.. code-block:: Python
+
+    H = Histogram(mesh=mesh)
+
+    # Update the histogram
+    H.update((np.random.randn(10000)*20.0) - 10.0)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-75
+
+Plot the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-87
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(211)
+    _ = H.plot()
+    plt.subplot(212)
+    _ = H.plot(normalize=True)
+
+    plt.figure()
+    H.plot()
+    H.plotCredibleIntervals()
+    H.plotMean()
+    H.plotMedian()
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_003.png
+         :alt: plot histogram 1d
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_003.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_004.png
+         :alt: plot histogram 1d
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_004.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
+
+We can plot the histogram as a pcolor plot
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-92
+
+.. code-block:: Python
+
+    plt.figure()
+    _ = H.pcolor(grid=True, transpose=True)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_005.png
+   :alt: plot histogram 1d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-96
+
+Histogram with linear space entries that are logged internally
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+Create some bins spaced logarithmically
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-98
+
+.. code-block:: Python
+
+    mesh = RectilinearMesh1D(edges = StatArray(np.logspace(-5, 3), 'positive bins'), log=10)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-100
+
+Instantiate the Histogram with log=10
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-102
+
+.. code-block:: Python
+
+    H = Histogram(mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-104
+
+The update takes in the numbers in linear space and takes their log=10
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-106
+
+.. code-block:: Python
+
+    H.update(10.0**(np.random.randn(1000)*2.0), trim=True)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 107-122
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(211)
+    _ = H.plot()
+
+    import h5py
+    with h5py.File('h1d.h5', 'w') as f:
+        H.toHdf(f, 'h1d')
+
+    with h5py.File('h1d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h1d'])
+
+    plt.subplot(212)
+    _ = H1.plot()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_1d_006.png
+   :alt: plot histogram 1d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_1d_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 123-124
+
+.. code-block:: Python
+
+    mesh = RectilinearMesh1D(edges=StatArray(np.linspace(-3.0, 3.0, 101), 'bins', 'm'))
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 125-126
+
+Set the histogram using the bins, and update
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-128
+
+.. code-block:: Python
+
+    H = Histogram(mesh=mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 129-130
+
+We can update the histogram with some new values
+
+.. GENERATED FROM PYTHON SOURCE LINES 130-156
+
+.. code-block:: Python
+
+    H.update(np.random.randn(1000), trim=True)
+
+    import h5py
+    with h5py.File('h1d.h5', 'w') as f:
+        H.createHdf(f, 'h1d', add_axis=StatArray(np.arange(3.0), "Name", "Units"))
+        H.writeHdf(f, 'h1d', index=0)
+        H.update(np.random.randn(1000), trim=True)
+        H.writeHdf(f, 'h1d', index=1)
+        H.update(np.random.randn(1000), trim=True)
+        H.writeHdf(f, 'h1d', index=2)
+
+    with h5py.File('h1d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h1d'])
+        H2 = Histogram.fromHdf(f['h1d'], index=0)
+        H3 = Histogram.fromHdf(f['h1d'], index=1)
+        H4 = Histogram.fromHdf(f['h1d'], index=2)
+
+
+    print(H4.summary)
+
+    # plt.figure()
+    # plt.subplot(211)
+    # _ = H1.plot()
+    # plt.subplot(212)
+    # _ = H4.plot()
+
+    plt.show()
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Histogram:
+    mesh:
+    |   RectilinearMesh1D
+    |   Number of Cells:
+    |   |   100
+    |   Cell Centres:
+    |   |   StatArray
+    |   |   Name:   bins (m)
+    |   |   Address:['0x17f6bebd0']
+    |   |   Shape:  (100,)
+    |   |   Values: [-2.97 -2.91 -2.85 ...  2.85  2.91  2.97]
+    |   |   Min:    -2.9699999999999998
+    |   |   Max:    2.9699999999999998
+    |   |   has_posterior: False
+    |   
+    |   Cell Edges:
+    |   |   StatArray
+    |   |   Name:   bins (m)
+    |   |   Address:['0x17f6bfad0']
+    |   |   Shape:  (101,)
+    |   |   Values: [-3.   -2.94 -2.88 ...  2.88  2.94  3.  ]
+    |   |   Min:    -3.0
+    |   |   Max:    3.0
+    |   |   has_posterior: False
+    |   
+    |   log:
+    |   |   None
+    |   relative_to:
+    |   |   StatArray
+    |   |   Name:   
+    |   |   Address:['0x17f088dd0']
+    |   |   Shape:  (1,)
+    |   |   Values: [0.]
+    |   |   Min:    0.0
+    |   |   Max:    0.0
+    |   |   has_posterior: False
+    |   
+    values:
+    |   DataArray
+    |   Name:   Frequency
+    |   Address:['0x17f08a550']
+    |   Shape:  (100,)
+    |   Values: [2 0 0 ... 0 2 1]
+    |   Min:    0
+    |   Max:    88
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 0.972 seconds)
+
+
+.. _sphx_glr_download_examples_Statistics_plot_histogram_1d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_histogram_1d.ipynb <plot_histogram_1d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_histogram_1d.py <plot_histogram_1d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_histogram_1d.zip <plot_histogram_1d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Statistics/plot_histogram_2d.rst.txt b/docs/_sources/examples/Statistics/plot_histogram_2d.rst.txt
new file mode 100644
index 00000000..4937c05b
--- /dev/null
+++ b/docs/_sources/examples/Statistics/plot_histogram_2d.rst.txt
@@ -0,0 +1,791 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Statistics/plot_histogram_2d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Statistics_plot_histogram_2d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Statistics_plot_histogram_2d.py:
+
+
+Histogram 2D
+------------
+
+This 2D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-21
+
+.. code-block:: Python
+
+    import h5py
+    import geobipy
+    from geobipy import StatArray
+    from geobipy import Histogram
+    import matplotlib.pyplot as plt
+    import matplotlib.gridspec as gridspec
+    from geobipy import RectilinearMesh2D
+    import numpy as np
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-23
+
+Create some histogram bins in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 23-27
+
+.. code-block:: Python
+
+    x = StatArray(np.linspace(-4.0, 4.0, 100), 'Variable 1')
+    y = StatArray(np.linspace(-4.0, 4.0, 105), 'Variable 2')
+
+    mesh = RectilinearMesh2D(x_edges=x, y_edges=y)
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 28-29
+
+Instantiate
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+.. code-block:: Python
+
+    H = Histogram(mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-33
+
+Generate some random numbers
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-36
+
+.. code-block:: Python
+
+    a = np.random.randn(1000000)
+    b = np.random.randn(1000000)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-38
+
+Update the histogram counts
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-40
+
+.. code-block:: Python
+
+    H.update(a, b)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-59
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(131)
+    plt.title("2D Histogram")
+    _ = H.plot(cmap='gray_r')
+    plt.subplot(132)
+    H.pdf.plot(cmap='gray_r')
+    plt.subplot(133)
+    H.pmf.plot(cmap='gray_r')
+
+
+    plt.figure()
+    plt.subplot(131)
+    H.cdf(axis=0).plot()
+    plt.subplot(132)
+    H.cdf(axis=1).plot()
+    plt.subplot(133)
+    H.cdf().plot()
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_001.png
+         :alt: 2D Histogram
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_001.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_002.png
+         :alt: plot histogram 2d
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_002.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Variable 1', ylabel='Variable 2'>, <matplotlib.collections.QuadMesh object at 0x17ed24a40>, <matplotlib.colorbar.Colorbar object at 0x17f1a32c0>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-61
+
+We can overlay the histogram with its credible intervals
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-67
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.title("90% credible intervals overlain")
+    H.pcolor(cmap='gray_r')
+    H.plotCredibleIntervals(axis=0, percent=95.0)
+    _ = H.plotCredibleIntervals(axis=1, percent=95.0)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_003.png
+   :alt: 90% credible intervals overlain
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Generate marginal histograms along an axis
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-72
+
+.. code-block:: Python
+
+    h1 = H.marginalize(axis=0)
+    h2 = H.marginalize(axis=1)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-74
+
+Note that the names of the variables are automatically displayed
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-81
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Marginals along each axis")
+    plt.subplot(121)
+    h1.plot()
+    plt.subplot(122)
+    _ = h2.plot()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_004.png
+   :alt: Marginals along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_004.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-84
+
+Create a combination plot with marginal histograms.
+sphinx_gallery_thumbnail_number = 3
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-102
+
+.. code-block:: Python
+
+    plt.figure()
+    gs = gridspec.GridSpec(5, 5)
+    gs.update(wspace=0.3, hspace=0.3)
+    ax = [plt.subplot(gs[1:, :4])]
+    H.pcolor(colorbar = False)
+
+    ax.append(plt.subplot(gs[:1, :4]))
+    h = H.marginalize(axis=0).plot()
+    plt.xlabel(''); plt.ylabel('')
+    plt.xticks([]); plt.yticks([])
+    ax[-1].spines["left"].set_visible(False)
+
+    ax.append(plt.subplot(gs[1:, 4:]))
+    h = H.marginalize(axis=1).plot(transpose=True)
+    plt.ylabel(''); plt.xlabel('')
+    plt.yticks([]); plt.xticks([])
+    ax[-1].spines["bottom"].set_visible(False)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_005.png
+   :alt: plot histogram 2d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_005.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-104
+
+Take the mean or median estimates from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-107
+
+.. code-block:: Python
+
+    mean = H.mean()
+    median = H.median()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 108-124
+
+.. code-block:: Python
+
+    plt.figure(figsize=(9.5, 5))
+    plt.suptitle("Mean, median, and credible interval overlain")
+    ax = plt.subplot(121)
+    H.pcolor(cmap='gray_r', colorbar=False)
+    H.plotCredibleIntervals(axis=0)
+    H.plotMedian(axis=0, color='g')
+    H.plotMean(axis=0, color='y')
+    plt.legend()
+
+    plt.subplot(122, sharex=ax, sharey=ax)
+    H.pcolor(cmap='gray_r', colorbar=False)
+    H.plotCredibleIntervals(axis=1)
+    H.plotMedian(axis=1, color='g')
+    H.plotMean(axis=1, color='y')
+    plt.legend()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_006.png
+   :alt: Mean, median, and credible interval overlain
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_006.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <matplotlib.legend.Legend object at 0x182f538f0>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 125-126
+
+Get the range between credible intervals
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-128
+
+.. code-block:: Python
+
+    H.credible_range(percent=95.0)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    StatArray([3.47474747, 4.44444444, 3.71717172, ..., 3.47474747,
+               3.47474747, 4.2020202 ])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 129-130
+
+We can map the credible range to an opacity or transparency
+
+.. GENERATED FROM PYTHON SOURCE LINES 130-148
+
+.. code-block:: Python
+
+    H.opacity()
+    H.transparency()
+
+    # H.animate(0, 'test.mp4')
+
+    import h5py
+    with h5py.File('h2d.h5', 'w') as f:
+        H.toHdf(f, 'h2d')
+
+    with h5py.File('h2d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h2d'])
+
+    plt.close('all')
+
+    x = StatArray(5.0 + np.linspace(-4.0, 4.0, 100), 'Variable 1')
+    y = StatArray(10.0 + np.linspace(-4.0, 4.0, 105), 'Variable 2')
+
+    mesh = RectilinearMesh2D(x_edges=x, x_relative_to=5.0, y_edges=y, y_relative_to=10.0)
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 149-150
+
+Instantiate
+
+.. GENERATED FROM PYTHON SOURCE LINES 150-152
+
+.. code-block:: Python
+
+    H = Histogram(mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 153-154
+
+Generate some random numbers
+
+.. GENERATED FROM PYTHON SOURCE LINES 154-157
+
+.. code-block:: Python
+
+    a = np.random.randn(1000000) + 5.0
+    b = np.random.randn(1000000) + 10.0
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 158-159
+
+Update the histogram counts
+
+.. GENERATED FROM PYTHON SOURCE LINES 159-161
+
+.. code-block:: Python
+
+    H.update(a, b)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 162-179
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.subplot(131)
+    plt.title("2D Histogram")
+    _ = H.plot(cmap='gray_r')
+    plt.subplot(132)
+    H.pdf.plot(cmap='gray_r')
+    plt.subplot(133)
+    H.pmf.plot(cmap='gray_r')
+
+    plt.figure()
+    plt.subplot(131)
+    H.cdf(axis=0).plot()
+    plt.subplot(132)
+    H.cdf(axis=1).plot()
+    plt.subplot(133)
+    H.cdf().plot()
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_007.png
+         :alt: 2D Histogram
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_007.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_008.png
+         :alt: plot histogram 2d
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_008.png
+         :class: sphx-glr-multi-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    (<Axes: xlabel='Variable 1', ylabel='Variable 2'>, <matplotlib.collections.QuadMesh object at 0x182ff9a00>, <matplotlib.colorbar.Colorbar object at 0x181bcaf00>)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 180-181
+
+We can overlay the histogram with its credible intervals
+
+.. GENERATED FROM PYTHON SOURCE LINES 181-191
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.title("90% credible intervals overlain")
+    H.pcolor(cmap='gray_r')
+    H.plotCredibleIntervals(axis=0, percent=95.0)
+    _ = H.plotCredibleIntervals(axis=1, percent=95.0)
+
+    # Generate marginal histograms along an axis
+    h1 = H.marginalize(axis=0)
+    h2 = H.marginalize(axis=1)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_009.png
+   :alt: 90% credible intervals overlain
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_009.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 192-193
+
+Note that the names of the variables are automatically displayed
+
+.. GENERATED FROM PYTHON SOURCE LINES 193-200
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Marginals along each axis")
+    plt.subplot(121)
+    h1.plot()
+    plt.subplot(122)
+    _ = h2.plot()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_010.png
+   :alt: Marginals along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_010.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 201-203
+
+Create a combination plot with marginal histograms.
+sphinx_gallery_thumbnail_number = 3
+
+.. GENERATED FROM PYTHON SOURCE LINES 203-221
+
+.. code-block:: Python
+
+    plt.figure()
+    gs = gridspec.GridSpec(5, 5)
+    gs.update(wspace=0.3, hspace=0.3)
+    ax = [plt.subplot(gs[1:, :4])]
+    H.pcolor(colorbar = False)
+
+    ax.append(plt.subplot(gs[:1, :4]))
+    h = H.marginalize(axis=0).plot()
+    plt.xlabel(''); plt.ylabel('')
+    plt.xticks([]); plt.yticks([])
+    ax[-1].spines["left"].set_visible(False)
+
+    ax.append(plt.subplot(gs[1:, 4:]))
+    h = H.marginalize(axis=1).plot(transpose=True)
+    plt.ylabel(''); plt.xlabel('')
+    plt.yticks([]); plt.xticks([])
+    ax[-1].spines["bottom"].set_visible(False)
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_011.png
+   :alt: plot histogram 2d
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_011.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 222-223
+
+Take the mean or median estimates from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 223-226
+
+.. code-block:: Python
+
+    mean = H.mean()
+    median = H.median()
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 227-243
+
+.. code-block:: Python
+
+    plt.figure(figsize=(9.5, 5))
+    plt.suptitle("Mean, median, and credible interval overlain")
+    ax = plt.subplot(121)
+    H.pcolor(cmap='gray_r', colorbar=False)
+    H.plotCredibleIntervals(axis=0)
+    H.plotMedian(axis=0, color='g')
+    H.plotMean(axis=0, color='y')
+    plt.legend()
+
+    plt.subplot(122, sharex=ax, sharey=ax)
+    H.pcolor(cmap='gray_r', colorbar=False)
+    H.plotCredibleIntervals(axis=1)
+    H.plotMedian(axis=1, color='g')
+    H.plotMean(axis=1, color='y')
+    plt.legend()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_012.png
+   :alt: Mean, median, and credible interval overlain
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_012.png
+   :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    <matplotlib.legend.Legend object at 0x181cf6b10>
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 244-245
+
+Get the range between credible intervals
+
+.. GENERATED FROM PYTHON SOURCE LINES 245-247
+
+.. code-block:: Python
+
+    H.credible_range(percent=95.0)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+
+    StatArray([4.2020202 , 4.44444444, 3.31313131, ..., 2.50505051,
+               4.52525253, 4.12121212])
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 248-249
+
+We can map the credible range to an opacity or transparency
+
+.. GENERATED FROM PYTHON SOURCE LINES 249-306
+
+.. code-block:: Python
+
+    H.opacity()
+    H.transparency()
+
+    # # H.animate(0, 'test.mp4')
+
+    with h5py.File('h2d.h5', 'w') as f:
+        H.toHdf(f, 'h2d')
+
+    with h5py.File('h2d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h2d'])
+
+    plt.figure(figsize=(9.5, 5))
+    plt.suptitle("Mean, median, and credible interval overlain")
+    ax = plt.subplot(121)
+    H1.pcolor(cmap='gray_r', colorbar=False)
+    H1.plotCredibleIntervals(axis=0)
+    H1.plotMedian(axis=0, color='g')
+    H1.plotMean(axis=0, color='y')
+    plt.legend()
+
+    plt.subplot(122, sharex=ax, sharey=ax)
+    H1.pcolor(cmap='gray_r', colorbar=False)
+    H1.plotCredibleIntervals(axis=1)
+    H1.plotMedian(axis=1, color='g')
+    H1.plotMean(axis=1, color='y')
+    plt.legend()
+
+    with h5py.File('h2d.h5', 'w') as f:
+        H.createHdf(f, 'h2d', add_axis=StatArray(np.arange(3.0), name='Easting', units="m"))
+        for i in range(3):
+            H.writeHdf(f, 'h2d', index=i)
+
+    with h5py.File('h2d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h2d'], index=0)
+
+    plt.figure(figsize=(9.5, 5))
+    plt.suptitle("Mean, median, and credible interval overlain")
+    ax = plt.subplot(121)
+    H1.pcolor(cmap='gray_r', colorbar=False)
+    H1.plotCredibleIntervals(axis=0)
+    H1.plotMedian(axis=0, color='g')
+    H1.plotMean(axis=0, color='y')
+    plt.legend()
+
+    plt.subplot(122, sharex=ax, sharey=ax)
+    H1.pcolor(cmap='gray_r', colorbar=False)
+    H1.plotCredibleIntervals(axis=1)
+    H1.plotMedian(axis=1, color='g')
+    H1.plotMean(axis=1, color='y')
+    plt.legend()
+
+    with h5py.File('h2d.h5', 'r') as f:
+        H1 = Histogram.fromHdf(f['h2d'])
+
+    # H1.pyvista_mesh().save('h3d_read.vtk')
+
+    plt.show()
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_013.png
+         :alt: Mean, median, and credible interval overlain
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_013.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_2d_014.png
+         :alt: Mean, median, and credible interval overlain
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_2d_014.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 5.012 seconds)
+
+
+.. _sphx_glr_download_examples_Statistics_plot_histogram_2d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_histogram_2d.ipynb <plot_histogram_2d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_histogram_2d.py <plot_histogram_2d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_histogram_2d.zip <plot_histogram_2d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Statistics/plot_histogram_3d.rst.txt b/docs/_sources/examples/Statistics/plot_histogram_3d.rst.txt
new file mode 100644
index 00000000..5121aeae
--- /dev/null
+++ b/docs/_sources/examples/Statistics/plot_histogram_3d.rst.txt
@@ -0,0 +1,467 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "examples/Statistics/plot_histogram_3d.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_examples_Statistics_plot_histogram_3d.py>`
+        to download the full example code.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_examples_Statistics_plot_histogram_3d.py:
+
+
+Histogram 3D
+------------
+
+This 3D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-19
+
+.. code-block:: Python
+
+    import geobipy
+    from geobipy import StatArray
+    from geobipy import Histogram
+    import matplotlib.pyplot as plt
+    from geobipy import RectilinearMesh3D
+    import numpy as np
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 20-21
+
+Create some histogram bins in x and y
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-27
+
+.. code-block:: Python
+
+    x = StatArray(np.linspace(-4.0, 4.0, 11), 'Variable 1')
+    y = StatArray(np.linspace(-4.0, 4.0, 21), 'Variable 2')
+    z = StatArray(np.linspace(-4.0, 4.0, 31), 'Variable 3')
+
+    mesh = RectilinearMesh3D(x_edges=x, y_edges=y, z_edges=z)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 28-29
+
+Instantiate
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-31
+
+.. code-block:: Python
+
+    H = Histogram(mesh=mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-33
+
+Generate some random numbers
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-39
+
+.. code-block:: Python
+
+    a = np.random.randn(100000)
+    b = np.random.randn(100000)
+    c = np.random.randn(100000)
+    # x = np.asarray([a, b, c])
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 40-41
+
+Update the histogram counts
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-43
+
+.. code-block:: Python
+
+    H.update(a, b, c)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 44-51
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Slice half way along each dimension")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        s = [5 if i  == axis else np.s_[:] for i in range(3)]
+        _ = H[tuple(s)].pcolor(cmap='gray_r')
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_001.png
+   :alt: Slice half way along each dimension
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 52-53
+
+Generate marginal histograms along an axis
+
+.. GENERATED FROM PYTHON SOURCE LINES 53-60
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Marginals along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.marginalize(axis=axis).plot()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_002.png
+   :alt: Marginals along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-62
+
+Take the mean estimate from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 62-68
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Mean along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.mean(axis=axis).pcolor()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_003.png
+   :alt: Mean along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_003.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-70
+
+Take the median estimate from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-100
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Median along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.median(axis=axis).pcolor()
+
+    # #%%
+    # # We can map the credible range to an opacity or transparency
+    # H.opacity()
+    # H.transparency()
+
+    H.animate(0, 'test.mp4')
+
+    H.to_vtk('h3d.vtk')
+
+
+
+
+    # Create some histogram bins in x and y
+    xx, yy = np.meshgrid(mesh.z.centres, mesh.y.centres)
+    x_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "x_re")
+
+    xx, yy = np.meshgrid(mesh.z.centres, mesh.x.centres)
+    y_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "y_re")
+
+    xx, yy = np.meshgrid(mesh.y.centres, mesh.x.centres)
+    z_re = StatArray(np.sin(np.sqrt(xx ** 2.0 + yy ** 2.0)), "z_re")
+
+    mesh = RectilinearMesh3D(x_edges=x, x_relative_to=x_re, y_edges=y, y_relative_to=y_re, z_edges=z, z_relative_to=z_re)
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_004.png
+         :alt: Median along each axis
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_004.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_005.png
+         :alt: 3.60
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_005.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-102
+
+Instantiate
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-104
+
+.. code-block:: Python
+
+    H = Histogram(mesh=mesh)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 105-106
+
+Generate some random numbers
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-111
+
+.. code-block:: Python
+
+    a = np.random.randn(100000)
+    b = np.random.randn(100000)
+    c = np.random.randn(100000)
+    # x = np.asarray([a, b, c])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 112-113
+
+Update the histogram counts
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-115
+
+.. code-block:: Python
+
+    H.update(a, b, c)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-123
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Slice half way along each dimension")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        s = [5 if i  == axis else np.s_[:] for i in range(3)]
+        _ = H[tuple(s)].pcolor(cmap='gray_r')
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_006.png
+   :alt: Slice half way along each dimension
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_006.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 124-125
+
+Generate marginal histograms along an axis
+
+.. GENERATED FROM PYTHON SOURCE LINES 125-132
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Marginals along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.marginalize(axis=axis).plot()
+
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_007.png
+   :alt: Marginals along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_007.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 133-134
+
+Take the mean estimate from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 134-140
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Mean along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.mean(axis=axis).pcolor()
+
+
+
+
+.. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_008.png
+   :alt: Mean along each axis
+   :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_008.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-142
+
+Take the median estimate from the histogram
+
+.. GENERATED FROM PYTHON SOURCE LINES 142-158
+
+.. code-block:: Python
+
+    plt.figure()
+    plt.suptitle("Median along each axis")
+    for axis in range(3):
+        plt.subplot(1, 3, axis+1)
+        _ = H.median(axis=axis).pcolor()
+
+    # #%%
+    # # We can map the credible range to an opacity or transparency
+    # H.opacity()
+    # H.transparency()
+
+    H.animate(0, 'test.mp4')
+
+    plt.show()
+
+    # H.to_vtk('h3d.vtk')
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_009.png
+         :alt: Median along each axis
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_009.png
+         :class: sphx-glr-multi-img
+
+    *
+
+      .. image-sg:: /examples/Statistics/images/sphx_glr_plot_histogram_3d_010.png
+         :alt: 3.60
+         :srcset: /examples/Statistics/images/sphx_glr_plot_histogram_3d_010.png
+         :class: sphx-glr-multi-img
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** (0 minutes 3.875 seconds)
+
+
+.. _sphx_glr_download_examples_Statistics_plot_histogram_3d.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: plot_histogram_3d.ipynb <plot_histogram_3d.ipynb>`
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: plot_histogram_3d.py <plot_histogram_3d.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-zip
+
+      :download:`Download zipped: plot_histogram_3d.zip <plot_histogram_3d.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/Statistics/readme.rst.txt b/docs/_sources/examples/Statistics/readme.rst.txt
new file mode 100644
index 00000000..5f81bd2e
--- /dev/null
+++ b/docs/_sources/examples/Statistics/readme.rst.txt
@@ -0,0 +1,2 @@
+Statistics
+==========
\ No newline at end of file
diff --git a/docs/_sources/examples/Statistics/sg_execution_times.rst.txt b/docs/_sources/examples/Statistics/sg_execution_times.rst.txt
new file mode 100644
index 00000000..7beb9fff
--- /dev/null
+++ b/docs/_sources/examples/Statistics/sg_execution_times.rst.txt
@@ -0,0 +1,49 @@
+
+:orphan:
+
+.. _sphx_glr_examples_Statistics_sg_execution_times:
+
+
+Computation times
+=================
+**00:12.629** total execution time for 5 files **from examples/Statistics**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_2d.py` (``plot_histogram_2d.py``)
+     - 00:05.012
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_3d.py` (``plot_histogram_3d.py``)
+     - 00:03.875
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_StatArray.py` (``plot_StatArray.py``)
+     - 00:02.767
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_1d.py` (``plot_histogram_1d.py``)
+     - 00:00.972
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_DataArray.py` (``plot_DataArray.py``)
+     - 00:00.003
+     - 0.0
diff --git a/docs/_sources/examples/index.rst.txt b/docs/_sources/examples/index.rst.txt
new file mode 100644
index 00000000..c14511d3
--- /dev/null
+++ b/docs/_sources/examples/index.rst.txt
@@ -0,0 +1,705 @@
+:orphan:
+
+########
+Examples
+########
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+Data
+====
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="3D Point Cloud class">
+
+.. only:: html
+
+  .. image:: /examples/Data/images/thumb/sphx_glr_plot_pointcloud3d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Data_plot_pointcloud3d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">3D Point Cloud class</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Frequency domain dataset">
+
+.. only:: html
+
+  .. image:: /examples/Data/images/thumb/sphx_glr_plot_frequency_dataset_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Data_plot_frequency_dataset.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Frequency domain dataset</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Skytem dataset">
+
+.. only:: html
+
+  .. image:: /examples/Data/images/thumb/sphx_glr_plot_skytem_dataset_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Data_plot_skytem_dataset.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Skytem dataset</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Tempest dataset">
+
+.. only:: html
+
+  .. image:: /examples/Data/images/thumb/sphx_glr_plot_tempest_dataset_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Data_plot_tempest_dataset.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Tempest dataset</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Data/plot_pointcloud3d
+   /examples/Data/plot_frequency_dataset
+   /examples/Data/plot_skytem_dataset
+   /examples/Data/plot_tempest_dataset
+
+Datapoints
+==========
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Frequency domain datapoint">
+
+.. only:: html
+
+  .. image:: /examples/Datapoints/images/thumb/sphx_glr_plot_resolve_datapoint_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Datapoints_plot_resolve_datapoint.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Frequency domain datapoint</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Skytem Datapoint Class">
+
+.. only:: html
+
+  .. image:: /examples/Datapoints/images/thumb/sphx_glr_plot_skytem_datapoint_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Datapoints_plot_skytem_datapoint.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Skytem Datapoint Class</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Tempest Datapoint Class">
+
+.. only:: html
+
+  .. image:: /examples/Datapoints/images/thumb/sphx_glr_plot_tempest_datapoint_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Datapoints_plot_tempest_datapoint.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Tempest Datapoint Class</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Datapoints/plot_resolve_datapoint
+   /examples/Datapoints/plot_skytem_datapoint
+   /examples/Datapoints/plot_tempest_datapoint
+
+Distributions
+=============
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Handles the initialization of different statistical distribution">
+
+.. only:: html
+
+  .. image:: /examples/Distributions/images/thumb/sphx_glr_plot_distributions_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Distributions_plot_distributions.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Distribution Class</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Distributions/plot_distributions
+
+HDF 5
+=====
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Inference for large scale datasets in GeoBIPy is handled using MPI and distributed memory systems. A common bottleneck with large parallel algorithms is the input output of information to disk. We use HDF5 to read and write data in order to leverage the parallel capabililties of the HDF5 API.">
+
+.. only:: html
+
+  .. image:: /examples/HDF5/images/thumb/sphx_glr_hdf5_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_HDF5_hdf5.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Using HDF5 within GeoBIPy</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/HDF5/hdf5
+
+1D Inference
+============
+
+There are a couple of ways to run an inference using geobipy, the first is via command line using
+
+.. code-block:: bash
+
+    geobipy skytem_options.py <output folder>
+
+The other is with a python script similar to the examples in this folder.
+In both cases, you will need to write an options file (also shown in these examples)
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Resolve data">
+
+.. only:: html
+
+  .. image:: /examples/Inference_1D/images/thumb/sphx_glr_plot_inference_1d_resolve_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_resolve.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Resolve data</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Skytem data">
+
+.. only:: html
+
+  .. image:: /examples/Inference_1D/images/thumb/sphx_glr_plot_inference_1d_skytem_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_skytem.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Skytem data</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Tempest data">
+
+.. only:: html
+
+  .. image:: /examples/Inference_1D/images/thumb/sphx_glr_plot_inference_1d_tempest_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_tempest.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Tempest data</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Inference_1D/plot_inference_1d_resolve
+   /examples/Inference_1D/plot_inference_1d_skytem
+   /examples/Inference_1D/plot_inference_1d_tempest
+
+2D Inference
+============
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class">
+
+.. only:: html
+
+  .. image:: /examples/Inference_2D/images/thumb/sphx_glr_plot_inference_2d_resolve_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_resolve.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Resolve inference</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class">
+
+.. only:: html
+
+  .. image:: /examples/Inference_2D/images/thumb/sphx_glr_plot_inference_2d_skytem_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_skytem.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Skytem inference</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class">
+
+.. only:: html
+
+  .. image:: /examples/Inference_2D/images/thumb/sphx_glr_plot_inference_2d_tempest_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_tempest.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Tempest inference</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Inference_2D/plot_inference_2d_resolve
+   /examples/Inference_2D/plot_inference_2d_skytem
+   /examples/Inference_2D/plot_inference_2d_tempest
+
+Meshes
+======
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="1D Rectilinear Mesh">
+
+.. only:: html
+
+  .. image:: /examples/Meshes/images/thumb/sphx_glr_plot_rectilinear_mesh_1d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_1d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">1D Rectilinear Mesh</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="It can be instantiated in two ways.">
+
+.. only:: html
+
+  .. image:: /examples/Meshes/images/thumb/sphx_glr_plot_rectilinear_mesh_2d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_2d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">2D Rectilinear Mesh</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="3D Rectilinear Mesh">
+
+.. only:: html
+
+  .. image:: /examples/Meshes/images/thumb/sphx_glr_plot_rectilinear_mesh_3d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_3d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">3D Rectilinear Mesh</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Meshes/plot_rectilinear_mesh_1d
+   /examples/Meshes/plot_rectilinear_mesh_2d
+   /examples/Meshes/plot_rectilinear_mesh_3d
+
+Models
+======
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="1D Model with an infinite halfspace">
+
+.. only:: html
+
+  .. image:: /examples/Models/images/thumb/sphx_glr_plot_model_1d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Models_plot_model_1d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">1D Model with an infinite halfspace</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="2D Rectilinear Model">
+
+.. only:: html
+
+  .. image:: /examples/Models/images/thumb/sphx_glr_plot_model_2d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Models_plot_model_2d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">2D Rectilinear Model</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="3D Rectilinear Model">
+
+.. only:: html
+
+  .. image:: /examples/Models/images/thumb/sphx_glr_plot_model_3d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Models_plot_model_3d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">3D Rectilinear Model</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Models/plot_model_1d
+   /examples/Models/plot_model_2d
+   /examples/Models/plot_model_3d
+
+Statistics
+==========
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Extends the numpy ndarray class to add extra attributes such as names, and units, and allows us to attach statistical descriptors of the array. The direct extension to numpy maintains speed and functionality of numpy arrays.">
+
+.. only:: html
+
+  .. image:: /examples/Statistics/images/thumb/sphx_glr_plot_DataArray_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Statistics_plot_DataArray.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">DataArray Class</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This histogram class allows efficient updating of histograms, plotting and saving as HDF5">
+
+.. only:: html
+
+  .. image:: /examples/Statistics/images/thumb/sphx_glr_plot_histogram_1d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Statistics_plot_histogram_1d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Histogram 1D</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This 2D histogram class allows efficient updating of histograms, plotting and saving as HDF5.">
+
+.. only:: html
+
+  .. image:: /examples/Statistics/images/thumb/sphx_glr_plot_histogram_2d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Statistics_plot_histogram_2d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Histogram 2D</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This 3D histogram class allows efficient updating of histograms, plotting and saving as HDF5.">
+
+.. only:: html
+
+  .. image:: /examples/Statistics/images/thumb/sphx_glr_plot_histogram_3d_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Statistics_plot_histogram_3d.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Histogram 3D</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Extends the numpy ndarray class to add extra attributes such as names, and units, and allows us to attach statistical descriptors of the array. The direct extension to numpy maintains speed and functionality of numpy arrays.">
+
+.. only:: html
+
+  .. image:: /examples/Statistics/images/thumb/sphx_glr_plot_StatArray_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_examples_Statistics_plot_StatArray.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">StatArray Class</div>
+    </div>
+
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /examples/Statistics/plot_DataArray
+   /examples/Statistics/plot_histogram_1d
+   /examples/Statistics/plot_histogram_2d
+   /examples/Statistics/plot_histogram_3d
+   /examples/Statistics/plot_StatArray
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: examples_python.zip </examples/examples_python.zip>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip </examples/examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/examples/readme.rst.txt b/docs/_sources/examples/readme.rst.txt
new file mode 100644
index 00000000..0db3c1d1
--- /dev/null
+++ b/docs/_sources/examples/readme.rst.txt
@@ -0,0 +1,3 @@
+########
+Examples
+########
\ No newline at end of file
diff --git a/docs/_sources/examples/sg_execution_times.rst.txt b/docs/_sources/examples/sg_execution_times.rst.txt
new file mode 100644
index 00000000..f7a0c03b
--- /dev/null
+++ b/docs/_sources/examples/sg_execution_times.rst.txt
@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_examples_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 0 files **from examples**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - N/A
+     - N/A
+     - N/A
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index d05d7185..b8d482f4 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -10,4 +10,21 @@ Application outside of these data types is in development.
 Currently there are two types of data that we have implemented; frequency domain electromagnetic data,
 and time domain electromagnetic data.
 The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller.
+See the section :ref:`Installing_time_domain_forward_modeller` for more information.
+
+Using GeoBIPy on Yeti
+~~~~~~~~~~~~~~~~~~~~~
+
+There is no need to install GeoBIPy on Yeti.
+Simply type "module load python/geobipy" for the serial version of the code, mainly used for plotting results,
+or "module load python/pGeobipy" for a parallel enabled version.
+
+`Codebase is here! <https://github.com/usgs/geobipy/>`_
+
+.. toctree::
+    :maxdepth: 2
+
+    content/getting_started/getting_started
+    content/api/api
+    examples/index
 
diff --git a/docs/_sources/sg_execution_times.rst.txt b/docs/_sources/sg_execution_times.rst.txt
new file mode 100644
index 00000000..e35f03ac
--- /dev/null
+++ b/docs/_sources/sg_execution_times.rst.txt
@@ -0,0 +1,112 @@
+
+:orphan:
+
+.. _sphx_glr_sg_execution_times:
+
+
+Computation times
+=================
+**06:08.049** total execution time for 26 files **from all galleries**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_tempest.py` (``examples/Inference_1D/plot_inference_1d_tempest.py``)
+     - 01:56.531
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_skytem.py` (``examples/Inference_1D/plot_inference_1d_skytem.py``)
+     - 01:37.169
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_1D_plot_inference_1d_resolve.py` (``examples/Inference_1D/plot_inference_1d_resolve.py``)
+     - 00:29.449
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_resolve.py` (``examples/Inference_2D/plot_inference_2d_resolve.py``)
+     - 00:21.524
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_tempest.py` (``examples/Inference_2D/plot_inference_2d_tempest.py``)
+     - 00:19.999
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_pointcloud3d.py` (``examples/Data/plot_pointcloud3d.py``)
+     - 00:19.063
+     - 0.0
+   * - :ref:`sphx_glr_examples_Inference_2D_plot_inference_2d_skytem.py` (``examples/Inference_2D/plot_inference_2d_skytem.py``)
+     - 00:18.965
+     - 0.0
+   * - :ref:`sphx_glr_examples_Datapoints_plot_resolve_datapoint.py` (``examples/Datapoints/plot_resolve_datapoint.py``)
+     - 00:07.271
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_2d.py` (``examples/Statistics/plot_histogram_2d.py``)
+     - 00:05.012
+     - 0.0
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_1d.py` (``examples/Meshes/plot_rectilinear_mesh_1d.py``)
+     - 00:04.585
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_tempest_dataset.py` (``examples/Data/plot_tempest_dataset.py``)
+     - 00:04.125
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_3d.py` (``examples/Statistics/plot_histogram_3d.py``)
+     - 00:03.875
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_frequency_dataset.py` (``examples/Data/plot_frequency_dataset.py``)
+     - 00:03.257
+     - 0.0
+   * - :ref:`sphx_glr_examples_Models_plot_model_1d.py` (``examples/Models/plot_model_1d.py``)
+     - 00:03.051
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_StatArray.py` (``examples/Statistics/plot_StatArray.py``)
+     - 00:02.767
+     - 0.0
+   * - :ref:`sphx_glr_examples_Datapoints_plot_skytem_datapoint.py` (``examples/Datapoints/plot_skytem_datapoint.py``)
+     - 00:02.306
+     - 0.0
+   * - :ref:`sphx_glr_examples_Data_plot_skytem_dataset.py` (``examples/Data/plot_skytem_dataset.py``)
+     - 00:02.295
+     - 0.0
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_2d.py` (``examples/Meshes/plot_rectilinear_mesh_2d.py``)
+     - 00:01.747
+     - 0.0
+   * - :ref:`sphx_glr_examples_Datapoints_plot_tempest_datapoint.py` (``examples/Datapoints/plot_tempest_datapoint.py``)
+     - 00:01.653
+     - 0.0
+   * - :ref:`sphx_glr_examples_Meshes_plot_rectilinear_mesh_3d.py` (``examples/Meshes/plot_rectilinear_mesh_3d.py``)
+     - 00:01.378
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_histogram_1d.py` (``examples/Statistics/plot_histogram_1d.py``)
+     - 00:00.972
+     - 0.0
+   * - :ref:`sphx_glr_examples_Models_plot_model_3d.py` (``examples/Models/plot_model_3d.py``)
+     - 00:00.788
+     - 0.0
+   * - :ref:`sphx_glr_examples_Models_plot_model_2d.py` (``examples/Models/plot_model_2d.py``)
+     - 00:00.212
+     - 0.0
+   * - :ref:`sphx_glr_examples_Distributions_plot_distributions.py` (``examples/Distributions/plot_distributions.py``)
+     - 00:00.050
+     - 0.0
+   * - :ref:`sphx_glr_examples_Statistics_plot_DataArray.py` (``examples/Statistics/plot_DataArray.py``)
+     - 00:00.003
+     - 0.0
+   * - :ref:`sphx_glr_examples_HDF5_hdf5.py` (``examples/HDF5/hdf5.py``)
+     - 00:00.000
+     - 0.0
diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 00000000..81415803
--- /dev/null
+++ b/docs/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/docs/_static/alabaster.css b/docs/_static/alabaster.css
deleted file mode 100644
index e3174bf9..00000000
--- a/docs/_static/alabaster.css
+++ /dev/null
@@ -1,708 +0,0 @@
-@import url("basic.css");
-
-/* -- page layout ----------------------------------------------------------- */
-
-body {
-    font-family: Georgia, serif;
-    font-size: 17px;
-    background-color: #fff;
-    color: #000;
-    margin: 0;
-    padding: 0;
-}
-
-
-div.document {
-    width: 940px;
-    margin: 30px auto 0 auto;
-}
-
-div.documentwrapper {
-    float: left;
-    width: 100%;
-}
-
-div.bodywrapper {
-    margin: 0 0 0 220px;
-}
-
-div.sphinxsidebar {
-    width: 220px;
-    font-size: 14px;
-    line-height: 1.5;
-}
-
-hr {
-    border: 1px solid #B1B4B6;
-}
-
-div.body {
-    background-color: #fff;
-    color: #3E4349;
-    padding: 0 30px 0 30px;
-}
-
-div.body > .section {
-    text-align: left;
-}
-
-div.footer {
-    width: 940px;
-    margin: 20px auto 30px auto;
-    font-size: 14px;
-    color: #888;
-    text-align: right;
-}
-
-div.footer a {
-    color: #888;
-}
-
-p.caption {
-    font-family: inherit;
-    font-size: inherit;
-}
-
-
-div.relations {
-    display: none;
-}
-
-
-div.sphinxsidebar {
-    max-height: 100%;
-    overflow-y: auto;
-}
-
-div.sphinxsidebar a {
-    color: #444;
-    text-decoration: none;
-    border-bottom: 1px dotted #999;
-}
-
-div.sphinxsidebar a:hover {
-    border-bottom: 1px solid #999;
-}
-
-div.sphinxsidebarwrapper {
-    padding: 18px 10px;
-}
-
-div.sphinxsidebarwrapper p.logo {
-    padding: 0;
-    margin: -10px 0 0 0px;
-    text-align: center;
-}
-
-div.sphinxsidebarwrapper h1.logo {
-    margin-top: -10px;
-    text-align: center;
-    margin-bottom: 5px;
-    text-align: left;
-}
-
-div.sphinxsidebarwrapper h1.logo-name {
-    margin-top: 0px;
-}
-
-div.sphinxsidebarwrapper p.blurb {
-    margin-top: 0;
-    font-style: normal;
-}
-
-div.sphinxsidebar h3,
-div.sphinxsidebar h4 {
-    font-family: Georgia, serif;
-    color: #444;
-    font-size: 24px;
-    font-weight: normal;
-    margin: 0 0 5px 0;
-    padding: 0;
-}
-
-div.sphinxsidebar h4 {
-    font-size: 20px;
-}
-
-div.sphinxsidebar h3 a {
-    color: #444;
-}
-
-div.sphinxsidebar p.logo a,
-div.sphinxsidebar h3 a,
-div.sphinxsidebar p.logo a:hover,
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diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index e5179b7a..f316efcb 100644
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+++ b/docs/_static/basic.css
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+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_static/sg_gallery-binder.css b/docs/_static/sg_gallery-binder.css
new file mode 100644
index 00000000..420005d2
--- /dev/null
+++ b/docs/_static/sg_gallery-binder.css
@@ -0,0 +1,11 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/docs/_static/sg_gallery-dataframe.css b/docs/_static/sg_gallery-dataframe.css
new file mode 100644
index 00000000..fac74c43
--- /dev/null
+++ b/docs/_static/sg_gallery-dataframe.css
@@ -0,0 +1,47 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+  width: auto;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/docs/_static/sg_gallery-rendered-html.css b/docs/_static/sg_gallery-rendered-html.css
new file mode 100644
index 00000000..93dc2ffb
--- /dev/null
+++ b/docs/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/docs/_static/sg_gallery.css b/docs/_static/sg_gallery.css
new file mode 100644
index 00000000..9bcd33c8
--- /dev/null
+++ b/docs/_static/sg_gallery.css
@@ -0,0 +1,367 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+
+.sphx-glr-thumbcontainer[tooltip]::before {
+  content: "";
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  z-index: 97;
+  background-color: var(--sg-tooltip-background);
+  backdrop-filter: blur(3px);
+  opacity: 0;
+  transition: opacity 0.3s;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::before {
+  opacity: 1;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::after {
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px 10px 5px;
+  z-index: 98;
+  width: 100%;
+  max-height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-line-clamp: 6;
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+div.sphx-glr-sidebar-item img {
+  max-height: 20px;
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
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diff --git a/docs/content/api/api.html b/docs/content/api/api.html
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+<!DOCTYPE html>
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+<h1>API<a class="headerlink" href="#api" title="Link to this heading"></a></h1>
+<p>The source code is split into two types of files.  Those that contain a set of python functions, and those that contain classes.</p>
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+<html class="writer-html5" lang="en" data-content_root="../../../">
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+  <title>Heirarchical Data Format (HDF) &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l2 current"><a class="reference internal" href="base.html">Core routines needed for GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="utilities.html">utilities</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="interpolation.html">Interpolation</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Heirarchical Data Format (HDF)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.find"><code class="docutils literal notranslate"><span class="pre">find()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.readKeyFromFile"><code class="docutils literal notranslate"><span class="pre">readKeyFromFile()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.readKeyFromFiles"><code class="docutils literal notranslate"><span class="pre">readKeyFromFiles()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.read_all"><code class="docutils literal notranslate"><span class="pre">read_all()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.read_groups_with_tag"><code class="docutils literal notranslate"><span class="pre">read_groups_with_tag()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfRead.read_item"><code class="docutils literal notranslate"><span class="pre">read_item()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.HDF.hdfWrite.write_nd"><code class="docutils literal notranslate"><span class="pre">write_nd()</span></code></a></li>
+</ul>
+</li>
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+</ul>
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+          <li class="breadcrumb-item"><a href="base.html">Core routines needed for GeoBIPy</a></li>
+      <li class="breadcrumb-item active">Heirarchical Data Format (HDF)</li>
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+            <a href="../../../_sources/content/api/base/HDF.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.HDF.hdfRead">
+<span id="heirarchical-data-format-hdf"></span><h1>Heirarchical Data Format (HDF)<a class="headerlink" href="#module-geobipy.src.base.HDF.hdfRead" title="Link to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.find">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">find</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.find" title="Link to this definition"></a></dt>
+<dd><p>Find the locations of all groups with ‘tag’ in their path.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – HDF5 file name</p></li>
+<li><p><strong>tag</strong> (<em>str</em>) – Sub string that appears in the group name.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – List of paths into the HDF5 file.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.readKeyFromFile">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">readKeyFromFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">h5obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groupName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.readKeyFromFile" title="Link to this definition"></a></dt>
+<dd><p>Reads in the keys from a file</p>
+<p>Iterates over group names and keys and reads them from a HDF5 file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>h5obj</strong> (<em>h5py._hl.files.File</em><em> or </em><em>h5py._hl.group.Group</em>) – An opened hdf5 handle or a h5py group object</p></li>
+<li><p><strong>fName</strong> (<em>str</em>) – The path and/or file name to the file that was opened</p></li>
+<li><p><strong>groupName</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – The group(s) path within the hdf5 file to read from. i.e. ‘/group1/group1a’</p></li>
+<li><p><strong>key</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – The key(s) in the group to read</p></li>
+<li><p><strong>index</strong> (<em>slice</em><em>, </em><em>optional</em>) – Specifies the index’th entry of the data to return. If the group was created using a createHDF procedure in parallel with the nRepeats option, index specifies the index’th entry from which to read the data.</p></li>
+<li><p><strong>necessary.</strong> (<em>Any other parameters in **kwargs are optional but may be necessary if an object's .fromHDF</em><em>(</em><em>) </em><em>procedure requires extra arguments. Refer to the object you wish to read in to determine whether extra arguments are</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Returns the read in entries as a list if there are multiple or as a single object if there is only one.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object or list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.readKeyFromFiles">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">readKeyFromFiles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fNames</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groupName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.readKeyFromFiles" title="Link to this definition"></a></dt>
+<dd><p>Reads in the keys from multiple files</p>
+<p>Iterates over filenames, group names, and keys and reads them from a HDF5 file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fNames</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – The path(s) and/or file name(s)</p></li>
+<li><p><strong>groupName</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – The group(s) path within the hdf5 file(s) to read from. i.e. ‘/group1/group1a’</p></li>
+<li><p><strong>key</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – The key(s) in the group to read</p></li>
+<li><p><strong>index</strong> (<em>slice</em><em>, </em><em>optional</em>) – Specifies the index’th entry of the data to return. If the group was created using a createHDF procedure in parallel with the nRepeats option, index specifies the index’th entry from which to read the data.</p></li>
+<li><p><strong>necessary.</strong> (<em>Any other parameters in **kwargs are optional but may be necessary if an object's .fromHDF</em><em>(</em><em>) </em><em>procedure requires extra arguments. Refer to the object you wish to read in to determine whether extra arguments are</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Returns the read in entries as a list if there are multiple or as a single object if there is only one.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object or list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.read_all">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">read_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fName</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.read_all" title="Link to this definition"></a></dt>
+<dd><p>Reads all the entries written to a HDF file</p>
+<p>Iterates through the highest set of keys in the hdf5 file, and reads each one to a list. If each entry has an attached .readHdf procedure, that will be used to read in an object (Those objects imported at the top of this file can be successfully read in using this attached procedure.) If an entry is a numpy array, that will be the return type.  This function will read in the entire file! Use this with caution if you are using large files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fName</strong> (<em>str</em>) – A path and/or file name.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A list of the read in items from the hdf5 file.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.read_groups_with_tag">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">read_groups_with_tag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.read_groups_with_tag" title="Link to this definition"></a></dt>
+<dd><p>Reads all groups with ‘tag’ in their path into memory.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – HDF5 file name</p></li>
+<li><p><strong>tag</strong> (<em>str</em>) – Sub string that appears in the group name.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – List of geobipy classes.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfRead.read_item">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfRead.</span></span><span class="sig-name descname"><span class="pre">read_item</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">h5obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfRead.read_item" title="Link to this definition"></a></dt>
+<dd><p>Read an object from a HDF file</p>
+<p>This function provides a flexible way to read in either a numpy hdf5 entry, or an object in this package.  The objects in this package may have an attached .createHdf and writeHdf procedure.  If so, this function will read in those objects and return that object.  If the entry is instead a numpy array, a numpy array will be returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hObj</strong> (<em>h5py._hl.dataset.Dataset</em><em> or </em><em>h5py._hl.group.Group</em>) – A h5py object from which to read entries.</p></li>
+<li><p><strong>index</strong> (<em>slice</em><em>, </em><em>optional</em>) – Specifies the index’th entry of the data to return. If the group was created using a createHDF procedure in parallel with the nRepeats option, index specifies the index’th entry from which to read the data.</p></li>
+<li><p><strong>necessary.</strong> (<em>Any other parameters in **kwargs are optional but may be necessary if an object's .fromHDF</em><em>(</em><em>) </em><em>procedure requires extra arguments. Refer to the object you wish to read in to determine whether extra arguments are</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – An object that has a .fromHdf() procedure or a numpy array of the returned variable.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object or numpy.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function" id="module-geobipy.src.base.HDF.hdfWrite">
+<dt class="sig sig-object py" id="geobipy.src.base.HDF.hdfWrite.write_nd">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.HDF.hdfWrite.</span></span><span class="sig-name descname"><span class="pre">write_nd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h5obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.HDF.hdfWrite.write_nd" title="Link to this definition"></a></dt>
+<dd><p>Writes a numpy array to a preallocated dataset in a h5py group object</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>h5obj</strong> (<em>h5py._hl.files.File</em><em> or </em><em>h5py._hl.group.Group</em>) – A HDF file or group object to write the contents to. The dataset must have already been allocated in the file.</p></li>
+<li><p><strong>myName</strong> (<em>str</em>) – The name of the h5py dataset key inside the h5py object. e.g. ‘/group1/group1a/dataset’</p></li>
+<li><p><strong>index</strong> (<em>slice</em><em>, </em><em>optional</em>) – Specifies the index’th entry of the data to return. If the group was created using a createHDF procedure in parallel with the nRepeats option, index specifies the index’th entry from which to read the data.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
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+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Bcast"><code class="docutils literal notranslate"><span class="pre">Bcast()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Bcast_1int"><code class="docutils literal notranslate"><span class="pre">Bcast_1int()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Bcast_list"><code class="docutils literal notranslate"><span class="pre">Bcast_list()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Irecv"><code class="docutils literal notranslate"><span class="pre">Irecv()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.IrecvFromLeft"><code class="docutils literal notranslate"><span class="pre">IrecvFromLeft()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.IrecvFromRight"><code class="docutils literal notranslate"><span class="pre">IrecvFromRight()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Irecv_1int"><code class="docutils literal notranslate"><span class="pre">Irecv_1int()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Isend"><code class="docutils literal notranslate"><span class="pre">Isend()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.IsendToLeft"><code class="docutils literal notranslate"><span class="pre">IsendToLeft()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.IsendToRight"><code class="docutils literal notranslate"><span class="pre">IsendToRight()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Isend_1int"><code class="docutils literal notranslate"><span class="pre">Isend_1int()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Scatterv"><code class="docutils literal notranslate"><span class="pre">Scatterv()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Scatterv_list"><code class="docutils literal notranslate"><span class="pre">Scatterv_list()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.Scatterv_numpy"><code class="docutils literal notranslate"><span class="pre">Scatterv_numpy()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.banner"><code class="docutils literal notranslate"><span class="pre">banner()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.bcastType"><code class="docutils literal notranslate"><span class="pre">bcastType()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.helloWorld"><code class="docutils literal notranslate"><span class="pre">helloWorld()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.loadBalance1D_shrinkingArrays"><code class="docutils literal notranslate"><span class="pre">loadBalance1D_shrinkingArrays()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.loadBalance3D_shrinkingArrays"><code class="docutils literal notranslate"><span class="pre">loadBalance3D_shrinkingArrays()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.ordered_print"><code class="docutils literal notranslate"><span class="pre">ordered_print()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.print"><code class="docutils literal notranslate"><span class="pre">print()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.MPI.rankPrint"><code class="docutils literal notranslate"><span class="pre">rankPrint()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
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+
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+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.MPI">
+<span id="mpi-wrapper-functions"></span><h1>MPI wrapper functions<a class="headerlink" href="#module-geobipy.src.base.MPI" title="Link to this heading"></a></h1>
+<p>Module containing custom MPI functions</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Bcast">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast a string or a numpy array</p>
+<p>Broadcast a string or a numpy array from a root rank to all ranks in an MPI communicator. Must be called collectively.
+In order to call this function collectively, the variable ‘self’ must be instantiated on every rank. See the example section for more details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>str</em><em> or </em><em>numpy.ndarray</em>) – A string or numpy array to broadcast from root.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The broadcast object on every rank.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>same type as self</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>TypeError</strong> – If self is a list, tell the user to use the specific Bcast_list function.  While it has less code and seems like it might be faster, MPI actually pickles the list, broadcasts that binary stream, and unpickles on the other side. For a large number of lists, this can take a long time. This way, the user is made aware of the time benefits of using numpy arrays.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Given a numpy array instantiated on the master rank 0, in order to broadcast it, I must also instantiate a variable with the same name on all other ranks.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mpi4py</span> <span class="kn">import</span> <span class="n">MPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy.src.base</span> <span class="kn">import</span> <span class="n">MPI</span> <span class="k">as</span> <span class="n">myMPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">world</span> <span class="o">=</span> <span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="n">world</span><span class="o">.</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">x</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Instantiate on all other ranks before broadcasting</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">x</span><span class="o">=</span><span class="kc">None</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">myMPI</span><span class="o">.</span><span class="n">Bcast</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">world</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># A string example</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="p">(</span><span class="n">world</span><span class="o">.</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;some string&#39;</span>  <span class="c1"># This may have been read in through an input file for production code</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span> <span class="o">=</span> <span class="n">myMPI</span><span class="o">.</span><span class="n">Bcast</span><span class="p">(</span><span class="n">s</span><span class="p">,</span><span class="n">world</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Bcast_1int">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Bcast_1int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Bcast_1int" title="Link to this definition"></a></dt>
+<dd><p>Broadcast a single integer</p>
+<p>In order to broadcast scalar values using the faster numpy approach, the value must cast into a 1D ndarray. Must be called collectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>int</em>) – The integer to broadcast.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The broadcast integer.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Given an integer instantiated on the master rank 0, in order to broadcast it, I must also instantiate a variable with the same name on all other ranks.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mpi4py</span> <span class="kn">import</span> <span class="n">MPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy.src.base</span> <span class="kn">import</span> <span class="n">MPI</span> <span class="k">as</span> <span class="n">myMPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">world</span> <span class="o">=</span> <span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="n">world</span><span class="o">.</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">i</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Instantiate on all other ranks before broadcasting</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">i</span><span class="o">=</span><span class="kc">None</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">myMPI</span><span class="o">.</span><span class="n">Bcast</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">world</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Bcast_list">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Bcast_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Bcast_list" title="Link to this definition"></a></dt>
+<dd><p>Broadcast a list by pickling, sending, and unpickling.  This is slower than using numpy arrays and uppercase (Bcast) mpi4py routines. Must be called collectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>list</em>) – A list to broadcast.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The broadcast list on every MPI rank.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Irecv">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Irecv" title="Link to this definition"></a></dt>
+<dd><p>Irecv a numpy array. Auto determines data type and shape. Must be accompanied by Isend on the source rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.IrecvFromLeft">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">IrecvFromLeft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.IrecvFromLeft" title="Link to this definition"></a></dt>
+<dd><p>Irecv an array from the rank left of world.rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.IrecvFromRight">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">IrecvFromRight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.IrecvFromRight" title="Link to this definition"></a></dt>
+<dd><p>IRecv an array from the rank right of world.rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Irecv_1int">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Irecv_1int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Irecv_1int" title="Link to this definition"></a></dt>
+<dd><p>Recv a single integer. Must be accompanied by Isend_1int on the source rank.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>int</em>) – Integer to Recv</p></li>
+<li><p><strong>source</strong> (<em>int</em>) – Receive from this rank.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The received integer.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Isend">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Isend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Isend" title="Link to this definition"></a></dt>
+<dd><p>Isend a numpy array. Auto determines data type and shape. Must be accompanied by Irecv on the dest rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.IsendToLeft">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">IsendToLeft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.IsendToLeft" title="Link to this definition"></a></dt>
+<dd><p>ISend an array to the rank left of world.rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.IsendToRight">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">IsendToRight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.IsendToRight" title="Link to this definition"></a></dt>
+<dd><p>ISend an array to the rank left of world.rank.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Isend_1int">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Isend_1int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Isend_1int" title="Link to this definition"></a></dt>
+<dd><p>Send a single integer. Must be accompanied by Irecv_1int on the dest rank.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>int</em>) – The integer to Send.</p></li>
+<li><p><strong>dest</strong> (<em>int</em>) – Rank to receive</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The sent integer.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Scatterv">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Scatterv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Scatterv" title="Link to this definition"></a></dt>
+<dd><p>ScatterV an array to all ranks in an MPI communicator.</p>
+<p>Each rank gets a chunk defined by a starting index and chunk size. Must be called collectively. The ‘starts’ and ‘chunks’ must be available on every MPI rank. Must be called collectively. See the example for more details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>numpy.ndarray</em>) – A numpy array to broadcast from root.</p></li>
+<li><p><strong>starts</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.</p></li>
+<li><p><strong>chunks</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) – Axis along which to Scatterv to the ranks if self is a 2D numpy array. Default is 0</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A chunk of self on each MPI rank with size chunk[world.rank].</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mpi4py</span> <span class="kn">import</span> <span class="n">MPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy.src.base</span> <span class="kn">import</span> <span class="n">MPI</span> <span class="k">as</span> <span class="n">myMPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">world</span> <span class="o">=</span> <span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Globally define a size N</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">N</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># On each rank, compute the starting indices and chunk size for the given world.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">starts</span><span class="p">,</span><span class="n">chunks</span><span class="o">=</span><span class="n">loadBalance_shrinkingArrays</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">world</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Create an array on the master rank</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="p">(</span><span class="n">world</span><span class="o">.</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">x</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Scatter the array x among ranks.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">myChunk</span> <span class="o">=</span> <span class="n">myMPI</span><span class="o">.</span><span class="n">Scatterv</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">starts</span><span class="p">,</span> <span class="n">chunks</span><span class="p">,</span> <span class="n">world</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Scatterv_list">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Scatterv_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Scatterv_list" title="Link to this definition"></a></dt>
+<dd><p>Scatterv a list by pickling, sending, receiving, and unpickling.  This is slower than using numpy arrays and uppercase (Scatterv) mpi4py routines. Must be called collectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>list</em>) – A list to scatterv.</p></li>
+<li><p><strong>starts</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.</p></li>
+<li><p><strong>chunks</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A chunk of self on each MPI rank with size chunk[world.rank].</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.Scatterv_numpy">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">Scatterv_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.Scatterv_numpy" title="Link to this definition"></a></dt>
+<dd><p>ScatterV a numpy array to all ranks in an MPI communicator.</p>
+<p>Each rank gets a chunk defined by a starting index and chunk size. Must be called collectively. The ‘starts’ and ‘chunks’ must be available on every MPI rank. See the example for more details. Must be called collectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>numpy.ndarray</em>) – A numpy array to broadcast from root.</p></li>
+<li><p><strong>starts</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.</p></li>
+<li><p><strong>chunks</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.</p></li>
+<li><p><strong>dtype</strong> (<em>type</em>) – The type of the numpy array being scattered. Must exist on all ranks.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) – Axis along which to Scatterv to the ranks if self is a 2D numpy array. Default is 0</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A chunk of self on each MPI rank with size chunk[world.rank].</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.banner">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">banner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aStr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\n'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.banner" title="Link to this definition"></a></dt>
+<dd><p>Prints a String with Separators above and below</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>aStr</strong> (<em>str</em>) – A string to print.</p></li>
+<li><p><strong>end</strong> (<em>str</em>) – string appended after the last value, default is a newline.</p></li>
+<li><p><strong>rank</strong> (<em>int</em>) – The rank to print from, default is the master rank, 0.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.bcastType">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">bcastType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.bcastType" title="Link to this definition"></a></dt>
+<dd><p>Gets the type of an object and broadcasts it to every rank in an MPI communicator.</p>
+<p>Adaptively broadcasts the type of an object. Must be called collectively.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>self</strong> (<em>object</em>) – For numpy arrays and numpy scalars, a numpy data type will be broadcast.
+For arbitrary objects, the attached __class__.__name__ will be broadcast.
+For lists, the data type will be list</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The data type broadcast to every rank including the rank broadcast from.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.helloWorld">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">helloWorld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.helloWorld" title="Link to this definition"></a></dt>
+<dd><p>Print hello from every rank in an MPI communicator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.loadBalance1D_shrinkingArrays">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">loadBalance1D_shrinkingArrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nChunks</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.loadBalance1D_shrinkingArrays" title="Link to this definition"></a></dt>
+<dd><p>Splits the length of an array into a number of chunks. Load balances the chunks in a shrinking arrays fashion.</p>
+<p>Given a length N, split N up into nChunks and return the starting index and size of each chunk.
+After being split equally among the chunks, the remainder is split so that the first remainder
+chunks get +1 in size. e.g. N=10, nChunks=3 would return starts=[0,4,7] chunks=[4,3,3]</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>N</strong> (<em>int</em>) – A size to split into chunks.</p></li>
+<li><p><strong>nChunks</strong> (<em>int</em>) – The number of chunks to split N into.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>starts</strong> (<em>ndarray of ints</em>) – The starting indices of each chunk.</p></li>
+<li><p><strong>chunks</strong> (<em>ndarray of ints</em>) – The size of each chunk.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.loadBalance3D_shrinkingArrays">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">loadBalance3D_shrinkingArrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nChunks</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.loadBalance3D_shrinkingArrays" title="Link to this definition"></a></dt>
+<dd><p>Splits three dimensions among nChunks.</p>
+<p>The number of chunks honours the relative difference in the values of shape. e.g. if shape is [600, 600, 300], then the number of chunks will be larger for the
+first two dimensions, and less for the third.
+Once the chunks are obtained, the start indices and chunk sizes for each dimension are returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>N</strong> (<em>array_like</em>) – A 3D shape to split.</p></li>
+<li><p><strong>nChunks</strong> (<em>int</em>) – The number of chunks to split shape into.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>starts</strong> (<em>ndarray of ints</em>) – The starting indices of each chunk.</p></li>
+<li><p><strong>chunks</strong> (<em>ndarray of ints</em>) – The size of each chunk.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.ordered_print">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">ordered_print</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">this</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.ordered_print" title="Link to this definition"></a></dt>
+<dd><p>Prints numbers from each rank in order of rank</p>
+<p>This routine will print an item from each rank in order of rank.
+This routine is SLOW due to lots of communication, but is useful for illustration purposes, or debugging.
+Do not use this in production code!  The title is used in a banner</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>this</strong> (<em>array_like</em>) – Variable to print, must exist on every rank in the communicator.</p></li>
+<li><p><strong>title</strong> (<em>str</em><em>, </em><em>optional</em>) – Creates a banner to separate output with a clear indication of what is being written.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.print">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aStr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\n'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.print" title="Link to this definition"></a></dt>
+<dd><p>Prints the str to sys.stdout and flushes the buffer so that printing is immediate</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>aStr</strong> (<em>str</em>) – A string to print.</p></li>
+<li><p><strong>end</strong> (<em>str</em>) – string appended after the last value, default is a newline.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.MPI.rankPrint">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.MPI.</span></span><span class="sig-name descname"><span class="pre">rankPrint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aStr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\n'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.MPI.rankPrint" title="Link to this definition"></a></dt>
+<dd><p>Prints only from the specified MPI rank</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – MPI parallel communicator.</p></li>
+<li><p><strong>aStr</strong> (<em>str</em>) – A string to print.</p></li>
+<li><p><strong>end</strong> (<em>str</em>) – string appended after the last value, default is a newline.</p></li>
+<li><p><strong>rank</strong> (<em>int</em>) – The rank to print from, default is the master rank, 0.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
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+  <title>Core routines needed for GeoBIPy &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Core routines needed for GeoBIPy</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="MPI.html">MPI wrapper functions</a></li>
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+      <li class="breadcrumb-item active">Core routines needed for GeoBIPy</li>
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+           <div itemprop="articleBody">
+             
+  <section id="core-routines-needed-for-geobipy">
+<h1>Core routines needed for GeoBIPy<a class="headerlink" href="#core-routines-needed-for-geobipy" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="utilities.html">utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="plotting.html">plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fileIO.html">fileIO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="interpolation.html">Interpolation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="base.html">Core routines needed for GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="utilities.html">utilities</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plotting.html">plotting</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">fileIO</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.bytes2readable"><code class="docutils literal notranslate"><span class="pre">bytes2readable()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.deleteFile"><code class="docutils literal notranslate"><span class="pre">deleteFile()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.dirExists"><code class="docutils literal notranslate"><span class="pre">dirExists()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.fileExists"><code class="docutils literal notranslate"><span class="pre">fileExists()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.filesExist"><code class="docutils literal notranslate"><span class="pre">filesExist()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.getFileExtension"><code class="docutils literal notranslate"><span class="pre">getFileExtension()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.getFileSize"><code class="docutils literal notranslate"><span class="pre">getFileSize()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.getNcolumns"><code class="docutils literal notranslate"><span class="pre">getNcolumns()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.getNlines"><code class="docutils literal notranslate"><span class="pre">getNlines()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.get_column_name"><code class="docutils literal notranslate"><span class="pre">get_column_name()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.get_real_numbers_from_line"><code class="docutils literal notranslate"><span class="pre">get_real_numbers_from_line()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.int2str"><code class="docutils literal notranslate"><span class="pre">int2str()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.isFileExtension"><code class="docutils literal notranslate"><span class="pre">isFileExtension()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.parseString"><code class="docutils literal notranslate"><span class="pre">parseString()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.fileIO.wccount"><code class="docutils literal notranslate"><span class="pre">wccount()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="interpolation.html">Interpolation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="HDF.html">Heirarchical Data Format (HDF)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="MPI.html">MPI wrapper functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../classes/classes.html">Classes used in GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../examples/index.html">Examples</a></li>
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+
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+          <li class="breadcrumb-item"><a href="base.html">Core routines needed for GeoBIPy</a></li>
+      <li class="breadcrumb-item active">fileIO</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../_sources/content/api/base/fileIO.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.fileIO">
+<span id="fileio"></span><h1>fileIO<a class="headerlink" href="#module-geobipy.src.base.fileIO" title="Link to this heading"></a></h1>
+<p>&#64;fileIO
+Module with custom file handling operations</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.bytes2readable">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">bytes2readable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nBytes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.bytes2readable" title="Link to this definition"></a></dt>
+<dd><p>Convert bytes to KB, MB, …, PB</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>nBytes</strong> (<em>float</em>) – The number of bytes</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The number of KB, MB, etc.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.deleteFile">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">deleteFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.deleteFile" title="Link to this definition"></a></dt>
+<dd><p>Deletes a file if it exists</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fName</strong> (<em>str</em>) – A path and/or file name</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.dirExists">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">dirExists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dirPath</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.dirExists" title="Link to this definition"></a></dt>
+<dd><p>Check if a directory exists on disk</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dirPath</strong> (<em>str</em>) – A directory path</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Whether the directory path exists</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.fileExists">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">fileExists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.fileExists" title="Link to this definition"></a></dt>
+<dd><p>Check if a single file exists on disk</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fname</strong> (<em>str</em>) – A file name</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Whether the file exists or not</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.filesExist">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">filesExist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fNames</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.filesExist" title="Link to this definition"></a></dt>
+<dd><p>Check if all files in fNames exist on disk</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fNames</strong> (<em>list</em><em> of </em><em>str</em>)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Whether all files exist or not</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.getFileExtension">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">getFileExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.getFileExtension" title="Link to this definition"></a></dt>
+<dd><p>Gets the extension of the filename</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fname</strong> (<em>str</em>) – A path and/or file name</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The extension of the filename</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.getFileSize">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">getFileSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fName</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.getFileSize" title="Link to this definition"></a></dt>
+<dd><p>Get the size of a file on disk</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fName</strong> (<em>str</em>) – A path and/or file name</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The file size in KB, MB, etc.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.getNcolumns">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">getNcolumns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nHeaders</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.getNcolumns" title="Link to this definition"></a></dt>
+<dd><p>Gets the number of columns in a file using the line after nHeaders</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fName</strong> (<em>str</em>) – A path and/or file name</p></li>
+<li><p><strong>nHeaders</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of header lines to skip before obtaining the number of columns</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The number of columns</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.getNlines">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">getNlines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nHeaders</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.getNlines" title="Link to this definition"></a></dt>
+<dd><p>Gets the number of lines in a file after taking into account the number of header lines</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fName</strong> (<em>str</em>) – A path and/or file name</p></li>
+<li><p><strong>nHeaders</strong> (<em>int</em><em>, </em><em>optional</em>) – Subtract the number of header lines from the total number of lines in the file</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Number of lines without the headers</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.get_column_name">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">get_column_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.get_column_name" title="Link to this definition"></a></dt>
+<dd><p>Read headers names from a line in a file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fName</strong> (<em>str</em>) – A path and/or file name.</p></li>
+<li><p><strong>i</strong> (<em>in</em><em> or </em><em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – The indices of the entries to read from the string.  By default, all entries are read in.</p></li>
+<li><p><strong>nHeaders</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of header lines to skip in the file.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A list of the parsed header entries.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list of str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.get_real_numbers_from_line">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">get_real_numbers_from_line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">line</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delimiters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">','</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.get_real_numbers_from_line" title="Link to this definition"></a></dt>
+<dd><p>Reads strictly the numbers from a string</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>line</strong> (<em>str</em>) – A string, or a line from a file.</p></li>
+<li><p><strong>i</strong> (<em>in</em><em> or </em><em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – The indices of the entries to read from the string.  By default, all entries are read in.</p></li>
+<li><p><strong>delimiters</strong> (<em>str</em>) – Splits the line based on these delimiters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The values read in from the string.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.int2str">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">int2str</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.int2str" title="Link to this definition"></a></dt>
+<dd><p>Converts an integer to a string with leading zeros in order to maintain the correct order in the file system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>i</strong> (<em>int</em>) – The integer to convert to a string.</p></li>
+<li><p><strong>N</strong> (<em>int</em>) – The maximum number of digits you wish to have in the integer. e.g. int2str(3,4)=’0003’.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The integer padded with zeroes on the front.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.isFileExtension">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">isFileExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ext</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.isFileExtension" title="Link to this definition"></a></dt>
+<dd><p>Check that the filename is of the given extension</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fname</strong> (<em>str</em>) – A path and/or file name</p></li>
+<li><p><strong>ext</strong> (<em>str</em>) – The extension you want to check the file name against</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Whether the extension of fname matches ext</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.parseString">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">parseString</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delimiters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">','</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.parseString" title="Link to this definition"></a></dt>
+<dd><p>Parse a string into its entries</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>this</strong> (<em>str</em>) – The string to parse.</p></li>
+<li><p><strong>delimiters</strong> (<em>str</em>) – Patterns to split against, e.g. ‘,’ splits at every comma.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A list of the parsed entries</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list of str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.fileIO.wccount">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.fileIO.</span></span><span class="sig-name descname"><span class="pre">wccount</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.fileIO.wccount" title="Link to this definition"></a></dt>
+<dd><p>Count the number of lines in a file using a wc system call</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>fName</strong> (<em>str</em>) – A path and/or file name</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The number of lines in the file</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plotting.html" class="btn btn-neutral float-left" title="plotting" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/docs/content/api/base/interpolation.html b/docs/content/api/base/interpolation.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Interpolation &mdash; GeoBIPy 2.3.1 documentation</title>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
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+          
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+          <a href="../../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
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+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="base.html">Core routines needed for GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="utilities.html">utilities</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plotting.html">plotting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="fileIO.html">fileIO</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Interpolation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="HDF.html">Heirarchical Data Format (HDF)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="MPI.html">MPI wrapper functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../classes/classes.html">Classes used in GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../examples/index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+          <li class="breadcrumb-item"><a href="base.html">Core routines needed for GeoBIPy</a></li>
+      <li class="breadcrumb-item active">Interpolation</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../_sources/content/api/base/interpolation.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.interpolation">
+<span id="interpolation"></span><h1>Interpolation<a class="headerlink" href="#module-geobipy.src.base.interpolation" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="fileIO.html" class="btn btn-neutral float-left" title="fileIO" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/docs/content/api/base/plotting.html b/docs/content/api/base/plotting.html
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index 00000000..72f22827
--- /dev/null
+++ b/docs/content/api/base/plotting.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>plotting &mdash; GeoBIPy 2.3.1 documentation</title>
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+      <link rel="stylesheet" type="text/css" href="../../../_static/sg_gallery-binder.css?v=f4aeca0c" />
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+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <link rel="next" title="fileIO" href="fileIO.html" />
+    <link rel="prev" title="utilities" href="utilities.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
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+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="base.html">Core routines needed for GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="utilities.html">utilities</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">plotting</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.bar"><code class="docutils literal notranslate"><span class="pre">bar()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.clabel"><code class="docutils literal notranslate"><span class="pre">clabel()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.generate_subplots"><code class="docutils literal notranslate"><span class="pre">generate_subplots()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.hillshade"><code class="docutils literal notranslate"><span class="pre">hillshade()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.hlines"><code class="docutils literal notranslate"><span class="pre">hlines()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.make_colourmap"><code class="docutils literal notranslate"><span class="pre">make_colourmap()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.pause"><code class="docutils literal notranslate"><span class="pre">pause()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.pcolor"><code class="docutils literal notranslate"><span class="pre">pcolor()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.pcolor_1D"><code class="docutils literal notranslate"><span class="pre">pcolor_1D()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.pcolor_as_bar"><code class="docutils literal notranslate"><span class="pre">pcolor_as_bar()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.plot"><code class="docutils literal notranslate"><span class="pre">plot()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.pretty"><code class="docutils literal notranslate"><span class="pre">pretty()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.scatter2D"><code class="docutils literal notranslate"><span class="pre">scatter2D()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.setAlphaPerPcolormeshPixel"><code class="docutils literal notranslate"><span class="pre">setAlphaPerPcolormeshPixel()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.sizeLegend"><code class="docutils literal notranslate"><span class="pre">sizeLegend()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.stackplot2D"><code class="docutils literal notranslate"><span class="pre">stackplot2D()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.step"><code class="docutils literal notranslate"><span class="pre">step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.plotting.vlines"><code class="docutils literal notranslate"><span class="pre">vlines()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="fileIO.html">fileIO</a></li>
+<li class="toctree-l3"><a class="reference internal" href="interpolation.html">Interpolation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="HDF.html">Heirarchical Data Format (HDF)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="MPI.html">MPI wrapper functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../classes/classes.html">Classes used in GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../examples/index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
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+
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+      <li class="breadcrumb-item active">plotting</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.plotting">
+<span id="plotting"></span><h1>plotting<a class="headerlink" href="#module-geobipy.src.base.plotting" title="Link to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.bar">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">bar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.bar" title="Link to this definition"></a></dt>
+<dd><p>Plot a bar chart.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – Bar values</p></li>
+<li><p><strong>edges</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – edges of the bars</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.hist</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.clabel">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">clabel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.clabel" title="Link to this definition"></a></dt>
+<dd><p>Create a colourbar label with default fontsizes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cb</strong> (<em>matplotlib.colorbar.Colorbar</em>) – A colourbar to label</p></li>
+<li><p><strong>label</strong> (<em>str</em>) – The colourbar label</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.generate_subplots">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">generate_subplots</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.generate_subplots" title="Link to this definition"></a></dt>
+<dd><p>Generates subplots depending on whats given</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n</strong> (<em>int</em>) – number of subplots</p></li>
+<li><p><strong>ax</strong> (<em>variable</em><em>, </em><em>optional</em>) – List of subplots.
+gridspec.GridSpec or gridspec.Subplotspec
+list of gridspec.Subplotspec
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.hillshade">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">hillshade</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">azimuth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">altitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.hillshade" title="Link to this definition"></a></dt>
+<dd><p>Create hillshade from a numpy array containing elevation data.</p>
+<p>Taken from <a class="reference external" href="https://github.com/royalosyin/Work-with-DEM-data-using-Python-from-Simple-to-Complicated/blob/master/ex07-Hillshade%20from%20a%20Digital%20Elevation%20Model%20(DEM).ipynb">https://github.com/royalosyin/Work-with-DEM-data-using-Python-from-Simple-to-Complicated/blob/master/ex07-Hillshade%20from%20a%20Digital%20Elevation%20Model%20(DEM).ipynb</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>arr</strong> (<em>numpy array</em><em> of </em><em>shape</em><em> (</em><em>rows</em><em>, </em><em>columns</em><em>)</em>) – Numpy array containing elevation values to be used to created hillshade.</p></li>
+<li><p><strong>azimuth</strong> (<em>float</em><em> (</em><em>default=30</em><em>)</em>) – The desired azimuth for the hillshade.</p></li>
+<li><p><strong>altitude</strong> (<em>float</em><em> (</em><em>default=30</em><em>)</em>) – The desired sun angle altitude for the hillshade.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A numpy array containing hillshade values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.hlines">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">hlines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.hlines" title="Link to this definition"></a></dt>
+<dd><p>Plot y against x</p>
+<p>If x and y are StatArrays, the axes are automatically labelled.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The abcissa</p></li>
+<li><p><strong>y</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The ordinate, can be upto 2 dimensions.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>labels</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the labels? Default is True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.make_colourmap">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">make_colourmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cname</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.make_colourmap" title="Link to this definition"></a></dt>
+<dd><p>Generate a Linear Segmented colourmap</p>
+<p>Generates a colourmap from the sequence given and registers the colourmap with matplotlib.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>seq</strong> (<em>array</em><em> of </em><em>hex colours.</em>) – e.g. [‘#000000’,’#00fcfd’,…]</p></li>
+<li><p><strong>cname</strong> (<em>str</em>) – Name of the colourmap.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.colors.LinearSegmentedColormap.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>out</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.pause">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">pause</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.pause" title="Link to this definition"></a></dt>
+<dd><p>Custom pause command to override matplotlib.pyplot.pause
+which keeps the figure on top of all others when using interactve mode.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>interval</strong> (<em>float</em>) – Pause for <em>interval</em> seconds.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.pcolor">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot of a 2D array, Actually uses pcolormesh for speed.</p>
+<p>Create a colour plot of a 2D array.
+If the arrays x, y, and values are geobipy.StatArray classes, the axes can be automatically labelled.
+Can take any other matplotlib arguments and keyword arguments e.g. cmap etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – A 2D array of colour values.</p></li>
+<li><p><strong>x</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Horizontal coordinates of the values edges.</p></li>
+<li><p><strong>y</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Vertical coordinates of the values edges.</p></li>
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>reciprocateX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Take the reciprocal of the X axis before other transforms</p></li>
+<li><p><strong>reciprocateY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Take the reciprocal of the Y axis before other transforms</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+<li><p><strong>classes</strong> (<em>dict</em><em>, </em><em>optional</em>) – A dictionary containing three entries.
+classes[‘id’] : array_like of same shape as self containing the class id of each element in self.
+classes[‘cmaps’] : list of matplotlib colourmaps.  The number of colourmaps should equal the number of classes.
+classes[‘labels’] : list of str.  The length should equal the number of classes.
+If classes is provided, alpha is ignored if provided.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.pcolor_1D">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">pcolor_1D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.pcolor_1D" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot of an array, Actually uses pcolormesh for speed.</p>
+<p>Create a colour plot of an array.
+If the arrays x, y, and values are geobipy.StatArray classes, the axes can be automatically labelled.
+Can take any other matplotlib arguments and keyword arguments e.g. cmap etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – An array of colour values.</p></li>
+<li><p><strong>x</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em>) – Horizontal coordinates of the values edges.</p></li>
+<li><p><strong>y</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Vertical coordinates of the values edges.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>clabel</strong> (<em>str</em><em>, </em><em>optional</em>) – colourbar label</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Show the grid lines</p></li>
+<li><p><strong>transpose</strong> (<em>bool</em><em>, </em><em>optional</em>) – Transpose the image</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.pcolor_as_bar">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">pcolor_as_bar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.pcolor_as_bar" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot of a 2D array, Actually uses pcolormesh for speed.</p>
+<p>Create a colour plot of a 2D array.
+If the arrays x, y, and values are geobipy.StatArray classes, the axes can be automatically labelled.
+Can take any other matplotlib arguments and keyword arguments e.g. cmap etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – A 2D array of colour values.</p></li>
+<li><p><strong>X</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Horizontal coordinates of the values edges.</p></li>
+<li><p><strong>Y</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Vertical coordinates of the values edges.</p></li>
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>alphaColour</strong> (<em>'trans'</em><em> or </em><em>length 3 array</em>) – If ‘trans’, low alpha values are mapped to transparency
+If 3 array, each entry is the RGB value of a colour to map to, e.g. white = [1, 1, 1].</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last locations that don’t equal the values in trim.</p></li>
+<li><p><strong>classes</strong> (<em>dict</em><em>, </em><em>optional</em>) – A dictionary containing three entries.
+classes[‘id’] : array_like of same shape as self containing the class id of each element in self.
+classes[‘cmaps’] : list of matplotlib colourmaps.  The number of colourmaps should equal the number of classes.
+classes[‘labels’] : list of str.  The length should equal the number of classes.
+If classes is provided, alpha is ignored if provided.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.plot">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.plot" title="Link to this definition"></a></dt>
+<dd><p>Plot y against x</p>
+<p>If x and y are StatArrays, the axes are automatically labelled.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The abcissa</p></li>
+<li><p><strong>y</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The ordinate, can be upto 2 dimensions.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>labels</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the labels? Default is True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.pretty">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">pretty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.pretty" title="Link to this definition"></a></dt>
+<dd><p>Make a plot with nice axes.</p>
+<p>Removes fluff from the axes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>ax</strong> (<em>matplotlib .Axes</em>) – A .Axes class from for example ax = plt.subplot(111), or ax = plt.gca()</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.scatter2D">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">scatter2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.scatter2D" title="Link to this definition"></a></dt>
+<dd><p>Create a 2D scatter plot.</p>
+<p>Create a 2D scatter plot, if the y values are not given, the colours are used instead.
+If the arrays x, y, and c are geobipy.StatArray classes, the axes can be automatically labelled.
+Can take any other matplotlib arguments and keyword arguments e.g. markersize etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em>) – Horizontal locations of the points to plot</p></li>
+<li><p><strong>c</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em>) – Colour values of the points</p></li>
+<li><p><strong>y</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Vertical locations of the points to plot, if y = None, then y = c.</p></li>
+<li><p><strong>i</strong> (<em>sequence</em><em> of </em><em>ints</em><em> or </em><em>numpy.slice</em><em>, </em><em>optional</em>) – Plot a geobipy_kwargs of x, y, c, using the indices in i.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to base ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.scatter</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.setAlphaPerPcolormeshPixel">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">setAlphaPerPcolormeshPixel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pcmesh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphaArray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.setAlphaPerPcolormeshPixel" title="Link to this definition"></a></dt>
+<dd><p>Set the opacity of each pixel in a pcolormesh</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pcmesh</strong> (<em>matplotlib.collections.QuadMesh</em>) – pcolormesh object</p></li>
+<li><p><strong>alphaArray</strong> (<em>array_like</em>) – Values per pixel each between 0 and 1.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.sizeLegend">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">sizeLegend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.sizeLegend" title="Link to this definition"></a></dt>
+<dd><p>Add a legend to a plot if the point sizes have been specified.</p>
+<p>If values is an StatArray, labels are generated automatically.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The array that was used as the size (s=) in a scatter function.</p></li>
+<li><p><strong>intervals</strong> (<em>array_like</em><em>, </em><em>optional</em>) – The legend will have items at each value in intervals.</p></li>
+<li><p><strong>**kwargs</strong> (<em>dict</em>) – kwargs are applied to plt.legend.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.stackplot2D">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">stackplot2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['#000000',</span> <span class="pre">'#FFFF00',</span> <span class="pre">'#1CE6FF',</span> <span class="pre">'#FF34FF',</span> <span class="pre">'#FF4A46',</span> <span class="pre">'#008941',</span> <span class="pre">'#006FA6',</span> <span class="pre">'#A30059',</span> <span class="pre">'#FFDBE5',</span> <span class="pre">'#7A4900',</span> <span class="pre">'#0000A6',</span> <span class="pre">'#63FFAC',</span> <span class="pre">'#B79762',</span> <span class="pre">'#004D43',</span> <span class="pre">'#8FB0FF',</span> <span class="pre">'#997D87',</span> <span class="pre">'#5A0007',</span> <span class="pre">'#809693',</span> <span class="pre">'#FEFFE6',</span> <span class="pre">'#1B4400',</span> <span class="pre">'#4FC601',</span> <span class="pre">'#3B5DFF',</span> <span class="pre">'#4A3B53',</span> <span class="pre">'#FF2F80',</span> <span class="pre">'#61615A',</span> <span class="pre">'#BA0900',</span> <span class="pre">'#6B7900',</span> <span class="pre">'#00C2A0',</span> <span class="pre">'#FFAA92',</span> <span class="pre">'#FF90C9',</span> <span class="pre">'#B903AA',</span> <span class="pre">'#D16100',</span> <span class="pre">'#DDEFFF',</span> <span class="pre">'#000035',</span> <span class="pre">'#7B4F4B',</span> <span class="pre">'#A1C299',</span> <span class="pre">'#300018',</span> <span class="pre">'#0AA6D8',</span> <span class="pre">'#013349',</span> <span class="pre">'#00846F',</span> <span class="pre">'#372101',</span> <span class="pre">'#FFB500',</span> <span class="pre">'#C2FFED',</span> <span class="pre">'#A079BF',</span> <span class="pre">'#CC0744',</span> <span class="pre">'#C0B9B2',</span> <span class="pre">'#C2FF99',</span> <span class="pre">'#001E09',</span> <span class="pre">'#00489C',</span> <span class="pre">'#6F0062',</span> <span class="pre">'#0CBD66',</span> <span class="pre">'#EEC3FF',</span> <span class="pre">'#456D75',</span> <span class="pre">'#B77B68',</span> <span class="pre">'#7A87A1',</span> <span class="pre">'#788D66',</span> <span class="pre">'#885578',</span> <span class="pre">'#FAD09F',</span> <span class="pre">'#FF8A9A',</span> <span class="pre">'#D157A0',</span> <span class="pre">'#BEC459',</span> <span class="pre">'#456648',</span> <span class="pre">'#0086ED',</span> <span class="pre">'#886F4C',</span> <span class="pre">'#34362D',</span> <span class="pre">'#B4A8BD',</span> <span class="pre">'#00A6AA',</span> <span class="pre">'#452C2C',</span> <span class="pre">'#636375',</span> <span class="pre">'#A3C8C9',</span> <span class="pre">'#FF913F',</span> <span class="pre">'#938A81',</span> <span class="pre">'#575329',</span> <span class="pre">'#00FECF',</span> <span class="pre">'#B05B6F',</span> <span class="pre">'#8CD0FF',</span> <span class="pre">'#3B9700',</span> <span class="pre">'#04F757',</span> <span class="pre">'#C8A1A1',</span> <span class="pre">'#1E6E00',</span> <span class="pre">'#7900D7',</span> <span class="pre">'#A77500',</span> <span class="pre">'#6367A9',</span> <span class="pre">'#A05837',</span> <span class="pre">'#6B002C',</span> <span class="pre">'#772600',</span> <span class="pre">'#D790FF',</span> <span class="pre">'#9B9700',</span> <span class="pre">'#549E79',</span> <span class="pre">'#FFF69F',</span> <span class="pre">'#201625',</span> <span class="pre">'#72418F',</span> <span class="pre">'#BC23FF',</span> <span class="pre">'#99ADC0',</span> <span class="pre">'#3A2465',</span> <span class="pre">'#922329',</span> <span class="pre">'#5B4534',</span> <span class="pre">'#FDE8DC',</span> <span class="pre">'#404E55',</span> <span class="pre">'#0089A3',</span> <span class="pre">'#CB7E98',</span> <span class="pre">'#A4E804',</span> <span class="pre">'#324E72',</span> <span class="pre">'#6A3A4C']</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.stackplot2D" title="Link to this definition"></a></dt>
+<dd><p>Plot a 2D array with column elements stacked on top of each other.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The abcissa.</p></li>
+<li><p><strong>y</strong> (<em>array_like</em><em> or </em><em>StatArray</em><em>, </em><em>2D</em>) – The cumulative sum along the columns is taken and stacked on top of each other.</p></li>
+<li><p><strong>labels</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – The labels to assign to each column.</p></li>
+<li><p><strong>colors</strong> (<em>matplotlib.colors.LinearSegmentedColormap</em><em> or </em><em>list</em><em> of </em><em>colours</em>) – The colour used for each column.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.scatterplot</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.step">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.step" title="Link to this definition"></a></dt>
+<dd><p>Plots y against x as a piecewise constant (step like) function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em>)</p></li>
+<li><p><strong>y</strong> (<em>array_like</em>)</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the y axis</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> – Flip the Y axis</p></li>
+<li><p><strong>noLabels</strong> (<em>bool</em><em>, </em><em>optional</em>) – Do not plot the labels</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.plotting.vlines">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.plotting.</span></span><span class="sig-name descname"><span class="pre">vlines</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.plotting.vlines" title="Link to this definition"></a></dt>
+<dd><p>Plot y against x</p>
+<p>If x and y are StatArrays, the axes are automatically labelled.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The abcissa</p></li>
+<li><p><strong>y</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – The ordinate, can be upto 2 dimensions.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>labels</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the labels? Default is True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
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diff --git a/docs/content/api/base/utilities.html b/docs/content/api/base/utilities.html
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+<li class="toctree-l2 current"><a class="reference internal" href="base.html">Core routines needed for GeoBIPy</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">utilities</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.Ax"><code class="docutils literal notranslate"><span class="pre">Ax()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.Det"><code class="docutils literal notranslate"><span class="pre">Det()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.Inv"><code class="docutils literal notranslate"><span class="pre">Inv()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.LogDet"><code class="docutils literal notranslate"><span class="pre">LogDet()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.cosSin1"><code class="docutils literal notranslate"><span class="pre">cosSin1()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.expReal"><code class="docutils literal notranslate"><span class="pre">expReal()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.findFirstLastNotValue"><code class="docutils literal notranslate"><span class="pre">findFirstLastNotValue()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.findFirstNonZeros"><code class="docutils literal notranslate"><span class="pre">findFirstNonZeros()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.findLastNonZeros"><code class="docutils literal notranslate"><span class="pre">findLastNonZeros()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.findNans"><code class="docutils literal notranslate"><span class="pre">findNans()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.findNotNans"><code class="docutils literal notranslate"><span class="pre">findNotNans()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.getName"><code class="docutils literal notranslate"><span class="pre">getName()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.getNameUnits"><code class="docutils literal notranslate"><span class="pre">getNameUnits()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.getUnits"><code class="docutils literal notranslate"><span class="pre">getUnits()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.histogramEqualize"><code class="docutils literal notranslate"><span class="pre">histogramEqualize()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.interleave"><code class="docutils literal notranslate"><span class="pre">interleave()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.isInt"><code class="docutils literal notranslate"><span class="pre">isInt()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.isIntorSlice"><code class="docutils literal notranslate"><span class="pre">isIntorSlice()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.isNumpy"><code class="docutils literal notranslate"><span class="pre">isNumpy()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.mergeComplex"><code class="docutils literal notranslate"><span class="pre">mergeComplex()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.rosenbrock"><code class="docutils literal notranslate"><span class="pre">rosenbrock()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.smooth"><code class="docutils literal notranslate"><span class="pre">smooth()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.splitComplex"><code class="docutils literal notranslate"><span class="pre">splitComplex()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.str_to_raw"><code class="docutils literal notranslate"><span class="pre">str_to_raw()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.tanh"><code class="docutils literal notranslate"><span class="pre">tanh()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#geobipy.src.base.utilities.trim_by_percentile"><code class="docutils literal notranslate"><span class="pre">trim_by_percentile()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="plotting.html">plotting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="fileIO.html">fileIO</a></li>
+<li class="toctree-l3"><a class="reference internal" href="interpolation.html">Interpolation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="HDF.html">Heirarchical Data Format (HDF)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="MPI.html">MPI wrapper functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../classes/classes.html">Classes used in GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../examples/index.html">Examples</a></li>
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+
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+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.base.utilities">
+<span id="utilities"></span><h1>utilities<a class="headerlink" href="#module-geobipy.src.base.utilities" title="Link to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.Ax">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">Ax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.Ax" title="Link to this definition"></a></dt>
+<dd><p>Custom matrix vector multiplication for different representations of the matrix.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>float</em><em> or </em><em>ndarray</em><em> of </em><em>floats</em>) – A scalar, 1D array, or 2D array.
+If A is scalar, assume it represents a diagonal matrix with constant value.
+If A is 1D, assume it represents a diagonal matrix and do an element wise multiply.
+If A is 2D, take the dot product.</p></li>
+<li><p><strong>x</strong> (<em>numpy.ndarray</em>) – The 1D vector to multiply A with.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Resultant matrix vector multiplication.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray of floats</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.Det">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">Det</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.Det" title="Link to this definition"></a></dt>
+<dd><p>Custom function to compute the determinant of a matrix.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>float</em><em> or </em><em>ndarray</em><em> of </em><em>floats</em>) – If A is 2D: Use numpy.linalg.det obtain determinant. Uses LU factorization.
+If A is 1D: Take the cumulative product of the numbers, assumes A represents a diagonal matrix.
+If A is scalar: Take the number to power N, assumes A represents a diagonal matrix with constant value.</p></li>
+<li><p><strong>N</strong> (<em>int</em><em>, </em><em>optional</em>) – If A is a scalar, N is the number of elements in the constant valued diagonal.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The determinant of the matrix.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.Inv">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">Inv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.Inv" title="Link to this definition"></a></dt>
+<dd><p>Custom matrix inversion upto 2 dimensions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>A</strong> (<em>float</em><em> or </em><em>ndarray</em><em> of </em><em>floats</em>) – A scalar, 1D array, or 2D array.
+If A is scalar, assume it represents a diagonal matrix with constant value and take the reciprocal.
+If A is 1D, assume it is the diagonal of a matrix: take reciprocal of entries.
+If A is 2D, invert using linalg.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The inversion of A.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float or ndarray of floats</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.LogDet">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">LogDet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.LogDet" title="Link to this definition"></a></dt>
+<dd><p>Custom function to get the natural logarithm of the determinant.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>float</em><em> or </em><em>numpy.ndarray</em><em> of </em><em>floats</em>) – If A is 2D: Use linalg.to obtain determinant. Uses LU factorization.
+If A is 1D: Take the cumulative product of the numbers, assumes A represents a diagonal matrix.
+If A is scalar: Take the number to power N, assumes A represents a diagonal matrix with constant value.</p></li>
+<li><p><strong>N</strong> (<em>int</em><em>, </em><em>optional</em>) – If A is a scalar, N is the number of elements in the constant valued diagonal.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The logged determinant of the matrix.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.cosSin1">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">cosSin1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.cosSin1" title="Link to this definition"></a></dt>
+<dd><p>Simple function for creating tests.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.expReal">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">expReal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.expReal" title="Link to this definition"></a></dt>
+<dd><p>Custom exponential of a number to allow a large negative exponent, overflow truncates without warning message.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>float</em>) – Real number to take exponential to.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – exp(this).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.findFirstLastNotValue">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">findFirstLastNotValue</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">invalid_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.findFirstLastNotValue" title="Link to this definition"></a></dt>
+<dd><p>Find the indices to the first and last non zero values along each axis</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>array_like</em>) – An array of numbers</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Indices of the first and last non zero values along each axisgg</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.findFirstNonZeros">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">findFirstNonZeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">invalid_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.findFirstNonZeros" title="Link to this definition"></a></dt>
+<dd><p>Find the indices to the first non zero values</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>this</strong> (<em>array_like</em>) – An array of numbers</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis along which to find first non zeros</p></li>
+<li><p><strong>invalid_val</strong> (<em>int</em>) – If all values along that axis are zero, use this value</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Indices of the first non zero values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.findLastNonZeros">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">findLastNonZeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">invalid_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.findLastNonZeros" title="Link to this definition"></a></dt>
+<dd><p>Find the indices to the last non zero values</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>this</strong> (<em>array_like</em>) – An array of numbers</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis along which to find last non zeros</p></li>
+<li><p><strong>invalid_val</strong> (<em>int</em>) – If all values along that axis are zero, use this value</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Indices of the last non zero values.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.findNans">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">findNans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.findNans" title="Link to this definition"></a></dt>
+<dd><p>Find the indicies to NaN values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>array_like</em>) – An array of numbers.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Integer array to locations of nans.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.findNotNans">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">findNotNans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.findNotNans" title="Link to this definition"></a></dt>
+<dd><p>Find the indicies to non NaN values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>array_like</em>) – An array of numbers.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Integer array to locations of non nans.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.getName">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">getName</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.getName" title="Link to this definition"></a></dt>
+<dd><p>Tries to obtain an attached name to a variable.</p>
+<p>If the variable is an object with a getName() procedure, that function will take precedence.
+If the variable does not have that procedure, a variable called name will be sought.
+If this fails, the specified default will be returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>any type</em>) – Any type of variable.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A string containing the variable’s name or the default.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.getNameUnits">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">getNameUnits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaultName</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaultUnits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.getNameUnits" title="Link to this definition"></a></dt>
+<dd><p>Tries to obtain any attached name and units to a variable. Any units are surrounded by round brackets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>any type</em>) – Any type of variable.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A string containing the variable’s name and units or the defaults.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.getUnits">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">getUnits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.getUnits" title="Link to this definition"></a></dt>
+<dd><p>Tries to obtain an attached units to a variable.</p>
+<p>If the variable is an object with a getUnits() procedure, that function will take precedence.
+If the variable does not have that procedure, a variable called units will be sought.
+If this fails, the specified default will be returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>any type</em>) – Any type of variable.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A string containing the variable’s units or the default.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.histogramEqualize">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">histogramEqualize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nBins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.histogramEqualize" title="Link to this definition"></a></dt>
+<dd><p>Equalize the histogram of the values so that all colours have an equal amount</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em>) – Values to be equalized.</p></li>
+<li><p><strong>nBins</strong> (<em>int</em>) – Number of bins to use.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>res</strong> (<em>array_like</em>) – Equalized values</p></li>
+<li><p><strong>cdf</strong> (<em>array_like</em>) – Cumulative Density Function.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.interleave">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">interleave</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.interleave" title="Link to this definition"></a></dt>
+<dd><p>Interleave two arrays together like zip</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>array_like</em>) – Interleave in [0::2]</p></li>
+<li><p><strong>b</strong> (<em>array_like</em>) – Interleave in [1::2]</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Interleaved arrays</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.isInt">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">isInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.isInt" title="Link to this definition"></a></dt>
+<dd><p>Check whether an entry is a subtype of an int</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>variable</em>) – Variable to check whether an int or not</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Is or is not an int</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.isIntorSlice">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">isIntorSlice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.isIntorSlice" title="Link to this definition"></a></dt>
+<dd><p>Check whether an entry is a subtype of an int or a slice</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>variable</em>) – Variable to check whether an int/slice or not</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Is or is not an int/slice</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.isNumpy">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">isNumpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.isNumpy" title="Link to this definition"></a></dt>
+<dd><p>Test that the variable is a compatible numpy type with built ins like .ndim</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>anything</em>) – A variable to check</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Whether the variable is a compatible numpy type</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.mergeComplex">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">mergeComplex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.mergeComplex" title="Link to this definition"></a></dt>
+<dd><p>Merge a 1D array containing a vertical concatenation of N real then N imaginary components   into an N/2 complex 1D array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>numpy.ndarray</em><em> of </em><em>float64</em>) – 1D array containing the vertical concatentation of real then imaginary values.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The combined real and imaginary components into a complex 1D array.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray of complex128</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.rosenbrock">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">rosenbrock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.rosenbrock" title="Link to this definition"></a></dt>
+<dd><p>Generates values from the Rosenbrock function.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.smooth">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">smooth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.smooth" title="Link to this definition"></a></dt>
+<dd><p>Smooth x by an LTI gaussian filter, forwards and backwards pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>array_like</em>) – signal to process</p></li>
+<li><p><strong>a</strong> (<em>scalar between 0.0 and 1.0</em>) – Weight</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Smoothed signal</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.splitComplex">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">splitComplex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.splitComplex" title="Link to this definition"></a></dt>
+<dd><p>Splits a vector of complex numbers into a vertical concatenation of the real and imaginary components.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>this</strong> (<em>numpy.ndarray</em><em> of </em><em>complex128</em>) – 1D array of complex numbers.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Vertically concatenated real then imaginary components of this.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray of float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.str_to_raw">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">str_to_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.str_to_raw" title="Link to this definition"></a></dt>
+<dd><p>Helper function for latex</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>s</strong> (<em>str</em>) – String with special latex commands.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – String with latex special characters.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.tanh">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">this</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.tanh" title="Link to this definition"></a></dt>
+<dd><p>Custom hyperbolic tangent, return correct overflow.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.base.utilities.trim_by_percentile">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.base.utilities.</span></span><span class="sig-name descname"><span class="pre">trim_by_percentile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percent</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.base.utilities.trim_by_percentile" title="Link to this definition"></a></dt>
+<dd><p>Trim an array by a given percentile from either end</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em>) – Values to trim</p></li>
+<li><p><strong>percent</strong> (<em>float</em>) – Percent from 0.0 to 100.0</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Trimmed values</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Classes used in GeoBIPy &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Classes used in GeoBIPy</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="data/data.html">Data classes</a></li>
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+</ul>
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+           <div itemprop="articleBody">
+             
+  <section id="classes-used-in-geobipy">
+<h1>Classes used in GeoBIPy<a class="headerlink" href="#classes-used-in-geobipy" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="core/core.html">Core classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data/data.html">Data classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="mesh/mesh.html">Mesh classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="model/model.html">Model classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="pointcloud/pointcloud.html">Pointcloud classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="statistics/statistics.html">Statistics classes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="system/system.html">System classes</a></li>
+</ul>
+</div>
+</section>
+
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+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>StatArray &mdash; GeoBIPy 2.3.1 documentation</title>
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+          
+          
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+              <div class="version">
+                2.3.1
+              </div>
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="core.html">Core classes</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="myObject.html">Core object class</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">StatArray</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
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+</ul>
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+</ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="statarray">
+<h1>StatArray<a class="headerlink" href="#statarray" title="Link to this heading"></a></h1>
+</section>
+
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <title>Core classes &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Core classes</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="myObject.html">Core object class</a></li>
+<li class="toctree-l4"><a class="reference internal" href="StatArray.html">StatArray</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../pointcloud/pointcloud.html">Pointcloud classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../statistics/statistics.html">Statistics classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../system/system.html">System classes</a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="core-classes">
+<h1>Core classes<a class="headerlink" href="#core-classes" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
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+<li class="toctree-l1"><a class="reference internal" href="myObject.html">Core object class</a></li>
+<li class="toctree-l1"><a class="reference internal" href="StatArray.html">StatArray</a></li>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+              </div>
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+  <form id="rtd-search-form" class="wy-form" action="../../../../search.html" method="get">
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+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="core.html">Core classes</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Core object class</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../statistics/statistics.html">Statistics classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../system/system.html">System classes</a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="module-geobipy.src.classes.core.myObject">
+<span id="core-object-class"></span><h1>Core object class<a class="headerlink" href="#module-geobipy.src.classes.core.myObject" title="Link to this heading"></a></h1>
+<p>o</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.core.myObject.myObject">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.core.myObject.</span></span><span class="sig-name descname"><span class="pre">myObject</span></span><a class="headerlink" href="#geobipy.src.classes.core.myObject.myObject" title="Link to this definition"></a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.core.myObject.myObject.getsizeof">
+<span class="sig-name descname"><span class="pre">getsizeof</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.core.myObject.myObject.getsizeof" title="Link to this definition"></a></dt>
+<dd><p>Get the size of the object in memory with nice output</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.core.myObject.myObject.toHdf">
+<span class="sig-name descname"><span class="pre">toHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">h5obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.core.myObject.myObject.toHdf" title="Link to this definition"></a></dt>
+<dd><p>Create and write to HDF.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>h5obj</strong> (<em>h5py._hl.files.File</em><em> or </em><em>h5py._hl.group.Group</em>) – A HDF file or group object to write the contents to.</p></li>
+<li><p><strong>myName</strong> (<em>str</em>) – The name of the group to write the StatArray to.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Data classes</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dataset/dataset.html">Dataset classes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="datapoint/datapointrst.html">Datapoint classes</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../model/model.html">Model classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../pointcloud/pointcloud.html">Pointcloud classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../statistics/statistics.html">Statistics classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../system/system.html">System classes</a></li>
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+             
+  <section id="emdatapoint">
+<h1>EmDataPoint<a class="headerlink" href="#emdatapoint" title="Link to this heading"></a></h1>
+digraph inheritance4ab37ba733 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;DataPoint&quot; [URL=&quot;datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a data point.&quot;];
+  &quot;Point&quot; -&gt; &quot;DataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;EmDataPoint&quot; [URL=&quot;#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Abstract EmDataPoint Class&quot;];
+  &quot;DataPoint&quot; -&gt; &quot;EmDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.datapoint.EmDataPoint">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.datapoint.EmDataPoint.</span></span><span class="sig-name descname"><span class="pre">EmDataPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels_per_system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictedData</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineNumber</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint" title="Link to this definition"></a></dt>
+<dd><p>Abstract EmDataPoint Class</p>
+<p>This is an abstract base class for TdemDataPoint and FdemDataPoint classes</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="FdemDataPoint.html#module-geobipy.src.classes.data.datapoint.FdemDataPoint" title="geobipy.src.classes.data.datapoint.FdemDataPoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.data.datapoint.FdemDataPoint</span></code></a>, <a class="reference internal" href="TdemDataPoint.html#module-geobipy.src.classes.data.datapoint.TdemDataPoint" title="geobipy.src.classes.data.datapoint.TdemDataPoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.data.datapoint.TdemDataPoint</span></code></a></p>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.active">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.active" title="Link to this definition"></a></dt>
+<dd><p>Gets the indices to the observed data values that are not NaN</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Indices into the observed data that are not NaN</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.find_best_halfspace">
+<span class="sig-name descname"><span class="pre">find_best_halfspace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minConductivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxConductivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nSamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.find_best_halfspace" title="Link to this definition"></a></dt>
+<dd><p>Computes the best value of a half space that fits the data.</p>
+<p>Carries out a brute force search of the halfspace conductivity that best fits the data.
+The profile of data misfit vs halfspace conductivity is not quadratic, so a bisection will not work.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>minConductivity</strong> (<em>float</em><em>, </em><em>optional</em>) – The minimum conductivity to search over</p></li>
+<li><p><strong>maxConductivity</strong> (<em>float</em><em>, </em><em>optional</em>) – The maximum conductivity to search over</p></li>
+<li><p><strong>nSamples</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of values between the min and max</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The best fitting log10 conductivity for the half space</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.plot_halfspace_responses">
+<span class="sig-name descname"><span class="pre">plot_halfspace_responses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">minConductivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxConductivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nSamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.plot_halfspace_responses" title="Link to this definition"></a></dt>
+<dd><p>Plots the reponses of different half space models.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>minConductivity</strong> (<em>float</em><em>, </em><em>optional</em>) – The minimum log10 conductivity to search over</p></li>
+<li><p><strong>maxConductivity</strong> (<em>float</em><em>, </em><em>optional</em>) – The maximum log10 conductivity to search over</p></li>
+<li><p><strong>nInc</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of increments between the min and max</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.predictedData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predictedData</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.predictedData" title="Link to this definition"></a></dt>
+<dd><p>The predicted data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.update_posteriors">
+<span class="sig-name descname"><span class="pre">update_posteriors</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.update_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Update any attached posteriors</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+             
+  <section id="fdemdatapoint">
+<h1>FdemDataPoint<a class="headerlink" href="#fdemdatapoint" title="Link to this heading"></a></h1>
+digraph inheritance205783337d {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;DataPoint&quot; [URL=&quot;datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a data point.&quot;];
+  &quot;Point&quot; -&gt; &quot;DataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;EmDataPoint&quot; [URL=&quot;EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Abstract EmDataPoint Class&quot;];
+  &quot;DataPoint&quot; -&gt; &quot;EmDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;FdemDataPoint&quot; [URL=&quot;#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a Frequency domain electromagnetic data point.&quot;];
+  &quot;EmDataPoint&quot; -&gt; &quot;FdemDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.data.datapoint.FdemDataPoint">&#64;FdemDataPoint_Class
+Module describing a frequency domain EMData Point that contains a single measurement.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.datapoint.FdemDataPoint.</span></span><span class="sig-name descname"><span class="pre">FdemDataPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictedData</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineNumber</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint" title="Link to this definition"></a></dt>
+<dd><p>Class defines a Frequency domain electromagnetic data point.</p>
+<p>Contains an easting, northing, height, elevation, observed and predicted data, and uncertainty estimates for the data.</p>
+<p>FdemDataPoint(x, y, z, elevation, data, std, system, lineNumber, fiducial)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>float</em>) – Easting co-ordinate of the data point</p></li>
+<li><p><strong>y</strong> (<em>float</em>) – Northing co-ordinate of the data point</p></li>
+<li><p><strong>z</strong> (<em>float</em>) – Height above ground of the data point</p></li>
+<li><p><strong>elevation</strong> (<em>float</em><em>, </em><em>optional</em>) – Elevation from sea level of the data point</p></li>
+<li><p><strong>data</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – Data values to assign the data of length 2*number of frequencies.
+* If None, initialized with zeros.</p></li>
+<li><p><strong>std</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – Estimated uncertainty standard deviation of the data of length 2*number of frequencies.
+* If None, initialized with ones if data is None, else 0.1*data values.</p></li>
+<li><p><strong>system</strong> (<em>str</em><em> or </em><em>geobipy.FdemSystem</em><em>, </em><em>optional</em>) – Describes the acquisition system with loop orientation and frequencies.
+* If str should be the path to a system file to read in.
+* If geobipy.FdemSystem, will be deepcopied.</p></li>
+<li><p><strong>lineNumber</strong> (<em>float</em><em>, </em><em>optional</em>) – The line number associated with the datapoint</p></li>
+<li><p><strong>fiducial</strong> (<em>float</em><em>, </em><em>optional</em>) – The fiducial associated with the datapoint</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.calibrate">
+<span class="sig-name descname"><span class="pre">calibrate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Predicted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.calibrate" title="Link to this definition"></a></dt>
+<dd><p>Apply calibration factors to the data point</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.forward" title="Link to this definition"></a></dt>
+<dd><p>Forward model the data from the given model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.frequencies">
+<span class="sig-name descname"><span class="pre">frequencies</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.frequencies" title="Link to this definition"></a></dt>
+<dd><p>Return the frequencies in an StatArray</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getFrequency">
+<span class="sig-name descname"><span class="pre">getFrequency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getFrequency" title="Link to this definition"></a></dt>
+<dd><p>Return the measurement frequency of the channel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Channel number</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – System number</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The measurement frequency of the channel</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getMeasurementType">
+<span class="sig-name descname"><span class="pre">getMeasurementType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getMeasurementType" title="Link to this definition"></a></dt>
+<dd><p>Returns the measurement type of the channel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Channel number</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – System number</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Either “In-Phase “ or “Quadrature “</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Frequency</span> <span class="pre">Domain</span> <span class="pre">EM</span> <span class="pre">Data'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_error_bars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot" title="Link to this definition"></a></dt>
+<dd><p>Plot the Inphase and Quadrature Data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>title</strong> (<em>str</em>) – Title of the plot</p></li>
+<li><p><strong>system</strong> (<em>int</em>) – If multiple system are present, select which one</p></li>
+<li><p><strong>with_error_bars</strong> (<em>bool</em>) – Plot vertical lines representing 1 standard deviation</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.errorbar</span></code></dt><dd><p>For more keyword arguements</p>
+</dd>
+</dl>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Figure axis</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.pyplot.ax</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot_predicted">
+<span class="sig-name descname"><span class="pre">plot_predicted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Frequency</span> <span class="pre">Domain</span> <span class="pre">EM</span> <span class="pre">Data'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot_predicted" title="Link to this definition"></a></dt>
+<dd><p>Plot the predicted Inphase and Quadrature Data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>title</strong> (<em>str</em>) – Title of the plot</p></li>
+<li><p><strong>system</strong> (<em>int</em>) – If multiple system are present, select which one</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.semilogx</span></code></dt><dd><p>For more keyword arguements</p>
+</dd>
+</dl>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Figure axis</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.pyplot.ax</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.sensitivity">
+<span class="sig-name descname"><span class="pre">sensitivity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.sensitivity" title="Link to this definition"></a></dt>
+<dd><p>Compute the sensitivty matrix for the given model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.updateSensitivity">
+<span class="sig-name descname"><span class="pre">updateSensitivity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.updateSensitivity" title="Link to this definition"></a></dt>
+<dd><p>Compute an updated sensitivity matrix based on the one already containined in the FdemDataPoint object</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.update_posteriors">
+<span class="sig-name descname"><span class="pre">update_posteriors</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.update_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Update any attached posteriors</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
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+           <div itemprop="articleBody">
+             
+  <section id="tdemdatapoint">
+<h1>TdemDataPoint<a class="headerlink" href="#tdemdatapoint" title="Link to this heading"></a></h1>
+digraph inheritance42929051d3 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;DataPoint&quot; [URL=&quot;datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a data point.&quot;];
+  &quot;Point&quot; -&gt; &quot;DataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;EmDataPoint&quot; [URL=&quot;EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Abstract EmDataPoint Class&quot;];
+  &quot;DataPoint&quot; -&gt; &quot;EmDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TdemDataPoint&quot; [URL=&quot;#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Initialize a Time domain EMData Point&quot;];
+  &quot;EmDataPoint&quot; -&gt; &quot;TdemDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.datapoint.TdemDataPoint">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.datapoint.TdemDataPoint.</span></span><span class="sig-name descname"><span class="pre">TdemDataPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">primary_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">secondary_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relative_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additive_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predicted_primary_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predicted_secondary_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transmitter_loop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">receiver_loop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineNumber</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint" title="Link to this definition"></a></dt>
+<dd><p>Initialize a Time domain EMData Point</p>
+<p>TdemDataPoint(x, y, z, elevation, data, std, system, transmitter_loop, receiver_loop, lineNumber, fiducial)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>float64</em>) – The easting co-ordinate of the data point</p></li>
+<li><p><strong>y</strong> (<em>float64</em>) – The northing co-ordinate of the data point</p></li>
+<li><p><strong>z</strong> (<em>float64</em>) – The height of the data point above ground</p></li>
+<li><p><strong>elevation</strong> (<em>float64</em><em>, </em><em>optional</em>) – The elevation of the data point, default is 0.0</p></li>
+<li><p><strong>data</strong> (<em>list</em><em> of </em><em>arrays</em><em>, </em><em>optional</em>) – A list of 1D arrays, where each array contains the data in each system.
+The arrays are vertically concatenated inside the TdemDataPoint object</p></li>
+<li><p><strong>std</strong> (<em>list</em><em> of </em><em>arrays</em><em>, </em><em>optional</em>) – A list of 1D arrays, where each array contains the errors in each system.
+The arrays are vertically concatenated inside the TdemDataPoint object</p></li>
+<li><p><strong>system</strong> (<a class="reference internal" href="../../system/TdemSystem.html#geobipy.src.classes.system.TdemSystem.TdemSystem" title="geobipy.src.classes.system.TdemSystem.TdemSystem"><em>TdemSystem</em></a><em>, </em><em>optional</em>) – Time domain system class</p></li>
+<li><p><strong>transmitter_loop</strong> (<a class="reference internal" href="../../system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop" title="geobipy.src.classes.system.EmLoop.EmLoop"><em>EmLoop</em></a><em>, </em><em>optional</em>) – Transmitter loop class</p></li>
+<li><p><strong>receiver_loop</strong> (<a class="reference internal" href="../../system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop" title="geobipy.src.classes.system.EmLoop.EmLoop"><em>EmLoop</em></a><em>, </em><em>optional</em>) – Receiver loop class</p></li>
+<li><p><strong>lineNumber</strong> (<em>float</em><em>, </em><em>optional</em>) – The line number associated with the datapoint</p></li>
+<li><p><strong>fiducial</strong> (<em>float</em><em>, </em><em>optional</em>) – The fiducial associated with the datapoint</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A time domain EM sounding</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint" title="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint">TdemDataPoint</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The data argument is a set of lists with length equal to the number of systems.
+These data are unpacked and vertically concatenated in this class.
+The parameter self._data will have length equal to the sum of the number of time gates in each system.
+The same is true for the errors, and the predicted data vector.</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.addressof">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">addressof</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.addressof" title="Link to this definition"></a></dt>
+<dd><p>Print a summary of the EMdataPoint</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.dualMoment">
+<span class="sig-name descname"><span class="pre">dualMoment</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.dualMoment" title="Link to this definition"></a></dt>
+<dd><p>Returns True if the number of systems is &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.forward" title="Link to this definition"></a></dt>
+<dd><p>Forward model the data from the given model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.iplotActive">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iplotActive</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.iplotActive" title="Link to this definition"></a></dt>
+<dd><p>Get the active data indices per system.  Used for plotting.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.off_time">
+<span class="sig-name descname"><span class="pre">off_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.off_time" title="Link to this definition"></a></dt>
+<dd><p>Return the window times in an StatArray</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.perturb" title="Link to this definition"></a></dt>
+<dd><p>Propose a new EM data point given the specified attached propsal distributions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>newHeight</strong> (<em>bool</em>) – Propose a new observation height.</p></li>
+<li><p><strong>newRelativeError</strong> (<em>bool</em>) – Propose a new relative error.</p></li>
+<li><p><strong>newAdditiveError</strong> (<em>bool</em>) – Propose a new additive error.</p></li>
+<li><p><strong>newCalibration</strong> (<em>bool</em>) – Propose new calibration parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The proposed data point</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>subclass of <a class="reference internal" href="EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint" title="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint">EmDataPoint</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated proposal must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a proposal has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Time</span> <span class="pre">Domain</span> <span class="pre">EM</span> <span class="pre">Data'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_error_bars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.plot" title="Link to this definition"></a></dt>
+<dd><p>Plot the Inphase and Quadrature Data for an EM measurement</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.predictedData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predictedData</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.predictedData" title="Link to this definition"></a></dt>
+<dd><p>The predicted data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the probability for the EM data point given the specified attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rEerr</strong> (<em>bool</em>) – Include the relative error when evaluating the prior</p></li>
+<li><p><strong>aEerr</strong> (<em>bool</em>) – Include the additive error when evaluating the prior</p></li>
+<li><p><strong>height</strong> (<em>bool</em>) – Include the elevation when evaluating the prior</p></li>
+<li><p><strong>calibration</strong> (<em>bool</em>) – Include the calibration parameters when evaluating the prior</p></li>
+<li><p><strong>verbose</strong> (<em>bool</em>) – Return the components of the probability, i.e. the individually evaluated priors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The evaluation of the probability using all assigned priors</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated prior must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a prior has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataFileName</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.read" title="Link to this definition"></a></dt>
+<dd><p>Read in a time domain data point from a file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataFileName</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – File names of the data point.  Multiple can be given for multiple moments at the same location.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Time domain data point</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.TdemDataPoint</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.sensitivity">
+<span class="sig-name descname"><span class="pre">sensitivity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_changed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.sensitivity" title="Link to this definition"></a></dt>
+<dd><p>Compute the sensitivty matrix for the given model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_posteriors">
+<span class="sig-name descname"><span class="pre">set_posteriors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Set the posteriors based on the attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>log</strong></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_proposals">
+<span class="sig-name descname"><span class="pre">set_proposals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">relative_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additive_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_proposals" title="Link to this definition"></a></dt>
+<dd><p>Set the proposals on the datapoint’s perturbable parameters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heightProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the height. Must be univariate</p></li>
+<li><p><strong>relativeErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, relativeErrorProposal is univariate.
+If there are more than one system, relativeErrorProposal is multivariate.</p></li>
+<li><p><strong>additiveErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, additiveErrorProposal is univariate.
+If there are more than one system, additiveErrorProposal is multivariate.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.std" title="Link to this definition"></a></dt>
+<dd><p>Updates the data errors</p>
+<p>Assumes a t^-0.5 behaviour e.g. logarithmic gate averaging
+V0 is assumed to be ln(Error &#64; 1ms)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>relativeErr</strong> (<em>list</em><em> of </em><em>scalars</em><em> or </em><em>list</em><em> of </em><em>array_like</em>) – A fraction percentage that is multiplied by the observed data. The list should have length equal to the number of systems. The entries in each item can be scalar or array_like.</p></li>
+<li><p><strong>additiveErr</strong> (<em>list</em><em> of </em><em>scalars</em><em> or </em><em>list</em><em> of </em><em>array_like</em>) – An absolute value of additive error. The list should have length equal to the number of systems. The entries in each item can be scalar or array_like.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>TypeError</strong> – If relativeErr or additiveErr is not a list</p></li>
+<li><p><strong>TypeError</strong> – If the length of relativeErr or additiveErr is not equal to the number of systems</p></li>
+<li><p><strong>TypeError</strong> – If any item in the relativeErr or additiveErr lists is not a scalar or array_like of length equal to the number of time channels</p></li>
+<li><p><strong>ValueError</strong> – If any relative or additive errors are &lt;= 0.0</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.summary" title="Link to this definition"></a></dt>
+<dd><p>Print a summary of the EMdataPoint</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.update_posteriors">
+<span class="sig-name descname"><span class="pre">update_posteriors</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.update_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Update any attached posteriors</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+        <a href="../../mesh/mesh.html" class="btn btn-neutral float-right" title="Mesh classes" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+             
+  <section id="tempest-datapoint">
+<h1>Tempest_datapoint<a class="headerlink" href="#tempest-datapoint" title="Link to this heading"></a></h1>
+digraph inheritance06e99732c6 {
+bgcolor=transparent;
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+  &quot;DataPoint&quot; [URL=&quot;datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a data point.&quot;];
+  &quot;Point&quot; -&gt; &quot;DataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;EmDataPoint&quot; [URL=&quot;EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Abstract EmDataPoint Class&quot;];
+  &quot;DataPoint&quot; -&gt; &quot;EmDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TdemDataPoint&quot; [URL=&quot;TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Initialize a Time domain EMData Point&quot;];
+  &quot;EmDataPoint&quot; -&gt; &quot;TdemDataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Tempest_datapoint&quot; [URL=&quot;#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Initialize a Tempest Time domain data point&quot;];
+  &quot;TdemDataPoint&quot; -&gt; &quot;Tempest_datapoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.datapoint.Tempest_datapoint">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.datapoint.Tempest_datapoint.</span></span><span class="sig-name descname"><span class="pre">Tempest_datapoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additive_error_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint" title="Link to this definition"></a></dt>
+<dd><p>Initialize a Tempest Time domain data point</p>
+<p>TdemDataPoint(x, y, z, elevation, data, std, system, transmitter_loop, receiver_loop, lineNumber, fiducial)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>float64</em>) – The easting co-ordinate of the data point</p></li>
+<li><p><strong>y</strong> (<em>float64</em>) – The northing co-ordinate of the data point</p></li>
+<li><p><strong>z</strong> (<em>float64</em>) – The height of the data point above ground</p></li>
+<li><p><strong>elevation</strong> (<em>float64</em><em>, </em><em>optional</em>) – The elevation of the data point, default is 0.0</p></li>
+<li><p><strong>data</strong> (<em>list</em><em> of </em><em>arrays</em><em>, </em><em>optional</em>) – A list of 1D arrays, where each array contains the data in each system.
+The arrays are vertically concatenated inside the TdemDataPoint object</p></li>
+<li><p><strong>std</strong> (<em>list</em><em> of </em><em>arrays</em><em>, </em><em>optional</em>) – A list of 1D arrays, where each array contains the errors in each system.
+The arrays are vertically concatenated inside the TdemDataPoint object</p></li>
+<li><p><strong>system</strong> (<a class="reference internal" href="../../system/TdemSystem.html#geobipy.src.classes.system.TdemSystem.TdemSystem" title="geobipy.src.classes.system.TdemSystem.TdemSystem"><em>TdemSystem</em></a><em>, </em><em>optional</em>) – Time domain system class</p></li>
+<li><p><strong>transmitter_loop</strong> (<a class="reference internal" href="../../system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop" title="geobipy.src.classes.system.EmLoop.EmLoop"><em>EmLoop</em></a><em>, </em><em>optional</em>) – Transmitter loop class</p></li>
+<li><p><strong>receiver_loop</strong> (<a class="reference internal" href="../../system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop" title="geobipy.src.classes.system.EmLoop.EmLoop"><em>EmLoop</em></a><em>, </em><em>optional</em>) – Receiver loop class</p></li>
+<li><p><strong>lineNumber</strong> (<em>float</em><em>, </em><em>optional</em>) – The line number associated with the datapoint</p></li>
+<li><p><strong>fiducial</strong> (<em>float</em><em>, </em><em>optional</em>) – The fiducial associated with the datapoint</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A time domain EM sounding</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint" title="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint">TdemDataPoint</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The data argument is a set of lists with length equal to the number of systems.
+These data are unpacked and vertically concatenated in this class.
+The parameter self._data will have length equal to the sum of the number of time gates in each system.
+The same is true for the errors, and the predicted data vector.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.perturb" title="Link to this definition"></a></dt>
+<dd><p>Propose a new EM data point given the specified attached propsal distributions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>newHeight</strong> (<em>bool</em>) – Propose a new observation height.</p></li>
+<li><p><strong>newRelativeError</strong> (<em>bool</em>) – Propose a new relative error.</p></li>
+<li><p><strong>newAdditiveError</strong> (<em>bool</em>) – Propose a new additive error.</p></li>
+<li><p><strong>newCalibration</strong> (<em>bool</em>) – Propose new calibration parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The proposed data point</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>subclass of <a class="reference internal" href="EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint" title="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint">EmDataPoint</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated proposal must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a proposal has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.plot" title="Link to this definition"></a></dt>
+<dd><p>Plot the Inphase and Quadrature Data for an EM measurement</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.predictedData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predictedData</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.predictedData" title="Link to this definition"></a></dt>
+<dd><p>The predicted data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the probability for the EM data point given the specified attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rEerr</strong> (<em>bool</em>) – Include the relative error when evaluating the prior</p></li>
+<li><p><strong>aEerr</strong> (<em>bool</em>) – Include the additive error when evaluating the prior</p></li>
+<li><p><strong>height</strong> (<em>bool</em>) – Include the elevation when evaluating the prior</p></li>
+<li><p><strong>calibration</strong> (<em>bool</em>) – Include the calibration parameters when evaluating the prior</p></li>
+<li><p><strong>verbose</strong> (<em>bool</em>) – Return the components of the probability, i.e. the individually evaluated priors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The evaluation of the probability using all assigned priors</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated prior must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a prior has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_additive_error_posterior">
+<span class="sig-name descname"><span class="pre">set_additive_error_posterior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_additive_error_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_posteriors">
+<span class="sig-name descname"><span class="pre">set_posteriors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Set the posteriors based on the attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>log</strong></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_proposals">
+<span class="sig-name descname"><span class="pre">set_proposals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">relative_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additive_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_proposals" title="Link to this definition"></a></dt>
+<dd><p>Set the proposals on the datapoint’s perturbable parameters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heightProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the height. Must be univariate</p></li>
+<li><p><strong>relativeErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, relativeErrorProposal is univariate.
+If there are more than one system, relativeErrorProposal is multivariate.</p></li>
+<li><p><strong>additiveErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, additiveErrorProposal is univariate.
+If there are more than one system, additiveErrorProposal is multivariate.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_relative_error_posterior">
+<span class="sig-name descname"><span class="pre">set_relative_error_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_relative_error_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.std" title="Link to this definition"></a></dt>
+<dd><p>Updates the data errors</p>
+<p>Assumes a t^-0.5 behaviour e.g. logarithmic gate averaging
+V0 is assumed to be ln(Error &#64; 1ms)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>relativeErr</strong> (<em>list</em><em> of </em><em>scalars</em><em> or </em><em>list</em><em> of </em><em>array_like</em>) – A fraction percentage that is multiplied by the observed data. The list should have length equal to the number of systems. The entries in each item can be scalar or array_like.</p></li>
+<li><p><strong>additiveErr</strong> (<em>list</em><em> of </em><em>scalars</em><em> or </em><em>list</em><em> of </em><em>array_like</em>) – An absolute value of additive error. The list should have length equal to the number of systems. The entries in each item can be scalar or array_like.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>TypeError</strong> – If relativeErr or additiveErr is not a list</p></li>
+<li><p><strong>TypeError</strong> – If the length of relativeErr or additiveErr is not equal to the number of systems</p></li>
+<li><p><strong>TypeError</strong> – If any item in the relativeErr or additiveErr lists is not a scalar or array_like of length equal to the number of time channels</p></li>
+<li><p><strong>ValueError</strong> – If any relative or additive errors are &lt;= 0.0</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
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+             
+  <section id="datapoint">
+<h1>DataPoint<a class="headerlink" href="#datapoint" title="Link to this heading"></a></h1>
+digraph inheritance476311a242 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;DataPoint&quot; [URL=&quot;#geobipy.src.classes.data.datapoint.DataPoint.DataPoint&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defines a data point.&quot;];
+  &quot;Point&quot; -&gt; &quot;DataPoint&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.datapoint.DataPoint">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.datapoint.DataPoint.</span></span><span class="sig-name descname"><span class="pre">DataPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictedData</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineNumber</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint" title="Link to this definition"></a></dt>
+<dd><p>Class defines a data point.</p>
+<p>Contains an easting, northing, height, elevation, observed and predicted data, and uncertainty estimates for the data.</p>
+<p>DataPoint(x, y, z, elevation, nChannels, data, std, units)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nChannelsPerSystem</strong> (<em>int</em><em> or </em><em>array_like</em>) – Number of data channels in the data
+* If int, a single acquisition system is assumed.
+* If array_like, each entry is the number of channels for each system.</p></li>
+<li><p><strong>x</strong> (<em>float</em>) – Easting co-ordinate of the data point</p></li>
+<li><p><strong>y</strong> (<em>float</em>) – Northing co-ordinate of the data point</p></li>
+<li><p><strong>z</strong> (<em>float</em>) – Height above ground of the data point</p></li>
+<li><p><strong>elevation</strong> (<em>float</em><em>, </em><em>optional</em>) – Elevation from sea level of the data point</p></li>
+<li><p><strong>data</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – Data values to assign the data of length sum(nChannelsPerSystem).
+* If None, initialized with zeros.</p></li>
+<li><p><strong>std</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – Estimated uncertainty standard deviation of the data of length sum(nChannelsPerSystem).
+* If None, initialized with ones if data is None, else 0.1*data values.</p></li>
+<li><p><strong>predictedData</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – Predicted data values to assign the data of length sum(nChannelsPerSystem).
+* If None, initialized with zeros.</p></li>
+<li><p><strong>units</strong> (<em>str</em><em>, </em><em>optional</em>) – Units of the data.  Default is “ppm”.</p></li>
+<li><p><strong>channel_names</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – Names of each channel of length sum(nChannelsPerSystem)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.active">
+<span class="sig-name descname"><span class="pre">active</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.active" title="Link to this definition"></a></dt>
+<dd><p>Gets the indices to the observed data values that are not NaN</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Indices into the observed data that are not NaN</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.addressof">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">addressof</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.addressof" title="Link to this definition"></a></dt>
+<dd><p>Print a summary of the EMdataPoint</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.data_misfit">
+<span class="sig-name descname"><span class="pre">data_misfit</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.data_misfit" title="Link to this definition"></a></dt>
+<dd><p>Compute the <span class="math notranslate nohighlight">\(L_{2}\)</span> norm squared misfit between the observed and predicted data</p>
+<div class="math notranslate nohighlight">
+\[\| \mathbf{W}_{d} (\mathbf{d}^{obs}-\mathbf{d}^{pre})\|_{2}^{2},\]</div>
+<p>where <span class="math notranslate nohighlight">\(\mathbf{W}_{d}\)</span> are the reciprocal data errors.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>squared</strong> (<em>bool</em>) – Return the squared misfit.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The misfit value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.deltaD">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">deltaD</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.deltaD" title="Link to this definition"></a></dt>
+<dd><p>Get the difference between the predicted and observed data,</p>
+<div class="math notranslate nohighlight">
+\[\delta \mathbf{d} = \mathbf{d}^{pre} - \mathbf{d}^{obs}.\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – The residual between the active observed and predicted data
+with size equal to the number of active channels.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>StatArray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.likelihood">
+<span class="sig-name descname"><span class="pre">likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.likelihood" title="Link to this definition"></a></dt>
+<dd><p>Compute the likelihood of the current predicted data given the observed data and assigned errors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Likelihood of the data point</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.perturb" title="Link to this definition"></a></dt>
+<dd><p>Propose a new EM data point given the specified attached propsal distributions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>newHeight</strong> (<em>bool</em>) – Propose a new observation height.</p></li>
+<li><p><strong>newRelativeError</strong> (<em>bool</em>) – Propose a new relative error.</p></li>
+<li><p><strong>newAdditiveError</strong> (<em>bool</em>) – Propose a new additive error.</p></li>
+<li><p><strong>newCalibration</strong> (<em>bool</em>) – Propose new calibration parameters.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The proposed data point</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>subclass of <a class="reference internal" href="EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint" title="geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint">EmDataPoint</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated proposal must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a proposal has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.predictedData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predictedData</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.predictedData" title="Link to this definition"></a></dt>
+<dd><p>The predicted data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the probability for the EM data point given the specified attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rEerr</strong> (<em>bool</em>) – Include the relative error when evaluating the prior</p></li>
+<li><p><strong>aEerr</strong> (<em>bool</em>) – Include the additive error when evaluating the prior</p></li>
+<li><p><strong>height</strong> (<em>bool</em>) – Include the elevation when evaluating the prior</p></li>
+<li><p><strong>calibration</strong> (<em>bool</em>) – Include the calibration parameters when evaluating the prior</p></li>
+<li><p><strong>verbose</strong> (<em>bool</em>) – Return the components of the probability, i.e. the individually evaluated priors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The evaluation of the probability using all assigned priors</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated prior must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a prior has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_additive_error_posterior">
+<span class="sig-name descname"><span class="pre">set_additive_error_posterior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_additive_error_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_posteriors">
+<span class="sig-name descname"><span class="pre">set_posteriors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Set the posteriors based on the attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>log</strong></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_proposals">
+<span class="sig-name descname"><span class="pre">set_proposals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">relative_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additive_error_proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_proposals" title="Link to this definition"></a></dt>
+<dd><p>Set the proposals on the datapoint’s perturbable parameters</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heightProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the height. Must be univariate</p></li>
+<li><p><strong>relativeErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, relativeErrorProposal is univariate.
+If there are more than one system, relativeErrorProposal is multivariate.</p></li>
+<li><p><strong>additiveErrorProposal</strong> (<em>geobipy.baseDistribution</em><em>, </em><em>optional</em>) – The proposal to attach to the relative error.
+If the datapoint has only one system, additiveErrorProposal is univariate.
+If there are more than one system, additiveErrorProposal is multivariate.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_relative_error_posterior">
+<span class="sig-name descname"><span class="pre">set_relative_error_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_relative_error_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.std" title="Link to this definition"></a></dt>
+<dd><p>Compute the data errors.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.summary" title="Link to this definition"></a></dt>
+<dd><p>Print a summary of the EMdataPoint</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.datapoint.DataPoint.DataPoint.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+      <li class="breadcrumb-item active">Data</li>
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+            <a href="../../../../../_sources/content/api/classes/data/dataset/Data.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="data">
+<h1>Data<a class="headerlink" href="#data" title="Link to this heading"></a></h1>
+digraph inheritance6003e78ca1 {
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+}
+<p id="module-geobipy.src.classes.data.dataset.Data">&#64;Data_Class
+Module describing a Data Set where values are associated with an xyz co-ordinate</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.dataset.Data.</span></span><span class="sig-name descname"><span class="pre">Data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels_per_system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictedData</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineNumber</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data" title="Link to this definition"></a></dt>
+<dd><p>Class defining a set of Data.</p>
+<p>Data(channels_per_system, x, y, z, data, std, predictedData, dataUnits, channel_names)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nPoints</strong> (<em>int</em>) – Number of points in the data.</p></li>
+<li><p><strong>channels_per_system</strong> (<em>int</em><em> or </em><em>array_like</em>) – Number of data channels in the data
+* If int, a single acquisition system is assumed.
+* If array_like, each item describes the number of points per acquisition system.</p></li>
+<li><p><strong>x</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – The x co-ordinates. Default is zeros of size nPoints.</p></li>
+<li><p><strong>y</strong> (<em>geobipy.StatArray</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – The y co-ordinates. Default is zeros of size nPoints.</p></li>
+<li><p><strong>z</strong> (<em>geobipy.StatArrayor array_like</em><em>, </em><em>optional</em>) – The z co-ordinates. Default is zeros of size nPoints.</p></li>
+<li><p><strong>data</strong> (<em>geobipy.StatArrayor array_like</em><em>, </em><em>optional</em>) – The values of the data.
+* If None, zeroes are assigned</p></li>
+<li><p><strong>std</strong> (<em>geobipy.StatArrayor array_like</em><em>, </em><em>optional</em>) – The uncertainty estimates of the data.
+* If None, ones are assigned if data is None, else 0.1*data</p></li>
+<li><p><strong>predictedData</strong> (<em>geobipy.StatArrayor array_like</em><em>, </em><em>optional</em>) – The predicted data.
+* If None, zeros are assigned.</p></li>
+<li><p><strong>dataUnits</strong> (<em>str</em>) – Units of the data.</p></li>
+<li><p><strong>channel_names</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – Names of each channel of length sum(channels_per_system)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Data class</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.data.dataset.Data.Data" title="geobipy.src.classes.data.dataset.Data.Data">Data</a></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast a Data object using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>world</strong> (<em>mpi4py.MPI.COMM_WORLD</em>) – MPI communicator</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Data broadcast to each core in the communicator</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.Data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.Scatterv">
+<span class="sig-name descname"><span class="pre">Scatterv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.Scatterv" title="Link to this definition"></a></dt>
+<dd><p>Scatterv a Data object using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>starts</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.</p></li>
+<li><p><strong>chunks</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – The MPI communicator over which to Scatterv.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The Data distributed amongst ranks.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.Data</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.active">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.active" title="Link to this definition"></a></dt>
+<dd><p>Logical array whether the channel is active or not.</p>
+<p>An inactive channel is one where channel values are NaN for all points.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Indices of non-NaN columns.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>bools</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.addToVTK">
+<span class="sig-name descname"><span class="pre">addToVTK</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vtk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['data',</span> <span class="pre">'predicted',</span> <span class="pre">'std']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.addToVTK" title="Link to this definition"></a></dt>
+<dd><p>Adds a member to a VTK handle.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>vtk</strong> (<em>pyvtk.VtkData</em>) – vtk handle returned from self.vtkStructure()</p></li>
+<li><p><strong>prop</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – List of the member to add to a VTK handle, either “data”, “predicted”, or “std”.</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system for which to add the data</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.additive_error">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">additive_error</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.additive_error" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.channel_index">
+<span class="sig-name descname"><span class="pre">channel_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.channel_index" title="Link to this definition"></a></dt>
+<dd><p>Gets the data in the specified channel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Index of the channel to return
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The index of the channel</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.data">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.data" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.data_misfit">
+<span class="sig-name descname"><span class="pre">data_misfit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.data_misfit" title="Link to this definition"></a></dt>
+<dd><p>Compute the <span class="math notranslate nohighlight">\(L_{2}\)</span> norm squared misfit between the observed and predicted data</p>
+<div class="math notranslate nohighlight">
+\[\| \mathbf{W}_{d} (\mathbf{d}^{obs}-\mathbf{d}^{pre})\|_{2}^{2},\]</div>
+<p>where <span class="math notranslate nohighlight">\(\mathbf{W}_{d}\)</span> are the reciprocal data errors.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>squared</strong> (<em>bool</em>) – Return the squared misfit.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The misfit value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.datapoint">
+<span class="sig-name descname"><span class="pre">datapoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.datapoint" title="Link to this definition"></a></dt>
+<dd><p>Get the ith data point from the data set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>i</strong> (<em>int</em>) – The data point to get</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The data point</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.DataPoint</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.deltaD">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">deltaD</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.deltaD" title="Link to this definition"></a></dt>
+<dd><p>Get the difference between the predicted and observed data,</p>
+<div class="math notranslate nohighlight">
+\[\delta \mathbf{d} = \mathbf{d}^{pre} - \mathbf{d}^{obs}.\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – The residual between the active observed and predicted data.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>StatArray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.line">
+<span class="sig-name descname"><span class="pre">line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">line</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.line" title="Link to this definition"></a></dt>
+<dd><p>Get the data from the given line number</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.mapData">
+<span class="sig-name descname"><span class="pre">mapData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.mapData" title="Link to this definition"></a></dt>
+<dd><p>Interpolate the data channel between the x, y co-ordinates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Index of the channel to return
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.mapPredictedData">
+<span class="sig-name descname"><span class="pre">mapPredictedData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.mapPredictedData" title="Link to this definition"></a></dt>
+<dd><p>Interpolate the predicted data channel between the x, y co-ordinates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Index of the channel to return
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.mapStd">
+<span class="sig-name descname"><span class="pre">mapStd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.mapStd" title="Link to this definition"></a></dt>
+<dd><p>Interpolate the standard deviation channel between the x, y co-ordinates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Index of the channel to return
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.nPointsPerLine">
+<span class="sig-name descname"><span class="pre">nPointsPerLine</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.nPointsPerLine" title="Link to this definition"></a></dt>
+<dd><p>Gets the number of points in each line.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of points in each line</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.plot_data">
+<span class="sig-name descname"><span class="pre">plot_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.plot_data" title="Link to this definition"></a></dt>
+<dd><p>Plots the specifed channels as a line plot.</p>
+<p>Plots the channels along a specified co-ordinate e.g. ‘x’. A legend is auto generated.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p></li>
+<li><p><strong>channels</strong> (<em>ints</em><em>, </em><em>optional</em>) – Indices of the channels to plot.  All are plotted if None
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>values</strong> (<em>arraylike</em><em>, </em><em>optional</em>) – Specifies values to plot against the chosen axis. Takes precedence over channels.</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+<li><p><strong>legend</strong> (<em>bool</em>) – Attach a legend to the plot.  Default is True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>ax</strong> (<em>matplotlib.axes</em>) – Plot axes handle</p></li>
+<li><p><strong>legend</strong> (<em>matplotlib.legend.Legend</em>) – The attached legend.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.plot</span></code></dt><dd><p>For additional keyword arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.plot_predicted">
+<span class="sig-name descname"><span class="pre">plot_predicted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xAxis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.plot_predicted" title="Link to this definition"></a></dt>
+<dd><p>Plots the specifed predicted data channels as a line plot.</p>
+<p>Plots the channels along a specified co-ordinate e.g. ‘x’. A legend is auto generated.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p></li>
+<li><p><strong>channels</strong> (<em>ints</em><em>, </em><em>optional</em>) – Indices of the channels to plot.  All are plotted if None
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+<li><p><strong>noLegend</strong> (<em>bool</em>) – Do not attach a legend to the plot.  Default is False, a legend is attached.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>ax</strong> (<em>matplotlib.axes</em>) – Plot axes handle</p></li>
+<li><p><strong>legend</strong> (<em>matplotlib.legend.Legend</em>) – The attached legend.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.plot</span></code></dt><dd><p>For additional keyword arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.predictedData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predictedData</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.predictedData" title="Link to this definition"></a></dt>
+<dd><p>The predicted data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.read_csv">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_filename</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.read_csv" title="Link to this definition"></a></dt>
+<dd><p>Reads the data and system parameters from file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain data file names</p></li>
+<li><p><strong>systemFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain system file names</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>File Format</p>
+<p>The data columns are read in according to the column names in the first line.
+The header line should contain at least the following column names.
+Extra columns may exist, but will be ignored. In this description,
+the column name or its alternatives are given followed by what the name represents.
+Optional columns are also described.</p>
+<p><strong>Required columns</strong></p>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>id or fid</dt><dd><p>Id number of the data point, these be unique</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point</p>
+</dd>
+<dt>z or dtm or dem_elev or dem_np or topo</dt><dd><p>Elevation of the ground at the data point</p>
+</dd>
+<dt>alt or laser or bheight</dt><dd><p>Altitude of the transmitter coil</p>
+</dd>
+<dt>Off[0] to Off[nWindows-1]  (with the number and brackets)</dt><dd><p>The measurements for each time specified in the accompanying system file under Receiver Window Times</p>
+</dd>
+</dl>
+<p><strong>Optional columns</strong></p>
+<p>If any loop orientation columns are omitted the loop is assumed to be horizontal.</p>
+<dl class="simple">
+<dt>TxPitch</dt><dd><p>Pitch of the transmitter loop</p>
+</dd>
+<dt>TxRoll</dt><dd><p>Roll of the transmitter loop</p>
+</dd>
+<dt>TxYaw</dt><dd><p>Yaw of the transmitter loop</p>
+</dd>
+<dt>RxPitch</dt><dd><p>Pitch of the receiver loop</p>
+</dd>
+<dt>RxRoll</dt><dd><p>Roll of the receiver loop</p>
+</dd>
+<dt>RxYaw</dt><dd><p>Yaw of the receiver loop</p>
+</dd>
+<dt>OffErr[0] to ErrOff[nWindows-1]</dt><dd><p>Error estimates for the data</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">INFORMATION</span></code></p>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.relative_error">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">relative_error</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.relative_error" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.std" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.summary" title="Link to this definition"></a></dt>
+<dd><p>Display a summary of the Data</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.Data.Data.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.Data.Data.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<html class="writer-html5" lang="en" data-content_root="../../../../../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+      <li class="breadcrumb-item active">FdemData</li>
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+             
+  <section id="fdemdata">
+<h1>FdemData<a class="headerlink" href="#fdemdata" title="Link to this heading"></a></h1>
+digraph inheritance66f423f7ef {
+bgcolor=transparent;
+rankdir=LR;
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+  &quot;Point&quot; -&gt; &quot;Data&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;FdemData&quot; [URL=&quot;#geobipy.src.classes.data.dataset.FdemData.FdemData&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class extension to geobipy.Data defining a Fourier domain electro magnetic data set&quot;];
+  &quot;Data&quot; -&gt; &quot;FdemData&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
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+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.data.dataset.FdemData">&#64;FdemData_Class
+Module describing an EMData Set where channels are associated with an xyz co-ordinate</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.dataset.FdemData.</span></span><span class="sig-name descname"><span class="pre">FdemData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData" title="Link to this definition"></a></dt>
+<dd><p>Class extension to geobipy.Data defining a Fourier domain electro magnetic data set</p>
+<p>FdemData(nPoints, nFrequencies, system)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nPoints</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of observations in the data set</p></li>
+<li><p><strong>nFrequencies</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of measurement frequencies</p></li>
+<li><p><strong>system</strong> (<em>str</em><em> or </em><em>geobipy.FdemSystem</em><em>, </em><em>optional</em>) – <ul>
+<li><p>If str: Must be a file name from which to read FD system information.</p></li>
+<li><p>If FdemSystem: A deepcopy is made.</p></li>
+</ul>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Contains x, y, z, elevation, and data values for a frequency domain dataset.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.data.dataset.FdemData.FdemData" title="geobipy.src.classes.data.dataset.FdemData.FdemData">FdemData</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>FdemData.read() requires a data filename and a system class or system filename to be specified.
+The data file is structured using columns with the first line containing header information.
+The header should contain the following entries
+Line [ID or FID] [X or N or northing] [Y or E or easting] [Z or DTM or dem_elev] [Alt or Laser or bheight] [I Q] … [I Q]
+Do not include brackets []
+[I Q] are the in-phase and quadrature values for each measurement frequency.</p>
+<p>If a system filename is given, it too is structured using columns with the first line containing header information
+Each subsequent row contains the information for each measurement frequency</p>
+<p>freq  tor  tmom  tx ty tz ror rmom  rx   ry rz
+378   z    1     0  0  0  z   1     7.93 0  0
+1776  z    1     0  0  0  z   1     7.91 0  0
+…</p>
+<p>where tor and ror are the orientations of the transmitter/reciever loops [x or z].
+tmom and rmom are the moments of the loops.
+t/rx,y,z are the loop offsets from the observation locations in the data file.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast the FdemData using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>world</strong> (<em>mpi4py.MPI.COMM_WORLD</em>) – MPI communicator</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A copy of the data on each core</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.FdemData</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mpi4py</span> <span class="kn">import</span> <span class="n">MPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemData</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">world</span> <span class="o">=</span> <span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rank</span> <span class="o">=</span> <span class="n">world</span><span class="o">.</span><span class="n">rank</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="p">(</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> <span class="c1"># Only the master reads in the data</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span> <span class="o">=</span> <span class="n">FdemData</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span> <span class="o">=</span> <span class="n">FdemData</span><span class="p">()</span> <span class="c1"># Must instantiate an empty object to Bcast</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">D2</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">Bcast</span><span class="p">(</span><span class="n">world</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.Scatterv">
+<span class="sig-name descname"><span class="pre">Scatterv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.Scatterv" title="Link to this definition"></a></dt>
+<dd><p>Distributes the FdemData between all cores using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>starts</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.</p></li>
+<li><p><strong>chunks</strong> (<em>array</em><em> of </em><em>ints</em>) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.COMM_WORLD</em>) – The MPI communicator</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The data distributed amongst cores</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.FdemData</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mpi4py</span> <span class="kn">import</span> <span class="n">MPI</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemData</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">world</span> <span class="o">=</span> <span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rank</span> <span class="o">=</span> <span class="n">world</span><span class="o">.</span><span class="n">rank</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">if</span> <span class="p">(</span><span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> <span class="c1"># Only the master reads in the data</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span> <span class="o">=</span> <span class="n">FdemData</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">else</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="n">D</span> <span class="o">=</span> <span class="n">FdemData</span><span class="p">()</span> <span class="c1"># Must instantiate an empty object to Bcast</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># In this example, assume there are 10 data and 4 cores</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">start</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">chunks</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">D2</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">Scatterv</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">chunks</span><span class="p">,</span> <span class="n">world</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.datapoint">
+<span class="sig-name descname"><span class="pre">datapoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.datapoint" title="Link to this definition"></a></dt>
+<dd><p>Get the ith data point from the data set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>index</strong> (<em>int</em><em>, </em><em>optional</em>) – Index of the data point to get.</p></li>
+<li><p><strong>fiducial</strong> (<em>float</em><em>, </em><em>optional</em>) – Fiducial of the data point to get.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The data point.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.FdemDataPoint</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>Exception</strong> – If neither an index or fiducial are given.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.fileInformation">
+<span class="sig-name descname"><span class="pre">fileInformation</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.fileInformation" title="Link to this definition"></a></dt>
+<dd><p>Description of the data file.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.getFrequency">
+<span class="sig-name descname"><span class="pre">getFrequency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.getFrequency" title="Link to this definition"></a></dt>
+<dd><p>Return the measurement frequency of the channel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Channel number</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – System number</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The measurement frequency of the channel</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.getMeasurementType">
+<span class="sig-name descname"><span class="pre">getMeasurementType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.getMeasurementType" title="Link to this definition"></a></dt>
+<dd><p>Returns the measurement type of the channel</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>channel</strong> (<em>int</em>) – Channel number</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – System number</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Either “In-Phase “ or “Quadrature “</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.nActiveData">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nActiveData</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.nActiveData" title="Link to this definition"></a></dt>
+<dd><p>Number of active data per data point.</p>
+<p>For each data point, counts the number of channels that are NOT nan.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of active data</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.plotLine">
+<span class="sig-name descname"><span class="pre">plotLine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">line</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.plotLine" title="Link to this definition"></a></dt>
+<dd><p>Plot the specified line</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.plot_data">
+<span class="sig-name descname"><span class="pre">plot_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.plot_data" title="Link to this definition"></a></dt>
+<dd><p>Plots the specifed channels as a line plot.</p>
+<p>Plots the channels along a specified co-ordinate e.g. ‘x’. A legend is auto generated.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p></li>
+<li><p><strong>channels</strong> (<em>ints</em><em>, </em><em>optional</em>) – The indices of the channels to plot. All are plotted if channels is None.</p></li>
+<li><p><strong>legend</strong> (<em>bool</em>) – Attach a legend to the plot.  Default is True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>ax</strong> (<em>matplotlib.axes</em>) – Plot axes handle</p></li>
+<li><p><strong>legend</strong> (<em>matplotlib.legend.Legend</em>) – The attached legend.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.plot</span></code></dt><dd><p>For additional keyword arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.readAarhusFile">
+<span class="sig-name descname"><span class="pre">readAarhusFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataFilename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.readAarhusFile" title="Link to this definition"></a></dt>
+<dd><p>Read in frequency domain data from an Aarhus workbench file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataFilename</strong> (<em>str</em>) – The data file.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.read_csv">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataFilename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.read_csv" title="Link to this definition"></a></dt>
+<dd><p>Read in both the Fdem data and FDEM system files</p>
+<p>The data file is structured using columns with the first line containing header information.
+The header should contain the following entries
+Line [ID or FID] [X or N or northing] [Y or E or easting] [Z or DTM or dem_elev] [Alt or Laser or bheight] [I Q] … [I Q]
+Do not include brackets []
+[I Q] are the in-phase and quadrature values for each measurement frequency.</p>
+<p>If a system filename is given, it too is structured using columns with the first line containing header information
+Each subsequent row contains the information for each measurement frequency</p>
+<p>freq  tor  tmom  tx ty tz ror rmom  rx   ry rz
+378   z    1     0  0  0  z   1     7.93 0  0
+1776  z    1     0  0  0  z   1     7.91 0  0
+…</p>
+<p>where tor and ror are the orientations of the transmitter/reciever loops [x or z].
+tmom and rmom are the moments of the loops.
+t/rx,y,z are the loop offsets from the observation locations in the data file.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.single">
+<span class="sig-name descname"><span class="pre">single</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.single" title="Link to this definition"></a></dt>
+<dd><p>alias of <a class="reference internal" href="../datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint" title="geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint"><code class="xref py py-class docutils literal notranslate"><span class="pre">FdemDataPoint</span></code></a></p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.FdemData.FdemData.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.FdemData.FdemData.std" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
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+      <li class="breadcrumb-item active">TdemData</li>
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+             
+  <section id="tdemdata">
+<h1>TdemData<a class="headerlink" href="#tdemdata" title="Link to this heading"></a></h1>
+digraph inheritanceeb2965ddb0 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Data&quot; [URL=&quot;Data.html#geobipy.src.classes.data.dataset.Data.Data&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a set of Data.&quot;];
+  &quot;Point&quot; -&gt; &quot;Data&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TdemData&quot; [URL=&quot;#geobipy.src.classes.data.dataset.TdemData.TdemData&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Time domain electro magnetic data set&quot;];
+  &quot;Data&quot; -&gt; &quot;TdemData&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.dataset.TdemData">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.dataset.TdemData.</span></span><span class="sig-name descname"><span class="pre">TdemData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData" title="Link to this definition"></a></dt>
+<dd><p>Time domain electro magnetic data set</p>
+<p>A time domain data set with easting, northing, height, and elevation values. Each sounding in the data set can be given a receiver and transmitter loop.</p>
+<p>TdemData(nPoints=1, nTimes=[1], nSystems=1)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nPoints</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of soundings in the data file</p></li>
+<li><p><strong>nTimes</strong> (<em>array</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Array of size nSystemsx1 containing the number of time gates in each system</p></li>
+<li><p><strong>nSystem</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of measurement systems</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Time domain data set</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.data.dataset.TdemData.TdemData" title="geobipy.src.classes.data.dataset.TdemData.TdemData">TdemData</a></p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">read()</span></code></dt><dd><p>For information on file format</p>
+</dd>
+</dl>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast the TdemData using MPI</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.Scatterv">
+<span class="sig-name descname"><span class="pre">Scatterv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.Scatterv" title="Link to this definition"></a></dt>
+<dd><p>Scatterv the TdemData using MPI</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.data">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.data" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.datapoint">
+<span class="sig-name descname"><span class="pre">datapoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fiducial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.datapoint" title="Link to this definition"></a></dt>
+<dd><p>Get the ith data point from the data set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>index</strong> (<em>int</em><em>, </em><em>optional</em>) – Index of the data point to get.</p></li>
+<li><p><strong>fiducial</strong> (<em>float</em><em>, </em><em>optional</em>) – Fiducial of the data point to get.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The data point.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.FdemDataPoint</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>Exception</strong> – If neither an index or fiducial are given.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.estimateAdditiveError">
+<span class="sig-name descname"><span class="pre">estimateAdditiveError</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.estimateAdditiveError" title="Link to this definition"></a></dt>
+<dd><p>Uses the late times after 1ms to estimate the additive errors and error bounds in the data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.fileInformation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fileInformation</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.fileInformation" title="Link to this definition"></a></dt>
+<dd><p>Description of PointCloud3D file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – File description.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.mapChannel">
+<span class="sig-name descname"><span class="pre">mapChannel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.mapChannel" title="Link to this definition"></a></dt>
+<dd><p>Create a map of the specified data channel</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.nPoints">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nPoints</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.nPoints" title="Link to this definition"></a></dt>
+<dd><p>Get the number of points</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.off_time">
+<span class="sig-name descname"><span class="pre">off_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.off_time" title="Link to this definition"></a></dt>
+<dd><p>Obtain the times from the system file</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">component</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yAxis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Plot the data in the given system as a 2D array</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.plot_data">
+<span class="sig-name descname"><span class="pre">plot_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.plot_data" title="Link to this definition"></a></dt>
+<dd><p>Plots the data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>system</strong> (<em>int</em>) – System to plot</p></li>
+<li><p><strong>channels</strong> (<em>sequence</em><em> of </em><em>ints</em>) – Channels to plot</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.plot_predicted">
+<span class="sig-name descname"><span class="pre">plot_predicted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.plot_predicted" title="Link to this definition"></a></dt>
+<dd><p>Plots the specifed predicted data channels as a line plot.</p>
+<p>Plots the channels along a specified co-ordinate e.g. ‘x’. A legend is auto generated.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p></li>
+<li><p><strong>channels</strong> (<em>ints</em><em>, </em><em>optional</em>) – Indices of the channels to plot.  All are plotted if None
+* If system is None, 0 &lt;= channel &lt; self.nChannels else 0 &lt;= channel &lt; self.nChannelsPerSystem[system]</p></li>
+<li><p><strong>system</strong> (<em>int</em><em>, </em><em>optional</em>) – The system to obtain the channel from.</p></li>
+<li><p><strong>noLegend</strong> (<em>bool</em>) – Do not attach a legend to the plot.  Default is False, a legend is attached.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>ax</strong> (<em>matplotlib.axes</em>) – Plot axes handle</p></li>
+<li><p><strong>legend</strong> (<em>matplotlib.legend.Legend</em>) – The attached legend.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.plot</span></code></dt><dd><p>For additional keyword arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_primary_field">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predicted_primary_field</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_primary_field" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_secondary_field">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">predicted_secondary_field</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_secondary_field" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.primary_field">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">primary_field</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.primary_field" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.read_csv">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.read_csv" title="Link to this definition"></a></dt>
+<dd><p>Reads the data and system parameters from file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain data file names</p></li>
+<li><p><strong>systemFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain system file names</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>File Format</p>
+<p>The data columns are read in according to the column names in the first line.
+The header line should contain at least the following column names.
+Extra columns may exist, but will be ignored.
+In this description, the column name or its alternatives are given followed by what the name represents.
+Optional columns are also described.</p>
+<p><strong>Required columns</strong></p>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>id or fid</dt><dd><p>Id number of the data point, these be unique</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point</p>
+</dd>
+<dt>z or dtm or dem_elev or dem_np or topo</dt><dd><p>Elevation of the ground at the data point</p>
+</dd>
+<dt>alt or laser or bheight</dt><dd><p>Altitude of the transmitter coil</p>
+</dd>
+<dt>Off[0] to Off[nWindows-1]  (with the number and brackets)</dt><dd><p>The measurements for each time specified in the accompanying system file under Receiver Window Times</p>
+</dd>
+</dl>
+<p><strong>Optional columns</strong></p>
+<p>If any loop orientation columns are omitted the loop is assumed to be horizontal.</p>
+<dl class="simple">
+<dt>TxPitch</dt><dd><p>Pitch of the transmitter loop</p>
+</dd>
+<dt>TxRoll</dt><dd><p>Roll of the transmitter loop</p>
+</dd>
+<dt>TxYaw</dt><dd><p>Yaw of the transmitter loop</p>
+</dd>
+<dt>RxPitch</dt><dd><p>Pitch of the receiver loop</p>
+</dd>
+<dt>RxRoll</dt><dd><p>Roll of the receiver loop</p>
+</dd>
+<dt>RxYaw</dt><dd><p>Yaw of the receiver loop</p>
+</dd>
+<dt>OffErr[0] to ErrOff[nWindows-1]</dt><dd><p>Error estimates for the data</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">INFORMATION</span></code></p>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.secondary_field">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">secondary_field</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.secondary_field" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.single">
+<span class="sig-name descname"><span class="pre">single</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.single" title="Link to this definition"></a></dt>
+<dd><p>alias of <a class="reference internal" href="../datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint" title="geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint"><code class="xref py py-class docutils literal notranslate"><span class="pre">TdemDataPoint</span></code></a></p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.std">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">std</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.std" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.summary" title="Link to this definition"></a></dt>
+<dd><p>Display a summary of the Data</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TdemData.TdemData.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TdemData.TdemData.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+      <li class="breadcrumb-item active">TempestData</li>
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+           <div itemprop="articleBody">
+             
+  <section id="tempestdata">
+<h1>TempestData<a class="headerlink" href="#tempestdata" title="Link to this heading"></a></h1>
+digraph inheritance92a4e4a9a8 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Data&quot; [URL=&quot;Data.html#geobipy.src.classes.data.dataset.Data.Data&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a set of Data.&quot;];
+  &quot;Point&quot; -&gt; &quot;Data&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TdemData&quot; [URL=&quot;TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Time domain electro magnetic data set&quot;];
+  &quot;Data&quot; -&gt; &quot;TdemData&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TempestData&quot; [URL=&quot;#geobipy.src.classes.data.dataset.TempestData.TempestData&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Time domain electro magnetic data set&quot;];
+  &quot;TdemData&quot; -&gt; &quot;TempestData&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.data.dataset.TempestData">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.data.dataset.TempestData.</span></span><span class="sig-name descname"><span class="pre">TempestData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData" title="Link to this definition"></a></dt>
+<dd><p>Time domain electro magnetic data set</p>
+<p>A time domain data set with easting, northing, height, and elevation values. Each sounding in the data set can be given a receiver and transmitter loop.</p>
+<p>TdemData(nPoints=1, nTimes=[1], nSxfystems=1)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nPoints</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of soundings in the data file</p></li>
+<li><p><strong>nTimes</strong> (<em>array</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Array of size nSystemsx1 containing the number of time gates in each system</p></li>
+<li><p><strong>nSystem</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of measurement systems</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Time domain data set</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData" title="geobipy.src.classes.data.dataset.TdemData.TdemData">TdemData</a></p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">read()</span></code></dt><dd><p>For information on file format</p>
+</dd>
+</dl>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.additive_error">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">additive_error</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.additive_error" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">myName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.plot_data">
+<span class="sig-name descname"><span class="pre">plot_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.plot_data" title="Link to this definition"></a></dt>
+<dd><p>Plots the data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>system</strong> (<em>int</em>) – System to plot</p></li>
+<li><p><strong>channels</strong> (<em>sequence</em><em> of </em><em>ints</em>) – Channels to plot</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.plot_predicted">
+<span class="sig-name descname"><span class="pre">plot_predicted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xAxis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.plot_predicted" title="Link to this definition"></a></dt>
+<dd><p>Plots the data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>system</strong> (<em>int</em>) – System to plot</p></li>
+<li><p><strong>channels</strong> (<em>sequence</em><em> of </em><em>ints</em>) – Channels to plot</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.read_csv">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system_filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.read_csv" title="Link to this definition"></a></dt>
+<dd><p>Reads the data and system parameters from file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain data file names</p></li>
+<li><p><strong>systemFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain system file names</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>File Format</p>
+<p>The data columns are read in according to the column names in the first line.
+The header line should contain at least the following column names.
+Extra columns may exist, but will be ignored. In this description,
+the column name or its alternatives are given followed by what the name represents.
+Optional columns are also described.</p>
+<p><strong>Required columns</strong></p>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>id or fid</dt><dd><p>Id number of the data point, these be unique</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point</p>
+</dd>
+<dt>z or dtm or dem_elev or dem_np or topo</dt><dd><p>Elevation of the ground at the data point</p>
+</dd>
+<dt>alt or laser or bheight</dt><dd><p>Altitude of the transmitter coil</p>
+</dd>
+<dt>Off[0] to Off[nWindows-1]  (with the number and brackets)</dt><dd><p>The measurements for each time specified in the accompanying system file under Receiver Window Times</p>
+</dd>
+</dl>
+<p><strong>Optional columns</strong></p>
+<p>If any loop orientation columns are omitted the loop is assumed to be horizontal.</p>
+<dl class="simple">
+<dt>TxPitch</dt><dd><p>Pitch of the transmitter loop</p>
+</dd>
+<dt>TxRoll</dt><dd><p>Roll of the transmitter loop</p>
+</dd>
+<dt>TxYaw</dt><dd><p>Yaw of the transmitter loop</p>
+</dd>
+<dt>RxPitch</dt><dd><p>Pitch of the receiver loop</p>
+</dd>
+<dt>RxRoll</dt><dd><p>Roll of the receiver loop</p>
+</dd>
+<dt>RxYaw</dt><dd><p>Yaw of the receiver loop</p>
+</dd>
+<dt>OffErr[0] to ErrOff[nWindows-1]</dt><dd><p>Error estimates for the data</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">INFORMATION</span></code></p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.read_netcdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_netcdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataFilename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">systemFilename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.read_netcdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the data and system parameters from file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain data file names</p></li>
+<li><p><strong>systemFilename</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em>) – Time domain system file names</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>File Format
+The data columns are read in according to the column names in the first line.
+The header line should contain at least the following column names.
+Extra columns may exist, but will be ignored. In this description, the column name or its
+alternatives are given followed by what the name represents. Optional columns are also described.</p>
+<p><strong>Required columns</strong></p>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>id or fid</dt><dd><p>Id number of the data point, these be unique</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point</p>
+</dd>
+<dt>z or dtm or dem_elev or dem_np or topo</dt><dd><p>Elevation of the ground at the data point</p>
+</dd>
+<dt>alt or laser or bheight</dt><dd><p>Altitude of the transmitter coil</p>
+</dd>
+<dt>Off[0] to Off[nWindows-1]  (with the number and brackets)</dt><dd><p>The measurements for each time specified in the accompanying system file under Receiver Window Times</p>
+</dd>
+</dl>
+<p><strong>Optional columns</strong>
+If any loop orientation columns are omitted the loop is assumed to be horizontal.</p>
+<dl class="simple">
+<dt>TxPitch</dt><dd><p>Pitch of the transmitter loop</p>
+</dd>
+<dt>TxRoll</dt><dd><p>Roll of the transmitter loop</p>
+</dd>
+<dt>TxYaw</dt><dd><p>Yaw of the transmitter loop</p>
+</dd>
+<dt>RxPitch</dt><dd><p>Pitch of the receiver loop</p>
+</dd>
+<dt>RxRoll</dt><dd><p>Roll of the receiver loop</p>
+</dd>
+<dt>RxYaw</dt><dd><p>Yaw of the receiver loop</p>
+</dd>
+<dt>OffErr[0] to ErrOff[nWindows-1]</dt><dd><p>Error estimates for the data</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">INFORMATION</span></code></p>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.relative_error">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">relative_error</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.relative_error" title="Link to this definition"></a></dt>
+<dd><p>The data.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.single">
+<span class="sig-name descname"><span class="pre">single</span></span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.single" title="Link to this definition"></a></dt>
+<dd><p>alias of <a class="reference internal" href="../datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint" title="geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tempest_datapoint</span></code></a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.data.dataset.TempestData.TempestData.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.data.dataset.TempestData.TempestData.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../../">
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+  <title>Dataset classes &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l2"><a class="reference internal" href="../../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../../core/core.html">Core classes</a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="dataset-classes">
+<h1>Dataset classes<a class="headerlink" href="#dataset-classes" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Data.html">Data</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="TdemData.html">TdemData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TempestData.html">TempestData</a></li>
+</ul>
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+
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diff --git a/docs/content/api/classes/mesh/RectilinearMesh1D.html b/docs/content/api/classes/mesh/RectilinearMesh1D.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>RectilinearMesh1D &mdash; GeoBIPy 2.3.1 documentation</title>
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+        <script src="../../../../_static/jquery.js?v=5d32c60e"></script>
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="mesh.html">Mesh classes</a><ul class="current">
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="rectilinearmesh1d">
+<h1>RectilinearMesh1D<a class="headerlink" href="#rectilinearmesh1d" title="Link to this heading"></a></h1>
+digraph inheritancef15b3b6232 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Mesh&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Abstract Base Class&quot;];
+  &quot;myObject&quot; -&gt; &quot;Mesh&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh1D&quot; [URL=&quot;#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a 1D rectilinear mesh with cell centres and edges.&quot;];
+  &quot;Mesh&quot; -&gt; &quot;RectilinearMesh1D&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.mesh.RectilinearMesh1D">&#64;RectilinearMesh1D_Class
+Module describing a 1D Rectilinear Mesh class</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.mesh.RectilinearMesh1D.</span></span><span class="sig-name descname"><span class="pre">RectilinearMesh1D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relative_to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimension</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D" title="Link to this definition"></a></dt>
+<dd><p>Class defining a 1D rectilinear mesh with cell centres and edges.</p>
+<p>Contains a simple 1D mesh with cell edges, widths, and centre locations.</p>
+<p>RectilinearMesh1D(centres, edges, edgesMin, edgesMax)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>centres</strong> (<em>geobipy.DataArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell. Only centres, edges, or widths can be given.</p></li>
+<li><p><strong>edges</strong> (<em>geobipy.DataArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges. Only centres, edges, or widths can be given.</p></li>
+<li><p><strong>widths</strong> (<em>geobipy.DataArray</em><em>, </em><em>optional</em>) – The widths of the cells.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Entries are given in linear space, but internally cells are logged.
+Plotting is in log space.</p></li>
+<li><p><strong>relative_to</strong> (<em>float</em><em>, </em><em>optional</em>) – If a float is given, updates will be relative to this value.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The 1D mesh.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">RectilinearMesh1D</a></p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>Exception</strong> – If both centres and edges are given.</p></li>
+<li><p><strong>TypeError</strong> – centres must be a geobipy.DataArray.</p></li>
+<li><p><strong>TypeError</strong> – edges must be a geobipy.DataArray.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.cellIndex">
+<span class="sig-name descname"><span class="pre">cellIndex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.cellIndex" title="Link to this definition"></a></dt>
+<dd><p>Get the index to the cell that each value in values falls in.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em>) – The values to find the cell indices for</p></li>
+<li><p><strong>clip</strong> (<em>bool</em>) – A negative index which would normally wrap will clip to 0 and self.bins.size instead.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em>) – Do not include out of axis indices. Negates clip, since they wont be included in the output.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The cell indices</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upcast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.delete_edge">
+<span class="sig-name descname"><span class="pre">delete_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.delete_edge" title="Link to this definition"></a></dt>
+<dd><p>Delete an edge from the mesh</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>i</strong> (<em>int</em>) – The edge to remove.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Mesh with edge removed.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">RectilinearMesh1D</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object froma HDF file</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.gradient" title="Link to this definition"></a></dt>
+<dd><p>Compute the gradient</p>
+<p>Parameter gradient <span class="math notranslate nohighlight">\(\nabla_{z}\sigma\)</span> at the ith layer is computed via</p>
+<div class="math notranslate nohighlight" id="equation-dpdz1">
+<span class="eqno">(1)<a class="headerlink" href="#equation-dpdz1" title="Link to this equation"></a></span>\[\nabla_{z}^{i}\sigma = \frac{\sigma_{i+1} - \sigma_{i}}{h_{i} - h_{min}}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\sigma_{i+1}\)</span> and <span class="math notranslate nohighlight">\(\sigma_{i}\)</span> are the log-parameters on either side of an interface, <span class="math notranslate nohighlight">\(h_{i}\)</span> is the log-thickness of the ith layer, and <span class="math notranslate nohighlight">\(h_{min}\)</span> is the minimum log thickness defined by</p>
+<div class="math notranslate nohighlight" id="equation-minthickness1">
+<span class="eqno">(2)<a class="headerlink" href="#equation-minthickness1" title="Link to this equation"></a></span>\[h_{min} = \frac{z_{max} - z_{min}}{2 k_{max}}\]</div>
+<p>where <span class="math notranslate nohighlight">\(k_{max}\)</span> is a maximum number of layers, set to be far greater than the expected final solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.hdfName">
+<span class="sig-name descname"><span class="pre">hdfName</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.hdfName" title="Link to this definition"></a></dt>
+<dd><p>Reprodicibility procedure</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.in_bounds">
+<span class="sig-name descname"><span class="pre">in_bounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.in_bounds" title="Link to this definition"></a></dt>
+<dd><p>Return whether values are inside the cell edges</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>values</strong> (<em>array_like</em>) – Check if these are inside left &lt;= values &lt; right.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Are the values inside.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bools</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.insert_edge">
+<span class="sig-name descname"><span class="pre">insert_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.insert_edge" title="Link to this definition"></a></dt>
+<dd><p>Insert a new edge.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>value</strong> (<em>numpy.float64</em>) – Location at which to insert a new edge</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Mesh with inserted edge.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.RectilinearMesh1D</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.map_to_pdf">
+<span class="sig-name descname"><span class="pre">map_to_pdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.map_to_pdf" title="Link to this definition"></a></dt>
+<dd><p>Creates a Hitmap from the model given the variance of each layer.</p>
+<p>For each depth, creates a normal distribution with a mean equal to the interpolated parameter
+at that depth and variance specified with variance.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>variance</strong> (<em>array_like</em>) – The variance of each layer</p></li>
+<li><p><strong>Hitmap</strong> (<em>geobipy.Hitmap</em>) – Hitmap to convert the model to.
+Must be instantiated before calling so that the model can be interpolated correctly</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.mask_cells">
+<span class="sig-name descname"><span class="pre">mask_cells</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.mask_cells" title="Link to this definition"></a></dt>
+<dd><p>Mask cells by a distance.</p>
+<p>If the edges of the cell are further than distance away, extra cells are inserted such that
+the cell’s new edges are at distance away from the centre.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>distance</strong> (<em>float</em>) – Distance to mask</p></li>
+<li><p><strong>values</strong> (<em>array_like</em><em>, </em><em>optional</em>) – If given, values will be remapped to the masked mesh.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>out</strong> (<em>RectilinearMesh1D</em>) – Masked mesh</p></li>
+<li><p><strong>indices</strong> (<em>ints</em>) – Location of the original centres in the expanded mesh</p></li>
+<li><p><strong>out_values</strong> (<em>array_like, optional</em>) – If values is given, values will be remapped to the masked mesh.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pad">
+<span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pad" title="Link to this definition"></a></dt>
+<dd><p>Copies the properties of a mesh including all priors or proposals, but pads memory to the given size</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>size</strong> (<em>int</em>) – Create memory upto this size.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Padded mesg</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">RectilinearMesh1D</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot.</p>
+<p>Can take any other matplotlib arguments and keyword arguments e.g. cmap etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em>) – The value of each cell.</p></li>
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.pcolor</span></code></dt><dd><p>For non matplotlib keywords.</p>
+</dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.perturb" title="Link to this definition"></a></dt>
+<dd><p>Perturb the mesh</p>
+<p>Generates a new mesh by perturbing the current mesh based on four probabilities.
+The probabilities correspond to
+* Birth, the insertion of a new interface
+* Death, the deletion of an interface
+* Change, change one the existing interfaces
+* No change, do nothing and return the original</p>
+<p>The methods.set_priors and setProposals must be used before calling self.perturb.</p>
+<p>If an interface is created, or an interface perturbed, any resulting cell width must be greater than the minimum width <span class="math notranslate nohighlight">\(h_{min}\)</span>.
+If the new cell width test fails, the birth or perturbation tries again. If the cycle fails after 10 tries, the entire process begins again
+such that a death, or no change is possible thus preventing any never-ending cycles.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – The perturbed mesh</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">RectilinearMesh1D</a></p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_priors" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_priors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RectilinearMesh1D.set_priors</span></code></a></dt><dd><p>Must be used before calling self.perturb</p>
+</dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">RectilinearMesh1D.setProposals</span></code></dt><dd><p>Must be used before calling self.perturb</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.piecewise_constant_interpolate">
+<span class="sig-name descname"><span class="pre">piecewise_constant_interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.piecewise_constant_interpolate" title="Link to this definition"></a></dt>
+<dd><p>Interpolate values of the cells to another RectilinearMesh in a piecewise constant manner.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>geobipy.StatArray</em>) – The values to interpolate. Has size self.nCells</p></li>
+<li><p><strong>mesh</strong> (<em>geobipy.RectilinearMeshND for N = 1</em><em>, </em><em>2</em><em>, </em><em>3.</em>) – A mesh to interpolate to.
+If 2D, axis must be given to specify which axis to interpolate against.</p></li>
+<li><p><strong>bound</strong> (<em>bool</em><em>, </em><em>optional</em>) – Interpolated values above the top of the model are nan.</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) – Axis to interpolate the value to.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The interpolated values at the cell centres of the other mesh.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot" title="Link to this definition"></a></dt>
+<dd><p>Plots values using the mesh as a line</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>reciprocateX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Take the reciprocal of the x axis</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>noLabels</strong> (<em>bool</em><em>, </em><em>optional</em>) – Do not plot the labels</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot_grid">
+<span class="sig-name descname"><span class="pre">plot_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot_grid" title="Link to this definition"></a></dt>
+<dd><p>Plot the grid lines of the mesh.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.StatArray.pcolor</span></code></dt><dd><p>For additional plotting arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the prior probability for the mesh.</p>
+<p>The following equation describes the components of the prior that correspond to the Model1D,</p>
+<div class="math notranslate nohighlight">
+\[p(k | I)p(\boldsymbol{e}| k, I),\]</div>
+<p>where <span class="math notranslate nohighlight">\(k, I, \boldsymbol{e}\)</span> are the number of cells, prior information, edge location respectively.</p>
+<p>The multiplication here can be turned into a summation by taking the log of the components.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>components</strong> (<em>bool</em><em>, </em><em>optional</em>) – Return all components used in the final probability as well as the final probability</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>probability</strong> (<em>numpy.float64</em>) – The probability</p></li>
+<li><p><strong>components</strong> (<em>array_like, optional</em>) – Return the components of the probability, i.e. the individually evaluated priors as a second return argument if comonents=True on input.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.range">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">range</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.range" title="Link to this definition"></a></dt>
+<dd><p>Get the difference between end edges.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_priors">
+<span class="sig-name descname"><span class="pre">set_priors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_cells_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edges_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_priors" title="Link to this definition"></a></dt>
+<dd><p>Setup the priors of the mesh.</p>
+<p>By default the following priors are set unless explictly specified.</p>
+<p><strong>Prior on the number of cells</strong></p>
+<p>Uninformative prior using a uniform distribution.</p>
+<div class="math notranslate nohighlight" id="equation-layers">
+<span class="eqno">(3)<a class="headerlink" href="#equation-layers" title="Link to this equation"></a></span>\[p(k | I) =
+\begin{cases}
+\frac{1}{k_{max} - 1} &amp; \quad 1 \leq k \leq k_{max} \newline
+0 &amp; \quad otherwise
+\end{cases}.\]</div>
+<p><strong>Prior on the cell edges</strong></p>
+<p>We use order statistics for the prior on cell edges.</p>
+<div class="math notranslate nohighlight" id="equation-depth">
+<span class="eqno">(4)<a class="headerlink" href="#equation-depth" title="Link to this equation"></a></span>\[p(\boldsymbol{e} | k, I) = \frac{(k -1)!}{\prod_{i=0}^{k-1} \Delta e_{i}},\]</div>
+<p>where the numerator describes the number of ways that <span class="math notranslate nohighlight">\((k - 1)\)</span> interfaces can be ordered and
+<span class="math notranslate nohighlight">\(\Delta e_{i} = (e_{max} - e_{min}) - 2 i h_{min}\)</span> describes the interval that is available to place an edge when there are already i edges in the model</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_edge</strong> (<em>float64</em>) – Minimum edge possible</p></li>
+<li><p><strong>max_edge</strong> (<em>float64</em>) – Maximum edge possible</p></li>
+<li><p><strong>max_cells</strong> (<em>int</em>) – Maximum number of cells allowable</p></li>
+<li><p><strong>min_width</strong> (<em>float64</em><em>, </em><em>optional</em>) – Minimum width of any layer. If min_width = None, min_width is computed from min_edge, max_edge, and max_cells (recommended).</p></li>
+<li><p><strong>prng</strong> (<em>numpy.random.RandomState</em><em>(</em><em>)</em><em>, </em><em>optional</em>) – Random number generator, if none is given, will use numpy’s global generator.</p></li>
+<li><p><strong>n_cells_prior</strong> (<em>geobipy.Distribution</em><em>, </em><em>optional</em>) – Distribution describing the prior on the number of cells. Overrides the default.</p></li>
+<li><p><strong>edge_prior</strong> (<em>geobipy.Distribution</em><em>, </em><em>optional</em>) – Distribution describing the prior on the cell edges. Overrides the default.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.perturb" title="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.perturb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RectilinearMesh1D.perturb</span></code></a></dt><dd><p>For a description of the perturbation cycle.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_proposals">
+<span class="sig-name descname"><span class="pre">set_proposals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probabilities</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_proposals" title="Link to this definition"></a></dt>
+<dd><p>Setup the proposal distibution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probabilities</strong> (<em>array_like</em>) – Probability of birth, death, perturb, and no change for the model
+e.g. probabilities = [0.5, 0.25, 0.15, 0.1]</p></li>
+<li><p><strong>parameterProposal</strong> (<em>geobipy.Distribution</em>) – The proposal  distribution for the parameter.</p></li>
+<li><p><strong>prng</strong> (<em>numpy.random.RandomState</em><em>(</em><em>)</em><em>, </em><em>optional</em>) – Random number generator, if none is given, will use numpy’s global generator.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.Model1D.perturb</span></code></dt><dd><p>For a description of the perturbation cycle.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.summary" title="Link to this definition"></a></dt>
+<dd><p>Summary of self</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.unperturb">
+<span class="sig-name descname"><span class="pre">unperturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.unperturb" title="Link to this definition"></a></dt>
+<dd><p>After a mesh has had its structure perturbed, remap back its previous state. Used for the reversible jump McMC step.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+  <section id="rectilinearmesh2d">
+<h1>RectilinearMesh2D<a class="headerlink" href="#rectilinearmesh2d" title="Link to this heading"></a></h1>
+digraph inheritancefab15836cf {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Mesh&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Abstract Base Class&quot;];
+  &quot;myObject&quot; -&gt; &quot;Mesh&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh2D&quot; [URL=&quot;#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a 2D rectilinear mesh with cell centres and edges.&quot;];
+  &quot;Mesh&quot; -&gt; &quot;RectilinearMesh2D&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.mesh.RectilinearMesh2D">&#64;RectilinearMesh2D_Class
+Module describing a 2D Rectilinear Mesh class with x and y axes specified</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.mesh.RectilinearMesh2D.</span></span><span class="sig-name descname"><span class="pre">RectilinearMesh2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D" title="Link to this definition"></a></dt>
+<dd><p>Class defining a 2D rectilinear mesh with cell centres and edges.</p>
+<p>Contains a simple 2D mesh with cell edges, widths, and centre locations.
+There are two ways of instantiating the RectilinearMesh2D.
+The first is by specifying the x and y cell centres or edges. In this case,
+the abscissa is the standard x axis, and y is the ordinate. The z co-ordinates are None.
+The second is by specifyin the x, y, and z cell centres or edges. In this case,
+The mesh is a 2D plane with the ordinate parallel to z, and the “horizontal” locations
+have co-ordinates (x, y).
+This allows you to, for example, create a vertical 2D mesh that is not parallel to either the
+x or y axis, like a typical line of data.
+If x, y, and z are specified, plots can be made against distance which calculated cumulatively between points.</p>
+<p>RectilinearMesh2D([x_centres or x_edges], [y_centres or y_edges], [z_centres or z_edges])</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>geobipy.RectilinearMesh1D</em><em>, </em><em>optional</em>) – text</p></li>
+<li><p><strong>y</strong> (<em>float</em><em>, </em><em>optional</em>) – text</p></li>
+<li><p><strong>z</strong> (<em>geobipy.RectilinearMesh1D</em><em>, </em><em>optional</em>) – text</p></li>
+<li><p><strong>relative_to</strong> (<em>geobipy.RectilinearMesh1D</em><em>, </em><em>optional</em>) – text</p></li>
+<li><p><strong>x_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “x” direction. Only x_centres or x_edges can be given.</p></li>
+<li><p><strong>x_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “x” direction. Only x_centres or x_edges can be given.</p></li>
+<li><p><strong>y_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “y” direction. Only y_centres or y_edges can be given.</p></li>
+<li><p><strong>y_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “y” direction. Only y_centres or y_edges can be given.</p></li>
+<li><p><strong>z_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “z” direction. Only z_centres or z_edges can be given.</p></li>
+<li><p><strong>z_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “z” direction. Only z_centres or z_edges can be given.</p></li>
+<li><p><strong>[</strong><strong>x</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for edgesMin description.</p></li>
+<li><p><strong>y</strong> – See geobipy.RectilinearMesh1D for edgesMin description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>edgesMin</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for edgesMin description.</p></li>
+<li><p><strong>[</strong><strong>x</strong> – See geobipy.RectilinearMesh1D for edgesMax description.</p></li>
+<li><p><strong>y</strong> – See geobipy.RectilinearMesh1D for edgesMax description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>edgesMax</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for edgesMax description.</p></li>
+<li><p><strong>[</strong><strong>x</strong> – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>y</strong> – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>[</strong><strong>x</strong> – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+<li><p><strong>y</strong> – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>relative_to</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The 2D mesh.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D" title="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D">RectilinearMesh2D</a></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndex">
+<span class="sig-name descname"><span class="pre">cellIndex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndex" title="Link to this definition"></a></dt>
+<dd><p>Return the cell indices of values along axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – Locations to obtain the cell index for</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis along which to obtain indices</p></li>
+<li><p><strong>clip</strong> (<em>bool</em>) – A negative index which would normally wrap will clip to 0 instead.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em>) – Do not include out of axis indices. Negates clip, since they wont be included in the output.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – indices for the locations along the axis</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndices">
+<span class="sig-name descname"><span class="pre">cellIndices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndices" title="Link to this definition"></a></dt>
+<dd><p>Return the cell indices in x and z for two floats.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – x location</p></li>
+<li><p><strong>y</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – y location (or z location if instantiated with 3 co-ordinates)</p></li>
+<li><p><strong>clip</strong> (<em>bool</em>) – A negative index which would normally wrap will clip to 0 instead.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em>) – Do not include out of axis indices. Negates clip, since they wont be included in the output.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – indices for the locations along [axis0, axis1]</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.centres">
+<span class="sig-name descname"><span class="pre">centres</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.centres" title="Link to this definition"></a></dt>
+<dd><p>Ravelled cell centres</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – ravelled cell centre locations.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upcast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.distance">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">distance</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.distance" title="Link to this definition"></a></dt>
+<dd><p>The distance along the top of the mesh using the x and y co-ordinates.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.edges">
+<span class="sig-name descname"><span class="pre">edges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.edges" title="Link to this definition"></a></dt>
+<dd><p>Gets the cell edges in the given dimension</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.hasSameSize">
+<span class="sig-name descname"><span class="pre">hasSameSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.hasSameSize" title="Link to this definition"></a></dt>
+<dd><p>Determines if the meshes have the same dimension sizes</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.in_bounds">
+<span class="sig-name descname"><span class="pre">in_bounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.in_bounds" title="Link to this definition"></a></dt>
+<dd><p>Return whether values are inside the cell edges</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>values</strong> (<em>array_like</em>) – Check if these are inside left &lt;= values &lt; right.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Are the values inside.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bools</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.intervalStatistic">
+<span class="sig-name descname"><span class="pre">intervalStatistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">statistic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.intervalStatistic" title="Link to this definition"></a></dt>
+<dd><p>Compute a statistic of the array between the intervals given along dimension dim.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>arr</strong> (<em>array_like</em>) – 2D array to take the mean over the given intervals</p></li>
+<li><p><strong>intervals</strong> (<em>array_like</em>) – A new set of mesh edges. The mean is computed between each two edges in the array.</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) – Which axis to take the mean</p></li>
+<li><p><strong>statistic</strong> (<em>string</em><em> or </em><em>callable</em><em>, </em><em>optional</em>) – <dl class="simple">
+<dt>The statistic to compute (default is ‘mean’).</dt><dd><p>The following statistics are available:</p>
+</dd>
+</dl>
+<ul>
+<li><p>’mean’ : compute the mean of values for points within each bin.
+Empty bins will be represented by NaN.</p></li>
+<li><p>’median’ : compute the median of values for points within each
+bin. Empty bins will be represented by NaN.</p></li>
+<li><p>’count’ : compute the count of points within each bin.  This is
+identical to an unweighted histogram.  <cite>values</cite> array is not
+referenced.</p></li>
+<li><p>’sum’ : compute the sum of values for points within each bin.
+This is identical to a weighted histogram.</p></li>
+<li><p>’min’ : compute the minimum of values for points within each bin.
+Empty bins will be represented by NaN.</p></li>
+<li><p>’max’ : compute the maximum of values for point within each bin.
+Empty bins will be represented by NaN.</p></li>
+<li><p>function : a user-defined function which takes a 1D array of
+values, and outputs a single numerical statistic. This function
+will be called on the values in each bin.  Empty bins will be
+represented by function([]), or NaN if this returns an error.</p></li>
+</ul>
+</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.stats.binned_statistic</span></code></dt><dd><p>for more information</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.mask_cells">
+<span class="sig-name descname"><span class="pre">mask_cells</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.mask_cells" title="Link to this definition"></a></dt>
+<dd><p>Mask cells by a distance.</p>
+<p>If the edges of the cell are further than distance away, extra cells are inserted such that
+the cell’s new edges are at distance away from the centre.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>xAxis</strong> (<em>array_like</em>) – Alternative axis to use for masking.  Must have size self.x.nEdges</p></li>
+<li><p><strong>x_distance</strong> (<em>float</em><em>, </em><em>optional</em>) – Mask along the x axis using this distance.
+Defaults to None.</p></li>
+<li><p><strong>y_distance</strong> (<em>float</em><em>, </em><em>optional</em>) – Mask along the y axis using this distance.
+Defaults to None.</p></li>
+<li><p><strong>values</strong> (<em>array_like</em><em>, </em><em>optional.</em>) – If given, values will be remapped to the masked mesh.
+Has shape (y.nCells, x.nCells)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>out</strong> (<em>RectilinearMesh2D</em>) – Masked mesh</p></li>
+<li><p><strong>x_indices</strong> (<em>ints, optional</em>) – Location of the original centres in the expanded mesh along the x axis.</p></li>
+<li><p><strong>y_indices</strong> (<em>ints, optional</em>) – Location of the original centres in the expanded mesh along the y axis.</p></li>
+<li><p><strong>out_values</strong> (<em>array_like, optional</em>) – If values is given, values will be remapped to the masked mesh.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nCells">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nCells</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nCells" title="Link to this definition"></a></dt>
+<dd><p>The number of cells in the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of cells</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nNodes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nNodes</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nNodes" title="Link to this definition"></a></dt>
+<dd><p>The number of nodes in the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of nodes</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nodes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nodes</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nodes" title="Link to this definition"></a></dt>
+<dd><p>Ravelled cell nodes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – ravelled cell node locations.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yAxis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absolute'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot of a 2D array using the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – A 2D array of colour values.</p></li>
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line</p></li>
+<li><p><strong>zAxis</strong> (<em>str</em>) – If zAxis is ‘absolute’ the vertical axis is the relative_to plus z.
+If zAxis is ‘relative’ the vertical axis is z.</p></li>
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_grid">
+<span class="sig-name descname"><span class="pre">plot_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_grid" title="Link to this definition"></a></dt>
+<dd><p>Plot the mesh grid lines.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal axis uses self.x
+If xAxis is ‘y’, the horizontal axis uses self.y
+If xAxis is ‘r’, the horizontal axis uses sqrt(self.x^2 + self.y^2)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_relative_to">
+<span class="sig-name descname"><span class="pre">plot_relative_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_relative_to" title="Link to this definition"></a></dt>
+<dd><p>Plot the relative_to of the mesh as a line.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.ravelIndices">
+<span class="sig-name descname"><span class="pre">ravelIndices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ixy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.ravelIndices" title="Link to this definition"></a></dt>
+<dd><p>Return a global index into a 1D array given the two cell indices in x and z.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>ixy</strong> (<em>tuple</em><em> of </em><em>array_like</em>) – A tuple of integer arrays, one array for each dimension.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Global index.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.shape" title="Link to this definition"></a></dt>
+<dd><p>The dimensions of the mesh</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Array of integers</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.summary" title="Link to this definition"></a></dt>
+<dd><p>Display a summary of the 3D Point Cloud</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.unravelIndex">
+<span class="sig-name descname"><span class="pre">unravelIndex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.unravelIndex" title="Link to this definition"></a></dt>
+<dd><p>Return a global index into a 1D array given the two cell indices in x and z.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>indices</strong> (<em>array_like</em>) – An integer array whose elements are indices into the flattened
+version of an array.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>unraveled_coords</strong> – Each array in the tuple has the same shape as the self.shape.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple of ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_centres">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">x_centres</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_centres" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the abscissa.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">x_edges</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_edges" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the ordinate</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_centres">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_centres</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_centres" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the abscissa.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_edges</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_edges" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the ordinate</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
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+  <section id="rectilinearmesh2d-stitched">
+<h1>RectilinearMesh2D_stitched<a class="headerlink" href="#rectilinearmesh2d-stitched" title="Link to this heading"></a></h1>
+digraph inheritance9774ecb1cc {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Mesh&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Abstract Base Class&quot;];
+  &quot;myObject&quot; -&gt; &quot;Mesh&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh2D&quot; [URL=&quot;RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a 2D rectilinear mesh with cell centres and edges.&quot;];
+  &quot;Mesh&quot; -&gt; &quot;RectilinearMesh2D&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh2D_stitched&quot; [URL=&quot;#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining stitched 1D rectilinear meshes.&quot;];
+  &quot;RectilinearMesh2D&quot; -&gt; &quot;RectilinearMesh2D_stitched&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.mesh.RectilinearMesh2D_stitched">&#64;RectilinearMesh2D_Class
+Module describing a 2D Rectilinear Mesh class with x and y axes specified</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.mesh.RectilinearMesh2D_stitched.</span></span><span class="sig-name descname"><span class="pre">RectilinearMesh2D_stitched</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_cells</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relative_to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nCells</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched" title="Link to this definition"></a></dt>
+<dd><p>Class defining stitched 1D rectilinear meshes.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upcast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.nCells">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nCells</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.nCells" title="Link to this definition"></a></dt>
+<dd><p>The number of cells in the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of cells</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Create a pseudocolour plot of a 2D array using the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em><em> or </em><em>StatArray</em>) – A 2D array of colour values.</p></li>
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line</p></li>
+<li><p><strong>zAxis</strong> (<em>str</em>) – If zAxis is ‘absolute’ the vertical axis is the relative_to plus z.
+If zAxis is ‘relative’ the vertical axis is z.</p></li>
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib .Axes</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ax</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.pyplot.pcolormesh</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.shape" title="Link to this definition"></a></dt>
+<dd><p>The dimensions of the mesh</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Array of integers</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.summary" title="Link to this definition"></a></dt>
+<dd><p>Display a summary of the 3D Point Cloud</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.y_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_edges</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.y_edges" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the ordinate</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+             
+  <section id="rectilinearmesh3d">
+<h1>RectilinearMesh3D<a class="headerlink" href="#rectilinearmesh3d" title="Link to this heading"></a></h1>
+digraph inheritanceb5cabbf6a5 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Mesh&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Abstract Base Class&quot;];
+  &quot;myObject&quot; -&gt; &quot;Mesh&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh2D&quot; [URL=&quot;RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a 2D rectilinear mesh with cell centres and edges.&quot;];
+  &quot;Mesh&quot; -&gt; &quot;RectilinearMesh2D&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;RectilinearMesh3D&quot; [URL=&quot;#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a 3D rectilinear mesh with cell centres and edges.&quot;];
+  &quot;RectilinearMesh2D&quot; -&gt; &quot;RectilinearMesh3D&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.mesh.RectilinearMesh3D">&#64;RectilinearMesh2D_Class
+Module describing a 2D Rectilinear Mesh class with x and y axes specified</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.mesh.RectilinearMesh3D.</span></span><span class="sig-name descname"><span class="pre">RectilinearMesh3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D" title="Link to this definition"></a></dt>
+<dd><p>Class defining a 3D rectilinear mesh with cell centres and edges.</p>
+<p>Contains a simple mesh with cell edges, widths, and centre locations.
+There are two ways of instantiating the RectilinearMesh2D.
+The first is by specifying the x and y cell centres or edges. In this case,
+the abscissa is the standard x axis, and y is the ordinate. The z co-ordinates are None.
+The second is by specifyin the x, y, and z cell centres or edges. In this case,
+The mesh is a 2D plane with the ordinate parallel to z, and the “horizontal” locations
+have co-ordinates (x, y).
+This allows you to, for example, create a vertical 2D mesh that is not parallel to either the
+x or y axis, like a typical line of data.
+If x, y, and z are specified, plots can be made against distance which calculated cumulatively between points.</p>
+<p>RectilinearMesh2D([x_centres or x_edges], [y_centres or y_edges], [z_centres or z_edges])</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “x” direction. Only x_centres or x_edges can be given.</p></li>
+<li><p><strong>x_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “x” direction. Only x_centres or x_edges can be given.</p></li>
+<li><p><strong>y_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “y” direction. Only y_centres or y_edges can be given.</p></li>
+<li><p><strong>y_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “y” direction. Only y_centres or y_edges can be given.</p></li>
+<li><p><strong>z_centres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the centre of each cell in the “z” direction. Only z_centres or z_edges can be given.</p></li>
+<li><p><strong>z_edges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The locations of the edges of each cell, including the outermost edges, in the “z” direction. Only z_centres or z_edges can be given.</p></li>
+<li><p><strong>relative_toCentres</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The relative_to of each point at the x, y locations. Only relative_toCentres or relative_toEdges can be given, not both.
+Has shape (y.nCells, x.nCells).</p></li>
+<li><p><strong>relative_toEdges</strong> (<em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The relative_to of each point at the x, y locations of the edges of each cell, including the outermost edges. Only relative_toCentres or relative_toEdges can be given, not both.
+Has shape (y.nEdges, x.nEdges).</p></li>
+<li><p><strong>[</strong><strong>x</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>y</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for log description.</p></li>
+<li><p><strong>[</strong><strong>x</strong> – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+<li><p><strong>y</strong> – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+<li><p><strong>z</strong><strong>]</strong><strong>relative_to</strong> (<em>float</em><em>, </em><em>optional</em>) – See geobipy.RectilinearMesh1D for relative_to description.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The 2D mesh.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D" title="geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D">RectilinearMesh2D</a></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.cellIndices">
+<span class="sig-name descname"><span class="pre">cellIndices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.cellIndices" title="Link to this definition"></a></dt>
+<dd><p>Return the cell indices in x and z for two floats.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – x location</p></li>
+<li><p><strong>y</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – y location (or z location if instantiated with 3 co-ordinates)</p></li>
+<li><p><strong>clip</strong> (<em>bool</em>) – A negative index which would normally wrap will clip to 0 instead.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em>) – Do not include out of axis indices. Negates clip, since they wont be included in the output.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – indices for the locations along [axis0, axis1]</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.centres">
+<span class="sig-name descname"><span class="pre">centres</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.centres" title="Link to this definition"></a></dt>
+<dd><p>Ravelled cell centres</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – ravelled cell centre locations.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upcast</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.edges">
+<span class="sig-name descname"><span class="pre">edges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.edges" title="Link to this definition"></a></dt>
+<dd><p>Gets the cell edges in the given dimension</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nCells">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nCells</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nCells" title="Link to this definition"></a></dt>
+<dd><p>The number of cells in the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of cells</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nNodes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nNodes</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nNodes" title="Link to this definition"></a></dt>
+<dd><p>The number of nodes in the mesh.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Number of nodes</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.plot_grid">
+<span class="sig-name descname"><span class="pre">plot_grid</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.plot_grid" title="Link to this definition"></a></dt>
+<dd><p>Plot the mesh grid lines.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal axis uses self.x
+If xAxis is ‘y’, the horizontal axis uses self.y
+If xAxis is ‘r’, the horizontal axis uses sqrt(self.x^2 + self.y^2)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.pyvista_mesh">
+<span class="sig-name descname"><span class="pre">pyvista_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.pyvista_mesh" title="Link to this definition"></a></dt>
+<dd><p>Creates a pyvista plotting object linked to VTK.</p>
+<p>Use mesh.plot(show_edges=True, show_grid=True) to plot the mesh.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.ravelIndices">
+<span class="sig-name descname"><span class="pre">ravelIndices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.ravelIndices" title="Link to this definition"></a></dt>
+<dd><p>Return a global index into a 1D array given the two cell indices in x and z.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>indices</strong> (<em>array_like</em>) – A tuple of integer arrays, one array for each dimension.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Global index.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.shape" title="Link to this definition"></a></dt>
+<dd><p>The dimensions of the mesh</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Array of integers</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.summary" title="Link to this definition"></a></dt>
+<dd><p>Display a summary of the 3D Point Cloud</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.unravelIndex">
+<span class="sig-name descname"><span class="pre">unravelIndex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.unravelIndex" title="Link to this definition"></a></dt>
+<dd><p>Return local indices given a global one.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>indices</strong> (<em>array_like</em>) – An integer array whose elements are indices into the flattened
+version of an array.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>unraveled_coords</strong> – Each array in the tuple has the same shape as the self.shape.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple of ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.xRange">
+<span class="sig-name descname"><span class="pre">xRange</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.xRange" title="Link to this definition"></a></dt>
+<dd><p>Get the range of x</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – The range of x</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy.float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_centres">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">x_centres</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_centres" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the abscissa.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">x_edges</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_edges" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the ordinate</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_centres">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_centres</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_centres" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the abscissa.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘x’, the horizontal xAxis uses self.x
+If xAxis is ‘y’, the horizontal xAxis uses self.y
+If xAxis is ‘r’, the horizontal xAxis uses cumulative distance along the line.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">y_edges</span></span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_edges" title="Link to this definition"></a></dt>
+<dd><p>Creates an array suitable for plt.pcolormesh for the ordinate</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.zRange">
+<span class="sig-name descname"><span class="pre">zRange</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.zRange" title="Link to this definition"></a></dt>
+<dd><p>Get the range of z</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – The range of z</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy.float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/docs/content/api/classes/mesh/mesh.html b/docs/content/api/classes/mesh/mesh.html
new file mode 100644
index 00000000..c1098aba
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+  <title>Mesh classes &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
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+</ul>
+</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="mesh-classes">
+<h1>Mesh classes<a class="headerlink" href="#mesh-classes" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="RectilinearMesh1D.html">RectilinearMesh1D</a></li>
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+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Model &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="model.html">Model classes</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Model</a></li>
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+      <li class="breadcrumb-item active">Model</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="model">
+<h1>Model<a class="headerlink" href="#model" title="Link to this heading"></a></h1>
+digraph inheritance456558ffd0 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Model&quot; [URL=&quot;#geobipy.src.classes.model.Model.Model&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Generic model class with an attached mesh.&quot;];
+  &quot;myObject&quot; -&gt; &quot;Model&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.model.Model">&#64;Model_Class
+Module describing a Model</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.model.Model.</span></span><span class="sig-name descname"><span class="pre">Model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model" title="Link to this definition"></a></dt>
+<dd><p>Generic model class with an attached mesh.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.gradient">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">gradient</span></span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.gradient" title="Link to this definition"></a></dt>
+<dd><p>Compute the gradient</p>
+<p>Parameter gradient :math:”nabla_{z}sigma” at the ith layer is computed via</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.gradient_probability">
+<span class="sig-name descname"><span class="pre">gradient_probability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.gradient_probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the prior for the gradient of the parameter with depth</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>hmin</strong> (<em>float64</em>) – The minimum thickness of any layer.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The probability given the prior on the gradient of the parameters with depth.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.float64</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.local_inverse_hessian">
+<span class="sig-name descname"><span class="pre">local_inverse_hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.local_inverse_hessian" title="Link to this definition"></a></dt>
+<dd><p>Generate a localized Hessian matrix using
+a dataPoint and the current realization of the Model1D.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>observation</strong> (<em>geobipy.DataPoint</em><em>, </em><em>geobipy.Dataset</em><em>, </em><em>optional</em>) – The observed data to use when computing the local estimate of the variance.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Hessian matrix</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.local_precision">
+<span class="sig-name descname"><span class="pre">local_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.local_precision" title="Link to this definition"></a></dt>
+<dd><p>Generate a localized inverse Hessian matrix using a dataPoint and the current realization of the Model1D.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>datapoint</strong> (<em>geobipy.DataPoint</em><em>, </em><em>optional</em>) – The data point to use when computing the local estimate of the variance.
+If None, only the prior derivative is used.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Inverse Hessian matrix</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.local_variance">
+<span class="sig-name descname"><span class="pre">local_variance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.local_variance" title="Link to this definition"></a></dt>
+<dd><p>Generate a localized inverse Hessian matrix using a dataPoint and the current realization of the Model1D.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>datapoint</strong> (<em>geobipy.DataPoint</em><em>, </em><em>optional</em>) – The data point to use when computing the local estimate of the variance.
+If None, only the prior derivative is used.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Inverse Hessian matrix</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.pad">
+<span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.pad" title="Link to this definition"></a></dt>
+<dd><p>Copies the properties of a model including all priors or proposals, but pads memory to the given size</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>size</strong> (<em>int</em><em>, </em><em>tuple</em>) – Create memory upto this size.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Padded model</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.Model1D</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Plot like an image</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.perturb" title="Link to this definition"></a></dt>
+<dd><p>Perturb a model’s structure and parameter values.</p>
+<p>Uses a stochastic newtown approach if a datapoint is provided.
+Otherwise, uses the existing proposal distribution attached to
+self.par to generate new values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>observation</strong> (<em>geobipy.DataPoint</em><em>, </em><em>optional</em>) – The datapoint to use to perturb using a stochastic Newton approach.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>remappedModel</strong> (<em>geobipy.Model</em>) – The current model remapped onto the perturbed dimension.</p></li>
+<li><p><strong>perturbedModel</strong> (<em>geobipy.Model</em>) – The model with perturbed structure and parameter values.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.probability">
+<span class="sig-name descname"><span class="pre">probability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">solve_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solve_gradient</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the prior probability for the 1D Model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sPar</strong> (<em>bool</em>) – Evaluate the prior on the parameters in the final probability</p></li>
+<li><p><strong>sGradient</strong> (<em>bool</em>) – Evaluate the prior on the parameter gradient in the final probability</p></li>
+<li><p><strong>components</strong> (<em>bool</em><em>, </em><em>optional</em>) – Return all components used in the final probability as well as the final probability</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>probability</strong> (<em>numpy.float64</em>) – The probability</p></li>
+<li><p><strong>components</strong> (<em>array_like, optional</em>) – Return the components of the probability, i.e. the individually evaluated priors as a second return argument if comonents=True on input.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.proposal_probabilities">
+<span class="sig-name descname"><span class="pre">proposal_probabilities</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">remapped_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">structure_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.proposal_probabilities" title="Link to this definition"></a></dt>
+<dd><p>Return the forward and reverse proposal probabilities for the model</p>
+<p>Returns the denominator and numerator for the model’s components of the proposal ratio.</p>
+<div class="math notranslate nohighlight" id="equation-proposal-numerator">
+<span class="eqno">(1)<a class="headerlink" href="#equation-proposal-numerator" title="Link to this equation"></a></span>\[q(k, \boldsymbol{z} | \boldsymbol{m}^{'})\]</div>
+<p>and</p>
+<div class="math notranslate nohighlight" id="equation-proposal-denominator">
+<span class="eqno">(2)<a class="headerlink" href="#equation-proposal-denominator" title="Link to this equation"></a></span>\[q(k^{'}, \boldsymbol{z}^{'} | \boldsymbol{m})\]</div>
+<p>Each component is dependent on the event that was chosen during perturbation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>remappedModel</strong> (<em>geobipy.Model1D</em>) – The current model, remapped onto the dimension of self.</p></li>
+<li><p><strong>observation</strong> (<em>geobipy.DataPoint</em>) – The perturbed datapoint that was used to generate self.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>forward</strong> (<em>float</em>) – The forward proposal probability</p></li>
+<li><p><strong>reverse</strong> (<em>float</em>) – The reverse proposal probability</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.set_priors">
+<span class="sig-name descname"><span class="pre">set_priors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.set_priors" title="Link to this definition"></a></dt>
+<dd><p>Setup the priors of a 1D model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>halfSpaceValue</strong> (<em>float</em>) – Value of the parameter for the halfspace.</p></li>
+<li><p><strong>min_edge</strong> (<em>float64</em>) – Minimum depth possible for the model</p></li>
+<li><p><strong>max_edge</strong> (<em>float64</em>) – Maximum depth possible for the model</p></li>
+<li><p><strong>max_cells</strong> (<em>int</em>) – Maximum number of layers allowable in the model</p></li>
+<li><p><strong>parameterPrior</strong> (<em>bool</em>) – Sets a prior on the parameter values</p></li>
+<li><p><strong>gradientPrior</strong> (<em>bool</em>) – Sets a prior on the gradient of the parameter values</p></li>
+<li><p><strong>parameterLimits</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Length 2 array with the bounds on the parameter values to impose.</p></li>
+<li><p><strong>min_width</strong> (<em>float64</em><em>, </em><em>optional</em>) – Minimum thickness of any layer. If min_width = None, min_width is computed from min_edge, max_edge, and max_cells (recommended).</p></li>
+<li><p><strong>factor</strong> (<em>float</em><em>, </em><em>optional</em>) – Tuning parameter used in the std of the parameter prior.</p></li>
+<li><p><strong>prng</strong> (<em>numpy.random.RandomState</em><em>(</em><em>)</em><em>, </em><em>optional</em>) – Random number generator, if none is given, will use numpy’s global generator.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.Model1D.perturb</span></code></dt><dd><p>For a description of the perturbation cycle.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.set_proposals">
+<span class="sig-name descname"><span class="pre">set_proposals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">proposal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.set_proposals" title="Link to this definition"></a></dt>
+<dd><p>Setup the proposals of a 1D model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>halfSpaceValue</strong> (<em>float</em>) – Value of the parameter for the halfspace.</p></li>
+<li><p><strong>probabilities</strong> (<em>array_like</em>) – Probability of birth, death, perturb, and no change for the model
+e.g. pWheel = [0.5, 0.25, 0.15, 0.1]</p></li>
+<li><p><strong>parameterProposal</strong> (<em>geobipy.Distribution</em>) – The proposal distribution for the parameter.</p></li>
+<li><p><strong>prng</strong> (<em>numpy.random.RandomState</em><em>(</em><em>)</em><em>, </em><em>optional</em>) – Random number generator, if none is given, will use numpy’s global generator.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.Model1D.perturb</span></code></dt><dd><p>For a description of the perturbation cycle.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.summary" title="Link to this definition"></a></dt>
+<dd><p>Summary of self</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.update_parameter_posterior">
+<span class="sig-name descname"><span class="pre">update_parameter_posterior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.update_parameter_posterior" title="Link to this definition"></a></dt>
+<dd><p>Imposes a model’s parameters with depth onto a 2D Hitmap.</p>
+<p>The cells that the parameter-depth profile passes through are accumulated by 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>Hitmap</strong> (<em>geobipy.Hitmap</em>) – The hitmap to add to</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.model.Model.Model.update_posteriors">
+<span class="sig-name descname"><span class="pre">update_posteriors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.model.Model.Model.update_posteriors" title="Link to this definition"></a></dt>
+<dd><p>Update any attached posterior distributions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>minimumRatio</strong> (<em>float</em>) – Only update the depth posterior if the layer parameter ratio
+is greater than this number.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <title>Point &mdash; GeoBIPy 2.3.1 documentation</title>
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+          </a>
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+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
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+<li class="toctree-l3 current"><a class="reference internal" href="pointcloud.html">Pointcloud classes</a><ul class="current">
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+      <li class="breadcrumb-item active">Point</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../../_sources/content/api/classes/pointcloud/Point.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="point">
+<h1>Point<a class="headerlink" href="#point" title="Link to this heading"></a></h1>
+digraph inheritancefff5d93699 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Point&quot; [URL=&quot;#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.pointcloud.Point">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.pointcloud.Point.</span></span><span class="sig-name descname"><span class="pre">Point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point" title="Link to this definition"></a></dt>
+<dd><p>3D Point Cloud with x,y,z co-ordinates</p>
+<p>Point(N, x, y, z)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>N</strong> (<em>int</em>) – Number of points</p></li>
+<li><p><strong>x</strong> (<em>array_like</em><em> or </em><em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The x co-ordinates. Default is zeros of size N</p></li>
+<li><p><strong>y</strong> (<em>array_like</em><em> or </em><em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The y co-ordinates. Default is zeros of size N</p></li>
+<li><p><strong>z</strong> (<em>array_like</em><em> or </em><em>geobipy.StatArray</em><em>, </em><em>optional</em>) – The z co-ordinates. Default is zeros of size N</p></li>
+<li><p><strong>units</strong> (<em>str</em><em>, </em><em>optional</em>) – The units of the co-ordinates.  Default is “m”</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The 3D point cloud</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.PointCloud3D</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast a PointCloud3D using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>world</strong> (<em>mpi4py.MPI.COMM_WORLD</em>) – MPI communicator</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – PointCloud3D broadcast to each rank</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.PointCloud3D</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.Scatterv">
+<span class="sig-name descname"><span class="pre">Scatterv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">starts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.Scatterv" title="Link to this definition"></a></dt>
+<dd><p>ScatterV a PointCloud3D using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>myStart</strong> (<em>sequence</em><em> of </em><em>ints</em>) – Indices into self that define the starting locations of the chunks to be sent to each rank.</p></li>
+<li><p><strong>myChunk</strong> (<em>sequence</em><em> of </em><em>ints</em>) – The size of each chunk that each rank will receive.</p></li>
+<li><p><strong>world</strong> (<em>mpi4py.MPI.Comm</em>) – The MPI communicator over which to Scatterv.</p></li>
+<li><p><strong>root</strong> (<em>int</em><em>, </em><em>optional</em>) – The MPI rank to broadcast from. Default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The PointCloud3D distributed amongst ranks.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.PointCloud3D</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.axis">
+<span class="sig-name descname"><span class="pre">axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.axis" title="Link to this definition"></a></dt>
+<dd><p>Obtain the axis against which to plot values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>axis</strong> (<em>str</em>) – If axis is ‘index’, returns numpy.arange(self.nPoints)
+If axis is ‘x’, returns self.x
+If axis is ‘y’, returns self.y
+If axis is ‘z’, returns self.z
+If axis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If axis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The requested axis.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.block_indices">
+<span class="sig-name descname"><span class="pre">block_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.block_indices" title="Link to this definition"></a></dt>
+<dd><p>Returns the indices of the points lying in blocks across the domain..</p>
+<p>Performed before a block median filter by extracting the point location within blocks across the domain.
+Idea taken from pygmt, however I extracted the indices and this was quite a bit faster.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dx</strong> (<em>float</em>) – Grid spacing in x.</p></li>
+<li><p><strong>dy</strong> (<em>float</em>) – Grid spacing in y.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>ints</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Index into self whose points are the median location within blocks across the domain.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.block_median">
+<span class="sig-name descname"><span class="pre">block_median</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.block_median" title="Link to this definition"></a></dt>
+<dd><p>Median point within juxtaposed blocks across the domain.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dx</strong> (<em>float</em>) – Increment in x.</p></li>
+<li><p><strong>dy</strong> (<em>float</em>) – Increment in y.</p></li>
+<li><p><strong>values</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Used to compute the median in each block.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>geobipt.PointCloud3d</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Contains one point in each block.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.block_median_indices">
+<span class="sig-name descname"><span class="pre">block_median_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.block_median_indices" title="Link to this definition"></a></dt>
+<dd><p>Index to the median point within juxtaposed blocks across the domain.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dx</strong> (<em>float</em>) – Increment in x.</p></li>
+<li><p><strong>dy</strong> (<em>float</em>) – Increment in y.</p></li>
+<li><p><strong>values</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Used to compute the median in each block.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>ints</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Index of the median point in each block.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.bounds">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bounds</span></span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.bounds" title="Link to this definition"></a></dt>
+<dd><p>Gets the bounding box of the data set</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.centred_grid_nodes">
+<span class="sig-name descname"><span class="pre">centred_grid_nodes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spacing</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.centred_grid_nodes" title="Link to this definition"></a></dt>
+<dd><p>Generates grid nodes centred over bounds</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>bounds</strong> (<em>array_like</em>) – bounds of the dimension</p></li>
+<li><p><strong>spacing</strong> (<em>float</em>) – distance between nodes</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.fileInformation">
+<span class="sig-name descname"><span class="pre">fileInformation</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.fileInformation" title="Link to this definition"></a></dt>
+<dd><p>Description of PointCloud3D file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – File description.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.interpolate">
+<span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mc'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.interpolate" title="Link to this definition"></a></dt>
+<dd><p>Interpolate values to a grid.</p>
+<p>The grid is automatically generated such that it is centred over the point cloud.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dx</strong> (<em>float</em>) – Grid spacing in x.</p></li>
+<li><p><strong>dy</strong> (<em>float</em>) – Grid spacing in y.</p></li>
+<li><p><strong>values</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Values to interpolate.  Must have size self.nPoints.
+Defaults to None.</p></li>
+<li><p><strong>method</strong> (<em>str</em><em>, </em><em>optional</em>) – <ul>
+<li><p>‘ct’ uses Clough Tocher interpolation. Default</p></li>
+<li><p>’mc’ uses Minimum curvature and requires pygmt to be installed.</p></li>
+</ul>
+</p></li>
+<li><p><strong>mask</strong> (<em>float</em><em>, </em><em>optional</em>) – Cells of distance mask away from points are NaN.
+Defaults to False.</p></li>
+<li><p><strong>clip</strong> (<em>bool</em><em>, </em><em>optional</em>) – Clip any overshot grid values to the min/max of values.
+Defaults to True.</p></li>
+<li><p><strong>i</strong> (<em>ints</em><em>, </em><em>optional</em>) – Use only the i locations during interpolation.
+Defaults to None.</p></li>
+<li><p><strong>block</strong> (<em>bool</em><em>, </em><em>optional</em>) – Perform a block median filter before interpolation. Inherrently smooths the final grid, but alleviates aliasing.
+Defaults to False.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>geobipy.Model</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Interpolated values.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.map">
+<span class="sig-name descname"><span class="pre">map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.map" title="Link to this definition"></a></dt>
+<dd><p>Create a map of a parameter</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.move">
+<span class="sig-name descname"><span class="pre">move</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dz</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.move" title="Link to this definition"></a></dt>
+<dd><p>Move the point by [dx,dy,dz]</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.nPoints">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nPoints</span></span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.nPoints" title="Link to this definition"></a></dt>
+<dd><p>Get the number of points</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.nearest">
+<span class="sig-name descname"><span class="pre">nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.nearest" title="Link to this definition"></a></dt>
+<dd><p>Obtain the k nearest neighbours</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">See</span></code></p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.perturb" title="Link to this definition"></a></dt>
+<dd><p>Propose a new point given the attached propsal distributions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'index'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.plot" title="Link to this definition"></a></dt>
+<dd><p>Line plot of values against a co-ordinate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>values</strong> (<em>array_like</em>) – Values to plot against a co-ordinate</p></li>
+<li><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>ax</strong> – Plot axes handle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib.axes</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.plot</span></code></dt><dd><p>For additional keyword arguments</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the probability for the EM data point given the specified attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rEerr</strong> (<em>bool</em>) – Include the relative error when evaluating the prior</p></li>
+<li><p><strong>aEerr</strong> (<em>bool</em>) – Include the additive error when evaluating the prior</p></li>
+<li><p><strong>height</strong> (<em>bool</em>) – Include the elevation when evaluating the prior</p></li>
+<li><p><strong>calibration</strong> (<em>bool</em>) – Include the calibration parameters when evaluating the prior</p></li>
+<li><p><strong>verbose</strong> (<em>bool</em>) – Return the components of the probability, i.e. the individually evaluated priors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The evaluation of the probability using all assigned priors</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated prior must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a prior has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.read_csv">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.read_csv" title="Link to this definition"></a></dt>
+<dd><p>Reads x y z co-ordinates from an ascii csv file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>filename</strong> (<em>str</em>) – Path to the file to read from.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.scatter2D">
+<span class="sig-name descname"><span class="pre">scatter2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.scatter2D" title="Link to this definition"></a></dt>
+<dd><p>Create a 2D scatter plot using the x, y coordinates of the point cloud.</p>
+<p>Can take any other matplotlib arguments and keyword arguments e.g. markersize etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>c</strong> (<em>1D array_like</em><em> or </em><em>StatArray</em><em>, </em><em>optional</em>) – Colour values of the points, default is the height of the points</p></li>
+<li><p><strong>i</strong> (<em>sequence</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Plot a subset of x, y, c, using the indices in i.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.plotting.Scatter2D</span></code></dt><dd><p>For additional keyword arguments you may use.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.set_kdtree">
+<span class="sig-name descname"><span class="pre">set_kdtree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ndim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.set_kdtree" title="Link to this definition"></a></dt>
+<dd><p>Creates a k-d tree of the point co-ordinates</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>nDims</strong> (<em>int</em>) – Either 2 or 3 to exclude or include the vertical co-ordinate</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.set_x_posterior">
+<span class="sig-name descname"><span class="pre">set_x_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.set_x_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.set_y_posterior">
+<span class="sig-name descname"><span class="pre">set_y_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.set_y_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.set_z_posterior">
+<span class="sig-name descname"><span class="pre">set_z_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.set_z_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.summary" title="Link to this definition"></a></dt>
+<dd><p>Summary of self</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.toVTK">
+<span class="sig-name descname"><span class="pre">toVTK</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fileName</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pointData</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.toVTK" title="Link to this definition"></a></dt>
+<dd><p>Save the PointCloud3D to a VTK file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fileName</strong> (<em>str</em>) – Filename to save to.</p></li>
+<li><p><strong>pointData</strong> (<em>geobipy.StatArray</em><em> or </em><em>list</em><em> of </em><em>geobipy.StatArray</em><em>, </em><em>optional</em>) – Data at each point in the point cloud. Each entry is saved as a separate
+vtk attribute.</p></li>
+<li><p><strong>format</strong> (<em>str</em><em>, </em><em>optional</em>) – “ascii” or “binary” format. Ascii is readable, binary is not but results in smaller files.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>TypeError</strong> – If pointData is not a geobipy.StatArray or list of them.</p></li>
+<li><p><strong>ValueError</strong> – If any pointData entry does not have size equal to the number of points.</p></li>
+<li><p><strong>ValueError</strong> – If any StatArray does not have a name or units. This is needed for the vtk attribute.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.vtkStructure">
+<span class="sig-name descname"><span class="pre">vtkStructure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.vtkStructure" title="Link to this definition"></a></dt>
+<dd><p>Generates a vtk mesh structure that can be used in a vtk file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> – Vtk data structure</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>pyvtk.VtkData</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.pointcloud.Point.Point.x_axis">
+<span class="sig-name descname"><span class="pre">x_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xAxis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.pointcloud.Point.Point.x_axis" title="Link to this definition"></a></dt>
+<dd><p>Obtain the xAxis against which to plot values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xAxis</strong> (<em>str</em>) – If xAxis is ‘index’, returns numpy.arange(self.nPoints)
+If xAxis is ‘x’, returns self.x
+If xAxis is ‘y’, returns self.y
+If xAxis is ‘z’, returns self.z
+If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y.
+If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The requested xAxis.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
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+  <section id="pointcloud-classes">
+<h1>Pointcloud classes<a class="headerlink" href="#pointcloud-classes" title="Link to this heading"></a></h1>
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+  <section id="distribution-wrapper">
+<h1>Distribution Wrapper<a class="headerlink" href="#distribution-wrapper" title="Link to this heading"></a></h1>
+<p id="module-geobipy.src.classes.statistics.Distribution">&#64;Distribution_Class
+Module describing statistical distributions</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Distribution.Distribution">
+<span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.Distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distributionType</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Distribution.Distribution" title="Link to this definition"></a></dt>
+<dd><p>Instantiate a statistical distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>distributionType</strong> (<em>str</em><em> or </em><em>subclass</em><em> of </em><a class="reference internal" href="baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution" title="geobipy.src.classes.statistics.baseDistribution.baseDistribution"><em>baseDistribution</em></a>) – If distributionType is str, choose between {Normal, MvNormal, Uniform, Gamma, Order, Categorical}
+if distributionType is subclass of baseDistribution, a copy is made</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Subclass of baseDistribution</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The distribution requested</p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">D</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">5.0</span><span class="p">,</span><span class="mf">5.0</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># To create a Distribution using a specific pseudo random number generator</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">prng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">D</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="NormalDistribution.html#module-geobipy.src.classes.statistics.NormalDistribution" title="geobipy.src.classes.statistics.NormalDistribution"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.NormalDistribution</span></code></a>, <a class="reference internal" href="MvNormalDistribution.html#module-geobipy.src.classes.statistics.MvNormalDistribution" title="geobipy.src.classes.statistics.MvNormalDistribution"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.MvNormalDistribution</span></code></a>, <a class="reference internal" href="UniformDistribution.html#module-geobipy.src.classes.statistics.UniformDistribution" title="geobipy.src.classes.statistics.UniformDistribution"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.UniformDistribution</span></code></a>, <a class="reference internal" href="GammaDistribution.html#module-geobipy.src.classes.statistics.GammaDistribution" title="geobipy.src.classes.statistics.GammaDistribution"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.GammaDistribution</span></code></a>, <a class="reference internal" href="OrderStatistics.html#module-geobipy.src.classes.statistics.OrderStatistics" title="geobipy.src.classes.statistics.OrderStatistics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.OrderStatistics</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">geobipy.src.classes.statistics.CategoricalDistribution</span></code></p>
+</div>
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+<li class="toctree-l4"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="UniformDistribution.html">Uniform distribution</a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="gamma-distribution">
+<h1>Gamma Distribution<a class="headerlink" href="#gamma-distribution" title="Link to this heading"></a></h1>
+digraph inheritance6948f34e9e {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;Gamma&quot; [URL=&quot;#geobipy.src.classes.statistics.GammaDistribution.Gamma&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a normal distribution&quot;];
+}
+<p id="module-geobipy.src.classes.statistics.GammaDistribution">&#64;GammaDistribution
+Module defining a gamma distribution with statistical procedures</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.GammaDistribution.Gamma">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.GammaDistribution.</span></span><span class="sig-name descname"><span class="pre">Gamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.GammaDistribution.Gamma" title="Link to this definition"></a></dt>
+<dd><p>Class defining a normal distribution</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.GammaDistribution.Gamma.pdf">
+<span class="sig-name descname"><span class="pre">pdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.GammaDistribution.Gamma.pdf" title="Link to this definition"></a></dt>
+<dd><p>set the PDF, for a gamma distribution</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+           <div itemprop="articleBody">
+             
+  <section id="histogram">
+<h1>Histogram<a class="headerlink" href="#histogram" title="Link to this heading"></a></h1>
+digraph inheritance05266a41c8 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Histogram&quot; [URL=&quot;#geobipy.src.classes.statistics.Histogram.Histogram&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;Model&quot; -&gt; &quot;Histogram&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Model&quot; [URL=&quot;../model/Model_.html#geobipy.src.classes.model.Model.Model&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Generic model class with an attached mesh.&quot;];
+  &quot;myObject&quot; -&gt; &quot;Model&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.statistics.Histogram">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.Histogram.</span></span><span class="sig-name descname"><span class="pre">Histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram" title="Link to this definition"></a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.credible_intervals">
+<span class="sig-name descname"><span class="pre">credible_intervals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">90.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.credible_intervals" title="Link to this definition"></a></dt>
+<dd><p>Gets the median and the credible intervals for the specified axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>percent</strong> (<em>float</em>) – Confidence percentage.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the credible intervals to a base. ‘e’ if log = ‘e’, or a number e.g. log = 10.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Along which axis to obtain the interval locations.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>med</strong> (<em>array_like</em>) – Contains the medians along the specified axis. Has size equal to arr.shape[axis].</p></li>
+<li><p><strong>low</strong> (<em>array_like</em>) – Contains the lower interval along the specified axis. Has size equal to arr.shape[axis].</p></li>
+<li><p><strong>high</strong> (<em>array_like</em>) – Contains the upper interval along the specified axis. Has size equal to arr.shape[axis].</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.credible_range">
+<span class="sig-name descname"><span class="pre">credible_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">90.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.credible_range" title="Link to this definition"></a></dt>
+<dd><p>Get the range of credibility with depth</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>percent</strong> (<em>float</em>) – Percent of the credible intervals</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – If None: The range is the difference in linear space of the credible intervals
+If ‘e’ or float: The range is the difference in log space, or ratio in linear space.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis along which to get the marginal histogram.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.fit_mixture_to_pdf_1d">
+<span class="sig-name descname"><span class="pre">fit_mixture_to_pdf_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mixture</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.fit_mixture_to_pdf_1d" title="Link to this definition"></a></dt>
+<dd><p>Find peaks in the histogram along an axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>intervals</strong> (<em>array_like</em><em>, </em><em>optional</em>) – Accumulate the histogram between these invervals before finding peaks</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) – Axis along which to find peaks.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.marginalize">
+<span class="sig-name descname"><span class="pre">marginalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.marginalize" title="Link to this definition"></a></dt>
+<dd><p>Get the marginal histogram along an axis</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>intervals</strong> (<em>array_like</em>) – Array of size 2 containing lower and upper limits between which to count.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Entries are given in linear space, but internally bins and values are logged.
+Plotting is in log space.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis along which to get the marginal histogram.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.Histogram1D</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.mean" title="Link to this definition"></a></dt>
+<dd><p>Gets the mean along the given axis.</p>
+<p>This is not the true mean of the original samples. It is the best estimated mean using the binned counts multiplied by the axis bin centres.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional.</em>) – Take the log of the mean to base “log”</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Axis to take the mean along.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The means along the axis.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.DataArray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.median">
+<span class="sig-name descname"><span class="pre">median</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.median" title="Link to this definition"></a></dt>
+<dd><p>Gets the median for the specified axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the median to a base. ‘e’ if log = ‘e’, or a number e.g. log = 10.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Along which axis to obtain the median.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The medians along the specified axis. Has size equal to arr.shape[axis].</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.mode">
+<span class="sig-name descname"><span class="pre">mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.mode" title="Link to this definition"></a></dt>
+<dd><p>Gets the median for the specified axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the median to a base. ‘e’ if log = ‘e’, or a number e.g. log = 10.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Along which axis to obtain the median.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The medians along the specified axis. Has size equal to arr.shape[axis].</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.opacity">
+<span class="sig-name descname"><span class="pre">opacity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">95.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.opacity" title="Link to this definition"></a></dt>
+<dd><p>Return an opacity between 0 and 1 based on the difference between credible invervals of the hitmap.</p>
+<p>Higher ranges in credibility map to less opaqueness.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>percent</strong> (<em>float</em><em>, </em><em>optional.</em>) – Confidence percentage.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional.</em>) – If None: Take the difference in credible intervals.
+Else: Take the ratio of the credible intervals.</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional.</em>) – Along which axis to obtain the interval locations.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Opacity along the axis.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.opacity_level">
+<span class="sig-name descname"><span class="pre">opacity_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">95.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.opacity_level" title="Link to this definition"></a></dt>
+<dd><p>Get the index along axis 1 from the bottom up that corresponds to the percent opacity</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.pcolor">
+<span class="sig-name descname"><span class="pre">pcolor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.pcolor" title="Link to this definition"></a></dt>
+<dd><p>Plot like an image</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>scalar</em><em> or </em><em>array_like</em><em>, </em><em>optional</em>) – If alpha is scalar, behaves like standard matplotlib alpha and opacity is applied to entire plot
+If array_like, each pixel is given an individual alpha value.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the colour to a base. ‘e’ if log = ‘e’, and a number e.g. log = 10.
+Values in c that are &lt;= 0 are masked.</p></li>
+<li><p><strong>equalize</strong> (<em>bool</em><em>, </em><em>optional</em>) – Equalize the histogram of the colourmap so that all colours have an equal amount.</p></li>
+<li><p><strong>nbins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to use for histogram equalization.</p></li>
+<li><p><strong>xscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the x axis? e.g. xscale = ‘linear’ or ‘log’</p></li>
+<li><p><strong>yscale</strong> (<em>str</em><em>, </em><em>optional</em>) – Scale the y axis? e.g. yscale = ‘linear’ or ‘log’.</p></li>
+<li><p><strong>flipX</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the X axis</p></li>
+<li><p><strong>flipY</strong> (<em>bool</em><em>, </em><em>optional</em>) – Flip the Y axis</p></li>
+<li><p><strong>grid</strong> (<em>bool</em><em>, </em><em>optional</em>) – Plot the grid</p></li>
+<li><p><strong>noColorbar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Turn off the colour bar, useful if multiple plotting plotting routines are used on the same figure.</p></li>
+<li><p><strong>trim</strong> (<em>bool</em><em>, </em><em>optional</em>) – Set the x and y limits to the first and last non zero values along each axis.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.percentile">
+<span class="sig-name descname"><span class="pre">percentile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reciprocate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.percentile" title="Link to this definition"></a></dt>
+<dd><p>Gets the median and the credible intervals for the specified axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>percent</strong> (<em>float</em>) – Confidence percentage.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Take the log of the credible intervals to a base. ‘e’ if log = ‘e’, or a number e.g. log = 10.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Along which axis to obtain the interval locations.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>med</strong> (<em>array_like</em>) – Contains the medians along the specified axis. Has size equal to arr.shape[axis].</p></li>
+<li><p><strong>low</strong> (<em>array_like</em>) – Contains the lower interval along the specified axis. Has size equal to arr.shape[axis].</p></li>
+<li><p><strong>high</strong> (<em>array_like</em>) – Contains the upper interval along the specified axis. Has size equal to arr.shape[axis].</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">overlay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.plot" title="Link to this definition"></a></dt>
+<dd><p>Plots the histogram</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.sample">
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.sample" title="Link to this definition"></a></dt>
+<dd><p>Generates samples from the histogram.</p>
+<p>A uniform distribution is used for each bin to generate samples.
+The number of samples generated per bin is scaled by the count for that bin using the requested number of samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>nSamples</strong> (<em>int</em>) – Number of samples to generate.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The samples.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.DataArray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.Histogram.Histogram.transparency">
+<span class="sig-name descname"><span class="pre">transparency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">95.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.Histogram.Histogram.transparency" title="Link to this definition"></a></dt>
+<dd><p>Return a transparency value between 0 and 1 based on the difference between credible invervals of the hitmap.</p>
+<p>Higher ranges in credibility are mapped to more transparency.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>percent</strong> (<em>float</em>) – Confidence percentage.</p></li>
+<li><p><strong>log</strong> (<em>'e'</em><em> or </em><em>float</em><em>, </em><em>optional.</em>) – If None: Take the difference in credible intervals.
+Else: Take the ratio of the credible intervals.</p></li>
+<li><p><strong>axis</strong> (<em>int</em>) – Along which axis to obtain the interval locations.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Transparency along the axis.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+             
+  <section id="mvnormal">
+<h1>MvNormal<a class="headerlink" href="#mvnormal" title="Link to this heading"></a></h1>
+digraph inheritanceea6c1a6edf {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;MvNormal&quot; [URL=&quot;#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class extension to geobipy.baseDistribution&quot;];
+  &quot;baseDistribution&quot; -&gt; &quot;MvNormal&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;baseDistribution&quot; [URL=&quot;baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Define an abstract base distribution class&quot;];
+  &quot;myObject&quot; -&gt; &quot;baseDistribution&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.statistics.MvNormalDistribution">&#64;MvNormalDistribution
+Module defining a multivariate normal distribution with statistical procedures</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.MvNormalDistribution.</span></span><span class="sig-name descname"><span class="pre">MvNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prng</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal" title="Link to this definition"></a></dt>
+<dd><p>Class extension to geobipy.baseDistribution</p>
+<p>Handles a multivariate normal distribution.  Uses Scipy to evaluate probabilities,
+but Numpy to generate random samples since scipy is slow.</p>
+<p>MvNormal(mean, variance, ndim, prng)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mean</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – Mean(s) for each dimension</p></li>
+<li><p><strong>variance</strong> (<em>scalar</em><em> or </em><em>array_like</em>) – Variance for each dimension</p></li>
+<li><p><strong>ndim</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of dimensions in the multivariate normal.
+Only used if mean and variance are scalars that are constant for all dimensions</p></li>
+<li><p><strong>prng</strong> (<em>numpy.random.RandomState</em><em>, </em><em>optional</em>) – A random state to generate random numbers. Required for parallel instantiation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Multivariate normal distribution.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal" title="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal">MvNormal</a></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.bins">
+<span class="sig-name descname"><span class="pre">bins</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nBins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nStd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.bins" title="Link to this definition"></a></dt>
+<dd><p>Discretizes a range given the mean and variance of the distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nBins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to return.</p></li>
+<li><p><strong>nStd</strong> (<em>float</em><em>, </em><em>optional</em>) – The bin edges = mean +- nStd * variance.</p></li>
+<li><p><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em>) – Get the bins of this dimension, if None, returns bins for all dimensions.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>bins</strong> – The bin edges.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>geobipy.StatArray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.ndim" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.pad">
+<span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.pad" title="Link to this definition"></a></dt>
+<dd><p>Pads the mean and variance to the given size
+N: Padded size</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.probability">
+<span class="sig-name descname"><span class="pre">probability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.probability" title="Link to this definition"></a></dt>
+<dd><p>For a realization x, compute the probability</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="NormalDistribution.html" class="btn btn-neutral float-left" title="Normal distribution" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="UniformDistribution.html" class="btn btn-neutral float-right" title="Uniform distribution" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">Normal distribution</a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="normal-distribution">
+<h1>Normal distribution<a class="headerlink" href="#normal-distribution" title="Link to this heading"></a></h1>
+digraph inheritance0a76c53a1d {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Normal&quot; [URL=&quot;#geobipy.src.classes.statistics.NormalDistribution.Normal&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Univariate normal distribution&quot;];
+  &quot;baseDistribution&quot; -&gt; &quot;Normal&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;baseDistribution&quot; [URL=&quot;baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Define an abstract base distribution class&quot;];
+  &quot;myObject&quot; -&gt; &quot;baseDistribution&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.statistics.NormalDistribution">&#64;NormalDistribution
+Module defining a normal distribution with statistical procedures</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.NormalDistribution.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prng</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal" title="Link to this definition"></a></dt>
+<dd><p>Univariate normal distribution</p>
+<p>Normal(mean, variance)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mean</strong> (<em>numpy.float</em>) – The mean of the distribution</p></li>
+<li><p><strong>variance</strong> (<em>numpy.float</em>) – The variance of the distribution</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal.bins">
+<span class="sig-name descname"><span class="pre">bins</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nBins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nStd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal.bins" title="Link to this definition"></a></dt>
+<dd><p>Discretizes a range given the mean and variance of the distribution</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal.cdf">
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal.cdf" title="Link to this definition"></a></dt>
+<dd><p>For a realization x, compute the probability</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal.ndim" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal.probability">
+<span class="sig-name descname"><span class="pre">probability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal.probability" title="Link to this definition"></a></dt>
+<dd><p>For a realization x, compute the probability</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.NormalDistribution.Normal.rng">
+<span class="sig-name descname"><span class="pre">rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.NormalDistribution.Normal.rng" title="Link to this definition"></a></dt>
+<dd><p>Generate random numbers</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>N</strong> (<em>int</em><em> or </em><em>sequence</em><em> of </em><em>ints</em>) – Number of samples to generate</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy.ndarray</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>out</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="baseDistribution.html" class="btn btn-neutral float-left" title="baseDistribution" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="MvNormalDistribution.html" class="btn btn-neutral float-right" title="MvNormal" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
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diff --git a/docs/content/api/classes/statistics/OrderStatistics.html b/docs/content/api/classes/statistics/OrderStatistics.html
new file mode 100644
index 00000000..4424a134
--- /dev/null
+++ b/docs/content/api/classes/statistics/OrderStatistics.html
@@ -0,0 +1,168 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <title>Order Statistics &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l4"><a class="reference internal" href="baseDistribution.html">baseDistribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="MvNormalDistribution.html">MvNormal</a></li>
+<li class="toctree-l4"><a class="reference internal" href="UniformDistribution.html">Uniform distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="GammaDistribution.html">Gamma Distribution</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Order Statistics</a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="order-statistics">
+<h1>Order Statistics<a class="headerlink" href="#order-statistics" title="Link to this heading"></a></h1>
+digraph inheritance76de29de59 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Order&quot; [URL=&quot;#geobipy.src.classes.statistics.OrderStatistics.Order&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining Order Statistics&quot;];
+  &quot;baseDistribution&quot; -&gt; &quot;Order&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;baseDistribution&quot; [URL=&quot;baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Define an abstract base distribution class&quot;];
+  &quot;myObject&quot; -&gt; &quot;baseDistribution&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.statistics.OrderStatistics">&#64;OrderStatistics
+Module defining modified order statistics as in Malinverno2002Parsimonious Bayesian Markov chain Monte Carlo inversion
+in a nonlinear geophysical problem,Geophysical Journal International</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.OrderStatistics.Order">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.OrderStatistics.</span></span><span class="sig-name descname"><span class="pre">Order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">denominator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.OrderStatistics.Order" title="Link to this definition"></a></dt>
+<dd><p>Class defining Order Statistics
+Specific application to Bayesian inversion of EM data</p>
+</dd></dl>
+
+</section>
+
+
+           </div>
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diff --git a/docs/content/api/classes/statistics/UniformDistribution.html b/docs/content/api/classes/statistics/UniformDistribution.html
new file mode 100644
index 00000000..2c1f19a5
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+++ b/docs/content/api/classes/statistics/UniformDistribution.html
@@ -0,0 +1,198 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+  <title>Uniform distribution &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l4"><a class="reference internal" href="Histogram.html">Histogram</a></li>
+<li class="toctree-l4"><a class="reference internal" href="baseDistribution.html">baseDistribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="MvNormalDistribution.html">MvNormal</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Uniform distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="GammaDistribution.html">Gamma Distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="OrderStatistics.html">Order Statistics</a></li>
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+      <li class="breadcrumb-item active">Uniform distribution</li>
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+            <a href="../../../../_sources/content/api/classes/statistics/UniformDistribution.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="uniform-distribution">
+<h1>Uniform distribution<a class="headerlink" href="#uniform-distribution" title="Link to this heading"></a></h1>
+digraph inheritancea4de341619 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;Uniform&quot; [URL=&quot;#geobipy.src.classes.statistics.UniformDistribution.Uniform&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Class defining a uniform distribution&quot;];
+  &quot;baseDistribution&quot; -&gt; &quot;Uniform&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;baseDistribution&quot; [URL=&quot;baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Define an abstract base distribution class&quot;];
+  &quot;myObject&quot; -&gt; &quot;baseDistribution&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.statistics.UniformDistribution">&#64;UniformDistribution
+Module defining a uniform distribution with statistical procedures</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.UniformDistribution.Uniform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.UniformDistribution.</span></span><span class="sig-name descname"><span class="pre">Uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prng</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.UniformDistribution.Uniform" title="Link to this definition"></a></dt>
+<dd><p>Class defining a uniform distribution</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.UniformDistribution.Uniform.bins">
+<span class="sig-name descname"><span class="pre">bins</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nBins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.UniformDistribution.Uniform.bins" title="Link to this definition"></a></dt>
+<dd><p>Discretizes a range given the min and max of the distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nBins</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of bins to return.</p></li>
+<li><p><strong>dim</strong> (<em>int</em><em>, </em><em>optional</em>) – Get the bins of this dimension, if None, returns bins for all dimensions.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>bins</strong> – The bin edges.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.UniformDistribution.Uniform.cdf">
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.UniformDistribution.Uniform.cdf" title="Link to this definition"></a></dt>
+<dd><p>Get the value of the cumulative distribution function for a x</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.UniformDistribution.Uniform.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#geobipy.src.classes.statistics.UniformDistribution.Uniform.ndim" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="MvNormalDistribution.html" class="btn btn-neutral float-left" title="MvNormal" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="GammaDistribution.html" class="btn btn-neutral float-right" title="Gamma Distribution" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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\ No newline at end of file
diff --git a/docs/content/api/classes/statistics/baseDistribution.html b/docs/content/api/classes/statistics/baseDistribution.html
new file mode 100644
index 00000000..19f2316c
--- /dev/null
+++ b/docs/content/api/classes/statistics/baseDistribution.html
@@ -0,0 +1,186 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>baseDistribution &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
+
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+        <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+        <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../../search.html" />
+    <link rel="next" title="Normal distribution" href="NormalDistribution.html" />
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+
+          
+          
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../model/model.html">Model classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../pointcloud/pointcloud.html">Pointcloud classes</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="statistics.html">Statistics classes</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="Distribution.html">Distribution Wrapper</a></li>
+<li class="toctree-l4"><a class="reference internal" href="Histogram.html">Histogram</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">baseDistribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="MvNormalDistribution.html">MvNormal</a></li>
+<li class="toctree-l4"><a class="reference internal" href="UniformDistribution.html">Uniform distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="GammaDistribution.html">Gamma Distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="OrderStatistics.html">Order Statistics</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../system/system.html">System classes</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../examples/index.html">Examples</a></li>
+</ul>
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+          <li class="breadcrumb-item"><a href="../classes.html">Classes used in GeoBIPy</a></li>
+          <li class="breadcrumb-item"><a href="statistics.html">Statistics classes</a></li>
+      <li class="breadcrumb-item active">baseDistribution</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../../_sources/content/api/classes/statistics/baseDistribution.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="basedistribution">
+<h1>baseDistribution<a class="headerlink" href="#basedistribution" title="Link to this heading"></a></h1>
+digraph inheritance78d6cc9397 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;baseDistribution&quot; [URL=&quot;#geobipy.src.classes.statistics.baseDistribution.baseDistribution&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Define an abstract base distribution class&quot;];
+  &quot;myObject&quot; -&gt; &quot;baseDistribution&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.statistics.baseDistribution">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.baseDistribution.baseDistribution">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.statistics.baseDistribution.</span></span><span class="sig-name descname"><span class="pre">baseDistribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prng</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.baseDistribution.baseDistribution" title="Link to this definition"></a></dt>
+<dd><p>Define an abstract base distribution class</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.baseDistribution.baseDistribution.bins">
+<span class="sig-name descname"><span class="pre">bins</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.baseDistribution.baseDistribution.bins" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.baseDistribution.baseDistribution.deepcopy">
+<span class="sig-name descname"><span class="pre">deepcopy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.statistics.baseDistribution.baseDistribution.deepcopy" title="Link to this definition"></a></dt>
+<dd><p>Place holder for children</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.baseDistribution.baseDistribution.moment">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">moment</span></span><a class="headerlink" href="#geobipy.src.classes.statistics.baseDistribution.baseDistribution.moment" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.statistics.baseDistribution.baseDistribution.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#geobipy.src.classes.statistics.baseDistribution.baseDistribution.ndim" title="Link to this definition"></a></dt>
+<dd><p>Place Holder for children</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="Histogram.html" class="btn btn-neutral float-left" title="Histogram" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="NormalDistribution.html" class="btn btn-neutral float-right" title="Normal distribution" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/docs/content/api/classes/statistics/statistics.html b/docs/content/api/classes/statistics/statistics.html
new file mode 100644
index 00000000..84ea7e9e
--- /dev/null
+++ b/docs/content/api/classes/statistics/statistics.html
@@ -0,0 +1,157 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Statistics classes &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css?v=19f00094" />
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+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-dataframe.css?v=2082cf3c" />
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+    <link rel="index" title="Index" href="../../../../genindex.html" />
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+          
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+<li class="toctree-l1 current"><a class="reference internal" href="../../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../base/base.html">Core routines needed for GeoBIPy</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../classes.html">Classes used in GeoBIPy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../core/core.html">Core classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../data/data.html">Data classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mesh/mesh.html">Mesh classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../model/model.html">Model classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../pointcloud/pointcloud.html">Pointcloud classes</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Statistics classes</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="Distribution.html">Distribution Wrapper</a></li>
+<li class="toctree-l4"><a class="reference internal" href="Histogram.html">Histogram</a></li>
+<li class="toctree-l4"><a class="reference internal" href="baseDistribution.html">baseDistribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="MvNormalDistribution.html">MvNormal</a></li>
+<li class="toctree-l4"><a class="reference internal" href="UniformDistribution.html">Uniform distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="GammaDistribution.html">Gamma Distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="OrderStatistics.html">Order Statistics</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../system/system.html">System classes</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../examples/index.html">Examples</a></li>
+</ul>
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+      <li class="breadcrumb-item active">Statistics classes</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../../../_sources/content/api/classes/statistics/statistics.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="statistics-classes">
+<h1>Statistics classes<a class="headerlink" href="#statistics-classes" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="Distribution.html">Distribution Wrapper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Histogram.html">Histogram</a></li>
+<li class="toctree-l1"><a class="reference internal" href="baseDistribution.html">baseDistribution</a></li>
+<li class="toctree-l1"><a class="reference internal" href="NormalDistribution.html">Normal distribution</a></li>
+<li class="toctree-l1"><a class="reference internal" href="MvNormalDistribution.html">MvNormal</a></li>
+<li class="toctree-l1"><a class="reference internal" href="UniformDistribution.html">Uniform distribution</a></li>
+<li class="toctree-l1"><a class="reference internal" href="GammaDistribution.html">Gamma Distribution</a></li>
+<li class="toctree-l1"><a class="reference internal" href="OrderStatistics.html">Order Statistics</a></li>
+</ul>
+</div>
+</section>
+
+
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+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../pointcloud/Point.html" class="btn btn-neutral float-left" title="Point" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="Distribution.html" class="btn btn-neutral float-right" title="Distribution Wrapper" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+
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+  </div>
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diff --git a/docs/content/api/classes/system/CircularLoop.html b/docs/content/api/classes/system/CircularLoop.html
new file mode 100644
index 00000000..bb7e9c68
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+             
+  <section id="circular-loop">
+<h1>Circular Loop<a class="headerlink" href="#circular-loop" title="Link to this heading"></a></h1>
+digraph inheritancea1db000af0 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;CircularLoop&quot; [URL=&quot;#geobipy.src.classes.system.CircularLoop.CircularLoop&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Defines a circular loop for EM acquisition systems&quot;];
+  &quot;EmLoop&quot; -&gt; &quot;CircularLoop&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;EmLoop&quot; [URL=&quot;EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Defines a loop in an EM system e.g. transmitter or reciever&quot;];
+  &quot;Point&quot; -&gt; &quot;EmLoop&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;ABC&quot; -&gt; &quot;EmLoop&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.system.CircularLoop">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.system.CircularLoop.</span></span><span class="sig-name descname"><span class="pre">CircularLoop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">moment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pitch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roll</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yaw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop" title="Link to this definition"></a></dt>
+<dd><p>Defines a circular loop for EM acquisition systems</p>
+<p>CircularLoop(orient, moment, x, y, z, pitch, roll, yaw, radius)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>orient</strong> (<em>str</em>) – Orientation of the loop, ‘x’ or ‘z’</p></li>
+<li><p><strong>moment</strong> (<em>int</em>) – Moment of the loop</p></li>
+<li><p><strong>x</strong> (<em>float</em>) – X location of the loop relative to an observation location</p></li>
+<li><p><strong>y</strong> (<em>float</em>) – Y location of the loop relative to an observation location</p></li>
+<li><p><strong>z</strong> (<em>float</em>) – Z location of the loop relative to an observation location</p></li>
+<li><p><strong>pitch</strong> (<em>float</em>) – Pitch of the loop</p></li>
+<li><p><strong>roll</strong> (<em>float</em>) – Roll of the loop</p></li>
+<li><p><strong>yaw</strong> (<em>float</em>) – Yaw of the loop</p></li>
+<li><p><strong>radius</strong> (<em>float</em>) – Radius of the loop</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast using MPI</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>world</strong> (<em>mpi4py.MPI.COMM_WORLD</em>) – An MPI communicator</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A CircularLoop on each core</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.system.CircularLoop.CircularLoop" title="geobipy.src.classes.system.CircularLoop.CircularLoop">CircularLoop</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.summary" title="Link to this definition"></a></dt>
+<dd><p>Print a summary</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.CircularLoop.CircularLoop.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.CircularLoop.CircularLoop.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
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@@ -0,0 +1,254 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+             
+  <section id="emloop">
+<h1>EmLoop<a class="headerlink" href="#emloop" title="Link to this heading"></a></h1>
+digraph inheritancef4d5cd9044 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;EmLoop&quot; [URL=&quot;#geobipy.src.classes.system.EmLoop.EmLoop&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Defines a loop in an EM system e.g. transmitter or reciever&quot;];
+  &quot;Point&quot; -&gt; &quot;EmLoop&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;ABC&quot; -&gt; &quot;EmLoop&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;Point&quot; [URL=&quot;../pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;3D Point Cloud with x,y,z co-ordinates&quot;];
+  &quot;myObject&quot; -&gt; &quot;Point&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.system.EmLoop">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.system.EmLoop.</span></span><span class="sig-name descname"><span class="pre">EmLoop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">moment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pitch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roll</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yaw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop" title="Link to this definition"></a></dt>
+<dd><p>Defines a loop in an EM system e.g. transmitter or reciever</p>
+<p>This is an abstract base class and should not be instantiated</p>
+<p>EmLoop()</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.append">
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.append" title="Link to this definition"></a></dt>
+<dd><p>Append pointclouds together</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> (<em>geobipy.PointCloud3D</em>) – 3D pointcloud</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.createHdf">
+<span class="sig-name descname"><span class="pre">createHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fillvalue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.createHdf" title="Link to this definition"></a></dt>
+<dd><p>Create the hdf group metadata in file
+parent: HDF object to create a group inside
+myName: Name of the group</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads in the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.perturb">
+<span class="sig-name descname"><span class="pre">perturb</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.perturb" title="Link to this definition"></a></dt>
+<dd><p>Propose a new point given the attached propsal distributions</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.probability" title="Link to this definition"></a></dt>
+<dd><p>Evaluate the probability for the EM data point given the specified attached priors</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rEerr</strong> (<em>bool</em>) – Include the relative error when evaluating the prior</p></li>
+<li><p><strong>aEerr</strong> (<em>bool</em>) – Include the additive error when evaluating the prior</p></li>
+<li><p><strong>height</strong> (<em>bool</em>) – Include the elevation when evaluating the prior</p></li>
+<li><p><strong>calibration</strong> (<em>bool</em>) – Include the calibration parameters when evaluating the prior</p></li>
+<li><p><strong>verbose</strong> (<em>bool</em>) – Return the components of the probability, i.e. the individually evaluated priors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – The evaluation of the probability using all assigned priors</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float64</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>For each boolean, the associated prior must have been set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>TypeError</strong> – If a prior has not been set on a requested parameter</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.set_pitch_posterior">
+<span class="sig-name descname"><span class="pre">set_pitch_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.set_pitch_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.set_roll_posterior">
+<span class="sig-name descname"><span class="pre">set_roll_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.set_roll_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.set_yaw_posterior">
+<span class="sig-name descname"><span class="pre">set_yaw_posterior</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.set_yaw_posterior" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.summary" title="Link to this definition"></a></dt>
+<dd><p>Print a summary</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.EmLoop.EmLoop.writeHdf">
+<span class="sig-name descname"><span class="pre">writeHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">withPosterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.EmLoop.EmLoop.writeHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the StatArray to an HDF object
+parent: Upper hdf file or group
+myName: object hdf name. Assumes createHdf has already been called
+create: optionally create the data set as well before writing</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="frequency-domain-system">
+<h1>Frequency domain system<a class="headerlink" href="#frequency-domain-system" title="Link to this heading"></a></h1>
+digraph inheritance3626e3b5d4 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;FdemSystem&quot; [URL=&quot;#geobipy.src.classes.system.FdemSystem.FdemSystem&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Defines a Frequency Domain ElectroMagnetic acquisition system&quot;];
+  &quot;myObject&quot; -&gt; &quot;FdemSystem&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<p id="module-geobipy.src.classes.system.FdemSystem">&#64;FdemSystem_Class
+Module describing a frequency domain EM acquisition system</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.system.FdemSystem.</span></span><span class="sig-name descname"><span class="pre">FdemSystem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frequencies</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transmitter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">receiver</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_frequencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem" title="Link to this definition"></a></dt>
+<dd><p>Defines a Frequency Domain ElectroMagnetic acquisition system</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.Bcast">
+<span class="sig-name descname"><span class="pre">Bcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">world</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.Bcast" title="Link to this definition"></a></dt>
+<dd><p>Broadcast the FdemSystem using MPI</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.component_id">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">component_id</span></span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.component_id" title="Link to this definition"></a></dt>
+<dd><p>For each coil orientation pair, adds the index of the frequency to the appropriate list
+e.g. two coils at the i$^{th}$ frequency with ‘x’ as their orientation cause i to be added to the ‘xx’ list.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.fileInformation">
+<span class="sig-name descname"><span class="pre">fileInformation</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.fileInformation" title="Link to this definition"></a></dt>
+<dd><p>Description of the system file.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.fromHdf">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fromHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grp</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.fromHdf" title="Link to this definition"></a></dt>
+<dd><p>Reads the object from a HDF file</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.read">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.read" title="Link to this definition"></a></dt>
+<dd><p>Read in a file containing the system information</p>
+<p>The system file is structured using columns with the first line containing header information
+Each subsequent row contains the information for each measurement frequency
+freq  tor  tmom  tx ty tz ror rmom  rx   ry rz
+378   z    1     0  0  0  z   1     7.93 0  0
+1776  z    1     0  0  0  z   1     7.91 0  0
+…</p>
+<p>where tor and ror are the orientations of the transmitter/reciever loops [x or z].
+tmom and rmom are the moments of the loops.
+t/rx,y,z are the loop offsets from the observation locations in the data file.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.summary">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summary</span></span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.summary" title="Link to this definition"></a></dt>
+<dd><p>Summary of the FdemSystem</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.tensor_id">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tensor_id</span></span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.tensor_id" title="Link to this definition"></a></dt>
+<dd><p>For each coil orientation pair, adds the index of the frequency to the appropriate list
+e.g. two coils at the i$^{th}$ frequency with ‘x’ as their orientation cause i to be added to the ‘xx’ list.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.FdemSystem.FdemSystem.toHdf">
+<span class="sig-name descname"><span class="pre">toHdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">h5obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.FdemSystem.FdemSystem.toHdf" title="Link to this definition"></a></dt>
+<dd><p>Write the object to a HDF file</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<html class="writer-html5" lang="en" data-content_root="../../../../">
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+           <div itemprop="articleBody">
+             
+  <section id="time-domain-system">
+<h1>Time domain system<a class="headerlink" href="#time-domain-system" title="Link to this heading"></a></h1>
+digraph inheritance997b7fa283 {
+bgcolor=transparent;
+rankdir=LR;
+size=&quot;8.0, 12.0&quot;;
+  &quot;ABC&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Helper class that provides a standard way to create an ABC using&quot;];
+  &quot;TDAEMSystem&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;TDAEMSystem Class&quot;];
+  &quot;TdemSystem&quot; [URL=&quot;#geobipy.src.classes.system.TdemSystem.TdemSystem&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;,tooltip=&quot;Initialize a Time domain system class&quot;];
+  &quot;TdemSystem_GAAEM&quot; -&gt; &quot;TdemSystem&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TdemSystem_GAAEM&quot; [fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,tooltip=&quot;Initialize a Time domain system class&quot;];
+  &quot;myObject&quot; -&gt; &quot;TdemSystem_GAAEM&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;TDAEMSystem&quot; -&gt; &quot;TdemSystem_GAAEM&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+  &quot;myObject&quot; [URL=&quot;../core/myObject.html#geobipy.src.classes.core.myObject.myObject&quot;,fillcolor=white,fontname=&quot;Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans&quot;,fontsize=10,height=0.25,shape=box,style=&quot;setlinewidth(0.5),filled&quot;,target=&quot;_top&quot;];
+  &quot;ABC&quot; -&gt; &quot;myObject&quot; [arrowsize=0.5,style=&quot;setlinewidth(0.5)&quot;];
+}
+<dl class="py class" id="module-geobipy.src.classes.system.TdemSystem">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.TdemSystem.TdemSystem">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">geobipy.src.classes.system.TdemSystem.</span></span><span class="sig-name descname"><span class="pre">TdemSystem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offTimes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transmitterLoop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">receiverLoop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loopOffset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">waveform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offTimeFilters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['z']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#geobipy.src.classes.system.TdemSystem.TdemSystem" title="Link to this definition"></a></dt>
+<dd><p>Initialize a Time domain system class</p>
+<p>TdemSystem(systemFileName)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>systemFileName</strong> (<em>str</em>) – The system file to read from</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – A time domain system class</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#geobipy.src.classes.system.TdemSystem.TdemSystem" title="geobipy.src.classes.system.TdemSystem.TdemSystem">TdemSystem</a></p>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.TdemSystem.TdemSystem.get_modellingTimes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_modellingTimes</span></span><a class="headerlink" href="#geobipy.src.classes.system.TdemSystem.TdemSystem.get_modellingTimes" title="Link to this definition"></a></dt>
+<dd><p>Generates regularly log spaced times that covers both the waveform and measurment times.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>waveformTimes</strong> (<em>array_like</em>) – Times of the waveform change points</p></li>
+<li><p><strong>measurementTimes</strong> (<em>array_like</em>) – Measurement times for the system</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Times spanning both the waveform and measrement times</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array_like</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="geobipy.src.classes.system.TdemSystem.TdemSystem.off_time">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">off_time</span></span><a class="headerlink" href="#geobipy.src.classes.system.TdemSystem.TdemSystem.off_time" title="Link to this definition"></a></dt>
+<dd><p>Time windows.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="FdemSystem.html" class="btn btn-neutral float-left" title="Frequency domain system" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../../../../examples/index.html" class="btn btn-neutral float-right" title="Examples" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+<li class="toctree-l3"><a class="reference internal" href="#installing-a-serial-version-of-geobipy">Installing a serial version of GeoBIPy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#installing-a-parallel-version-of-geobipy">Installing a parallel version of GeoBIPy</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#installing-mpi-and-mpi4py">Installing MPI and mpi4py</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#installing-parallel-hdf5-and-h5py">Installing parallel HDF5 and h5py</a></li>
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+  <section id="installing-geobipy">
+<h1>Installing GeoBIPy<a class="headerlink" href="#installing-geobipy" title="Link to this heading"></a></h1>
+<p>First things first, install a Python 3.5+ distribution.  This is the minimum version that we have tested with.
+You will also need to install Numpy and a Fortran compiler.</p>
+<p>This package has a few requirements depending on what you wish to do with it.</p>
+<p>If you require a serial version of the code, see <a class="reference internal" href="#installing-a-serial-version-of-geobipy">Installing a serial version of GeoBIPy</a>.</p>
+<p>If you require an parallel implementation, you will need to install an MPI library, and Python’s mpi4py module. See <a class="reference internal" href="#installing-mpi-and-mpi4py">Installing MPI and mpi4py</a>.</p>
+<p>If you require parallel file reading and writing, you will also need to install an MPI enabled HDF5 library, as well as Python’s h5py wrapper to that library. It is important to read the notes below on installing h5py on top of a parallel HDF library.  The traditional “pip install h5py” will not work correctly. See <a class="reference internal" href="#installing-parallel-hdf5-and-h5py">Installing parallel HDF5 and h5py</a> to do this correctly.</p>
+<p>If you need to install the parallel IO version of the code, we would recommend that you start with a clean install of Python. This makes it easier to determine whether you have installed and linked the correct version of the parallel HDF5 library.</p>
+<p>There are two versions when installing GeoBIPy, a serial version, and a parallel version. Since GeoBIPy uses a Fortran backend for forward modelling frequency domain data, you will need to have a Fortran compiler installed. Make sure that the compiler can handle derived data types since I make use of object oriented programming in Fortran.</p>
+<section id="installing-a-serial-version-of-geobipy">
+<h2>Installing a serial version of GeoBIPy<a class="headerlink" href="#installing-a-serial-version-of-geobipy" title="Link to this heading"></a></h2>
+<p>This is the easiest installation and provides access to a serial implementation of the code.</p>
+<p>Simply clone the git repository, navigate to the package folder that contains the setup.py file, and type “pip install .”</p>
+<p>You should then be able to import modules from geobipy.  For this type of installation mpi will not need to be installed, and the serial version of h5py will suffice i.e. the standard “pip install h5py” is fine.  h5py will automatically be installed during the install of GeoBIPy since it is a dependency.</p>
+<p><strong>Side note:</strong>  Let’s say you ran a production run on a parallel machine with MPI and parallel HDF capabilities. You generated all the results, copied them back to your local machine, and wish to make plots and images.  You will only need to install the serial version of the code on your local machine to do this.</p>
+</section>
+<section id="installing-a-parallel-version-of-geobipy">
+<h2>Installing a parallel version of GeoBIPy<a class="headerlink" href="#installing-a-parallel-version-of-geobipy" title="Link to this heading"></a></h2>
+<p>Installing the parallel version of the code is a little trickier due to the dependencies necessary between the OpenMPI and/or HDF libraries, and how Python’s mpi4py and h5py wrap around those.</p>
+<section id="installing-mpi-and-mpi4py">
+<h3>Installing MPI and mpi4py<a class="headerlink" href="#installing-mpi-and-mpi4py" title="Link to this heading"></a></h3>
+<p>To run this code in parallel you will need both an MPI library and the python wrapper, mpi4py.  You must install MPI first before mpi4py.</p>
+<section id="mpi">
+<h4>MPI<a class="headerlink" href="#mpi" title="Link to this heading"></a></h4>
+<p>If you are installing GeoBIPy on a parallel machine, I would think that you have access to prebuilt MPI libraries.
+If you are on a local laptop, you will need to install one.</p>
+</section>
+<section id="mpi4py">
+<h4>mpi4py<a class="headerlink" href="#mpi4py" title="Link to this heading"></a></h4>
+<p>At this point, if you have an mpi4py module already installed, please remove it (you can check with “pip list”).
+If you started with a clean installation you should not have to worry about this.
+To test whether a new install of mpi4py will see the mpi library you have, just type “which mpicc”.
+The path that you see should point to the implementation that you want mpi4py to link to.
+Make sure you are about to install mpi4py to the correct python installation.
+If you type ‘which python’ it should return the path to the correct python distribution.
+If you are using environments, make sure you have activated the correct one.</p>
+<p>Next, use “env MPICC=&lt;Your mpicc compiler&gt; python -m pip install mpi4py”.  This last option is very important, without it, pip might install its own MPI library called MPICH2.
+I would try to avoid this because if you need to install the HDF5 library you will need know which directories to link to (see <a class="reference internal" href="#installing-parallel-hdf5-and-h5py">Installing parallel HDF5 and h5py</a>).</p>
+<p>At the end of the day,  h5py needs to communicate with both the correct HDF5 library and mpi4py, and both of those need to communicate with the same MPI library.</p>
+</section>
+</section>
+<section id="installing-parallel-hdf5-and-h5py">
+<h3>Installing parallel HDF5 and h5py<a class="headerlink" href="#installing-parallel-hdf5-and-h5py" title="Link to this heading"></a></h3>
+<p>If a parallel HDF5 library is not available, you will need to install one. First make sure you follow <a class="reference internal" href="#installing-mpi-and-mpi4py">Installing MPI and mpi4py</a> so that an MPI library is available to you. You must install a HDF5 library first before h5py.</p>
+<section id="hdf5">
+<h4>HDF5<a class="headerlink" href="#hdf5" title="Link to this heading"></a></h4>
+<p>When you install HDF5, make sure that the correct MPI library can be seen by typing “which mpicc”.  When you configure the HDF5 library, be sure to use the –enable-parallel option.</p>
+</section>
+<section id="h5py">
+<h4>h5py<a class="headerlink" href="#h5py" title="Link to this heading"></a></h4>
+<p>Once the HDF5 library is installed you will need to install a parallel enabled <a class="reference external" href="https://github.com/h5py/h5py">h5py package</a></p>
+<p>Make sure you are about to install h5py to the correct python installation.  If you type ‘which python’ it should return the path to the correct python installation.</p>
+<p>First check the following</p>
+<ul class="simple">
+<li><p>HDF5_DIR = Get the path to the HDF5 installation.</p></li>
+<li><p>Check that ‘which mpicc’ returns the correct version of an mpi enabled compiler. This needs to point to the same MPI library that mpi4py was installed on top of.</p></li>
+<li><p>Do the following, replacing items in &lt; &gt; with your mpicc compiler and you HDF5 install directory.</p></li>
+</ul>
+<p>This will install h5py and compile the source.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">CC</span><span class="o">=</span>&lt;Your<span class="w"> </span>mpicc<span class="w"> </span>compiler&gt;<span class="w"> </span><span class="nv">HDF5_MPI</span><span class="o">=</span><span class="s2">&quot;ON&quot;</span><span class="w"> </span><span class="nv">HDF5_DIR</span><span class="o">=</span>&lt;Your<span class="w"> </span>HDF5_DIR&gt;<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--no-binary<span class="o">=</span>h5py<span class="w"> </span>h5py
+</pre></div>
+</div>
+</section>
+</section>
+</section>
+<section id="installing-the-time-domain-forward-modeller">
+<span id="installing-time-domain-forward-modeller"></span><h2>Installing the time domain forward modeller<a class="headerlink" href="#installing-the-time-domain-forward-modeller" title="Link to this heading"></a></h2>
+<p>Ross Brodie at Geoscience Australia has written a great forward modeller, gatdaem1D,  in C++ with a python interface.
+You can obtain that code here at the <a class="reference external" href="https://github.com/GeoscienceAustralia/ga-aem">GA repository</a></p>
+<p>Go ahead and “git clone” that repository.</p>
+<p>These instructions only describe how to install Ross’ forward modeller, but it is part of a larger code base for inversion.
+If you wish to install his entire package, please follow his instructions.</p>
+<section id="prerequisites">
+<h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h3>
+<p>To compile his forward modeller, you will need a c++ compiler, and <a class="reference external" href="http://www.fftw.org/">FFTW</a></p>
+<p>On a Mac, installing these two items is easy if you use a package manager such as <a class="reference external" href="https://brew.sh/">homebrew</a></p>
+<p>If you use brew, simply do the following</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>brew<span class="w"> </span>install<span class="w"> </span>gcc
+brew<span class="w"> </span>install<span class="w"> </span>fftw
+</pre></div>
+</div>
+<p>If you do not have brew, or use a package manager, you can install fftw from source instead.</p>
+<p>Download fftw-3.3.7.tar.gz from the <a class="reference external" href="http://www.fftw.org/download.html">FFTW downloads</a> .</p>
+<p>Untar the folder and install fftw using the following.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>tar<span class="w"> </span>-zxvf<span class="w"> </span>fftw-3.3.7.tar.gz
+<span class="nb">cd</span><span class="w"> </span>fftw-3.3.7
+mkdir<span class="w"> </span>build
+<span class="nb">cd</span><span class="w"> </span>build
+../configure<span class="w"> </span>--prefix<span class="o">=</span>path-to-install-to/fftw-3.3.7<span class="w"> </span>--enable-threads
+make
+make<span class="w"> </span>install
+</pre></div>
+</div>
+<p>where, path-to-install-to is the location where you want fftw to be installed.</p>
+</section>
+<section id="compile-the-gatdaem1d-shared-library">
+<h3>Compile the gatdaem1d shared library<a class="headerlink" href="#compile-the-gatdaem1d-shared-library" title="Link to this heading"></a></h3>
+<p>Next, within the gatdaem1d folder, navigate to the makefiles folder and modify the top part of the file “gatdaem1d_python.make” to the following</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">SHELL</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>/bin/sh
+.SUFFIXES:
+.SUFFIXES:<span class="w"> </span>.cpp<span class="w"> </span>.o
+<span class="nv">cxx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>g++
+<span class="nv">cxxflags</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>-std<span class="o">=</span>c++11<span class="w"> </span>-O3<span class="w"> </span>-Wall<span class="w"> </span>-fPIC
+<span class="nv">FFTW_DIR</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>path-to-fftw
+
+<span class="nv">ldflags</span><span class="w">    </span><span class="o">+=</span><span class="w"> </span>-shared
+<span class="nv">bindir</span><span class="w">     </span><span class="o">=</span><span class="w"> </span>../python/gatdaem1d
+
+<span class="nv">srcdir</span><span class="w">     </span><span class="o">=</span><span class="w"> </span>../src
+<span class="nv">objdir</span><span class="w">     </span><span class="o">=</span><span class="w"> </span>./obj
+<span class="nv">includes</span><span class="w">   </span><span class="o">=</span><span class="w"> </span>-I<span class="k">$(</span>srcdir<span class="k">)</span><span class="w"> </span>-I<span class="k">$(</span>FFTW_DIR<span class="k">)</span>/include
+<span class="nv">libs</span><span class="w">       </span><span class="o">=</span><span class="w"> </span>-L<span class="k">$(</span>FFTW_DIR<span class="k">)</span>/lib<span class="w"> </span>-lfftw3
+<span class="nv">library</span><span class="w">    </span><span class="o">=</span><span class="w"> </span><span class="k">$(</span>bindir<span class="k">)</span>/gatdaem1d.so
+</pre></div>
+</div>
+<p>You can find out where brew installed fftw by typing</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>brew<span class="w"> </span>info<span class="w"> </span>fftw
+</pre></div>
+</div>
+<p>Which may return something like “/usr/local/Cellar/fftw/3.3.5”</p>
+<p>In this case, path-to-fftw is “/usr/local/Cellar/fftw/3.3.5”</p>
+<p>If you installed fftw from source, then path-to-fftw is that install path.</p>
+<p>Next, type the following to compile the gatdaem1d c++ code.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-f<span class="w"> </span>gatdaem1d_python.make
+</pre></div>
+</div>
+</section>
+<section id="installing-the-python-bindings">
+<h3>Installing the Python Bindings<a class="headerlink" href="#installing-the-python-bindings" title="Link to this heading"></a></h3>
+<p>Finally, to install the python wrapper to gatdaem1d, navigate to the python folder of the gatdaem1d repository.
+Type,</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>.
+</pre></div>
+</div>
+<p>You should now have access to the time domain forward modeller within geobipy.</p>
+</section>
+</section>
+</section>
+
+
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">Frequency domain dataset</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#defining-data-using-a-frequency-domain-system">Defining data using a frequency domain system</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#reading-in-the-data">Reading in the Data</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#obtain-a-line-from-the-data-set">Obtain a line from the data set</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#file-format-for-frequency-domain-data">File Format for frequency domain data</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#file-format-for-a-frequency-domain-system">File Format for a frequency domain system</a></li>
+</ul>
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+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-data-plot-frequency-dataset-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="frequency-domain-dataset">
+<span id="sphx-glr-examples-data-plot-frequency-dataset-py"></span><h1>Frequency domain dataset<a class="headerlink" href="#frequency-domain-dataset" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">CircularLoop</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemSystem</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemData</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<section id="defining-data-using-a-frequency-domain-system">
+<h2>Defining data using a frequency domain system<a class="headerlink" href="#defining-data-using-a-frequency-domain-system" title="Link to this heading"></a></h2>
+<p>We can start by defining the frequencies, transmitter loops, and receiver loops
+For each frequency we need to define a pair of loops</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mf">395.0</span><span class="p">,</span> <span class="mf">822.0</span><span class="p">,</span> <span class="mf">3263.0</span><span class="p">,</span> <span class="mf">8199.0</span><span class="p">,</span> <span class="mf">38760.0</span><span class="p">,</span> <span class="mf">128755.0</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>Transmitter positions are defined relative to the observation locations in the data
+This is usually a constant offset for all data points.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">transmitters</span> <span class="o">=</span> <span class="n">CircularLoop</span><span class="p">(</span><span class="n">orientation</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">],</span>
+                             <span class="n">moment</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                             <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">pitch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">roll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">yaw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">radius</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>Receiver positions are defined relative to the transmitter</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">receivers</span> <span class="o">=</span> <span class="n">CircularLoop</span><span class="p">(</span><span class="n">orientation</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">],</span>
+                             <span class="n">moment</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                             <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">9.03</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.89</span><span class="p">],</span>
+                             <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">pitch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">roll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">yaw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">radius</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="c1"># Instantiate the system for the data</span>
+<span class="n">system</span> <span class="o">=</span> <span class="n">FdemSystem</span><span class="p">(</span><span class="n">frequencies</span><span class="o">=</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">transmitter</span><span class="o">=</span><span class="n">transmitters</span><span class="p">,</span> <span class="n">receiver</span><span class="o">=</span><span class="n">receivers</span><span class="p">)</span>
+
+<span class="c1"># Create some data with random co-ordinates</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">FdemData</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=-</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">system</span> <span class="o">=</span> <span class="n">system</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="reading-in-the-data">
+<h2>Reading in the Data<a class="headerlink" href="#reading-in-the-data" title="Link to this heading"></a></h2>
+<p>Of course measured field data is stored on disk. So instead we can read data from file.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//data//&quot;</span>
+<span class="c1"># The data file name</span>
+<span class="n">dataFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Resolve2.txt&#39;</span>
+<span class="c1"># The EM system file name</span>
+<span class="n">systemFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;FdemSystem2.stm&#39;</span>
+</pre></div>
+</div>
+<p>Read in a data set from file.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">FD1</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Take a look at the channel names</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">FD1</span><span class="o">.</span><span class="n">channel_names</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Get data points by slicing</span>
+<span class="c1"># FDa = FD1[10:]</span>
+<span class="c1"># FD1 = FD1[:10]</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Append data sets together</span>
+<span class="c1"># FD1.append(FDa)</span>
+
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Plot the locations of the data points</span>
+<span class="c1"># plt.figure(figsize=(8,6))</span>
+<span class="c1"># _ = FD1.scatter2D();</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Plot all the data along the specified line</span>
+<span class="c1"># plt.figure(figsize=(8,6))</span>
+<span class="c1"># _ = FD1.plotLine(30010.0, log=10);</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Or, plot specific channels in the data</span>
+<span class="c1"># plt.figure(figsize=(8,6))</span>
+<span class="c1"># _ = FD1.plot(channels=[0,11,8], log=10, linewidth=0.5);</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>In_Phase 380.0
+In_Phase 1776.0
+In_Phase 3345.0
+In_Phase 8171.0
+In_Phase 41020.0
+In_Phase 129550.0
+Quadrature 380.0
+Quadrature 1776.0
+Quadrature 3345.0
+Quadrature 8171.0
+Quadrature 41020.0
+Quadrature 129550.0
+</pre></div>
+</div>
+<p>Read in a second data set</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">FD2</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">dataFilename</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Resolve1.txt&#39;</span><span class="p">,</span> <span class="n">system</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;FdemSystem1.stm&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Warning: Your data contains values that are &lt;= 0.0
+</pre></div>
+</div>
+<p>We can create maps of the elevations in two separate figures</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD1</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">50.0</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">50.0</span><span class="p">,</span> <span class="n">mask</span> <span class="o">=</span> <span class="mf">200.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_001.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_001.png" alt="plot frequency dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>surface [WARNING]: 66659 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+(np.float64(584494.28), np.float64(590194.28), np.float64(4639054.24), np.float64(4661854.24))
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD2</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">50.0</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">50.0</span><span class="p">,</span> <span class="n">mask</span> <span class="o">=</span> <span class="mf">200.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_002.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_002.png" alt="plot frequency dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>surface [WARNING]: 123487 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+(np.float64(662822.398), np.float64(668372.398), np.float64(4560028.655), np.float64(4600678.655))
+</pre></div>
+</div>
+<p>Or, we can plot both data sets in one figure to see their positions relative
+to each other.</p>
+<p>In this case, I use a 2D scatter plot of the data point co-ordinates, and pass
+one of the channels as the colour.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD1</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">s</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">FD1</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD2</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">s</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">FD2</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_003.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_003.png" alt="plot frequency dataset" class = "sphx-glr-single-img"/><p>Or, interpolate the values to create a gridded “map”. mapChannel will
+interpolate the specified channel number.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD1</span><span class="o">.</span><span class="n">mapData</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mi">250</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_004.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_004.png" alt="In_Phase 8171.0" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>surface [WARNING]: 70336 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+(np.float64(584419.28), np.float64(590219.28), np.float64(4638979.24), np.float64(4661979.24))
+</pre></div>
+</div>
+<p>Export the data to VTK</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">FD1</span><span class="o">.</span><span class="n">to_vtk</span><span class="p">(</span><span class="s1">&#39;FD_one.vtk&#39;</span><span class="p">)</span>
+<span class="c1"># FD2.to_vtk(&#39;FD_two.vtk&#39;)</span>
+</pre></div>
+</div>
+</section>
+<section id="obtain-a-line-from-the-data-set">
+<h2>Obtain a line from the data set<a class="headerlink" href="#obtain-a-line-from-the-data-set" title="Link to this heading"></a></h2>
+<p>Take a look at the line numbers in the dataset</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">FD1</span><span class="o">.</span><span class="n">lineNumber</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[30010 30020 30030 ... 30100 39010 39020]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">L</span> <span class="o">=</span> <span class="n">FD1</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="mf">30010.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>A summary will now show the properties of the line.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">L</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>FdemData
+x:
+|   StatArray
+|   Name:   Easting (m)
+|   Address:[&#39;0x156368350&#39;]
+|   Shape:  (6710,)
+|   Values: [586852.29 586852.23 586852.17 ... 586123.57 586123.2  586122.82]
+|   Min:    586122.82
+|   Max:    586852.29
+|   has_posterior: False
+
+y:
+|   StatArray
+|   Name:   Northing (m)
+|   Address:[&#39;0x156369ed0&#39;]
+|   Shape:  (6710,)
+|   Values: [4639119.38 4639122.68 4639125.98 ... 4661765.26 4661768.84 4661772.42]
+|   Min:    4639119.38
+|   Max:    4661772.42
+|   has_posterior: False
+
+z:
+|   StatArray
+|   Name:   Height (m)
+|   Address:[&#39;0x1563681d0&#39;]
+|   Shape:  (6710,)
+|   Values: [36.115 36.498 36.835 ... 27.799 27.704 27.601]
+|   Min:    23.830000000000002
+|   Max:    50.567
+|   has_posterior: False
+
+elevation:
+|   StatArray
+|   Name:   Elevation (m)
+|   Address:[&#39;0x15468a450&#39;]
+|   Shape:  (6710,)
+|   Values: [1246.84 1246.71 1246.61 ... 1337.94 1337.96 1338.02]
+|   Min:    1213.18
+|   Max:    1338.02
+|   has_posterior: False
+
+channel names:
+|   In_Phase 380.0, In_Phase 1776.0, In_Phase 3345.0, In_Phase 8171.0, In_Phase 41020.0,
+|   In_Phase 129550.0, Quadrature 380.0, Quadrature 1776.0, Quadrature 3345.0, Quadrature 8171.0,
+|   Quadrature 41020.0, Quadrature 129550.0
+data:
+|   DataArray
+|   Name:   Data (ppm)
+|   Address:[&#39;0x17eb61950&#39;]
+|   Shape:  (80520,)
+|   Values: [145.3 435.8 260.6 ... 749.2 976.5 928.3]
+|   Min:    37.7
+|   Max:    3726.9
+
+predicted data:
+|   DataArray
+|   Name:   Predicted Data (ppm)
+|   Address:[&#39;0x17eb61950&#39;]
+|   Shape:  (80520,)
+|   Values: [0. 0. 0. ... 0. 0. 0.]
+|   Min:    0.0
+|   Max:    0.0
+
+std:
+|   DataArray
+|   Name:   std (ppm)
+|   Address:[&#39;0x17eb604d0&#39;]
+|   Shape:  (80520,)
+|   Values: [1.453 4.358 2.606 ... 7.492 9.765 9.283]
+|   Min:    0.37700000000000006
+|   Max:    37.269
+
+line number:
+|   DataArray
+|   Name:   Line number
+|   Address:[&#39;0x17ec01150&#39;]
+|   Shape:  (6710,)
+|   Values: [30010. 30010. 30010. ... 30010. 30010. 30010.]
+|   Min:    30010.0
+|   Max:    30010.0
+
+fiducial:
+|   DataArray
+|   Name:   Fiducial
+|   Address:[&#39;0x156368250&#39;]
+|   Shape:  (6710,)
+|   Values: [30000 30000 30000 ... 30670 30670 30670]
+|   Min:    30000
+|   Max:    30670
+
+relative error:
+|   DataArray
+|   Name:   Relative error (%)
+|   Address:[&#39;0x15468ad50&#39;]
+|   Shape:  (6710, 1)
+|   Values: [[0.01]
+|    [0.01]
+|    [0.01]
+|    ...
+|    [0.01]
+|    [0.01]
+|    [0.01]]
+|   Min:    0.01
+|   Max:    0.01
+
+additive error:
+|   DataArray
+|   Name:   Additive error (ppm)
+|   Address:[&#39;0x15468ac50&#39;]
+|   Shape:  (6710, 1)
+|   Values: [[0.]
+|    [0.]
+|    [0.]
+|    ...
+|    [0.]
+|    [0.]
+|    [0.]]
+|   Min:    0.0
+|   Max:    0.0
+</pre></div>
+</div>
+<p>And we can scatter2D the points in the line.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">L</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">();</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_005.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_005.png" alt="plot frequency dataset" class = "sphx-glr-single-img"/><p>We can specify the axis along which to plot.
+xAxis can be index, x, y, z, r2d, r3d</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">FD1</span><span class="o">.</span><span class="n">plot_data</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">);</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">FD1</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdem&#39;</span><span class="p">)</span>
+    <span class="n">FD1</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdem&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">FD3</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;fdem&#39;</span><span class="p">])</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">fdp</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;fdem&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Obtain a single datapoint from the data set</span>
+<span class="c1"># # +++++++++++++++++++++++++++++++++++++++++++</span>
+<span class="c1"># #</span>
+<span class="c1"># # Checkout :ref:`Frequency domain datapoint` for an example</span>
+<span class="c1"># # about how to use a datapoint once it is instantiated.</span>
+<span class="c1"># dp = FD1.datapoint(0)</span>
+
+<span class="c1"># # Prepare the dataset so that we can read a point at a time.</span>
+<span class="c1"># Dataset = FdemData._initialize_sequential_reading(dataFile, systemFile)</span>
+<span class="c1"># # Get a datapoint from the file.</span>
+<span class="c1"># DataPoint = Dataset._read_record()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_frequency_dataset_006.png" srcset="../../_images/sphx_glr_plot_frequency_dataset_006.png" alt="plot frequency dataset" class = "sphx-glr-single-img"/></section>
+<section id="file-format-for-frequency-domain-data">
+<h2>File Format for frequency domain data<a class="headerlink" href="#file-format-for-frequency-domain-data" title="Link to this heading"></a></h2>
+<p>Here we describe the file format for frequency domain data.</p>
+<p>The data columns are read in according to the column names in the first line.</p>
+<p>In this description, the column name or its alternatives are given followed by what the name represents.
+Optional columns are also described.</p>
+<section id="required-columns">
+<h3>Required columns<a class="headerlink" href="#required-columns" title="Link to this heading"></a></h3>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>fid</dt><dd><p>Unique identification number of the data point</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point, (m)</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point, (m)</p>
+</dd>
+<dt>z or alt</dt><dd><p>Altitude of the transmitter coil above ground level (m)</p>
+</dd>
+<dt>elevation</dt><dd><p>Elevation of the ground at the data point (m)</p>
+</dd>
+<dt>I_&lt;frequency[0]&gt; Q_&lt;frequency[0]&gt; … I_&lt;frequency[last]&gt; Q_&lt;frequency[last]&gt;  - with the number and square brackets</dt><dd><p>The measurements for each frequency specified in the accompanying system file.
+I is the real inphase measurement in (ppm)
+Q is the imaginary quadrature measurement in (ppm)</p>
+</dd>
+</dl>
+</section>
+<section id="optional-columns">
+<h3>Optional columns<a class="headerlink" href="#optional-columns" title="Link to this heading"></a></h3>
+<dl class="simple">
+<dt>InphaseErr[0] QuadratureErr[0] … InphaseErr[nFrequencies] QuadratureErr[nFrequencies]</dt><dd><p>Estimates of standard deviation for each inphase and quadrature measurement.
+These must appear after the data colums.</p>
+</dd>
+</dl>
+</section>
+<section id="example-header">
+<h3>Example Header<a class="headerlink" href="#example-header" title="Link to this heading"></a></h3>
+<p>Line fid easting northing elevation height I_380 Q_380 … … I_129550 Q_129550</p>
+</section>
+</section>
+<section id="file-format-for-a-frequency-domain-system">
+<h2>File Format for a frequency domain system<a class="headerlink" href="#file-format-for-a-frequency-domain-system" title="Link to this heading"></a></h2>
+<p>The system file is structured using columns with the first line containing header information</p>
+<p>Each subsequent row contains the information for each measurement frequency</p>
+<dl class="simple">
+<dt>freq</dt><dd><p>Frequency of the channel</p>
+</dd>
+<dt>tor</dt><dd><p>Orientation of the transmitter loop ‘x’, or ‘z’</p>
+</dd>
+<dt>tmom</dt><dd><p>Transmitter moment</p>
+</dd>
+<dt>tx, ty, tx</dt><dd><p>Offset of the transmitter with respect to the observation locations</p>
+</dd>
+<dt>ror</dt><dd><p>Orientation of the receiver loop ‘x’, or ‘z’</p>
+</dd>
+<dt>rmom</dt><dd><p>Receiver moment</p>
+</dd>
+<dt>rx, ry, rz</dt><dd><p>Offset of the receiver with respect to the transmitter location</p>
+</dd>
+</dl>
+<p>Example system files are contained in
+<a class="reference external" href="https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data">the supplementary folder</a> in this repository</p>
+<p>See the Resolve.stm files.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.257 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-data-plot-frequency-dataset-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/ffa7a17936c32d098025040a94f8af46/plot_frequency_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_frequency_dataset.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/8a8596f0390404145eca225a8f310218/plot_frequency_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_frequency_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/dd5f000fe10de32b5e5e1f6a210e1afb/plot_frequency_dataset.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_frequency_dataset.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
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+<li class="toctree-l3"><a class="reference internal" href="plot_frequency_dataset.html">Frequency domain dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_skytem_dataset.html">Skytem dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_tempest_dataset.html">Tempest dataset</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
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+      <li class="breadcrumb-item active">3D Point Cloud class</li>
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+            <a href="../../_sources/examples/Data/plot_pointcloud3d.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-data-plot-pointcloud3d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-point-cloud-class">
+<span id="sphx-glr-examples-data-plot-pointcloud3d-py"></span><h1>3D Point Cloud class<a class="headerlink" href="#d-point-cloud-class" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Point</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+
+<span class="n">nPoints</span> <span class="o">=</span> <span class="mi">200</span>
+</pre></div>
+</div>
+<p>Create a quick test example using random points
+$z=x(1-x)cos(4pi x)sin(4pi y^{2})^{2}$</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">nPoints</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">nPoints</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">PC3D</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Append pointclouds together</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">nPoints</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">nPoints</span><span class="p">))</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">Other_PC</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Other_PC</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;geobipy.src.classes.pointcloud.Point.Point object at 0x10efdda80&gt;
+</pre></div>
+</div>
+<p>Write a summary of the contents of the point cloud</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">PC3D</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Point
+x:
+|   StatArray
+|   Name:   Easting (m)
+|   Address:[&#39;0x1462b7250&#39;]
+|   Shape:  (400,)
+|   Values: [-0.75575613 -0.37227056 -0.5082486  ...  0.91062364  0.56073342
+|     0.52761544]
+|   Min:    -0.9805553827696698
+|   Max:    0.9916814356778278
+|   has_posterior: False
+
+y:
+|   StatArray
+|   Name:   Northing (m)
+|   Address:[&#39;0x1462b72d0&#39;]
+|   Shape:  (400,)
+|   Values: [-0.15201495 -0.55717559 -0.53823673 ...  0.30290861  0.17254113
+|     0.23398822]
+|   Min:    -0.9899649834962019
+|   Max:    0.9979698382486921
+|   has_posterior: False
+
+z:
+|   StatArray
+|   Name:   Height (m)
+|   Address:[&#39;0x1462b7350&#39;]
+|   Shape:  (400,)
+|   Values: [-0.43897973  0.19632561 -0.01971922 ... -0.06368326 -0.02409831
+|    -0.01448342]
+|   Min:    -1.9063110648103332
+|   Max:    0.22889687797869024
+|   has_posterior: False
+
+elevation:
+|   StatArray
+|   Name:   Elevation (m)
+|   Address:[&#39;0x1462b73d0&#39;]
+|   Shape:  (400,)
+|   Values: [0. 0. 0. ... 0. 0. 0.]
+|   Min:    0.0
+|   Max:    0.0
+|   has_posterior: False
+</pre></div>
+</div>
+<p>Get a single location from the point as a 3x1 vector</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Point</span> <span class="o">=</span> <span class="n">PC3D</span><span class="p">[</span><span class="mi">50</span><span class="p">]</span>
+<span class="c1"># Print the point to the screen</span>
+</pre></div>
+</div>
+<p>Plot the locations with Height as colour</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_pointcloud3d_001.png" srcset="../../_images/sphx_glr_plot_pointcloud3d_001.png" alt="plot pointcloud3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Easting (m)&#39;, ylabel=&#39;Northing (m)&#39;&gt;, &lt;matplotlib.collections.PathCollection object at 0x143a91b20&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x144cb0c50&gt;)
+</pre></div>
+</div>
+<p>Plotting routines take matplotlib arguments for customization</p>
+<p>For example, plotting the size of the points according to the absolute value of height</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">PC3D</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">PC3D</span><span class="o">.</span><span class="n">z</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_pointcloud3d_002.png" srcset="../../_images/sphx_glr_plot_pointcloud3d_002.png" alt="plot pointcloud3d" class = "sphx-glr-single-img"/><p>Interpolate the points to a 2D rectilinear mesh</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span><span class="p">,</span> <span class="n">dum</span> <span class="o">=</span> <span class="n">PC3D</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">PC3D</span><span class="o">.</span><span class="n">z</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sibson&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+
+<span class="c1"># We can save that mesh to VTK</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">to_vtk</span><span class="p">(</span><span class="s1">&#39;pc3d.vtk&#39;</span><span class="p">)</span>
+<span class="n">mesh</span><span class="o">.</span><span class="n">to_vtk</span><span class="p">(</span><span class="s1">&#39;interpolated_pc3d.vtk&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Grid the points using a triangulated CloughTocher, or minimum curvature interpolation</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">331</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;ct&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">332</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mc&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">333</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sibson&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">334</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;ct&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">335</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mc&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">336</span><span class="p">)</span>
+<span class="n">PC3D</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sibson&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_pointcloud3d_003.png" srcset="../../_images/sphx_glr_plot_pointcloud3d_003.png" alt="plot pointcloud3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>surface [WARNING]: 5 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+surface [WARNING]: 5 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+(&lt;Axes: &gt;, &lt;matplotlib.collections.QuadMesh object at 0x17eb0c470&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x17eca7650&gt;)
+</pre></div>
+</div>
+<p>For lots of points, these surfaces can look noisy. Using a block filter will help</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">PCsub</span> <span class="o">=</span> <span class="n">PC3D</span><span class="o">.</span><span class="n">block_median</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">337</span><span class="p">)</span>
+<span class="n">PCsub</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;ct&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">338</span><span class="p">)</span>
+<span class="n">PCsub</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mc&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">339</span><span class="p">)</span>
+<span class="n">PCsub</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">dx</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sibson&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_pointcloud3d_004.png" srcset="../../_images/sphx_glr_plot_pointcloud3d_004.png" alt="plot pointcloud3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>surface [WARNING]: 2 unusable points were supplied; these will be ignored.
+surface [WARNING]: You should have pre-processed the data with block-mean, -median, or -mode.
+surface [WARNING]: Check that previous processing steps write results with enough decimals.
+surface [WARNING]: Possibly some data were half-way between nodes and subject to IEEE 754 rounding.
+
+(&lt;Axes: &gt;, &lt;matplotlib.collections.QuadMesh object at 0x17ec7aea0&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x17eeb34d0&gt;)
+</pre></div>
+</div>
+<p>We can perform spatial searches on the 3D point cloud</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">PC3D</span><span class="o">.</span><span class="n">set_kdtree</span><span class="p">(</span><span class="n">ndim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">=</span> <span class="n">PC3D</span><span class="o">.</span><span class="n">nearest</span><span class="p">((</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">),</span> <span class="n">k</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>.nearest returns the distances and indices into the point cloud of the nearest points.
+We can then obtain those points as another point cloud</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># pNear = PC3D[p[1]]</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># ax1 = plt.subplot(1,2,1)</span>
+<span class="c1"># pNear.scatter2D()</span>
+<span class="c1"># plt.plot(0.0, 0.0, &#39;x&#39;)</span>
+<span class="c1"># plt.subplot(1,2,2, sharex=ax1, sharey=ax1)</span>
+<span class="c1"># ax, sc, cb = PC3D.scatter2D(edgecolor=&#39;k&#39;)</span>
+<span class="c1"># searchRadius = plt.Circle((0.0, 0.0), 0.3, color=&#39;b&#39;, fill=False)</span>
+<span class="c1"># ax.add_artist(searchRadius)</span>
+<span class="c1"># plt.plot(0.0, 0.0, &#39;x&#39;)</span>
+</pre></div>
+</div>
+<p>Read in the xyz co-ordinates in columns 2,3,4 from a file. Skip 1 header line.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//Data//&quot;</span>
+
+<span class="n">PC3D</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Resolve1.txt&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;geobipy.src.classes.pointcloud.Point.Point object at 0x17ed3c0e0&gt;
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">f</span> <span class="o">=</span> <span class="n">PC3D</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;test.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">PC3D</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+    <span class="n">PC3D</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;test.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">PC3D1</span> <span class="o">=</span> <span class="n">Point</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;test.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">point</span> <span class="o">=</span> <span class="n">Point</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_pointcloud3d_005.png" srcset="../../_images/sphx_glr_plot_pointcloud3d_005.png" alt="plot pointcloud3d" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 19.063 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-data-plot-pointcloud3d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/651af48d5bb0348e72f0e99cb3cfd75a/plot_pointcloud3d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_pointcloud3d.ipynb</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/6c96ba056672a7e415f479abafdaf81d/plot_pointcloud3d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_pointcloud3d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/54daa4e7c10c4176fd98102617f46076/plot_pointcloud3d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_pointcloud3d.zip</span></code></a></p>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#data">Data</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_pointcloud3d.html">3D Point Cloud class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_frequency_dataset.html">Frequency domain dataset</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Skytem dataset</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#reading-in-the-data">Reading in the Data</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#file-format-for-time-domain-data">File Format for time domain data</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#file-format-for-a-time-domain-system">File Format for a time domain system</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="plot_tempest_dataset.html">Tempest dataset</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
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+      <li class="breadcrumb-item active">Skytem dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Data/plot_skytem_dataset.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-data-plot-skytem-dataset-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="skytem-dataset">
+<span id="sphx-glr-examples-data-plot-skytem-dataset-py"></span><h1>Skytem dataset<a class="headerlink" href="#skytem-dataset" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">plotting</span> <span class="k">as</span> <span class="n">cP</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TdemData</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+</pre></div>
+</div>
+<section id="reading-in-the-data">
+<h2>Reading in the Data<a class="headerlink" href="#reading-in-the-data" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//data//&quot;</span>
+<span class="c1"># The data file name</span>
+<span class="n">dataFiles</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;skytem_saline_clay.csv&#39;</span>
+<span class="c1"># dataFiles = dataFolder + &#39;Skytem.csv&#39;</span>
+<span class="c1"># The EM system file name</span>
+<span class="n">systemFiles</span><span class="o">=</span><span class="p">[</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;SkytemHM.stm&#39;</span><span class="p">,</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;SkytemLM.stm&#39;</span><span class="p">]</span>
+
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">systemFiles</span><span class="p">[:</span><span class="mi">1</span><span class="p">]:</span>
+    <span class="n">txt</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read_text</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>System Begin
+        Name = SkyTemHighMoment-ElkHills
+        Type = Time Domain
+        Transmitter Begin
+                NumberOfTurns = 1
+                PeakCurrent   = 1
+                LoopArea      = 1
+                BaseFrequency = 30.0
+                WaveformDigitisingFrequency = 491520
+                WaveFormCurrent Begin
+-4.00E-03       0.00E+00
+-3.91E-03       3.17E-01
+-3.81E-03       6.30E-01
+-3.72E-03       8.79E-01
+-3.68E-03       9.61E-01
+-2.30E-03       9.74E-01
+-1.01E-03       9.88E-01
+0.00E+00        1.00E+00
+3.25E-06        9.91E-01
+1.00E-04        7.02E-01
+2.02E-04        3.78E-01
+2.82E-04        1.16E-01
+3.08E-04        2.79E-02
+3.13E-04        1.21E-02
+3.15E-04        6.61E-03
+3.17E-04        3.03E-03
+3.19E-04        0.00E+00
+0.012666667     0.00E+00
+
+                WaveFormCurrent End
+        Transmitter End
+Receiver Begin
+        NumberOfWindows = 26
+        WindowWeightingScheme = AreaUnderCurve
+        WindowTimes Begin
+3.796E-04       3.872E-04
+3.876E-04       3.972E-04
+3.976E-04       4.102E-04
+4.106E-04       4.262E-04
+4.266E-04       4.462E-04
+4.466E-04       4.712E-04
+4.716E-04       5.022E-04
+5.026E-04       5.422E-04
+5.426E-04       5.932E-04
+5.936E-04       6.562E-04
+6.566E-04       7.372E-04
+7.376E-04       8.382E-04
+8.386E-04       9.652E-04
+9.656E-04       1.126E-03
+1.127E-03       1.328E-03
+1.329E-03       1.583E-03
+1.584E-03       1.905E-03
+1.906E-03       2.311E-03
+2.312E-03       2.822E-03
+2.823E-03       3.468E-03
+3.469E-03       4.260E-03
+4.261E-03       5.228E-03
+5.229E-03       6.413E-03
+6.414E-03       7.865E-03
+7.866E-03       9.641E-03
+9.642E-03       1.182E-02
+
+                WindowTimes End
+                LowPassFilter Begin
+                        CutOffFrequency = 300000 210000
+                        Order           = 1       2
+                LowPassFilter End
+        Receiver End
+ForwardModelling Begin
+                //TX loop area is was 340.82 m^2 -&gt; r = sqrt(340.82/pi)
+                ModellingLoopRadius = 10.416
+                OutputType = dB/dt
+                XOutputScaling = 0
+                YOutputScaling = 0
+                ZOutputScaling = 1
+                SecondaryFieldNormalisation  =  none
+                FrequenciesPerDecade = 5
+                NumberOfAbsiccaInHankelTransformEvaluation = 21
+        ForwardModelling End
+
+System End
+</pre></div>
+</div>
+<p>Read in the data from file</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">TD</span> <span class="o">=</span> <span class="n">TdemData</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">dataFiles</span><span class="p">,</span> <span class="n">systemFiles</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<p>Plot the locations of the data points</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_dataset_001.png" srcset="../../_images/sphx_glr_plot_skytem_dataset_001.png" alt="plot skytem dataset" class = "sphx-glr-single-img"/><p>Plot all the data along the specified line</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">plotLine</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_dataset_002.png" srcset="../../_images/sphx_glr_plot_skytem_dataset_002.png" alt="plot skytem dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<p>Or, plot specific channels in the data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">plot_data</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_dataset_003.png" srcset="../../_images/sphx_glr_plot_skytem_dataset_003.png" alt="plot skytem dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">xscale</span><span class="o">=</span><span class="s1">&#39;log&#39;</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">xscale</span><span class="o">=</span><span class="s1">&#39;log&#39;</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_dataset_004.png" srcset="../../_images/sphx_glr_plot_skytem_dataset_004.png" alt="plot skytem dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">c</span><span class="o">=</span><span class="n">TD</span><span class="o">.</span><span class="n">secondary_field</span><span class="p">[:,</span> <span class="n">TD</span><span class="o">.</span><span class="n">channel_index</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="mi">6</span><span class="p">)],</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># with h5py.File(&#39;tdem.h5&#39;, &#39;w&#39;) as f:</span>
+<span class="c1">#     TD.createHdf(f, &#39;tdem&#39;)</span>
+<span class="c1">#     TD.writeHdf(f, &#39;tdem&#39;)</span>
+
+<span class="c1"># with h5py.File(&#39;tdem.h5&#39;, &#39;r&#39;) as f:</span>
+<span class="c1">#     TD3 = TdemData.fromHdf(f[&#39;tdem&#39;])</span>
+
+<span class="c1"># with h5py.File(&#39;tdem.h5&#39;, &#39;r&#39;) as f:</span>
+<span class="c1">#     tdp = TdemData.fromHdf(f[&#39;tdem&#39;], index=0)</span>
+
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Obtain a line from the data set</span>
+<span class="c1"># # +++++++++++++++++++++++++++++++</span>
+<span class="c1"># line = TD.line(0.0)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># plt.figure(6)</span>
+<span class="c1"># _ = line.scatter2D(c=line.secondary_field[:, line.channel_index(system=0, channel=6)], log=10)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># plt.figure(7)</span>
+<span class="c1"># _ = line.plot(xAxis=&#39;index&#39;, log=10)</span>
+
+<span class="c1"># Prepare the dataset so that we can read a point at a time.</span>
+<span class="n">Dataset</span> <span class="o">=</span> <span class="n">TdemData</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">dataFiles</span><span class="p">,</span> <span class="n">systemFiles</span><span class="p">)</span>
+<span class="c1"># Get a datapoint from the file.</span>
+<span class="n">DataPoint</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">_read_record</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_dataset_005.png" srcset="../../_images/sphx_glr_plot_skytem_dataset_005.png" alt="plot skytem dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+</section>
+<section id="file-format-for-time-domain-data">
+<h2>File Format for time domain data<a class="headerlink" href="#file-format-for-time-domain-data" title="Link to this heading"></a></h2>
+<p>Here we describe the file format for time domain data.</p>
+<p>The data columns are read in according to the column names in the first line</p>
+<p>In this description, the column name or its alternatives are given followed by what the name represents
+Optional columns are also described.</p>
+<section id="required-columns">
+<h3>Required columns<a class="headerlink" href="#required-columns" title="Link to this heading"></a></h3>
+<dl class="simple">
+<dt>line</dt><dd><p>Line number for the data point</p>
+</dd>
+<dt>fid</dt><dd><p>Unique identification number of the data point</p>
+</dd>
+<dt>x or northing or n</dt><dd><p>Northing co-ordinate of the data point, (m)</p>
+</dd>
+<dt>y or easting or e</dt><dd><p>Easting co-ordinate of the data point, (m)</p>
+</dd>
+<dt>z or alt</dt><dd><p>Altitude of the transmitter coil above ground level (m)</p>
+</dd>
+<dt>elevation</dt><dd><p>Elevation of the ground at the data point (m)</p>
+</dd>
+<dt>txrx_dx</dt><dd><p>Distance in x between transmitter and reciever (m)</p>
+</dd>
+<dt>txrx_dy</dt><dd><p>Distance in y between transmitter and reciever (m)</p>
+</dd>
+<dt>txrx_dz</dt><dd><p>Distance in z between transmitter and reciever (m)</p>
+</dd>
+<dt>Tx_Pitch</dt><dd><p>Pitch of the transmitter loop</p>
+</dd>
+<dt>Tx_Roll</dt><dd><p>Roll of the transmitter loop</p>
+</dd>
+<dt>Tx_Yaw</dt><dd><p>Yaw of the transmitter loop</p>
+</dd>
+<dt>Rx_Pitch</dt><dd><p>Pitch of the receiver loop</p>
+</dd>
+<dt>Rx_Roll</dt><dd><p>Roll of the receiver loop</p>
+</dd>
+<dt>Rx_Yaw</dt><dd><p>Yaw of the receiver loop</p>
+</dd>
+<dt>Off_time[0] Off_time[1] … Off_time[last]  - with the number and square brackets</dt><dd><p>The measurements for each time gate specified in the accompanying system file under Receiver Window Times
+The total number of off_time columns should equal the sum of the receiver windows in all system files.</p>
+</dd>
+</dl>
+</section>
+<section id="optional-columns">
+<h3>Optional columns<a class="headerlink" href="#optional-columns" title="Link to this heading"></a></h3>
+<dl class="simple">
+<dt>Off_time_Error[0] Off_time_Error[1] … Off_time_Error[last]</dt><dd><p>Estimates of standard deviation for each off time measurement</p>
+</dd>
+</dl>
+</section>
+<section id="example-header">
+<h3>Example Header<a class="headerlink" href="#example-header" title="Link to this heading"></a></h3>
+<p>Line fid easting northing elevation height txrx_dx txrx_dy txrx_dz TxPitch TxRoll TxYaw RxPitch RxRoll RxYaw Off[0] Off[1]</p>
+</section>
+</section>
+<section id="file-format-for-a-time-domain-system">
+<h2>File Format for a time domain system<a class="headerlink" href="#file-format-for-a-time-domain-system" title="Link to this heading"></a></h2>
+<p>Please see Page 13 of Ross Brodie’s <a class="reference external" href="https://github.com/GeoscienceAustralia/ga-aem/blob/master/docs/GA%20AEM%20Programs%20User%20Manual.pdf">instructions</a></p>
+<p>We use GA-AEM for our airborne time domain forward modeller.</p>
+<p>Example system files are contained in
+<a class="reference external" href="https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data">the supplementary folder</a> in this repository</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.295 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-data-plot-skytem-dataset-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/7c29b9dd89aefd9a6dbcb1839dec629b/plot_skytem_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_skytem_dataset.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/762d4f393eb8c7294ca843748ae360e3/plot_skytem_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_skytem_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b30e33468a3aa2492e5eaedce425508f/plot_skytem_dataset.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_skytem_dataset.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_frequency_dataset.html" class="btn btn-neutral float-left" title="Frequency domain dataset" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="plot_tempest_dataset.html" class="btn btn-neutral float-right" title="Tempest dataset" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+  <hr/>
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+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>Tempest dataset &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">Tempest dataset</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#reading-in-the-data">Reading in the Data</a></li>
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+      <li class="breadcrumb-item active">Tempest dataset</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Data/plot_tempest_dataset.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-data-plot-tempest-dataset-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="tempest-dataset">
+<span id="sphx-glr-examples-data-plot-tempest-dataset-py"></span><h1>Tempest dataset<a class="headerlink" href="#tempest-dataset" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">plotting</span> <span class="k">as</span> <span class="n">cP</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TempestData</span>
+</pre></div>
+</div>
+<section id="reading-in-the-data">
+<h2>Reading in the Data<a class="headerlink" href="#reading-in-the-data" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//data//&quot;</span>
+
+<span class="c1"># # The data file name</span>
+<span class="c1"># dataFiles = dataFolder + &#39;Tempest.nc&#39;</span>
+<span class="c1"># # The EM system file name</span>
+<span class="c1"># systemFiles = dataFolder + &#39;Tempest.stm&#39;</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Read in the data from file</span>
+<span class="c1"># TD = TempestData.read_netcdf(dataFiles, systemFiles)</span>
+
+<span class="c1"># TD.write_csv(dataFolder + &#39;Tempest.csv&#39;)</span>
+<span class="n">TD</span> <span class="o">=</span> <span class="n">TempestData</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;tempest_saline_clay.csv&#39;</span><span class="p">,</span> <span class="n">system_filename</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Tempest.stm&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+</pre></div>
+</div>
+<p>Plot the locations of the data points</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Scatter plot&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_dataset_001.png" srcset="../../_images/sphx_glr_plot_tempest_dataset_001.png" alt="Scatter plot" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;Scatter plot&#39;)
+</pre></div>
+</div>
+<p>Plot all the data along the specified line</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">plotLine</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Line </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mf">225401.0</span><span class="p">))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_dataset_002.png" srcset="../../_images/sphx_glr_plot_tempest_dataset_002.png" alt="Line 225401.0" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+
+Text(0.5, 1.0, &#39;Line 225401.0&#39;)
+</pre></div>
+</div>
+<p>Or, plot specific channels in the data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">plot_data</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">18</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;3 channels of data&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_dataset_003.png" srcset="../../_images/sphx_glr_plot_tempest_dataset_003.png" alt="3 channels of data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+
+Text(0.5, 1.0, &#39;3 channels of data&#39;)
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Data as an array&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_dataset_004.png" srcset="../../_images/sphx_glr_plot_tempest_dataset_004.png" alt="Data as an array" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+
+Text(0.5, 1.0, &#39;Data as an array&#39;)
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">TD</span><span class="o">.</span><span class="n">scatter2D</span><span class="p">(</span><span class="n">c</span><span class="o">=</span><span class="n">TD</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="n">TD</span><span class="o">.</span><span class="n">channel_index</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="mi">10</span><span class="p">)],</span> <span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;scatter plot of channel </span><span class="si">{</span><span class="n">TD</span><span class="o">.</span><span class="n">channel_index</span><span class="p">(</span><span class="n">system</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">channel</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;tdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">TD</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;tdem&#39;</span><span class="p">)</span>
+    <span class="n">TD</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;tdem&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;tdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">TD3</span> <span class="o">=</span> <span class="n">TempestData</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;tdem&#39;</span><span class="p">])</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;tdem.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">tdp</span> <span class="o">=</span> <span class="n">TempestData</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;tdem&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Obtain a line from the data set</span>
+<span class="c1"># # +++++++++++++++++++++++++++++++</span>
+<span class="c1"># line = TD.line(0.0)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># _ = line.scatter2D()</span>
+<span class="c1"># plt.title(&#39;Channel&#39;)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># _ = line.plot_data(xAxis=&#39;index&#39;, log=10)</span>
+<span class="c1"># plt.title(&quot;All data along line&quot;)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_dataset_005.png" srcset="../../_images/sphx_glr_plot_tempest_dataset_005.png" alt="scatter plot of channel 10" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.125 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-data-plot-tempest-dataset-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/748b6f52da77ee25ec08c56b7a0bf079/plot_tempest_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tempest_dataset.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/cad36827134302329c83d6bd810a1239/plot_tempest_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tempest_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/d11ca9540d0abc72d005f3af4dbab908/plot_tempest_dataset.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_tempest_dataset.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+  <ul class="wy-breadcrumbs">
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+      <li class="breadcrumb-item active">Data</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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+             
+  <section id="data">
+<h1>Data<a class="headerlink" href="#data" title="Link to this heading"></a></h1>
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diff --git a/docs/examples/Data/sg_execution_times.html b/docs/examples/Data/sg_execution_times.html
new file mode 100644
index 00000000..2b2ae5b3
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+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-data-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:28.741</strong> total execution time for 4 files <strong>from examples/Data</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
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+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_pointcloud3d.html#sphx-glr-examples-data-plot-pointcloud3d-py"><span class="std std-ref">3D Point Cloud class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_pointcloud3d.py</span></code>)</p></td>
+<td><p>00:19.063</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_tempest_dataset.html#sphx-glr-examples-data-plot-tempest-dataset-py"><span class="std std-ref">Tempest dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tempest_dataset.py</span></code>)</p></td>
+<td><p>00:04.125</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_frequency_dataset.html#sphx-glr-examples-data-plot-frequency-dataset-py"><span class="std std-ref">Frequency domain dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_frequency_dataset.py</span></code>)</p></td>
+<td><p>00:03.257</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_skytem_dataset.html#sphx-glr-examples-data-plot-skytem-dataset-py"><span class="std std-ref">Skytem dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_skytem_dataset.py</span></code>)</p></td>
+<td><p>00:02.295</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
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\ No newline at end of file
diff --git a/docs/examples/Datapoints/plot_resolve_datapoint.html b/docs/examples/Datapoints/plot_resolve_datapoint.html
new file mode 100644
index 00000000..e273522f
--- /dev/null
+++ b/docs/examples/Datapoints/plot_resolve_datapoint.html
@@ -0,0 +1,393 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
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+  <title>Frequency domain datapoint &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#datapoints">Datapoints</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Frequency domain datapoint</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_skytem_datapoint.html">Skytem Datapoint Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_tempest_datapoint.html">Tempest Datapoint Class</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
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+      <li class="breadcrumb-item active">Frequency domain datapoint</li>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-datapoints-plot-resolve-datapoint-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="frequency-domain-datapoint">
+<span id="sphx-glr-examples-datapoints-plot-resolve-datapoint-py"></span><h1>Frequency domain datapoint<a class="headerlink" href="#frequency-domain-datapoint" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">CircularLoop</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemSystem</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemData</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemDataPoint</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+
+<span class="c1"># Instantiating a frequency domain data point</span>
+<span class="c1"># +++++++++++++++++++++++++++++++++++++++++++</span>
+<span class="c1">#</span>
+<span class="c1"># To instantiate a frequency domain datapoint we need to define some</span>
+<span class="c1"># characteristics of the acquisition system.</span>
+<span class="c1">#</span>
+<span class="c1"># We need to define the frequencies in Hz of the transmitter,</span>
+<span class="c1"># and the geometery of the loops used for each frequency.</span>
+
+<span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mf">380.0</span><span class="p">,</span> <span class="mf">1776.0</span><span class="p">,</span> <span class="mf">3345.0</span><span class="p">,</span> <span class="mf">8171.0</span><span class="p">,</span> <span class="mf">41020.0</span><span class="p">,</span> <span class="mf">129550.0</span><span class="p">])</span>
+
+<span class="c1"># Transmitter positions are defined relative to the observation locations in the data</span>
+<span class="c1"># This is usually a constant offset for all data points.</span>
+<span class="n">transmitters</span> <span class="o">=</span> <span class="n">CircularLoop</span><span class="p">(</span><span class="n">orientation</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">],</span>
+                             <span class="n">moment</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                             <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">pitch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">roll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">yaw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">radius</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="c1"># Receiver positions are defined relative to the transmitter</span>
+<span class="n">receivers</span> <span class="o">=</span> <span class="n">CircularLoop</span><span class="p">(</span><span class="n">orientation</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">,</span><span class="s1">&#39;z&#39;</span><span class="p">],</span>
+                             <span class="n">moment</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                             <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">9.03</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.91</span><span class="p">,</span> <span class="mf">7.89</span><span class="p">],</span>
+                             <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">pitch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">roll</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">yaw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                             <span class="n">radius</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="c1"># Now we can instantiate the system.</span>
+<span class="n">fds</span> <span class="o">=</span> <span class="n">FdemSystem</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">transmitters</span><span class="p">,</span> <span class="n">receivers</span><span class="p">)</span>
+
+<span class="c1"># And use the system to instantiate a datapoint</span>
+<span class="c1">#</span>
+<span class="c1"># Note the extra arguments that can be used to create the data point.</span>
+<span class="c1"># data is for any observed data one might have, while std are the estimated standard</span>
+<span class="c1"># deviations of those observed data.</span>
+<span class="c1">#</span>
+<span class="c1"># Define some in-phase then quadrature data for each frequency.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">145.3</span><span class="p">,</span> <span class="mf">435.8</span><span class="p">,</span> <span class="mf">260.6</span><span class="p">,</span> <span class="mf">875.1</span><span class="p">,</span> <span class="mf">1502.7</span><span class="p">,</span> <span class="mf">1516.9</span><span class="p">,</span>
+             <span class="mf">217.9</span><span class="p">,</span> <span class="mf">412.5</span><span class="p">,</span> <span class="mf">178.7</span><span class="p">,</span> <span class="mf">516.5</span><span class="p">,</span> <span class="mf">405.7</span><span class="p">,</span> <span class="mf">255.7</span><span class="p">]</span>
+
+<span class="n">fdp</span> <span class="o">=</span> <span class="n">FdemDataPoint</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="mf">30.0</span><span class="p">,</span> <span class="n">elevation</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
+                    <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">predictedData</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="n">system</span><span class="o">=</span><span class="n">fds</span><span class="p">,</span> <span class="n">lineNumber</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">fiducial</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+
+<span class="c1"># plt.figure()</span>
+<span class="c1"># _ = fdp.plot()</span>
+
+<span class="c1"># Obtaining a datapoint from a dataset</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++++++</span>
+<span class="c1">#</span>
+<span class="c1"># More often than not, our observed data is stored in a file on disk.</span>
+<span class="c1"># We can read in a dataset and pull datapoints from it.</span>
+<span class="c1">#</span>
+<span class="c1"># For more information about the frequency domain data set see :ref:`Frequency domain dataset`</span>
+
+<span class="c1"># Set some paths and file names</span>
+<span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//Data//&quot;</span>
+<span class="c1"># The data file name</span>
+<span class="n">dataFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Resolve2.txt&#39;</span>
+<span class="c1"># The EM system file name</span>
+<span class="n">systemFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;FdemSystem2.stm&#39;</span>
+</pre></div>
+</div>
+<p>Initialize and read an EM data set
+Prepare the dataset so that we can read a point at a time.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Dataset</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+<span class="c1"># Get a datapoint from the file.</span>
+<span class="n">fdp</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">_read_record</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># # Initialize and read an EM data set</span>
+<span class="c1"># D = FdemData.read_csv(dataFile,systemFile)</span>
+
+<span class="c1"># # Get a data point from the dataset</span>
+<span class="c1"># fdp = D.datapoint(0)</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># _ = fdp.plot()</span>
+
+<span class="c1"># Using a resolve datapoint</span>
+<span class="c1"># +++++++++++++++++++++++++</span>
+
+<span class="c1"># We can define a 1D layered earth model, and use it to predict some data</span>
+<span class="n">nCells</span> <span class="o">=</span> <span class="mi">19</span>
+<span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">nCells</span><span class="p">),</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">depth</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nCells</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mf">10.0</span><span class="p">,</span> <span class="s2">&quot;Depth&quot;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">depth</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">depth</span><span class="p">),</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+
+<span class="c1"># Forward model the data</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">plot_predicted</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+<span class="c1"># Compute the sensitivity matrix for a given model</span>
+<span class="n">J</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">sensitivity</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">J</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Attaching statistical descriptors to the resolve datapoint</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="c1"># Set values of relative and additive error for both systems.</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">relative_error</span> <span class="o">=</span> <span class="mf">0.05</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">additive_error</span> <span class="o">=</span> <span class="mf">10.0</span>
+<span class="c1"># Define a multivariate log normal distribution as the prior on the predicted data.</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">predictedData</span><span class="o">.</span><span class="n">prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvLogNormal&#39;</span><span class="p">,</span> <span class="n">fdp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">fdp</span><span class="o">.</span><span class="n">active</span><span class="p">],</span> <span class="n">fdp</span><span class="o">.</span><span class="n">std</span><span class="p">[</span><span class="n">fdp</span><span class="o">.</span><span class="n">active</span><span class="p">]</span><span class="o">**</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+<span class="c1"># This allows us to evaluate the likelihood of the predicted data</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">fdp</span><span class="o">.</span><span class="n">likelihood</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="c1"># Or the misfit</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">fdp</span><span class="o">.</span><span class="n">data_misfit</span><span class="p">())</span>
+
+<span class="c1"># Plot the misfits for a range of half space conductivities</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">plot_halfspace_responses</span><span class="p">(</span><span class="o">-</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Halfspace responses&quot;</span><span class="p">);</span>
+
+<span class="c1"># We can perform a quick search for the best fitting half space</span>
+<span class="n">halfspace</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">find_best_halfspace</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Best half space conductivity is </span><span class="si">{}</span><span class="s1"> $S/m$&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">halfspace</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">fdp</span><span class="o">.</span><span class="n">plot_predicted</span><span class="p">()</span>
+
+<span class="c1"># Compute the misfit between observed and predicted data</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">fdp</span><span class="o">.</span><span class="n">data_misfit</span><span class="p">())</span>
+
+<span class="c1"># We can attach priors to the height of the datapoint,</span>
+<span class="c1"># the relative error multiplier, and the additive error noise floor</span>
+
+
+<span class="c1"># Define the distributions used as priors.</span>
+<span class="n">zPrior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">fdp</span><span class="o">.</span><span class="n">z</span> <span class="o">-</span> <span class="mf">2.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">fdp</span><span class="o">.</span><span class="n">z</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">relativePrior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">additivePrior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">z_prior</span><span class="o">=</span><span class="n">zPrior</span><span class="p">,</span> <span class="n">relative_error_prior</span><span class="o">=</span><span class="n">relativePrior</span><span class="p">,</span> <span class="n">additive_error_prior</span><span class="o">=</span><span class="n">additivePrior</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+
+<span class="c1"># In order to perturb our solvable parameters, we need to attach proposal distributions</span>
+<span class="n">z_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">fdp</span><span class="o">.</span><span class="n">z</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">relativeProposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">fdp</span><span class="o">.</span><span class="n">relative_error</span><span class="p">,</span> <span class="n">variance</span><span class="o">=</span><span class="mf">2.5e-7</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">additiveProposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvLogNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">fdp</span><span class="o">.</span><span class="n">additive_error</span><span class="p">,</span> <span class="n">variance</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">set_proposals</span><span class="p">(</span><span class="n">relativeProposal</span><span class="p">,</span> <span class="n">additiveProposal</span><span class="p">,</span> <span class="n">z_proposal</span><span class="o">=</span><span class="n">z_proposal</span><span class="p">)</span>
+
+<span class="c1"># With priors set we can auto generate the posteriors</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">set_posteriors</span><span class="p">()</span>
+
+<span class="n">nCells</span> <span class="o">=</span> <span class="mi">19</span>
+<span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">nCells</span><span class="p">),</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">depth</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nCells</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mf">10.0</span><span class="p">,</span> <span class="s2">&quot;Depth&quot;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">depth</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">depth</span><span class="p">),</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+<span class="c1"># Perturb the datapoint and record the perturbations</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">fdp</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+    <span class="n">fdp</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+
+
+<span class="c1"># Plot the posterior distributions</span>
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">fdp</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">overlay</span><span class="o">=</span><span class="n">fdp</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdp.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">fdp</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdp&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">fdp</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdp&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdp.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">fdp1</span> <span class="o">=</span> <span class="n">FdemDataPoint</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;fdp&#39;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">fdp1</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">overlay</span><span class="o">=</span><span class="n">fdp1</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdp.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">fdp</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdp&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">))</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+        <span class="n">fdp</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;fdp&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">FdemData</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;fdp.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">fdp1</span> <span class="o">=</span> <span class="n">FdemDataPoint</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;fdp&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">fdp2</span> <span class="o">=</span> <span class="n">FdemData</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;fdp&#39;</span><span class="p">])</span>
+
+<span class="n">fdp1</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">overlay</span><span class="o">=</span><span class="n">fdp1</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="c1"># %%</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_001.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_001.png" alt="Frequency Domain EM Data" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_002.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_002.png" alt="plot resolve datapoint" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_003.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_003.png" alt="Halfspace responses" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_004.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_004.png" alt="Frequency Domain EM Data" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_005.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_005.png" alt="Frequency Domain EM Data" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_resolve_datapoint_006.png" srcset="../../_images/sphx_glr_plot_resolve_datapoint_006.png" alt="Frequency Domain EM Data, Frequency Domain EM Data" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-733.5454886696688
+1367.81945885548
+Best half space conductivity is [0.097701] $S/m$
+45286.828623928755
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.271 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-datapoints-plot-resolve-datapoint-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/521357f099bff44b6fc76f69b371108f/plot_resolve_datapoint.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_resolve_datapoint.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/950360871c2acdf8ad1b1627e54009da/plot_resolve_datapoint.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_resolve_datapoint.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/d879a27c2deb3b0230b4bdd74daa15f2/plot_resolve_datapoint.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_resolve_datapoint.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-datapoints-plot-skytem-datapoint-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="skytem-datapoint-class">
+<span id="sphx-glr-examples-datapoints-plot-skytem-datapoint-py"></span><h1>Skytem Datapoint Class<a class="headerlink" href="#skytem-datapoint-class" title="Link to this heading"></a></h1>
+<p>Credits:
+We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+<a class="reference external" href="https://github.com/GeoscienceAustralia/ga-aem">https://github.com/GeoscienceAustralia/ga-aem</a></p>
+<p>For ground-based time domain data, we are using Dieter Werthmuller’s python package Empymod
+<a class="reference external" href="https://empymod.github.io/">https://empymod.github.io/</a></p>
+<p>Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Waveform</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">SquareLoop</span><span class="p">,</span> <span class="n">CircularLoop</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">butterworth</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TdemSystem</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TdemData</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TdemDataPoint</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+
+<span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//data//&quot;</span>
+
+<span class="c1"># Obtaining a datapoint from a dataset</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++++++</span>
+<span class="c1"># More often than not, our observed data is stored in a file on disk.</span>
+<span class="c1"># We can read in a dataset and pull datapoints from it.</span>
+<span class="c1">#</span>
+<span class="c1"># For more information about the time domain data set, see :ref:`Time domain dataset`</span>
+
+<span class="c1"># The data file name</span>
+<span class="n">dataFile</span><span class="o">=</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;skytem_saline_clay.csv&#39;</span>
+<span class="c1"># The EM system file name</span>
+<span class="n">systemFile</span><span class="o">=</span><span class="p">[</span><span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;SkytemHM.stm&#39;</span><span class="p">,</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;SkytemLM.stm&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Initialize and read an EM data set
+Prepare the dataset so that we can read a point at a time.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Dataset</span> <span class="o">=</span> <span class="n">TdemData</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+<span class="c1"># Get a datapoint from the file.</span>
+<span class="n">tdp</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">_read_record</span><span class="p">()</span>
+
+<span class="n">Dataset</span><span class="o">.</span><span class="n">_file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<section id="using-a-time-domain-datapoint">
+<h2>Using a time domain datapoint<a class="headerlink" href="#using-a-time-domain-datapoint" title="Link to this heading"></a></h2>
+<p>We can define a 1D layered earth model, and use it to predict some data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">500.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">],</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">75.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]),</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Forward model the data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">tdp</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot_predicted</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_001.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_001.png" alt="Time Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/Users/nfoks/codes/repositories/geobipy_docs/geobipy/src/classes/data/datapoint/TdemDataPoint.py:363: RuntimeWarning: divide by zero encountered in log
+  additive_error = exp(log(self.additive_error[i]) - 0.5 * (log(off_times) - log(1e-3)))
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">plotDataResidual</span><span class="p">(</span><span class="n">yscale</span><span class="o">=</span><span class="s1">&#39;log&#39;</span><span class="p">,</span> <span class="n">xscale</span><span class="o">=</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;new&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_002.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_002.png" alt="new" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;new&#39;)
+</pre></div>
+</div>
+<p>Compute the sensitivity matrix for a given model</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">J</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">sensitivity</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">J</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_003.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_003.png" alt="plot skytem datapoint" class = "sphx-glr-single-img"/></section>
+<section id="attaching-statistical-descriptors-to-the-skytem-datapoint">
+<h2>Attaching statistical descriptors to the skytem datapoint<a class="headerlink" href="#attaching-statistical-descriptors-to-the-skytem-datapoint" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="c1"># Set values of relative and additive error for both systems.</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">relative_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">]</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">additive_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">1e-14</span><span class="p">,</span> <span class="mf">1e-13</span><span class="p">]</span>
+<span class="c1"># Define a multivariate normal distribution as the prior on the predicted data.</span>
+<span class="n">data_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">tdp</span><span class="o">.</span><span class="n">active</span><span class="p">],</span> <span class="n">tdp</span><span class="o">.</span><span class="n">std</span><span class="p">[</span><span class="n">tdp</span><span class="o">.</span><span class="n">active</span><span class="p">]</span><span class="o">**</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+<span class="n">tdp</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">data_prior</span><span class="o">=</span><span class="n">data_prior</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This allows us to evaluate the likelihood of the predicted data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">likelihood</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="c1"># Or the misfit</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">data_misfit</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-320327.7331520327
+643134.8665682999
+</pre></div>
+</div>
+<p>Plot the misfits for a range of half space conductivities</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot_halfspace_responses</span><span class="p">(</span><span class="o">-</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Halfspace responses&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_004.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_004.png" alt="Halfspace responses" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;Halfspace responses&#39;)
+</pre></div>
+</div>
+<p>We can perform a quick search for the best fitting half space</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">halfspace</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">find_best_halfspace</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Best half space conductivity is </span><span class="si">{}</span><span class="s1"> $S/m$&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">halfspace</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot_predicted</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_005.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_005.png" alt="Time Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best half space conductivity is [0.01047616] $S/m$
+</pre></div>
+</div>
+<p>Compute the misfit between observed and predicted data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">data_misfit</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>19656.31514467744
+</pre></div>
+</div>
+<p>We can attach priors to the height of the datapoint,
+the relative error multiplier, and the additive error noise floor</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the distributions used as priors.</span>
+<span class="n">z_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="mf">2.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">relativePrior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">],</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">additivePrior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">1e-16</span><span class="p">,</span> <span class="mf">1e-16</span><span class="p">],</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">1e-10</span><span class="p">,</span> <span class="mf">1e-10</span><span class="p">],</span> <span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">relative_error_prior</span><span class="o">=</span><span class="n">relativePrior</span><span class="p">,</span> <span class="n">additive_error_prior</span><span class="o">=</span><span class="n">additivePrior</span><span class="p">,</span> <span class="n">z_prior</span><span class="o">=</span><span class="n">z_prior</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_skytem_datapoint.py:135: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  z_prior = Distribution(&#39;Uniform&#39;, min=np.float64(tdp.z) - 2.0, max=np.float64(tdp.z) + 2.0, prng=prng)
+</pre></div>
+</div>
+<p>In order to perturb our solvable parameters, we need to attach proposal distributions</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">z_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">z</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">relativeProposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">relative_error</span><span class="p">,</span> <span class="n">variance</span><span class="o">=</span><span class="mf">2.5e-7</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">additiveProposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvLogNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">additive_error</span><span class="p">,</span> <span class="n">variance</span><span class="o">=</span><span class="mf">2.5e-3</span><span class="p">,</span> <span class="n">linearSpace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">set_proposals</span><span class="p">(</span><span class="n">relativeProposal</span><span class="p">,</span> <span class="n">additiveProposal</span><span class="p">,</span> <span class="n">z_proposal</span><span class="o">=</span><span class="n">z_proposal</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>With priorss set we can auto generate the posteriors</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">tdp</span><span class="o">.</span><span class="n">set_posteriors</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Perturb the datapoint and record the perturbations
+Note we are not using the priors to accept or reject perturbations.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">tdp</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+    <span class="n">tdp</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Plot the posterior distributions</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">overlay</span><span class="o">=</span><span class="n">tdp</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_skytem_datapoint_006.png" srcset="../../_images/sphx_glr_plot_skytem_datapoint_006.png" alt="Time Domain EM Data" class = "sphx-glr-single-img"/></section>
+<section id="file-format-for-a-time-domain-datapoint">
+<h2>File Format for a time domain datapoint<a class="headerlink" href="#file-format-for-a-time-domain-datapoint" title="Link to this heading"></a></h2>
+<p>Here we describe the file format for a time domain datapoint.</p>
+<p>For individual datapoints we are using the AarhusInv data format.</p>
+<p>Here we take the description for the AarhusInv TEM data file, modified to reflect what we can
+currently handle in GeoBIPy.</p>
+<dl class="simple">
+<dt>Line 1 :: string</dt><dd><p>User-defined label describing the TEM datapoint.
+This line must contain the following, separated by semicolons.
+XUTM=
+YUTM=
+Elevation=
+StationNumber=
+LineNumber=
+Current=</p>
+</dd>
+<dt>Line 2 :: first integer, sourceType</dt><dd><p>7 = Rectangular loop source parallel to the x - y plane</p>
+</dd>
+<dt>Line 2 :: second integer, polarization</dt><dd><p>3 = Vertical magnetic field</p>
+</dd>
+<dt>Line 3 :: 6 floats, transmitter and receiver offsets relative to X/Y UTM location.</dt><dd><p>If sourceType = 7, Position of the center loop sounding.</p>
+</dd>
+<dt>Line 4 :: Transmitter loop dimensions</dt><dd><p>If sourceType = 7, 2 floats.  Loop side length in the x and y directions</p>
+</dd>
+<dt>Line 5 :: Fixed</dt><dd><p>3 3 3</p>
+</dd>
+<dt>Line 6 :: first integer, transmitter waveform type. Fixed</dt><dd><p>3 = User defined waveform.</p>
+</dd>
+<dt>Line 6 :: second integer, number of transmitter waveforms. Fixed</dt><dd><p>1</p>
+</dd>
+<dt>Line 7 :: transmitter waveform definition</dt><dd><p>A user-defined waveform with piecewise linear segments.
+A full transmitter waveform definition consists of a number of linear segments
+This line contains an integer as the first entry, which specifies the number of
+segments, followed by each segment with 4 floats each. The 4 floats per segment
+are the start and end times, and start and end amplitudes of the waveform. e.g.
+3  -8.333e-03 -8.033e-03 0.0 1.0 -8.033e-03 0.0 1.0 1.0 0.0 5.4e-06 1.0 0.0</p>
+</dd>
+<dt>Line 8 :: On time information. Not used but needs specifying.</dt><dd><p>1 1 1</p>
+</dd>
+<dt>Line 9 :: On time low-pass filters.  Not used but need specifying.</dt><dd><p>0</p>
+</dd>
+<dt>Line 10 :: On time high-pass filters. Not used but need specifying.</dt><dd><p>0</p>
+</dd>
+<dt>Line 11 :: Front-gate time. Not used but need specifying.</dt><dd><p>0.0</p>
+</dd>
+<dt>Line 12 :: first integer, Number of off time filters</dt><dd><p>Number of filters</p>
+</dd>
+<dt>Line 12 :: second integer, Order of the butterworth filter</dt><dd><p>1 or 2</p>
+</dd>
+<dt>Line 12 :: cutoff frequencies Hz, one per the number of filters</dt><dd><p>e.g. 4.5e5</p>
+</dd>
+<dt>Line 13 :: Off time high pass filters.</dt><dd><p>See Line 12</p>
+</dd>
+</dl>
+<p>Lines after 13 contain 3 columns that pertain to
+Measurement Time, Data Value, Estimated Standard Deviation</p>
+<p>Example data files are contained in
+<a class="reference external" href="https://github.com/usgs/geobipy/tree/master/documentation_source/source/examples/supplementary/Data">the supplementary folder</a> in this repository</p>
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+<p><a class="reference download internal" download="" href="../../_downloads/2dd62cc642303de6b0d2af1a789eccaa/plot_skytem_datapoint.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_skytem_datapoint.py</span></code></a></p>
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+    <link rel="search" title="Search" href="../../search.html" />
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#datapoints">Datapoints</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_resolve_datapoint.html">Frequency domain datapoint</a></li>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">Tempest Datapoint Class</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#using-a-tempest-domain-datapoint">Using a tempest domain datapoint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#attaching-statistical-descriptors-to-the-tempest-datapoint">Attaching statistical descriptors to the tempest datapoint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+      <li class="breadcrumb-item active">Tempest Datapoint Class</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Datapoints/plot_tempest_datapoint.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-datapoints-plot-tempest-datapoint-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="tempest-datapoint-class">
+<span id="sphx-glr-examples-datapoints-plot-tempest-datapoint-py"></span><h1>Tempest Datapoint Class<a class="headerlink" href="#tempest-datapoint-class" title="Link to this heading"></a></h1>
+<p>Credits:
+We would like to thank Ross Brodie at Geoscience Australia for his airborne time domain forward modeller
+<a class="reference external" href="https://github.com/GeoscienceAustralia/ga-aem">https://github.com/GeoscienceAustralia/ga-aem</a></p>
+<p>For ground-based time domain data, we are using Dieter Werthmuller’s python package Empymod
+<a class="reference external" href="https://empymod.github.io/">https://empymod.github.io/</a></p>
+<p>Thanks to Dieter for his help getting Empymod ready for incorporation into GeoBIPy</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">TempestData</span>
+<span class="c1"># from geobipy import TemDataPoint</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">get_prng</span>
+
+<span class="n">dataFolder</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//data//&quot;</span>
+<span class="c1"># dataFolder = &quot;source//examples//supplementary//Data&quot;</span>
+
+<span class="c1"># Obtaining a tempest datapoint from a dataset</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++++++++++++++</span>
+<span class="c1"># More often than not, our observed data is stored in a file on disk.</span>
+<span class="c1"># We can read in a dataset and pull datapoints from it.</span>
+<span class="c1">#</span>
+<span class="c1"># For more information about the time domain data set, see :ref:`Time domain dataset`</span>
+
+<span class="c1"># The data file name</span>
+<span class="n">dataFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;tempest_saline_clay.csv&#39;</span>
+<span class="c1"># The EM system file name</span>
+<span class="n">systemFile</span> <span class="o">=</span> <span class="n">dataFolder</span> <span class="o">+</span> <span class="s1">&#39;Tempest.stm&#39;</span>
+
+<span class="c1"># Prepare the dataset so that we can read a point at a time.</span>
+<span class="n">Dataset</span> <span class="o">=</span> <span class="n">TempestData</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">dataFile</span><span class="p">,</span> <span class="n">systemFile</span><span class="p">)</span>
+<span class="c1"># Get a datapoint from the file.</span>
+<span class="n">tdp</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">_read_record</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">prng</span> <span class="o">=</span> <span class="n">get_prng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">146100583096709124601953385843316024947</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_datapoint_001.png" srcset="../../_images/sphx_glr_plot_tempest_datapoint_001.png" alt="Time Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+</pre></div>
+</div>
+<section id="using-a-tempest-domain-datapoint">
+<h2>Using a tempest domain datapoint<a class="headerlink" href="#using-a-tempest-domain-datapoint" title="Link to this heading"></a></h2>
+<p>We can define a 1D layered earth model, and use it to predict some data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">50.0</span><span class="p">,</span> <span class="mf">75.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]),</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+
+<span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">350</span><span class="p">,</span> <span class="mi">31</span><span class="p">);</span> <span class="n">e</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">e</span><span class="p">),</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Forward model the data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">tdp</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;primary&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">primary_field</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;sx&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">secondary_field</span><span class="p">[:</span><span class="mi">15</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;sz&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">secondary_field</span><span class="p">[</span><span class="mi">15</span><span class="p">:])</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># plt.subplot(121)</span>
+<span class="c1"># _ = mod.pcolor(transpose=True)</span>
+<span class="c1"># plt.subplot(122)</span>
+<span class="c1"># _ = tdp.plot()</span>
+<span class="c1"># _ = tdp.plot_predicted()</span>
+<span class="c1"># plt.tight_layout()</span>
+<span class="c1"># plt.suptitle(&#39;Model and response&#39;)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # plt.figure()</span>
+<span class="c1"># # tdp.plotDataResidual(xscale=&#39;log&#39;)</span>
+<span class="c1"># # plt.title(&#39;data residual&#39;)</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # Compute the sensitivity matrix for a given model</span>
+<span class="n">J</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">sensitivity</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+<span class="c1"># plt.figure()</span>
+<span class="c1"># _ = np.abs(J).pcolor(equalize=True, log=10, flipY=True)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;J&#39;</span><span class="p">,</span> <span class="n">J</span><span class="p">)</span>
+<span class="c1"># print(&#39;J shape&#39;, J.shape)</span>
+<span class="c1"># print(&#39;sx 0&#39;, J[:16, 0])</span>
+
+<span class="n">tdp</span><span class="o">.</span><span class="n">fm_dlogc</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;new primary&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">primary_field</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;sx&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">secondary_field</span><span class="p">[:</span><span class="mi">15</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;sz&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">secondary_field</span><span class="p">[</span><span class="mi">15</span><span class="p">:])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;new J&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">sensitivity_matrix</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>primary [34.27253219 17.55503397]
+sx [4.46362582 2.52720951 2.10544857 ... 0.34346631 0.27359586 0.19875285]
+sz [6.47100177 4.53101158 3.87594468 ... 1.05345525 0.79969548 0.56994112]
+J [[ 1.13463137e-01  1.49920887e-01  1.76789170e-01 ... -1.01809840e-09
+   1.13341751e-11  7.27489718e-13]
+ [ 2.09383016e-02  3.20412212e-02  4.74815387e-02 ... -1.02489023e-09
+   1.15994185e-11  7.25910166e-13]
+ [ 1.04188675e-02  1.61552555e-02  2.45575508e-02 ... -1.03167228e-09
+   1.18645190e-11  7.24296662e-13]
+ ...
+ [ 7.20880061e-05  1.13758034e-04  1.79138645e-04 ... -6.62044639e-09
+   1.91310127e-10  4.83737910e-13]
+ [ 3.95655826e-05  6.25753935e-05  9.87713313e-05 ... -6.33418159e-09
+   2.26727066e-10 -1.05451995e-12]
+ [ 1.60007270e-05  3.08385747e-05  5.05626410e-05 ... -1.28316523e-09
+   1.11041966e-10 -2.41585978e-12]]
+new primary [34.27253219 17.55503397]
+sx [4.46362582 2.52720951 2.10544857 ... 0.34346631 0.27359586 0.19875285]
+sz [6.47100177 4.53101158 3.87594468 ... 1.05345525 0.79969548 0.56994112]
+new J [[ 1.13463137e-01  1.49920887e-01  1.76789170e-01 ... -1.01809840e-09
+   1.13341751e-11  7.27489718e-13]
+ [ 2.09383016e-02  3.20412212e-02  4.74815387e-02 ... -1.02489023e-09
+   1.15994185e-11  7.25910166e-13]
+ [ 1.04188675e-02  1.61552555e-02  2.45575508e-02 ... -1.03167228e-09
+   1.18645190e-11  7.24296662e-13]
+ ...
+ [ 7.20880061e-05  1.13758034e-04  1.79138645e-04 ... -6.62044639e-09
+   1.91310127e-10  4.83737910e-13]
+ [ 3.95655826e-05  6.25753935e-05  9.87713313e-05 ... -6.33418159e-09
+   2.26727066e-10 -1.05451995e-12]
+ [ 1.60007270e-05  3.08385747e-05  5.05626410e-05 ... -1.28316523e-09
+   1.11041966e-10 -2.41585978e-12]]
+</pre></div>
+</div>
+</section>
+<section id="attaching-statistical-descriptors-to-the-tempest-datapoint">
+<h2>Attaching statistical descriptors to the tempest datapoint<a class="headerlink" href="#attaching-statistical-descriptors-to-the-tempest-datapoint" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="c1"># Set relative errors for the primary fields, and secondary fields.</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">relative_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">]</span>
+
+<span class="c1"># Set the additive errors for</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">additive_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([[</span><span class="mf">0.011474</span><span class="p">,</span> <span class="mf">0.012810</span><span class="p">,</span> <span class="mf">0.008507</span><span class="p">,</span> <span class="mf">0.005154</span><span class="p">,</span> <span class="mf">0.004742</span><span class="p">,</span> <span class="mf">0.004477</span><span class="p">,</span> <span class="mf">0.004168</span><span class="p">,</span> <span class="mf">0.003539</span><span class="p">,</span> <span class="mf">0.003352</span><span class="p">,</span> <span class="mf">0.003213</span><span class="p">,</span> <span class="mf">0.003161</span><span class="p">,</span> <span class="mf">0.003122</span><span class="p">,</span> <span class="mf">0.002587</span><span class="p">,</span> <span class="mf">0.002038</span><span class="p">,</span> <span class="mf">0.002201</span><span class="p">],</span>
+                                <span class="p">[</span><span class="mf">0.007383</span><span class="p">,</span> <span class="mf">0.005693</span><span class="p">,</span> <span class="mf">0.005178</span><span class="p">,</span> <span class="mf">0.003659</span><span class="p">,</span> <span class="mf">0.003426</span><span class="p">,</span> <span class="mf">0.003046</span><span class="p">,</span> <span class="mf">0.003095</span><span class="p">,</span> <span class="mf">0.003247</span><span class="p">,</span> <span class="mf">0.002775</span><span class="p">,</span> <span class="mf">0.002627</span><span class="p">,</span> <span class="mf">0.002460</span><span class="p">,</span> <span class="mf">0.002178</span><span class="p">,</span> <span class="mf">0.001754</span><span class="p">,</span> <span class="mf">0.001405</span><span class="p">,</span> <span class="mf">0.001283</span><span class="p">]])</span>
+<span class="c1"># Define a multivariate log normal distribution as the prior on the predicted data.</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">predictedData</span><span class="o">.</span><span class="n">prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvLogNormal&#39;</span><span class="p">,</span> <span class="n">tdp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">tdp</span><span class="o">.</span><span class="n">active</span><span class="p">],</span> <span class="n">tdp</span><span class="o">.</span><span class="n">std</span><span class="p">[</span><span class="n">tdp</span><span class="o">.</span><span class="n">active</span><span class="p">]</span><span class="o">**</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This allows us to evaluate the likelihood of the predicted data</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">likelihood</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="c1"># Or the misfit</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">data_misfit</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-36389.6500813217
+72940.71365767403
+</pre></div>
+</div>
+<p>Plot the misfits for a range of half space conductivities</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot_halfspace_responses</span><span class="p">(</span><span class="o">-</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Halfspace responses&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tempest_datapoint_002.png" srcset="../../_images/sphx_glr_plot_tempest_datapoint_002.png" alt="Halfspace responses" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;Halfspace responses&#39;)
+</pre></div>
+</div>
+<p>We can perform a quick search for the best fitting half space</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">halfspace</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">find_best_halfspace</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Best half space conductivity is </span><span class="si">{}</span><span class="s1"> $S/m$&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">halfspace</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">tdp</span><span class="o">.</span><span class="n">plot_predicted</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">plot_secondary_field</span><span class="p">()</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">plot_predicted_secondary_field</span><span class="p">()</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # We can attach priors to the height of the datapoint,</span>
+<span class="c1"># # the relative error multiplier, and the additive error noise floor</span>
+
+<span class="c1"># Define the distributions used as priors.</span>
+<span class="n">relative_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">],</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_x_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_z_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_pitch_prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">pitch</span> <span class="o">-</span> <span class="mf">5.0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">pitch</span> <span class="o">+</span> <span class="mf">5.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">relative_error_prior</span><span class="o">=</span><span class="n">relative_prior</span><span class="p">,</span> <span class="n">receiver_x_prior</span><span class="o">=</span><span class="n">receiver_x_prior</span><span class="p">,</span> <span class="n">receiver_z_prior</span><span class="o">=</span><span class="n">receiver_z_prior</span><span class="p">,</span> <span class="n">receiver_pitch_prior</span><span class="o">=</span><span class="n">receiver_pitch_prior</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_tempest_datapoint_003.png" srcset="../../_images/sphx_glr_plot_tempest_datapoint_003.png" alt="Time Domain EM Data" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_tempest_datapoint_004.png" srcset="../../_images/sphx_glr_plot_tempest_datapoint_004.png" alt="plot tempest datapoint" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best half space conductivity is [0.01830738] $S/m$
+/Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_tempest_datapoint.py:156: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  receiver_x_prior = Distribution(&#39;Uniform&#39;, min=np.float64(tdp.receiver.x) - 1.0, max=np.float64(tdp.receiver.x) + 1.0, prng=prng)
+/Users/nfoks/codes/repositories/geobipy_docs/documentation_source/source/examples/Datapoints/plot_tempest_datapoint.py:157: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  receiver_z_prior = Distribution(&#39;Uniform&#39;, min=np.float64(tdp.receiver.z) - 1.0, max=np.float64(tdp.receiver.z) + 1.0, prng=prng)
+</pre></div>
+</div>
+<p>In order to perturb our solvable parameters, we need to attach proposal distributions</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">relative_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">relative_error</span><span class="p">,</span> <span class="n">variance</span><span class="o">=</span><span class="mf">2.5e-4</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_x_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_z_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">z</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">receiver_pitch_proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">tdp</span><span class="o">.</span><span class="n">receiver</span><span class="o">.</span><span class="n">pitch</span><span class="p">,</span> <span class="n">variance</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">tdp</span><span class="o">.</span><span class="n">set_proposals</span><span class="p">(</span><span class="n">relative_error_proposal</span><span class="o">=</span><span class="n">relative_proposal</span><span class="p">,</span>
+                  <span class="n">receiver_x_proposal</span><span class="o">=</span><span class="n">receiver_x_proposal</span><span class="p">,</span>
+                  <span class="n">receiver_z_proposal</span><span class="o">=</span><span class="n">receiver_z_proposal</span><span class="p">,</span>
+                  <span class="n">receiver_pitch_proposal</span><span class="o">=</span><span class="n">receiver_pitch_proposal</span><span class="p">,</span>
+                  <span class="n">solve_additive_error</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">additive_error_proposal_variance</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>With priors set we can auto generate the posteriors</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">tdp</span><span class="o">.</span><span class="n">set_posteriors</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Perturb the datapoint and record the perturbations
+Note we are not using the priors to accept or reject perturbations.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">tdp</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+    <span class="n">tdp</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.653 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-datapoints-plot-tempest-datapoint-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/93b8636d5d11f2858e28438141a2ee46/plot_tempest_datapoint.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tempest_datapoint.ipynb</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/439c06574d36795305525eb492c7861b/plot_tempest_datapoint.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tempest_datapoint.py</span></code></a></p>
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+<h1>Datapoints<a class="headerlink" href="#datapoints" title="Link to this heading"></a></h1>
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+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
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+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-datapoints-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:11.231</strong> total execution time for 3 files <strong>from examples/Datapoints</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
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+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_resolve_datapoint.html#sphx-glr-examples-datapoints-plot-resolve-datapoint-py"><span class="std std-ref">Frequency domain datapoint</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_resolve_datapoint.py</span></code>)</p></td>
+<td><p>00:07.271</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_skytem_datapoint.html#sphx-glr-examples-datapoints-plot-skytem-datapoint-py"><span class="std std-ref">Skytem Datapoint Class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_skytem_datapoint.py</span></code>)</p></td>
+<td><p>00:02.306</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_tempest_datapoint.html#sphx-glr-examples-datapoints-plot-tempest-datapoint-py"><span class="std std-ref">Tempest Datapoint Class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tempest_datapoint.py</span></code>)</p></td>
+<td><p>00:01.653</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#distributions">Distributions</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Distribution Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#univariate-normal-distribution">Univariate Normal Distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#multivariate-normal-distribution">Multivariate Normal Distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
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+      <li class="breadcrumb-item active">Distribution Class</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Distributions/plot_distributions.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-distributions-plot-distributions-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="distribution-class">
+<span id="sphx-glr-examples-distributions-plot-distributions-py"></span><h1>Distribution Class<a class="headerlink" href="#distribution-class" title="Link to this heading"></a></h1>
+<p>Handles the initialization of different statistical distribution</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">plotting</span> <span class="k">as</span> <span class="n">cP</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="univariate-normal-distribution">
+<h1>Univariate Normal Distribution<a class="headerlink" href="#univariate-normal-distribution" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">D</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+<span class="c1"># Get the bins of the Distribution from +- 4 standard deviations of the mean</span>
+<span class="n">bins</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">bins</span><span class="p">()</span>
+
+<span class="c1"># Grab random samples from the distribution</span>
+<span class="n">D</span><span class="o">.</span><span class="n">rng</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># We can then get the Probability Density Function for those bins</span>
+<span class="n">pdf</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># And we can plot that PDF</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">pdf</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_distributions_001.png" srcset="../../_images/sphx_glr_plot_distributions_001.png" alt="plot distributions" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x17f679190&gt;]
+</pre></div>
+</div>
+</section>
+<section id="multivariate-normal-distribution">
+<h1>Multivariate Normal Distribution<a class="headerlink" href="#multivariate-normal-distribution" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">D</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,[</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">],[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">],</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">D</span><span class="o">.</span><span class="n">rng</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([ 0.64050649,  1.77177243, -0.34500474])
+</pre></div>
+</div>
+<p>Uniform Distribution</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">D</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">D</span><span class="o">.</span><span class="n">bins</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>DataArray([0.        , 0.01010101, 0.02020202, ..., 0.97979798,
+           0.98989899, 1.        ])
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.050 seconds)</p>
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index 00000000..685f3361
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Distributions &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+            GeoBIPy
+          </a>
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+                2.3.1
+              </div>
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+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
+
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Distributions</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Distributions/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="distributions">
+<h1>Distributions<a class="headerlink" href="#distributions" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
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+    <p>&#169; Copyright None.</p>
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\ No newline at end of file
diff --git a/docs/examples/Distributions/sg_execution_times.html b/docs/examples/Distributions/sg_execution_times.html
new file mode 100644
index 00000000..0a20ee33
--- /dev/null
+++ b/docs/examples/Distributions/sg_execution_times.html
@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+                2.3.1
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+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
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+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
+
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+            <a href="../../_sources/examples/Distributions/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-distributions-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:00.050</strong> total execution time for 1 file <strong>from examples/Distributions</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
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+</style>
+<script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_distributions.html#sphx-glr-examples-distributions-plot-distributions-py"><span class="std std-ref">Distribution Class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_distributions.py</span></code>)</p></td>
+<td><p>00:00.050</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
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\ No newline at end of file
diff --git a/docs/examples/HDF5/hdf5.html b/docs/examples/HDF5/hdf5.html
new file mode 100644
index 00000000..c92db005
--- /dev/null
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@@ -0,0 +1,241 @@
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+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Using HDF5 within GeoBIPy &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+</head>
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+
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+          
+          <a href="../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+    <input type="hidden" name="area" value="default" />
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+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Using HDF5 within GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
+</ul>
+</li>
+</ul>
+
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+
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+      <li class="breadcrumb-item active">Using HDF5 within GeoBIPy</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/HDF5/hdf5.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-hdf5-hdf5-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="using-hdf5-within-geobipy">
+<span id="sphx-glr-examples-hdf5-hdf5-py"></span><h1>Using HDF5 within GeoBIPy<a class="headerlink" href="#using-hdf5-within-geobipy" title="Link to this heading"></a></h1>
+<p>Inference for large scale datasets in GeoBIPy is handled using MPI and distributed memory systems.
+A common bottleneck with large parallel algorithms is the input output of information to disk.
+We use HDF5 to read and write data in order to leverage the parallel capabililties of the HDF5 API.</p>
+<p>Each object within GeoBIPy has a create_hdf, write_hdf, and read_hdf routine.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+</pre></div>
+</div>
+<p>StatArray</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Instantiate a StatArray</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;an Array&#39;</span><span class="p">,</span> <span class="n">units</span> <span class="o">=</span> <span class="s1">&#39;some units&#39;</span><span class="p">)</span>
+
+<span class="c1"># Write the StatArray to a HDF file.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
+
+<span class="c1"># Read the StatArray back in.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>There are actually steps within the “toHdf” function.
+First, space is created within the HDF file and second, the data is written to that space
+These functions are split because during the execution of a parallel enabled program,
+all the space within the HDF file needs to be allocated before we can write to the file
+using multiple cores.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write the StatArray to a HDF file.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
+
+<span class="c1"># Read the StatArray back in.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The create and write HDF methods also allow extra space to be allocated so that
+the extra memory can be written later, perhaps by multiple cores.
+Here we specify space for 2 arrays, the memory is stored contiguously as a numpy array.
+We then write to only the first index.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write the StatArray to a HDF file.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">nRepeats</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Read the StatArray back in.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The duplication can also be a shape.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write the StatArray to a HDF file.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">nRepeats</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+<span class="c1"># Read the StatArray back in.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Similarly, we can duplicate a 2D array with an extra 2D duplication</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;an Array&#39;</span><span class="p">,</span> <span class="n">units</span> <span class="o">=</span> <span class="s1">&#39;some units&#39;</span><span class="p">)</span>
+<span class="c1"># Write the StatArray to a HDF file.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">nRepeats</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">x</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+<span class="c1"># Read the StatArray back in.</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s2">&quot;x.h5&quot;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
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+<h1>HDF 5<a class="headerlink" href="#hdf-5" title="Link to this heading"></a></h1>
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+</ul>
+
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+
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+      </li>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-hdf5-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:00.000</strong> total execution time for 1 file <strong>from examples/HDF5</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+<link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
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+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="hdf5.html#sphx-glr-examples-hdf5-hdf5-py"><span class="std std-ref">Using HDF5 within GeoBIPy</span></a> (<code class="docutils literal notranslate"><span class="pre">hdf5.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
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diff --git a/docs/examples/Inference_1D/plot_inference_1d_resolve.html b/docs/examples/Inference_1D/plot_inference_1d_resolve.html
new file mode 100644
index 00000000..228812aa
--- /dev/null
+++ b/docs/examples/Inference_1D/plot_inference_1d_resolve.html
@@ -0,0 +1,253 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Running GeoBIPy to invert Resolve data &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
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+    <link rel="index" title="Index" href="../../genindex.html" />
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+    <link rel="next" title="Running GeoBIPy to invert Skytem data" href="plot_inference_1d_skytem.html" />
+    <link rel="prev" title="Using HDF5 within GeoBIPy" href="../HDF5/hdf5.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#d-inference">1D Inference</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Running GeoBIPy to invert Resolve data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_skytem.html">Running GeoBIPy to invert Skytem data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_tempest.html">Running GeoBIPy to invert Tempest data</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+      </nav>
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+      <li class="breadcrumb-item active">Running GeoBIPy to invert Resolve data</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Inference_1D/plot_inference_1d_resolve.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-1d-plot-inference-1d-resolve-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="running-geobipy-to-invert-resolve-data">
+<span id="sphx-glr-examples-inference-1d-plot-inference-1d-resolve-py"></span><h1>Running GeoBIPy to invert Resolve data<a class="headerlink" href="#running-geobipy-to-invert-resolve-data" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">pathlib</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference3D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">user_parameters</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">get_prng</span>
+
+<span class="k">def</span> <span class="nf">checkCommandArguments</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Check the users command line arguments. &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">argparse</span>
+    <span class="c1"># warnings.filterwarnings(&#39;error&#39;)</span>
+
+    <span class="n">Parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;GeoBIPy&quot;</span><span class="p">,</span>
+                                     <span class="n">formatter_class</span><span class="o">=</span><span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentDefaultsHelpFormatter</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--index&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;job array index 0-18&#39;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--data&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Data type. Choose from [&#39;skytem_512&#39;, &#39;tempest&#39;, &#39;resolve&#39;]&quot;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--model&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Model type. Choose from [&#39;glacial&#39;, &#39;saline_clay&#39;, &#39;resistive_dolomites&#39;, &#39;resistive_basement&#39;, &#39;coastal_salt_water&#39;, &#39;ice_over_salt_water&#39;]&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">args</span> <span class="o">=</span> <span class="n">checkCommandArguments</span><span class="p">()</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
+
+<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+<span class="n">data_type</span> <span class="o">=</span> <span class="s2">&quot;Resolve&quot;</span>
+<span class="n">model_type</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>The directory where HDF files will be stored
+%%</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">)</span>
+<span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">islink</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Failed to delete </span><span class="si">%s</span><span class="s1">. Reason: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">file_path</span>
+
+<span class="n">data_filename</span> <span class="o">=</span> <span class="n">data_type</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="n">model_type</span>
+
+<span class="n">supplementary</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//&quot;</span>
+
+<span class="n">parameter_file</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//options_files//</span><span class="si">{}</span><span class="s2">_options&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">)</span>
+<span class="n">inputFile</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">inputFile</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Cannot find input file </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inputFile</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">output_directory</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">output_directory</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Make sure the output directory exists </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using user input file </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Output files will be produced at </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="n">kwargs</span> <span class="o">=</span> <span class="n">user_parameters</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">inputFile</span><span class="p">)</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;n_markov_chains&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5000</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s1">&#39;//data//&#39;</span> <span class="o">+</span> <span class="n">data_filename</span> <span class="o">+</span> <span class="s1">&#39;.csv&#39;</span>
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//data//&quot;</span> <span class="o">+</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]</span>
+
+<span class="c1"># Everyone needs the system classes read in early.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">])</span>
+
+<span class="c1"># Start keeping track of time.</span>
+<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+<span class="n">seed</span> <span class="o">=</span> <span class="mi">146100583096709124601953385843316024947</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">get_prng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+<span class="n">inference3d</span> <span class="o">=</span> <span class="n">Inference3D</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">inference3d</span><span class="o">.</span><span class="n">create_hdf5</span><span class="p">(</span><span class="n">directory</span><span class="o">=</span><span class="n">output_directory</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Created hdf5 files in </span><span class="si">{}</span><span class="s2"> h:m:s&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))))</span>
+
+<span class="n">inference3d</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_inference_1d_resolve_001.png" srcset="../../_images/sphx_glr_plot_inference_1d_resolve_001.png" alt="Fiducial [30], Frequency Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using user input file ..//..//supplementary////options_files//Resolve_options
+Output files will be produced at Resolve/glacial
+Creating HDF5 files, this may take a few minutes...
+Files are being created for data files ..//..//supplementary////data//Resolve_glacial.csv and system files ..//..//supplementary////data//..//data/FdemSystem2.stm
+Created hdf5 file for line 0.0 with 79 data points
+Created hdf5 files 79 total data points
+Created hdf5 files in 0:00:00.164874 h:m:s
+i=5000, k=1, acc=*49.200, 0.006 s/Model, 28.026 s Elapsed, eta=--:--:-- h:m:s
+
+Remaining Points -30/1 || Elapsed Time: 0:00:28.993323 h:m:s || ETA 0:00:00.935268 h:m:s
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 29.449 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-1d-plot-inference-1d-resolve-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/0a7e593d4f3c0d61cfffc73460926f3c/plot_inference_1d_resolve.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_1d_resolve.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/09f048ba9debdfb740a9a828a370954b/plot_inference_1d_resolve.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_1d_resolve.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/427deb048b69f9c7dc66abcfe303e6d9/plot_inference_1d_resolve.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_1d_resolve.zip</span></code></a></p>
+</div>
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diff --git a/docs/examples/Inference_1D/plot_inference_1d_skytem.html b/docs/examples/Inference_1D/plot_inference_1d_skytem.html
new file mode 100644
index 00000000..87666570
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+++ b/docs/examples/Inference_1D/plot_inference_1d_skytem.html
@@ -0,0 +1,326 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <title>Running GeoBIPy to invert Skytem data &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#d-inference">1D Inference</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_resolve.html">Running GeoBIPy to invert Resolve data</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Running GeoBIPy to invert Skytem data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_tempest.html">Running GeoBIPy to invert Tempest data</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
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+      <li class="breadcrumb-item active">Running GeoBIPy to invert Skytem data</li>
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+            <a href="../../_sources/examples/Inference_1D/plot_inference_1d_skytem.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-1d-plot-inference-1d-skytem-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="running-geobipy-to-invert-skytem-data">
+<span id="sphx-glr-examples-inference-1d-plot-inference-1d-skytem-py"></span><h1>Running GeoBIPy to invert Skytem data<a class="headerlink" href="#running-geobipy-to-invert-skytem-data" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">pathlib</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference3D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">user_parameters</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">get_prng</span>
+
+<span class="k">def</span> <span class="nf">checkCommandArguments</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Check the users command line arguments. &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">argparse</span>
+    <span class="c1"># warnings.filterwarnings(&#39;error&#39;)</span>
+
+    <span class="n">Parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;GeoBIPy&quot;</span><span class="p">,</span>
+                                     <span class="n">formatter_class</span><span class="o">=</span><span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentDefaultsHelpFormatter</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--index&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;job array index 0-18&#39;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--data&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Data type. Choose from [&#39;skytem&#39;, &#39;tempest&#39;, &#39;resolve&#39;]&quot;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--model&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Model type. Choose from [&#39;glacial&#39;, &#39;saline_clay&#39;, &#39;resistive_dolomites&#39;, &#39;resistive_basement&#39;, &#39;coastal_salt_water&#39;, &#39;ice_over_salt_water&#39;]&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">args</span> <span class="o">=</span> <span class="n">checkCommandArguments</span><span class="p">()</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
+
+<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+
+<span class="n">data_type</span> <span class="o">=</span> <span class="s2">&quot;Skytem&quot;</span>
+<span class="n">model_type</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>The directory where HDF files will be stored
+%%</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">)</span>
+<span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">islink</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Failed to delete </span><span class="si">%s</span><span class="s1">. Reason: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">file_path</span>
+
+<span class="n">data_filename</span> <span class="o">=</span> <span class="n">data_type</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="n">model_type</span>
+
+<span class="n">supplementary</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//&quot;</span>
+<span class="n">parameter_file</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//options_files//</span><span class="si">{}</span><span class="s2">_options&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">)</span>
+<span class="n">inputFile</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">inputFile</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Cannot find input file </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inputFile</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">output_directory</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">output_directory</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Make sure the output directory exists </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using user input file </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Output files will be produced at </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="n">kwargs</span> <span class="o">=</span> <span class="n">user_parameters</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">inputFile</span><span class="p">)</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;n_markov_chains&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5000</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s1">&#39;//data//&#39;</span> <span class="o">+</span> <span class="n">data_filename</span> <span class="o">+</span> <span class="s1">&#39;.csv&#39;</span>
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//data//&quot;</span> <span class="o">+</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]]</span>
+
+<span class="c1"># Everyone needs the system classes read in early.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">])</span>
+
+<span class="c1"># Start keeping track of time.</span>
+<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+<span class="n">seed</span> <span class="o">=</span> <span class="mi">146100583096709124601953385843316024947</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">get_prng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+<span class="n">inference3d</span> <span class="o">=</span> <span class="n">Inference3D</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">inference3d</span><span class="o">.</span><span class="n">create_hdf5</span><span class="p">(</span><span class="n">directory</span><span class="o">=</span><span class="n">output_directory</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Created hdf5 files in </span><span class="si">{}</span><span class="s2"> h:m:s&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))))</span>
+
+<span class="n">inference3d</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_inference_1d_skytem_001.png" srcset="../../_images/sphx_glr_plot_inference_1d_skytem_001.png" alt="Fiducial [2.], Time Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using user input file ..//..//supplementary////options_files//Skytem_options
+Output files will be produced at Skytem/glacial
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Creating HDF5 files, this may take a few minutes...
+Files are being created for data files ..//..//supplementary////data//Skytem_glacial.csv and system files [&#39;..//..//supplementary////data//..//data//SkytemHM.stm&#39;, &#39;..//..//supplementary////data//..//data//SkytemLM.stm&#39;]
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Created hdf5 file for line 0.0 with 79 data points
+Created hdf5 files 79 total data points
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Created hdf5 files in 0:00:00.368412 h:m:s
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+i=5000, k=6, acc=*25.760, 0.019 s/Model, 94.498 s Elapsed, eta=--:--:-- h:m:s
+
+Remaining Points -2/1 || Elapsed Time: 0:01:36.342864 h:m:s || ETA 0:00:32.114288 h:m:s
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 37.169 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-1d-plot-inference-1d-skytem-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b5df6fd797c0d747b0f432edeb4de1b6/plot_inference_1d_skytem.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_1d_skytem.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/a209de4dae1a6f3f3daeab70fe6f557a/plot_inference_1d_skytem.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_1d_skytem.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5cf20ebc694f8382ff502459c2eae019/plot_inference_1d_skytem.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_1d_skytem.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_inference_1d_resolve.html" class="btn btn-neutral float-left" title="Running GeoBIPy to invert Resolve data" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="plot_inference_1d_tempest.html" class="btn btn-neutral float-right" title="Running GeoBIPy to invert Tempest data" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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\ No newline at end of file
diff --git a/docs/examples/Inference_1D/plot_inference_1d_tempest.html b/docs/examples/Inference_1D/plot_inference_1d_tempest.html
new file mode 100644
index 00000000..fedb9565
--- /dev/null
+++ b/docs/examples/Inference_1D/plot_inference_1d_tempest.html
@@ -0,0 +1,327 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Running GeoBIPy to invert Tempest data &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#d-inference">1D Inference</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_resolve.html">Running GeoBIPy to invert Resolve data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_1d_skytem.html">Running GeoBIPy to invert Skytem data</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Running GeoBIPy to invert Tempest data</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
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+            <a href="../../_sources/examples/Inference_1D/plot_inference_1d_tempest.rst.txt" rel="nofollow"> View page source</a>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-1d-plot-inference-1d-tempest-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="running-geobipy-to-invert-tempest-data">
+<span id="sphx-glr-examples-inference-1d-plot-inference-1d-tempest-py"></span><h1>Running GeoBIPy to invert Tempest data<a class="headerlink" href="#running-geobipy-to-invert-tempest-data" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">pathlib</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference3D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">user_parameters</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">get_prng</span>
+
+<span class="k">def</span> <span class="nf">checkCommandArguments</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Check the users command line arguments. &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">argparse</span>
+    <span class="c1"># warnings.filterwarnings(&#39;error&#39;)</span>
+
+    <span class="n">Parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;GeoBIPy&quot;</span><span class="p">,</span>
+                                     <span class="n">formatter_class</span><span class="o">=</span><span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentDefaultsHelpFormatter</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--index&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;job array index 0-18&#39;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--data&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Data type. Choose from [&#39;skytem_512&#39;, &#39;tempest&#39;, &#39;resolve&#39;]&quot;</span><span class="p">)</span>
+    <span class="n">Parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--model&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Model type. Choose from [&#39;glacial&#39;, &#39;saline_clay&#39;, &#39;resistive_dolomites&#39;, &#39;resistive_basement&#39;, &#39;coastal_salt_water&#39;, &#39;ice_over_salt_water&#39;]&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">args</span> <span class="o">=</span> <span class="n">checkCommandArguments</span><span class="p">()</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
+
+<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+
+<span class="n">data_type</span> <span class="o">=</span> <span class="s2">&quot;Tempest&quot;</span>
+<span class="n">model_type</span> <span class="o">=</span> <span class="n">models</span><span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>The directory where HDF files will be stored
+%%</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">)</span>
+<span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">islink</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Failed to delete </span><span class="si">%s</span><span class="s1">. Reason: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">file_path</span>
+
+<span class="n">data_filename</span> <span class="o">=</span> <span class="n">data_type</span> <span class="o">+</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="n">model_type</span>
+
+<span class="n">supplementary</span> <span class="o">=</span> <span class="s2">&quot;..//..//supplementary//&quot;</span>
+
+<span class="n">parameter_file</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//options_files//</span><span class="si">{}</span><span class="s2">_options&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">)</span>
+<span class="n">inputFile</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">inputFile</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Cannot find input file </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inputFile</span><span class="p">))</span>
+
+<span class="n">output_directory</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">output_directory</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">output_directory</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Make sure the output directory exists </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using user input file </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter_file</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Output files will be produced at </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_directory</span><span class="p">))</span>
+
+<span class="n">kwargs</span> <span class="o">=</span> <span class="n">user_parameters</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">inputFile</span><span class="p">)</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;n_markov_chains&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5000</span>
+
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s1">&#39;//data//&#39;</span> <span class="o">+</span> <span class="n">data_filename</span> <span class="o">+</span> <span class="s1">&#39;.csv&#39;</span>
+<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">supplementary</span> <span class="o">+</span> <span class="s2">&quot;//data//&quot;</span> <span class="o">+</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">]</span>
+
+<span class="c1"># Everyone needs the system classes read in early.</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">_initialize_sequential_reading</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;data_filename&#39;</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;system_filename&#39;</span><span class="p">])</span>
+
+<span class="c1"># Start keeping track of time.</span>
+<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+<span class="n">seed</span> <span class="o">=</span> <span class="mi">146100583096709124601953385843316024947</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">get_prng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+<span class="n">inference3d</span> <span class="o">=</span> <span class="n">Inference3D</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">inference3d</span><span class="o">.</span><span class="n">create_hdf5</span><span class="p">(</span><span class="n">directory</span><span class="o">=</span><span class="n">output_directory</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Created hdf5 files in </span><span class="si">{}</span><span class="s2"> h:m:s&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))))</span>
+
+<span class="n">inference3d</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_inference_1d_tempest_001.png" srcset="../../_images/sphx_glr_plot_inference_1d_tempest_001.png" alt="Fiducial [2.], Time Domain EM Data" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using user input file ..//..//supplementary////options_files//Tempest_options
+Output files will be produced at Tempest/glacial
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+Creating HDF5 files, this may take a few minutes...
+Files are being created for data files ..//..//supplementary////data//Tempest_glacial.csv and system files ..//..//supplementary////data//..//data/tempest.stm
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+Created hdf5 file for line 0.0 with 79 data points
+Created hdf5 files 79 total data points
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+Created hdf5 files in 0:00:00.544066 h:m:s
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+i=5000, k=5, acc=*24.800, 0.023 s/Model, 113.655 s Elapsed, eta=--:--:-- h:m:s
+
+Remaining Points -2/1 || Elapsed Time: 0:01:55.563057 h:m:s || ETA 0:00:38.521019 h:m:s
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 56.531 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-1d-plot-inference-1d-tempest-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/d846da4c19c00a52fbf7a1e0d8828a4b/plot_inference_1d_tempest.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_1d_tempest.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e7cc18385c0a8722277c7651cb3e9fdb/plot_inference_1d_tempest.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_1d_tempest.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/0dde35d89bbe5f91c6bdca4eb7376ef5/plot_inference_1d_tempest.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_1d_tempest.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_inference_1d_skytem.html" class="btn btn-neutral float-left" title="Running GeoBIPy to invert Skytem data" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+    <p>&#169; Copyright None.</p>
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+
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new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>1D Inference &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" /> 
+</head>
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+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
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+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">1D Inference</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Inference_1D/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="d-inference">
+<h1>1D Inference<a class="headerlink" href="#d-inference" title="Link to this heading"></a></h1>
+<p>There are a couple of ways to run an inference using geobipy, the first is via command line using</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>geobipy<span class="w"> </span>skytem_options.py<span class="w"> </span>&lt;output<span class="w"> </span>folder&gt;
+</pre></div>
+</div>
+<p>The other is with a python script similar to the examples in this folder.
+In both cases, you will need to write an options file (also shown in these examples)</p>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
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diff --git a/docs/examples/Inference_1D/sg_execution_times.html b/docs/examples/Inference_1D/sg_execution_times.html
new file mode 100644
index 00000000..0a297009
--- /dev/null
+++ b/docs/examples/Inference_1D/sg_execution_times.html
@@ -0,0 +1,151 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
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+
+  
+  <!--[if lt IE 9]>
+    <script src="../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=67b02a41"></script>
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+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+      <li class="breadcrumb-item active">Computation times</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Inference_1D/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-inference-1d-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>04:03.149</strong> total execution time for 3 files <strong>from examples/Inference_1D</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+<link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+</style>
+<script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_inference_1d_tempest.html#sphx-glr-examples-inference-1d-plot-inference-1d-tempest-py"><span class="std std-ref">Running GeoBIPy to invert Tempest data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_1d_tempest.py</span></code>)</p></td>
+<td><p>01:56.531</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_inference_1d_skytem.html#sphx-glr-examples-inference-1d-plot-inference-1d-skytem-py"><span class="std std-ref">Running GeoBIPy to invert Skytem data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_1d_skytem.py</span></code>)</p></td>
+<td><p>01:37.169</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_inference_1d_resolve.html#sphx-glr-examples-inference-1d-plot-inference-1d-resolve-py"><span class="std std-ref">Running GeoBIPy to invert Resolve data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_1d_resolve.py</span></code>)</p></td>
+<td><p>00:29.449</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
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+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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\ No newline at end of file
diff --git a/docs/examples/Inference_2D/plot_inference_2d_resolve.html b/docs/examples/Inference_2D/plot_inference_2d_resolve.html
new file mode 100644
index 00000000..b58c9f93
--- /dev/null
+++ b/docs/examples/Inference_2D/plot_inference_2d_resolve.html
@@ -0,0 +1,308 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>2D Posterior analysis of Resolve inference &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+
+  
+  <!--[if lt IE 9]>
+    <script src="../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" />
+    <link rel="next" title="2D Posterior analysis of Skytem inference" href="plot_inference_2d_skytem.html" />
+    <link rel="prev" title="Running GeoBIPy to invert Tempest data" href="../Inference_1D/plot_inference_1d_tempest.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
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+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#id1">2D Inference</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">2D Posterior analysis of Resolve inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_skytem.html">2D Posterior analysis of Skytem inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_tempest.html">2D Posterior analysis of Tempest inference</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
+
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
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+          <li class="breadcrumb-item"><a href="../index.html">Examples</a></li>
+      <li class="breadcrumb-item active">2D Posterior analysis of Resolve inference</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Inference_2D/plot_inference_2d_resolve.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-2d-plot-inference-2d-resolve-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-posterior-analysis-of-resolve-inference">
+<span id="sphx-glr-examples-inference-2d-plot-inference-2d-resolve-py"></span><h1>2D Posterior analysis of Resolve inference<a class="headerlink" href="#d-posterior-analysis-of-resolve-inference" title="Link to this heading"></a></h1>
+<p>All plotting in GeoBIPy can be carried out using the 3D inference class</p>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_001.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_001.png" alt="resolve glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_002.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_002.png" alt="resolve saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_003.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_003.png" alt="resolve resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_004.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_004.png" alt="resolve resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_005.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_005.png" alt="resolve coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_resolve_006.png" srcset="../../_images/sphx_glr_plot_inference_2d_resolve_006.png" alt="resolve ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference2D</span>
+
+<span class="k">def</span> <span class="nf">plot_2d_summary</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">):</span>
+   <span class="c1">#%%</span>
+   <span class="c1"># Inference for a line of inferences</span>
+   <span class="c1"># ++++++++++++++++++++++++++++++++++</span>
+   <span class="c1">#</span>
+   <span class="c1"># We can instantiate the inference handler by providing a path to the directory containing</span>
+   <span class="c1"># HDF5 files generated by GeoBIPy.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The InfereceXD classes are low memory.  They only read information from the HDF5 files</span>
+   <span class="c1"># as and when it is needed.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The first time you use these classes to create plots, expect longer initial processing times.</span>
+   <span class="c1"># I precompute expensive properties and store them in the HDF5 files for later use.</span>
+
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+   <span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+   <span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="n">results_2d</span> <span class="o">=</span> <span class="n">Inference2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/0.0.h5&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">),</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+   <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+         <span class="s2">&quot;log&quot;</span> <span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+         <span class="s2">&quot;cmap&quot;</span> <span class="p">:</span> <span class="s1">&#39;jet&#39;</span>
+         <span class="p">}</span>
+
+   <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+   <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">))</span>
+   <span class="n">gs0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_gridspec</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+   <span class="n">true_model</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">create_synthetic_model</span><span class="p">(</span><span class="n">model_type</span><span class="p">)</span>
+   <span class="n">true_model</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">y_edges</span> <span class="o">=</span> <span class="n">true_model</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">y_edges</span> <span class="o">/</span> <span class="mf">10.0</span>
+
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+
+   <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+   <span class="n">true_model</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+
+   <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">160</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mean_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># By adding the useVariance keyword, we can make regions of lower confidence more transparent</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mode_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># # # # # We can also choose to keep parameters above the DOI opaque.</span>
+   <span class="c1"># # # # results_2d.compute_doi()</span>
+   <span class="c1"># # # # plt.subplot(313)</span>
+   <span class="c1"># # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);</span>
+   <span class="c1"># # # # results_2d.plot_data_elevation(linewidth=0.3);</span>
+   <span class="c1"># # # # results_2d.plot_elevation(linewidth=0.3);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_best_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Best model&#39;</span><span class="p">)</span>
+
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span>
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;5%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;50%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;95%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can plot the parameter values that produced the highest posterior</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_k_layers</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+
+   <span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="p">,</span> <span class="n">hh</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span><span class="o">.</span><span class="n">bounds</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="o">*</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">hh</span><span class="p">])</span>
+
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_channel_saturation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_burned_in</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">underlay</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># Now we can start plotting some more interesting posterior properties.</span>
+   <span class="c1"># How about the confidence?</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_confidence</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can take the interface depth posterior for each data point,</span>
+   <span class="c1"># and display an interface probability cross section</span>
+   <span class="c1"># This posterior can be washed out, so the clim_scaling keyword lets me saturate</span>
+   <span class="c1"># the top and bottom 0.5% of the colour range</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;P(Interface)&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_interfaces</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_entropy</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+
+   <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+   <span class="c1"># plt.savefig(&#39;{}_{}.png&#39;.format(data_type, model_type), dpi=300)</span>
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+   <span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+
+   <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+      <span class="k">try</span><span class="p">:</span>
+         <span class="n">plot_2d_summary</span><span class="p">(</span><span class="s2">&quot;../../../Parallel_Inference/&quot;</span><span class="p">,</span> <span class="s2">&quot;resolve&quot;</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+      <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+         <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+         <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
+         <span class="k">pass</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 21.524 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-2d-plot-inference-2d-resolve-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1663fc70a797174c7ea5a88d52e4b2b2/plot_inference_2d_resolve.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_2d_resolve.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_2d_resolve.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/517d2f5356b7a3ac11e723033c4b0fb3/plot_inference_2d_resolve.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_2d_resolve.zip</span></code></a></p>
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new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <title>2D Posterior analysis of Skytem inference &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#id1">2D Inference</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_resolve.html">2D Posterior analysis of Resolve inference</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">2D Posterior analysis of Skytem inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_tempest.html">2D Posterior analysis of Tempest inference</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
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+            <a href="../../_sources/examples/Inference_2D/plot_inference_2d_skytem.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-2d-plot-inference-2d-skytem-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-posterior-analysis-of-skytem-inference">
+<span id="sphx-glr-examples-inference-2d-plot-inference-2d-skytem-py"></span><h1>2D Posterior analysis of Skytem inference<a class="headerlink" href="#d-posterior-analysis-of-skytem-inference" title="Link to this heading"></a></h1>
+<p>All plotting in GeoBIPy can be carried out using the 3D inference class</p>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_001.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_001.png" alt="skytem glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_002.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_002.png" alt="skytem saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_003.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_003.png" alt="skytem resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_004.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_004.png" alt="skytem resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_005.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_005.png" alt="skytem coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_skytem_006.png" srcset="../../_images/sphx_glr_plot_inference_2d_skytem_006.png" alt="skytem ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17f00d0d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17f00ebd0&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x182cba8d0&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17f00fa50&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17f00e9d0&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x17ec00950&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x17f00d650&#39;]
+|   Shape:  (79, 132)
+|   Values: [[0.01120778 0.01120778 0.01089737 ... 0.01842322 0.01842322 0.01842322]
+|    [0.01004315 0.01004315 0.01001518 ... 0.01627435 0.01627435 0.01627435]
+|    [0.0098209  0.0098209  0.00983342 ... 0.01667789 0.01667789 0.01667789]
+|    ...
+|    [0.06755182 0.06755182 0.11932536 ... 0.0915802  0.0915802  0.0915802 ]
+|    [0.08104576 0.08104576 0.09688295 ... 0.09217019 0.09217019 0.09217019]
+|    [0.09121028 0.09121028 0.09943253 ... 0.11213815 0.11213815 0.11213815]]
+|   Min:    0.007485002271267226
+|   Max:    0.21717666705189165
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x182cb9c50&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17f114d50&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x182c2d4d0&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17f115b50&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17ec01c50&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x17ec036d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x17ec006d0&#39;]
+|   Shape:  (79, 132)
+|   Values: [[0.01022279 0.01022279 0.01016321 ... 0.0112021  0.0112021  0.0112021 ]
+|    [0.01121804 0.01121804 0.01062572 ... 0.0110357  0.0110357  0.0110357 ]
+|    [0.01049917 0.01049917 0.01007536 ... 0.01354095 0.01354095 0.01354095]
+|    ...
+|    [0.06896877 0.06896877 0.082946   ... 0.07112381 0.07112381 0.07112381]
+|    [0.08055856 0.08055856 0.0985627  ... 0.07903887 0.07903887 0.07903887]
+|    [0.09166633 0.09166633 0.09878492 ... 0.11154601 0.11154601 0.11154601]]
+|   Min:    0.006775009348709323
+|   Max:    0.8077378262817626
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x18229f350&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17f21de50&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x182600c50&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x18229d5d0&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x18229dbd0&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x18229e6d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x18229cb50&#39;]
+|   Shape:  (79, 132)
+|   Values: [[0.02104953 0.02104953 0.02099504 ... 0.01952426 0.01952426 0.01952426]
+|    [0.02008789 0.02008789 0.01997577 ... 0.02017019 0.02017019 0.02017019]
+|    [0.0198301  0.0198301  0.01986773 ... 0.0145253  0.0145253  0.0145253 ]
+|    ...
+|    [0.00511314 0.00511314 0.0029654  ... 0.01258484 0.01258484 0.01258484]
+|    [0.00392961 0.00392961 0.00385047 ... 0.00870505 0.00870505 0.00870505]
+|    [0.00279718 0.00279718 0.00269725 ... 0.00924302 0.00924302 0.00924302]]
+|   Min:    0.0008600086897131668
+|   Max:    9.774547726888374
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17eab3d50&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x17eab07d0&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x17f2d60d0&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17eab31d0&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x17eab0bd0&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x182fd6bd0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x182fd5650&#39;]
+|   Shape:  (79, 132)
+|   Values: [[0.01147797 0.01147797 0.01033699 ... 0.00131732 0.00131732 0.00131732]
+|    [0.00999299 0.00999299 0.01000167 ... 0.00120333 0.00120333 0.00120333]
+|    [0.01007022 0.01007022 0.01005285 ... 0.00844038 0.00844038 0.00844038]
+|    ...
+|    [0.06763163 0.06763163 0.11942951 ... 0.09924038 0.09924038 0.09924038]
+|    [0.08122468 0.08122468 0.09651811 ... 0.09608009 0.09608009 0.09608009]
+|    [0.09162505 0.09162505 0.09883382 ... 0.11272764 0.11272764 0.11272764]]
+|   Min:    0.0007661982574834428
+|   Max:    0.21045876958147633
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x181fa95d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x181fa88d0&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x181f49a50&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x181fa8450&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x181fa8150&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x181fa9150&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x181fa87d0&#39;]
+|   Shape:  (79, 132)
+|   Values: [[1.02310541 1.02310541 1.02438661 ... 1.05490484 1.05490484 1.05490484]
+|    [1.02229478 1.02229478 1.02483553 ... 0.83077926 0.83077926 0.83077926]
+|    [1.02256309 1.02256309 1.02298923 ... 1.16206135 1.16206135 1.16206135]
+|    ...
+|    [0.18317234 0.18317234 0.18317234 ... 0.04611609 0.04611609 0.04611609]
+|    [0.13296225 0.13296225 0.13296225 ... 0.02326468 0.02326468 0.02326468]
+|    [0.08897776 0.08897776 0.08897776 ... 0.02807133 0.02807133 0.02807133]]
+|   Min:    0.005922638381206899
+|   Max:    15.778868401978407
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+Model:
+mesh:
+|   RectilinearMesh2D:
+|   Shape: : (79, 132)
+|   x
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   79
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x180d2a5d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [ 0.  1.  2. ... 76. 77. 78.]
+|   |   Min:    0.0
+|   |   Max:    78.0
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   Easting (m)
+|   |   Address:[&#39;0x180d2aad0&#39;]
+|   |   Shape:  (80,)
+|   |   Values: [-0.5  0.5  1.5 ... 76.5 77.5 78.5]
+|   |   Min:    -0.5
+|   |   Max:    78.5
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x17f866cd0&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+|   y
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   132
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x180d2a750&#39;]
+|   |   Shape:  (132,)
+|   |   Values: [  -3.1875   -7.7625  -12.3375 ... -593.3625 -597.9375 -602.5125]
+|   |   Min:    -602.5125
+|   |   Max:    -3.1875
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   elevation (m)
+|   |   Address:[&#39;0x180d2ac50&#39;]
+|   |   Shape:  (133,)
+|   |   Values: [  -0.9     -5.475  -10.05  ... -595.65  -600.225 -604.8  ]
+|   |   Min:    -604.8
+|   |   Max:    -0.9
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:   Elevation (m)
+|   |   Address:[&#39;0x180d216d0&#39;]
+|   |   Shape:  (79,)
+|   |   Values: [0. 0. 0. ... 0. 0. 0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   StatArray
+|   Name:   Mean Conductivity ($\frac{S}{m}$)
+|   Address:[&#39;0x180d21350&#39;]
+|   Shape:  (79, 132)
+|   Values: [[0.00056777 0.00056777 0.00056795 ... 0.00059446 0.00059446 0.00059446]
+|    [0.00052266 0.00052266 0.00052266 ... 0.00071041 0.00071041 0.00071041]
+|    [0.00080071 0.00080071 0.00080071 ... 0.00104502 0.00104502 0.00104502]
+|    ...
+|    [0.00854703 0.00854703 0.00857136 ... 0.00167267 0.00167267 0.00167267]
+|    [0.00894549 0.00894549 0.00908742 ... 0.00255047 0.00255047 0.00255047]
+|    [0.00925526 0.00925526 0.00948505 ... 0.00507188 0.00507188 0.00507188]]
+|   Min:    0.0004901610032814771
+|   Max:    0.7309893437585234
+|   has_posterior: False
+
+
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+self.n_components=1, self.nTimes=array([26, 19])
+</pre></div>
+</div>
+<div class="line-block">
+<div class="line"><br /></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference2D</span>
+
+<span class="k">def</span> <span class="nf">plot_2d_summary</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">):</span>
+   <span class="c1">#%%</span>
+   <span class="c1"># Inference for a line of inferences</span>
+   <span class="c1"># ++++++++++++++++++++++++++++++++++</span>
+   <span class="c1">#</span>
+   <span class="c1"># We can instantiate the inference handler by providing a path to the directory containing</span>
+   <span class="c1"># HDF5 files generated by GeoBIPy.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The InfereceXD classes are low memory.  They only read information from the HDF5 files</span>
+   <span class="c1"># as and when it is needed.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The first time you use these classes to create plots, expect longer initial processing times.</span>
+   <span class="c1"># I precompute expensive properties and store them in the HDF5 files for later use.</span>
+
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+   <span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+   <span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="n">results_2d</span> <span class="o">=</span> <span class="n">Inference2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/0.0.h5&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">),</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+   <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+         <span class="s2">&quot;log&quot;</span> <span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+         <span class="s2">&quot;cmap&quot;</span> <span class="p">:</span> <span class="s1">&#39;jet&#39;</span>
+         <span class="p">}</span>
+
+   <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+   <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">))</span>
+   <span class="n">gs0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_gridspec</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+   <span class="n">true_model</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">create_synthetic_model</span><span class="p">(</span><span class="n">model_type</span><span class="p">)</span>
+
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+
+   <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+   <span class="n">true_model</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+
+   <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">550</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+
+   <span class="nb">print</span><span class="p">(</span><span class="n">results_2d</span><span class="o">.</span><span class="n">mean_parameters</span><span class="p">()</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mean_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># By adding the useVariance keyword, we can make regions of lower confidence more transparent</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mode_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># # # # # We can also choose to keep parameters above the DOI opaque.</span>
+   <span class="c1"># # # # results_2d.compute_doi()</span>
+   <span class="c1"># # # # plt.subplot(313)</span>
+   <span class="c1"># # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);</span>
+   <span class="c1"># # # # results_2d.plot_data_elevation(linewidth=0.3);</span>
+   <span class="c1"># # # # results_2d.plot_elevation(linewidth=0.3);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_best_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Best model&#39;</span><span class="p">)</span>
+
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span>
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;5%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;50%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;95%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can plot the parameter values that produced the highest posterior</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_k_layers</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+
+   <span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="p">,</span> <span class="n">hh</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span><span class="o">.</span><span class="n">bounds</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="o">*</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">hh</span><span class="p">])</span>
+
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_channel_saturation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_burned_in</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">underlay</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># Now we can start plotting some more interesting posterior properties.</span>
+   <span class="c1"># How about the confidence?</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_confidence</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can take the interface depth posterior for each data point,</span>
+   <span class="c1"># and display an interface probability cross section</span>
+   <span class="c1"># This posterior can be washed out, so the clim_scaling keyword lets me saturate</span>
+   <span class="c1"># the top and bottom 0.5% of the colour range</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;P(Interface)&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_interfaces</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_entropy</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># plt.show()</span>
+   <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">.png&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+   <span class="n">types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+
+   <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">types</span><span class="p">:</span>
+      <span class="c1"># try:</span>
+         <span class="n">plot_2d_summary</span><span class="p">(</span><span class="s1">&#39;../../../Parallel_Inference/&#39;</span><span class="p">,</span> <span class="s2">&quot;skytem&quot;</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+      <span class="c1"># except Exception as e:</span>
+      <span class="c1">#    print(model)</span>
+      <span class="c1">#    print(e)</span>
+      <span class="c1">#    pass</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.965 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-2d-plot-inference-2d-skytem-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/69580c6cbad9fa0c5835bd892b01fc6c/plot_inference_2d_skytem.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_2d_skytem.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/fd0d450029ed4426f6b6d51ec196e9de/plot_inference_2d_skytem.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_2d_skytem.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/c95b0f836ba0fba698e8506132e0a081/plot_inference_2d_skytem.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_2d_skytem.zip</span></code></a></p>
+</div>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+        <a href="plot_inference_2d_resolve.html" class="btn btn-neutral float-left" title="2D Posterior analysis of Resolve inference" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/docs/examples/Inference_2D/plot_inference_2d_tempest.html b/docs/examples/Inference_2D/plot_inference_2d_tempest.html
new file mode 100644
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+++ b/docs/examples/Inference_2D/plot_inference_2d_tempest.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#id1">2D Inference</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_resolve.html">2D Posterior analysis of Resolve inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_inference_2d_skytem.html">2D Posterior analysis of Skytem inference</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">2D Posterior analysis of Tempest inference</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
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+          <li class="breadcrumb-item"><a href="../index.html">Examples</a></li>
+      <li class="breadcrumb-item active">2D Posterior analysis of Tempest inference</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Inference_2D/plot_inference_2d_tempest.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-inference-2d-plot-inference-2d-tempest-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-posterior-analysis-of-tempest-inference">
+<span id="sphx-glr-examples-inference-2d-plot-inference-2d-tempest-py"></span><h1>2D Posterior analysis of Tempest inference<a class="headerlink" href="#d-posterior-analysis-of-tempest-inference" title="Link to this heading"></a></h1>
+<p>All plotting in GeoBIPy can be carried out using the 3D inference class</p>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_001.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_001.png" alt="tempest glacial, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_002.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_002.png" alt="tempest saline_clay, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_003.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_003.png" alt="tempest resistive_dolomites, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_004.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_004.png" alt="tempest resistive_basement, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_005.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_005.png" alt="tempest coastal_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_inference_2d_tempest_006.png" srcset="../../_images/sphx_glr_plot_inference_2d_tempest_006.png" alt="tempest ice_over_salt_water, Best model, 5%, 50%, 95%, P(# of Layers), P(Interface)" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
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+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
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+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
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+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
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+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
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+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+self.n_components=2, self.nTimes=array([15])
+</pre></div>
+</div>
+<div class="line-block">
+<div class="line"><br /></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Inference2D</span>
+
+<span class="k">def</span> <span class="nf">plot_2d_summary</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">):</span>
+   <span class="c1">#%%</span>
+   <span class="c1"># Inference for a line of inferences</span>
+   <span class="c1"># ++++++++++++++++++++++++++++++++++</span>
+   <span class="c1">#</span>
+   <span class="c1"># We can instantiate the inference handler by providing a path to the directory containing</span>
+   <span class="c1"># HDF5 files generated by GeoBIPy.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The InfereceXD classes are low memory.  They only read information from the HDF5 files</span>
+   <span class="c1"># as and when it is needed.</span>
+   <span class="c1">#</span>
+   <span class="c1"># The first time you use these classes to create plots, expect longer initial processing times.</span>
+   <span class="c1"># I precompute expensive properties and store them in the HDF5 files for later use.</span>
+
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+   <span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+   <span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+   <span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="n">results_2d</span> <span class="o">=</span> <span class="n">Inference2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/0.0.h5&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">),</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+
+   <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+         <span class="s2">&quot;log&quot;</span> <span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+         <span class="s2">&quot;cmap&quot;</span> <span class="p">:</span> <span class="s1">&#39;jet&#39;</span>
+         <span class="p">}</span>
+
+   <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+   <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">))</span>
+   <span class="n">gs0</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_gridspec</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+   <span class="n">true_model</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">create_synthetic_model</span><span class="p">(</span><span class="n">model_type</span><span class="p">)</span>
+
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+   <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">true_model</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+
+   <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+   <span class="n">true_model</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+
+   <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">550</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mean_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># By adding the useVariance keyword, we can make regions of lower confidence more transparent</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_mode_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># # # # # We can also choose to keep parameters above the DOI opaque.</span>
+   <span class="c1"># # # # results_2d.compute_doi()</span>
+   <span class="c1"># # # # plt.subplot(313)</span>
+   <span class="c1"># # # # results_2d.plot_mean_model(use_variance=True, mask_below_doi=True, **kwargs);</span>
+   <span class="c1"># # # # results_2d.plot_data_elevation(linewidth=0.3);</span>
+   <span class="c1"># # # # results_2d.plot_elevation(linewidth=0.3);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_best_model</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Best model&#39;</span><span class="p">)</span>
+
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span>
+   <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;5%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;50%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">);</span> <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;95%&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_percentile</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">wrap_clabel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can plot the parameter values that produced the highest posterior</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_k_layers</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+
+   <span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="p">,</span> <span class="n">hh</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span><span class="o">.</span><span class="n">bounds</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">ll</span><span class="p">,</span> <span class="n">bb</span><span class="p">,</span> <span class="n">ww</span><span class="o">*</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">hh</span><span class="p">])</span>
+
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_channel_saturation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">wrap_ylabel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_burned_in</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">underlay</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># Now we can start plotting some more interesting posterior properties.</span>
+   <span class="c1"># How about the confidence?</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_confidence</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">);</span>
+
+   <span class="c1">#%%</span>
+   <span class="c1"># We can take the interface depth posterior for each data point,</span>
+   <span class="c1"># and display an interface probability cross section</span>
+   <span class="c1"># This posterior can be washed out, so the clim_scaling keyword lets me saturate</span>
+   <span class="c1"># the top and bottom 0.5% of the colour range</span>
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;P(Interface)&#39;</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_interfaces</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs0</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_entropy</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Greys&#39;</span><span class="p">,</span> <span class="n">clim_scaling</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_data_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+   <span class="n">results_2d</span><span class="o">.</span><span class="n">plot_elevation</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">);</span>
+
+   <span class="c1"># plt.show()</span>
+   <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">.png&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">model_type</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+   <span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;glacial&#39;</span><span class="p">,</span> <span class="s1">&#39;saline_clay&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_dolomites&#39;</span><span class="p">,</span> <span class="s1">&#39;resistive_basement&#39;</span><span class="p">,</span> <span class="s1">&#39;coastal_salt_water&#39;</span><span class="p">,</span> <span class="s1">&#39;ice_over_salt_water&#39;</span><span class="p">]</span>
+
+   <span class="c1"># import warnings</span>
+   <span class="c1"># warnings.filterwarnings(&#39;error&#39;)</span>
+   <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+      <span class="k">try</span><span class="p">:</span>
+         <span class="n">plot_2d_summary</span><span class="p">(</span><span class="s1">&#39;../../../Parallel_Inference/&#39;</span><span class="p">,</span> <span class="s2">&quot;tempest&quot;</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+      <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+         <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+         <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
+         <span class="k">pass</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 19.999 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-inference-2d-plot-inference-2d-tempest-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/a883c12cffc3e60393a274d75531e179/plot_inference_2d_tempest.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_inference_2d_tempest.ipynb</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/54ce350904e65ae0e6717397118ed43f/plot_inference_2d_tempest.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_inference_2d_tempest.py</span></code></a></p>
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+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/537a99c8a809708fd1370175a0022351/plot_inference_2d_tempest.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_inference_2d_tempest.zip</span></code></a></p>
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+<h1>2D Inference<a class="headerlink" href="#d-inference" title="Link to this heading"></a></h1>
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diff --git a/docs/examples/Inference_2D/sg_execution_times.html b/docs/examples/Inference_2D/sg_execution_times.html
new file mode 100644
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+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+            <a href="../../_sources/examples/Inference_2D/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-inference-2d-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>01:00.488</strong> total execution time for 3 files <strong>from examples/Inference_2D</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
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+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_inference_2d_resolve.html#sphx-glr-examples-inference-2d-plot-inference-2d-resolve-py"><span class="std std-ref">2D Posterior analysis of Resolve inference</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_2d_resolve.py</span></code>)</p></td>
+<td><p>00:21.524</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_inference_2d_tempest.html#sphx-glr-examples-inference-2d-plot-inference-2d-tempest-py"><span class="std std-ref">2D Posterior analysis of Tempest inference</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_2d_tempest.py</span></code>)</p></td>
+<td><p>00:19.999</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_inference_2d_skytem.html#sphx-glr-examples-inference-2d-plot-inference-2d-skytem-py"><span class="std std-ref">2D Posterior analysis of Skytem inference</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_inference_2d_skytem.py</span></code>)</p></td>
+<td><p>00:18.965</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
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diff --git a/docs/examples/Meshes/plot_rectilinear_mesh_1d.html b/docs/examples/Meshes/plot_rectilinear_mesh_1d.html
new file mode 100644
index 00000000..a36f7c3a
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@@ -0,0 +1,540 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#meshes">Meshes</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">1D Rectilinear Mesh</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#the-basics">The basics</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#log-space-rectilinear-mesh">Log-space rectilinear mesh</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#relative-to">relative_to</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#randomness-and-model-perturbations">Randomness and Model Perturbations</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="plot_rectilinear_mesh_2d.html">2D Rectilinear Mesh</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_rectilinear_mesh_3d.html">3D Rectilinear Mesh</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
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+      <li class="breadcrumb-item active">1D Rectilinear Mesh</li>
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+            <a href="../../_sources/examples/Meshes/plot_rectilinear_mesh_1d.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-meshes-plot-rectilinear-mesh-1d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-rectilinear-mesh">
+<span id="sphx-glr-examples-meshes-plot-rectilinear-mesh-1d-py"></span><h1>1D Rectilinear Mesh<a class="headerlink" href="#d-rectilinear-mesh" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">DataArray</span><span class="p">,</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span><span class="p">,</span> <span class="n">RectilinearMesh2D</span><span class="p">,</span> <span class="n">RectilinearMesh2D_stitched</span>
+<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+</pre></div>
+</div>
+<section id="the-basics">
+<h2>The basics<a class="headerlink" href="#the-basics" title="Link to this heading"></a></h2>
+<p>Instantiate a new 1D rectilinear mesh by specifying cell centres, edges, or widths.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Cell edges</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">centres</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">widths</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can plot the grid of the mesh
+Or Pcolor the mesh showing. An array of cell values is used as the colour.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">arr</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="s2">&quot;Name&quot;</span><span class="p">,</span> <span class="s2">&quot;Units&quot;</span><span class="p">)</span>
+<span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Mask the mesh cells by a distance</span>
+<span class="n">rm_masked</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">arr2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">mask_cells</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">arr</span><span class="p">)</span>
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm_masked</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr2</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Writing and reading to/from HDF5</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_001.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_001.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_002.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_002.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_003.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_003.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_004.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_004.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+</section>
+<section id="log-space-rectilinear-mesh">
+<h2>Log-space rectilinear mesh<a class="headerlink" href="#log-space-rectilinear-mesh" title="Link to this heading"></a></h2>
+<p>Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+Here we use edges</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># We can plot the grid of the mesh</span>
+<span class="c1"># Or Pcolor the mesh showing. An array of cell values is used as the colour.</span>
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">nCells</span><span class="p">),</span> <span class="s2">&quot;Name&quot;</span><span class="p">,</span> <span class="s2">&quot;Units&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Writing and reading to/from HDF5</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_005.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_005.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_006.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_006.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_007.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_007.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+</section>
+<section id="relative-to">
+<h2>relative_to<a class="headerlink" href="#relative-to" title="Link to this heading"></a></h2>
+<p>Instantiate a new 1D rectilinear mesh by specifying cell centres or edges.
+Here we use edges</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">11.0</span><span class="p">),</span> <span class="s1">&#39;Deviation&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">relative_to</span><span class="o">=</span><span class="mf">5.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can plot the grid of the mesh
+Or Pcolor the mesh showing. An array of cell values is used as the colour.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">nCells</span><span class="p">),</span> <span class="s2">&quot;Name&quot;</span><span class="p">,</span> <span class="s2">&quot;Units&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Writing and reading to/from HDF5</span>
+<span class="c1"># ++++++++++++++++++++++++++++++++</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">)</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">+=</span> <span class="mf">0.5</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+
+<span class="c1"># Making a mesh perturbable</span>
+<span class="c1"># +++++++++++++++++++++++++</span>
+<span class="n">n_cells</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">widths</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_cells</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">widths</span><span class="o">=</span><span class="n">widths</span><span class="p">,</span> <span class="n">relative_to</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_008.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_008.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_009.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_009.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_010.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_010.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+</section>
+<section id="randomness-and-model-perturbations">
+<h2>Randomness and Model Perturbations<a class="headerlink" href="#randomness-and-model-perturbations" title="Link to this heading"></a></h2>
+<p>We can set the priors on the 1D model by assigning minimum and maximum layer
+depths and a maximum number of layers.  These are used to create priors on
+the number of cells in the model, a new depth interface, new parameter values
+and the vertical gradient of those parameters.
+The halfSpaceValue is used as a reference value for the parameter prior.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="c1"># Set the priors</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">min_edge</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+              <span class="n">max_edge</span> <span class="o">=</span> <span class="mf">150.0</span><span class="p">,</span>
+              <span class="n">max_cells</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span>
+              <span class="n">prng</span> <span class="o">=</span> <span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can evaluate the prior of the model using depths only</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Log probability of the Mesh given its priors: &#39;</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">probability</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Log probability of the Mesh given its priors:  -3.367295829986474
+</pre></div>
+</div>
+<p>To propose new meshes, we specify the probabilities of creating, removing, perturbing, and not changing
+an edge interface
+Here we force the creation of a layer.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span><span class="o">.</span><span class="n">set_proposals</span><span class="p">(</span><span class="n">probabilities</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">],</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">set_posteriors</span><span class="p">()</span>
+
+<span class="n">rm0</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">rm</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can then perturb the layers of the model</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="n">rm</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">_init_posterior_plots</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+
+<span class="n">rm</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm1</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">rm1</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">rm1</span><span class="o">.</span><span class="n">_init_posterior_plots</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="n">rm1</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_011.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_011.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_012.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_012.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_013.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_013.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;Axes: xlabel=&#39;# of Layers&#39;, ylabel=&#39;Density&#39;&gt;, &lt;Axes: xlabel=&#39;test&#39;, ylabel=&#39;Density&#39;&gt;]
+</pre></div>
+</div>
+<p>Expanded</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">max_cells</span><span class="p">)</span>
+    <span class="n">tmp</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">3.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;m&quot;</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+
+    <span class="n">rm</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">=</span> <span class="mf">5.0</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="n">rm</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">rm0</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
+        <span class="n">rm</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">perturb</span><span class="p">();</span> <span class="n">rm</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">=</span> <span class="mf">10.0</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">rm</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">rm0</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
+        <span class="n">rm</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">perturb</span><span class="p">();</span> <span class="n">rm</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">=</span> <span class="mf">25.0</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;rm1d&#39;</span><span class="p">,</span> <span class="n">withPosterior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">])</span>
+
+<span class="n">p</span><span class="o">+=</span><span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">max_cells</span><span class="p">)</span> <span class="o">*</span> <span class="mi">10</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm0</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="n">rm0</span><span class="o">.</span><span class="n">nCells</span><span class="o">.</span><span class="n">item</span><span class="p">()],</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh2D</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;rm1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">edges</span><span class="o">.</span><span class="n">posterior</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_014.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_014.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_1d_015.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_1d_015.png" alt="plot rectilinear mesh 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;nCells&#39;, &#39;x&#39;, &#39;y&#39;]
+RectilinearMesh1D
+Number of Cells:
+|   StatArray
+|   Name:   Number of cells
+|   Address:[&#39;0x154b0fd50&#39; &#39;0x182c1a750&#39; &#39;0x180970e10&#39; &#39;0x1809733f0&#39;]
+|   Shape:  (1,)
+|   Values: [15]
+|   Min:    15
+|   Max:    15
+|   Prior:
+|   |   Uniform Distribution:
+|   |   Min: 1
+|   |   Max: 30
+|   has_posterior: True
+
+Cell Centres:
+|   StatArray
+|   Name:   test
+|   Address:[&#39;0x17f3b4850&#39;]
+|   Shape:  (15,)
+|   Values: [  1.22331766   3.28906551   6.36608642 ...  66.85590133 108.99206549
+|    148.449825  ]
+|   Min:    1.2233176602046558
+|   Max:    148.4498250024555
+|   has_posterior: False
+
+Cell Edges:
+|   StatArray
+|   Name:   test
+|   Address:[&#39;0x1834e3950&#39; &#39;0x17ebbc1a0&#39; &#39;0x182174450&#39; &#39;0x17f387aa0&#39; &#39;0x1808cd750&#39;
+|    &#39;0x1808ced70&#39;]
+|   Shape:  (16,)
+|   Values: [  0.           2.44663532   4.13149571 ...  70.19008745 147.79404353
+|    149.10560647]
+|   Min:    0.0
+|   Max:    149.1056064716983
+|   Prior:
+|   |   Order Statistics:
+|   |   None
+|   Proposal:
+|   |   Uniform Distribution:
+|   |   Min: 1.0
+|   |   Max: 149.99999999999997
+|   has_posterior: True
+
+log:
+|   None
+relative_to:
+|   StatArray
+|   Name:
+|   Address:[&#39;0x17ef83e50&#39;]
+|   Shape:  (1,)
+|   Values: [5.]
+|   Min:    5.0
+|   Max:    5.0
+|   has_posterior: False
+</pre></div>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-meshes-plot-rectilinear-mesh-2d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-rectilinear-mesh">
+<span id="sphx-glr-examples-meshes-plot-rectilinear-mesh-2d-py"></span><h1>2D Rectilinear Mesh<a class="headerlink" href="#d-rectilinear-mesh" title="Link to this heading"></a></h1>
+<p>This 2D rectilinear mesh defines a grid with straight cell boundaries.</p>
+<p>It can be instantiated in two ways.</p>
+<p>The first is by providing the cell centres or
+cell edges in two dimensions.</p>
+<p>The second embeds the 2D mesh in 3D by providing the cell centres or edges in three dimensions.
+The first two dimensions specify the mesh coordinates in the horiztontal cartesian plane
+while the third discretizes in depth. This allows us to characterize a mesh whose horizontal coordinates
+do not follow a line that is parallel to either the “x” or “y” axis.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span><span class="p">,</span> <span class="n">RectilinearMesh2D</span><span class="p">,</span> <span class="n">RectilinearMesh3D</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Specify some cell centres in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">20.0</span><span class="p">),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_centres</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_centres</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can plot the grid lines of the mesh.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">p</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span>  <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># Intersecting multisegment lines with a mesh</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">i</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">line_indices</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
+<span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">i</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">values</span> <span class="o">=</span> <span class="n">arr</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_001.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_001.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_002.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_002.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Easting (m)&#39;, ylabel=&#39;Depth (m)&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x180d19bb0&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x1824970b0&gt;)
+</pre></div>
+</div>
+<p>We can pcolor the mesh by providing cell values.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;Values&quot;</span><span class="p">)</span>
+
+<span class="n">rm2</span><span class="p">,</span> <span class="n">values2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span>
+
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_003.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_003.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><p>Mask the x axis cells by a distance</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm_masked</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">arr2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">mask_cells</span><span class="p">(</span><span class="n">x_distance</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">arr</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm_masked</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr2</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_004.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_004.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><p>Mask the z axis cells by a distance</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm_masked</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">arr2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">mask_cells</span><span class="p">(</span><span class="n">y_distance</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">arr</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm_masked</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr2</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_005.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_005.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><p>Mask axes by a distance</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm_masked</span><span class="p">,</span> <span class="n">x_indices</span><span class="p">,</span> <span class="n">z_indices</span><span class="p">,</span> <span class="n">arr2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">mask_cells</span><span class="p">(</span><span class="n">x_distance</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">y_distance</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">arr</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">rm_masked</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">StatArray</span><span class="p">(</span><span class="n">arr2</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">15.0</span><span class="p">)),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_centres</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_centres</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_006.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_006.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><p>We can perform some interval statistics on the cell values of the mesh
+Generate some values</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">nCells</span><span class="o">+</span><span class="mi">1</span><span class="p">))[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">nCells</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Compute the mean over an interval for the mesh.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span><span class="o">.</span><span class="n">intervalStatistic</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="p">[</span><span class="mf">6.8</span><span class="p">,</span> <span class="mf">12.4</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">statistic</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(array([[9., 9., 9., ..., 9., 9., 9.]]), [6.8, 12.4])
+</pre></div>
+</div>
+<p>Compute the mean over multiple intervals for the mesh.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span><span class="o">.</span><span class="n">intervalStatistic</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="p">[</span><span class="mf">6.8</span><span class="p">,</span> <span class="mf">12.4</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">,</span> <span class="mf">40.0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">statistic</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(array([[ 9.,  9.,  9., ...,  9.,  9.,  9.],
+       [nan, nan, nan, ..., nan, nan, nan],
+       [nan, nan, nan, ..., nan, nan, nan]]), [6.8, 12.4, 20.0, 40.0])
+</pre></div>
+</div>
+<p>We can specify either axis</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span><span class="o">.</span><span class="n">intervalStatistic</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="p">[</span><span class="mf">2.8</span><span class="p">,</span> <span class="mf">4.2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">statistic</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(array([[ 1.],
+       [ 2.],
+       [ 3.],
+       ...,
+       [ 8.],
+       [ 9.],
+       [10.]]), [2.8, 4.2])
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm</span><span class="o">.</span><span class="n">intervalStatistic</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="p">[</span><span class="mf">2.8</span><span class="p">,</span> <span class="mf">4.2</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">,</span> <span class="mf">8.4</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">statistic</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(array([[ 1., nan,  1.],
+       [ 2., nan,  2.],
+       [ 3., nan,  3.],
+       ...,
+       [ 8., nan,  8.],
+       [ 9., nan,  9.],
+       [10., nan, 10.]]), [2.8, 4.2, 5.1, 8.4])
+</pre></div>
+</div>
+<p>Slice the 2D mesh to retrieve either a 2D mesh or 1D mesh</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_007.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_007.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><p>Resample a grid</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">values</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+<span class="n">rm2</span><span class="p">,</span> <span class="n">values2</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span>
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">values2</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_008.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_008.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: &gt;, &lt;matplotlib.collections.QuadMesh object at 0x17faebb00&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x17f487b60&gt;)
+</pre></div>
+</div>
+<section id="axes-in-log-space">
+<h2>Axes in log space<a class="headerlink" href="#axes-in-log-space" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_log</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+
+<span class="c1"># We can plot the grid lines of the mesh.</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">_</span>  <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_009.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_009.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_010.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_010.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;x&#39;, ylabel=&#39;y&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x182aca390&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x17f6df0b0&gt;)
+</pre></div>
+</div>
+</section>
+<section id="relative-to">
+<h2>relative_to<a class="headerlink" href="#relative-to" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;Northing&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">20.0</span><span class="p">),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_centres</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_centres</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">_</span>  <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_centres</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="mf">0.2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">y_centres</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_relative_to</span><span class="o">=</span><span class="mf">0.2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span>  <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># relative_to single</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">rm</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># relative_to expanded</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">centres</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">3.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;m&quot;</span><span class="p">),</span> <span class="n">relative_to</span> <span class="o">=</span> <span class="mf">0.2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">+=</span> <span class="mf">0.5</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">relative_to</span> <span class="o">+=</span> <span class="mf">0.5</span>
+        <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm3</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">311</span><span class="p">)</span>
+<span class="n">rm</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">312</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">p</span> <span class="o">+=</span> <span class="mi">1</span><span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+<span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">rm3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">311</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">rm3</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+<span class="n">mesh</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">312</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">rm3</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">mesh</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:],</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">313</span><span class="p">)</span>
+<span class="n">rm3</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">arr</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_011.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_011.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_012.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_012.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_013.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_013.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_rectilinear_mesh_2d_014.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_2d_014.png" alt="plot rectilinear mesh 2d" class = "sphx-glr-multi-img"/></li>
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+<p><a class="reference download internal" download="" href="../../_downloads/6dec5e41149e94d435270c1dffe803e1/plot_rectilinear_mesh_2d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_rectilinear_mesh_2d.ipynb</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/48c7722b252e7ddf33d95322115dcaf5/plot_rectilinear_mesh_2d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rectilinear_mesh_2d.py</span></code></a></p>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-meshes-plot-rectilinear-mesh-3d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-rectilinear-mesh">
+<span id="sphx-glr-examples-meshes-plot-rectilinear-mesh-3d-py"></span><h1>3D Rectilinear Mesh<a class="headerlink" href="#d-rectilinear-mesh" title="Link to this heading"></a></h1>
+<p>This 3D rectilinear mesh defines a grid with straight cell boundaries.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh3D</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+</pre></div>
+</div>
+<p>Specify some cell centres in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">15.0</span><span class="p">),</span> <span class="s1">&#39;Northing&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">20.0</span><span class="p">),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">)</span>
+
+<span class="n">rm1</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_3d_001.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_3d_001.png" alt="plot rectilinear mesh 3d" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">234</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">235</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">236</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_3d_002.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_3d_002.png" alt="plot rectilinear mesh 3d" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">rm</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;rm3d.vtk&#39;</span><span class="p">)</span>
+
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">z_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;z_re&quot;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+
+<span class="n">rm1</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_3d_003.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_3d_003.png" alt="plot rectilinear mesh 3d" class = "sphx-glr-single-img"/><p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;rm3d_re1.vtk&#39;</span><span class="p">)</span>
+
+<span class="n">x_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">rm</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">nCells</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="s2">&quot;x_re&quot;</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">z_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;z_re&quot;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="n">x_re</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+
+<span class="n">rm1</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_3d_004.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_3d_004.png" alt="plot rectilinear mesh 3d" class = "sphx-glr-single-img"/><p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;rm3d_re2.vtk&#39;</span><span class="p">)</span>
+
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">x_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;x_re&quot;</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">y_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;y_re&quot;</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">rm</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">rm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">z_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;z_re&quot;</span><span class="p">)</span>
+<span class="n">rm</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="n">x_re</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_relative_to</span><span class="o">=</span><span class="n">y_re</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+
+<span class="n">rm1</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">rm2</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">rm3</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">rm4</span> <span class="o">=</span> <span class="n">rm</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">rm2</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">rm3</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">rm4</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_rectilinear_mesh_3d_005.png" srcset="../../_images/sphx_glr_plot_rectilinear_mesh_3d_005.png" alt="plot rectilinear mesh 3d" class = "sphx-glr-single-img"/><p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;rm3d_re3.vtk&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;rm3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">rm2</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">rm2</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;rm3d_read.vtk&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.378 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-meshes-plot-rectilinear-mesh-3d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b7483b08f4f927c644b97f4bf2cf523c/plot_rectilinear_mesh_3d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_rectilinear_mesh_3d.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/7f3bb43ebb9eba21697953fbfc04f6d2/plot_rectilinear_mesh_3d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rectilinear_mesh_3d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/dc3e882d376317616b04bc390b62a137/plot_rectilinear_mesh_3d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_rectilinear_mesh_3d.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_rectilinear_mesh_2d.html" class="btn btn-neutral float-left" title="2D Rectilinear Mesh" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Meshes &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+          </a>
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+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Meshes</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Meshes/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="meshes">
+<h1>Meshes<a class="headerlink" href="#meshes" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
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+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/examples/Meshes/sg_execution_times.html b/docs/examples/Meshes/sg_execution_times.html
new file mode 100644
index 00000000..f35888c7
--- /dev/null
+++ b/docs/examples/Meshes/sg_execution_times.html
@@ -0,0 +1,151 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../../_static/sg_gallery-dataframe.css?v=2082cf3c" />
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+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
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+      <li class="breadcrumb-item active">Computation times</li>
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+            <a href="../../_sources/examples/Meshes/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-meshes-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:07.710</strong> total execution time for 3 files <strong>from examples/Meshes</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+<link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+</style>
+<script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_rectilinear_mesh_1d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-1d-py"><span class="std std-ref">1D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rectilinear_mesh_1d.py</span></code>)</p></td>
+<td><p>00:04.585</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_rectilinear_mesh_2d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-2d-py"><span class="std std-ref">2D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rectilinear_mesh_2d.py</span></code>)</p></td>
+<td><p>00:01.747</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_rectilinear_mesh_3d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-3d-py"><span class="std std-ref">3D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rectilinear_mesh_3d.py</span></code>)</p></td>
+<td><p>00:01.378</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
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+
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">1D Model with an infinite halfspace</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instantiate-the-1d-model-with-a-half-space">Instantiate the 1D Model with a Half Space</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#randomness-and-model-perturbations">Randomness and Model Perturbations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#perturbing-a-model-multiple-times">Perturbing a model multiple times</a></li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-models-plot-model-1d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-model-with-an-infinite-halfspace">
+<span id="sphx-glr-examples-models-plot-model-1d-py"></span><h1>1D Model with an infinite halfspace<a class="headerlink" href="#d-model-with-an-infinite-halfspace" title="Link to this heading"></a></h1>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Distribution</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<section id="instantiate-the-1d-model-with-a-half-space">
+<h2>Instantiate the 1D Model with a Half Space<a class="headerlink" href="#instantiate-the-1d-model-with-a-half-space" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Make a test model with 10 layers, and increasing parameter values</span>
+<span class="n">nLayers</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">par</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">,</span> <span class="n">nLayers</span><span class="p">),</span> <span class="s2">&quot;Conductivity&quot;</span><span class="p">,</span> <span class="s2">&quot;$</span><span class="se">\\</span><span class="s2">frac</span><span class="si">{S}{m}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">thk</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">nLayers</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">10.0</span><span class="p">))</span>
+<span class="n">thk</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">widths</span> <span class="o">=</span> <span class="n">thk</span><span class="p">)</span>
+
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span> <span class="o">=</span> <span class="n">mesh</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">par</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">plot_grid</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_1d_001.png" srcset="../../_images/sphx_glr_plot_model_1d_001.png" alt="plot model 1d" class = "sphx-glr-single-img"/></section>
+<section id="randomness-and-model-perturbations">
+<h2>Randomness and Model Perturbations<a class="headerlink" href="#randomness-and-model-perturbations" title="Link to this heading"></a></h2>
+<p>We can set the priors on the 1D model by assigning minimum and maximum layer
+depths and a maximum number of layers.  These are used to create priors on
+the number of cells in the model, a new depth interface, new parameter values
+and the vertical gradient of those parameters.
+The halfSpaceValue is used as a reference value for the parameter prior.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="c1"># Set the priors</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">set_priors</span><span class="p">(</span><span class="n">value_mean</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
+              <span class="n">min_edge</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+              <span class="n">max_edge</span><span class="o">=</span><span class="mf">150.0</span><span class="p">,</span>
+              <span class="n">max_cells</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+              <span class="n">solve_value</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+              <span class="n">solve_gradient</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+              <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can evaluate the prior of the model using depths only</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Log probability of the Model given its priors: &#39;</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
+<span class="c1"># Or with priors on its parameters, and parameter gradient with depth.</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Log probability of the Model given its priors: &#39;</span><span class="p">,</span> <span class="n">mod</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Log probability of the Model given its priors:  -3.367295829986474
+Log probability of the Model given its priors:  -9.157731937350919
+</pre></div>
+</div>
+<p>To propose new models, we specify the probabilities of creating, removing, perturbing, and not changing
+a layer interface</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pProposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;LogNormal&#39;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span><span class="o">**</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">linearSpace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">set_proposals</span><span class="p">(</span><span class="n">probabilities</span><span class="o">=</span><span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">],</span> <span class="n">proposal</span><span class="o">=</span><span class="n">pProposal</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can then perturb the layers of the model</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">remapped</span><span class="p">,</span> <span class="n">perturbed</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>  <span class="c1"># , grid=True)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">perturbed</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>  <span class="c1"># , grid=True)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_1d_002.png" srcset="../../_images/sphx_glr_plot_model_1d_002.png" alt="plot model 1d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: &gt;
+</pre></div>
+</div>
+<p>We can evaluate the prior of the model using depths only</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Log probability of the Model given its priors: &#39;</span><span class="p">,</span><span class="n">perturbed</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
+<span class="c1"># Or with priors on its parameters, and parameter gradient with depth.</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Log probability of the Model given its priors: &#39;</span><span class="p">,</span><span class="n">perturbed</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Log probability of the Model given its priors:  -3.367295829986474
+Log probability of the Model given its priors:  -8.559817917094882
+</pre></div>
+</div>
+</section>
+<section id="perturbing-a-model-multiple-times">
+<h2>Perturbing a model multiple times<a class="headerlink" href="#perturbing-a-model-multiple-times" title="Link to this heading"></a></h2>
+<p>In the stochasitic inference process, we perturb the model structure,
+and parameter values, multiple times.
+Each time the model is perturbed, we can record its state
+in a posterior distribution.</p>
+<p>For a 1D model, the parameter posterior is a 2D hitmap with depth in one dimension
+and the parameter value in the other.
+We also attach a 1D histogram for the number of layers,
+and a 1D histogram for the locations of interfaces.</p>
+<p>Since we have already set the priors on the Model, we can set the posteriors
+based on bins from from the priors.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="o">.</span><span class="n">set_posteriors</span><span class="p">()</span>
+
+<span class="n">mod0</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Now we randomly perturb the model, and update its posteriors.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1001</span><span class="p">):</span>
+    <span class="n">remapped</span><span class="p">,</span> <span class="n">perturbed</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+
+    <span class="c1"># And update the model posteriors</span>
+    <span class="n">perturbed</span><span class="o">.</span><span class="n">update_posteriors</span><span class="p">()</span>
+
+    <span class="n">mod</span> <span class="o">=</span> <span class="n">perturbed</span>
+</pre></div>
+</div>
+<p>We can now plot the posteriors of the model.</p>
+<p>Remember in this case, we are simply perturbing the model structure and parameter values
+The proposal for the parameter values is fixed and centred around a single value.
+fig = plt.figure(figsize=(8, 6))</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># plt.subplot(131)</span>
+<span class="c1"># mod.nCells.posterior.plot()</span>
+<span class="c1"># ax = plt.subplot(132)</span>
+<span class="c1"># mod.values.posterior.pcolor(cmap=&#39;gray_r&#39;, colorbar=False, flipY=True, logX=10)</span>
+<span class="c1"># plt.subplot(133, sharey=ax)</span>
+<span class="c1"># mod.mesh.edges.posterior.plot(transpose=True, flipY=True)</span>
+
+<span class="c1"># plt.figure()</span>
+<span class="c1"># mod.plot_posteriors(**{&quot;cmap&quot;: &#39;gray_r&#39;,</span>
+<span class="c1">#                   &quot;xscale&quot;: &#39;log&#39;,</span>
+<span class="c1">#                   &quot;noColorbar&quot;: True,</span>
+<span class="c1">#                   &quot;flipY&quot;: True,</span>
+<span class="c1">#                   &#39;credible_interval_kwargs&#39;:{&#39;axis&#39;: 1,</span>
+<span class="c1">#                                           &#39;reciprocate&#39;: True,</span>
+<span class="c1">#                                           &#39;xscale&#39;: &#39;log&#39;}})</span>
+<span class="c1"># mod.par.posterior.plotCredibleIntervals(xscale=&#39;log&#39;, axis=1)</span>
+
+
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="c1"># gs = fig.add_gridspec(nrows=1, ncols=1)</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">plot_posteriors</span><span class="p">(</span><span class="n">axes</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span>
+                    <span class="n">edges_kwargs</span> <span class="o">=</span> <span class="p">{</span>
+                        <span class="s2">&quot;transpose&quot;</span><span class="p">:</span><span class="kc">True</span><span class="p">,</span>
+                        <span class="s2">&quot;flipY&quot;</span><span class="p">:</span><span class="kc">True</span>
+                    <span class="p">},</span>
+                    <span class="n">parameter_kwargs</span> <span class="o">=</span> <span class="p">{</span>
+                        <span class="s2">&quot;cmap&quot;</span><span class="p">:</span> <span class="s1">&#39;gray_r&#39;</span><span class="p">,</span>
+                        <span class="s2">&quot;xscale&quot;</span><span class="p">:</span> <span class="s1">&#39;log&#39;</span><span class="p">,</span>
+                        <span class="s2">&quot;colorbar&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+                        <span class="s2">&quot;flipY&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+                        <span class="s1">&#39;credible_interval_kwargs&#39;</span><span class="p">:{</span>
+                              <span class="s1">&#39;reciprocate&#39;</span><span class="p">:</span><span class="kc">True</span><span class="p">,</span>
+                            <span class="c1">#   &#39;axis&#39;: 1,</span>
+                              <span class="s1">&#39;xscale&#39;</span><span class="p">:</span> <span class="s1">&#39;log&#39;</span>
+                        <span class="p">}</span>
+                    <span class="p">},</span>
+                    <span class="n">best</span> <span class="o">=</span> <span class="n">mod</span><span class="p">)</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_1d_003.png" srcset="../../_images/sphx_glr_plot_model_1d_003.png" alt="plot model 1d" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.051 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-models-plot-model-1d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/7d8af6f7b40a771fd3b189fc4fd80360/plot_model_1d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_model_1d.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/f48e5c05cfcaf1411cc06bb7a0ed1eb0/plot_model_1d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_model_1d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/315f11f9fe5f088152f7ee4638bd2209/plot_model_1d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_model_1d.zip</span></code></a></p>
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+<html class="writer-html5" lang="en" data-content_root="../../">
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#models">Models</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_model_1d.html">1D Model with an infinite halfspace</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">2D Rectilinear Model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_model_3d.html">3D Rectilinear Model</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#statistics">Statistics</a></li>
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+
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+      <li class="breadcrumb-item active">2D Rectilinear Model</li>
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+            <a href="../../_sources/examples/Models/plot_model_2d.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-models-plot-model-2d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-rectilinear-model">
+<span id="sphx-glr-examples-models-plot-model-2d-py"></span><h1>2D Rectilinear Model<a class="headerlink" href="#d-rectilinear-model" title="Link to this heading"></a></h1>
+<p>This 2D rectilinear model defines a grid with straight cell boundaries.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh2D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Specify some cell centres in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">11.0</span><span class="p">),</span> <span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">11.0</span><span class="p">),</span> <span class="s1">&#39;Northing&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">values</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;Values&quot;</span><span class="p">)</span>
+
+<span class="n">mod</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">values</span> <span class="o">=</span> <span class="n">values</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">mod</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+
+<span class="n">mod2</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+<span class="n">mod3</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">mod2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">mod3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+
+
+<span class="c1"># #%%</span>
+<span class="c1"># # We can plot the mesh in 3D!</span>
+<span class="c1"># pv = rm.pyvista_plotter()</span>
+<span class="c1"># pv.show()</span>
+
+<span class="c1"># rm.to_vtk(&#39;Model3D.vtk&#39;)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;Model2D.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">mod</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;model&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;Model2D.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">mod2</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">])</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_model_2d_001.png" srcset="../../_images/sphx_glr_plot_model_2d_001.png" alt="plot model 2d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_model_2d_002.png" srcset="../../_images/sphx_glr_plot_model_2d_002.png" alt="plot model 2d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.212 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-models-plot-model-2d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/36c869e825ae3b16a184379512e44203/plot_model_2d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_model_2d.ipynb</span></code></a></p>
+</div>
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+<p><a class="reference download internal" download="" href="../../_downloads/b65392b576618a906ad35b58f026489b/plot_model_2d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_model_2d.py</span></code></a></p>
+</div>
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+<p><a class="reference download internal" download="" href="../../_downloads/1380a312ea4fdf4a772aca7bd6fd33ce/plot_model_2d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_model_2d.zip</span></code></a></p>
+</div>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+            <a href="../../_sources/examples/Models/plot_model_3d.rst.txt" rel="nofollow"> View page source</a>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-models-plot-model-3d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="d-rectilinear-model">
+<span id="sphx-glr-examples-models-plot-model-3d-py"></span><h1>3D Rectilinear Model<a class="headerlink" href="#d-rectilinear-model" title="Link to this heading"></a></h1>
+<p>This 3D rectilinear model defines a grid with straight cell boundaries.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh3D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">3D Rectilinear Mesh</span>
+<span class="sd">-------------------</span>
+<span class="sd">This 3D rectilinear mesh defines a grid with straight cell boundaries.</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;\n3D Rectilinear Mesh\n-------------------\nThis 3D rectilinear mesh defines a grid with straight cell boundaries.\n\n&#39;
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh3D</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Model</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+</pre></div>
+</div>
+<p>Specify some cell centres in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">15.0</span><span class="p">),</span> <span class="s1">&#39;Northing&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">20.0</span><span class="p">),</span> <span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">values</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;Height&quot;</span><span class="p">)</span>
+<span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">values</span><span class="p">[:,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">mesh</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">nCells</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">values</span><span class="p">)</span>
+
+<span class="n">model1</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">model2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">model3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">model4</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_3d_001.png" srcset="../../_images/sphx_glr_plot_model_3d_001.png" alt="plot model 3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Northing (m)&#39;, ylabel=&#39;Depth (m)&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x1828e1340&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x182d6cb30&gt;)
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">model2</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">234</span><span class="p">)</span>
+<span class="n">model2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">235</span><span class="p">)</span>
+<span class="n">model3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">236</span><span class="p">)</span>
+<span class="n">model4</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_3d_002.png" srcset="../../_images/sphx_glr_plot_model_3d_002.png" alt="plot model 3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: xlabel=&#39;Depth (m)&#39;&gt;
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;model3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;model3d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">model2</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;model3d.vtk&#39;</span><span class="p">)</span>
+
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">z_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;z_re&quot;</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">values</span><span class="p">)</span>
+
+<span class="n">model1</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">model2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">model3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">model4</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_3d_003.png" srcset="../../_images/sphx_glr_plot_model_3d_003.png" alt="plot model 3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Northing (m)&#39;, ylabel=&#39;z_re (m)&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x17f18acc0&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x182510410&gt;)
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;model3d_re1.vtk&#39;</span><span class="p">)</span>
+
+
+<span class="n">x_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">mesh</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">nCells</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="s2">&quot;x_re&quot;</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="n">x_re</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">values</span><span class="p">)</span>
+
+<span class="n">model1</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">model2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">model3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">model4</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_3d_004.png" srcset="../../_images/sphx_glr_plot_model_3d_004.png" alt="plot model 3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Northing (m)&#39;, ylabel=&#39;z_re (m)&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x17ee3a0f0&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x154bb9a30&gt;)
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;model3d_re2.vtk&#39;</span><span class="p">)</span>
+
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">y_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;y_re&quot;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="n">x_re</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_relative_to</span><span class="o">=</span><span class="n">y_re</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">values</span><span class="p">)</span>
+
+<span class="n">model1</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">5</span><span class="p">]</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">model</span><span class="p">[:,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">231</span><span class="p">)</span>
+<span class="n">model2</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">232</span><span class="p">)</span>
+<span class="n">model3</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">233</span><span class="p">)</span>
+<span class="n">model4</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_model_3d_005.png" srcset="../../_images/sphx_glr_plot_model_3d_005.png" alt="plot model 3d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;y_re (m)&#39;, ylabel=&#39;z_re (m)&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x17faea990&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x15e95dc10&gt;)
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pv</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">pyvista_plotter</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>We can plot the mesh in 3D!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pyvista_mesh</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;model3d_re3.vtk&#39;</span><span class="p">)</span>
+
+<span class="c1"># with h5py.File(&#39;mesh3d.h5&#39;, &#39;w&#39;) as f:</span>
+<span class="c1">#     mesh.toHdf(f, &#39;test&#39;)</span>
+
+<span class="c1"># with h5py.File(&#39;mesh3d.h5&#39;, &#39;r&#39;) as f:</span>
+<span class="c1">#     mesh2 = RectilinearMesh3D.fromHdf(f[&#39;test&#39;])</span>
+
+<span class="c1"># mesh2.pyvista_mesh().save(&#39;mesh3d_read.vtk&#39;)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.788 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-models-plot-model-3d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/525bc65bfe238dea5610d6856bece19c/plot_model_3d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_model_3d.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/2b313676e6ab4e7fef5cb85211b43803/plot_model_3d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_model_3d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/dab51a848a47b0a54695528258f289e6/plot_model_3d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_model_3d.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_model_2d.html" class="btn btn-neutral float-left" title="2D Rectilinear Model" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../Statistics/plot_DataArray.html" class="btn btn-neutral float-right" title="DataArray Class" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/docs/examples/Models/readme.html b/docs/examples/Models/readme.html
new file mode 100644
index 00000000..987fd79d
--- /dev/null
+++ b/docs/examples/Models/readme.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Models &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+          <a href="../../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+        <div class="rst-content">
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+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Models</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Models/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="models">
+<h1>Models<a class="headerlink" href="#models" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
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diff --git a/docs/examples/Models/sg_execution_times.html b/docs/examples/Models/sg_execution_times.html
new file mode 100644
index 00000000..4fffeaa1
--- /dev/null
+++ b/docs/examples/Models/sg_execution_times.html
@@ -0,0 +1,151 @@
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+  <section id="computation-times">
+<span id="sphx-glr-examples-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:04.051</strong> total execution time for 3 files <strong>from examples/Models</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
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+<script type="text/javascript" class="init">
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+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_model_1d.html#sphx-glr-examples-models-plot-model-1d-py"><span class="std std-ref">1D Model with an infinite halfspace</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_model_1d.py</span></code>)</p></td>
+<td><p>00:03.051</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_model_3d.html#sphx-glr-examples-models-plot-model-3d-py"><span class="std std-ref">3D Rectilinear Model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_model_3d.py</span></code>)</p></td>
+<td><p>00:00.788</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_model_2d.html#sphx-glr-examples-models-plot-model-2d-py"><span class="std std-ref">2D Rectilinear Model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_model_2d.py</span></code>)</p></td>
+<td><p>00:00.212</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#statistics">Statistics</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">DataArray Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_1d.html">Histogram 1D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_2d.html">Histogram 2D</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="plot_StatArray.html">StatArray Class</a></li>
+</ul>
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+            <a href="../../_sources/examples/Statistics/plot_DataArray.rst.txt" rel="nofollow"> View page source</a>
+      </li>
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+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-statistics-plot-dataarray-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="dataarray-class">
+<span id="sphx-glr-examples-statistics-plot-dataarray-py"></span><h1>DataArray Class<a class="headerlink" href="#dataarray-class" title="Link to this heading"></a></h1>
+<p>Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>DataArray
+Name:   1
+Address:[&#39;0x17f629d50&#39;]
+Shape:  (1,)
+Values: [0.]
+Min:    0.0
+Max:    0.0
+
+DataArray
+Name:   10
+Address:[&#39;0x17f629450&#39;]
+Shape:  (10,)
+Values: [0. 0. 0. ... 0. 0. 0.]
+Min:    0.0
+Max:    0.0
+
+DataArray
+Name:   (2, 10)
+Address:[&#39;0x17f62aad0&#39;]
+Shape:  (2, 10)
+Values: [[0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+Min:    0.0
+Max:    0.0
+
+DataArray
+Name:   (2, 10)
+Address:[&#39;0x154ab7a50&#39;]
+Shape:  (2,)
+Values: [ 2 10]
+Min:    2
+Max:    10
+
+DataArray
+Name:   45.454
+Address:[&#39;0x154ab6dd0&#39;]
+Shape:  (1,)
+Values: [45.454]
+Min:    45.454
+Max:    45.454
+
+DataArray
+Name:   45.454
+Address:[&#39;0x17f62aad0&#39;]
+Shape:  (1,)
+Values: [45.454]
+Min:    45.454
+Max:    45.454
+
+DataArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x154ab6dd0&#39;]
+Shape:  (1,)
+Values: [0.00257118]
+Min:    0.002571182431510025
+Max:    0.002571182431510025
+
+DataArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x17f62aad0&#39;]
+Shape:  (10,)
+Values: [0. 1. 2. ... 7. 8. 9.]
+Min:    0.0
+Max:    9.0
+
+DataArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x154ab7a50&#39;]
+Shape:  (10,)
+Values: [0. 1. 2. ... 7. 8. 9.]
+Min:    0.0
+Max:    9.0
+</pre></div>
+</div>
+<div class="line-block">
+<div class="line"><br /></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span>  <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">DataArray</span><span class="p">,</span> <span class="n">StatArray</span>
+
+<span class="c1"># Integer</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;1&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 0&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;10&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 1&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="c1"># tuple/Shape</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;(2, 10)&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span>  <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 2&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;(2, 10)&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span>  <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">]),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 2&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="c1"># float</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="mf">45.454</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;45.454&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">45.454</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 3&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="mf">45.454</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;45.454&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">45.454</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 4&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="c1"># array</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 5&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 6&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 6&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+</pre></div>
+</div>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#statistics">Statistics</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_DataArray.html">DataArray Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_1d.html">Histogram 1D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_2d.html">Histogram 2D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_3d.html">Histogram 3D</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">StatArray Class</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instantiating-a-new-statarray-class">Instantiating a new StatArray class</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#attaching-prior-and-proposal-distributions-to-a-statarray">Attaching Prior and Proposal Distributions to a StatArray</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#attaching-a-histogram-to-capture-the-posterior-distribution">Attaching a Histogram to capture the posterior distribution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#attach-a-multivariate-normal-distribution-as-the-prior-and-proposal">Attach a multivariate normal distribution as the prior and proposal</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#basic-manipulation">Basic manipulation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#plotting">Plotting</a></li>
+</ul>
+</li>
+</ul>
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+</ul>
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+            <a href="../../_sources/examples/Statistics/plot_StatArray.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-statistics-plot-statarray-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="statarray-class">
+<span id="sphx-glr-examples-statistics-plot-statarray-py"></span><h1>StatArray Class<a class="headerlink" href="#statarray-class" title="Link to this heading"></a></h1>
+<p>Extends the numpy ndarray class to add extra attributes such as names, and
+units, and allows us to attach statistical descriptors of the array.
+The direct extension to numpy maintains speed and functionality of numpy arrays.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">DataArray</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">,</span> <span class="n">Histogram</span><span class="p">,</span> <span class="n">Distribution</span><span class="p">,</span> <span class="n">RectilinearMesh1D</span>
+
+
+<span class="c1"># plt.style.use(&#39;seaborn-pastel&#39;)</span>
+</pre></div>
+</div>
+<section id="instantiating-a-new-statarray-class">
+<h2>Instantiating a new StatArray class<a class="headerlink" href="#instantiating-a-new-statarray-class" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Integer</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;1&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 0&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;10&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 1&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="c1"># tuple/Shape</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;(2, 10)&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span>  <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 2&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;(2, 10)&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">test</span> <span class="o">==</span>  <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">]),</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 2&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="c1"># float</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mf">45.454</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;45.454&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">45.454</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 3&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">(</span><span class="mf">45.454</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;45.454&#39;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mf">45.454</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 4&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="c1"># array</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">1</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 5&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 6&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+
+<span class="n">test</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">StatArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">test</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span>  <span class="mi">10</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da 6&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+
+
+
+<span class="c1"># The StatArray can take any numpy function that returns an array as an input.</span>
+<span class="c1"># The name and units of the variable can be assigned to the StatArray.</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>StatArray
+Name:   1
+Address:[&#39;0x154bfe950&#39;]
+Shape:  (1,)
+Values: [0.]
+Min:    0.0
+Max:    0.0
+has_posterior: False
+
+StatArray
+Name:   10
+Address:[&#39;0x17f089a50&#39;]
+Shape:  (10,)
+Values: [0. 0. 0. ... 0. 0. 0.]
+Min:    0.0
+Max:    0.0
+has_posterior: False
+
+StatArray
+Name:   (2, 10)
+Address:[&#39;0x17f08a7d0&#39;]
+Shape:  (2, 10)
+Values: [[0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+Min:    0.0
+Max:    0.0
+has_posterior: False
+
+StatArray
+Name:   (2, 10)
+Address:[&#39;0x17f6825d0&#39;]
+Shape:  (2,)
+Values: [ 2 10]
+Min:    2
+Max:    10
+has_posterior: False
+
+StatArray
+Name:   45.454
+Address:[&#39;0x17f08a7d0&#39;]
+Shape:  (1,)
+Values: [45.454]
+Min:    45.454
+Max:    45.454
+has_posterior: False
+
+StatArray
+Name:   45.454
+Address:[&#39;0x17f680a50&#39;]
+Shape:  (1,)
+Values: [45.454]
+Min:    45.454
+Max:    45.454
+has_posterior: False
+
+StatArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x17f08a7d0&#39;]
+Shape:  (1,)
+Values: [-0.70419088]
+Min:    -0.7041908829549965
+Max:    -0.7041908829549965
+has_posterior: False
+
+StatArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x17f6819d0&#39;]
+Shape:  (10,)
+Values: [0. 1. 2. ... 7. 8. 9.]
+Min:    0.0
+Max:    9.0
+has_posterior: False
+
+StatArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x17f680a50&#39;]
+Shape:  (10,)
+Values: [0. 1. 2. ... 7. 8. 9.]
+Min:    0.0
+Max:    9.0
+has_posterior: False
+</pre></div>
+</div>
+</section>
+<section id="attaching-prior-and-proposal-distributions-to-a-statarray">
+<h2>Attaching Prior and Proposal Distributions to a StatArray<a class="headerlink" href="#attaching-prior-and-proposal-distributions-to-a-statarray" title="Link to this heading"></a></h2>
+<p>The StatArray class has been built so that we may easily
+attach not only names and units, but statistical distributions too.
+We won’t go into too much detail about the different distribution</p>
+<p>Two types of distributions can be attached to the StatArray.</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>Prior Distribution</dt><dd><p>The prior represents how the user believes the variable should
+behave from a statistical standpoint.
+The values of the variable can be evaluated against the attached prior,
+to determine how likely they are to have occured <a class="reference external" href="https://en.wikipedia.org/wiki/Prior_probability">https://en.wikipedia.org/wiki/Prior_probability</a></p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>Proposal Distribution</dt><dd><p>The proposal describes a probability distribution from which to
+sample when we wish to perturb the variable
+<a class="reference external" href="https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm">https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm</a></p>
+</dd>
+</dl>
+</li>
+</ul>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Obtain an instantiation of a random number generator.</span>
+<span class="c1"># This is optional, but is an important consideration for parallel programming.</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">Generator</span>
+<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">PCG64DXSM</span>
+<span class="n">generator</span> <span class="o">=</span> <span class="n">PCG64DXSM</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">prng</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span>
+
+<span class="n">Density</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+
+<span class="n">Density</span><span class="o">.</span><span class="n">prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Uniform&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can also attach a proposal distribution</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span><span class="o">.</span><span class="n">proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;Normal&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Class type of the prior: &quot;</span><span class="p">,</span><span class="nb">type</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">prior</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Class type of the proposal: &quot;</span><span class="p">,</span><span class="nb">type</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">proposal</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>StatArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x154bfd7d0&#39; &#39;0x17f652360&#39; &#39;0x181310930&#39; ... &#39;0x17f652180&#39; &#39;0x181313b10&#39;
+ &#39;0x1813134b0&#39;]
+Shape:  (1,)
+Values: [10.]
+Min:    10.0
+Max:    10.0
+Prior:
+|   Uniform Distribution:
+|   Min: -2.0
+|   Max: 2.0
+Proposal:
+|   Normal
+|       Mean:0.0
+|   Variance:1.0
+has_posterior: False
+
+Class type of the prior:  &lt;class &#39;geobipy.src.classes.statistics.UniformDistribution.Uniform&#39;&gt;
+Class type of the proposal:  &lt;class &#39;geobipy.src.classes.statistics.NormalDistribution.Normal&#39;&gt;
+</pre></div>
+</div>
+<p>The values in the variable can be evaluated against the prior.
+In this case, we have 3 elements in the variable, and a univariate Normal for the prior.
+Therefore each element is evaluated to get 3 probabilities, one for each element.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.0
+</pre></div>
+</div>
+<p>The univariate proposal distribution can generate random samples from itself.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">propose</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.1375024404290368
+</pre></div>
+</div>
+<p>From a sampling stand point we can either sample using only the proposal
+Or we can only generate samples that simultaneously satisfy the prior.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">propose</span><span class="p">(</span><span class="n">relative</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[10.53816627]
+</pre></div>
+</div>
+<p>We can perturb the variable by drawing from the attached proposal distribution.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>StatArray
+Name:   test ($\frac{g}{cc}$)
+Address:[&#39;0x154bfd7d0&#39; &#39;0x17f652360&#39; &#39;0x181310930&#39; ... &#39;0x17f652180&#39; &#39;0x181313b10&#39;
+ &#39;0x1813134b0&#39;]
+Shape:  (1,)
+Values: [0.38188467]
+Min:    0.38188466718060166
+Max:    0.38188466718060166
+Prior:
+|   Uniform Distribution:
+|   Min: -2.0
+|   Max: 2.0
+Proposal:
+|   Normal
+|       Mean:0.0
+|   Variance:1.0
+has_posterior: False
+</pre></div>
+</div>
+</section>
+<section id="attaching-a-histogram-to-capture-the-posterior-distribution">
+<h2>Attaching a Histogram to capture the posterior distribution<a class="headerlink" href="#attaching-a-histogram-to-capture-the-posterior-distribution" title="Link to this heading"></a></h2>
+<p>The StatArray can perturb itself, evaluate its current probability given its priors
+and a histogram can be attached to capture its posterior distribution.
+As an example, lets create a Histogram class with bins generated from the prior.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">bins</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">prior</span><span class="o">.</span><span class="n">bins</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Attach the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span><span class="o">.</span><span class="n">posterior</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">bins</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>In an iterative sense, we can propose and evaluate new values, and update the posterior</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="n">Density</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+    <span class="n">p</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">p</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">:</span> <span class="c1"># This is a simple example!</span>
+        <span class="n">Density</span><span class="o">.</span><span class="n">update_posterior</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">summaryPlot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_001.png" srcset="../../_images/sphx_glr_plot_StatArray_001.png" alt="Prior, Proposal, Posterior" class = "sphx-glr-single-img"/></section>
+<section id="attach-a-multivariate-normal-distribution-as-the-prior-and-proposal">
+<h2>Attach a multivariate normal distribution as the prior and proposal<a class="headerlink" href="#attach-a-multivariate-normal-distribution-as-the-prior-and-proposal" title="Link to this heading"></a></h2>
+<p>Attach the multivariate prior</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">prior</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">variance</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Since the prior is multivariate, the appropriate equations are used to
+evaluate the probability for all elements in the StatArray.
+This produces a single probability.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">probability</span><span class="p">(</span><span class="n">log</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.16541198455442488
+</pre></div>
+</div>
+<p>Attach the multivariate proposal</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">proposal</span> <span class="o">=</span> <span class="n">Distribution</span><span class="p">(</span><span class="s1">&#39;MvNormal&#39;</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">variance</span><span class="p">,</span> <span class="n">prng</span><span class="o">=</span><span class="n">prng</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Perturb the variables using the multivariate proposal.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span><span class="o">.</span><span class="n">perturb</span><span class="p">()</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">summary</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;statarray.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">Density</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;statarray&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">Density</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;statarray&#39;</span><span class="p">,</span> <span class="n">withPosterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;statarray.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;statarray&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">skip_posterior</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;statarray.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;statarray&#39;</span><span class="p">,</span> <span class="n">skip_posterior</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="basic-manipulation">
+<h2>Basic manipulation<a class="headerlink" href="#basic-manipulation" title="Link to this heading"></a></h2>
+<p>The StatArray contains other functions to perform basic array manipulations</p>
+<p>These routines essentially wrap around numpy functions,
+but the result will have the same name and units,
+and if any prior or proposal are set, those will be carried through too.</p>
+<section id="d-example">
+<h3>1D example<a class="headerlink" href="#d-example" title="Link to this heading"></a></h3>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.0</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ -0.  -1.  -3. ... -28. -36. -45.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="mf">999.0</span><span class="p">,</span> <span class="mf">999.0</span><span class="p">]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[999.  -0.  -1. ... -36. 999. -45.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">prepend</span><span class="p">(</span><span class="mf">999.0</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[999.  -0.  -1. ... -28. -36. -45.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">prepend</span><span class="p">([</span><span class="mf">998.0</span><span class="p">,</span> <span class="mf">999.0</span><span class="p">]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[998. 999.  -0. ... -28. -36. -45.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mf">998.0</span><span class="p">,</span> <span class="mf">999.0</span><span class="p">]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ -0.  -1.  -3. ... -45. 998. 999.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="mi">14</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[-0. -1. -3. ... -1. -3. -6.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">delete</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span><span class="mi">8</span><span class="p">]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ -0.  -1.  -3. ... -21. -28. -45.]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[  0.5  -0.5  -2.  ... -32.  -40.5 -49.5]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">internalEdges</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ -0.5  -2.   -4.5 ... -24.5 -32.  -40.5]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">firstNonZero</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">lastNonZero</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>10
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ 0.  1.  3. ... 28. 36. 45.]
+</pre></div>
+</div>
+</section>
+<section id="id1">
+<h3>2D example<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0 -2  3]
+ [ 3  0 -1]
+ [ 1  2  0]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 4  4  4]
+ [ 0 -2  3]
+ [ 3  0 -1]
+ [ 1  2  0]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">values</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0 -2  5  3  5]
+ [ 3  0  5 -1  5]
+ [ 1  2  5  0  5]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0 -2 10  3]
+ [ 3  0 11 -1]
+ [ 1  2 12  0]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">prepend</span><span class="p">(</span><span class="mi">999</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[999 999 999]
+ [  0  -2   3]
+ [  3   0  -1]
+ [  1   2   0]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">prepend</span><span class="p">([</span><span class="mi">999</span><span class="p">,</span> <span class="mi">998</span><span class="p">,</span> <span class="mi">997</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[999 998 997   0  -2   3]
+ [999 998 997   3   0  -1]
+ [999 998 997   1   2   0]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="mi">999</span><span class="p">,</span> <span class="mi">998</span><span class="p">,</span> <span class="mi">997</span><span class="p">]]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[  0  -2   3]
+ [  3   0  -1]
+ [  1   2   0]
+ [999 998 997]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">resize</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0 -2  3  3  0]
+ [-1  1  2  0  0]
+ [-2  3  3  0 -1]
+ [ 1  2  0  0 -2]
+ [ 3  3  0 -1  1]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ 0 -2  3 ...  1  2  0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0 -2  3]
+ [ 3  0 -1]]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">firstNonZero</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[1 0 0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">lastNonZero</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[3 3 2]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">firstNonZero</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[1 0 0]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">lastNonZero</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[3 3 2]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[0 2 3]
+ [3 0 1]
+ [1 2 0]]
+</pre></div>
+</div>
+</section>
+</section>
+<section id="plotting">
+<h2>Plotting<a class="headerlink" href="#plotting" title="Link to this heading"></a></h2>
+<p>We can easily plot the StatArray with its built in plotting functions.
+All plotting functions can take matplotlib keywords</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The simplest is to just plot the array</span>
+
+<span class="n">Density</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Density&quot;</span><span class="p">,</span><span class="n">units</span><span class="o">=</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">Time</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Time&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;s&#39;</span><span class="p">)</span>
+<span class="n">Depth</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">exponential</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Depth&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;m&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_002.png" srcset="../../_images/sphx_glr_plot_StatArray_002.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can quickly plot a bar graph.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_003.png" srcset="../../_images/sphx_glr_plot_StatArray_003.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can scatter the contents of the StatArray if it is 1D</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_004.png" srcset="../../_images/sphx_glr_plot_StatArray_004.png" alt="plot StatArray" class = "sphx-glr-single-img"/><section id="histogram-equalization">
+<h3>Histogram Equalization<a class="headerlink" href="#histogram-equalization" title="Link to this heading"></a></h3>
+<p>A neat trick with colourmaps is histogram equalization.
+This approach forces all colours in the images to have an equal weight.
+This distorts the colour bar, but can really highlight the lower and higher
+ends of whatever you are plotting. Just add the equalize keyword!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_005.png" srcset="../../_images/sphx_glr_plot_StatArray_005.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Take the log base(x) of the data</p>
+<p>We can also take the data to a log, log10, log2, or a custom number!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span><span class="n">log</span><span class="o">=</span><span class="s1">&#39;e&#39;</span><span class="p">)</span> <span class="c1"># could also use log=&#39;e&#39;, log=2, log=x) where x is the base you require</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_006.png" srcset="../../_images/sphx_glr_plot_StatArray_006.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>X and Y axes</p>
+<p>We can specify the x axis of the scatter plot.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_007.png" srcset="../../_images/sphx_glr_plot_StatArray_007.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Notice that I never specified the y axis, so the y axis defaulted to the values in the StatArray.
+In this case, any operations applied to the colours, are also applied to the y axis, e.g. log=10.
+When I take the values of Density to log base 10, because I do not specify the y plotting locations, those locations are similarly affected.</p>
+<p>I can however force the y co-ordinates by specifying it as input.
+In the second subplot I explicitly plot distance on the y axis.
+In the first subplot, the y axis is the same as the colourbar.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">Depth</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_008.png" srcset="../../_images/sphx_glr_plot_StatArray_008.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Point sizes</p>
+<p>Since the plotting functions take matplotlib keywords, I can also specify the size of each points.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">))))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">Depth</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">legend_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+<span class="c1">#Density.scatter(x=Time, y=Depth, s=s, alpha=0.7,edgecolor=&#39;k&#39;, sizeLegend=[1.0, 100, 200, 300])</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Density</span><span class="p">)</span><span class="o">+</span><span class="mf">1.0</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">Depth</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">legend_size</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_009.png" srcset="../../_images/sphx_glr_plot_StatArray_009.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Of course we can still take the log, or equalize the colour histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">Depth</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span><span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_010.png" srcset="../../_images/sphx_glr_plot_StatArray_010.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Typically pcolor only works with 2D arrays. The StatArray has a pcolor method that will pcolor a 1D array</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">221</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">222</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">Time</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">223</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">224</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">Time</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_011.png" srcset="../../_images/sphx_glr_plot_StatArray_011.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can add grid lines, and add opacity to each element in the pcolor image</p>
+<p>This is useful if the colour values need to be scaled by another variable e.g. variance.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">Density</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_012.png" srcset="../../_images/sphx_glr_plot_StatArray_012.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can plot a histogram of the StatArray</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_013.png" srcset="../../_images/sphx_glr_plot_StatArray_013.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can write the StatArray to a HDF5 file.  HDF5 files are binary files that can include compression.  They allow quick and easy access to parts of the file, and can also be written to and read from in parallel!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;1Dtest.h5&#39;</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">Density</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can then read the StatArray from the file
+Here x is a new variable, that is read in from the hdf5 file we just wrote.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="s1">&#39;1Dtest.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x has the same values as Density? &#39;</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="n">Density</span><span class="p">))</span>
+<span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mf">5.0</span> <span class="c1"># Change one of the values in x</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x has its own memory allocated (not a reference/pointer)? &#39;</span><span class="p">,</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">id</span><span class="p">(</span><span class="n">Density</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x has the same values as Density?  True
+x has its own memory allocated (not a reference/pointer)?  True
+</pre></div>
+</div>
+<p>We can also define a 2D array</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="s2">&quot;Density&quot;</span><span class="p">,</span><span class="s2">&quot;$</span><span class="se">\f</span><span class="s2">rac</span><span class="si">{g}{cc}</span><span class="s2">$&quot;</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">summary</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&quot;StatArray\nName:   Density ($\\frac{g}{cc}$)\nAddress:[&#39;0x17f4e05d0&#39;]\nShape:  (50, 100)\nValues: [[ 0.14104158  0.47155933  1.95502179 ...  1.76214533  1.24396239\n   0.1215391 ]\n [-1.85725407 -1.83481902  1.04987135 ...  0.85985878 -0.34529634\n  -0.85558253]\n [ 0.76292177  1.13113089 -1.35816931 ...  1.31159774  0.46698778\n   1.68630769]\n ...\n [ 0.48384681  0.22774246 -0.51285518 ... -0.24617681  0.81498656\n   1.57095942]\n [-0.13056782  1.72508382 -1.33506224 ... -1.17319374  0.27482725\n   0.39378825]\n [ 1.36799972  0.26714623 -0.02588039 ... -0.28979426 -0.8747471\n  -0.98512505]]\nMin:    -3.2750329304041186\nMax:    3.445969826454679\nhas_posterior: False\n&quot;
+</pre></div>
+</div>
+<p>The StatArray Class’s functions work whether it is 1D or 2D</p>
+<p>We can still do a histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_014.png" srcset="../../_images/sphx_glr_plot_StatArray_014.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>And we can use pcolor to plot the 2D array</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_015.png" srcset="../../_images/sphx_glr_plot_StatArray_015.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>The StatArray comes with extra plotting options</p>
+<p>Here we specify the x and y axes for the 2D array using two other 1D StatArrays</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">101</span><span class="p">),</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x Axis&#39;</span><span class="p">,</span><span class="n">units</span> <span class="o">=</span> <span class="s1">&#39;mm&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">51</span><span class="p">),</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y Axis&#39;</span><span class="p">,</span><span class="n">units</span> <span class="o">=</span> <span class="s1">&#39;elephants&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_016.png" srcset="../../_images/sphx_glr_plot_StatArray_016.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can plot using a log10 scale, in this case, we have values that are less
+than or equal to 0.0.  Plotting with the log option will by default mask any
+of those values, and will let you know that it has done so!</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span><span class="n">log</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_017.png" srcset="../../_images/sphx_glr_plot_StatArray_017.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>A neat trick with colourmaps is histogram equalization.
+This approach forces all colours in the image to have an equal amount.
+This distorts the colours, but can really highlight the lower and higher
+ends of whatever you are plotting</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_018.png" srcset="../../_images/sphx_glr_plot_StatArray_018.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can equalize the log10 plot too :)</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span><span class="n">equalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_019.png" srcset="../../_images/sphx_glr_plot_StatArray_019.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>We can add opacity to each pixel in the image</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">Density</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="s1">&#39;Opacity from 0.0 to 1.0&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">flipY</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_020.png" srcset="../../_images/sphx_glr_plot_StatArray_020.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>If the array potentially has a lot of white space around the edges, we can trim the image</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">Density</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">Density</span><span class="p">[</span><span class="o">-</span><span class="mi">10</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">Density</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">Density</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">10</span><span class="p">:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">Density</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">trim</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_021.png" srcset="../../_images/sphx_glr_plot_StatArray_021.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p>Create a stacked area plot of a 2D StatArray</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span><span class="mi">100</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Variable&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;units&quot;</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x Axis&#39;</span><span class="p">,</span><span class="n">units</span> <span class="o">=</span> <span class="s1">&#39;mm&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">A</span><span class="o">.</span><span class="n">stackedAreaPlot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">stackedAreaPlot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">i</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">s_</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],:],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_StatArray_022.png" srcset="../../_images/sphx_glr_plot_StatArray_022.png" alt="plot StatArray" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.767 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-statistics-plot-statarray-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/e43324a965564fecaa251424c676fc0e/plot_StatArray.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_StatArray.ipynb</span></code></a></p>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">Histogram 1D</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#histogram-with-regular-bins">Histogram with regular bins</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#histogram-with-irregular-bins">Histogram with irregular bins</a></li>
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+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-statistics-plot-histogram-1d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="histogram-1d">
+<span id="sphx-glr-examples-statistics-plot-histogram-1d-py"></span><h1>Histogram 1D<a class="headerlink" href="#histogram-1d" title="Link to this heading"></a></h1>
+<p>This histogram class allows efficient updating of histograms, plotting and
+saving as HDF5</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">geobipy.src.classes.mesh.RectilinearMesh1D</span> <span class="kn">import</span> <span class="n">RectilinearMesh1D</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Histogram</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+</div>
+<section id="histogram-with-regular-bins">
+<h2>Histogram with regular bins<a class="headerlink" href="#histogram-with-regular-bins" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create regularly spaced bins</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span> <span class="s1">&#39;bins&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>Set the histogram using the bins, and update</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can update the histogram with some new values</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">),</span> <span class="n">trim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot the histogram</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">221</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">222</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">pdf</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">223</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pmf</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">224</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">()</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_1d_001.png" srcset="../../_images/sphx_glr_plot_histogram_1d_001.png" alt="plot histogram 1d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: xlabel=&#39;bins (m)&#39;, ylabel=&#39;Cumulative Density Function&#39;&gt;
+</pre></div>
+</div>
+<p>Get the median, and 95% confidence values</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">credible_intervals</span><span class="p">(</span><span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">))</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_1d_002.png" srcset="../../_images/sphx_glr_plot_histogram_1d_002.png" alt="plot histogram 1d" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(np.float64(-0.030000000000000027), np.float64(-1.83), np.float64(1.8299999999999996))
+</pre></div>
+</div>
+</section>
+<section id="histogram-with-irregular-bins">
+<h2>Histogram with irregular bins<a class="headerlink" href="#histogram-with-irregular-bins" title="Link to this heading"></a></h2>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create irregularly spaced bins</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">))</span>
+<span class="n">irregularBins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="o">-</span><span class="n">x</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
+</pre></div>
+</div>
+<p>Create a named StatArray</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">edges</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">irregularBins</span><span class="p">,</span> <span class="s1">&#39;irregular bins&#39;</span><span class="p">)</span>
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span> <span class="o">=</span> <span class="n">edges</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Instantiate the histogram with bin edges</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">)</span>
+
+<span class="c1"># Update the histogram</span>
+<span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span><span class="o">*</span><span class="mf">20.0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">10.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Plot the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_1d_003.png" srcset="../../_images/sphx_glr_plot_histogram_1d_003.png" alt="plot histogram 1d" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_1d_004.png" srcset="../../_images/sphx_glr_plot_histogram_1d_004.png" alt="plot histogram 1d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<p>We can plot the histogram as a pcolor plot</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_1d_005.png" srcset="../../_images/sphx_glr_plot_histogram_1d_005.png" alt="plot histogram 1d" class = "sphx-glr-single-img"/></section>
+<section id="histogram-with-linear-space-entries-that-are-logged-internally">
+<h2>Histogram with linear space entries that are logged internally<a class="headerlink" href="#histogram-with-linear-space-entries-that-are-logged-internally" title="Link to this heading"></a></h2>
+<p>Create some bins spaced logarithmically</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s1">&#39;positive bins&#39;</span><span class="p">),</span> <span class="n">log</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Instantiate the Histogram with log=10</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The update takes in the numbers in linear space and takes their log=10</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mf">10.0</span><span class="o">**</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span><span class="o">*</span><span class="mf">2.0</span><span class="p">),</span> <span class="n">trim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h1d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h1d&#39;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H1</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_1d_006.png" srcset="../../_images/sphx_glr_plot_histogram_1d_006.png" alt="plot histogram 1d" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh1D</span><span class="p">(</span><span class="n">edges</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span> <span class="s1">&#39;bins&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>Set the histogram using the bins, and update</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can update the histogram with some new values</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">),</span> <span class="n">trim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h1d&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">3.0</span><span class="p">),</span> <span class="s2">&quot;Name&quot;</span><span class="p">,</span> <span class="s2">&quot;Units&quot;</span><span class="p">))</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">),</span> <span class="n">trim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">),</span> <span class="n">trim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h1d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h1d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h1d&#39;</span><span class="p">])</span>
+    <span class="n">H2</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">H3</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">H4</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h1d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+
+<span class="nb">print</span><span class="p">(</span><span class="n">H4</span><span class="o">.</span><span class="n">summary</span><span class="p">)</span>
+
+<span class="c1"># plt.figure()</span>
+<span class="c1"># plt.subplot(211)</span>
+<span class="c1"># _ = H1.plot()</span>
+<span class="c1"># plt.subplot(212)</span>
+<span class="c1"># _ = H4.plot()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Histogram:
+mesh:
+|   RectilinearMesh1D
+|   Number of Cells:
+|   |   100
+|   Cell Centres:
+|   |   StatArray
+|   |   Name:   bins (m)
+|   |   Address:[&#39;0x17f6bebd0&#39;]
+|   |   Shape:  (100,)
+|   |   Values: [-2.97 -2.91 -2.85 ...  2.85  2.91  2.97]
+|   |   Min:    -2.9699999999999998
+|   |   Max:    2.9699999999999998
+|   |   has_posterior: False
+|
+|   Cell Edges:
+|   |   StatArray
+|   |   Name:   bins (m)
+|   |   Address:[&#39;0x17f6bfad0&#39;]
+|   |   Shape:  (101,)
+|   |   Values: [-3.   -2.94 -2.88 ...  2.88  2.94  3.  ]
+|   |   Min:    -3.0
+|   |   Max:    3.0
+|   |   has_posterior: False
+|
+|   log:
+|   |   None
+|   relative_to:
+|   |   StatArray
+|   |   Name:
+|   |   Address:[&#39;0x17f088dd0&#39;]
+|   |   Shape:  (1,)
+|   |   Values: [0.]
+|   |   Min:    0.0
+|   |   Max:    0.0
+|   |   has_posterior: False
+|
+values:
+|   DataArray
+|   Name:   Frequency
+|   Address:[&#39;0x17f08a550&#39;]
+|   Shape:  (100,)
+|   Values: [2 0 0 ... 0 2 1]
+|   Min:    0
+|   Max:    88
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.972 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-statistics-plot-histogram-1d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/6651de36bdd3440bb761887dd2b00831/plot_histogram_1d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_histogram_1d.ipynb</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/9570534d71f4f6a00b8f20360000c3ff/plot_histogram_1d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_histogram_1d.py</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/2144e9773886d1394652515ba8d8dc41/plot_histogram_1d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_histogram_1d.zip</span></code></a></p>
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#statistics">Statistics</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_DataArray.html">DataArray Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_1d.html">Histogram 1D</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Histogram 2D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_3d.html">Histogram 3D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_StatArray.html">StatArray Class</a></li>
+</ul>
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+      <li class="breadcrumb-item active">Histogram 2D</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Statistics/plot_histogram_2d.rst.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-statistics-plot-histogram-2d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="histogram-2d">
+<span id="sphx-glr-examples-statistics-plot-histogram-2d-py"></span><h1>Histogram 2D<a class="headerlink" href="#histogram-2d" title="Link to this heading"></a></h1>
+<p>This 2D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">import</span> <span class="nn">geobipy</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Histogram</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh2D</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Create some histogram bins in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="s1">&#39;Variable 1&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">105</span><span class="p">),</span> <span class="s1">&#39;Variable 2&#39;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Instantiate</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Generate some random numbers</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000000</span><span class="p">)</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000000</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Update the histogram counts</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;2D Histogram&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pmf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_001.png" srcset="../../_images/sphx_glr_plot_histogram_2d_001.png" alt="2D Histogram" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_002.png" srcset="../../_images/sphx_glr_plot_histogram_2d_002.png" alt="plot histogram 2d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Variable 1&#39;, ylabel=&#39;Variable 2&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x17ed24a40&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x17f1a32c0&gt;)
+</pre></div>
+</div>
+<p>We can overlay the histogram with its credible intervals</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;90</span><span class="si">% c</span><span class="s2">redible intervals overlain&quot;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_003.png" srcset="../../_images/sphx_glr_plot_histogram_2d_003.png" alt="90% credible intervals overlain" class = "sphx-glr-single-img"/><p>Generate marginal histograms along an axis</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">h1</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">h2</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Note that the names of the variables are automatically displayed</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Marginals along each axis&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">h1</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_004.png" srcset="../../_images/sphx_glr_plot_histogram_2d_004.png" alt="Marginals along each axis" class = "sphx-glr-single-img"/><p>Create a combination plot with marginal histograms.
+sphinx_gallery_thumbnail_number = 3</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">gs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])]</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">colorbar</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[:</span><span class="mi">1</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]))</span>
+<span class="n">h</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([]);</span> <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">4</span><span class="p">:]))</span>
+<span class="n">h</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([]);</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([])</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_005.png" srcset="../../_images/sphx_glr_plot_histogram_2d_005.png" alt="plot histogram 2d" class = "sphx-glr-single-img"/><p>Take the mean or median estimates from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">median</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">9.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean, median, and credible interval overlain&quot;</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_006.png" srcset="../../_images/sphx_glr_plot_histogram_2d_006.png" alt="Mean, median, and credible interval overlain" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x182f538f0&gt;
+</pre></div>
+</div>
+<p>Get the range between credible intervals</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">credible_range</span><span class="p">(</span><span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>StatArray([3.47474747, 4.44444444, 3.71717172, ..., 3.47474747,
+           3.47474747, 4.2020202 ])
+</pre></div>
+</div>
+<p>We can map the credible range to an opacity or transparency</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">opacity</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">transparency</span><span class="p">()</span>
+
+<span class="c1"># H.animate(0, &#39;test.mp4&#39;)</span>
+
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h2d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h2d&#39;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="s1">&#39;all&#39;</span><span class="p">)</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mf">5.0</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="s1">&#39;Variable 1&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="mf">10.0</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">105</span><span class="p">),</span> <span class="s1">&#39;Variable 2&#39;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh2D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_relative_to</span><span class="o">=</span><span class="mf">10.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Instantiate</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Generate some random numbers</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000000</span><span class="p">)</span> <span class="o">+</span> <span class="mf">5.0</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000000</span><span class="p">)</span> <span class="o">+</span> <span class="mf">10.0</span>
+</pre></div>
+</div>
+<p>Update the histogram counts</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;2D Histogram&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pmf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">132</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">133</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">cdf</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_007.png" srcset="../../_images/sphx_glr_plot_histogram_2d_007.png" alt="2D Histogram" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_008.png" srcset="../../_images/sphx_glr_plot_histogram_2d_008.png" alt="plot histogram 2d" class = "sphx-glr-multi-img"/></li>
+</ul>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&lt;Axes: xlabel=&#39;Variable 1&#39;, ylabel=&#39;Variable 2&#39;&gt;, &lt;matplotlib.collections.QuadMesh object at 0x182ff9a00&gt;, &lt;matplotlib.colorbar.Colorbar object at 0x181bcaf00&gt;)
+</pre></div>
+</div>
+<p>We can overlay the histogram with its credible intervals</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;90</span><span class="si">% c</span><span class="s2">redible intervals overlain&quot;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+
+<span class="c1"># Generate marginal histograms along an axis</span>
+<span class="n">h1</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">h2</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_009.png" srcset="../../_images/sphx_glr_plot_histogram_2d_009.png" alt="90% credible intervals overlain" class = "sphx-glr-single-img"/><p>Note that the names of the variables are automatically displayed</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Marginals along each axis&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">h1</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_010.png" srcset="../../_images/sphx_glr_plot_histogram_2d_010.png" alt="Marginals along each axis" class = "sphx-glr-single-img"/><p>Create a combination plot with marginal histograms.
+sphinx_gallery_thumbnail_number = 3</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">gs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:</span><span class="mi">4</span><span class="p">])]</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">colorbar</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[:</span><span class="mi">1</span><span class="p">,</span> <span class="p">:</span><span class="mi">4</span><span class="p">]))</span>
+<span class="n">h</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([]);</span> <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">4</span><span class="p">:]))</span>
+<span class="n">h</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">transpose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([]);</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([])</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">&quot;bottom&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_011.png" srcset="../../_images/sphx_glr_plot_histogram_2d_011.png" alt="plot histogram 2d" class = "sphx-glr-single-img"/><p>Take the mean or median estimates from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">median</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">9.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean, median, and credible interval overlain&quot;</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_2d_012.png" srcset="../../_images/sphx_glr_plot_histogram_2d_012.png" alt="Mean, median, and credible interval overlain" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x181cf6b10&gt;
+</pre></div>
+</div>
+<p>Get the range between credible intervals</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">credible_range</span><span class="p">(</span><span class="n">percent</span><span class="o">=</span><span class="mf">95.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>StatArray([4.2020202 , 4.44444444, 3.31313131, ..., 2.50505051,
+           4.52525253, 4.12121212])
+</pre></div>
+</div>
+<p>We can map the credible range to an opacity or transparency</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">opacity</span><span class="p">()</span>
+<span class="n">H</span><span class="o">.</span><span class="n">transparency</span><span class="p">()</span>
+
+<span class="c1"># # H.animate(0, &#39;test.mp4&#39;)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">toHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h2d&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h2d&#39;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">9.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean, median, and credible interval overlain&quot;</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H</span><span class="o">.</span><span class="n">createHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h2d&#39;</span><span class="p">,</span> <span class="n">add_axis</span><span class="o">=</span><span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">3.0</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s2">&quot;m&quot;</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+        <span class="n">H</span><span class="o">.</span><span class="n">writeHdf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;h2d&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h2d&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">9.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean, median, and credible interval overlain&quot;</span><span class="p">)</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">,</span> <span class="n">colorbar</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotCredibleIntervals</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMedian</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+<span class="n">H1</span><span class="o">.</span><span class="n">plotMean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;h2d.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">H1</span> <span class="o">=</span> <span class="n">Histogram</span><span class="o">.</span><span class="n">fromHdf</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="s1">&#39;h2d&#39;</span><span class="p">])</span>
+
+<span class="c1"># H1.pyvista_mesh().save(&#39;h3d_read.vtk&#39;)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_013.png" srcset="../../_images/sphx_glr_plot_histogram_2d_013.png" alt="Mean, median, and credible interval overlain" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_2d_014.png" srcset="../../_images/sphx_glr_plot_histogram_2d_014.png" alt="Mean, median, and credible interval overlain" class = "sphx-glr-multi-img"/></li>
+</ul>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.012 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-statistics-plot-histogram-2d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/36d592b131669bd825431209f21f3531/plot_histogram_2d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_histogram_2d.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/066a96b3e7333f9a789eb7e687c45875/plot_histogram_2d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_histogram_2d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5c85bfce6697082c125824937647a640/plot_histogram_2d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_histogram_2d.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
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+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="plot_histogram_1d.html" class="btn btn-neutral float-left" title="Histogram 1D" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="plot_histogram_3d.html" class="btn btn-neutral float-right" title="Histogram 3D" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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diff --git a/docs/examples/Statistics/plot_histogram_3d.html b/docs/examples/Statistics/plot_histogram_3d.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>Histogram 3D &mdash; GeoBIPy 2.3.1 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../index.html#data">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#datapoints">Datapoints</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#distributions">Distributions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#hdf-5">HDF 5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#d-inference">1D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#id1">2D Inference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#meshes">Meshes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../index.html#models">Models</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html#statistics">Statistics</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="plot_DataArray.html">DataArray Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_1d.html">Histogram 1D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_histogram_2d.html">Histogram 2D</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Histogram 3D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plot_StatArray.html">StatArray Class</a></li>
+</ul>
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+</ul>
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+      <li class="breadcrumb-item active">Histogram 3D</li>
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+            <a href="../../_sources/examples/Statistics/plot_histogram_3d.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-examples-statistics-plot-histogram-3d-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code.</p>
+</div>
+<section class="sphx-glr-example-title" id="histogram-3d">
+<span id="sphx-glr-examples-statistics-plot-histogram-3d-py"></span><h1>Histogram 3D<a class="headerlink" href="#histogram-3d" title="Link to this heading"></a></h1>
+<p>This 3D histogram class allows efficient updating of histograms, plotting and
+saving as HDF5.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">geobipy</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">StatArray</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">Histogram</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">geobipy</span> <span class="kn">import</span> <span class="n">RectilinearMesh3D</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Create some histogram bins in x and y</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="s1">&#39;Variable 1&#39;</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">21</span><span class="p">),</span> <span class="s1">&#39;Variable 2&#39;</span><span class="p">)</span>
+<span class="n">z</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mi">31</span><span class="p">),</span> <span class="s1">&#39;Variable 3&#39;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Instantiate</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Generate some random numbers</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="c1"># x = np.asarray([a, b, c])</span>
+</pre></div>
+</div>
+<p>Update the histogram counts</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Slice half way along each dimension&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span> <span class="k">if</span> <span class="n">i</span>  <span class="o">==</span> <span class="n">axis</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">s_</span><span class="p">[:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">s</span><span class="p">)]</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_001.png" srcset="../../_images/sphx_glr_plot_histogram_3d_001.png" alt="Slice half way along each dimension" class = "sphx-glr-single-img"/><p>Generate marginal histograms along an axis</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Marginals along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_002.png" srcset="../../_images/sphx_glr_plot_histogram_3d_002.png" alt="Marginals along each axis" class = "sphx-glr-single-img"/><p>Take the mean estimate from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_003.png" srcset="../../_images/sphx_glr_plot_histogram_3d_003.png" alt="Mean along each axis" class = "sphx-glr-single-img"/><p>Take the median estimate from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Median along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # We can map the credible range to an opacity or transparency</span>
+<span class="c1"># H.opacity()</span>
+<span class="c1"># H.transparency()</span>
+
+<span class="n">H</span><span class="o">.</span><span class="n">animate</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;test.mp4&#39;</span><span class="p">)</span>
+
+<span class="n">H</span><span class="o">.</span><span class="n">to_vtk</span><span class="p">(</span><span class="s1">&#39;h3d.vtk&#39;</span><span class="p">)</span>
+
+
+
+
+<span class="c1"># Create some histogram bins in x and y</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">x_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;x_re&quot;</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">y_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;y_re&quot;</span><span class="p">)</span>
+
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">mesh</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">centres</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">centres</span><span class="p">)</span>
+<span class="n">z_re</span> <span class="o">=</span> <span class="n">StatArray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">xx</span> <span class="o">**</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span> <span class="s2">&quot;z_re&quot;</span><span class="p">)</span>
+
+<span class="n">mesh</span> <span class="o">=</span> <span class="n">RectilinearMesh3D</span><span class="p">(</span><span class="n">x_edges</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">x_relative_to</span><span class="o">=</span><span class="n">x_re</span><span class="p">,</span> <span class="n">y_edges</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">y_relative_to</span><span class="o">=</span><span class="n">y_re</span><span class="p">,</span> <span class="n">z_edges</span><span class="o">=</span><span class="n">z</span><span class="p">,</span> <span class="n">z_relative_to</span><span class="o">=</span><span class="n">z_re</span><span class="p">)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_3d_004.png" srcset="../../_images/sphx_glr_plot_histogram_3d_004.png" alt="Median along each axis" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_3d_005.png" srcset="../../_images/sphx_glr_plot_histogram_3d_005.png" alt="3.60" class = "sphx-glr-multi-img"/></li>
+</ul>
+<p>Instantiate</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">Histogram</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Generate some random numbers</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
+<span class="c1"># x = np.asarray([a, b, c])</span>
+</pre></div>
+</div>
+<p>Update the histogram counts</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Slice half way along each dimension&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span> <span class="k">if</span> <span class="n">i</span>  <span class="o">==</span> <span class="n">axis</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">s_</span><span class="p">[:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)]</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">s</span><span class="p">)]</span><span class="o">.</span><span class="n">pcolor</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray_r&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_006.png" srcset="../../_images/sphx_glr_plot_histogram_3d_006.png" alt="Slice half way along each dimension" class = "sphx-glr-single-img"/><p>Generate marginal histograms along an axis</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Marginals along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">marginalize</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_007.png" srcset="../../_images/sphx_glr_plot_histogram_3d_007.png" alt="Marginals along each axis" class = "sphx-glr-single-img"/><p>Take the mean estimate from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Mean along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_histogram_3d_008.png" srcset="../../_images/sphx_glr_plot_histogram_3d_008.png" alt="Mean along each axis" class = "sphx-glr-single-img"/><p>Take the median estimate from the histogram</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Median along each axis&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">_</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">pcolor</span><span class="p">()</span>
+
+<span class="c1"># #%%</span>
+<span class="c1"># # We can map the credible range to an opacity or transparency</span>
+<span class="c1"># H.opacity()</span>
+<span class="c1"># H.transparency()</span>
+
+<span class="n">H</span><span class="o">.</span><span class="n">animate</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;test.mp4&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># H.to_vtk(&#39;h3d.vtk&#39;)</span>
+</pre></div>
+</div>
+<ul class="sphx-glr-horizontal">
+<li><img src="../../_images/sphx_glr_plot_histogram_3d_009.png" srcset="../../_images/sphx_glr_plot_histogram_3d_009.png" alt="Median along each axis" class = "sphx-glr-multi-img"/></li>
+<li><img src="../../_images/sphx_glr_plot_histogram_3d_010.png" srcset="../../_images/sphx_glr_plot_histogram_3d_010.png" alt="3.60" class = "sphx-glr-multi-img"/></li>
+</ul>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.875 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-examples-statistics-plot-histogram-3d-py">
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/93e086b68a2d08aac2f637ab24f17e8a/plot_histogram_3d.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_histogram_3d.ipynb</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3bf606a1e1bcade69bb1094393e978cd/plot_histogram_3d.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_histogram_3d.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-zip docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/3c764380cfa74d0cf19eca0975ff0c0d/plot_histogram_3d.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_histogram_3d.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+           </div>
+          </div>
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+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Statistics &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+                2.3.1
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+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Statistics</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/examples/Statistics/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="statistics">
+<h1>Statistics<a class="headerlink" href="#statistics" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
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+
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+    <p>&#169; Copyright None.</p>
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+</html>
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diff --git a/docs/examples/Statistics/sg_execution_times.html b/docs/examples/Statistics/sg_execution_times.html
new file mode 100644
index 00000000..7a631943
--- /dev/null
+++ b/docs/examples/Statistics/sg_execution_times.html
@@ -0,0 +1,159 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+      <link rel="stylesheet" type="text/css" href="../../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
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+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+              <div class="version">
+                2.3.1
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+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+<li class="toctree-l1"><a class="reference internal" href="../../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">Examples</a></li>
+</ul>
+
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+    </nav>
+
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">GeoBIPy</a>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+      <li class="breadcrumb-item active">Computation times</li>
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+            <a href="../../_sources/examples/Statistics/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-statistics-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:12.629</strong> total execution time for 5 files <strong>from examples/Statistics</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+<link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+</style>
+<script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_histogram_2d.html#sphx-glr-examples-statistics-plot-histogram-2d-py"><span class="std std-ref">Histogram 2D</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_histogram_2d.py</span></code>)</p></td>
+<td><p>00:05.012</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_histogram_3d.html#sphx-glr-examples-statistics-plot-histogram-3d-py"><span class="std std-ref">Histogram 3D</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_histogram_3d.py</span></code>)</p></td>
+<td><p>00:03.875</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_StatArray.html#sphx-glr-examples-statistics-plot-statarray-py"><span class="std std-ref">StatArray Class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_StatArray.py</span></code>)</p></td>
+<td><p>00:02.767</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_histogram_1d.html#sphx-glr-examples-statistics-plot-histogram-1d-py"><span class="std std-ref">Histogram 1D</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_histogram_1d.py</span></code>)</p></td>
+<td><p>00:00.972</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_DataArray.html#sphx-glr-examples-statistics-plot-dataarray-py"><span class="std std-ref">DataArray Class</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_DataArray.py</span></code>)</p></td>
+<td><p>00:00.003</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
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+
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+    <p>&#169; Copyright None.</p>
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diff --git a/docs/examples/index.html b/docs/examples/index.html
new file mode 100644
index 00000000..a4f9f766
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+++ b/docs/examples/index.html
@@ -0,0 +1,315 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Examples &mdash; GeoBIPy 2.3.1 documentation</title>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="3D Point Cloud class" href="Data/plot_pointcloud3d.html" />
+    <link rel="prev" title="Time domain system" href="../content/api/classes/system/TdemSystem.html" /> 
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+<body class="wy-body-for-nav"> 
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+      <div class="wy-side-scroll">
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+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../content/api/api.html">API</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#data">Data</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Data/plot_pointcloud3d.html">3D Point Cloud class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Data/plot_frequency_dataset.html">Frequency domain dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Data/plot_skytem_dataset.html">Skytem dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Data/plot_tempest_dataset.html">Tempest dataset</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#datapoints">Datapoints</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Datapoints/plot_resolve_datapoint.html">Frequency domain datapoint</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Datapoints/plot_skytem_datapoint.html">Skytem Datapoint Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Datapoints/plot_tempest_datapoint.html">Tempest Datapoint Class</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#distributions">Distributions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Distributions/plot_distributions.html">Distribution Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Distributions/plot_distributions.html#univariate-normal-distribution">Univariate Normal Distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Distributions/plot_distributions.html#multivariate-normal-distribution">Multivariate Normal Distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#hdf-5">HDF 5</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="HDF5/hdf5.html">Using HDF5 within GeoBIPy</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#d-inference">1D Inference</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Inference_1D/plot_inference_1d_resolve.html">Running GeoBIPy to invert Resolve data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Inference_1D/plot_inference_1d_skytem.html">Running GeoBIPy to invert Skytem data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Inference_1D/plot_inference_1d_tempest.html">Running GeoBIPy to invert Tempest data</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#id1">2D Inference</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Inference_2D/plot_inference_2d_resolve.html">2D Posterior analysis of Resolve inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Inference_2D/plot_inference_2d_skytem.html">2D Posterior analysis of Skytem inference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Inference_2D/plot_inference_2d_tempest.html">2D Posterior analysis of Tempest inference</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#meshes">Meshes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Meshes/plot_rectilinear_mesh_1d.html">1D Rectilinear Mesh</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Meshes/plot_rectilinear_mesh_2d.html">2D Rectilinear Mesh</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Meshes/plot_rectilinear_mesh_3d.html">3D Rectilinear Mesh</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#models">Models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Models/plot_model_1d.html">1D Model with an infinite halfspace</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Models/plot_model_2d.html">2D Rectilinear Model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Models/plot_model_3d.html">3D Rectilinear Model</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#statistics">Statistics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Statistics/plot_DataArray.html">DataArray Class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Statistics/plot_histogram_1d.html">Histogram 1D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Statistics/plot_histogram_2d.html">Histogram 2D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Statistics/plot_histogram_3d.html">Histogram 3D</a></li>
+<li class="toctree-l3"><a class="reference internal" href="Statistics/plot_StatArray.html">StatArray Class</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">GeoBIPy</a>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Examples</li>
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+            <a href="../_sources/examples/index.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h1>
+<div class="sphx-glr-thumbnails"></div><section id="data">
+<h2>Data<a class="headerlink" href="#data" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="3D Point Cloud class"><img alt="" src="../_images/sphx_glr_plot_pointcloud3d_thumb.png" />
+<p><a class="reference internal" href="Data/plot_pointcloud3d.html#sphx-glr-examples-data-plot-pointcloud3d-py"><span class="std std-ref">3D Point Cloud class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">3D Point Cloud class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Frequency domain dataset"><img alt="" src="../_images/sphx_glr_plot_frequency_dataset_thumb.png" />
+<p><a class="reference internal" href="Data/plot_frequency_dataset.html#sphx-glr-examples-data-plot-frequency-dataset-py"><span class="std std-ref">Frequency domain dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Frequency domain dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Skytem dataset"><img alt="" src="../_images/sphx_glr_plot_skytem_dataset_thumb.png" />
+<p><a class="reference internal" href="Data/plot_skytem_dataset.html#sphx-glr-examples-data-plot-skytem-dataset-py"><span class="std std-ref">Skytem dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Skytem dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tempest dataset"><img alt="" src="../_images/sphx_glr_plot_tempest_dataset_thumb.png" />
+<p><a class="reference internal" href="Data/plot_tempest_dataset.html#sphx-glr-examples-data-plot-tempest-dataset-py"><span class="std std-ref">Tempest dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tempest dataset</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="datapoints">
+<h2>Datapoints<a class="headerlink" href="#datapoints" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Frequency domain datapoint"><img alt="" src="../_images/sphx_glr_plot_resolve_datapoint_thumb.png" />
+<p><a class="reference internal" href="Datapoints/plot_resolve_datapoint.html#sphx-glr-examples-datapoints-plot-resolve-datapoint-py"><span class="std std-ref">Frequency domain datapoint</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Frequency domain datapoint</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Skytem Datapoint Class"><img alt="" src="../_images/sphx_glr_plot_skytem_datapoint_thumb.png" />
+<p><a class="reference internal" href="Datapoints/plot_skytem_datapoint.html#sphx-glr-examples-datapoints-plot-skytem-datapoint-py"><span class="std std-ref">Skytem Datapoint Class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Skytem Datapoint Class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Tempest Datapoint Class"><img alt="" src="../_images/sphx_glr_plot_tempest_datapoint_thumb.png" />
+<p><a class="reference internal" href="Datapoints/plot_tempest_datapoint.html#sphx-glr-examples-datapoints-plot-tempest-datapoint-py"><span class="std std-ref">Tempest Datapoint Class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Tempest Datapoint Class</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="distributions">
+<h2>Distributions<a class="headerlink" href="#distributions" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Handles the initialization of different statistical distribution"><img alt="" src="../_images/sphx_glr_plot_distributions_thumb.png" />
+<p><a class="reference internal" href="Distributions/plot_distributions.html#sphx-glr-examples-distributions-plot-distributions-py"><span class="std std-ref">Distribution Class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Distribution Class</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="hdf-5">
+<h2>HDF 5<a class="headerlink" href="#hdf-5" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Inference for large scale datasets in GeoBIPy is handled using MPI and distributed memory systems. A common bottleneck with large parallel algorithms is the input output of information to disk. We use HDF5 to read and write data in order to leverage the parallel capabililties of the HDF5 API."><img alt="" src="../_images/sphx_glr_hdf5_thumb.png" />
+<p><a class="reference internal" href="HDF5/hdf5.html#sphx-glr-examples-hdf5-hdf5-py"><span class="std std-ref">Using HDF5 within GeoBIPy</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Using HDF5 within GeoBIPy</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="d-inference">
+<h2>1D Inference<a class="headerlink" href="#d-inference" title="Link to this heading"></a></h2>
+<p>There are a couple of ways to run an inference using geobipy, the first is via command line using</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>geobipy<span class="w"> </span>skytem_options.py<span class="w"> </span>&lt;output<span class="w"> </span>folder&gt;
+</pre></div>
+</div>
+<p>The other is with a python script similar to the examples in this folder.
+In both cases, you will need to write an options file (also shown in these examples)</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Resolve data"><img alt="" src="../_images/sphx_glr_plot_inference_1d_resolve_thumb.png" />
+<p><a class="reference internal" href="Inference_1D/plot_inference_1d_resolve.html#sphx-glr-examples-inference-1d-plot-inference-1d-resolve-py"><span class="std std-ref">Running GeoBIPy to invert Resolve data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Resolve data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Skytem data"><img alt="" src="../_images/sphx_glr_plot_inference_1d_skytem_thumb.png" />
+<p><a class="reference internal" href="Inference_1D/plot_inference_1d_skytem.html#sphx-glr-examples-inference-1d-plot-inference-1d-skytem-py"><span class="std std-ref">Running GeoBIPy to invert Skytem data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Skytem data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Running GeoBIPy to invert Tempest data"><img alt="" src="../_images/sphx_glr_plot_inference_1d_tempest_thumb.png" />
+<p><a class="reference internal" href="Inference_1D/plot_inference_1d_tempest.html#sphx-glr-examples-inference-1d-plot-inference-1d-tempest-py"><span class="std std-ref">Running GeoBIPy to invert Tempest data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Running GeoBIPy to invert Tempest data</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="id1">
+<h2>2D Inference<a class="headerlink" href="#id1" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class"><img alt="" src="../_images/sphx_glr_plot_inference_2d_resolve_thumb.png" />
+<p><a class="reference internal" href="Inference_2D/plot_inference_2d_resolve.html#sphx-glr-examples-inference-2d-plot-inference-2d-resolve-py"><span class="std std-ref">2D Posterior analysis of Resolve inference</span></a></p>
+  <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Resolve inference</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class"><img alt="" src="../_images/sphx_glr_plot_inference_2d_skytem_thumb.png" />
+<p><a class="reference internal" href="Inference_2D/plot_inference_2d_skytem.html#sphx-glr-examples-inference-2d-plot-inference-2d-skytem-py"><span class="std std-ref">2D Posterior analysis of Skytem inference</span></a></p>
+  <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Skytem inference</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="All plotting in GeoBIPy can be carried out using the 3D inference class"><img alt="" src="../_images/sphx_glr_plot_inference_2d_tempest_thumb.png" />
+<p><a class="reference internal" href="Inference_2D/plot_inference_2d_tempest.html#sphx-glr-examples-inference-2d-plot-inference-2d-tempest-py"><span class="std std-ref">2D Posterior analysis of Tempest inference</span></a></p>
+  <div class="sphx-glr-thumbnail-title">2D Posterior analysis of Tempest inference</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="meshes">
+<h2>Meshes<a class="headerlink" href="#meshes" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="1D Rectilinear Mesh"><img alt="" src="../_images/sphx_glr_plot_rectilinear_mesh_1d_thumb.png" />
+<p><a class="reference internal" href="Meshes/plot_rectilinear_mesh_1d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-1d-py"><span class="std std-ref">1D Rectilinear Mesh</span></a></p>
+  <div class="sphx-glr-thumbnail-title">1D Rectilinear Mesh</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="It can be instantiated in two ways."><img alt="" src="../_images/sphx_glr_plot_rectilinear_mesh_2d_thumb.png" />
+<p><a class="reference internal" href="Meshes/plot_rectilinear_mesh_2d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-2d-py"><span class="std std-ref">2D Rectilinear Mesh</span></a></p>
+  <div class="sphx-glr-thumbnail-title">2D Rectilinear Mesh</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="3D Rectilinear Mesh"><img alt="" src="../_images/sphx_glr_plot_rectilinear_mesh_3d_thumb.png" />
+<p><a class="reference internal" href="Meshes/plot_rectilinear_mesh_3d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-3d-py"><span class="std std-ref">3D Rectilinear Mesh</span></a></p>
+  <div class="sphx-glr-thumbnail-title">3D Rectilinear Mesh</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="models">
+<h2>Models<a class="headerlink" href="#models" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="1D Model with an infinite halfspace"><img alt="" src="../_images/sphx_glr_plot_model_1d_thumb.png" />
+<p><a class="reference internal" href="Models/plot_model_1d.html#sphx-glr-examples-models-plot-model-1d-py"><span class="std std-ref">1D Model with an infinite halfspace</span></a></p>
+  <div class="sphx-glr-thumbnail-title">1D Model with an infinite halfspace</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="2D Rectilinear Model"><img alt="" src="../_images/sphx_glr_plot_model_2d_thumb.png" />
+<p><a class="reference internal" href="Models/plot_model_2d.html#sphx-glr-examples-models-plot-model-2d-py"><span class="std std-ref">2D Rectilinear Model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">2D Rectilinear Model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="3D Rectilinear Model"><img alt="" src="../_images/sphx_glr_plot_model_3d_thumb.png" />
+<p><a class="reference internal" href="Models/plot_model_3d.html#sphx-glr-examples-models-plot-model-3d-py"><span class="std std-ref">3D Rectilinear Model</span></a></p>
+  <div class="sphx-glr-thumbnail-title">3D Rectilinear Model</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="statistics">
+<h2>Statistics<a class="headerlink" href="#statistics" title="Link to this heading"></a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Extends the numpy ndarray class to add extra attributes such as names, and units, and allows us to attach statistical descriptors of the array. The direct extension to numpy maintains speed and functionality of numpy arrays."><img alt="" src="../_images/sphx_glr_plot_DataArray_thumb.png" />
+<p><a class="reference internal" href="Statistics/plot_DataArray.html#sphx-glr-examples-statistics-plot-dataarray-py"><span class="std std-ref">DataArray Class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">DataArray Class</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This histogram class allows efficient updating of histograms, plotting and saving as HDF5"><img alt="" src="../_images/sphx_glr_plot_histogram_1d_thumb.png" />
+<p><a class="reference internal" href="Statistics/plot_histogram_1d.html#sphx-glr-examples-statistics-plot-histogram-1d-py"><span class="std std-ref">Histogram 1D</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Histogram 1D</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This 2D histogram class allows efficient updating of histograms, plotting and saving as HDF5."><img alt="" src="../_images/sphx_glr_plot_histogram_2d_thumb.png" />
+<p><a class="reference internal" href="Statistics/plot_histogram_2d.html#sphx-glr-examples-statistics-plot-histogram-2d-py"><span class="std std-ref">Histogram 2D</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Histogram 2D</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This 3D histogram class allows efficient updating of histograms, plotting and saving as HDF5."><img alt="" src="../_images/sphx_glr_plot_histogram_3d_thumb.png" />
+<p><a class="reference internal" href="Statistics/plot_histogram_3d.html#sphx-glr-examples-statistics-plot-histogram-3d-py"><span class="std std-ref">Histogram 3D</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Histogram 3D</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Extends the numpy ndarray class to add extra attributes such as names, and units, and allows us to attach statistical descriptors of the array. The direct extension to numpy maintains speed and functionality of numpy arrays."><img alt="" src="../_images/sphx_glr_plot_StatArray_thumb.png" />
+<p><a class="reference internal" href="Statistics/plot_StatArray.html#sphx-glr-examples-statistics-plot-statarray-py"><span class="std std-ref">StatArray Class</span></a></p>
+  <div class="sphx-glr-thumbnail-title">StatArray Class</div>
+</div></div><div class="toctree-wrapper compound">
+</div>
+<div class="sphx-glr-footer sphx-glr-footer-gallery docutils container">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">examples_python.zip</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Jupyter</span> <span class="pre">notebooks:</span> <span class="pre">examples_jupyter.zip</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../content/api/classes/system/TdemSystem.html" class="btn btn-neutral float-left" title="Time domain system" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Examples</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/examples/readme.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h1>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/examples/sg_execution_times.html b/docs/examples/sg_execution_times.html
new file mode 100644
index 00000000..36ae07d8
--- /dev/null
+++ b/docs/examples/sg_execution_times.html
@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Computation times &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
+
+  
+  <!--[if lt IE 9]>
+    <script src="../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=67b02a41"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="index.html">Examples</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">GeoBIPy</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Computation times</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/examples/sg_execution_times.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="computation-times">
+<span id="sphx-glr-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>00:00.000</strong> total execution time for 0 files <strong>from examples</strong>:</p>
+<div class="docutils container">
+<style scoped>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+<link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+</style>
+<script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+<script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+<script type="text/javascript" class="init">
+$(document).ready( function () {
+    $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+} );
+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>N/A</p></td>
+<td><p>N/A</p></td>
+<td><p>N/A</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright None.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
index faeb0d8e..f1779d0b 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -1,99 +1,1639 @@
 <!DOCTYPE html>
-
-<html lang="en" data-content_root="./">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Index &#8212; geobipy 2.3.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d1102ebc" />
-    <link rel="stylesheet" type="text/css" href="_static/alabaster.css?v=12dfc556" />
-    <script src="_static/documentation_options.js?v=67b02a41"></script>
-    <script src="_static/doctools.js?v=9a2dae69"></script>
-    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-    <link rel="index" title="Index" href="#" />
-    <link rel="search" title="Search" href="search.html" />
-   
-  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
-  
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Index &mdash; GeoBIPy 2.3.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
   
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=67b02a41"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" /> 
+</head>
 
-  </head><body>
-  
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
 
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
           
+          
+          <a href="index.html" class="icon icon-home">
+            GeoBIPy
+          </a>
+              <div class="version">
+                2.3.1
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="content/getting_started/getting_started.html">Getting Started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="content/api/api.html">API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="examples/index.html">Examples</a></li>
+</ul>
 
-          <div class="body" role="main">
-            
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">GeoBIPy</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Index</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
 
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#X"><strong>X</strong></a>
+ | <a href="#Y"><strong>Y</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
  
 </div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.active">active (geobipy.src.classes.data.datapoint.DataPoint.DataPoint attribute)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.active">(geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.active">(geobipy.src.classes.data.dataset.Data.Data property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.additive_error">additive_error (geobipy.src.classes.data.dataset.Data.Data property)</a>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="Main">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="index.html">geobipy</a></h1>
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.additive_error">(geobipy.src.classes.data.dataset.TempestData.TempestData property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.addressof">addressof (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.addressof">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.addToVTK">addToVTK() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.append">append() (geobipy.src.classes.data.dataset.Data.Data method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.append">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.append">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.append">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.append">(geobipy.src.classes.system.CircularLoop.CircularLoop method)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.append">(geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.Ax">Ax() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.axis">axis() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.banner">banner() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.bar">bar() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/statistics/baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution">baseDistribution (class in geobipy.src.classes.statistics.baseDistribution)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.Bcast">Bcast() (geobipy.src.classes.data.dataset.Data.Data method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.Bcast">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.Bcast">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.Bcast">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.Bcast">(geobipy.src.classes.system.CircularLoop.CircularLoop method)</a>
+</li>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.Bcast">(geobipy.src.classes.system.FdemSystem.FdemSystem method)</a>
+</li>
+        <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Bcast">(in module geobipy.src.base.MPI)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Bcast_1int">Bcast_1int() (in module geobipy.src.base.MPI)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Bcast_list">Bcast_list() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.bcastType">bcastType() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/classes/statistics/baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution.bins">bins() (geobipy.src.classes.statistics.baseDistribution.baseDistribution method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.bins">(geobipy.src.classes.statistics.MvNormalDistribution.MvNormal method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal.bins">(geobipy.src.classes.statistics.NormalDistribution.Normal method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/UniformDistribution.html#geobipy.src.classes.statistics.UniformDistribution.Uniform.bins">(geobipy.src.classes.statistics.UniformDistribution.Uniform method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.block_indices">block_indices() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.block_median">block_median() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.block_median_indices">block_median_indices() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.bounds">bounds (geobipy.src.classes.pointcloud.Point.Point property)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.bytes2readable">bytes2readable() (in module geobipy.src.base.fileIO)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.calibrate">calibrate() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal.cdf">cdf() (geobipy.src.classes.statistics.NormalDistribution.Normal method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/statistics/UniformDistribution.html#geobipy.src.classes.statistics.UniformDistribution.Uniform.cdf">(geobipy.src.classes.statistics.UniformDistribution.Uniform method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.cellIndex">cellIndex() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
 
-<h3>Navigation</h3>
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndex">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.cellIndices">cellIndices() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
 
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="index.html">Documentation overview</a><ul>
-  </ul></li>
-</ul>
-</div>
-<search id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</search>
-<script>document.getElementById('searchbox').style.display = "block"</script>
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.cellIndices">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.centred_grid_nodes">centred_grid_nodes() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.centres">centres() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.centres">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.channel_index">channel_index() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop">CircularLoop (class in geobipy.src.classes.system.CircularLoop)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.clabel">clabel() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.component_id">component_id (geobipy.src.classes.system.FdemSystem.FdemSystem property)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.cosSin1">cosSin1() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.createHdf">createHdf() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.createHdf">(geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.createHdf">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.createHdf">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.createHdf">(geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.createHdf">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.createHdf">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.createHdf">(geobipy.src.classes.data.dataset.TempestData.TempestData method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.createHdf">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.createHdf">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.createHdf">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.createHdf">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.createHdf">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.createHdf">(geobipy.src.classes.system.CircularLoop.CircularLoop method)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.createHdf">(geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.credible_intervals">credible_intervals() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.credible_range">credible_range() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data">Data (class in geobipy.src.classes.data.dataset.Data)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.data">data (geobipy.src.classes.data.dataset.Data.Data property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.data">(geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.data_misfit">data_misfit() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.data_misfit">(geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint">DataPoint (class in geobipy.src.classes.data.datapoint.DataPoint)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.datapoint">datapoint() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.datapoint">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.datapoint">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution.deepcopy">deepcopy() (geobipy.src.classes.statistics.baseDistribution.baseDistribution method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.delete_edge">delete_edge() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.deleteFile">deleteFile() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.deltaD">deltaD (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.deltaD">(geobipy.src.classes.data.dataset.Data.Data property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.Det">Det() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.dirExists">dirExists() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.distance">distance (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Distribution.html#geobipy.src.classes.statistics.Distribution.Distribution">Distribution() (in module geobipy.src.classes.statistics.Distribution)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.dualMoment">dualMoment() (geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.edges">edges() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.edges">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint">EmDataPoint (class in geobipy.src.classes.data.datapoint.EmDataPoint)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop">EmLoop (class in geobipy.src.classes.system.EmLoop)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.estimateAdditiveError">estimateAdditiveError() (geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.expReal">expReal() (in module geobipy.src.base.utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData">FdemData (class in geobipy.src.classes.data.dataset.FdemData)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint">FdemDataPoint (class in geobipy.src.classes.data.datapoint.FdemDataPoint)</a>
+</li>
+      <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem">FdemSystem (class in geobipy.src.classes.system.FdemSystem)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.fileExists">fileExists() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.fileInformation">fileInformation() (geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.fileInformation">(geobipy.src.classes.data.dataset.TdemData.TdemData static method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.fileInformation">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.fileInformation">(geobipy.src.classes.system.FdemSystem.FdemSystem method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.filesExist">filesExist() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.find">find() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.find_best_halfspace">find_best_halfspace() (geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.findFirstLastNotValue">findFirstLastNotValue() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.findFirstNonZeros">findFirstNonZeros() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.findLastNonZeros">findLastNonZeros() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.findNans">findNans() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.findNotNans">findNotNans() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.fit_mixture_to_pdf_1d">fit_mixture_to_pdf_1d() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.forward">forward() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.forward">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.frequencies">frequencies() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.fromHdf">fromHdf() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint class method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.fromHdf">(geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint class method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.fromHdf">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint class method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.fromHdf">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.fromHdf">(geobipy.src.classes.data.dataset.Data.Data class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.fromHdf">(geobipy.src.classes.data.dataset.FdemData.FdemData class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.fromHdf">(geobipy.src.classes.data.dataset.TdemData.TdemData class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.fromHdf">(geobipy.src.classes.data.dataset.TempestData.TempestData class method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.fromHdf">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D class method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.fromHdf">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched class method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.fromHdf">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D class method)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.fromHdf">(geobipy.src.classes.model.Model.Model class method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.fromHdf">(geobipy.src.classes.pointcloud.Point.Point class method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.fromHdf">(geobipy.src.classes.statistics.Histogram.Histogram class method)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.fromHdf">(geobipy.src.classes.system.CircularLoop.CircularLoop class method)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.fromHdf">(geobipy.src.classes.system.EmLoop.EmLoop class method)</a>
+</li>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.fromHdf">(geobipy.src.classes.system.FdemSystem.FdemSystem class method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/GammaDistribution.html#geobipy.src.classes.statistics.GammaDistribution.Gamma">Gamma (class in geobipy.src.classes.statistics.GammaDistribution)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.generate_subplots">generate_subplots() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li>
+    geobipy.src.base.fileIO
+
+      <ul>
+        <li><a href="content/api/base/fileIO.html#module-geobipy.src.base.fileIO">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.HDF.hdfRead
+
+      <ul>
+        <li><a href="content/api/base/HDF.html#module-geobipy.src.base.HDF.hdfRead">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.HDF.hdfWrite
+
+      <ul>
+        <li><a href="content/api/base/HDF.html#module-geobipy.src.base.HDF.hdfWrite">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.interpolation
+
+      <ul>
+        <li><a href="content/api/base/interpolation.html#module-geobipy.src.base.interpolation">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.MPI
+
+      <ul>
+        <li><a href="content/api/base/MPI.html#module-geobipy.src.base.MPI">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.plotting
+
+      <ul>
+        <li><a href="content/api/base/plotting.html#module-geobipy.src.base.plotting">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.base.utilities
+
+      <ul>
+        <li><a href="content/api/base/utilities.html#module-geobipy.src.base.utilities">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.core.myObject
+
+      <ul>
+        <li><a href="content/api/classes/core/myObject.html#module-geobipy.src.classes.core.myObject">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.datapoint.DataPoint
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/datapoint.html#module-geobipy.src.classes.data.datapoint.DataPoint">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.datapoint.EmDataPoint
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#module-geobipy.src.classes.data.datapoint.EmDataPoint">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.datapoint.FdemDataPoint
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#module-geobipy.src.classes.data.datapoint.FdemDataPoint">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.datapoint.TdemDataPoint
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#module-geobipy.src.classes.data.datapoint.TdemDataPoint">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.datapoint.Tempest_datapoint
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#module-geobipy.src.classes.data.datapoint.Tempest_datapoint">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.dataset.Data
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/Data.html#module-geobipy.src.classes.data.dataset.Data">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.dataset.FdemData
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#module-geobipy.src.classes.data.dataset.FdemData">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.dataset.TdemData
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#module-geobipy.src.classes.data.dataset.TdemData">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.data.dataset.TempestData
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#module-geobipy.src.classes.data.dataset.TempestData">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.mesh.RectilinearMesh1D
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#module-geobipy.src.classes.mesh.RectilinearMesh1D">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.mesh.RectilinearMesh2D
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#module-geobipy.src.classes.mesh.RectilinearMesh2D">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.mesh.RectilinearMesh2D_stitched
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#module-geobipy.src.classes.mesh.RectilinearMesh2D_stitched">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.mesh.RectilinearMesh3D
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#module-geobipy.src.classes.mesh.RectilinearMesh3D">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.model.Model
+
+      <ul>
+        <li><a href="content/api/classes/model/Model_.html#module-geobipy.src.classes.model.Model">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    geobipy.src.classes.pointcloud.Point
+
+      <ul>
+        <li><a href="content/api/classes/pointcloud/Point.html#module-geobipy.src.classes.pointcloud.Point">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.baseDistribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/baseDistribution.html#module-geobipy.src.classes.statistics.baseDistribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.Distribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/Distribution.html#module-geobipy.src.classes.statistics.Distribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.GammaDistribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/GammaDistribution.html#module-geobipy.src.classes.statistics.GammaDistribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.Histogram
+
+      <ul>
+        <li><a href="content/api/classes/statistics/Histogram.html#module-geobipy.src.classes.statistics.Histogram">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.MvNormalDistribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#module-geobipy.src.classes.statistics.MvNormalDistribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.NormalDistribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/NormalDistribution.html#module-geobipy.src.classes.statistics.NormalDistribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.OrderStatistics
+
+      <ul>
+        <li><a href="content/api/classes/statistics/OrderStatistics.html#module-geobipy.src.classes.statistics.OrderStatistics">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.statistics.UniformDistribution
+
+      <ul>
+        <li><a href="content/api/classes/statistics/UniformDistribution.html#module-geobipy.src.classes.statistics.UniformDistribution">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.system.CircularLoop
+
+      <ul>
+        <li><a href="content/api/classes/system/CircularLoop.html#module-geobipy.src.classes.system.CircularLoop">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.system.EmLoop
 
+      <ul>
+        <li><a href="content/api/classes/system/EmLoop.html#module-geobipy.src.classes.system.EmLoop">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.system.FdemSystem
 
+      <ul>
+        <li><a href="content/api/classes/system/FdemSystem.html#module-geobipy.src.classes.system.FdemSystem">module</a>
+</li>
+      </ul></li>
+      <li>
+    geobipy.src.classes.system.TdemSystem
 
+      <ul>
+        <li><a href="content/api/classes/system/TdemSystem.html#module-geobipy.src.classes.system.TdemSystem">module</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.get_column_name">get_column_name() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/system/TdemSystem.html#geobipy.src.classes.system.TdemSystem.TdemSystem.get_modellingTimes">get_modellingTimes (geobipy.src.classes.system.TdemSystem.TdemSystem property)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.get_real_numbers_from_line">get_real_numbers_from_line() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.getFileExtension">getFileExtension() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.getFileSize">getFileSize() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getFrequency">getFrequency() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.getFrequency">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.getMeasurementType">getMeasurementType() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.getMeasurementType">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.getName">getName() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.getNameUnits">getNameUnits() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.getNcolumns">getNcolumns() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.getNlines">getNlines() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/classes/core/myObject.html#geobipy.src.classes.core.myObject.myObject.getsizeof">getsizeof() (geobipy.src.classes.core.myObject.myObject method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.getUnits">getUnits() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.gradient">gradient (geobipy.src.classes.model.Model.Model property)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.gradient">gradient() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.gradient_probability">gradient_probability() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.hasSameSize">hasSameSize() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.hdfName">hdfName() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.helloWorld">helloWorld() (in module geobipy.src.base.MPI)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.hillshade">hillshade() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram">Histogram (class in geobipy.src.classes.statistics.Histogram)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.histogramEqualize">histogramEqualize() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.hlines">hlines() (in module geobipy.src.base.plotting)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.in_bounds">in_bounds() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
 
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.in_bounds">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.insert_edge">insert_edge() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.int2str">int2str() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.interleave">interleave() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.interpolate">interpolate() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.intervalStatistic">intervalStatistic() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.Inv">Inv() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.iplotActive">iplotActive (geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Irecv">Irecv() (in module geobipy.src.base.MPI)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Irecv_1int">Irecv_1int() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.IrecvFromLeft">IrecvFromLeft() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.IrecvFromRight">IrecvFromRight() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Isend">Isend() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Isend_1int">Isend_1int() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.IsendToLeft">IsendToLeft() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.IsendToRight">IsendToRight() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.isFileExtension">isFileExtension() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.isInt">isInt() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.isIntorSlice">isIntorSlice() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.isNumpy">isNumpy() (in module geobipy.src.base.utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.likelihood">likelihood() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.line">line() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.loadBalance1D_shrinkingArrays">loadBalance1D_shrinkingArrays() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.loadBalance3D_shrinkingArrays">loadBalance3D_shrinkingArrays() (in module geobipy.src.base.MPI)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.local_inverse_hessian">local_inverse_hessian() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.local_precision">local_precision() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.local_variance">local_variance() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.LogDet">LogDet() (in module geobipy.src.base.utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.make_colourmap">make_colourmap() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.map">map() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.map_to_pdf">map_to_pdf() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.mapChannel">mapChannel() (geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.mapData">mapData() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.mapPredictedData">mapPredictedData() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.mapStd">mapStd() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.marginalize">marginalize() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.mask_cells">mask_cells() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.mask_cells">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.mean">mean() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.median">median() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.mergeComplex">mergeComplex() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.mode">mode() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model">Model (class in geobipy.src.classes.model.Model)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="content/api/base/fileIO.html#module-geobipy.src.base.fileIO">geobipy.src.base.fileIO</a>
+</li>
+        <li><a href="content/api/base/HDF.html#module-geobipy.src.base.HDF.hdfRead">geobipy.src.base.HDF.hdfRead</a>
+</li>
+        <li><a href="content/api/base/HDF.html#module-geobipy.src.base.HDF.hdfWrite">geobipy.src.base.HDF.hdfWrite</a>
+</li>
+        <li><a href="content/api/base/interpolation.html#module-geobipy.src.base.interpolation">geobipy.src.base.interpolation</a>
+</li>
+        <li><a href="content/api/base/MPI.html#module-geobipy.src.base.MPI">geobipy.src.base.MPI</a>
+</li>
+        <li><a href="content/api/base/plotting.html#module-geobipy.src.base.plotting">geobipy.src.base.plotting</a>
+</li>
+        <li><a href="content/api/base/utilities.html#module-geobipy.src.base.utilities">geobipy.src.base.utilities</a>
+</li>
+        <li><a href="content/api/classes/core/myObject.html#module-geobipy.src.classes.core.myObject">geobipy.src.classes.core.myObject</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/datapoint.html#module-geobipy.src.classes.data.datapoint.DataPoint">geobipy.src.classes.data.datapoint.DataPoint</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#module-geobipy.src.classes.data.datapoint.EmDataPoint">geobipy.src.classes.data.datapoint.EmDataPoint</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#module-geobipy.src.classes.data.datapoint.FdemDataPoint">geobipy.src.classes.data.datapoint.FdemDataPoint</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#module-geobipy.src.classes.data.datapoint.TdemDataPoint">geobipy.src.classes.data.datapoint.TdemDataPoint</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#module-geobipy.src.classes.data.datapoint.Tempest_datapoint">geobipy.src.classes.data.datapoint.Tempest_datapoint</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#module-geobipy.src.classes.data.dataset.Data">geobipy.src.classes.data.dataset.Data</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#module-geobipy.src.classes.data.dataset.FdemData">geobipy.src.classes.data.dataset.FdemData</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#module-geobipy.src.classes.data.dataset.TdemData">geobipy.src.classes.data.dataset.TdemData</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#module-geobipy.src.classes.data.dataset.TempestData">geobipy.src.classes.data.dataset.TempestData</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#module-geobipy.src.classes.mesh.RectilinearMesh1D">geobipy.src.classes.mesh.RectilinearMesh1D</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#module-geobipy.src.classes.mesh.RectilinearMesh2D">geobipy.src.classes.mesh.RectilinearMesh2D</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#module-geobipy.src.classes.mesh.RectilinearMesh2D_stitched">geobipy.src.classes.mesh.RectilinearMesh2D_stitched</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#module-geobipy.src.classes.mesh.RectilinearMesh3D">geobipy.src.classes.mesh.RectilinearMesh3D</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#module-geobipy.src.classes.model.Model">geobipy.src.classes.model.Model</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#module-geobipy.src.classes.pointcloud.Point">geobipy.src.classes.pointcloud.Point</a>
+</li>
+        <li><a href="content/api/classes/statistics/baseDistribution.html#module-geobipy.src.classes.statistics.baseDistribution">geobipy.src.classes.statistics.baseDistribution</a>
+</li>
+        <li><a href="content/api/classes/statistics/Distribution.html#module-geobipy.src.classes.statistics.Distribution">geobipy.src.classes.statistics.Distribution</a>
+</li>
+        <li><a href="content/api/classes/statistics/GammaDistribution.html#module-geobipy.src.classes.statistics.GammaDistribution">geobipy.src.classes.statistics.GammaDistribution</a>
+</li>
+        <li><a href="content/api/classes/statistics/Histogram.html#module-geobipy.src.classes.statistics.Histogram">geobipy.src.classes.statistics.Histogram</a>
+</li>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#module-geobipy.src.classes.statistics.MvNormalDistribution">geobipy.src.classes.statistics.MvNormalDistribution</a>
+</li>
+        <li><a href="content/api/classes/statistics/NormalDistribution.html#module-geobipy.src.classes.statistics.NormalDistribution">geobipy.src.classes.statistics.NormalDistribution</a>
+</li>
+        <li><a href="content/api/classes/statistics/OrderStatistics.html#module-geobipy.src.classes.statistics.OrderStatistics">geobipy.src.classes.statistics.OrderStatistics</a>
+</li>
+        <li><a href="content/api/classes/statistics/UniformDistribution.html#module-geobipy.src.classes.statistics.UniformDistribution">geobipy.src.classes.statistics.UniformDistribution</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#module-geobipy.src.classes.system.CircularLoop">geobipy.src.classes.system.CircularLoop</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#module-geobipy.src.classes.system.EmLoop">geobipy.src.classes.system.EmLoop</a>
+</li>
+        <li><a href="content/api/classes/system/FdemSystem.html#module-geobipy.src.classes.system.FdemSystem">geobipy.src.classes.system.FdemSystem</a>
+</li>
+        <li><a href="content/api/classes/system/TdemSystem.html#module-geobipy.src.classes.system.TdemSystem">geobipy.src.classes.system.TdemSystem</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution.moment">moment (geobipy.src.classes.statistics.baseDistribution.baseDistribution property)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.move">move() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/MvNormalDistribution.html#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal">MvNormal (class in geobipy.src.classes.statistics.MvNormalDistribution)</a>
+</li>
+      <li><a href="content/api/classes/core/myObject.html#geobipy.src.classes.core.myObject.myObject">myObject (class in geobipy.src.classes.core.myObject)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.nActiveData">nActiveData (geobipy.src.classes.data.dataset.FdemData.FdemData property)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nCells">nCells (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.nCells">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nCells">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/statistics/baseDistribution.html#geobipy.src.classes.statistics.baseDistribution.baseDistribution.ndim">ndim (geobipy.src.classes.statistics.baseDistribution.baseDistribution property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.ndim">(geobipy.src.classes.statistics.MvNormalDistribution.MvNormal property)</a>
+</li>
+        <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal.ndim">(geobipy.src.classes.statistics.NormalDistribution.Normal property)</a>
+</li>
+        <li><a href="content/api/classes/statistics/UniformDistribution.html#geobipy.src.classes.statistics.UniformDistribution.Uniform.ndim">(geobipy.src.classes.statistics.UniformDistribution.Uniform property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.nearest">nearest() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nNodes">nNodes (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.nNodes">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.nodes">nodes (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+</li>
+      <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal">Normal (class in geobipy.src.classes.statistics.NormalDistribution)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.nPoints">nPoints (geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.nPoints">(geobipy.src.classes.pointcloud.Point.Point property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.nPointsPerLine">nPointsPerLine() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/system/TdemSystem.html#geobipy.src.classes.system.TdemSystem.TdemSystem.off_time">off_time (geobipy.src.classes.system.TdemSystem.TdemSystem property)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.off_time">off_time() (geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.off_time">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.opacity">opacity() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.opacity_level">opacity_level() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/OrderStatistics.html#geobipy.src.classes.statistics.OrderStatistics.Order">Order (class in geobipy.src.classes.statistics.OrderStatistics)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.ordered_print">ordered_print() (in module geobipy.src.base.MPI)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pad">pad() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.pad">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.pad">(geobipy.src.classes.statistics.MvNormalDistribution.MvNormal method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.parseString">parseString() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.pause">pause() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.pcolor">pcolor() (geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.pcolor">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.pcolor">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.pcolor">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched method)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.pcolor">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.pcolor">(geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+        <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.pcolor">(in module geobipy.src.base.plotting)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.pcolor_1D">pcolor_1D() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.pcolor_as_bar">pcolor_as_bar() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/statistics/GammaDistribution.html#geobipy.src.classes.statistics.GammaDistribution.Gamma.pdf">pdf() (geobipy.src.classes.statistics.GammaDistribution.Gamma method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.percentile">percentile() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.perturb">perturb() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.perturb">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.perturb">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.perturb">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.perturb">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.perturb">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.perturb">(geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.piecewise_constant_interpolate">piecewise_constant_interpolate() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot">plot() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.plot">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.plot">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.plot">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.plot">(geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+        <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.plot">(in module geobipy.src.base.plotting)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.plot_data">plot_data() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.plot_data">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.plot_data">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.plot_data">(geobipy.src.classes.data.dataset.TempestData.TempestData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.plot_grid">plot_grid() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_grid">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.plot_grid">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.plot_halfspace_responses">plot_halfspace_responses() (geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.plot_predicted">plot_predicted() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.plot_predicted">(geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.plot_predicted">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.plot_predicted">(geobipy.src.classes.data.dataset.TempestData.TempestData method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.plot_relative_to">plot_relative_to() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.plotLine">plotLine() (geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point">Point (class in geobipy.src.classes.pointcloud.Point)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_primary_field">predicted_primary_field (geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.predicted_secondary_field">predicted_secondary_field (geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.predictedData">predictedData (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.predictedData">(geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.predictedData">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.predictedData">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.predictedData">(geobipy.src.classes.data.dataset.Data.Data property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.pretty">pretty() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.primary_field">primary_field (geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.print">print() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.probability">probability (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.probability">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.probability">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.probability">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D property)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.probability">(geobipy.src.classes.pointcloud.Point.Point property)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.probability">(geobipy.src.classes.system.EmLoop.EmLoop property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.probability">probability() (geobipy.src.classes.model.Model.Model method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/statistics/MvNormalDistribution.html#geobipy.src.classes.statistics.MvNormalDistribution.MvNormal.probability">(geobipy.src.classes.statistics.MvNormalDistribution.MvNormal method)</a>
+</li>
+        <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal.probability">(geobipy.src.classes.statistics.NormalDistribution.Normal method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.proposal_probabilities">proposal_probabilities() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.pyvista_mesh">pyvista_mesh() (geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.range">range (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D property)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.rankPrint">rankPrint() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.ravelIndices">ravelIndices() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.ravelIndices">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.read">read() (geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.read">(geobipy.src.classes.system.FdemSystem.FdemSystem class method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.read_all">read_all() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.read_csv">read_csv() (geobipy.src.classes.data.dataset.Data.Data class method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.read_csv">(geobipy.src.classes.data.dataset.FdemData.FdemData class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.read_csv">(geobipy.src.classes.data.dataset.TdemData.TdemData class method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.read_csv">(geobipy.src.classes.data.dataset.TempestData.TempestData class method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.read_csv">(geobipy.src.classes.pointcloud.Point.Point class method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.read_groups_with_tag">read_groups_with_tag() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.read_item">read_item() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.read_netcdf">read_netcdf() (geobipy.src.classes.data.dataset.TempestData.TempestData class method)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.readAarhusFile">readAarhusFile() (geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.readKeyFromFile">readKeyFromFile() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfRead.readKeyFromFiles">readKeyFromFiles() (in module geobipy.src.base.HDF.hdfRead)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D">RectilinearMesh1D (class in geobipy.src.classes.mesh.RectilinearMesh1D)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D">RectilinearMesh2D (class in geobipy.src.classes.mesh.RectilinearMesh2D)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched">RectilinearMesh2D_stitched (class in geobipy.src.classes.mesh.RectilinearMesh2D_stitched)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D">RectilinearMesh3D (class in geobipy.src.classes.mesh.RectilinearMesh3D)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.relative_error">relative_error (geobipy.src.classes.data.dataset.Data.Data property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.relative_error">(geobipy.src.classes.data.dataset.TempestData.TempestData property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/statistics/NormalDistribution.html#geobipy.src.classes.statistics.NormalDistribution.Normal.rng">rng() (geobipy.src.classes.statistics.NormalDistribution.Normal method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.rosenbrock">rosenbrock() (in module geobipy.src.base.utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.sample">sample() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.scatter2D">scatter2D() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+
+      <ul>
+        <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.scatter2D">(in module geobipy.src.base.plotting)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.Scatterv">Scatterv() (geobipy.src.classes.data.dataset.Data.Data method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.Scatterv">(geobipy.src.classes.data.dataset.FdemData.FdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.Scatterv">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.Scatterv">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Scatterv">(in module geobipy.src.base.MPI)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Scatterv_list">Scatterv_list() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/base/MPI.html#geobipy.src.base.MPI.Scatterv_numpy">Scatterv_numpy() (in module geobipy.src.base.MPI)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.secondary_field">secondary_field (geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.sensitivity">sensitivity() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.sensitivity">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_additive_error_posterior">set_additive_error_posterior() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_additive_error_posterior">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.set_kdtree">set_kdtree() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.set_pitch_posterior">set_pitch_posterior() (geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_posteriors">set_posteriors() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_posteriors">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_posteriors">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_priors">set_priors() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.set_priors">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_proposals">set_proposals() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.set_proposals">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_proposals">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.set_proposals">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.set_proposals">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.set_relative_error_posterior">set_relative_error_posterior() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.set_relative_error_posterior">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.set_roll_posterior">set_roll_posterior() (geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.set_x_posterior">set_x_posterior() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.set_y_posterior">set_y_posterior() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.set_yaw_posterior">set_yaw_posterior() (geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.set_z_posterior">set_z_posterior() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.setAlphaPerPcolormeshPixel">setAlphaPerPcolormeshPixel() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.shape">shape (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.shape">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.shape">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.single">single (geobipy.src.classes.data.dataset.FdemData.FdemData attribute)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.single">(geobipy.src.classes.data.dataset.TdemData.TdemData attribute)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.single">(geobipy.src.classes.data.dataset.TempestData.TempestData attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.sizeLegend">sizeLegend() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.smooth">smooth() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.splitComplex">splitComplex() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.stackplot2D">stackplot2D() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.std">std (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.std">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.std">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.std">(geobipy.src.classes.data.dataset.Data.Data property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/FdemData.html#geobipy.src.classes.data.dataset.FdemData.FdemData.std">(geobipy.src.classes.data.dataset.FdemData.FdemData property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.std">(geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.step">step() (in module geobipy.src.base.plotting)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.str_to_raw">str_to_raw() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.summary">summary (geobipy.src.classes.data.datapoint.DataPoint.DataPoint property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.summary">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.summary">(geobipy.src.classes.data.dataset.Data.Data property)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.summary">(geobipy.src.classes.data.dataset.TdemData.TdemData property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.summary">(geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.summary">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.summary">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.summary">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.summary">(geobipy.src.classes.model.Model.Model property)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.summary">(geobipy.src.classes.pointcloud.Point.Point property)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.summary">(geobipy.src.classes.system.CircularLoop.CircularLoop property)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.summary">(geobipy.src.classes.system.EmLoop.EmLoop property)</a>
+</li>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.summary">(geobipy.src.classes.system.FdemSystem.FdemSystem property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.tanh">tanh() (in module geobipy.src.base.utilities)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData">TdemData (class in geobipy.src.classes.data.dataset.TdemData)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint">TdemDataPoint (class in geobipy.src.classes.data.datapoint.TdemDataPoint)</a>
+</li>
+      <li><a href="content/api/classes/system/TdemSystem.html#geobipy.src.classes.system.TdemSystem.TdemSystem">TdemSystem (class in geobipy.src.classes.system.TdemSystem)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint">Tempest_datapoint (class in geobipy.src.classes.data.datapoint.Tempest_datapoint)</a>
+</li>
+      <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData">TempestData (class in geobipy.src.classes.data.dataset.TempestData)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.tensor_id">tensor_id (geobipy.src.classes.system.FdemSystem.FdemSystem property)</a>
+</li>
+      <li><a href="content/api/classes/core/myObject.html#geobipy.src.classes.core.myObject.myObject.toHdf">toHdf() (geobipy.src.classes.core.myObject.myObject method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/system/FdemSystem.html#geobipy.src.classes.system.FdemSystem.FdemSystem.toHdf">(geobipy.src.classes.system.FdemSystem.FdemSystem method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.toVTK">toVTK() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/statistics/Histogram.html#geobipy.src.classes.statistics.Histogram.Histogram.transparency">transparency() (geobipy.src.classes.statistics.Histogram.Histogram method)</a>
+</li>
+      <li><a href="content/api/base/utilities.html#geobipy.src.base.utilities.trim_by_percentile">trim_by_percentile() (in module geobipy.src.base.utilities)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/statistics/UniformDistribution.html#geobipy.src.classes.statistics.UniformDistribution.Uniform">Uniform (class in geobipy.src.classes.statistics.UniformDistribution)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh1D.html#geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D.unperturb">unperturb() (geobipy.src.classes.mesh.RectilinearMesh1D.RectilinearMesh1D method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.unravelIndex">unravelIndex() (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.unravelIndex">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.update_parameter_posterior">update_parameter_posterior() (geobipy.src.classes.model.Model.Model method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/data/datapoint/EmDataPoint.html#geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint.update_posteriors">update_posteriors() (geobipy.src.classes.data.datapoint.EmDataPoint.EmDataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.update_posteriors">(geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.update_posteriors">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/model/Model_.html#geobipy.src.classes.model.Model.Model.update_posteriors">(geobipy.src.classes.model.Model.Model method)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/data/datapoint/FdemDataPoint.html#geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint.updateSensitivity">updateSensitivity() (geobipy.src.classes.data.datapoint.FdemDataPoint.FdemDataPoint method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/plotting.html#geobipy.src.base.plotting.vlines">vlines() (in module geobipy.src.base.plotting)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.vtkStructure">vtkStructure() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/base/fileIO.html#geobipy.src.base.fileIO.wccount">wccount() (in module geobipy.src.base.fileIO)</a>
+</li>
+      <li><a href="content/api/base/HDF.html#geobipy.src.base.HDF.hdfWrite.write_nd">write_nd() (in module geobipy.src.base.HDF.hdfWrite)</a>
+</li>
+      <li><a href="content/api/classes/data/datapoint/datapoint.html#geobipy.src.classes.data.datapoint.DataPoint.DataPoint.writeHdf">writeHdf() (geobipy.src.classes.data.datapoint.DataPoint.DataPoint method)</a>
+
+      <ul>
+        <li><a href="content/api/classes/data/datapoint/TdemDataPoint.html#geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint.writeHdf">(geobipy.src.classes.data.datapoint.TdemDataPoint.TdemDataPoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint.writeHdf">(geobipy.src.classes.data.datapoint.Tempest_datapoint.Tempest_datapoint method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/Data.html#geobipy.src.classes.data.dataset.Data.Data.writeHdf">(geobipy.src.classes.data.dataset.Data.Data method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TdemData.html#geobipy.src.classes.data.dataset.TdemData.TdemData.writeHdf">(geobipy.src.classes.data.dataset.TdemData.TdemData method)</a>
+</li>
+        <li><a href="content/api/classes/data/dataset/TempestData.html#geobipy.src.classes.data.dataset.TempestData.TempestData.writeHdf">(geobipy.src.classes.data.dataset.TempestData.TempestData method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.writeHdf">(geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D method)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.writeHdf">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+        <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.writeHdf">(geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+        <li><a href="content/api/classes/system/CircularLoop.html#geobipy.src.classes.system.CircularLoop.CircularLoop.writeHdf">(geobipy.src.classes.system.CircularLoop.CircularLoop method)</a>
+</li>
+        <li><a href="content/api/classes/system/EmLoop.html#geobipy.src.classes.system.EmLoop.EmLoop.writeHdf">(geobipy.src.classes.system.EmLoop.EmLoop method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="X">X</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/pointcloud/Point.html#geobipy.src.classes.pointcloud.Point.Point.x_axis">x_axis() (geobipy.src.classes.pointcloud.Point.Point method)</a>
+</li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_centres">x_centres (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_centres">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.x_edges">x_edges (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.x_edges">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+      <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.xRange">xRange() (geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Y">Y</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_centres">y_centres (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_centres">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="content/api/classes/mesh/RectilinearMesh2D.html#geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D.y_edges">y_edges (geobipy.src.classes.mesh.RectilinearMesh2D.RectilinearMesh2D property)</a>
+
+      <ul>
+        <li><a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched.y_edges">(geobipy.src.classes.mesh.RectilinearMesh2D_stitched.RectilinearMesh2D_stitched property)</a>
+</li>
+        <li><a href="content/api/classes/mesh/RectilinearMesh3D.html#geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D.y_edges">(geobipy.src.classes.mesh.RectilinearMesh3D.RectilinearMesh3D property)</a>
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   <section id="welcome-to-geobipy-geophysical-bayesian-inference-in-python">
-<h1>Welcome to GeoBIPy: Geophysical Bayesian Inference in Python<a class="headerlink" href="#welcome-to-geobipy-geophysical-bayesian-inference-in-python" title="Link to this heading">¶</a></h1>
+<h1>Welcome to GeoBIPy: Geophysical Bayesian Inference in Python<a class="headerlink" href="#welcome-to-geobipy-geophysical-bayesian-inference-in-python" title="Link to this heading"></a></h1>
 <p>This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
 derive posterior distributions of subsurface and measured data properties.
 The current implementation is applied to time and frequency domain electromagnetic data.
 Application outside of these data types is in development.</p>
 <p>Currently there are two types of data that we have implemented; frequency domain electromagnetic data,
 and time domain electromagnetic data.
-The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller.</p>
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+The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller.
+See the section <a class="reference internal" href="content/getting_started/installation.html#installing-time-domain-forward-modeller"><span class="std std-ref">Installing the time domain forward modeller</span></a> for more information.</p>
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+       <td><img src="_static/minus.png" class="toggler"
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+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/base/interpolation.html#module-geobipy.src.base.interpolation"><code class="xref">geobipy.src.base.interpolation</code></a></td><td>
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+       <a href="content/api/base/MPI.html#module-geobipy.src.base.MPI"><code class="xref">geobipy.src.base.MPI</code></a></td><td>
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+     <tr class="cg-1">
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+       <a href="content/api/base/plotting.html#module-geobipy.src.base.plotting"><code class="xref">geobipy.src.base.plotting</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/base/utilities.html#module-geobipy.src.base.utilities"><code class="xref">geobipy.src.base.utilities</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/core/myObject.html#module-geobipy.src.classes.core.myObject"><code class="xref">geobipy.src.classes.core.myObject</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
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+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/datapoint/datapoint.html#module-geobipy.src.classes.data.datapoint.DataPoint"><code class="xref">geobipy.src.classes.data.datapoint.DataPoint</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/datapoint/EmDataPoint.html#module-geobipy.src.classes.data.datapoint.EmDataPoint"><code class="xref">geobipy.src.classes.data.datapoint.EmDataPoint</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/datapoint/FdemDataPoint.html#module-geobipy.src.classes.data.datapoint.FdemDataPoint"><code class="xref">geobipy.src.classes.data.datapoint.FdemDataPoint</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/datapoint/TdemDataPoint.html#module-geobipy.src.classes.data.datapoint.TdemDataPoint"><code class="xref">geobipy.src.classes.data.datapoint.TdemDataPoint</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/datapoint/Tempest_dataPoint.html#module-geobipy.src.classes.data.datapoint.Tempest_datapoint"><code class="xref">geobipy.src.classes.data.datapoint.Tempest_datapoint</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/dataset/Data.html#module-geobipy.src.classes.data.dataset.Data"><code class="xref">geobipy.src.classes.data.dataset.Data</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/dataset/FdemData.html#module-geobipy.src.classes.data.dataset.FdemData"><code class="xref">geobipy.src.classes.data.dataset.FdemData</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/dataset/TdemData.html#module-geobipy.src.classes.data.dataset.TdemData"><code class="xref">geobipy.src.classes.data.dataset.TdemData</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/data/dataset/TempestData.html#module-geobipy.src.classes.data.dataset.TempestData"><code class="xref">geobipy.src.classes.data.dataset.TempestData</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/mesh/RectilinearMesh1D.html#module-geobipy.src.classes.mesh.RectilinearMesh1D"><code class="xref">geobipy.src.classes.mesh.RectilinearMesh1D</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/mesh/RectilinearMesh2D.html#module-geobipy.src.classes.mesh.RectilinearMesh2D"><code class="xref">geobipy.src.classes.mesh.RectilinearMesh2D</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/mesh/RectilinearMesh2D_stitched.html#module-geobipy.src.classes.mesh.RectilinearMesh2D_stitched"><code class="xref">geobipy.src.classes.mesh.RectilinearMesh2D_stitched</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/mesh/RectilinearMesh3D.html#module-geobipy.src.classes.mesh.RectilinearMesh3D"><code class="xref">geobipy.src.classes.mesh.RectilinearMesh3D</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/model/Model_.html#module-geobipy.src.classes.model.Model"><code class="xref">geobipy.src.classes.model.Model</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/pointcloud/Point.html#module-geobipy.src.classes.pointcloud.Point"><code class="xref">geobipy.src.classes.pointcloud.Point</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/baseDistribution.html#module-geobipy.src.classes.statistics.baseDistribution"><code class="xref">geobipy.src.classes.statistics.baseDistribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/Distribution.html#module-geobipy.src.classes.statistics.Distribution"><code class="xref">geobipy.src.classes.statistics.Distribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/GammaDistribution.html#module-geobipy.src.classes.statistics.GammaDistribution"><code class="xref">geobipy.src.classes.statistics.GammaDistribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/Histogram.html#module-geobipy.src.classes.statistics.Histogram"><code class="xref">geobipy.src.classes.statistics.Histogram</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/MvNormalDistribution.html#module-geobipy.src.classes.statistics.MvNormalDistribution"><code class="xref">geobipy.src.classes.statistics.MvNormalDistribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/NormalDistribution.html#module-geobipy.src.classes.statistics.NormalDistribution"><code class="xref">geobipy.src.classes.statistics.NormalDistribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/OrderStatistics.html#module-geobipy.src.classes.statistics.OrderStatistics"><code class="xref">geobipy.src.classes.statistics.OrderStatistics</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/statistics/UniformDistribution.html#module-geobipy.src.classes.statistics.UniformDistribution"><code class="xref">geobipy.src.classes.statistics.UniformDistribution</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/system/CircularLoop.html#module-geobipy.src.classes.system.CircularLoop"><code class="xref">geobipy.src.classes.system.CircularLoop</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/system/EmLoop.html#module-geobipy.src.classes.system.EmLoop"><code class="xref">geobipy.src.classes.system.EmLoop</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/system/FdemSystem.html#module-geobipy.src.classes.system.FdemSystem"><code class="xref">geobipy.src.classes.system.FdemSystem</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="content/api/classes/system/TdemSystem.html#module-geobipy.src.classes.system.TdemSystem"><code class="xref">geobipy.src.classes.system.TdemSystem</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
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+          </div>
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+  <section id="computation-times">
+<span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
+<p><strong>06:08.049</strong> total execution time for 26 files <strong>from all galleries</strong>:</p>
+<div class="docutils container">
+<style scoped>
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+</script><table class="table table-striped sg-datatable docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+<th class="head"><p>Time</p></th>
+<th class="head"><p>Mem (MB)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Inference_1D/plot_inference_1d_tempest.html#sphx-glr-examples-inference-1d-plot-inference-1d-tempest-py"><span class="std std-ref">Running GeoBIPy to invert Tempest data</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_1D/plot_inference_1d_tempest.py</span></code>)</p></td>
+<td><p>01:56.531</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Inference_1D/plot_inference_1d_skytem.html#sphx-glr-examples-inference-1d-plot-inference-1d-skytem-py"><span class="std std-ref">Running GeoBIPy to invert Skytem data</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_1D/plot_inference_1d_skytem.py</span></code>)</p></td>
+<td><p>01:37.169</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Inference_1D/plot_inference_1d_resolve.html#sphx-glr-examples-inference-1d-plot-inference-1d-resolve-py"><span class="std std-ref">Running GeoBIPy to invert Resolve data</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_1D/plot_inference_1d_resolve.py</span></code>)</p></td>
+<td><p>00:29.449</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Inference_2D/plot_inference_2d_resolve.html#sphx-glr-examples-inference-2d-plot-inference-2d-resolve-py"><span class="std std-ref">2D Posterior analysis of Resolve inference</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_2D/plot_inference_2d_resolve.py</span></code>)</p></td>
+<td><p>00:21.524</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Inference_2D/plot_inference_2d_tempest.html#sphx-glr-examples-inference-2d-plot-inference-2d-tempest-py"><span class="std std-ref">2D Posterior analysis of Tempest inference</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_2D/plot_inference_2d_tempest.py</span></code>)</p></td>
+<td><p>00:19.999</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Data/plot_pointcloud3d.html#sphx-glr-examples-data-plot-pointcloud3d-py"><span class="std std-ref">3D Point Cloud class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Data/plot_pointcloud3d.py</span></code>)</p></td>
+<td><p>00:19.063</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Inference_2D/plot_inference_2d_skytem.html#sphx-glr-examples-inference-2d-plot-inference-2d-skytem-py"><span class="std std-ref">2D Posterior analysis of Skytem inference</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Inference_2D/plot_inference_2d_skytem.py</span></code>)</p></td>
+<td><p>00:18.965</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Datapoints/plot_resolve_datapoint.html#sphx-glr-examples-datapoints-plot-resolve-datapoint-py"><span class="std std-ref">Frequency domain datapoint</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Datapoints/plot_resolve_datapoint.py</span></code>)</p></td>
+<td><p>00:07.271</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Statistics/plot_histogram_2d.html#sphx-glr-examples-statistics-plot-histogram-2d-py"><span class="std std-ref">Histogram 2D</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Statistics/plot_histogram_2d.py</span></code>)</p></td>
+<td><p>00:05.012</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Meshes/plot_rectilinear_mesh_1d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-1d-py"><span class="std std-ref">1D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Meshes/plot_rectilinear_mesh_1d.py</span></code>)</p></td>
+<td><p>00:04.585</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Data/plot_tempest_dataset.html#sphx-glr-examples-data-plot-tempest-dataset-py"><span class="std std-ref">Tempest dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Data/plot_tempest_dataset.py</span></code>)</p></td>
+<td><p>00:04.125</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Statistics/plot_histogram_3d.html#sphx-glr-examples-statistics-plot-histogram-3d-py"><span class="std std-ref">Histogram 3D</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Statistics/plot_histogram_3d.py</span></code>)</p></td>
+<td><p>00:03.875</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Data/plot_frequency_dataset.html#sphx-glr-examples-data-plot-frequency-dataset-py"><span class="std std-ref">Frequency domain dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Data/plot_frequency_dataset.py</span></code>)</p></td>
+<td><p>00:03.257</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Models/plot_model_1d.html#sphx-glr-examples-models-plot-model-1d-py"><span class="std std-ref">1D Model with an infinite halfspace</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Models/plot_model_1d.py</span></code>)</p></td>
+<td><p>00:03.051</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Statistics/plot_StatArray.html#sphx-glr-examples-statistics-plot-statarray-py"><span class="std std-ref">StatArray Class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Statistics/plot_StatArray.py</span></code>)</p></td>
+<td><p>00:02.767</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Datapoints/plot_skytem_datapoint.html#sphx-glr-examples-datapoints-plot-skytem-datapoint-py"><span class="std std-ref">Skytem Datapoint Class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Datapoints/plot_skytem_datapoint.py</span></code>)</p></td>
+<td><p>00:02.306</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Data/plot_skytem_dataset.html#sphx-glr-examples-data-plot-skytem-dataset-py"><span class="std std-ref">Skytem dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Data/plot_skytem_dataset.py</span></code>)</p></td>
+<td><p>00:02.295</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Meshes/plot_rectilinear_mesh_2d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-2d-py"><span class="std std-ref">2D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Meshes/plot_rectilinear_mesh_2d.py</span></code>)</p></td>
+<td><p>00:01.747</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Datapoints/plot_tempest_datapoint.html#sphx-glr-examples-datapoints-plot-tempest-datapoint-py"><span class="std std-ref">Tempest Datapoint Class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Datapoints/plot_tempest_datapoint.py</span></code>)</p></td>
+<td><p>00:01.653</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Meshes/plot_rectilinear_mesh_3d.html#sphx-glr-examples-meshes-plot-rectilinear-mesh-3d-py"><span class="std std-ref">3D Rectilinear Mesh</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Meshes/plot_rectilinear_mesh_3d.py</span></code>)</p></td>
+<td><p>00:01.378</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Statistics/plot_histogram_1d.html#sphx-glr-examples-statistics-plot-histogram-1d-py"><span class="std std-ref">Histogram 1D</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Statistics/plot_histogram_1d.py</span></code>)</p></td>
+<td><p>00:00.972</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Models/plot_model_3d.html#sphx-glr-examples-models-plot-model-3d-py"><span class="std std-ref">3D Rectilinear Model</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Models/plot_model_3d.py</span></code>)</p></td>
+<td><p>00:00.788</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Models/plot_model_2d.html#sphx-glr-examples-models-plot-model-2d-py"><span class="std std-ref">2D Rectilinear Model</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Models/plot_model_2d.py</span></code>)</p></td>
+<td><p>00:00.212</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/Distributions/plot_distributions.html#sphx-glr-examples-distributions-plot-distributions-py"><span class="std std-ref">Distribution Class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Distributions/plot_distributions.py</span></code>)</p></td>
+<td><p>00:00.050</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="examples/Statistics/plot_DataArray.html#sphx-glr-examples-statistics-plot-dataarray-py"><span class="std std-ref">DataArray Class</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/Statistics/plot_DataArray.py</span></code>)</p></td>
+<td><p>00:00.003</p></td>
+<td><p>0.0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="examples/HDF5/hdf5.html#sphx-glr-examples-hdf5-hdf5-py"><span class="std std-ref">Using HDF5 within GeoBIPy</span></a> (<code class="docutils literal notranslate"><span class="pre">examples/HDF5/hdf5.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
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