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DOI-USGS · Jun 5, 2024 · c624dbf · c624dbf
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diff --git a/docs/_downloads/021b6cf14de4fd76c7a3e2f90a173578/hdf5.ipynb b/docs/_downloads/021b6cf14de4fd76c7a3e2f90a173578/hdf5.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/16ec1efc89ed8eeea10bd844b92557de/plot_rectilinear_mesh_1d.ipynb b/docs/_downloads/16ec1efc89ed8eeea10bd844b92557de/plot_rectilinear_mesh_1d.ipynb
@@ -277,7 +277,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/36c869e825ae3b16a184379512e44203/plot_model_2d.ipynb b/docs/_downloads/36c869e825ae3b16a184379512e44203/plot_model_2d.ipynb
@@ -64,7 +64,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/36d592b131669bd825431209f21f3531/plot_histogram_2d.ipynb b/docs/_downloads/36d592b131669bd825431209f21f3531/plot_histogram_2d.ipynb
@@ -450,7 +450,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py b/docs/_downloads/48f0be9987c0cd931b23ce187c305d56/plot_inference_2d_resolve.py
@@ -38,6 +38,9 @@ def plot_2d_summary(folder, data_type, model_type):
          "cmap" : 'jet'
          }
 
+   import warnings
+   warnings.filterwarnings('error')
+
    fig = plt.figure(figsize=(16, 8))
    plt.suptitle("{} {}".format(data_type, model_type))
    gs0 = fig.add_gridspec(6, 2, hspace=1.0)
@@ -134,17 +137,16 @@ def plot_2d_summary(folder, data_type, model_type):
    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)
+   # 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:
+      # try:
          plot_2d_summary("../../../Parallel_Inference/", "resolve", model)
-      except Exception as e:
-         print(model)
-         print(e)
-         pass
+      # except Exception as e:
+      #    print(model)
+      #    print(e)
+      #    pass
diff --git a/docs/_downloads/525bc65bfe238dea5610d6856bece19c/plot_model_3d.ipynb b/docs/_downloads/525bc65bfe238dea5610d6856bece19c/plot_model_3d.ipynb
@@ -205,7 +205,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/6651de36bdd3440bb761887dd2b00831/plot_histogram_1d.ipynb b/docs/_downloads/6651de36bdd3440bb761887dd2b00831/plot_histogram_1d.ipynb
@@ -320,7 +320,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/6dec5e41149e94d435270c1dffe803e1/plot_rectilinear_mesh_2d.ipynb b/docs/_downloads/6dec5e41149e94d435270c1dffe803e1/plot_rectilinear_mesh_2d.ipynb
@@ -320,7 +320,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/7d8af6f7b40a771fd3b189fc4fd80360/plot_model_1d.ipynb b/docs/_downloads/7d8af6f7b40a771fd3b189fc4fd80360/plot_model_1d.ipynb
@@ -219,7 +219,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/83471875085bd5964f72b6b955c92f62/test.py b/docs/_downloads/83471875085bd5964f72b6b955c92f62/test.py
@@ -0,0 +1,75 @@
+"""
+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'
+         }
+
+   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))
+
+   t = results_2d.inference_1d(index=7)
+
+   plt.figure()
+   t.model.plot_posteriors(edges_kwargs={
+                           'transpose': True,
+                           'trim': False},
+                           values_kwargs={
+                           'colorbar': False,
+                           'flipY': True,
+                           'xscale': 'log',
+                           'credible_interval_kwargs': {
+                           }})
+
+   tmp = t.model.values.posterior
+
+
+   plt.show(block=True)
+   # 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
diff --git a/docs/_downloads/93b8636d5d11f2858e28438141a2ee46/plot_tempest_datapoint.ipynb b/docs/_downloads/93b8636d5d11f2858e28438141a2ee46/plot_tempest_datapoint.ipynb
@@ -222,7 +222,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/93e086b68a2d08aac2f637ab24f17e8a/plot_histogram_3d.ipynb b/docs/_downloads/93e086b68a2d08aac2f637ab24f17e8a/plot_histogram_3d.ipynb
@@ -302,7 +302,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/b7483b08f4f927c644b97f4bf2cf523c/plot_rectilinear_mesh_3d.ipynb b/docs/_downloads/b7483b08f4f927c644b97f4bf2cf523c/plot_rectilinear_mesh_3d.ipynb
@@ -194,7 +194,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/docs/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/docs/_downloads/e43324a965564fecaa251424c676fc0e/plot_StatArray.ipynb b/docs/_downloads/e43324a965564fecaa251424c676fc0e/plot_StatArray.ipynb
@@ -1074,7 +1074,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

diff --git a/docs/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/docs/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/docs/_downloads/fbbc3367a4d5c657e951c760d8b85bb4/plot_distributions.ipynb b/docs/_downloads/fbbc3367a4d5c657e951c760d8b85bb4/plot_distributions.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.1"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,

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diff --git a/docs/_sources/content/getting_started/installation.rst.txt b/docs/_sources/content/getting_started/installation.rst.txt
@@ -40,21 +40,21 @@ To run this code in parallel you will need both an MPI library and the python wr
 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. 
+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.  
+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 "pip install mpi4py --no-cache-dir".  This last option is very important, without it, pip might install its own MPI library called MPICH2. 
+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.
@@ -92,14 +92,14 @@ This will install h5py and compile the source.
 
 Installing the time domain forward modeller
 ===========================================
-Ross Brodie at Geoscience Australia has written a great forward modeller, gatdaem1D,  in C++ with a python interface.  
+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. 
+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
@@ -192,4 +192,3 @@ Type,
   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
@@ -457,31 +457,31 @@ A summary will now show the properties of the line.
 
     FdemData
     x:
-    |   Name: Easting (m) 0x174e7e4d0
+    |   Name: Easting (m) 0x171f2ff50
     |   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:
-    |   Name: Northing (m) 0x174e7ecd0
+    |   Name: Northing (m) 0x171f2fe50
     |   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:
-    |   Name: Height (m) 0x174e7e150
+    |   Name: Height (m) 0x171f2f950
     |   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:
-    |   Name: Elevation (m) 0x174e7e050
+    |   Name: Elevation (m) 0x171f2f9d0
     |   Shape: (6710,)
     |   Values: [1246.84 1246.71 1246.61 ... 1337.94 1337.96 1338.02]
     |   Min: 1213.18
@@ -493,47 +493,47 @@ A summary will now show the properties of the line.
     |   In_Phase 129550.0, Quadrature 380.0, Quadrature 1776.0, Quadrature 3345.0, Quadrature 8171.0, 
     |   Quadrature 41020.0, Quadrature 129550.0
     data:
-    |   Name: Data (ppm) 0x174f4d350
+    |   Name: Data (ppm) 0x171dd6c50
     |   Shape: (80520,)
     |   Values: [145.3 435.8 260.6 ... 749.2 976.5 928.3]
     |   Min: 37.7
     |   Max: 3726.9
     |   has_posterior: False
 
     predicted data:
-    |   Name: Predicted Data (ppm) 0x174f4d350
+    |   Name: Predicted Data (ppm) 0x1744559d0
     |   Shape: (80520,)
     |   Values: [0. 0. 0. ... 0. 0. 0.]
     |   Min: 0.0
     |   Max: 0.0
     |   has_posterior: False
 
     std:
-    |   Name: std (ppm) 0x174f4d350
+    |   Name: std (ppm) 0x1744559d0
     |   Shape: (80520,)
     |   Values: [1.453 4.358 2.606 ... 7.492 9.765 9.283]
     |   Min: 0.37700000000000006
     |   Max: 37.269
     |   has_posterior: False
 
     line number:
-    |   Name: Line number 0x174e7d250
+    |   Name: Line number 0x171f2fb50
     |   Shape: (6710,)
     |   Values: [30010. 30010. 30010. ... 30010. 30010. 30010.]
     |   Min: 30010.0
     |   Max: 30010.0
     |   has_posterior: False
 
     fiducial:
-    |   Name: Fiducial 0x174e7da50
+    |   Name: Fiducial 0x171f2fbd0
     |   Shape: (6710,)
     |   Values: [30000 30000 30000 ... 30670 30670 30670]
     |   Min: 30000
     |   Max: 30670
     |   has_posterior: False
 
     relative error:
-    |   Name: Relative error (%) 0x174e7e7d0
+    |   Name: Relative error (%) 0x171f2eb50
     |   Shape: (6710, 1)
     |   Values: [[0.01]
     |    [0.01]
@@ -547,7 +547,7 @@ A summary will now show the properties of the line.
     |   has_posterior: False
 
     additive error:
-    |   Name: Additive error (ppm) 0x174e7e750
+    |   Name: Additive error (ppm) 0x171f2ebd0
     |   Shape: (6710, 1)
     |   Values: [[0.]
     |    [0.]
@@ -714,7 +714,7 @@ See the Resolve.stm files.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.948 seconds)
+   **Total running time of the script:** (0 minutes 3.419 seconds)
 
 
 .. _sphx_glr_download_examples_Data_plot_frequency_dataset.py: