Merge branch 'master' of https://github.com/rustychris/stompy

.github/workflows/publish-to-test-pypi.yml

@@ -0,0 +1,4 @@
+name: Publish Python distribution to PyPI and TestPyPI
+
+on: push
+

.readthedocs.yaml

@@ -0,0 +1,13 @@
+version: 2
+
+#build:
+#  tools:
+
+
+sphinx:
+  configuration: docs/conf.py
+
+python:
+  install:
+    - requirements-rtd.txt
+

README.md

@@ -1,38 +1,33 @@
-# stompy
+# stompy: spatial tools for ocean modeling in python
 
 Various python modules related to modeling and oceanographic data analysis.
 
-## Installation
+## Prerequisites
 
-There is not yet a pip or conda installer setup. (if you use this code and would find that useful, please
-add an issue on github so I know somebody cares).
+ `stompy` requires a Python 3 environment with `gdal` installed. There are many ways to set this up. The recommended method would be creating a `mamba` (or `conda`) environment:
 
-### Requirements
+ ```
+mamba create -n <env-name> python "gdal<3.9" "numpy<2.0"
+mamba activate <env-name>
+```
 
-`stompy` makes extensive use of the core packages of a modern scientific python installation,
-plus a few slightly more specialized modules:
+## Installation
 
- * python 2.7 or 3
- * six
- * numpy
- * scipy
- * gdal
- * shapely
- * matplotlib
- * xarray
- * pandas
- * netCDF
-
-### Installation
 
-Python must be able to find the `stompy` subdirectory of the repository.  So on Linux, this might look like:
+### Installing with `pip`
 
 ```
-   cd $HOME/src
-   git clone https://github.com/rustychris/stompy.git
-   export PYTHONPATH=$PYTHONPATH:$HOME/src/stompy
+pip install stompy-ocean
 ```
 
-At this point, you should be able to start python and successfully run `import stompy.grid.unstructured_grid`, for example.
+### Installing with `mamba`/`conda`
+
+Coming soon
+
+## Documentation
+
+See the [Documentation pages](https://stompy.readthedocs.io/en/latest/) for descriptions of the various modules.
+
+## Issues
 
- 
+If you run into any bugs using `stompy`, you are encouraged to submit an [Issue](https://github.com/rustychris/stompy/issues) or PR to this repo.

pyproject.toml

@@ -0,0 +1,47 @@
+# pyproject.toml
+
+[build-system]
+requires = ["setuptools>=67.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "stompy-ocean"
+version = "0.0.3"
+description = "Various python modules related to modeling and oceanographic data analysis."
+readme = {file = "README.md", content-type = "text/markdown"}
+authors = [{ name = "Rusty Holleman", email = "[email protected]" }]
+license = { file = "LICENSE" }
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+]
+keywords = ["modeling", "oceanography", "grid"]
+dependencies = ["cmocean",
+"matplotlib",
+"netCDF4",
+"pandas",
+"requests",
+"scipy",
+"Shapely",
+"six",
+"xarray",
+"seawater",
+"gdal<3.9",
+"numpy<2.0",
+"cgal"
+]
+requires-python = ">=3.0"
+
+[tool.setuptools]
+packages=['stompy', 'stompy.grid', 'stompy.io', 'stompy.io.local',
+              'stompy.model', 'stompy.model.delft', 'stompy.model.fvcom',
+              'stompy.model.pypart', 'stompy.model.suntans',
+              'stompy.plot', 'stompy.plot.cmaps',
+              'stompy.spatial']
+
+[tool.setuptools.package-data]
+stompy = ["tide_consts.txt"]
+
+[project.urls]
+Homepage = "https://github.com/rustychris/stompy"

requirements.txt

@@ -1,8 +1,6 @@
 cmocean
-future
 gdal
 matplotlib
-pyqt
 netCDF4
 numpy
 pandas

stompy/filters.py

@@ -49,7 +49,7 @@ def lowpass_gotin(data,in_t_days,*args,**kwargs):
     return lowpass_godin(data,in_t_days,*args,**kwargs)
 
 def lowpass_godin(data,in_t_days=None,ends='pass',
-                  mean_dt_h = None,
+                  mean_dt_h = None,axis=-1,
                   *args,**kwargs):
     """ Approximate Godin's tidal filter
     Note that in preserving the length of the dataset, the ends aren't really
@@ -80,22 +80,35 @@ def lowpass_godin(data,in_t_days=None,ends='pass',
     A24 = np.ones(N24) / float(N24)
     A25 = np.ones(N25) / float(N25)
 
-    if ends=='nan':
-        # Add nan at start/end, which will carry through
-        # the convolution to mark any samples affected
-        # by the ends
-        data=np.concatenate( ( [np.nan],data,[np.nan] ) )
-    data = np.convolve(data,A24,'same')
-    data = np.convolve(data,A24,'same')
-    data = np.convolve(data,A25,'same')
-
-    if ends=='nan':
-        data=data[1:-1]
+    # numpy convolve only takes 1D inputs.
+    # so iterate over any extra dimensions
+    iter_shape=list(data.shape)
+    del iter_shape[axis]
+    data=data.copy()
+    for idx in np.ndindex(*iter_shape):
+        expanded=list(idx)
+        expanded.insert(axis,slice(None))
+        expanded=tuple(expanded)
+        slc=data[expanded]
+
+        if ends=='nan':
+            # Add nan at start/end, which will carry through
+            # the convolution to mark any samples affected
+            # by the ends
+            slc=np.concatenate( ( [np.nan],slc,[np.nan] ) )
+        slc=np.convolve(slc,A24,'same')
+        slc=np.convolve(slc,A24,'same')
+        slc=np.convolve(slc,A25,'same')
+
+        if ends=='nan':
+            slc=slc[1:-1]
+        data[expanded]=slc            
 
     return data
 
 def lowpass_fir(x,winsize,ignore_nan=True,axis=-1,mode='same',use_fft=False,
-                nan_weight_threshold=0.49999,window='hanning'):
+                nan_weight_threshold=0.49999,window='hanning',
+                dim='time'):
     """
     In the absence of exact filtering needs, choose the window 
     size to match the cutoff period.  Signals with a frequency corresponding to
@@ -115,6 +128,8 @@ def lowpass_fir(x,winsize,ignore_nan=True,axis=-1,mode='same',use_fft=False,
     nan_weight_threshold: items with a weight less than this will be marked nan
       the default value is slightly less than half, to avoid numerical roundoff
       issues with 0.49999999 < 0.5
+    dim: if x is a xarray.DataArray, gives the dimension name along which to
+      filter.
 
     N.B. In the case of an input with nan only at the beginning and end, to 
     guarantee that the output will have the same nans as the input winsize must
@@ -123,22 +138,39 @@ def lowpass_fir(x,winsize,ignore_nan=True,axis=-1,mode='same',use_fft=False,
     # hanning windows have first/last elements==0.
     # but it's counter-intuitive - so force a window with nonzero
     # elements matching the requested size
+    import xarray as xr
     if isinstance(winsize,np.ndarray):
         win=winsize
         winsize=len(win)
-    elif window=='hanning':
-        win=np.hanning(winsize+2)[1:-1]
-    elif window=='boxcar':
-        win=np.ones(winsize)
     else:
-        raise Exception("window must be one of hanning, boxcar")
+        if isinstance(winsize, np.timedelta64):
+            assert isinstance(x,xr.DataArray)
+            dts=np.diff(x.time)
+            dts.sort()
+            dt=dts[len(dts)//2]
+            winsize=max(1,int(winsize / dt))
+
+        if window=='hanning':
+            win=np.hanning(winsize+2)[1:-1]
+        elif window=='boxcar':
+            win=np.ones(winsize)
+        else:
+            raise Exception("window must be one of hanning, boxcar")
     win/=win.sum()
 
     if use_fft:
         convolve=scipy.signal.fftconvolve
     else:    
         convolve=scipy.signal.convolve
 
+    if isinstance(x,xr.DataArray):
+        x_da=x
+        x=x_da.values
+        if dim is not None:
+            axis=x_da.get_axis_num(dim)
+    else:
+        x_da=None
+
     slices=[None]*x.ndim
     slices[axis]=slice(None)
     win=win[tuple(slices)] # expand to get the right broadcasting
@@ -156,8 +188,13 @@ def lowpass_fir(x,winsize,ignore_nan=True,axis=-1,mode='same',use_fft=False,
         result[has_weight] = result[has_weight] / weights[has_weight]
         result[~has_weight]=0 # redundant, but in case of roundoff.
         result[weights<nan_weight_threshold]=np.nan
-
-    return result
+
+    if x_da is not None:
+        result_da=x_da.copy()
+        result_da.values[...] = result
+        return result_da
+    else:
+        return result
 
 # xarray time series versions:
 def lowpass_xr(da,cutoff,**kw):

stompy/grid/cli.py

@@ -54,7 +54,10 @@ def format_list(cls):
                          lambda fmt,path: unstructured_grid.UnstructuredGrid.read_dfm(fn=path)]
 ReadGrid.formats['sms']=['SMS grd',
                          lambda fmt,path: unstructured_grid.UnstructuredGrid.read_sms(path)]
-
+ReadGrid.formats['ras2d']=(
+    ['RAS2D h5', lambda fmt,path: unstructured_grid.UnstructuredGrid.read_ras2d(path,subedges='subedges') ]
+)
+
 class Dualify(Op):
     def run(self,args):
         g=stack.pop()