update tide tools following pyTMD changes (#1)

interpolate GOT model over complex constituent space

nearest and linear interpolation with regulargridinterpolators

add EPSG option to spatial coordinate option

add function doc strings for tide programs

add PR flake8 lint check with ubuntu, macos, windows

using conda to install dependencies from environment.yml

flake8 updates of programs

test corrtide.py

Author: tsutterley

.github/workflows/python-publish.yml

@@ -0,0 +1,32 @@
+# This workflows will upload a Python Package using Twine when a release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel twine
+    - name: Build and publish
+      env:
+        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
+        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+      run: |
+        python setup.py sdist bdist_wheel
+        twine upload dist/*
+

.github/workflows/python-request.yml

@@ -0,0 +1,38 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Python on pull request
+
+on:
+  pull_request:
+
+jobs:
+
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-20.04, macos-latest, windows-latest]
+        python-version: [3.6, 3.7, 3.8]
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: ${{ matrix.python-version }}
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Conda ${{ matrix.python-version }}
+      uses: goanpeca/[email protected]
+      with:
+        auto-update-conda: true
+        auto-activate-base: true
+        python-version: ${{ matrix.python-version }}
+        environment-file: environment.yml
+        activate-environment: captoolkit
+    - name: Lint with flake8
+      shell: bash -l {0}
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

captoolkit/fitsec.py

@@ -203,7 +203,7 @@
 dmin = args.radius[0] * 1e3         # min search radius (km -> m)
 dmax = args.radius[1] * 1e3 + 1e-4  # max search radius (km -> m)
 dres_ = args.resparam[0]            # resolution param for weighting func [1]
-nreloc = args.nreloc[0]             # number of relocations 
+nreloc = args.nreloc[0]             # number of relocations
 nlim = args.minobs[0]               # min obs for solution
 niter = args.niter[0]               # number of iterations for solution
 tspan = args.tspan                  # min/max time for solution (d.yr)
@@ -229,7 +229,7 @@
 # [4] If err and id cols = -1, then they are not used.
 
 
-def binning(x, y, xmin=None, xmax=None, dx=1/12., 
+def binning(x, y, xmin=None, xmax=None, dx=1/12.,
             window=3/12., interp=False, median=False):
     """Time-series binning (w/overlapping windows).
 
@@ -304,7 +304,7 @@ def sigma_filter(x, y, order=1, window=3/12., n_iter=3, n_sigma=3):
         if len(idx) == 0: break  # if no data to filter, stop iterating
         y_res[idx] = np.nan
         if np.sum(~np.isnan(y_res)) < 10: break  ##NOTE: Arbitrary min obs
-    y_filt[np.isnan(y_res)] = np.nan    
+    y_filt[np.isnan(y_res)] = np.nan
     return y_filt
 
 
@@ -360,7 +360,7 @@ def get_radius_idx(x, y, x0, y0, r, Tree, n_reloc=0):
     # Either no relocation or not enough points to do relocation
     if n_reloc < 1 or len(idx) < 2: return idx, reloc_dist
 
-    # Relocate center of search radius and query again 
+    # Relocate center of search radius and query again
     for k in range(n_reloc):
 
         # Compute new search location => relocate initial center
@@ -400,7 +400,7 @@ def is_empty(ifile):
 
 # Main function for computing parameters
 def main(ifile, n='', robust_fit=True, n_iter=niter):
-    
+
     # Check for empty file
     if is_empty(ifile):
         print(('SKIP FILE: EMPTY OR CORRUPTED FILE:', ifile))
@@ -471,9 +471,9 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
 
         # Check bbox for obs.
         if len(x[Ig]) == 0:
-	    print(('SKIP FILE: NO DATA POINTS INSIDE BBOX:', ifile))
+            print(('SKIP FILE: NO DATA POINTS INSIDE BBOX:', ifile))
             return
-            
+
         print(('Number of obs. edited by bbox!', 'before:', len(x), 'after:', len(x[Ig])))
 
         # Only select wanted data
@@ -519,7 +519,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
         # Grid solution - defined by nodes
         Xi, Yi = make_grid(xmin, xmax, ymin, ymax, dx, dy)
 
-        xi, yi = Xi.ravel(), Yi.ravel() 
+        xi, yi = Xi.ravel(), Yi.ravel()
         coord = list(zip(x.ravel(), y.ravel()))
 
         print('building the k-d tree ...')
@@ -563,11 +563,11 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
 
             if len(i_cell) < nlim: continue  # use larger radius
 
-            tcap, hcap  = time[i_cell], height[i_cell] 
+            tcap, hcap  = time[i_cell], height[i_cell]
 
             Nb = sum(~np.isnan(hcap))  # length before editing
 
-            # 3-sigma filter 
+            # 3-sigma filter
             if SIGMAFILT:
                 #hcap = sigma_filter(tcap, hcap, order=1, n_sigma=3, n_iter=3)  ##NOTE: It removes too much!!!
                 hcap[np.abs( hcap - np.nanmedian(hcap) ) > mad_std(hcap) * 3] = np.nan
@@ -578,7 +578,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
             n_mon, t_span = n_months(tcap, hcap, tstep=tstep)
 
             ##NOTE: Not using n_mon and t_span to constrain the solution! <<<<<<<<<<<<<<<<<<<<<
-            # If enough data accept radius 
+            # If enough data accept radius
             #if Na >= nlim and n_mon >= MINMONTHS and t_span >= dtlim:
             if Na >= nlim:
                 break
@@ -609,8 +609,8 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
             xc = np.median(xcap)  # update inversion cell coords
             yc = np.median(ycap)
 
-        # Define resolution param (a fraction of the accepted radius) 
-        dres = dres_ * rad 
+        # Define resolution param (a fraction of the accepted radius)
+        dres = dres_ * rad
 
         # Estimate variance
         vcap = scap * scap
@@ -622,7 +622,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
             tref = np.nanmean(tcap)
         else:
             tref = np.float(tref_)
-            
+
         # Design matrix elements
         c0 = np.ones(len(xcap))  # intercept    (0)
         c1 = xcap - xc           # dx           (1)
@@ -645,7 +645,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
         Wcap = 1.0 / (vcap * (1.0 + (dist/dres)*(dist/dres)))
 
         # Create some intermediate output variables
-        sx, sy, at, ae, bi = np.nan, np.nan, np.nan, np.nan, np.nan 
+        sx, sy, at, ae, bi = np.nan, np.nan, np.nan, np.nan, np.nan
 
         # Setup design matrix
         if model == 0:
@@ -666,7 +666,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
             mcol = [6, 7, 8, 9]
 
         has_bias = False  # bias flag
-        
+
         # Check if bias is needed
         if len(np.unique(mcap)) > 1:
             # Add bias to design matrix
@@ -694,10 +694,10 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
         Cm = model_fit.params    # coeffs
         Ce = model_fit.bse       # std err
         resid = model_fit.resid  # data - model
-        
+
         # Check rate and error
         if np.abs(Cm[-1]) > dhlim or np.isinf(Ce[-1]): continue                            ##NOTE: Important for ICESat !!!
-        
+
         # Residuals dH = H - A * Cm (remove linear trend)
         dh = hcap - np.dot(Acap, Cm)
 
@@ -796,7 +796,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
         DATA2 = np.delete(DATA2.T, i_nan, 1).T
     else:
         ##NOTE: NaNs are not removed in case a grid soluction (n_reloc=0) is selected.
-        if not nreloc: grids = [d.reshape(Xi.shape) for d in DATA0.T]  # 1d -> 2d (grids) 
+        if not nreloc: grids = [d.reshape(Xi.shape) for d in DATA0.T]  # 1d -> 2d (grids)
 
         variables = ['lat', 'lon', 'trend', 'trend_err', 'accel', 'accel_err',
                      'height', 'height_err', 'model_rms', 'slope_x', 'slope_y',
@@ -830,7 +830,7 @@ def main(ifile, n='', robust_fit=True, n_iter=niter):
             for v,a in zip(variables, DATA0.T): fo0[v] = a  # 1d arrays
         else:
             for v,g in zip(variables, grids): fo0[v] = g    # 2d arrays
-            fo0['x'], fo0['y'] = Xi[0,:], Yi[:,0] 
+            fo0['x'], fo0['y'] = Xi[0,:], Yi[:,0]
 
     # Save binned time series values
     with h5py.File(ofile1, 'w') as fo1: