Cut scikit-image dependency

README.md

@@ -44,8 +44,8 @@ git clone https://github.com/insarlab/PySolid.git
 # option 1: use conda to install dependencies into an existing, activated environment
 conda install -c conda-forge fortran-compiler --file PySolid/requirements.txt
 
-# option 2: use conda to create a new environment named "pysolid"
-conda env create -f PySolid/environment.yml
+# option 2: use conda to install dependencies into a new environment, e.g. named "pysolid"
+conda create --name pysolid fortran-compiler --file PySolid/requirements.txt
 conda activate pysolid
 
 # option 3: have a Fortran compiler already installed and use pip to install the rest dependencies

pyproject.toml

@@ -25,7 +25,6 @@ dependencies = [
     "numpy",
     "scipy",
     "matplotlib",
-    "scikit-image",
 ]
 dynamic = ["version"]
 

requirements.txt

@@ -2,7 +2,6 @@
 numpy
 scipy
 matplotlib
-scikit-image
 # for packaging and installation
 #fortran-compiler   # Fortran compiler across platforms through conda-forge channel
 pip

setup.py

@@ -43,7 +43,6 @@ def readme():
         "numpy",
         "scipy",
         "matplotlib",
-        "scikit-image",
     ],
 
     # package discovery

src/pysolid/grid.py

@@ -14,7 +14,7 @@
 import os
 
 import numpy as np
-from skimage.transform import resize
+from scipy import ndimage
 
 
 ##################################  Earth tides - grid mode  ###################################
@@ -79,13 +79,16 @@ def calc_solid_earth_tides_grid(dt_obj, atr, step_size=1e3, display=False, verbo
                                         lon0, lon_step, width)
 
     # resample to the input size
+    # via scipy.ndimage.zoom or skimage.transform.resize
     if num_step > 1:
+        in_shape = tide_e.shape
         out_shape = (int(atr['LENGTH']), int(atr['WIDTH']))
         vprint('PYSOLID: resize data to the shape of {} using order-1 spline interpolation'.format(out_shape))
-        kwargs = dict(order=1, mode='edge', anti_aliasing=True, preserve_range=True)
-        tide_e = resize(tide_e, out_shape, **kwargs)
-        tide_n = resize(tide_n, out_shape, **kwargs)
-        tide_u = resize(tide_u, out_shape, **kwargs)
+
+        enu = np.stack([tide_e, tide_n, tide_u])
+        zoom_factors = [1, *np.divide(out_shape, in_shape)]
+        kwargs = dict(order=1, mode="nearest", grid_mode=True)
+        tide_e, tide_n, tide_u = ndimage.zoom(enu, zoom_factors, **kwargs)
 
     # plot
     if display: