Merge pull request #193 from OpenPIV/add_testing_github_workflow

Create testing.yml

Author: alexlib

.github/workflows/testing.yml

@@ -0,0 +1,32 @@
+name: Python package
+
+on: [push]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      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
+    - name: Test with pytest
+      run: |
+        pytest

openpiv/filters.py

@@ -67,7 +67,7 @@ def gaussian_kernel(sigma, truncate=4.0):
 
 def gaussian(u, v, half_width=1):
     """Smooths the velocity field with a Gaussian kernel.
-    
+
     Parameters
     ----------
     u : 2d np.ndarray
@@ -86,7 +86,7 @@ def gaussian(u, v, half_width=1):
         the smoothed u velocity component field
 
     vf : 2d np.ndarray
-        the smoothed v velocity component field    
+        the smoothed v velocity component field
 
     """
     g = _gaussian_kernel(half_width=half_width)
@@ -150,17 +150,17 @@ def replace_outliers(u, v, w=None, method="localmean",
 
     """
     uf = replace_nans(
-        u, method=method, max_iter=max_iter, tol=tol, 
+        u, method=method, max_iter=max_iter, tol=tol,
         kernel_size=kernel_size
     )
     vf = replace_nans(
-        v, method=method, max_iter=max_iter, tol=tol, 
+        v, method=method, max_iter=max_iter, tol=tol,
         kernel_size=kernel_size
     )
 
     if isinstance(w, np.ndarray):
         wf = replace_nans(
-            w, method=method, max_iter=max_iter, tol=tol, 
+            w, method=method, max_iter=max_iter, tol=tol,
             kernel_size=kernel_size
         )
         return uf, vf, wf

openpiv/lib.py

@@ -44,7 +44,7 @@ def replace_nans(array, max_iter, tol, kernel_size=2, method="disk"):
 
       array : 2d or 3d np.ndarray
           an array containing NaN elements that have to be replaced
-          if array is a masked array (numpy.ma.MaskedArray), then 
+          if array is a masked array (numpy.ma.MaskedArray), then
           the mask is reapplied after the replacement
 
       max_iter : int
@@ -90,7 +90,9 @@ def replace_nans(array, max_iter, tol, kernel_size=2, method="disk"):
 
     # list of kernel array indices
     # kernel_indices = np.indices(kernel.shape)
-    # kernel_indices = np.reshape(kernel_indices, (n_dim, (2 * kernel_size + 1) ** n_dim), order="C").T
+    # kernel_indices = np.reshape(kernel_indices,
+    #   (n_dim, (2 * kernel_size + 1) ** n_dim),
+    #   order="C").T
 
     # indices where array is NaN
     nan_indices = np.array(np.nonzero(np.isnan(array))).T.astype(int)
@@ -104,7 +106,7 @@ def replace_nans(array, max_iter, tol, kernel_size=2, method="disk"):
 
     # make several passes
     # until we reach convergence
-    for it in range(max_iter):
+    for _ in range(max_iter):
         # note: identifying new nan indices and looping other the new indices
         # would give slightly different result
 
@@ -116,11 +118,10 @@ def replace_nans(array, max_iter, tol, kernel_size=2, method="disk"):
             # init to 0.0
             replaced_new[k] = 0.0
 
-            # generating a list of indices of the convolution window in the 
+            # generating a list of indices of the convolution window in the
             # array
-            slice_indices = np.array(
-                np.meshgrid(*[range(i - kernel_size, i + kernel_size + 1) for i in ind])
-            )
+            slice_indices = np.array(np.meshgrid(*[range(i - kernel_size,
+                                     i + kernel_size + 1) for i in ind]))
 
             # identifying all indices strictly inside the image edges:
             in_mask = np.array(

openpiv/piv.py

@@ -46,12 +46,13 @@ def simple_piv(im1, im2, plot=True):
     x, y = pyprocess.get_coordinates(image_size=im1.shape,
                                      search_area_size=32, overlap=16)
 
-    valid = s2n > np.percentile(s2n,5)
+    valid = s2n > np.percentile(s2n, 5)
 
     if plot:
         _, ax = plt.subplots(figsize=(6, 6))
         ax.imshow(im1, cmap=plt.get_cmap("gray"), alpha=0.5, origin="upper")
-        ax.quiver(x[valid], y[valid], u[valid], -v[valid], scale=70, color='r',width=.005)
+        ax.quiver(x[valid], y[valid], u[valid], -v[valid], scale=70,
+                  color='r', width=.005)
         plt.show()
 
     return x, y, u, v
@@ -85,7 +86,7 @@ def piv_example():
     # each frame
     ims = []
     for i in range(2):
-        im = ax.imshow(images[i % 2], animated=True, cmap=plt.cm.gray)
+        im = ax.imshow(images[i % 2], animated=True, cmap="gray")
         ims.append([im])
 
     _ = animation.ArtistAnimation(fig, ims, interval=500, blit=False,
@@ -105,7 +106,7 @@ def piv_example():
     fig, ax = plt.subplots(1, 2, figsize=(11, 8))
     ax[0].imshow(frame_a, cmap=plt.get_cmap("gray"), alpha=0.8)
     ax[0].quiver(x, y, vel[0], -vel[1], scale=50, color="r")
-    ax[1].quiver(x, y[::-1,:], vel[0], -vel[1], scale=50, color="b")
+    ax[1].quiver(x, y[::-1, :], vel[0], -1*vel[1], scale=50, color="b")
     ax[1].set_aspect(1)
     # ax[1].invert_yaxis()
     plt.show()

openpiv/pyprocess.py

@@ -467,9 +467,9 @@ def fft_correlate_images(image_a, image_b,
     """
 
     if normalized_correlation:
-        # remove the effect of stronger laser or 
+        # remove the effect of stronger laser or
         # longer exposure for frame B
-        # image_a = match_histograms(image_a, image_b) 
+        # image_a = match_histograms(image_a, image_b)
 
         # remove mean background, normalize to 0..1 range
         image_a = normalize_intensity(image_a)
@@ -521,7 +521,8 @@ def normalize_intensity(window):
     window -= window.mean(axis=(-2, -1),
                           keepdims=True, dtype=np.float32)
     tmp = window.std(axis=(-2, -1), keepdims=True)
-    window = np.divide(window, tmp, out=np.zeros_like(window), where=(tmp != 0))
+    window = np.divide(window, tmp, out=np.zeros_like(window),
+                       where=(tmp != 0))
     return np.clip(window, 0, window.max())
 
 
@@ -699,7 +700,7 @@ def extended_search_area_piv(
        the size of the interrogation window in the second frame,
        default is the same interrogation window size and it is a
        fallback to the simplest FFT based PIV
-    
+
     normalized_correlation: bool
         if True, then the image intensity will be modified by removing
         the mean, dividing by the standard deviation and
@@ -828,10 +829,10 @@ def correlation_to_displacement(corr, n_rows, n_cols,
                             subpixel_method=subpixel_method)) -\
                             default_peak_position
 
-            # the horizontal shift from left to right is the u
-            # the vertical displacement from top to bottom (increasing row) is v
-            # x the vertical shift from top to bottom is row-wise shift is now
-            # a negative vertical
+        # the horizontal shift from left to right is the u
+        # the vertical displacement from top to bottom (increasing row) is v
+        # x the vertical shift from top to bottom is row-wise shift is now
+        # a negative vertical
             u[k, m], v[k, m] = peak[1], peak[0]
 
     return (u, v)

openpiv/scaling.py

@@ -20,36 +20,25 @@
 """
 
 
-import numpy as np
-
-
 def uniform(x, y, u, v, scaling_factor):
     """
     Apply an uniform scaling
-    
+
     Parameters
     ----------
     x : 2d np.ndarray
-    
     y : 2d np.ndarray
-    
     u : 2d np.ndarray
-    
     v : 2d np.ndarray
-    
     scaling_factor : float
         the image scaling factor in pixels per meter
-    
+
     Return
     ----------
     x : 2d np.ndarray
-    
     y : 2d np.ndarray
-    
     u : 2d np.ndarray
-    
     v : 2d np.ndarray
-        
     """
     return (
         x / scaling_factor,