Merge pull request #309 from alexlib/master

tested simple_multipass

Author: alexlib

openpiv/test/test_process.py

@@ -1,5 +1,5 @@
+""" Testing basic PIV processes """
 import numpy as np
-import matplotlib.pyplot as plt
 from skimage.util import random_noise
 from skimage import img_as_ubyte
 from scipy.ndimage import shift as shift_img
@@ -8,9 +8,11 @@
 from openpiv.pyprocess import extended_search_area_piv as piv
 from openpiv.pyprocess import fft_correlate_images, \
                               correlation_to_displacement
+from openpiv import tools 
 
 
-threshold = 0.25
+
+THRESHOLD = 0.25
 
 # define "PIV" shift, i.e. creating u,v values that we want to get
 # -5.5 pixels to the left and 3.2 pixels upwards
@@ -19,18 +21,18 @@
 # the second value is 1 pixel positive to the right 
 # shifted_digit_image=shift(some_digit_image,[2,1]) 
 # so we expect to get later
-# shift(image, [-1*shift_v, shift_u])
+# shift(image, [-1*SHIFT_V, SHIFT_U])
 
 
 # <------
-shift_u = -3.5  # shift to the left, should be placed in columns, axis=1
+SHIFT_U = -3.5  # shift to the left, should be placed in columns, axis=1
 # ^
 # |
 # |
-shift_v = 2.5   # shift upwards, should be placed in rows, axis=0 
+SHIFT_V = 2.5   # shift upwards, should be placed in rows, axis=0 
 
 
-def create_pair(image_size=32, u=shift_u, v=shift_v):
+def create_pair(image_size=32, u=SHIFT_U, v=SHIFT_V):
     """ creates a pair of images with a roll/shift """
     frame_a = np.zeros((image_size, image_size))
     frame_a = random_noise(frame_a)
@@ -60,16 +62,16 @@ def test_piv():
     # extended_search_area_piv returns image based coordinate system
     u, v, _ = piv(frame_a, frame_b, window_size=32)
     print(u, v)
-    assert np.allclose(u, shift_u, atol=threshold)
-    assert np.allclose(v, shift_v, atol=threshold)
+    assert np.allclose(u, SHIFT_U, atol=THRESHOLD)
+    assert np.allclose(v, SHIFT_V, atol=THRESHOLD)
 
 
 def test_piv_smaller_window():
     """ test of the search area larger than the window """
     frame_a, frame_b = create_pair(image_size=32, u=-3.5, v=-2.1)
     u, v, _ = piv(frame_a, frame_b, window_size=16, search_area_size=32)
-    assert np.allclose(u, -3.5, atol=threshold)
-    assert np.allclose(v, -2.1, atol=threshold)
+    assert np.allclose(u, -3.5, atol=THRESHOLD)
+    assert np.allclose(v, -2.1, atol=THRESHOLD)
 
 
 def test_extended_search_area():
@@ -80,10 +82,10 @@ def test_extended_search_area():
                   search_area_size=32, 
                   overlap=0)
 
-    assert np.allclose(u, -3.5, atol=threshold)
-    assert np.allclose(v, -2.1, atol=threshold)
-    # assert dist(u, shift_u) < threshold
-    # assert dist(v, shift_v) < threshold
+    assert np.allclose(u, -3.5, atol=THRESHOLD)
+    assert np.allclose(v, -2.1, atol=THRESHOLD)
+    # assert dist(u, SHIFT_U) < THRESHOLD
+    # assert dist(v, SHIFT_V) < THRESHOLD
 
 
 def test_extended_search_area_overlap():
@@ -94,8 +96,8 @@ def test_extended_search_area_overlap():
                   search_area_size=32,
                   overlap=8)
     print(f"\n u={u}\n v={v}\n")
-    assert np.allclose(u, shift_u, atol=threshold)
-    assert np.allclose(v, shift_v, atol=threshold)
+    assert np.allclose(u, SHIFT_U, atol=THRESHOLD)
+    assert np.allclose(v, SHIFT_V, atol=THRESHOLD)
 
 
 def test_extended_search_area_sig2noise():
@@ -110,8 +112,8 @@ def test_extended_search_area_sig2noise():
         subpixel_method="gaussian"
     )
 
-    assert np.allclose(u, -3.5, atol=threshold)
-    assert np.allclose(v, 2.1, atol=threshold)
+    assert np.allclose(u, -3.5, atol=THRESHOLD)
+    assert np.allclose(v, 2.1, atol=THRESHOLD)
 
 
 def test_process_extended_search_area():
@@ -120,13 +122,12 @@ def test_process_extended_search_area():
     u, v, _ = piv(frame_a, frame_b, window_size=16,
                   search_area_size=32, dt=2., overlap=0)
 
-    assert np.allclose(u, shift_u/2., atol=threshold)
-    assert np.allclose(v, shift_v/2., atol=threshold)
+    assert np.allclose(u, SHIFT_U/2., atol=THRESHOLD)
+    assert np.allclose(v, SHIFT_V/2., atol=THRESHOLD)
 
 
 def test_sig2noise_ratio():
     """ s2n ratio test """
-    from openpiv import tools 
     im1 = files('openpiv.data').joinpath('test1/exp1_001_a.bmp')
     im2 = files('openpiv.data').joinpath('test1/exp1_001_b.bmp')
 
@@ -148,9 +149,10 @@ def test_sig2noise_ratio():
 
 
 def test_fft_correlate():
+    """ test of the fft correlation """
     frame_a, frame_b = create_pair(image_size=32)
     corr = fft_correlate_images(frame_a, frame_b)
     u, v = correlation_to_displacement(corr[np.newaxis, ...], 1, 1)
-    assert np.allclose(u, shift_u, atol=threshold)
-    assert np.allclose(v, shift_v, atol=threshold)
+    assert np.allclose(u, SHIFT_U, atol=THRESHOLD)
+    assert np.allclose(v, SHIFT_V, atol=THRESHOLD)
 

openpiv/test/test_validation.py

@@ -1,61 +1,56 @@
+""" Testing validation functions """
+from typing import Tuple
+import numpy as np
+from importlib_resources import files
+
 from openpiv.pyprocess import extended_search_area_piv as piv
 from openpiv.tools import imread
-import pathlib
-
-import numpy as np
-from .test_process import create_pair, shift_u, shift_v, threshold
 from openpiv import validation
 
-from scipy.ndimage import generic_filter, median_filter
-from scipy.signal import convolve2d
-
-import matplotlib.pyplot as plt
-from typing import List, Tuple
-import numpy.typing as npt
 
-
-file_a = pathlib.Path(__file__).parent / '../data/test1/exp1_001_a.bmp'
-file_b = pathlib.Path(__file__).parent / '../data/test1/exp1_001_b.bmp'
+file_a = files('openpiv.data').joinpath('test1/exp1_001_a.bmp')
+file_b = files('openpiv.data').joinpath('test1/exp1_001_b.bmp')
 
 frame_a = imread(file_a)
 frame_b = imread(file_b)
 
-frame_a = frame_a[:32,:32]
-frame_b = frame_b[:32,:32]
+frame_a = frame_a[:32, :32]
+frame_b = frame_b[:32, :32]
 
 
 def test_validation_peak2mean():
     """test of the simplest PIV run
     default window_size = 32
     """
-    _, _, s2n = piv(frame_a, frame_b, 
-                    window_size=32, 
+    _, _, s2n = piv(frame_a, frame_b,
+                    window_size=32,
                     sig2noise_method="peak2mean")
 
-    assert np.allclose(s2n.min(),1.443882)
+    assert np.allclose(s2n.min(), 1.443882)
+
 
 def test_validation_peak2peak():
     """test of the simplest PIV run
     default window_size = 32
     """
-    _, _, s2n = piv(frame_a, frame_b, 
-                    window_size=32, 
+    _, _, s2n = piv(frame_a, frame_b,
+                    window_size=32,
                     sig2noise_method="peak2peak")
     assert np.allclose(np.min(s2n), 1.24009)
 
+
 def test_sig2noise_val():
     """ tests sig2noise validation """
-    u = np.ones((5,5))
-    v = np.ones((5,5))
+    u = np.ones((5, 5))
+    v = np.ones((5, 5))
     s2n_threshold = 1.05
-    s2n = np.ones((5,5))*s2n_threshold
-    s2n[2,2] -= 0.1
-  
+    s2n = np.ones((5, 5))*s2n_threshold
+    s2n[2, 2] -= 0.1
+
     mask = s2n < s2n_threshold
-  
-    assert not mask[0,0] # should be False
-    assert mask[2,2]
 
+    assert not mask[0, 0]  # should be False
+    assert mask[2, 2]
 
 
 def test_local_median_validation(u_threshold=3, N=3):
@@ -66,49 +61,46 @@ def test_local_median_validation(u_threshold=3, N=3):
         N (int, optional): _description_. Defaults to 3.
         size (int, optional): _description_. Defaults to 1.
     """
-    
-    u = np.random.rand(2*N+1, 2*N+1)
-    u[N,N] = np.median(u)*10
-
 
+    u = np.random.rand(2*N+1, 2*N+1)
+    u[N, N] = np.median(u)*10
 
     # and masked array copy
     u = np.ma.copy(u)
-    u[N+1:,N+1:-1] = np.ma.masked
+    u[N+1:, N+1:-1] = np.ma.masked
 
-    # now we test our function which is just a decoration 
+    # now we test our function which is just a decoration
     # of the above steps
-    flag = validation.local_median_val(u,u,u_threshold,u_threshold)
+    flag = validation.local_median_val(u, u, u_threshold, u_threshold)
+
+    assert flag[N, N]
 
-    assert flag[N,N]
 
 def test_global_val():
     """ tests global validation
     """
-    N: int=2
-    U: Tuple[int,int]=(-10,10)
+    N: int = 2
+    U: Tuple[int, int] = (-10, 10)
 
     u = np.random.rand(2*N+1, 2*N+1)
     u[N, N] = U[0]-.2
-    u[0,0] = U[1]+.2
+    u[0, 0] = U[1]+.2
 
     u = np.ma.copy(u)
     v = np.ma.copy(u)
-    v[N+1,N+1] = np.ma.masked
+    v[N+1, N+1] = np.ma.masked
 
-    mask = validation.global_val(u,u,U,U)
+    mask = validation.global_val(u, u, U, U)
 
-    assert mask[N,N]
-    assert mask[0,0]
+    assert mask[N, N]
+    assert mask[0, 0]
 
     # masked array test
 
+    mask1 = validation.global_val(v, v, U, U)
 
-    
-    mask1 = validation.global_val(v,v,U,U)
-
-    assert mask1[N,N]
-    assert mask1[0,0]
+    assert mask1[N, N]
+    assert mask1[0, 0]
 
 
 def test_global_std():
@@ -126,32 +118,31 @@ def test_global_std():
     # print(np.nanstd(u))
 
     u[N, N] = 10.
-    u[0,0] =  -10.
+    u[0, 0] = -10.
 
     v = np.ma.copy(u)
-    v[N+1,N+1] = np.ma.masked
-
+    v[N+1, N+1] = np.ma.masked
 
     mask = validation.global_std(u, u, 3)
 
-    assert mask[N,N]
-    assert mask[0,0] 
+    assert mask[N, N]
+    assert mask[0, 0]
 
     mask1 = validation.global_std(v, v, std_threshold=3)
 
-    assert mask1[N,N]
-    assert mask1[0,0]
+    assert mask1[N, N]
+    assert mask1[0, 0]
 
-def test_uniform_shift_std(N: int=2):
+
+def test_uniform_shift_std(N: int = 2):
     """ test for uniform shift """
     u = np.ones((2*N+1, 2*N+1))
     v = np.ma.copy(u)
-    v[N+1,N+1] = np.ma.masked
+    v[N+1, N+1] = np.ma.masked
 
     mask = validation.global_std(u, u, std_threshold=3)
 
-    assert ~mask[N,N]
-
+    assert ~mask[N, N]
 
     # print(f'v.data \n {v.data}')
     # print(f'v before \n {v}')
@@ -160,5 +151,5 @@ def test_uniform_shift_std(N: int=2):
 
     v[mask1] = np.ma.masked
 
-    assert v.data[N,N] == 1.0
-    assert v.mask[N+1,N+1]
+    assert v.data[N, N] == 1.0
+    assert v.mask[N+1, N+1]