Copy some tests into 'interactive tests'

Moving forward the 'tests' directory will be used for unit type tests
that will be run with pytest.  They will use dummy data to test if the
software is functioning right and producing correct values for simulated
cases.

'interactive tests' will be used to test and troubleshoot with real
files.

python/interactive tests/test_libs.py

@@ -0,0 +1,16 @@
+from clij2fft.libs import getlib
+from clij2fft.richardson_lucy import richardson_lucy
+import numpy as np
+
+
+lib = getlib()
+lib.print_platforms_and_devices()
+
+img= np.ones((256, 256, 128), dtype=np.float32)
+psf = np.ones((128, 128, 64), dtype=np.float32)
+
+result = richardson_lucy(img, psf, 100, 0, platform=0, device=0)
+#result = richardson_lucy(img, psf, 100, 0, platform=1, device=0)
+
+print()
+print(result.shape, result.mean())

python/interactive tests/test_richardson_lucy_dask.py

@@ -0,0 +1,51 @@
+from clij2fft.richardson_lucy_dask import richardson_lucy_dask
+from skimage.io import imread
+import numpy as np
+from matplotlib import pyplot as plt
+import pyopencl as cl
+import os
+
+use_ones = False
+
+# we have the option of just using arrays of ones to test that the code runs...
+if use_ones:
+    img = np.ones((256, 256, 128), dtype=np.float32)
+    psf = np.ones((128, 128, 64), dtype=np.float32)    
+# ...or we can use real images
+else:
+    clij2fft_images_path = r'/home/bnorthan/images/clij2-fft-images'
+
+    img_name=os.path.join(clij2fft_images_path, 'Bars-G10-P15-stack.tif')
+    psf_name=os.path.join(clij2fft_images_path, 'PSF-Bars-stack.tif')
+
+    img=imread(img_name)
+    print('image shape is',img.shape)
+    psf=imread(psf_name)
+
+pad_z=50
+pad_y=50
+pad_x=50
+mem_to_use=1
+
+img = np.pad(img, [(pad_z,pad_z),(pad_y, pad_y),(pad_x, pad_x)], mode = 'constant', constant_values = 0)
+print('image shape is',img.shape)
+
+platforms = cl.get_platforms()
+
+for platform in platforms:
+    # print number of devices per platform
+    print("Platform: {} has {} devices".format(platform.name, len(platform.get_devices())))
+    for device in platform.get_devices():
+        print('    ',device)
+
+platform_to_use = 0
+decon=richardson_lucy_dask(img, psf, 50, 0.0001, mem_to_use=mem_to_use, platform = 0, num_devices = len(platforms[0].get_devices()), debug = False)
+
+fig, ax = plt.subplots(1,2)
+ax[0].imshow(img.max(axis=0))
+ax[0].set_title('img')
+
+ax[1].imshow(decon.max(axis=0))
+ax[1].set_title('deconvolution')
+
+plt.show()

python/tests/test_libs.py

@@ -2,14 +2,16 @@
 from clij2fft.richardson_lucy import richardson_lucy
 import numpy as np
 
-lib = getlib()
-lib.print_platforms_and_devices()
 
-img= np.ones((256, 256, 128), dtype=np.float32)
-psf = np.ones((128, 128, 64), dtype=np.float32)
+def test_libs():
+    lib = getlib()
+    lib.print_platforms_and_devices()
 
-result = richardson_lucy(img, psf, 100, 0, platform=0, device=0)
-#result = richardson_lucy(img, psf, 100, 0, platform=1, device=0)
+    img= np.ones((256, 256, 128), dtype=np.float32)
+    psf = np.ones((128, 128, 64), dtype=np.float32)
 
-print()
-print(result.shape, result.mean())
+    result = richardson_lucy(img, psf, 100, 0, platform=0, device=0)
+    #result = richardson_lucy(img, psf, 100, 0, platform=1, device=0)
+
+    print()
+    print(result.shape, result.mean())

python/tests/test_richardson_lucy_dask.py

@@ -5,47 +5,27 @@
 import pyopencl as cl
 import os
 
-use_ones = False
+def test_richardson_lucy_dask():
 
-# we have the option of just using arrays of ones to test that the code runs...
-if use_ones:
     img = np.ones((256, 256, 128), dtype=np.float32)
     psf = np.ones((128, 128, 64), dtype=np.float32)    
-# ...or we can use real images
-else:
-    clij2fft_images_path = r'/home/bnorthan/images/clij2-fft-images'
 
-    img_name=os.path.join(clij2fft_images_path, 'Bars-G10-P15-stack.tif')
-    psf_name=os.path.join(clij2fft_images_path, 'PSF-Bars-stack.tif')
+    pad_z=50
+    pad_y=50
+    pad_x=50
+    mem_to_use=1
 
-    img=imread(img_name)
+    img = np.pad(img, [(pad_z,pad_z),(pad_y, pad_y),(pad_x, pad_x)], mode = 'constant', constant_values = 0)
     print('image shape is',img.shape)
-    psf=imread(psf_name)
 
-pad_z=50
-pad_y=50
-pad_x=50
-mem_to_use=1
+    platforms = cl.get_platforms()
 
-img = np.pad(img, [(pad_z,pad_z),(pad_y, pad_y),(pad_x, pad_x)], mode = 'constant', constant_values = 0)
-print('image shape is',img.shape)
+    for platform in platforms:
+        # print number of devices per platform
+        print("Platform: {} has {} devices".format(platform.name, len(platform.get_devices())))
+        for device in platform.get_devices():
+            print('    ',device)
 
-platforms = cl.get_platforms()
+    platform_to_use = 0
+    decon=richardson_lucy_dask(img, psf, 50, 0.0001, mem_to_use=mem_to_use, platform = 0, num_devices = len(platforms[0].get_devices()), debug = False)
 
-for platform in platforms:
-    # print number of devices per platform
-    print("Platform: {} has {} devices".format(platform.name, len(platform.get_devices())))
-    for device in platform.get_devices():
-        print('    ',device)
-
-platform_to_use = 0
-decon=richardson_lucy_dask(img, psf, 50, 0.0001, mem_to_use=mem_to_use, platform = 0, num_devices = len(platforms[0].get_devices()), debug = False)
-
-fig, ax = plt.subplots(1,2)
-ax[0].imshow(img.max(axis=0))
-ax[0].set_title('img')
-
-ax[1].imshow(decon.max(axis=0))
-ax[1].set_title('deconvolution')
-
-plt.show()