Generalize covariance function to any image shapes

.gitignore

@@ -63,3 +63,6 @@ ttkvenv
 # latex
 *.aux
 *.log
+
+# code OSS
+.vscode

tools/measurements/covariance.py

@@ -84,7 +84,7 @@ def covarianceAlongAxis(im, d, mask=None, axis=[0, 1, 2]):
         print('spam.measurements.covariance.covarianceAlongAxis: d={}. Should be a list of integers.'.format(type(d[0])))
         print('exit function.')
         return -1
-    if max(d) >= im.shape[0] or max(d) >= im.shape[1] or max(d) >= im.shape[2]:
+    if any([max(d) >= im.shape[i] for i in axis]):
         print('spam.measurements.covariance.covarianceAlongAxis: max(d)={}. Should be smaller than the image.'.format(max(d)))
         print('exit function.')
         return -1
@@ -115,8 +115,7 @@ def covarianceAlongAxis(im, d, mask=None, axis=[0, 1, 2]):
                 im_multi = numpy.multiply(im_multi, mask_eff, dtype=numpy.float32)
             else:
                 # Step 2.1: Compute the pairs of numbers
-                size = im.shape[a]
-                n = (size - x) * size**2
+                n = (im.shape[a] - x) * numpy.prod([s for i, s in enumerate(im.shape) if i != a])  # 
                 # Step 2.2: Multiply the image
                 im_multi = numpy.multiply(im, spam.helpers.singleShift(im, x, a, sub=0), dtype=numpy.float32)
             # n_multi = numpy.sum(im_multi)