If all data pixel are on NaN, then assign a value of 0.0 for each pixel.

AFM_thumbs/Linalg.py

@@ -119,7 +119,7 @@ def linalg_line_fit(size_x, size_y, data):
         xy = sum(line*x_vec)
 
         B  = scipy.mat([[xy],[y]])
-        c,resid,rank,sigma = linalg.lstsq(M,B)
+        c, resid, rank, sigma = linalg.lstsq(M, B)
 
         plane.append([i * c[0][0] + c[1][0] for i in range(size_x)])
 
@@ -198,7 +198,12 @@ def check_image_properties(data, data_file):
 
         list_nans = np.where(np.isnan(data))[0]
         data_clean = np.delete(data, list_nans)
-        data_mean = np.mean(data_clean)
+
+        # If all pixels are on NaN, then put the mean value onto 0.0.
+        if len(list_nans) == len(data):
+            data_mean = 0.0
+        else:
+            data_mean = np.mean(data_clean)
 
         for nan in list_nans:
             data[nan] = data_mean