STYLE: Prefer obtaining the cluster filename automatically from Python

Prefer obtaining the module filenames automatically from Python: fixes
messages where the filename is incorrect.

Left behind in commit becfe63.

bin/wm_quality_control_cluster_measurements.py

@@ -47,7 +47,7 @@ def main():
 
     ## output_dir = args.outputDirectory
     ## if not os.path.exists(output_dir):
-    ##     print "<register> Output directory", output_dir, "does not exist, creating it."
+    ##     print f"{os.path.basename(__file__)} Output directory", output_dir, "does not exist, creating it."
     ##     os.makedirs(output_dir)
 
     measurement_list = wma.tract_measurement.load_measurement_in_folder(args.inputDirectory, hierarchy = 'Column', separator = 'Tab')

bin/wm_register_multisubject_faster.py

@@ -106,7 +106,7 @@ def main():
 
 
     #points_per_fiber = args.pointsPerFiber
-    #print "<register> Number of points for fiber representation: ", points_per_fiber
+    #print f"<{os.path.basename(__file__)}> Number of points for fiber representation: ", points_per_fiber
 
     if args.flag_norender:
         print(f"<{os.path.basename(__file__)}> No rendering (for compute servers without X connection).")

whitematteranalysis/cluster.py

@@ -93,18 +93,18 @@ def save(self, directory, atlas_name):
     def load(self, directory, atlas_name, verbose=False):
         if not os.path.isdir(directory):
             print("Error: Atlas directory", directory, "does not exist or is not a directory.")
-            raise "<cluster.py> I/O error"
+            raise f"{os.path.basename(__file__)} I/O error"
 
         fname_base = os.path.join(directory,atlas_name)
         fname_atlas = fname_base+'.p'
         fname_polydata = fname_base+'.vtp'
 
         if not os.path.exists(fname_atlas):
             print("Error: Atlas file", fname_atlas, "does not exist.")
-            raise "<cluster.py> I/O error"
+            raise f"{os.path.basename(__file__)} I/O error"
         if not os.path.exists(fname_polydata):
             print("Error: Atlas file", fname_polydata, "does not exist.")
-            raise "<cluster.py> I/O error"
+            raise f"{os.path.basename(__file__)} I/O error"
 
         try:
             atlas = pickle.load(open(fname_atlas,'rb'))
@@ -333,33 +333,33 @@ def spectral(input_polydata, number_of_clusters=200,
             # C is not computed.
             # Calculate the sum of the partial rows we've computed:
             atlas.row_sum_1 = np.sum(A, axis=0) + np.sum(B.T, axis=0)
-            #print "<cluster.py> A size:", A.shape
-            #print "<cluster.py> B size:", B.shape
-            #print "<cluster.py> A-B matrix row sums range (should be > 0):", np.min(atlas.row_sum_1), np.max(atlas.row_sum_1)
-            
+            #print f"<{os.path.basename(__file__)}> A size:", A.shape
+            #print f"<{os.path.basename(__file__)}> B size:", B.shape
+            #print f"<{os.path.basename(__file__)}> A-B matrix row sums range (should be > 0):", np.min(atlas.row_sum_1), np.max(atlas.row_sum_1)
+
             # Approximate the sum of the rest of the data (including C)
             # These are weighted sums of the columns we did compute
             # where the weight depends on how similar that fiber 
             # was to each path in A.  This uses the dual basis
             # of the columns in A.
             # Approximate the inverse of A for dual basis
-            #print "<cluster.py> Using numpy linalg pinv A"
+            #print f"<{os.path.basename(__file__)}> Using numpy linalg pinv A"
             atlas.pinv_A = np.linalg.pinv(A)
 
             #e_val, e_vec = np.linalg.eigh(atlas.pinv_A)
-            #print "<cluster.py> test of non-normalized A pseudoinverse Eigenvalue range:", e_val[0], e_val[-1]  
+            #print f"<{os.path.basename(__file__)}> test of non-normalized A pseudoinverse Eigenvalue range:", e_val[0], e_val[-1]
 
             # row sum formula:
             # dhat = [a_r + b_r; b_c + B^T*A-1*b_r]
             # this matrix is A^-1 * b_r, where b_r are the row sums of B
             # matlab was: atlas.approxRowSumMatrix = sum(B',1)*atlas.pseudoInverseA;
             atlas.row_sum_matrix = np.dot(np.sum(B.T, axis=0), atlas.pinv_A)
             #test = np.sum(B.T, axis=0)
-            #print "<cluster.py> B column sums range (should be > 0):", np.min(test), np.max(test)
+            #print f"<{os.path.basename(__file__)}> B column sums range (should be > 0):", np.min(test), np.max(test)
             print(f"<{os.path.basename(__file__)}> Range of row sum weights:", np.min(atlas.row_sum_matrix), np.max(atlas.row_sum_matrix))
-            #print "<cluster.py> First 10 entries in weight matrix:", atlas.row_sum_matrix[0:10]
+            #print f"<{os.path.basename(__file__)}> First 10 entries in weight matrix:", atlas.row_sum_matrix[0:10]
             #test = np.dot(atlas.row_sum_matrix, B)
-            #print "<cluster.py> Test partial sum estimation for B:", np.min(test), np.max(test)
+            #print f"<{os.path.basename(__file__)}> Test partial sum estimation for B:", np.min(test), np.max(test)
             #del test
 
             # row sum estimate for current B part of the matrix
@@ -503,7 +503,7 @@ def spectral(input_polydata, number_of_clusters=200,
         centroid_order = render.argsort_by_jet_lookup_table(color)
         atlas.centroids = centroids[centroid_order,:]
         cluster_idx, dist = scipy.cluster.vq.vq(embed, atlas.centroids)
-        #print "<cluster.py> Distortion metric:", cluster_metric
+        #print f"<{os.path.basename(__file__)}> Distortion metric:", cluster_metric
         if 0:
             # This is extremely slow, but leave code here if ever wanted for testing
             cluster_metric = metrics.silhouette_score(embed, cluster_idx, metric='sqeuclidean')
@@ -513,7 +513,7 @@ def spectral(input_polydata, number_of_clusters=200,
         print("ERROR: Unknown centroid finder", centroid_finder)
         ## # This found fewer clusters than we need to represent the anatomy well
         ## # Leave code here in case wanted in future for more testing.
-        ## print '<cluster.py> Affinity Propagation clustering in embedding space.'
+        ## print f'<{os.path.basename(__file__)}> Affinity Propagation clustering in embedding space.'
         ## af = AffinityPropagation(preference=-50).fit(embed)
         ## cluster_centers_indices = af.cluster_centers_indices_
         ## labels = af.labels_
@@ -1041,7 +1041,7 @@ def output_and_quality_control_cluster_atlas(atlas, output_polydata_s, subject_f
         else:
             cluster_colors.append([0,0,0])
         del pd_c
-    print("\n<cluster.py> Finished saving individual clusters as polydata files.")
+    print(f"\n<{os.path.basename(__file__)}> Finished saving individual clusters as polydata files.")
 
     # Notify user if some clusters empty
     empty_count = 0

whitematteranalysis/filter.py

@@ -287,7 +287,7 @@ def downsample(inpd, output_number_of_lines, return_indices=False, preserve_poin
     outpd = mask(inpd, fiber_mask, preserve_point_data=preserve_point_data, preserve_cell_data=preserve_cell_data, verbose=verbose)
 
     # final line count
-    #print "<filter.py> Number of lines selected:", outpd.GetNumberOfLines()
+    #print f"<{os.path.basename(__file__)}> Number of lines selected:", outpd.GetNumberOfLines()
     if return_indices:
         # return sorted indices, this is the line ordering of output
         # polydata (because we mask rather than changing input line order)
@@ -1134,7 +1134,7 @@ def pd_to_array(inpd, dims=225):
         # progress
         #if verbose:
         #    if lidx % 1 == 0:
-        #        print "<filter.py> Line:", lidx, "/", inpd.GetNumberOfLines()
+        #        print f"<{os.path.basename(__file__)}> Line:", lidx, "/", inpd.GetNumberOfLines()
         inpd.GetLines().GetNextCell(ptids)
         num_points = ptids.GetNumberOfIds()
         for pidx in range(0, num_points):
@@ -1176,7 +1176,7 @@ def measure_line_lengths(inpd):
         # progress
         #if verbose:
         #    if lidx % 1 == 0:
-        #        print "<filter.py> Line:", lidx, "/", inpd.GetNumberOfLines()
+        #        print f"<{os.path.basename(__file__)}> Line:", lidx, "/", inpd.GetNumberOfLines()
         inpd.GetLines().GetNextCell(ptids)
         output_lengths[lidx] = ptids.GetNumberOfIds()
     return(output_lengths)

whitematteranalysis/io.py

@@ -349,7 +349,7 @@ def transform_polydatas_from_diskUNSAFE(input_dir, transforms, output_dir, paral
             os.remove(fname)
 
     #for idx in range(0, len(input_pd_fnames)):
-        #print "<io.py>  ", idx + 1, "/",  num_pd, subject_id, " Transforming ", in_filename, "->", out_filename, "..."
+        #print f"<{os.path.basename(__file__)}>  ", idx + 1, "/",  num_pd, subject_id, " Transforming ", in_filename, "->", out_filename, "..."
         #transform_polydata_from_disk(in_filename, transform, out_filename)
 
 def transform_polydatas_from_diskOLD(input_dir, transforms, output_dir):
@@ -707,7 +707,7 @@ def write(self, dirname, savedist=False):
         ##         matplotlib.pyplot.close()
         ##         print "g"
         ##     except Exception:
-        ##         print "<io.py> matplotlib was unable to write histogram."
+        ##         print(f"<{os.path.basename(__file__)}> matplotlib was unable to write histogram.")
         ##         raise
         ## print "1"
         # generate fiber visualization
@@ -716,8 +716,8 @@ def write(self, dirname, savedist=False):
             #ren = render.render(self.polydata)
             #ren.save_views(dirname)
         #except Exception:
-        #    print "<io.py> vtk or rendering issue. Failed to save views."
-        #    print "<io.py> polydata was saved to disk so you can re-render."
+        #    print(f"<{os.path.basename(__file__)}> vtk or rendering issue. Failed to save views.")
+        #    print(f"<{os.path.basename(__file__)}> polydata was saved to disk so you can re-render.")
         #    raise
 
         #print "IMPLEMENT SAVING OF PARAMETERS TOO"