remove comments

common_python_scripts/create_subset.py

@@ -12,7 +12,6 @@
 
 def main(image_paths_list, subset_data_y_x_interval, transform_file, detected_transcripts_file, technology, tiles_dimension, overlap, amount_of_VMs, transcript_plot_as_channel, sigma, algorithm, trim_amount=50):
 
-    print("inside subset script")
     channel_images = []
     mean_intensity_of_channels = {}
 
@@ -66,7 +65,6 @@ def main(image_paths_list, subset_data_y_x_interval, transform_file, detected_tr
             trx_subset = pd.concat([trx_subset, trx_subset_temp])
 
     elif technology == 'XENIUM':
-        print("start subset procedure")
         transformation_matrix = pd.read_csv(transform_file).values[:3,:3]
 
         inverse_transformation_matrix = np.linalg.inv(transformation_matrix)
@@ -134,7 +132,6 @@ def main(image_paths_list, subset_data_y_x_interval, transform_file, detected_tr
     ])
 
     np.savetxt('subset_transformation_matrix.csv', transformation_matrix_subset, delimiter=' ', fmt='%d')
-    print("done subset procedure")
     for image_index, image_path in enumerate(image_paths_list):
 
         with tiff.TiffFile(image_path, is_ome=False) as image_file:
@@ -151,8 +148,6 @@ def main(image_paths_list, subset_data_y_x_interval, transform_file, detected_tr
     mean_intensity_of_channels_df = pd.DataFrame(mean_intensity_of_channels, index=[0])
     mean_intensity_of_channels_df.to_csv('mean_intensity_of_channels.csv', index=False)
 
-    print("done image channel intensity")
-
     if transcript_plot_as_channel == 1:
         array_x = trx_subset[x_col].values
         array_y = trx_subset[y_col].values
@@ -185,8 +180,6 @@ def main(image_paths_list, subset_data_y_x_interval, transform_file, detected_tr
         for shard_index in range(num_VMs_in_use):
 
             tiling_script(subset_multi_channel_image, listed_intervals, shard_index, out_path)
-
-    print("complete")
 
 if __name__ == '__main__':