Version 0.1.6

Fixed various issues with numpy 2.0 as submitted by Maria Kisch. Provided more information on some errors. Set xslsx writer option to default to False.

setup.py

@@ -20,7 +20,7 @@
 EMAIL = '[email protected]'
 AUTHOR = 'Julian Schwanbeck'
 REQUIRES_PYTHON = '>=3.6.0'
-VERSION = '0.1.5'
+VERSION = '0.1.6'
 
 # What packages are required for this module to be executed?
 REQUIRED = [

ysmr/__version__.py

@@ -8,6 +8,6 @@
    #     ####   ####       ####  #      ### #    #     #    # ######        #    #  ####   ####  #    # #####
 or YoghurtYetis Software for Motility Recognition
 """
-VERSION = (0, 1, 5)
+VERSION = (0, 1, 6)
 
 __version__ = '.'.join(map(str, VERSION))

ysmr/helper_file.py

@@ -101,7 +101,12 @@ def collate_results_csv_to_xlsx(path=None, save_path=None, csv_extension='statis
     try:
         import xlsxwriter
     except ImportError:
-        logger.warning('Could not import module \'xlsxwriter\', saving as .xlsx file is not possible.')
+        logger.warning(
+            'Could not import module \'xlsxwriter\', saving as .xlsx file is not possible.',
+            exc_info=True,
+        )
+        logger.warning('Please install xlsxwriter to save the file, or deactivate the option in tracking.ini:')
+        logger.warning('"collate results csv to xlsx = False"')
         return
     if save_path is None:
         save_path = './'
@@ -527,7 +532,9 @@ def get_any_paths(prev_dir=None, rename=False, file_types=None, settings=None):
     _config.read(settings['tracking_ini_filepath'])
 
     try:  # In case of headless or otherwise non-Tkinter cases
-        from tkinter import filedialog, Tk
+        # https://stackoverflow.com/questions/45533932/python-3-6-attributeerror-module-tkinter-has-no-attribute-filedialog/59624202#59624202
+        import tkinter.filedialog as filedialog
+        from tkinter import Tk
     except ImportError:
         logger.exception('Cannot import tkinter; files have to be specified manually.')
         return None

ysmr/main.py

@@ -191,6 +191,7 @@ def ysmr(paths=None, settings=None, result_folder=None, multiprocess=False):
     :return: list of (finished path, results)
     :rtype paths_finished: list
     """
+
     t_one = datetime.now()  # to get rough time estimation
     settings = get_configs(settings)  # Get settings
     if settings is None:
@@ -317,7 +318,10 @@ def ysmr(paths=None, settings=None, result_folder=None, multiprocess=False):
         else:
             logger.info('Finished with all files.')
         if settings['collate results csv to xlsx']:
-            collate_results_csv_to_xlsx(path=result_folder, save_path=result_folder)  # folder_path
+            try:
+                collate_results_csv_to_xlsx(path=result_folder, save_path=result_folder)  # folder_path
+            finally:
+                pass
 
     if settings['shut down after analysis']:
         shutdown()

ysmr/track_eval.py

@@ -624,8 +624,8 @@ def select_tracks(path_to_file=None, df=None, results_directory=None, fps=None,
     initial_length, initial_size = (len(track_change), df.shape[0])
 
     df['area'] = df['WIDTH'] * df['HEIGHT']  # calculate area of bacteria in px**2
-    # In general, area is set to np.NaN if anything is wrong, so we later only have
-    # to search for np.NaNs in one column in order to know which rows to remove
+    # In general, area is set to np.nan if anything is wrong, so we later only have
+    # to search for np.nans in one column in order to know which rows to remove
     if settings['verbose']:
         logger.debug('Starting to set NaNs')
     # Remove rough outliers
@@ -634,19 +634,19 @@ def select_tracks(path_to_file=None, df=None, results_directory=None, fps=None,
         (df['average_area'] >= settings['extreme area outliers lower end in px*px']) &
         (df['average_area'] <= settings['extreme area outliers upper end in px*px']),
         df['area'],  # track is fine
-        np.NaN  # delete otherwise
+        np.nan  # delete otherwise
     )
     # Remove frames where bacterial area is x times average area
     if settings['exclude measurement when above x times average area']:
         df['area'] = np.where(
             df['area'] <= (df['average_area'] * settings['exclude measurement when above x times average area']),
             df['area'],  # track is fine
-            np.NaN  # delete otherwise
+            np.nan  # delete otherwise
         )
     # set zeroes in area to NaN
     # tracker.py sets width/height as 0 if it can't connect tracks,
     # thus every data point with area == 0 is suspect
-    df.loc[df['area'] == 0, 'area'] = np.NaN
+    df.loc[df['area'] == 0, 'area'] = np.nan
 
     # remove too short frames
     df['length'] = (df.groupby('TRACK_ID')['POSITION_T'].transform('last') -
@@ -655,15 +655,15 @@ def select_tracks(path_to_file=None, df=None, results_directory=None, fps=None,
     df['area'] = np.where(
         df['length'] >= minimal_length_frames,
         df['area'],  # track is fine
-        np.NaN  # delete otherwise
+        np.nan  # delete otherwise
     )
 
     # Remove frames where GSFF predicted bacterium to be outside of frame
     # df['area'] = np.where(
     #     (0 <= df['POSITION_X'] <= frame_width) &
     #     (0 <= df['POSITION_Y'] <= frame_height),
     #     df['area'],  # track is fine
-    #     np.NaN  # delete otherwise
+    #     np.nan  # delete otherwise
     # )
     # -> moved to select_tracks()
 
@@ -1175,7 +1175,7 @@ def evaluate_tracks(path_to_file, results_directory, df=None, settings=None, fps
     else:
         cut_off_precursor = [(a, b, '{:.2f} - {:.2f}'.format(a, b)) for a, b in
                              zip(cut_off_list[:-1], cut_off_list[1:])]
-    cut_off_list = [(np.NINF, np.inf, name_all_categories)]  # So one x contains all values
+    cut_off_list = [(-np.inf, np.inf, name_all_categories)]  # So one x contains all values
     cut_off_list.extend(cut_off_precursor)
 
     # Calculate df_stats_seaborne for statistical plots
@@ -1184,7 +1184,7 @@ def evaluate_tracks(path_to_file, results_directory, df=None, settings=None, fps
     df_stats_seaborne = df_stats.copy()
 
     # To drop unassigned values later
-    df_stats_seaborne[cut_off_category] = np.NaN
+    df_stats_seaborne[cut_off_category] = np.nan
     for index_cut_off, (low, high, category) in enumerate(cut_off_list):
         # Since we'll concatenate df_stats to df_stats_seaborne, which already contains all
         if category == name_all_categories:
@@ -1196,7 +1196,7 @@ def evaluate_tracks(path_to_file, results_directory, df=None, settings=None, fps
             index_cut_off,
             df_stats_seaborne[cut_off_category]
         )
-    # All np.NaN in column get replaced with str when str values are set within column -> easier to exchange later
+    # All np.nan in column get replaced with str when str values are set within column -> easier to exchange later
     df_stats_seaborne.dropna(subset=[cut_off_category], inplace=True)
 
     # Exchange int values in 'Categories' for correct labels