def local_data_test():
local_path = OverallAnalysis.folder_path_settings.get_local_test_recording_path()
spatial_firing = pd.read_pickle(local_path + '/DataFrames/spatial_firing.pkl')
position_data = pd.read_pickle(local_path + '/DataFrames/position.pkl')
field_df = data_frame_utility.get_field_data_frame(spatial_firing, position_data)
field_df = shuffle_field_data(field_df, local_path, number_of_bins=20, number_of_times_to_shuffle=1000)
field_df = analyze_shuffled_data(field_df, local_path, 30, number_of_bins=20)
field_df.to_pickle(local_path + 'shuffle_analysis/shuffled_fields.pkl')
def main():
"""
This is just here for testing.
"""
path = '/DataFrames/'
position_path = path + 'position.pkl'
position = pd.read_pickle(position_path)
spatial_firing_path = path + 'spatial_firing.pkl'
spatial_firing = pd.read_pickle(spatial_firing_path)
position = add_heading_direction_to_position_data_frame(position)
# spatial_firing, position = add_heading_direction_to_spatial_firing_data_frame(spatial_firing, position)
field_df = data_frame_utility.get_field_data_frame(spatial_firing, position)
field_df, position = add_heading_direction_to_fields_frame(field_df, position)
def get_shuffled_field_data(spatial_firing,
position_data,
shuffle_type='distributive',
sampling_rate_video=50):
field_df = data_frame_utility.get_field_data_frame(spatial_firing,
position_data)
field_df = OverallAnalysis.shuffle_field_analysis.add_rate_map_values_to_field_df_session(
spatial_firing, field_df)
field_df = OverallAnalysis.shuffle_field_analysis.shuffle_field_data(
field_df,
analysis_path,
number_of_bins=20,
number_of_times_to_shuffle=1000,
shuffle_type=shuffle_type)
field_df = OverallAnalysis.shuffle_field_analysis.analyze_shuffled_data(
field_df,
analysis_path,
sampling_rate_video,
number_of_bins=20,
shuffle_type=shuffle_type)
return field_df
def process_recordings(server_path, sampling_rate_video, spike_sorter='/MountainSort', df_path='/DataFrames', redo_existing=True, shuffle_type='occupancy'):
if os.path.exists(server_path):
print('I see the server.')
for recording_folder in glob.glob(server_path + '*'):
if os.path.isdir(recording_folder):
spike_data_frame_path = recording_folder + spike_sorter + df_path + '/spatial_firing.pkl'
position_data_frame_path = recording_folder + spike_sorter + df_path + '/position.pkl'
if shuffle_type == 'occupancy':
shuffled_data_frame_path = recording_folder + spike_sorter + df_path + '/shuffled_fields.pkl'
else:
shuffled_data_frame_path = recording_folder + spike_sorter + df_path + '/shuffled_fields_distributive.pkl'
if os.path.exists(spike_data_frame_path):
print('I found a firing data frame.')
if redo_existing is False:
if os.path.exists(shuffled_data_frame_path):
shuffled_data = pd.read_pickle(shuffled_data_frame_path)
if 'shuffled_hd_distribution' in shuffled_data:
print('This was shuffled earlier.')
print(recording_folder)
continue
spatial_firing = pd.read_pickle(spike_data_frame_path)
position_data = pd.read_pickle(position_data_frame_path)
field_df = data_frame_utility.get_field_data_frame(spatial_firing, position_data)
if not field_df.empty:
print(field_df.session_id)
if shuffle_type == 'distributive':
field_df = add_rate_map_values_to_field_df_session(spatial_firing, field_df)
field_df = shuffle_field_data(field_df, recording_folder + spike_sorter + '/', number_of_bins=20, number_of_times_to_shuffle=1000, shuffle_type=shuffle_type)
field_df = analyze_shuffled_data(field_df, recording_folder + spike_sorter + '/', sampling_rate_video, number_of_bins=20, shuffle_type=shuffle_type)
try:
if shuffle_type == 'occupancy':
field_df.to_pickle(recording_folder + spike_sorter + df_path + '/shuffled_fields.pkl')
else:
field_df.to_pickle(recording_folder + spike_sorter + df_path + '/shuffled_fields_distributive.pkl')
print('I finished analyzing ' + recording_folder)
except OSError as error:
print('ERROR I failed to analyze ' + recording_folder)
print(error)