def process_a_dir(dir_name):
print('')
print('All folders in {} will be processed.'.format(dir_name))
if prm.get_is_windows():
prm.set_date(dir_name.rsplit('\\', 2)[-2])
if prm.get_is_ubuntu():
prm.set_date(dir_name.rsplit('/', 2)[-2])
prm.set_filepath(dir_name)
file_utility.set_continuous_data_path(prm)
dead_channels.get_dead_channel_ids(prm) # read dead_channels.txt
file_utility.create_folder_structure(prm)
if prm.get_is_tetrode_by_tetrode() is True:
print('------------------------------------------')
print('I am making one mda file for each tetrode.')
print('------------------------------------------')
make_sorting_database.create_sorting_folder_structure_separate_tetrodes(prm)
convert_open_ephys_to_mda.convert_continuous_to_mda(prm)
print('All 4 tetrodes were converted to separate mda files.')
print('*****************************************************')
if prm.get_is_all_tetrodes_together() is True:
print('-------------------------------------------------------------------------')
print('I am converting all channels into one mda file. This will take some time.')
print('-------------------------------------------------------------------------')
make_sorting_database.create_sorting_folder_structure(prm)
convert_open_ephys_to_mda.convert_all_tetrodes_to_mda(prm)
print('The big mda file is created, it is in Electrophysiology/Spike_sorting/all_tetrodes/data')
print('***************************************************************************************')
if prm.is_vr is True:
vr_process_movement.save_or_open_movement_arrays(prm)
def convert_continuous_to_mda(prm):
file_utility.create_folder_structure(prm)
# make_sorting_database.create_sorting_folder_structure(prm)
number_of_tetrodes = prm.get_num_tetrodes()
folder_path = prm.get_filepath()
spike_data_path = prm.get_spike_path() + '\\'
continuous_file_name = prm.get_continuous_file_name()
if os.path.isfile(spike_data_path + 't1_' + prm.get_date() +
'\\raw.mda') is False:
file_utility.create_ephys_folder_structure(prm)
for tetrode in range(number_of_tetrodes):
channel_data_all = []
for channel in range(4):
file_path = folder_path + continuous_file_name + str(
tetrode * 4 + channel + 1) + '.continuous'
channel_data = open_ephys_IO.get_data_continuous(prm, file_path)
channel_data_all.append(channel_data)
recording_length = len(channel_data_all[0])
channels_tetrode = np.zeros((4, recording_length))
for ch in range(4):
channels_tetrode[ch, :] = channel_data_all[ch]
mdaio.writemda16i(
channels_tetrode, spike_data_path + 't' + str(tetrode + 1) + '_' +
prm.get_date() + '_continuous\\data\\raw.mda')
def convert_spk_to_mda(prm):
file_utility.create_folder_structure(prm)
folder_path = prm.get_filepath()
spike_data_path = prm.get_spike_path() + '\\'
number_of_tetrodes = prm.get_num_tetrodes()
samples_per_spike = prm.get_waveform_size()
if os.path.isfile(spike_data_path + 't1_' + prm.get_date() +
'\\raw.nt1.mda') is False:
file_utility.create_ephys_folder_structure(prm)
for tetrode in range(number_of_tetrodes):
file_path = folder_path + 'TT' + str(tetrode) + '.spikes'
waveforms, timestamps = open_ephys_IO.get_data_spike(
folder_path, file_path, 'TT' + str(tetrode + 1))
np.save(
spike_data_path + 't' + str(tetrode + 1) + '_' +
prm.get_date() + '\\TT' + str(tetrode + 1) + '_timestamps',
timestamps
) # todo: this is shifted by 10 seconds relative to light and location!
padded_array = get_padded_array(waveforms, samples_per_spike)
mdaio.writemda16i(
padded_array, spike_data_path + 't' + str(tetrode + 1) + '_' +
prm.get_date() + '\\raw.nt' + str(tetrode + 1) + '.mda')
peak_indices = get_peak_indices(waveforms, samples_per_spike)
mdaio.writemda32i(
peak_indices, spike_data_path + 't' + str(tetrode + 1) + '_' +
prm.get_date() + '\\event_times.nt' + str(tetrode + 1) +
'.mda')
mdaio.writemda32(
timestamps, spike_data_path + 't' + str(tetrode + 1) + '_' +
prm.get_date() + '\\timestamps.nt' + str(tetrode + 1) + '.mda')
def save_or_open_movement_arrays(prm):
file_utility.create_folder_structure(prm)
for file in os.listdir(prm.get_filepath()):
os.chdir(prm.get_filepath())
if file.endswith(".npy") and os.path.getsize(file) == 0:
print('---FILE ERROR: The size of ' + file +
' is 0, something is wrong.---')
location, speed, velocity = cached_calculate_movement(prm)
moves_indices, stationary_indices = cached_stationary_movement(prm, speed)
os.chdir('..')
return
def cached_stationary_movement(prm, speed):
file_utility.create_folder_structure(prm)
data_path = prm.get_behaviour_data_path()
if os.path.isfile(data_path +
"\\stationary.npy") is False or os.path.isfile(
data_path + "\\moves.npy") is False:
stationary, moves = moves_or_stationary(prm, speed)
np.save(data_path + "\\stationary", stationary)
np.save(data_path + "\\moves", moves)
else:
stationary = np.load(data_path + "\\stationary.npy")
moves = np.load(data_path + "\\moves.npy")
return moves, stationary
def convert_all_tetrodes_to_mda(prm):
file_utility.create_folder_structure(prm)
make_sorting_database.organize_files_for_ms(prm)
number_of_tetrodes = prm.get_num_tetrodes()
folder_path = prm.get_filepath()
spike_data_path = prm.get_spike_path() + '\\'
path = spike_data_path + 'all_tetrodes\\data\\raw.mda'
file_path = folder_path + '100_CH' + str(1) + '.continuous'
first_ch = open_ephys_IO.get_data_continuous(prm, file_path)
recording_length = len(first_ch)
channels_all = np.zeros((number_of_tetrodes * 4, recording_length))
channels_all[0, :] = first_ch
for channel in range(15):
file_path = folder_path + '100_CH' + str(channel + 2) + '.continuous'
channel_data = open_ephys_IO.get_data_continuous(prm, file_path)
channels_all[channel + 1, :] = channel_data
mdaio.writemda16i(channels_all, path)