kilosort_folder_denoised = join(denoised_folder, 'Kilosort')
clean.cleanup_kilosorted_data(kilosort_folder_denoised,
number_of_channels_in_binary_file=const.
NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE,
binary_data_filename=binary_data_filename,
prb_file=const.prb_file,
type_of_binary=const.BINARY_FILE_ENCODING,
order_of_binary='F',
sampling_frequency=20000,
num_of_shanks_for_vis=5)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# CREATE TEMPLATE INFO ----------------------------------------------------
spp.generate_probe_positions_of_templates(kilosort_folder_denoised)
spp.view_grouped_templates_positions(kilosort_folder_denoised,
const.BRAIN_REGIONS,
const.PROBE_DIMENSIONS,
const.POSITION_MULT)
#template_info = preproc_kilo.generate_template_info_after_cleaning(kilosort_folder_denoised, sampling_freq)
template_info = np.load(join(kilosort_folder_denoised, 'template_info.df'))
templates_in_cortex = template_info[
template_info['position Y'] *
const.POSITION_MULT > const.BRAIN_REGIONS['CA1']]
templates_in_cortex.index = np.arange(len(templates_in_cortex))
templates_in_cortex['number of spikes'].sum()
np.argwhere(np.in1d(spike_templates_v, templates_clean_index_v)))
spike_templates_clean_v = spike_templates_v[spikes_clean_index_v]
template_markings_a = np.load(join(base_folder_a, 'template_marking.npy'))
spike_templates_a = np.load(join(base_folder_a, 'spike_templates.npy'))
templates_clean_index_a = np.argwhere(template_markings_a)
spikes_clean_index_a = np.squeeze(
np.argwhere(np.in1d(spike_templates_a, templates_clean_index_a)))
spike_templates_clean_a = spike_templates_a[spikes_clean_index_a]
weighted_average_postions_v, spike_distance_on_probe_v = \
pos.generate_probe_positions_of_spikes(base_folder_v, binary_v, number_of_channels_in_binary_file,
spikes_clean_index_v)
weighted_template_positions_v = pos.generate_probe_positions_of_templates(
base_folder_v, threshold=0.1)
weighted_average_postions_a, spike_distance_on_probe_a = \
pos.generate_probe_positions_of_spikes(base_folder_a, binary_a, number_of_channels_in_binary_file,
spikes_clean_index_a)
weighted_template_positions_a = pos.generate_probe_positions_of_templates(
base_folder_a, threshold=0.1)
position_mult = 2.25
probe_dimensions = [100, 8100]
brain_regions_v = {
'AuD': 5208,
'Au1': 4748,
'AuV': 2890,
number_of_channels_in_binary_file=const.
NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE,
binary_data_filename=binary_data_filename,
prb_file=const.prb_file,
type_of_binary=const.BINARY_FILE_ENCODING,
order_of_binary='F',
sampling_frequency=20000,
num_of_shanks_for_vis=5)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# STEP 5: CREATE TEMPLATE INFO OF ALL THE CLEANED TEMPLATES
# a) Create the positions of the templates on the probe (and have a look)
spp.generate_probe_positions_of_templates(kilosort_folder_denoised)
spp.view_grouped_templates_positions(kilosort_folder_denoised,
const.BRAIN_REGIONS,
const.PROBE_DIMENSIONS,
const.POSITION_MULT)
# b) Create the template_info.df dataframe (or load it if you already have it)
# template_info = preproc_kilo.generate_template_info_after_cleaning(kilosort_folder_denoised, sampling_freq)
template_info = np.load(join(kilosort_folder_denoised, 'template_info.df'),
allow_pickle=True)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# STEP 6: PICK THE CORTICAL SPIKES AND T-SNE THEM
print('Single: {}'.format(len(np.argwhere(template_marking == 1))))
print('Contaminated: {}'.format(len(np.argwhere(template_marking == 2))))
print('Putative: {}'.format(len(np.argwhere(template_marking == 3))))
print('Multi: {}'.format(len(np.argwhere(template_marking == 4))))
print('Non Multi: {}'.format(len(np.argwhere(template_marking == 1)) + len(np.argwhere(template_marking == 2))
+len(np.argwhere(template_marking == 3))))
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = "STEP 2: CREATE TEMPLATE INFO OF ALL THE CLEANED TEMPLATES"
# a) Create the positions of the templates on the probe (and have a look)
_ = spp.generate_probe_positions_of_templates(kilosort_folder)
bad_channel_positions = spp.get_y_spread_regions_of_bad_channel_groups(kilosort_folder, const.bad_channels)
# b) Create the template_info.df dataframe (or load it if you already have it)
# template_info = preproc_kilo.generate_template_info_after_cleaning(kilosort_folder, sampling_freq)
template_info = np.load(join(kilosort_folder, 'template_info.df'), allow_pickle=True)
# c) Make the spike info from the initial, cleaned, kilosort results
# spike_info = preproc_kilo.generate_spike_info_after_cleaning(kilosort_folder)
spike_info = np.load(join(kilosort_folder, 'spike_info_after_cleaning.df'), allow_pickle=True)
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
type_of_binary = np.uint16
# CLEANING KILOSORT RESULTS -------------------------
cdc.generate_average_over_spikes_per_template_multiprocess(base_folder=kilosort_folder,
binary_data_filename=join(data_folder, amplifier_file),
number_of_channels_in_binary_file=number_of_channels_in_binary_file,
cut_time_points_around_spike=80)
ckt.cleanup_kilosorted_data(kilosort_folder, number_of_channels_in_binary_file=number_of_channels_in_binary_file,
binary_data_filename=join(data_folder, amplifier_file), prb_file=prb_file,
type_of_binary=type_of_binary, order_of_binary='F', sampling_frequency=30000)
# ---------------------------------------------------
# Generate positions on probe
weighted_template_positions = pos.generate_probe_positions_of_templates(kilosort_folder, threshold=0.1)
np.save(join(kilosort_folder, 'weighted_template_positions.npy'), weighted_template_positions)
# ---------------------------
# REDOING TSNE ON CLEAN KILOSORT RESULTS ------------
spike_clusters = np.load(join(kilosort_folder, 'spike_clusters.npy'))
template_marking = np.load(join(kilosort_folder, 'template_marking.npy'))
spikes_used = np.array([spike_index for spike_index in np.arange(len(spike_clusters)) if template_marking[spike_clusters[spike_index]] > 0])
np.save(join(tsne_folder, 'indices_of_spikes_used.npy'), spikes_used)
template_features_sparse_clean = \
preproc.calculate_template_features_matrix_for_tsne(kilosort_folder, save_to_folder=tsne_folder,
spikes_used_with_original_indexing=spikes_used)
template_features_sparse_clean = np.load(join(tsne_folder, 'data_to_tsne_(272886, 140).npy'))