transform_name='space_denoised_data')
# -------------------------------------------------------------------------
# CLEAN DENOISED SPIKESORT (RIGHT AFTER KILOSORT) -------------------------
# To create averages of templates use cmd (because the create_data_cubes doesn't work when called from a REPL):
# E:\Software\Develop\Source\Repos\spikesorting_tsne_guis\spikesorting_tsne_guis>python create_data_cubes.py original D:\Data\George\AK_33.1\2018_04_30-11_38\Analysis\Denoised\Kilosort D:\Data\George\AK_33.1\2018_04_30-11_38\Data\Amplifier_APs.bin 1368 50
# To clean:
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)
# Create once the data cube of the average template spike
c_cubes.generate_average_over_spikes_per_template_multiprocess(kilosort_folder,
binary_data_filename,
const.NUMBER_OF_CHANNELS_IN_BINARY_FILE,
cut_time_points_around_spike=100)
c_cubes.generate_average_over_spikes_per_template_multiprocess(spyking_circus_folder,
binary_data_filename,
const.NUMBER_OF_CHANNELS_IN_BINARY_FILE,
cut_time_points_around_spike=50)
# Run the GUI that helps clean the templates
clean.cleanup_kilosorted_data(spyking_circus_folder,
number_of_channels_in_binary_file=const.NUMBER_OF_CHANNELS_IN_BINARY_FILE,
binary_data_filename=binary_data_filename,
prb_file=const.prb_file,
type_of_binary=np.float16,
order_of_binary='F',
sampling_frequency=20000,
num_of_shanks_for_vis=5)
# ----------------------------------------------------------------
# T-SNE THE CLEANED SPIKES ---------------------------------------
# Find how many spikes are clean (demarcated as not noise)
templates_of_spikes = np.load(join(kilosort_folder, 'spike_templates.npy'))
number_of_raw_spikes = len(templates_of_spikes)
template_markings = preproc_kilo.get_template_marking(kilosort_folder)
clean_templates = np.argwhere(template_markings > 0)
number_of_clean_templates = len(clean_templates)
clean_spikes = np.argwhere(np.in1d(templates_of_spikes, clean_templates) > 0)
