def cut_data(start, end):
print('Cut {}'.format(str(start / number_of_points)))
raw_data = ns_funcs.load_binary_amplifier_data(
binary_data_filename,
number_of_channels=const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
data = copy(raw_data[:, int(start):int(end)])
del raw_data
return data
# Load data
event_dataframes = ns_funcs.load_events_dataframes(events_folder,
sync_funcs.event_types)
ev_video = event_dataframes['ev_video']
template_info = pd.read_pickle(join(kilosort_folder, 'template_info.df'))
spike_info = pd.read_pickle(
join(kilosort_folder, 'spike_info_after_cortex_sorting.df'))
spike_rates = np.load(spike_rates_per_video_frame_filename)
distances_rat_to_poke_all_frames = np.load(
join(patterned_vs_non_patterned_folder,
'distances_rat_to_poke_all_frames.npy'))
lfps = ns_funcs.load_binary_amplifier_data(
lfps_filename, const.NUMBER_OF_LFP_CHANNELS_IN_BINARY_FILE)
trial_pokes_timepoints = np.load(trial_pokes_timepoints_filename)
non_trial_pokes_timepoints = np.load(non_trial_pokes_timepoints_filename)
window_time = 8
window_timepoints = int(window_time * const.SAMPLING_FREQUENCY)
window_downsampled = int(window_timepoints / const.LFP_DOWNSAMPLE_FACTOR)
possible_events = {
'tp': trial_pokes_timepoints,
'ntp': non_trial_pokes_timepoints
}
lfp_probe_positions = np.empty(const.NUMBER_OF_LFP_CHANNELS_IN_BINARY_FILE)
lfp_probe_positions[np.arange(
# FOLDERS NAMES --------------------------------------------------
date = 5
kilosort_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Analysis', 'Kilosort')
data_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Data')
binary_data_filename = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Data',
'Amplifier_APs.bin')
tsne_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Analysis', 'Tsne')
barnes_hut_exe_dir = r'E:\Software\Develop\Source\Repos\spikesorting_tsne_bhpart\Barnes_Hut\win\x64\Release'
raw_data = ns_funcs.load_binary_amplifier_data(
binary_data_filename,
number_of_channels=const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
sampling_freq = 20000
denoised_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date], 'Analysis', 'Denoised')
denoised_data_filename = join(denoised_folder, 'Data',
'Amplifier_APs_Denoised.bin')
# -----------------------------------------------------------------
data_cor = raw_data[1050:, :]
data_hyp = raw_data[850:1050, :]
data_th = raw_data[370:850, :]
tsne_folder = join(analysis_folder, const.decimation_type_folder, 'TSNE')
tsne_exe_dir = r'E:\Software\Develop\Source\Repos\spikesorting_tsne_bhpart\Barnes_Hut\win\x64\Release'
sampling_freq = const.SAMPLING_FREQUENCY
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = Create the ned data set
group_channels = const.group_channels
raw_data = ns_funcs.load_binary_amplifier_data(
binary_data_filename, const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE,
const.BINARY_FILE_ENCODING)
number_of_chunks = 1000
chunk_size = raw_data.shape[1] / number_of_chunks
decimated_data = np.memmap(decimated_data_filename,
const.BINARY_FILE_ENCODING,
mode='w+',
shape=raw_data.shape,
order='F')
for c in np.arange(number_of_chunks):
start = int(c * chunk_size)
end = int(start + chunk_size)
data_chunk = raw_data[:, start:end]
data_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date_folder], 'Data')
denoised_data_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date_folder], 'Analysis',
'Denoised', 'Data')
spikes_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date_folder], 'Analysis', 'Denoised',
'Kilosort')
events_folder = join(data_folder, "events")
time_points_buffer = 5200
ap_data = ns_funcs.load_binary_amplifier_data(
join(data_folder, 'Amplifier_APs.bin'),
number_of_channels=const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
ap_data_panes = np.swapaxes(
np.reshape(ap_data,
(ap_data.shape[0], int(ap_data.shape[1] / time_points_buffer),
time_points_buffer)), 0, 1)
ap_den_data = ns_funcs.load_binary_amplifier_data(
join(denoised_data_folder, 'Amplifier_APs_Denoised.bin'),
number_of_channels=const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
ap_den_data_panes = np.swapaxes(
np.reshape(
ap_den_data,
(ap_den_data.shape[0], int(
ap_den_data.shape[1] / time_points_buffer), time_points_buffer)),
const.date_folders[date_folder], 'Data')
events_folder = join(data_folder, "events")
analysis_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date_folder], 'Analysis')
kilosort_folder = join(analysis_folder, 'Kilosort')
results_folder = join(analysis_folder, 'Results')
events_definitions_folder = join(results_folder, 'EventsDefinitions')
lfp_average_data_folder = join(results_folder, 'Lfp', 'Averages')
ap_data_filename = join(data_folder, 'Amplifier_APs.bin')
# Load data
ap_data = ns_funcs.load_binary_amplifier_data(
ap_data_filename, const_comm.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
trials = {
's':
np.load(
join(events_definitions_folder,
'events_pokes_of_successful_trial.npy')),
'tb':
np.load(join(events_definitions_folder, 'events_touch_ball.npy'))
}
if date_folder != 6:
minimum_delay = 5
nst = np.load(
join(
events_definitions_folder,
analysis_folder = join(const.base_save_folder, const.rat_folder,
const.date_folders[date_folder], 'Analysis')
results_folder = join(analysis_folder, 'Results')
spike_lfp_folder = join(results_folder, 'SpikeLfpCorrelations',
'RandomSpikesAlongRecording')
spike_lfp_images_folder = join(spike_lfp_folder, 'SingleNeuronImages')
template_info = pd.read_pickle(join(kilosort_folder, 'template_info.df'))
spike_info = pd.read_pickle(
join(kilosort_folder, 'spike_info_after_cleaning.df'))
lfps_file = join(data_folder, 'Amplifier_LFPs_Downsampled_x4.bin')
lfps = ns_funcs.load_binary_amplifier_data(
(lfps_file),
number_of_channels=const.NUMBER_OF_LFP_CHANNELS_IN_BINARY_FILE)
imfs_file = join(analysis_folder, 'Lfp', 'EMD', 'imfs.bin')
imfs = emd.load_memmaped_imfs(imfs_file, const.NUMBER_OF_IMFS,
const.NUMBER_OF_LFP_CHANNELS_IN_BINARY_FILE)
num_of_spikes = 4000
half_time_window = 2000
# </editor-fold>
# -------------------------------------------------
# <editor-fold desc="CREATE TIMES OF RANDOM SPIKES FOR ALL HIGH FIRING NEURONS">
neurons_with_high_frs = template_info[
event_dataframes = ns_funcs.load_events_dataframes(events_folder,
sync_funcs.event_types)
file_to_save_to = join(kilosort_folder,
'firing_rate_with_video_frame_window.npy')
template_info = pd.read_pickle(join(kilosort_folder, 'template_info.df'))
spike_info = pd.read_pickle(
join(kilosort_folder, 'spike_info_after_cleaning.df'))
video_frame_spike_rates_filename = join(
kilosort_folder, 'firing_rate_with_video_frame_window.npy')
spike_rates = np.load(video_frame_spike_rates_filename)
raw_data_filename = join(data_folder, 'Amplifier_APs.bin')
raw_data = ns_funcs.load_binary_amplifier_data(
raw_data_filename,
number_of_channels=const_comm.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE)
# </editor-fold>
# -------------------------------------------------
# <editor-fold desc="GET TIMES AND FRAMES AROUND DIFFERENT TRIALS">
trials = {
's':
np.load(
join(events_definitions_folder,
'events_pokes_of_successful_trial.npy')),
'tb':
np.load(join(events_definitions_folder, 'events_touch_ball.npy'))
}
binary_a = r'Exp2and3_2017_03_28T18_48_25_Amp_S16_LP3p5KHz_mV.bin' number_of_channels_in_binary_file = 1440 ''' template_markings_v = np.load(join(base_folder_v, 'template_marking.npy')) spike_templates_v = np.load(join(base_folder_v, 'spike_templates.npy')) spike_times_v = np.load(join(base_folder_v, 'spike_times.npy')) templates_clean_index_v = np.argwhere(template_markings_v) spikes_clean_index_v = np.squeeze(np.argwhere(np.in1d(spike_templates_v, templates_clean_index_v))) spike_templates_clean_v = spike_templates_v[spikes_clean_index_v] spike_times_clean_v = spike_times_v[spikes_clean_index_v] ''' channel_map = np.squeeze(np.load(join(base_folder_a, 'channel_map.npy'))) template_markings_a = np.load(join(base_folder_a, 'template_marking.npy')) spike_templates_a = np.load(join(base_folder_a, 'spike_templates.npy')) spike_times_a = np.load(join(base_folder_a, 'spike_times.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] spike_times_clean_a = spike_times_a[spikes_clean_index_a] binary_file_a = join(base_folder_a, binary_a) raw_extracellular_data_a = nf.load_binary_amplifier_data(binary_file_a)
import numpy as np
import matplotlib.pyplot as plt
from os.path import join
import BrainDataAnalysis.neuroseeker_specific_functions as nsf
from matplotlib.widgets import Button
data_folder = r'D:\Data\George\Neuroseeker Chronic Rat 22_1\2017_05_22\16_49_50' # first day
data_file = r'2017_05_22T16_49_50_Amp_S16_HP500Hz_LP3p5KHz_mV.bin'
data_folder = r'D:\Data\George\Neuroseeker Chronic Rat 22_1\2017_06_06\13_34_05' # last day
data_file = r'2017_06_06T13_34_05_Amp_S16_HP500Hz_LP3p5KHz_mV.bin'
data_folder = r'D:\Data\George\Neuroseeker Chronic Rat 22_1\2017_06_02\13_20_21'
data_file = r'InBehaviour_2017-06-02T13_20_21_Amp_S16_mV.bin'
full_data = nsf.load_binary_amplifier_data(join(data_folder, data_file))
lfp_channels = np.arange(9, 1440, 20)
bad_channels = np.concatenate(
(np.arange(720, 760), np.arange(1200, 1240), np.arange(1320, 1440)))
channels_to_remove = np.concatenate(
(lfp_channels, bad_channels, nsf.references)).astype(np.int)
channels_to_remove = np.sort(channels_to_remove)
good_channels = np.arange(nsf.number_of_channels_in_binary_file)
good_channels = np.delete(good_channels, channels_to_remove).astype(np.int)
brain_regions = {
'Parietal_Cortex': 8000,
