setup = 'onebin'
ANALYSIS_DIR, analysis_num_str, R_tot_onebin, T_D_onebin, T, R_onebin, R_onebin_CI_lo, R_onebin_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
neuron_index,
setup,
CODE_DIR,
regularization_method='shuffling',
use_settings_path=use_settings_path)
R_tot_onebin, T_D_index_onebin, max_valid_index_onebin = plots.get_R_tot(
T, R_onebin, R_onebin_CI_lo)
R_tot_onebin_CA1 += [R_tot_onebin / R_tot_bbc]
R_tot_shuffling_CA1_median = np.median(R_tot_shuffling_CA1)
R_tot_shuffling_CA1_median_loCI, R_tot_shuffling_CA1_median_hiCI = plots.get_CI_median(
R_tot_shuffling_CA1)
R_tot_fivebins_CA1_median = np.median(R_tot_fivebins_CA1)
R_tot_fivebins_CA1_median_loCI, R_tot_fivebins_CA1_median_hiCI = plots.get_CI_median(
R_tot_fivebins_CA1)
R_tot_onebin_CA1_median = np.median(R_tot_onebin_CA1)
R_tot_onebin_CA1_median_loCI, R_tot_onebin_CA1_median_hiCI = plots.get_CI_median(
R_tot_onebin_CA1)
R_tot_glm_CA1_median = np.median(R_tot_glm_CA1)
R_tot_glm_CA1_median_loCI, R_tot_glm_CA1_median_hiCI = plots.get_CI_median(
R_tot_glm_CA1)
recorded_system = 'retina'
rec_length = '90min'
number_valid_neurons = 28
R_tot_shuffling_Retina = []
rate_CA1 = []
CV_CA1 = []
medianISI_CA1 = []
autocorrelation_time_CA1 = []
for neuron_index in range(number_valid_neurons):
tau_R, R_tot = get_tau_R_and_R_tot(T_0, setup, regularization_method, recorded_system, rec_length, neuron_index, CODE_DIR, use_settings_path)
stats = get_stats(bin_size_ms,T_0_ms, neuron_index, recorded_system, CODE_DIR)
R_tot_CA1 += [R_tot]
tau_R_CA1 += [tau_R*1000]
rate_CA1 += [stats['rate']]
medianISI_CA1 += [stats['medianISI']]
CV_CA1 += [stats['CV']]
autocorrelation_time_CA1 += [stats['autocorrelation_time']]
R_tot_CA1_median = np.median(R_tot_CA1)
R_tot_CA1_median_loCI, R_tot_CA1_median_hiCI = plots.get_CI_median(R_tot_CA1)
tau_R_CA1_median = np.median(tau_R_CA1)
tau_R_CA1_median_loCI, tau_R_CA1_median_hiCI = plots.get_CI_median(tau_R_CA1)
rate_CA1_median = np.median(rate_CA1)
rate_CA1_median_loCI, rate_CA1_median_hiCI = plots.get_CI_median(rate_CA1)
medianISI_CA1_median = np.median(medianISI_CA1)
medianISI_CA1_median_loCI, medianISI_CA1_median_hiCI = plots.get_CI_median(medianISI_CA1)
CV_CA1_median = np.median(CV_CA1)
CV_CA1_median_loCI, CV_CA1_median_hiCI = plots.get_CI_median(CV_CA1)
autocorrelation_time_CA1_median = np.median(autocorrelation_time_CA1)
autocorrelation_time_CA1_median_loCI, autocorrelation_time_CA1_median_hiCI = plots.get_CI_median(autocorrelation_time_CA1)
recorded_system = 'retina'
number_valid_neurons = 111
R_tot_retina = []
tau_R_retina = []
T_0 = T_0_ms / 1000.
"""Load data"""
recorded_system = 'CA1'
number_valid_neurons = 28
R_tot_CA1 = []
tau_R_CA1 = []
for neuron_index in range(number_valid_neurons):
tau_R, R_tot = get_tau_R_and_R_tot(T_0, setup, regularization_method,
recorded_system, rec_length,
neuron_index, CODE_DIR,
use_settings_path)
R_tot_CA1 += [R_tot]
tau_R_CA1 += [tau_R * 1000]
R_tot_CA1_median = np.median(R_tot_CA1)
R_tot_CA1_median_loCI, R_tot_CA1_median_hiCI = plots.get_CI_median(R_tot_CA1)
tau_R_CA1_median = np.median(tau_R_CA1)
tau_R_CA1_median_loCI, tau_R_CA1_median_hiCI = plots.get_CI_median(tau_R_CA1)
recorded_system = 'retina'
number_valid_neurons = 111
R_tot_retina = []
tau_R_retina = []
for neuron_index in range(number_valid_neurons):
tau_R, R_tot = get_tau_R_and_R_tot(T_0, setup, regularization_method,
recorded_system, rec_length,
neuron_index, CODE_DIR,
use_settings_path)
R_tot_retina += [R_tot]
tau_R_retina += [tau_R * 1000]
recorded_system = 'CA1'
number_valid_neurons = 28
tau_R_shuffling_CA1 = []
tau_R_fivebins_CA1 = []
tau_R_onebin_CA1 = []
for neuron_index in range(number_valid_neurons):
tau_R_bbc, tau_R_shuffling, tau_R_fivebins, tau_R_onebin = get_tau_R_for_different_estimators(
T_0, recorded_system, rec_length, neuron_index, CODE_DIR,
use_settings_path)
tau_R_shuffling_CA1 += [tau_R_shuffling / tau_R_bbc]
tau_R_fivebins_CA1 += [tau_R_fivebins / tau_R_bbc]
tau_R_onebin_CA1 += [tau_R_onebin / tau_R_bbc]
tau_R_shuffling_CA1_median = np.median(tau_R_shuffling_CA1)
tau_R_shuffling_CA1_median_loCI, tau_R_shuffling_CA1_median_hiCI = plots.get_CI_median(
tau_R_shuffling_CA1)
tau_R_fivebins_CA1_median = np.median(tau_R_fivebins_CA1)
tau_R_fivebins_CA1_median_loCI, tau_R_fivebins_CA1_median_hiCI = plots.get_CI_median(
tau_R_fivebins_CA1)
tau_R_onebin_CA1_median = np.median(tau_R_onebin_CA1)
tau_R_onebin_CA1_median_loCI, tau_R_onebin_CA1_median_hiCI = plots.get_CI_median(
tau_R_onebin_CA1)
recorded_system = 'retina'
number_valid_neurons = 111
tau_R_shuffling_retina = []
tau_R_fivebins_retina = []
tau_R_onebin_retina = []
for neuron_index in range(number_valid_neurons):
tau_R_bbc, tau_R_shuffling, tau_R_fivebins, tau_R_onebin = get_tau_R_for_different_estimators(
def median_relative_mean_R_tot_and_T_avg(recorded_system, setup, N_neurons, rec_lengths, rec_lengths_Nsamples, CODE_DIR):
if recorded_system == 'CA1':
DATA_DIR = '{}/data/CA1/'.format(CODE_DIR)
if recorded_system == 'retina':
DATA_DIR = '{}/data/retina/'.format(CODE_DIR)
if recorded_system == 'culture':
DATA_DIR = '{}/data/culture/'.format(CODE_DIR)
validNeurons = np.load(
'{}validNeurons.npy'.format(DATA_DIR)).astype(int)
R_tot_relative_mean = {}
T_avg_relative_mean = {}
np.random.seed(41)
neuron_selection = np.random.choice(len(validNeurons), N_neurons, replace=False)
for rec_length in rec_lengths:
# arrays containing R_tot and mean T_avg for different neurons
R_tot_mean_arr = []
T_avg_mean_arr = []
N_samples = rec_lengths_Nsamples[rec_length]
for j in range(N_neurons):
neuron_index = neuron_selection[j]
R_tot_arr = []
T_avg_arr = []
for sample_index in range(N_samples):
# Get run index
run_index = j * N_samples + sample_index
"""Load data five bins"""
if not rec_length == '90min':
setup_subsampled = '{}_subsampled'.format(setup)
else:
run_index = neuron_index
setup_subsampled = setup
if setup == 'full_bbc':
analysis_results = plots.load_analysis_results(
recorded_system, rec_length, run_index, setup_subsampled, CODE_DIR, regularization_method='bbc', use_settings_path=use_settings_path)
else:
analysis_results = plots.load_analysis_results(
recorded_system, rec_length, run_index, setup_subsampled, CODE_DIR, regularization_method='shuffling', use_settings_path=use_settings_path)
if not analysis_results == None:
ANALYSIS_DIR, analysis_num_str, R_tot, T_D, T, R, R_CI_lo, R_CI_hi = analysis_results
if not len(R) == 0:
R_tot_analysis_results = plots.get_R_tot(T, R, R_CI_lo)
if not R_tot_analysis_results == None:
R_tot, T_D_index, max_valid_index = R_tot_analysis_results
# R_running_avg = plots.get_running_avg(R)
dR = plots.get_dR(T,R,R_tot)
T_avg = plots.get_T_avg(T, dR, T_0)
T_avg_arr += [T_avg]
R_tot_arr += [R_tot]
else:
print('CI_fail', recorded_system, setup, rec_length, run_index, neuron_index, sample_index)
else:
print('no valid embeddings', recorded_system, rec_length, setup, analysis_num_str)
else:
print('analysis_fail', recorded_system, rec_length, setup, run_index, neuron_index, sample_index)
R_tot_mean_arr += [np.mean(R_tot_arr)]
T_avg_mean_arr += [np.mean(T_avg_arr)]
R_tot_relative_mean[rec_length] = np.array(R_tot_mean_arr)
T_avg_relative_mean[rec_length] = np.array(T_avg_mean_arr)
median_R_tot_relative_mean = []
median_CI_R_tot_relative_mean = []
median_T_avg_relative_mean = []
median_CI_T_avg_relative_mean = []
for rec_length in rec_lengths:
R_tot_relative_mean_arr = R_tot_relative_mean[rec_length] / R_tot_relative_mean['90min']*100
T_avg_relative_mean_arr = T_avg_relative_mean[rec_length] / T_avg_relative_mean['90min']*100
# If no valid embeddings were found for BBC for all samples, the mean is nan so the neuron is not considered in the median operation
R_tot_relative_mean_arr = R_tot_relative_mean_arr[~np.isnan(R_tot_relative_mean_arr)]
T_avg_relative_mean_arr = T_avg_relative_mean_arr[~np.isnan(T_avg_relative_mean_arr)]
# Computing the median and 95% CIs over the 10 neurons
median_R_tot_relative_mean += [np.median(R_tot_relative_mean_arr)]
median_CI_R_tot_relative_mean += [plots.get_CI_median(R_tot_relative_mean_arr)]
median_T_avg_relative_mean += [np.median(T_avg_relative_mean_arr)]
median_CI_T_avg_relative_mean += [plots.get_CI_median(T_avg_relative_mean_arr)]
return np.array(median_R_tot_relative_mean), np.array(median_CI_R_tot_relative_mean), np.array(median_T_avg_relative_mean), np.array(median_CI_T_avg_relative_mean)
