def get_tau_R_and_R_tot(T_0, setup, regularization_method, recorded_system, rec_length, neuron_index, CODE_DIR, use_settings_path):
ANALYSIS_DIR, analysis_num_str, R_tot, T_D, T, R, R_CI_lo, R_CI_hi = plots.load_analysis_results(
recorded_system, rec_length, neuron_index, setup, CODE_DIR, regularization_method = regularization_method, use_settings_path = use_settings_path)
R_tot = plots.get_R_tot(T, R, R_CI_lo)[0]
dR = plots.get_dR(T,R,R_tot)
tau_R = plots.get_T_avg(T, dR, T_0)
return tau_R, R_tot
def main():
# Load glm settings
with open('{}/settings/{}_glm.yaml'.format(CODE_DIR,recorded_system), 'r') as glm_settings_file:
glm_settings = yaml.load(glm_settings_file, Loader=yaml.BaseLoader)
# Load the 900 minute simulated recording
DATA_DIR = '{}/{}'.format(data_path,recorded_system)
spiketimes = np.load('{}/spiketimes_900min.npy'.format(DATA_DIR))
# Preprocess spiketimes and compute binary counts for current spiking
spiketimes, counts = glm.preprocess_spiketimes(spiketimes, glm_settings)
# Load embedding-optimized estimates
ANALYSIS_DIR, analysis_num_str, R_tot, T_D, T, R, R_CI_lo, R_CI_hi = plots.load_analysis_results(recorded_system, rec_length, sample_index, setup, CODE_DIR, regularization_method = regularization_method, use_settings_path = use_settings_path)
R_tot, T_D_index, max_valid_index = plots.get_R_tot(T, R, R_CI_lo)
# Load embedding parameters from embedding optimization
embedding_parameters, analysis_num_str = glm.load_embedding_parameters(
rec_length, sample_index, ANALYSIS_DIR, regularization_method = regularization_method)
# Compute glm for optimized embedding parameters for temporal depth, only if sample_index = 0 compute for all T
if sample_index > 0:
embedding_parameters_benchmark = embedding_parameters[:,T_D_index:max_valid_index]
else:
embedding_parameters_benchmark = embedding_parameters
# Compute history dependence with GLM for the same embeddings as found with bbc/shuffling
glm_benchmark = glm.compute_benchmark(embedding_parameters_benchmark, spiketimes, counts, glm_settings)
# Save results to glm_benchmarks.csv
glm.save_glm_benchmark_to_CSV(glm_benchmark, embedding_parameters_benchmark,
ANALYSIS_DIR, analysis_num_str, regularization_method=regularization_method)
return EXIT_SUCCESS
import hde_plotutils as plots
"""Load data"""
recorded_system = 'CA1'
rec_length = '90min'
number_valid_neurons = 28
R_tot_shuffling_CA1 = []
R_tot_fivebins_CA1 = []
R_tot_onebin_CA1 = []
R_tot_glm_CA1 = []
for neuron_index in range(number_valid_neurons):
setup = 'full_bbc'
ANALYSIS_DIR, analysis_num_str, R_tot_bbc, T_D_bbc, T, R_bbc, R_bbc_CI_lo, R_bbc_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
neuron_index,
setup,
CODE_DIR,
regularization_method='bbc',
use_settings_path=use_settings_path)
R_tot_bbc, T_D_index_bbc, max_valid_index_bbc = plots.get_R_tot(
T, R_bbc, R_bbc_CI_lo)
# Load GLM R_tot
R_tot_glm = plots.load_analysis_results_glm(ANALYSIS_DIR, analysis_num_str)
R_tot_glm_CA1 += [R_tot_glm / R_tot_bbc]
setup = 'full_shuffling'
ANALYSIS_DIR, analysis_num_str, R_tot_shuffling, T_D_shuffling, T, R_shuffling, R_shuffling_CI_lo, R_shuffling_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
neuron_index,
DATA_DIR = '{}/data/{}'.format(CODE_DIR, recorded_system)
"""Compute measures"""
tau_list = [70, 85, 100, 120, 150, 198, 300, 500]
m_list = []
R_tot_list = []
tau_R_list = []
for tau in tau_list:
if tau == 198:
sample_index = 0
else:
sample_index = tau
m = np.exp(-bin_size * 1000 / tau)
ANALYSIS_DIR, analysis_num_str, R_tot, T_D, T_R, R, R_CI_lo, R_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
sample_index,
setup,
CODE_DIR,
regularization_method='shuffling',
use_settings_path=use_settings_path)
R_tot, T_D_index, max_valid_index = plots.get_R_tot(T_R, R, R_CI_lo)
dR_arr = plots.get_dR(T_R, R, R_tot)
# transform T to ms
T_R_ms = T_R * 1000
tau_R = plots.get_T_avg(T_R_ms, dR_arr, T_0_ms)
m_list += [m]
R_tot_list += [R_tot]
tau_R_list += [tau_R]
"""Plotting"""
rc('text', usetex=True)
matplotlib.rcParams['font.size'] = '15.0'
matplotlib.rcParams['xtick.labelsize'] = '15'
setup_index = i % 2
rec_length_index = int(i / 2)
rec_length = rec_lengths[rec_length_index]
if setup_index == 0:
bbc_setup = 'full_bbc'
shuffling_setup = 'full_shuffling'
else:
bbc_setup = 'full_bbc_withCV'
shuffling_setup = 'full_shuffling_withCV'
"""Load data"""
# Load settings from yaml file
ANALYSIS_DIR, analysis_num_str, R_tot_bbc, T_D_bbc, T_bbc, R_bbc, R_bbc_CI_lo, R_bbc_CI_hi = plots.load_analysis_results(
recorded_system,
rec_length,
sample_index,
bbc_setup,
CODE_DIR,
regularization_method='bbc',
use_settings_path=use_settings_path)
R_tot_bbc, T_D_index_bbc, max_valid_index_bbc = plots.get_R_tot(
T_bbc, R_bbc, R_bbc_CI_lo)
dR_bbc = plots.get_dR(T_bbc, R_bbc, R_tot_bbc)
tau_R_bbc = plots.get_T_avg(T_bbc, dR_bbc, T_0)
glm_bbc_csv_file_name = '{}/ANALYSIS{}/glm_benchmark_bbc.csv'.format(
ANALYSIS_DIR, analysis_num_str)
glm_bbc_pd = pd.read_csv(glm_bbc_csv_file_name)
R_glm_bbc = np.array(glm_bbc_pd['R_GLM'])
T_glm_bbc = np.array(glm_bbc_pd['T'])
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)
