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How to use auto_kernel method in autotest

Best Python code snippet using autotest_python

compute_rate_time_series.py

Source:compute_rate_time_series.py

1import json2import neo3import numpy as np4import os5import quantities as pq6from multiarea_model.analysis_helpers import pop_rate_time_series7from elephant.statistics import instantaneous_rate8from multiarea_model import MultiAreaModel9import sys10"""11Compute time series of population-averaged spike rates for a given12area from raw spike files of a given simulation.13Implements three different methods:14- binned spike histograms on all neurons ('full')15- binned spike histograms on a subsample of 140 neurons ('subsample')16- spike histograms convolved with a Gaussian kernel of optimal width17  after Shimazaki et al. (2010)18"""19assert(len(sys.argv) == 5)20data_path = sys.argv[1]21label = sys.argv[2]22area = sys.argv[3]23method = sys.argv[4]24assert(method in ['subsample', 'full', 'auto_kernel'])25# subsample : subsample spike data to 140 neurons to match the Chu 2014 data26# full : use spikes of all neurons and compute spike histogram with bin size 1 ms27# auto_kernel : use spikes of all neurons and convolve with Gaussian28#               kernel of optimal width using the method of Shimazaki et al. (2012)29#               (see Method parts of the paper)30load_path = os.path.join(data_path,31                         label,32                         'recordings')33save_path = os.path.join(data_path,34                         label,35                         'Analysis',36                         'rate_time_series_{}'.format(method))37try:38    os.mkdir(save_path)39except FileExistsError:40    pass41with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:42    sim_params = json.load(f)43T = sim_params['T']44"""45Create MultiAreaModel instance to have access to data structures46"""47M = MultiAreaModel({})48time_series_list = []49N_list = []50for pop in M.structure[area]:51    fp = '-'.join((label,52                   'spikes',  # assumes that the default label for spike files was used53                   area,54                   pop))55    fn = '{}/{}.npy'.format(load_path, fp)56    spike_data = np.load(fn)57    spike_data = spike_data[np.logical_and(spike_data[:, 1] > 500.,58                                           spike_data[:, 1] <= T)]59    if method == 'subsample':60        all_gid = np.unique(spike_data[:, 0])61        N = int(np.round(140 * M.N[area][pop] / M.N[area]['total']))62        i = 063        s = 064        gid_list = []65        while s < N:66            rate = spike_data[:, 1][spike_data[:, 0] == all_gid[i]].size / (1e-3 * (T - 500.))67            if rate > 0.56:68                gid_list.append(all_gid[i])69                s += 170            i += 171        spike_data = spike_data[np.isin(spike_data[:, 0], gid_list)]72        kernel = 'binned_subsample'73    if method == 'full':74        N = M.N[area][pop]  # Assumes that all neurons were recorded75        kernel = 'binned'76        77    if method in ['subsample', 'full']:78        time_series = pop_rate_time_series(spike_data, N, 500., T,79                                           resolution=1.)80    if method == 'auto_kernel':81        # To reduce the computational load, the time series is only computed until 10500. ms82        T = 10500.83        N = M.N[area][pop]  # Assumes that all neurons were recorded84        st = neo.SpikeTrain(spike_data[:, 1] * pq.ms, t_stop=T*pq.ms)85        time_series = instantaneous_rate(st, 1.*pq.ms, t_start=500.*pq.ms, t_stop=T*pq.ms)86        time_series = np.array(time_series)[:, 0] / N87        kernel = 'auto'88        89    time_series_list.append(time_series)90    N_list.append(N)91    92    fp = '_'.join(('rate_time_series',93                   method,94                   area,95                   pop))96    np.save('{}/{}.npy'.format(save_path, fp), time_series)97time_series_list = np.array(time_series_list)98area_time_series = np.average(time_series_list, axis=0, weights=N_list)99fp = '_'.join(('rate_time_series',100               method,101               area))102np.save('{}/{}.npy'.format(save_path, fp), area_time_series)103par = {'areas': M.area_list,104       'pops': 'complete',105       'kernel': kernel,106       'resolution': 1.,107       't_min': 500.,108       't_max': T}109fp = '_'.join(('rate_time_series',110               method,111               'Parameters.json'))112with open('{}/{}'.format(save_path, fp), 'w') as f:...

source.py

Source:source.py

...37    estimation_dif_rule_histogram(sampled_data, gm1d)38elif estimation_method == 'kernel':39    estimation_kernel(sampled_data, gm1d, kernel_h)40elif estimation_method == 'auto_kernel':41    estimation_auto_kernel(sampled_data, gm1d)42elif estimation_method == 'knn':...

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