Source code for bmtk.analyzer.firing_rates

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


[docs]def convert_rates(rates_file): rates_df = pd.read_csv(rates_file, sep=' ', names=['gid', 'time', 'rate']) rates_sorted_df = rates_df.sort_values(['gid', 'time']) rates_dict = {} for gid, rates in rates_sorted_df.groupby('gid'): start = rates['time'].iloc[0] end = rates['time'].iloc[-1] dt = float(end - start)/len(rates) rates_dict[gid] = {'start': start, 'end': end, 'dt': dt, 'rates': np.array(rates['rate'])} return rates_dict
[docs]def firing_rates_equal(rates_file1, rates_file2, err=0.0001): trial_1 = convert_rates(rates_file1) trial_2 = convert_rates(rates_file2) if set(trial_1.keys()) != set(trial_2.keys()): return False for gid, rates_data1 in trial_1.items(): rates_data2 = trial_2[gid] if rates_data1['dt'] != rates_data2['dt'] or rates_data1['start'] != rates_data2['start'] or rates_data1['end'] != rates_data2['end']: return False for r1, r2 in zip(rates_data1['rates'], rates_data2['rates']): if abs(r1 - r2) > err: return False return True
[docs]def plot_rates_popnet(cell_models_file, rates_file, model_keys=None, save_as=None, show_plot=True): """Initial method for plotting popnet output :param cell_models_file: :param rates_file: :param model_keys: :param save_as: :param show_plot: :return: """ pops_df = pd.read_csv(cell_models_file, sep=' ') lookup_col = model_keys if model_keys is not None else 'node_type_id' pop_keys = {str(r['node_type_id']): r[lookup_col] for _, r in pops_df.iterrows()} # organize the rates file by population # rates = {pop_name: ([], []) for pop_name in pop_keys.keys()} rates_df = pd.read_csv(rates_file, sep=' ', names=['id', 'times', 'rates']) for grp_key, grp_df in rates_df.groupby('id'): grp_label = pop_keys[str(grp_key)] plt.plot(grp_df['times'], grp_df['rates'], label=grp_label) plt.legend(fontsize='x-small') plt.xlabel('time (s)') plt.ylabel('firing rates (Hz)') if save_as is not None: plt.savefig(save_as) if show_plot: plt.show()