# Copyright 2017. Allen Institute. All rights reserved
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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()
