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plot_figure3_ring_network_coherence_analysis_input_strength.py
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"""
Created on Mon Mar 05 13:41:23 2018
@author: DanielM
"""
from neuron import h, gui # gui necessary for some parameters to h namespace
import numpy as np
from pydentate import net_basket_cell_ring, neuron_tools, oscillations_analysis
from pydentate.inputs import inhom_poiss, homogeneous_poisson_process, sigmoid
import os
import argparse
import scipy.stats as stats
import platform
import matplotlib.pyplot as plt
from scipy.signal import windows, convolve
from scipy.ndimage import convolve
from scipy.spatial import distance_matrix
from numpy.fft import fft, fftshift, fftfreq
import sys
import tables
import networkx
from pydentate import spike_to_x
from pydentate import oscillations_analysis
import pdb
import pandas as pd
import seaborn as sns
from distutils.util import strtobool
dirname = os.path.dirname(__file__)
data_dir = os.path.join(dirname, 'output', 'figure_3_input_strength')
all_files = [os.path.join(data_dir, x) for x in os.listdir(data_dir)]
bin_size_ms = [0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]
d = {'seed': [],
'pp_weight': [],
'rec_weight': [],
'input_rate': [],
'n_pvbcs': [],
'n_input_syns': [],
'n_inputs': [],
'gap_resistance': [],
'gap_junctions': [],
'mean_frequency': [],
'theta_power': [], # 4-12 Hz
'gamma_power': [], # 20-100 Hz
'low_gamma_power': [], # 20-40
'high_gamma_power': [], # 60-100
'ripple_power': [], # 120-250
'total_coherence': [],
'small_delta_coherence': [],
'intermediate_delta_coherence': [],
'file': [],
'mean_rec_synapses': [],
'k_point_1_over_f': [],
'k_1_over_f': [],
'one_ms_delta_coherence': [],
'one_ms_delta_slope_coherence': [],
# 'shuffling_coherence': []
'area_over_line': [],
'area_over_line_normalized': [],
'input_strength': [],
'mean_pearson_r': []
}
for curr_file in all_files:
curr_data = tables.open_file(curr_file, mode='r')
fname = curr_file.split(os.path.sep)[-1]
d['seed'].append(curr_data.root.parameters.seed.read())
d['pp_weight'].append(curr_data.root.parameters.pp_bc_weight.read())
d['rec_weight'].append(curr_data.root.parameters.rec_weight.read())
d['input_rate'].append(curr_data.root.parameters.input_rate.read())
d['n_pvbcs'].append(curr_data.root.parameters.n_pvbcs.read())
d['n_input_syns'].append(curr_data.root.parameters.n_input_syns.read())
d['n_inputs'].append(curr_data.root.parameters.n_inputs.read())
d['gap_resistance'].append(curr_data.root.parameters.gap_resistance.read())
d['gap_junctions'].append(curr_data.root.parameters.gap_junction.read())
d['mean_frequency'].append(curr_data.root.analysis.frequencies.read().mean())
d['mean_rec_synapses'].append(curr_data.root.parameters.n_rec_synapses.read().mean())
d['input_strength'].append(curr_data.root.parameters.input_current.read())
"""Calculate Powers"""
freq = curr_data.root.analysis.fft.freq.read()
amp = curr_data.root.analysis.fft.amp.read()
theta_band = np.logical_and(12 > freq, freq > 4)
gamma_band = np.logical_and(100 > freq, freq > 20)
low_gamma_band = np.logical_and(40 > freq, freq > 20)
high_gamma_band = np.logical_and(100 > freq, freq > 60)
ripple_band = np.logical_and(250 > freq, freq > 120)
d['theta_power'].append(amp[theta_band].mean())
d['gamma_power'].append(amp[gamma_band].mean())
d['low_gamma_power'].append(amp[low_gamma_band].mean())
d['high_gamma_power'].append(amp[high_gamma_band].mean())
d['ripple_power'].append(amp[ripple_band].mean())
d['total_coherence'].append(np.nanmean(np.array(curr_data.root.analysis.coherence.mean_k.read())))
d['small_delta_coherence'].append(curr_data.root.analysis.coherence.mean_k.read()[0])
d['one_ms_delta_coherence'].append(curr_data.root.analysis.coherence.mean_k.read()[1])
d['one_ms_delta_slope_coherence'].append(curr_data.root.analysis.coherence.mean_k.read()[2] - curr_data.root.analysis.coherence.mean_k.read()[1])
d['intermediate_delta_coherence'].append(curr_data.root.analysis.coherence.mean_k.read()[5])
d['file'].append(curr_data)
d['k_point_1_over_f'].append(float(curr_data.root.analysis.coherence.k_point_1_over_f.read()))
d['k_1_over_f'].append(float(curr_data.root.analysis.coherence.k_1_over_f.read()))
d['area_over_line'].append(float(curr_data.root.analysis.coherence.area_over_line.read()))
d['area_over_line_normalized'].append(float(curr_data.root.analysis.coherence.area_over_line_normalized.read()))
d['mean_pearson_r'].append(float(curr_data.root.analysis.coherence.mean_pearson_r.read()))
# n_shuffles=10
# shuffle = oscillations_analysis.shuffling_coherence(curr_data.root.times.read(), curr_data.root.parameters.dt.read(), curr_data.root.parameters.duration.read(), n_shuffles=n_shuffles)
# d['shuffling_coherence'].append(np.abs(shuffle[0] - shuffle[1]).sum())
"""
curr_data.create_array('/analysis/coherence', 'delta_t_point_1', obj=delta_t_point_1)
curr_data.create_array('/analysis/coherence', 'k_point_1_over_f', obj=k_point_one_over_f.mean())
delta_t_1 = (1 / frequencies.mean()) * 1000
k_one_over_f = oscillations_analysis.pairwise_coherence(curr_binary, bin_size=int(delta_t_1 / curr_dt))
curr_data.create_array('/analysis/coherence', 'delta_t_1', obj=delta_t_1)
curr_data.create_array('/analysis/coherence', 'k_1_over_f', obj=k_one_over_f.mean())
"""
df = pd.DataFrame(d)
df['inhibition_on'] = df['rec_weight'] > 0
"""PLOTTING"""
colors = ['#C70019', '#0D6B9A', '#EE9A20', '#6389A5', '#EA521C', '#8A963F']
plt.rcParams['svg.fonttype'] = 'none'
plt.rcParams.update({'font.size': 22})
min_synapses_idx = df['input_strength'].argmin()
max_synapses_idx = df['input_strength'].argmax()
min_synapses_file = df['file'][min_synapses_idx]
max_synapses_file = df['file'][max_synapses_idx]
def exp_decay(t, tau, V):
'''Exponential decay helper function'''
return V * np.e ** (-t / tau)
"""Create the synaptic Kernel"""
decay_tau = 0.0018
t = np.arange(0,0.020, 0.0001)
kernel = exp_decay(t, decay_tau, 1)
kernel = kernel / kernel.sum()
curr_dt = 0.1
curr_binary = spike_to_x.units_times_to_binary(np.array(max_synapses_file.root.units.read(), dtype=int), max_synapses_file.root.times.read(),
n_units=curr_data.root.parameters.n_pvbcs.read(),
dt=curr_dt,
total_time=curr_data.root.parameters.duration.read() * 1000 + 1)
curr_binary_convolved = np.array([np.convolve(x, kernel, 'same') for x in curr_binary])
synaptic_field = curr_binary_convolved.sum(axis=0)
t = np.arange(0, 2001.0, 0.1)
t_conv_full = np.arange(0, min_synapses_file.root.analysis.coherence.synaptic_field.read().shape[0] * 0.1, 0.1)
fig, ax = plt.subplots(3, 3)
ax[0, 0].scatter(min_synapses_file.root.times, min_synapses_file.root.units, marker='|', color=colors[3])
ax[0, 0].set_xlim((0, 200))
ax[0, 0].set_xlabel("Time (ms)")
ax[0, 0].set_ylabel("Neuron #")
ax[0, 0].set_title("Mean $f_{\mu}$:\n" + f"{min_synapses_file.root.analysis.frequencies.read().mean():.2f}")
ax[0, 1].scatter(max_synapses_file.root.times, max_synapses_file.root.units, marker='|', color=colors[4])
ax[0, 1].set_xlim((0, 200))
ax[0, 1].set_xlabel("Time (ms)")
ax[0, 1].set_ylabel("Neuron #")
ax[0, 1].set_title("Mean $f_{\mu}$:\n" + f"{max_synapses_file.root.analysis.frequencies.read().mean():.2f}")
ax[1, 0].plot(t_conv_full, min_synapses_file.root.analysis.coherence.synaptic_field.read(), color=colors[3])
ax[1, 0].set_xlim((0, 200))
#ax[1, 0].set_ylim((0, 2))
ax[1, 0].set_xlabel("Time (ms)")
ax[1, 0].set_ylabel("Synaptic Field")
ax[1, 1].plot(t_conv_full, max_synapses_file.root.analysis.coherence.synaptic_field.read(), color=colors[4])
ax[1, 1].set_xlim((0, 200))
#ax[1, 1].set_ylim((0, 2))
ax[1, 1].set_xlabel("Time (ms)")
ax[1, 1].set_ylabel("Synaptic Field")
ax[2, 0].plot(bin_size_ms, min_synapses_file.root.analysis.coherence.mean_k.read(), color=colors[3], marker='o')
ax[2, 1].plot(bin_size_ms, max_synapses_file.root.analysis.coherence.mean_k.read(), color=colors[4], marker='o')
ax[2, 0].set_xlabel("Bin $\\tau$ (ms)")
ax[2, 0].set_ylabel("Coherence Measure $k(\\tau)$")
ax[2, 1].set_xlabel("Bin $\\tau$ (ms)")
ax[2, 1].set_ylabel("Coherence Measure $k(\\tau)$")
dt = min_synapses_file.root.parameters.dt.read()
ax[2, 1].set_xlim((0, 8))
ax[2, 1].set_ylim((0, 1.3))
async_binary = spike_to_x.units_times_to_binary(
np.array(min_synapses_file.root.units.read(), dtype=int),
min_synapses_file.root.times.read(),
n_units=min_synapses_file.root.parameters.n_pvbcs.read(),
dt=dt,
total_time=min_synapses_file.root.parameters.duration.read() * 1000 + 1)
sync_binary = spike_to_x.units_times_to_binary(
np.array(max_synapses_file.root.units.read(), dtype=int),
max_synapses_file.root.times.read(),
n_units=max_synapses_file.root.parameters.n_pvbcs.read(),
dt=dt,
total_time=max_synapses_file.root.parameters.duration.read() * 1000 + 1)
async_peak_tau = (1 / df.iloc[min_synapses_idx]['mean_frequency']) * 1000 # in ms
sync_peak_tau = (1 / df.iloc[max_synapses_idx]['mean_frequency']) * 1000 # in ms
async_peak_coherence = np.nanmean(oscillations_analysis.pairwise_coherence(async_binary, bin_size=int(async_peak_tau / dt)))
sync_peak_coherence = np.nanmean(oscillations_analysis.pairwise_coherence(sync_binary, bin_size=int(sync_peak_tau / dt)))
ax[2, 0].plot([0, async_peak_tau], [0, async_peak_coherence], color='k', marker='o', linestyle='dashed')
ax[2, 1].plot([0, sync_peak_tau], [0, sync_peak_coherence], color='k', marker='o', linestyle='dashed')
sns.scatterplot(data=df, x='input_strength', y='mean_pearson_r', ax=ax[0, 2], color='k')
sns.scatterplot(data=df, x='input_strength', y='k_point_1_over_f', ax=ax[1, 2], color='k')
sns.scatterplot(data=df, x='input_strength', y='area_over_line', ax=ax[2, 2], color='k')
# linearity_n_points = 30
# oscillations_analysis.linearity_analysis(curr_binary, curr_dt, curr_duration, n_points=linearity_n_points)
fig, ax = plt.subplots(1, 4)
sns.scatterplot(data=df, x='input_strength', y='small_delta_coherence', ax=ax[0], color='k')
sns.scatterplot(data=df, x='input_strength', y='one_ms_delta_coherence', ax=ax[1], color='k')
sns.scatterplot(data=df, x='input_strength', y='k_point_1_over_f', ax=ax[2], color='k')
sns.scatterplot(data=df, x='input_strength', y='one_ms_delta_slope_coherence', ax=ax[3], color='k')
"""
plt.figure()
sns.boxplot(data=df, x='inhibition_on', y='k_point_1_over_f', hue='gap_junctions')
plt.figure()
sns.boxplot(data=df, x='inhibition_on', y='total_coherence', hue='gap_junctions')
plt.figure()
sns.boxplot(data=df, x='inhibition_on', y='k_1_over_f', hue='gap_junctions')
"""
"""
example_file = "pvring_seed_pp-weight_rec-weight_input-rate_n-pvbcs_n-input-syns_n-inputs_gap-resistance_gap-junctions_145_0.001_0.0076_2_120_100_192_600.0_True.h5"
example_path = os.path.join(results_dir, example_file)
file = tables.open_file(example_path)
times = file.root.times.read()
units = file.root.units.read()
duration = file.root.parameters.duration.read()
dt = file.root.parameters.dt.read()
n_timepoints = int((duration * 1000) / dt)
spike_counts = np.unique(times, return_counts=True)
spike_occurences_indices = np.array(spike_counts[0] / dt, dtype=int)
spike_counts_full = np.zeros(n_timepoints+1)
spike_counts_full[spike_occurences_indices] = spike_counts[1]
gaussian_sigma = 1 / dt
gaussian_width = gaussian_sigma * 10
gaussian_kernel = windows.gaussian(gaussian_width, gaussian_sigma)
gaussian_kernel = gaussian_kernel / gaussian_kernel.sum()
spike_counts_full_convolved = convolve(spike_counts_full, gaussian_kernel)
# hist, bins = np.histogram(output_spiketimes, bins=1000)
sampling_rate = 1 / (dt / 1000)
# sampling_rate = 5000
sp = fftshift(fft(spike_counts_full_convolved))
freq = fftshift(fftfreq(len(spike_counts_full_convolved), 1/sampling_rate))
amp = np.abs(sp)
fig, ax = plt.subplots(2, 1)
ax[1].plot(freq[len(freq)//2:], amp[len(amp)//2:])
ax[1].set_title('FFT')
ax[1].set_xlabel('Frequency (Hz)')
ax[1].set_ylabel('Amplitude')
ax[1].set_xlim(0,300)
ax[1].set_ylim(0, 10000)
fig.tight_layout()
ax[0].scatter(curr_times, curr_units, marker='|')
"""