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analyze_eta.py
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analyze_eta.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Dec 15 17:48:41 2016
@author: daniel
"""
import pandas as pd
import json
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
def mg_block(mg, alpha, v, eta = 1/3.57, x_offset = 0):
block = 1 / (1 + mg * eta * np.exp(-alpha * (v + x_offset)) )
return block
def deriv_mg_block(mg, alpha, v, eta = 1/3.57, x_offset = 0):
block = 1 / (1 + mg * eta * np.exp(-alpha * (v + x_offset)) )
return block * (1-block) *(alpha)
v = np.linspace(-100, 50, 15000);
alpha = 0.086
#etas = np.arange(0.02, 0.4, 0.04)
etas = np.exp([-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1])
v12 = [-48, -40, -32, -24, -16, -8, 0, 8, 16]
eta = 0.38
mg = 1.0
vs_widths = []
vs_amps = []
vs = []
for eta in etas:
# filename = './results/alpha/data_no_spillover_alpha_%.3f.dat' % alpha
filename = './results/eta/no_spillover_eta_%.3f.dat' % eta
with open(filename, 'r', encoding = 'utf-8') as f:
fload = json.load(f)
res_dict = json.loads(fload)
num_syns = res_dict['num_syns']
independent_dends = res_dict['independent_dends']
trials = res_dict['trials']
vswidths = res_dict['vs_widths']
# vdwidths = res_dict['vd_widths']
vsamps = res_dict['vs_amps']
# vdamps = res_dict['vd_amps']
vs.append(res_dict['vs'])
vs_widths.append(vswidths)
vs_amps.append(vsamps)
# vd_widths.append(vdwidths)
# vd_amps.append(vdamps)
vs_widths_diff = []
vs = np.reshape(vs, (len(etas), len(num_syns), trials, len(independent_dends), 500))
vs_widths = np.reshape(vs_widths, (len(etas), len(num_syns), trials, len(independent_dends)))
vs_amps = np.max(vs, axis = (4))
d1 = vs_amps.shape[0]; d2 = vs_amps.shape[1]; d3 = vs_amps.shape[2]; d4 = vs_amps.shape[3];
for i in range(0,d1):
for j in range(0,d2):
for k in range(0,d3):
for l in range(0,d4):
if vs_amps[i,j,k,l] > -50:
vs_amps[i,j,k,l] = -50
vs_widths_mean = np.mean(vs_widths, axis = (2,3))
vs_amps_mean = np.mean(vs_amps, axis = (2))
vs_amps_diff = vs_amps[:,1:,:,:] - vs_amps[:,0:(len(num_syns)-1),:,:]
max_amps_diff = np.max(vs_amps_diff, axis = 1)
mean_max_amps_diff = np.mean(max_amps_diff, axis = (1,2))
sd_max_amps_diff = np.std(max_amps_diff, axis = (1,2))
#for d, a in zip(vs_widths, vs_amps):
# end = len(d)
# ddiff = np.asarray(d[1:]) - np.asarray(d[0:(end-1)])
# adiff = np.asarray(a[1:]) - np.asarray(a[0:(end-1)])
# vs_widths_diff.append(ddiff.tolist())
# vs_amps_diff.append(adiff.tolist())
vs_dur_df = pd.DataFrame(vs_widths_mean, index = etas.round(2),
columns = [i for i in range(1, 21)] )
vs_amps_df = pd.DataFrame(vs_amps_mean[:,:,0], index = v12,
columns = [i for i in range(1, 21)] )
#ax1 = sns.heatmap(vs_amps_df, cmap = "icefire", cbar_kws = {'label': 'plateau width (ms)'})
#ax1.set_xlabel('cluster size')
#ax1.set_ylabel('steepness of Mg block (%)')
sns.set(font_scale = 1.5)
sns.set_style("ticks")
vmin = -78; vmax = -55
ax2 = sns.heatmap(vs_amps_df, cmap = "icefire", cbar_kws = {'label': 'soma Vm (mV)'},
vmax = vmax, vmin = vmax)
ax2.set_xlabel('cluster size')
ax2.set_ylabel('V$_{1/2}$ (mv)')
colors = sns.color_palette("icefire", len(num_syns))
fig_vs = plt.figure()
ax_vs = fig_vs.add_subplot(111)
for i in range(0,len(num_syns)):
ax_vs.plot(vs[8, i, 40, 0, :], color = colors[i])
ax_vs.set_xlabel('t (ms)')
ax_vs.set_yticks([-80, -75, -70, -65])
ax_vs.set_ylim([-84,-60])
ax_vs.set_ylabel('soma Vm (mV)')
fig_bar = plt.figure()
ax_bar = fig_bar.add_subplot(111)
ax_bar.bar(etas, mean_max_amps_diff, yerr = sd_max_amps_diff)
plt.show()