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nmda_plateaus_d2_x_multi.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Aug 2 17:50:36 2019
@author: daniel
"""
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 15 17:48:41 2016
@author: daniel
"""
from neuron import h,rxd
# import d1msn as msn
import iMSN as msn
import spillover_experiment as pe
import pickle
import parameters_2 as p
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import json
import scipy.signal as ss
import os, sys, traceback
from multiprocessing import Pool, TimeoutError
# --- 1. Create a cell and other useful stuff
dMSN_library = 'D1_71bestFit_updRheob.pkl'
iMSN_library = 'D2_34bestFit_updRheob.pkl'
with open(iMSN_library, 'rb') as f:
model_sets = pickle.load(f, encoding="latin1")
cell_ID = 58
cell_ids = list(model_sets.keys())
# pool = Pool(len(cell_ids))
# def run_cell_sim(cell_id):
for cell_id in cell_ids:
print (" cell id ", cell_id)
if cell_id <2:
continue
variables = model_sets[cell_id]['variables']
print(variables)
cell = msn.iMSN(variables = variables)
for d in p.input_dends:
print (" input dendrite ", d)
for x in cell.dendlist:
print (x)
cell.dendlist[d].nseg *=5
dend_record_list = [22] #[3,4,9,10,21,22,24,26,35,36,51,52]
dend_stim_list = []#[3,4,9,10,35,36]
plateau_cluster_list = [22]
plateau_cluster_size = np.arange(1,31,1)
vs = []
vspine = []
vd = []
legend = []
max_vs = []
max_vspine = []
max_vd = []
g_nmda = []
i_nmda = []
sns.set(font_scale = 1.0)
sns.set_style('whitegrid')
fig_vs = plt.figure();
fig_vspine = plt.figure();
fig_vd = plt.figure();
ax_vs = fig_vs.add_subplot(111); ax_vs.set_ylabel('Vs (mV)'); ax_vs.set_xlabel('t (ms)')
ax_vspine = fig_vspine.add_subplot(111); ax_vspine.set_ylabel('Vspine (mV)'); ax_vspine.set_xlabel('t (ms)')
ax_vd = fig_vd.add_subplot(111); ax_vd.set_ylabel('Vd (mV)'); ax_vd.set_xlabel('t (ms)')
colors = sns.color_palette("coolwarm", plateau_cluster_size.max())
add_spine = 0
on_spine = 1
cell.insert_spines(plateau_cluster_list, p.cluster_start_pos, p.cluster_end_pos, num_spines = p.plateau_cluster_size_max)
sns.set_style("ticks")
for index_plateau, num_syns in enumerate(plateau_cluster_size):
print (" index_plateau ", index_plateau)
ex = pe.Spillover_Experiment('record_ca', cell)
ex.insert_synapses('noise_SPN')
ex.insert_synapses('my_spillover', plateau_cluster_list, deterministic = 0,
num_syns = num_syns, add_spine = add_spine, on_spine = on_spine)
ex.set_up_recording(dend_record_list)
ex.simulate()
tv = ex.tv.to_python()
t = ex.t.to_python()
legend.append("%d syns" % num_syns)
if add_spine == 1 or on_spine == 1:
vspine.append(ex.vspine[0].to_python())
max_vspine.append(max(ex.vspine[0]))
ax_vspine.plot(tv, ex.vspine[0].to_python(), color = colors[num_syns-1])
vd.append(ex.vdlist[0].to_python())
max_vd.append(max(ex.vdlist[0]))
max_vs.append(max(ex.vs))
vs.append(ex.vs.to_python())
cell.esyn = []
ex.estim = []
ex.enc = []
for s in cell.spines:
s.syn_on = 0
cell.isyn = []
ex.istim = []
ex.inc = []
vs_indices = []; vs_widths = []
vd_indices = []; vd_widths = []
vspine_indices = []; vspine_wid = []
for v in vs:
vs_indices.append(ss.find_peaks(v))
vs_widths.append(ss.peak_widths(v, (vs_indices[-1])[0], rel_height = 0.15))
for v in vd:
vd_indices.append(ss.find_peaks(v))
vd_widths.append(ss.peak_widths(v, (vd_indices[-1])[0] ,rel_height = 0.15))
for i in range(0, len(vs)):
if add_spine ==0 and on_spine ==0:
ax_vd.plot(tv, vd[i]);
ax_vs.plot(tv, vs[i], color = colors[i]); ax_vs.set_title("weight = %.2f, Cdur_factor = %d" % (p.weight, p.eCdur_factor))
ax_vd.plot(tv, vd[i], color = colors[i]); ax_vs.set_title("weight = %.2f, Cdur_factor = %d" % (p.weight, p.eCdur_factor))
ax_vd.set_title("cell_id %d weight = %.2f, Cdur_factor = %d" % (cell_id, p.weight, p.eCdur_factor))
sns.despine()
res_dict = {'t': t,
'vs': vs,
'vspine': vspine}
to_save = json.dumps(res_dict)
#filename = './results/data_spillover_steep.dat'
#with open(filename,'w', encoding = 'utf-8') as f:
# json.dump(to_save, f)
# plt.show()
plt.savefig("d2_results_" + str(cell_id) + ".png")
# except:
# traceback.print_exc(file=sys.stdout)
# funclist = []
# for cell_id in cell_ids:
# print (" cell id ", cell_id)
# # if cell_id == 1:
# # continue
# f = pool.map(run_cell_sim,(cell_id))
# funclist.append(f)
# results = []
# for f in funclist:
# try:
# results.append(f.get())
# except:
# pass