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soma.py
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soma.py
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#!/usr/bin/python
import dill as pickle
import itertools
import pandas as pd
import numpy as np
from scoop import futures
from scipy.stats import ortho_group
import time
dt = 0.025
number_of_models = 1
simulation_length = 2000
v_init = -82
extract_output_args = []
result_indices_parameters = True
# Input parameters
feature_names = ['gamma_CaDynamics_E2',
'decay_CaDynamics_E2',
'gCa_LVAstbar_Ca_LVAst',
'gCa_HVAbar_Ca_HVA',
'gSKv3_1bar_SKv3_1',
'gSK_E2bar_SK_E2',
'gNap_Et2bar_Nap_Et2',
'gNaTa_tbar_NaTa_t',
'gK_Pstbar_K_Pst',
'gK_Tstbar_K_Tst',
'g_pas']
feature_names = sorted(feature_names)
original_input_param = {
'g_pas' : 0.0000338 ,
'gSK_E2bar_SK_E2' : 0.0441 ,
'gK_Tstbar_K_Tst' : 0.0812 ,
'gK_Pstbar_K_Pst' : 0.00223 ,
'gNap_Et2bar_Nap_Et2' : 0.00172,
'gCa_LVAstbar_Ca_LVAst' : 0.00343 ,
'gCa_HVAbar_Ca_HVA' : 0.000992,
'gSKv3_1bar_SKv3_1' : 0.693 ,
'gNaTa_tbar_NaTa_t' : 2.04 ,
'gamma_CaDynamics_E2' : 0.000501 ,
'decay_CaDynamics_E2' : 460.0 }
feature_limits = {}
for input_param in original_input_param.keys():
feature_limits[input_param] = (original_input_param[input_param] - original_input_param[input_param] * 1.00, original_input_param[input_param] + original_input_param[input_param] * 1.00)
m = len(feature_names)
feature_pairs = sorted([sorted(pair) for pair in itertools.combinations(range(len(feature_names)), 2)])
feature_pairs = ['{} and {}'.format(feature_names[p[0]], feature_names[p[1]]) for p in feature_pairs]
normalization_feature_pairs = []
for feature_ind_1 in range(len(feature_names)):
for feature_ind_2 in range(feature_ind_1 + 1, len(feature_names)):
normalization_feature_pairs.append('{} and {}'.format(feature_names[feature_ind_1],feature_names[feature_ind_2]))
perturbation_feature_pairs = []
for feature_ind_1 in range(len(feature_names)):
for feature_ind_2 in range(feature_ind_1, len(feature_names)):
perturbation_feature_pairs.append('{} and {}'.format(feature_names[feature_ind_1],feature_names[feature_ind_2]))
perturbation_status_columns = []
perturbation_status_columns.append('core')
for feature_ind_1 in range(len(feature_names)):
perturbation_status_columns.append(feature_names[feature_ind_1])
for feature_ind_2 in range(feature_ind_1, len(feature_names)):
perturbation_status_columns.append('{} and {}'.format(feature_names[feature_ind_1],feature_names[feature_ind_2]))
output_names = ['ISI_CV']
def set_ground_truth(number_of_core_samples, step_size, name, output_path):
print('This is a currently studied system, there is still no ground truth')
def extract_outputs(raw_results):
outputs = pd.DataFrame(0, index = np.arange(raw_results.shape[0]), columns = output_names)
outputs['Integral'] = np.sum(raw_results + 90) / 40.0
return outputs
def simulate_single_param(args):
"""
Simulates a specific input parameter vector
:param args: The parameters for the simulation:
The list of excitatory presynpatic inputs,
The list of inhibitory presynpatic inputs,
and the input parameter dictionary
:returns: The voltage trace of the simulation
"""
from neuron import h
from neuron import gui
h.load_file("nrngui.hoc")
h.load_file("import3d.hoc")
param_dict = args[0]
hoc_code = '''h("create soma")
h("access soma")
h("nseg = 1")
h("L = 20")
h("diam = 20")
h("insert pas")
h("cm = 1")
h("Ra = 150")
h("forall nseg = 1")
h("forall e_pas = -90")
h("soma insert Ca_LVAst ")
h("soma insert Ca_HVA ")
h("soma insert SKv3_1 ")
h("soma insert SK_E2 ")
h("soma insert K_Tst ")
h("soma insert K_Pst ")
h("soma insert Nap_Et2 ")
h("soma insert NaTa_t")
h("soma insert CaDynamics_E2")
h("soma insert Ih")
h("ek = -85")
h("ena = 50")
h("gIhbar_Ih = 0.0002")
h("g_pas = 1.0 / 12000 ")
h("celsius = 36")
'''
exec(hoc_code)
exec('h("tstop = {}")'.format(simulation_length))
exec('h("v_init = {}")'.format(v_init))
for key in param_dict.keys():
h(key + " = " + str(param_dict[key]))
iclamp = h.IClamp(0.5)
iclamp.delay = 500
iclamp.dur = 1400
amp = 0.05
iclamp.amp = amp
im = h.Impedance()
h("access soma")
h("nseg = 1")
im.loc(0.5)
im.compute(0)
Ri = im.input(0.5)
semitrial_start = time.time()
### Set the protocol
h.finitialize()
### Simulate!
voltageVector = h.Vector()
voltageVector.record(eval("h.soma(0.5)._ref_v"))
timeVector = h.Vector()
timeVector.record(h._ref_t)
h.run()
timeVector = np.array(timeVector)
voltageVector = np.array(voltageVector)
trace = {}
trace['T'] = timeVector[4000:]
trace['V'] = np.array(voltageVector)[4000:]
trace['stim_start'] = [500]
trace['stim_end'] = [1900]
del voltageVector, timeVector
return trace
def get_ground_truth(output_path,number_of_core_samples, step_size, name):
print('This is a currently studied system, there is still no ground truth')
def generate_feature_vectors(number_of_core_samples, step_size):
start = time.time()
lower_limits = np.array([feature_limits[f][0] for f in feature_names])
upper_limits = np.array([feature_limits[f][1] for f in feature_names])
x = lower_limits + (np.random.rand(number_of_core_samples, len(feature_names))) * (upper_limits - lower_limits)
perturbation_status_columns = []
perturbation_status_columns.append('core')
for feature_ind_1 in range(len(feature_names)):
perturbation_status_columns.append(feature_names[feature_ind_1])
for feature_ind_2 in range(len(feature_names)):
perturbation_status_columns.append('{} and {}'.format(feature_names[feature_ind_1],feature_names[feature_ind_2]))
data = []
for ind in range(number_of_core_samples):
data.append([])
data[-1].append([x[ind, feature_names.index(key)] for key in feature_names])
for feature_ind_1 in range(len(feature_names)):
data[-1].append([x[ind, feature_names.index(key)] for key in feature_names])
data[-1][-1][feature_ind_1] += (upper_limits[feature_ind_1] - lower_limits[feature_ind_1]) * step_size
for feature_ind_2 in range(feature_ind_1 + 1, len(feature_names)):
data[-1].append([x[ind, feature_names.index(key)] for key in feature_names])
data[-1][-1][feature_ind_1] += (upper_limits[feature_ind_1] - lower_limits[feature_ind_1]) * step_size
data[-1][-1][feature_ind_2] += (upper_limits[feature_ind_2] - lower_limits[feature_ind_2]) * step_size
data = np.array(data)
feature_vectors = pd.DataFrame(data.reshape(data.shape[0], data.shape[1] * data.shape[2]), index = np.arange(number_of_core_samples), columns = pd.MultiIndex.from_product([perturbation_status_columns, feature_names], names=['perturbation_status','features']))
end = time.time()
print('Sampling features took {}'.format(end - start))
return feature_vectors, []