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Functions and plotting scripts for each model, sensitivity analysis in each model and KCNA1 mutations in each model

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nkoch1
2022-08-28 15:57:33 -04:00
parent 64e663d857
commit 47f5c1db02
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110
Code/Sensitivity_Analysis/SA_Cb_stellate.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 08:24:33 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns import SA_Cb_stellate
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 9
num_current = 6
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['n_A_mut'][:] = np.array([-27, -13.2, 1.])
b_param['h_A_mut'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["A_mut"] = False
currents_included["A"] = True
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 3.4 * surf_area
g["Kd"] = 9.0556 * surf_area
g["A_mut"] = 0.
g["A"] = 15.0159 * surf_area
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './SA/Cb_stellate'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 'n_A', 'h_A'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'A', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Cb_stellate)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 08:24:33 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 11
num_current = 7
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["A"] = False
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 34 * surf_area
g["Kd"] = 9.0556 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 1.50159 / 2 * surf_area
else:
g["Kv"] = 1.50159 / 2 * surf_area * 2
g["Kv_mut"] = 1.50159 / 2 * surf_area
g["A"] = 0 * surf_area
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './SA/Cb_stellate_Kv_only'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Cb_stellate_Kv)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 08:24:33 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 11
num_current = 7
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["A"] = True
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 3.4 * surf_area
g["Kd"] = 9.0556 * (1-Kv_ratio) * surf_area
g["Kv"] = 9.0556 * Kv_ratio * surf_area
g["Kv_mut"] = 0.
g["A"] = 15.0159 * surf_area
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './Sensitivity_Analysis/Cb_stellate_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 'n_A', 'h_A', 's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'A', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Cb_stellate_Kv)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
# #%% Get pd Dataframes for certain variables
# import pandas as pd
# AUC = pd.DataFrame(columns=mutations.keys())
# AUC_rel = pd.DataFrame(columns=mutations.keys())
# rheobase = pd.DataFrame(columns=mutations.keys())
# rheobase_fit = pd.DataFrame(columns=mutations.keys())
# rheobase_null_fit = pd.DataFrame(columns=mutations.keys())
# for mut in list(mutations.keys()):
# fname = os.path.join(folder, "{}.hdf5".format(mut))
# f = h5py.File(fname, "r")
# AUC['{}'.format(mut)] = f['analysis']['AUC']
# AUC_rel['{}'.format(mut)] = f['analysis']['AUC_rel']
# rheobase['{}'.format(mut)] = f['analysis']['rheobase']
# rheobase_fit['{}'.format(mut)] = f['analysis']['rheobase_fit']
# rheobase_null_fit['{}'.format(mut)] = f['analysis']['rheobase_null_fit']
# AUC.to_json(os.path.join(folder, 'AUC.json'))
# AUC_rel.to_json(os.path.join(folder, 'AUC_rel.json'))
# rheobase.to_json(os.path.join(folder, 'rheobase.json'))
# rheobase_fit.to_json(os.path.join(folder, 'rheobase_fit.json'))
# rheobase_null_fit.to_json(os.path.join(folder, 'rheobase_null_fit.json'))

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Code/Sensitivity_Analysis/SA_FS.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 18:14:31 2021
@author: nils
Original Pospischil FS Sensitivity analysis (no Kv1.1)
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(56.9, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 502
V_init = -70
V_T = -57.9 # Threshold
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# create dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'][:] = np.array([-57.37, 20.98, 1.])
b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.4
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = False
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 58. * surf_area
g["Kd"] = 3.9 * surf_area
g["M"] = 0.075 * surf_area
g["Kv"] = 0.
g["Kv_mut"] = 0.
g["L"] = 0.
g["Leak"] = 0.038 * surf_area
# folder to save to
folder = './Sensitivity_Analysis/FS'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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Code/Sensitivity_Analysis/SA_FS_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 18:14:31 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(56.9, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 502
V_init = -70
V_T = -57.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'][:] = np.array([-57.37, 20.98, 1.])
b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# mut_act_Vhalf_wt = -30.01851851851851
# mut_act_k_wt = -7.7333333333333325
# s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
# s_diff_k = mut_act_k_wt - b_param['s'][1]
# b_param['s'][1] = b_param['s'][1] + s_diff_k
# b_param['u'][1] = b_param['u'][1] + s_diff_k
# b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
# b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
# b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
# b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
# b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
# b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.4
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.10
g["Na"] = 58. * surf_area
g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
g["Kv"] = 3.9 * Kv_ratio * surf_area
g["Kv_mut"] = 0.
g["L"] = 0. * surf_area
g["Leak"] = 0.038 * surf_area
# save folder
folder = './Sensitivity_Analysis/FS_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,20, base=2)
log_array = np.append(log_array,1)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
# #%% Get pd Dataframes for certain variables
# import pandas as pd
# AUC = pd.DataFrame(columns=mutations.keys())
# AUC_rel = pd.DataFrame(columns=mutations.keys())
# rheobase = pd.DataFrame(columns=mutations.keys())
# rheobase_fit = pd.DataFrame(columns=mutations.keys())
# rheobase_null_fit = pd.DataFrame(columns=mutations.keys())
# for mut in list(mutations.keys()):
# fname = os.path.join(folder, "{}.hdf5".format(mut))
# f = h5py.File(fname, "r")
# AUC['{}'.format(mut)] = f['analysis']['AUC']
# AUC_rel['{}'.format(mut)] = f['analysis']['AUC_rel']
# rheobase['{}'.format(mut)] = f['analysis']['rheobase']
# rheobase_fit['{}'.format(mut)] = f['analysis']['rheobase_fit']
# rheobase_null_fit['{}'.format(mut)] = f['analysis']['rheobase_null_fit']
# AUC.to_json(os.path.join(folder, 'AUC.json'))
# AUC_rel.to_json(os.path.join(folder, 'AUC_rel.json'))
# rheobase.to_json(os.path.join(folder, 'rheobase.json'))
# rheobase_fit.to_json(os.path.join(folder, 'rheobase_fit.json'))
# rheobase_null_fit.to_json(os.path.join(folder, 'rheobase_null_fit.json'))

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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 18:14:31 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.00035
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.8, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 934
V_init = -70
V_T = -67.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'][:] = np.array([-57.37, 20.98, 1.])
b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -56.2
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = False
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 10. * surf_area
g["Kd"] = 2.1 * surf_area
g["M"] = 0.0098 * surf_area
g["Kv"] = 0.
g["Kv_mut"] = 0.
g["L"] = 0.
g["Leak"] = 0.0205 * surf_area
# save folder
folder = './Sensitivity_Analysis/RS_inhib'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale,
b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 18:14:31 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.00035
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.8, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 934
V_init = -70
V_T = -67.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'][:] = np.array([-57.37, 20.98, 1.])
b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -56.2
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 10. * surf_area
g["Kd"] = 2.1 * (1 - Kv_ratio) * surf_area
g["M"] = 0.0098 * surf_area
g["Kv"] = 2.1 * Kv_ratio * surf_area
g["Kv_mut"] = 0.
g["L"] = 0. * surf_area
g["Leak"] = 0.0205 * surf_area
# save folder
folder = './Sensitivity_Analysis/RS_inhib_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 08:24:33 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.4, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 608
V_init = -70
V_T = -56.2
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.0])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.0])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
# reversal potential
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.3
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = False
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 56. * surf_area
g["Kd"] = 6. * surf_area
g["M"] = 0.075 * surf_area
g["Kv"] = 0.
g["Kv_mut"] = 0.
g["L"] = 0.
g["Leak"] = 0.0205 * surf_area
folder = './Sensitivity_Analysis/RS_pyramidal'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale,
b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 08:24:33 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import SA_Pospischil
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.4, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 608
V_init = -70
V_T = -56.2
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# create dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.0])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.0])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.3
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 56. * surf_area
g["Kd"] = 6. * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
g["Kv"] = 6. * Kv_ratio * surf_area
g["Kv_mut"] = 0.
g["L"] = 0. * surf_area
g["Leak"] = 0.0205 * surf_area
# save folder
folder = './Sensitivity_Analysis/RS_pyramidal_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 's', 'u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 19:44:04 2021
@author: nils
original STN model SA
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns import SA_STN
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 15
num_current = 8
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc', 'E_Ca']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r','Ca_conc','E_Ca']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'L', 'A_mut', 'T', 'Leak', 'Ca_K']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-40., -1., 1.])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-45.5, 1., 1., 0.05])
b_param['n'][:] = np.array([-41., -1., 1.])
b_param['p'][:] = np.array([-56., -1., 1.])
b_param['q'][:] = np.array([-85., 1., 1.])
b_param['r'][:] = np.array([0.17, -0.08, 1.])
b_param['a'][:] = np.array([-45., -14.7, 1.])
b_param['b'][:] = np.array([-90., 7.5, 1.])
b_param['a_mut'][:] = np.array([-45., -14.7, 1.])
b_param['b_mut'][:] = np.array([-90., 7.5, 1.])
b_param['c'][:] = np.array([-30.6, -5., 1.])
b_param['d1'][:] = np.array([-60, 7.5, 1.])
b_param['d2'][:] = np.array([0.1, 0.02, 1.])
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["A_mut"] =False
currents_included["Leak"] = True
# model conductances
g["Na"] = 49. * surf_area
g["Kd"] = 57. * surf_area
g["A"] = 5. * surf_area
g["A_mut"] = 0.
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
# save folder
folder = '../Sensitivity_Analysis/STN'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 'a', 'b'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'A', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% run SA with multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period, sec, vol,
lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 19:44:04 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns_Kv import SA_Kv_STN
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 17
num_current = 9
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-40., -1. , 1.])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-45.5, 1. , 1., 0.05])
b_param['n'][:] = np.array([-41., -1., 1.])
b_param['p'][:] = np.array([-56., -1., 1.])
b_param['q'][:] = np.array([-85., 1., 1.])
b_param['r'][:] = np.array([0.17, -0.08, 1.])
b_param['a'][:] = np.array([-45., -14.7, 1.])
b_param['b'][:] = np.array([-90., 7.5, 1.])
b_param['c'][:] = np.array([-30.6, -5., 1.])
b_param['d1'][:] = np.array([-60, 7.5, 1.])
b_param['d2'][:] = np.array([0.1, 0.02, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.01
g["Na"] = 49. * surf_area
g["Kd"] = 57. * surf_area
g["Kv"] = 0.5 * surf_area
g["Kv_mut"] = 0
g["A"] = 0. * surf_area
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
V_init = -70
# save folder
folder = './Sensitivity_Analysis/STN_Kv_only'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 's','u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% run SA with multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Kv_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period,
sec, vol, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))

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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 5 19:44:04 2021
@author: nils
"""
import numpy as np
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
import os
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns_Kv import SA_Kv_STN
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 17
num_current = 9
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-40., -1. , 1.])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-45.5, 1. , 1., 0.05])
b_param['n'][:] = np.array([-41., -1., 1.])
b_param['p'][:] = np.array([-56., -1., 1.])
b_param['q'][:] = np.array([-85., 1., 1.])
b_param['r'][:] = np.array([0.17, -0.08, 1.])
b_param['a'][:] = np.array([-45., -14.7, 1.])
b_param['b'][:] = np.array([-90., 7.5, 1.])
b_param['c'][:] = np.array([-30.6, -5., 1.])
b_param['d1'][:] = np.array([-60, 7.5, 1.])
b_param['d2'][:] = np.array([0.1, 0.02, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["Kv"] = True
currents_included["Kv_mut"] = False
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.01
g["Na"] = 49. * surf_area
g["Kd"] = 57. * (1-Kv_ratio)* surf_area
g["Kv"] = 57. * Kv_ratio * surf_area
g["Kv_mut"] = 0
g["A"] = 5. * surf_area
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
# save folder
folder = '../Sensitivity_Analysis/STN_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#%% setup for one-factor-at-a-time SA
var = np.array(['m', 'h', 'n', 'a', 'b', 's','u'])
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
np.array(['slope' for i in range(var.shape[0])]))
cur = np.array(['Na', 'Kd', 'A','Kv', 'Leak'])
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
var = np.append(var, var)
var = np.append(var, cur)
alt_types = np.c_[var, type_names]
lin_array = np.arange(-10, 11, 1)
log_array = np.logspace(-1,1,21, base=2)
# %% run SA with multiprocessing
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(SA_Kv_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period, sec,
vol, lin_array, log_array, alt_types, alt_ind, alt)
for alt_ind in range(alt_types.shape[0]) for alt in range(21))