121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Script to run sensitivity analysis for FS model
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"""
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__author__ = "Nils A. Koch"
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__copyright__ = "Copyright 2022, Nils A. Koch"
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__license__ = "MIT"
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import numpy as np
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from numba import types
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from numba.typed import Dict
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from joblib import Parallel, delayed
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import os
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from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
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from Code.Functions.Pospischil_fxns import SA_Pospischil
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# model parameters
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dt = 0.01
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sec = 2
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low = 0
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high = 0.001
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number_steps = 200
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initial_period = 1000
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num_gating = 10
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num_current = 7
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C, surf_area = capacitance(56.9, 1)
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stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
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shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
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np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
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tau_max_p = 502
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V_init = -70
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V_T = -57.9 # Threshold
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# initialize arrays
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current = np.zeros((num_current, stim_num))
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gating = np.zeros((num_gating, stim_num))
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# create dictionary
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ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
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i = 0
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for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
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ind_dict[var] = i
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i += 1
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i = 0
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for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
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ind_dict[var] = i
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i += 1
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# gating parameters
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b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
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b_param['h'] = np.zeros(4)
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b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
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b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
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b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
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b_param['r'][:] = np.array([-57.37, 20.98, 1.])
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b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
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b_param['s'][:] = np.array([-14.16, -10.15, 1.])
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b_param['u'] = np.zeros(4)
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b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
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b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
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b_param['u_mut'] = np.zeros(4)
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b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
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# reversal potentials
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E["Na"] = 50.
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E["K"] = -90.
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E["Ca"] = 120.
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E["Leak"] = -70.4
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# model currents
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currents_included["Na"] = True
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currents_included["Kd"] = True
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currents_included["Kv"] = False
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currents_included["Kv_mut"] = False
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currents_included["L"] = False
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currents_included["M"] = True
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currents_included["Leak"] = True
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# model conductances
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g["Na"] = 58. * surf_area
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g["Kd"] = 3.9 * surf_area
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g["M"] = 0.075 * surf_area
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g["Kv"] = 0.
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g["Kv_mut"] = 0.
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g["L"] = 0.
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g["Leak"] = 0.038 * surf_area
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# folder to save to
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folder = '../Sensitivity_Analysis/Data/FS'
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if not os.path.isdir(folder):
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os.makedirs(folder)
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#%% setup for one-factor-at-a-time SA
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var = np.array(['m', 'h', 'n'])
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type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
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np.array(['slope' for i in range(var.shape[0])]))
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cur = np.array(['Na', 'Kd', 'Leak'])
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type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
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var = np.append(var, var)
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var = np.append(var, cur)
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alt_types = np.c_[var, type_names]
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lin_array = np.arange(-10, 11, 1)
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log_array = np.logspace(-1,1,21, base=2)
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# %% multiprocessing
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prominence = 50
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desired_AUC_width = high/5
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Parallel(n_jobs=8, verbose=9)(
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delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
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b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
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number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
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for alt_ind in range(alt_types.shape[0]) for alt in range(21))
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