# Print total time taken
solve_time = time.time() - start_time
if solve_time < 60.0:
print("\n\n--- Took {0:.0f} seconds to solve ---".format(solve_time))
else:
minutes, seconds = divmod(solve_time, 60)
print(
"\n\n--- Took {0:.0f} minutes and {1:.0f} seconds to solve ---".format(
minutes, seconds))
del start_time, solve_time
# Extract the state variables for easy plotting etc
v = initialise_var_names(u, idx, 'out function')
# Extract the algebraic variables for easy plotting etc
a = set_algebraic_variables(u, t, idx, input_data, total_waveform,
'out function')
del total_waveform, input_data
# Solve for the normalised CBF, CBV, CMRO2, HbO, HbR, HbT and BOLD and put into 'a' (solved separately from odeint as they depend on the steady state conditions)
a = solve_normalised_hemodynamics(a, v, t)
# The variables that can be plotted sorted into alphabetical order
state_vars = sorted(vars(v).keys())
#print("\nState variables: \n", state_vars, "\n")
alg_vars = sorted(vars(a).keys())
#print("Algebraic variables: \n", alg_vars,"\n")
'''Plotting parameters'''
if p.startpulse < p.Tend:
time = t / 1e3 - p.startpulse / 1e3 # Time in seconds, normalised so t=0 at the start of stimulation
xlim1 = p.startpulse / 1e3 - 10 # Set left xlimit to 10 sec before stimulation begins
else:
def func(u, t, idx, input_data, total_waveform, start_time):
# Initialise variable names for clarity
v = initialise_var_names(u, idx, 'in function')
# Set the algebraic_variables
a = set_algebraic_variables(u, t, idx, input_data, total_waveform,
'in function')
# RHS of ODEs
du = [None] * len(idx) # Initialise with correct size
if p.InputCase == 'ThalamicTriangles' or p.InputCase == 'ThalamicTrianglesZheng' or p.InputCase == 'ThalamicSquarePulse': # Using thalamic input T(t) rather than P(t), Q(t)
du[idx['E_t']] = 1 / p.tau_e * (-v.E_t + a.f_arg_e)
du[idx['I_t']] = 1 / p.tau_i * (-v.I_t + a.f_arg_i)
else:
du[idx['E_t']] = 1 / p.tau_e * (-v.E_t +
(a.k_e - p.r_e * v.E_t) * a.s_arg_e)
du[idx['I_t']] = 1 / p.tau_i * (-v.I_t +
(a.k_i - p.r_i * v.I_t) * a.s_arg_i)
# "Neuron"
du[idx['K_e']] = -p.beta_K_e * (v.K_e -
p.K_eBase) + p.alpha_K_e * p.beta_K_e * (
(abs(v.E_t - v.I_t) - p.EImin) /
(p.EI_relative - p.EImin))
du[idx['Na_sa']] = -p.beta_Na_sa * (
v.Na_sa - p.Na_saBase) + p.alpha_Na_sa * p.beta_Na_sa * (
(abs(v.E_t - v.I_t) - p.EImin) / (p.EI_relative - p.EImin))
du[idx['Na_d']] = -p.beta_Na_d * (
v.Na_d - p.Na_dBase) + p.alpha_Na_d * p.beta_Na_d * (
(abs(v.E_t - v.I_t) - p.EImin) / (p.EI_relative - p.EImin))
# haemodynamics
du[idx['O2']] = a.J_O2_vascular - a.J_O2_background - a.J_O2_pump
du[idx['CBV']] = 1 / (p.tau_MTT + p.tau_TAT) * (a.f_in - v.CBV**(1 / p.d))
du[idx['HbR']] = 1 / p.tau_MTT * (a.f_in * a.OEF / p.E_0 -
v.HbR / v.CBV * a.f_out)
# Neuron
du[idx['Ca_n']] = (a.I_Ca_tot / (2 * p.Farad * p.V_spine) -
(p.k_ex * (v.Ca_n - p.Ca_rest))) / (1 + p.lambda_buf)
du[idx['nNOS']] = p.NOswitch_NE * (p.V_maxNOS * a.CaM /
(p.K_actNOS + a.CaM) - p.mu2_n * v.nNOS)
du[idx['NO_n']] = a.p_NO_n - a.c_NO_n + a.d_NO_n
du[idx['K_p']] = a.J_BK_k / (p.VR_pa) + a.J_KIR_i / p.VR_ps - p.R_decay * (
v.K_p - p.K_p_min)
du[idx[
'Ca_p']] = a.J_TRPV_k / p.VR_pa + a.J_VOCC_i / p.VR_ps - p.Ca_decay_k * (
v.Ca_p - p.Capmin_k)
du[idx['v_k']] = p.gamma_i * (-a.J_BK_k - a.J_K_k - a.J_Cl_k - a.J_NBC_k -
a.J_Na_k - a.J_NaK_k - 2 * a.J_TRPV_k
) # - a.J_GABA_k)
du[idx['Na_k']] = -a.J_Na_k - 3 * a.J_NaK_k + a.J_NKCC1_k + a.J_NBC_k
du[idx[
'K_k']] = -a.J_K_k + 2 * a.J_NaK_k + a.J_NKCC1_k + a.J_KCC1_k - a.J_BK_k
du[idx['HCO3_k']] = 2 * a.J_NBC_k
du[idx['Ca_k']] = a.B_cyt * (a.J_IP3 - a.J_pump + a.J_ER_leak -
a.J_TRPV_k / p.r_buff + a.J_CICR_k)
du[idx['Cl_k']] = du[idx['Na_k']] + du[idx['K_k']] - du[
idx['HCO3_k']] + p.z_Ca * du[idx['Ca_k']]
du[idx['K_s']] = 1 / p.VR_sa * (a.J_K_k - 2 * a.J_NaK_k - a.J_NKCC1_k -
a.J_KCC1_k) + a.J_K_NEtoSC
du[idx['Na_s']] = 1 / p.VR_sa * (a.J_Na_k + 3 * a.J_NaK_k - a.J_NKCC1_k -
a.J_NBC_k) + a.J_Na_NEtoSC
du[idx['HCO3_s']] = 1 / p.VR_sa * (-2 * a.J_NBC_k)
du[idx['w_k']] = a.phi_w * (a.w_inf - v.w_k)
du[idx['I_k']] = p.r_h * a.G - p.k_deg * v.I_k
du[idx['h_k']] = p.k_on * (p.K_inh - (v.Ca_k + p.K_inh) * v.h_k)
du[idx['s_k']] = -(a.B_cyt *
(a.J_IP3 - a.J_pump + a.J_ER_leak + a.J_CICR_k)) / (
p.VR_ER_cyt)
du[idx['m_k']] = p.trpv_switch * ((a.minf_k - v.m_k) / p.t_TRPV_k)
du[idx['eet_k']] = p.V_eet * max(v.Ca_k - p.Ca_k_min,
0) - p.k_eet * v.eet_k
du[idx['NO_k']] = a.p_NO_k - a.c_NO_k + a.d_NO_k
du[idx['AA_k']] = (p.AA_m * p.AA_max) / (p.AA_m + max(
v.Ca_k - p.Ca0, 0))**2 * du[idx['Ca_k']] + (v.AA_i - v.AA_k) / p.tau_AA
du[idx[
'Ca_i']] = a.J_IP3_i - a.J_SR_uptake_i - a.J_extrusion_i + a.J_SR_leak_i - a.J_VOCC_i + a.J_CICR_i + a.J_NaCa_i - 0.1 * a.J_stretch_i + a.J_Ca_coup_i
du[idx['s_i']] = a.J_SR_uptake_i - a.J_CICR_i - a.J_SR_leak_i
du[idx['v_i']] = p.gamma_i * (
-a.J_NaK_i - a.J_Cl_i - 2 * a.J_VOCC_i - a.J_NaCa_i - a.J_K_i -
a.J_stretch_i - a.J_KIR_i) + a.V_coup_i # - a.J_GABA_i) + a.V_coup_i
du[idx['w_i']] = p.lambda_i * (a.K_act_i - v.w_i)
du[idx['I_i']] = a.J_IP3_coup_i - a.J_degrad_i
du[idx['NO_i']] = a.p_NO_i - a.c_NO_i + a.d_NO_i
du[idx['E_b']] = -p.k1 * v.E_b * v.NO_i + p.k_1 * v.E_6c + a.k4 * a.E_5c
du[idx['E_6c']] = p.k1 * v.E_b * v.NO_i - (
p.k_1 + p.k2) * v.E_6c - p.k3 * v.E_6c * v.NO_i
du[idx['cGMP_i']] = p.V_max_sGC * a.E_5c - a.V_max_pde * v.cGMP_i / (
p.K_m_pde + v.cGMP_i)
du[idx['H_i']] = p.HETswitch_20HETE * (a.f_NO * p.V_a * v.AA_i /
(p.K_a + v.AA_i) + p.V_f * v.AA_i /
(p.K_f + v.AA_i) -
p.lambda_h * v.H_i)
du[idx['AA_i']] = (v.AA_k - v.AA_i) / p.tau_AA
du[idx[
'Ca_j']] = a.J_IP3_j - a.J_ER_uptake_j + a.J_CICR_j - a.J_extrusion_j + a.J_ER_leak_j + a.J_cation_j + p.J_0_j - a.J_stretch_j - a.J_Ca_coup_i
du[idx['s_j']] = a.J_ER_uptake_j - a.J_CICR_j - a.J_ER_leak_j
du[idx['v_j']] = -1 / p.C_m_j * (a.J_K_j + a.J_R_j) - a.V_coup_i
du[idx['I_j']] = p.J_PLC - a.J_degrad_j - a.J_IP3_coup_i
du[idx['eNOS']] = p.gam_eNOS * a.Act_eNOS_Ca * p.NOswitch_EC_CA + (
1 - p.gam_eNOS) * a.Act_eNOS_wss * p.NOswitch_EC_WSS - p.mu2_j * v.eNOS
du[idx['NO_j']] = a.p_NO_j - a.c_NO_j + a.d_NO_j
du[idx['Mp']] = p.wallMech * (p.K_4 * v.AMp + a.K_1 * a.M -
(a.K_2 + p.K_3) * v.Mp)
du[idx['AMp']] = p.wallMech * (p.K_3 * v.Mp + a.K_6 * v.AM -
(p.K_4 + a.K_5) * v.AMp)
du[idx['AM']] = p.wallMech * (a.K_5 * v.AMp - (p.K_7 + a.K_6) * v.AM)
du[idx['R']] = p.R_init / p.eta_R * (v.R * p.trans_p / a.h - a.E *
(v.R - a.R_0) / a.R_0)
# du[idx['GABA']] = - a.kappa_GABA * (v.GABA - p.GABAbase ) + p.alpha_GABA * (v.I_t - p.Imin) / (p.I_relative - p.Imin)
# du[idx['NPY']] = - p.beta_NPY * (v.NPY - p.NPYbase) + p.alpha_NPY * (v.I_t - p.Imin) / (p.I_relative - p.Imin)
# du[idx['Glu']] = - p.beta_Glu * v.Glu + p.GluSwitch * (a.f_Ke + a.f_GABA)
# Print to console the current time and percentage completed
time_elapsed = time.time() - start_time
print(
'\r-- Time {0:.2f} / {1:.0f} -- {2:.1f}% complete -- {3:.0f} sec elapsed --'
.format(t / 1e3, p.Tend / 1e3, t / p.Tend * 100, time_elapsed),
end='')
return du
def single_eval(QoI_flag, Param_Index, change_value):
iteration = [Param_Index, change_value]
data_choice = 'pre'
import sys
sys.path.append("./Model_Codes/")
# Import modules
from scipy.integrate import odeint
from scipy import io
from scipy.interpolate import interp1d
import numpy as np
import time
import matplotlib.pyplot as plt
import warnings
from matplotlib.patches import Rectangle
# Local files
from indices import set_indices
from ICs import set_initial_conditions
from ODEsystem import func
from algebraic_variables import set_algebraic_variables
from state_variable_names import initialise_var_names
from normalised_hemo import solve_normalised_hemodynamics
from plotting_functions import plot_variables_singles, plot_variables_samegraph
from model_functions import T_pulses
from parameters import p_function
import import_mat_files_no_fig as im
from multipliers import V_IC
if data_choice == 'pre':
p = p_function(iteration, 'normal')
elif data_choice == 'post':
p = p_function(iteration, 'LNAME')
# Calculate the start time
start_time = time.time()
# Initialise indices, initial conditions and time vector
idx = set_indices()
u0 = set_initial_conditions(idx, V_IC)
t = np.arange(start=0, stop=p.Tend + p.dt, step=p.dt)
# Import the experimental neural input profile from mat file, for InputCase = 'ZhengData', 'ThalamicTrianglesZheng' or 'ZhengFittedParams'
input_data = im.import_Zheng_neural_data(p, t)
# Generate the multiple triangular pulse input profile for the thalamic input T(t), for InputCase = 'ThalamicTriangles' or 'ThalamicTrianglesZheng'
total_waveform = T_pulses(p)
# Solve ODE system
#print("\n Model simulation successfully started\n")
u, solver_details = odeint(func,
u0,
t,
args=(p, idx, input_data, total_waveform,
start_time),
hmax=1e2,
full_output=1)
if solver_details['message'] == 'Integration successful.':
del solver_details
# Print total time taken
solve_time = time.time() - start_time
if solve_time < 60.0:
print("\n\n--- Took {0:.0f} seconds to solve ---".format(solve_time))
else:
minutes, seconds = divmod(solve_time, 60)
print("\n\n--- Took {0:.0f} minutes and {1:.0f} seconds to solve ---".
format(minutes, seconds))
del start_time, solve_time
# Extract the state variables for easy plotting etc
v = initialise_var_names(u, idx, 'out function')
# Extract the algebraic variables for easy plotting etc
a = set_algebraic_variables(p, u, t, idx, input_data, total_waveform,
'out function')
del total_waveform, input_data
# Solve for the normalised CBF, CBV, CMRO2, HbO, HbR, HbT and BOLD and put into 'a' (solved separately from odeint as they depend on the steady state conditions)
a = solve_normalised_hemodynamics(p, a, v, t)
# The variables that can be plotted sorted into alphabetical order
state_vars = sorted(vars(v).keys())
#print("\nState variables: \n", state_vars, "\n")
alg_vars = sorted(vars(a).keys())
#print("Algebraic variables: \n", alg_vars,"\n")
if p.startpulse < p.Tend:
time = t / 1e3 - p.startpulse / 1e3 # Time in seconds, normalised so t=0 at the start of stimulation
xlim1 = p.startpulse / 1e3 - 10 # Set left xlimit to 10 sec before stimulation begins
else:
time = t / 1e3 # Time in seconds, not normalised if there is no stimulation
xlim1 = 0 # Set left xlimit to 0 if there is no stimulation
xlim2 = p.Tend / 1e3 # Set right xlimit to end of simulation
del t
pulse_marker = np.where(time >= 0.0)
pulse_marker = pulse_marker[0][0]
pre_pulse_marker = np.where(time >= -5.0)
pre_pulse_marker = pre_pulse_marker[0][0]
pre_end_marker = np.where(time >= time[-1] - 5.0)
pre_end_marker = pre_end_marker[0][0]
radius = v.R
radius = radius / radius[pulse_marker]
clean_flag = 0
if not np.all(np.isfinite(radius)):
clean_flag = -2
elif not np.all(np.isreal(radius)):
clean_flag = -3
elif np.amax(abs(radius[pre_pulse_marker:pulse_marker + 1] - 1)) > 1e-3:
clean_flag = -4
elif np.amax(abs(radius[pre_end_marker:] - 1)) > 1e-3:
clean_flag = -5
elif np.amin(abs(radius[pulse_marker:])) < 0.9:
clean_flag = -6
else:
clean_flag = 1
if clean_flag != 1:
QoI = 100
else:
# Set custom xlims if wanted
if data_choice == 'pre':
dataset = 'tots_LNAME_pre'
elif data_choice == 'post':
dataset = 'tots_LNAME_post'
#sys.path.remove("./Model_Codes/")
Data = im.import_Berwick_HET_LNAME_Data(dataset, area='Whisker')
if QoI_flag == 0:
interpolator = interp1d(time, a.HBO_N)
HBO_interp = interpolator(Data.time)
Error_HBO = HBO_interp - Data.HbOwhisk_mean
Error_HBO = list(map(lambda x: x * x, Error_HBO))
Error = Error_HBO
elif QoI_flag == 1:
interpolator = interp1d(time, a.HBR_N)
HBR_interp = interpolator(Data.time)
Error_HBR = HBR_interp - Data.HbRwhisk_mean
Error_HBR = list(map(lambda x: x * x, Error_HBR))
Error = Error_HBR
QoI = Error
return QoI