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:
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
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