- Author:
- WeiweiAi <wai484@aucklanduni.ac.nz>
- Date:
- 2021-07-06 11:22:35+12:00
- Desc:
- Change the figures size to fit the Physiome paper;
Adjust the solver setting to remove the notch in Fig6.
Minor editting in README.
- Permanent Source URI:
- https://models.physiomeproject.org/workspace/692/rawfile/b98a10f69ee76a70243efdb648c598db5a866a2c/Simulation/Fig5_sim.py
# To reproduce the data needed for Figure 5 in associated original paper,
# execute this script in the Python console in OpenCOR. This can be done
# with the following commands at the prompt in the OpenCOR Python console:
#
# In [1]: cd path/to/folder_this_file_is_in
# In [2]: run Fig5_sim.py
import opencor as oc
import numpy as np
# The prefix of the saved output file name
prefilename = 'simFig5'
# Load the simulation file
simfile='Patch_clampXi_experiment.sedml'
simulation = oc.open_simulation(simfile)
# The data object houses all the relevant information
# and pointers to the OpenCOR internal data representations
data = simulation.data()
# Set the experiments
Vholding, t_ss = -70, 1000
Vtest = range (-70, 70, 10)
duration = 50
Cai =[0.001, 0.0003]
items = ['a', 'b']
T=297
# Define the interval of interest for this simulation experiment
start, end, pointInterval = 0, t_ss+duration, 0.1
data.set_starting_point(start)
data.set_ending_point(end)
data.set_point_interval(pointInterval)
# Data to save
varName = np.array(["V", "P_BK"])
vars = np.reshape(varName, (1, len(varName)))
row_start=int(t_ss/pointInterval-50) # to get the peak
r = np.zeros((len(Vtest),len(varName)))
for j, iCai in enumerate(Cai):
for i, V in enumerate(Vtest):
# Reset states and parameters
simulation.reset(True)
# Set constant parameter values
data.constants()['Clamp_parameters/V_actHolding'] = Vholding
data.constants()['Clamp_parameters/Cai'] = iCai
data.constants()['Clamp_parameters/V_actTest'] = V
data.constants()['Clamp_parameters/T'] = T
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a specific algebraic variable results
r[i,0] = V
temp = results.states()['outputs/P_BK'].values()[row_start:]
r[i,1] = max(abs(temp))
# clear the results except the last run
simulation.clear_results()
# Save the simulation result of the last run
filename='%s%s.csv' % (prefilename,items[j])
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
