- Author:
- WeiweiAi <wai484@aucklanduni.ac.nz>
- Date:
- 2021-09-30 13:14:32+13:00
- Desc:
- fix the typos and tables in doc
- Permanent Source URI:
- https://models.cellml.org/workspace/6bb/rawfile/5766fc294ca1fb80e47e776c78d754cad7f5fc7f/Simulation/src/Fig8B_sim.py
# To reproduce the data needed for Figure 8B 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 Fig8B_sim.py
import opencor as oc
import numpy as np
# Load the simulation file
simfile='../Nai_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
K_Cahalf = 11
duration = 46
t_ss = duration+1
V_actHolding = -50
Cai=[200, 450, 700]
# Define the interval of interest for this simulation experiment
start, pointInterval = 0, 0.001
data.set_starting_point(start)
data.set_point_interval(pointInterval)
# Data to save
varName = np.array(["time", "J_NaCa", "Nai"])
vars = np.reshape(varName, (1, len(varName)))
# start to save when the test voltage returns to holding
rows=int((duration)/pointInterval+1)
r = np.zeros((rows,len(varName)))
# The prefix of the saved output file name
prefilename = 'simFig8B'
inhPump=1
for j, iCai in enumerate(Cai):
data.set_ending_point(duration)
# Reset states and parameters
simulation.reset(True)
# Set constant parameter values
data.constants()['control_para/inhPump'] = inhPump
data.constants()['control_para/K_Cahalf'] = K_Cahalf
data.constants()['control_para/Cai'] = iCai*1e-6
data.constants()['Vstim_para/t_ss'] = t_ss
data.constants()['Vstim_para/V_actHolding'] = V_actHolding
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a specific algebraic variable results
r[:,0] = results.voi().values()[-rows:]
r[:,1] = results.algebraic()['outputs/J_NaCa'].values()[-rows:]
r[:,2] = results.states()['Nai/Nai'].values()[-rows:]
# clear the results except the last run
simulation.clear_results()
# Save the simulation result of the last run
filename='../simulatedData/%s_%d.csv' % (prefilename,j)
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
