- Author:
- Leyla <lnor300>
- Date:
- 2022-09-27 19:24:59+13:00
- Desc:
- ..
- Permanent Source URI:
- https://models.cellml.org/workspace/83c/rawfile/881bedcb01f191d01798f0d6266263a935197ee3/Simulations/ICC_Lees_Green.py
import opencor as oc
import numpy as np
import json
# Author: Leyla Noroozbabaee
# Date: 12/ 09/2021
# To reproduce the data needed for Figure 3 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 ICC_Lees_Green.py
# To reproduce 4A-4E; the json file 'HCl_NaV.json' is loaded to update the model's parameters and initial conditions
# for model simulations accordingly. Data is saved in the cvs files: Fig4_1a and Fig4_1b.
# To reproduce 4B-4F; 'HCl_NSV.json' is loaded. Data is saved in the cvs files: Fig4_2a and Fig4_2b
# To reproduce 4C-4G; 'HCl_NSCC.json' is loaded. Data is saved in the cvs files: Fig4_3a and Fig4_3b
# To reproduce 4D-4H; 'HCl_CaV.json' is loaded. Data is saved in the cvs files: Fig4_4a and Fig4_4b
# To reproduce 5A-5C; 'LCl_NaV.json' is loaded. Data is saved in the cvs files: Fig5_1a and Fig5_1b
# To reproduce 5B-5D; 'LCl_NSCC.json' is loaded. Data is saved in the cvs files: Fig5_2a and Fig5_2b
simfile = 'ICC_Lees_2021.sedml'
simulation = oc.open_simulation(simfile)
data = simulation.data()
# Set constant parameter values
start = 0
end = 30
pointInterval = 0.01
data.set_starting_point(start)
data.set_ending_point(end)
data.set_point_interval(pointInterval)
rows = 30 * 100 + 1
# Setting the new parameters and initial conditions
prefilename = ['Fig4_1', 'Fig4_2', 'Fig4_3', 'Fig4_4','Fig5_1','Fig5_2']
param_file = ['HCl_NaV' ,'HCl_NSV' ,'HCl_NSCC' ,'HCl_CaV' ,'LCl_NaV' ,'LCl_NSCC']
for i in range(6):
# Reset states and parameters
simulation.reset(True)
param_filename = '%s.json' % param_file [ i ]
print('param_filename', param_filename)
with open(param_filename,'r') as fp:
variables = json.load(fp)
# print(data.constants())
for k, v in variables['constants'].items():
data.constants()[k] = v
for k, v in variables['states'].items():
data.states()[k] = v
# Run the simulation for WT scenarios
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a selected algebraic variable results
varName = np.array([ "time", "Vm", "Ca_i"])
vars = np.reshape(varName, (1, 3))
r = np.zeros((rows, len(varName)))
r[:,0] = results.voi().values()
r[:,1] = results.states()['ICC_Membrane/Vm'].values()
r[:,2] = results.states()['ICC_Membrane/Ca_i'].values()
# Save the simulation results
filename = '%sa.csv' % prefilename[i]
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
# Run the simulation for KO scenarios
data.constants()['ICC_Membrane/I_Ano1/g_Ano1'] = 0
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a selected algebraic variable results
varName = np.array([ "time", "Vm", "Ca_i"])
vars = np.reshape(varName, (1, 3))
r = np.zeros((rows, len(varName)))
r[:,0] = results.voi().values()
r[:,1] = results.states()['ICC_Membrane/Vm'].values()
r[:,2] = results.states()['ICC_Membrane/Ca_i'].values()
# Save the simulation result of the last run
filename = '%sb.csv' % prefilename[i]
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
