- Author:
- WeiweiAi <wai484@aucklanduni.ac.nz>
- Date:
- 2021-06-09 17:59:55+12:00
- Desc:
- Update figure titles and documentation;
- Permanent Source URI:
- https://models.cellml.org/workspace/692/rawfile/4de8d2594e8e71c62b4f118f49fde8a277df70d7/Simulation/Fig13_sim.py
# To reproduce the data needed for Figure 13 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]: Fig13_sim.py
import opencor as oc
import numpy as np
# The prefix of the saved output file name
prefilename = 'simFig13_'
# Load the simulation file
simfile='Periodic_stimulation_SA.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()
# Define the interval of interest for this simulation experiment
pointInterval = 0.1
data.set_point_interval(pointInterval)
N=30
varName = np.array(["time","V", "Cai"])
vars = np.reshape(varName, (1, 3))
rows=20*1000*10
r = np.zeros((rows,len(varName)))
# control
# Reset states and parameters
simulation.reset(True)
for i in range(N):
# Set constant parameter values
start=i*60000
end=start+60000
data.set_starting_point(start)
data.set_ending_point(end)
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a specific algebraic variable results
r[:,0] = results.voi().values()[0:rows]
r[:,1] = results.states()['outputs/V'].values()[0:rows]
r[:,2] = results.states()['outputs/Cai'].values()[0:rows]
# clear the results except the last run
simulation.clear_results()
# Save the simulation result of the last run
filename='%s0.csv' % (prefilename)
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
# Parameters to change
gs = [1.0217, 80, 25.1, 1.44]
gnames = ['g_Kv', 'g_BK', 'g_Na', 'g_CaL']
files = ['a', 'b', 'c', 'd']
for j , Gmax in enumerate(gs):
# Set constant parameter values
gkey = 'g_parameters/%s' % gnames[j]
for n in range(2):
if n == 0: #increase 50%
# Reset states and parameters
simulation.reset(True)
data.constants()[gkey] = Gmax*1.5
else: # decrease 50%
# Reset states and parameters
simulation.reset(True)
data.constants()[gkey] = Gmax*0.5
for i in range(N):
# Set constant parameter values
start=i*60000
end=start+60000
data.set_starting_point(start)
data.set_ending_point(end)
simulation.run()
# Access simulation results
results = simulation.results()
# Grab a specific algebraic variable results
r[:,0] = results.voi().values()[0:rows]
r[:,1] = results.states()['outputs/V'].values()[0:rows]
r[:,2] = results.states()['outputs/Cai'].values()[0:rows]
# clear the results except the last run
simulation.clear_results()
# Save the simulation result of the last run
filename='%s%s%d.csv' % (prefilename,files[j],(n+1))
np.savetxt(filename, vars, fmt='%s',delimiter=",")
with open(filename, "ab") as f:
np.savetxt(f, r, delimiter=",")
f.close
