Location: Hodgkin & Huxley (1952) model @ bd02dedb491f / sed-ml / originalFig20_sim.py

Author:

WeiweiAi <wai484@aucklanduni.ac.nz>

Date:

2021-11-24 17:49:12+13:00

Desc:

Add BG params related scripts and matrix

Permanent Source URI:

http://models.cellml.org/workspace/64f/rawfile/bd02dedb491faf9960ccd23496c19ccd3e339db1/sed-ml/originalFig20_sim.py

# To reproduce the data needed for Figure 20 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 originalFig20_sim.py

import opencor as oc
import get_init
import imp
import numpy as np
imp.reload(get_init)

# The prefix of the saved output file name 
prefilename = 'simFig20'
# Load the simulation file
simfile='periodic-stimulus.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
start, end, pointInterval = 0, 22, 0.0001
data.set_starting_point(start)
data.set_ending_point(end)
data.set_point_interval(pointInterval)

# Compute initial value based on T and V_b
T, V_b = 6, 0
m, n, h = get_init.init_gate(T, V_b)

iV_initial = -15
V_stim = -90
t_stim = [20, 4.7284, 5.7302, 7.7352]
suffixfile=['A', 'B', 'C', 'D',]
varName = np.array(['outputs/time', 'outputs/minus_V'])
vars = np.reshape(varName, (1, len(varName)))
rows=int(end/pointInterval+2)
r = np.zeros((rows,len(varName)))

for i, iend in enumerate(t_stim):

   filename ='%s_%s.csv' % (prefilename, suffixfile[i])
   # Reset states and parameters
   simulation.reset(True)
   # Set constant parameter values
   data.states()['outputs/V'] = iV_initial
   data.constants()['parameters/T'] = T
   data.states()['outputs/m'] = m
   data.states()['outputs/n'] = n
   data.states()['outputs/h'] = h
   # Run simulation from 0 to iend
   data.set_starting_point(start)
   data.set_ending_point(iend)
   row1=int(iend/pointInterval+1)
   simulation.run()
   # Access simulation results
   results = simulation.results()
   # Grab a specific algebraic variable results 
   row1=len(results.voi().values())
   r[0:row1,0] = results.voi().values()
   r[0:row1,1] = results.algebraic()['outputs/minus_V'].values()
          
   # Stimulate at iend and run till end
   data.states()['outputs/V'] = V_stim+data.states()['outputs/V']
   data.set_starting_point(iend)
   data.set_ending_point(end)
   simulation.run()
    # Access simulation results
   results = simulation.results()
   # Grab a specific algebraic variable results 
   r[row1:,0] = results.voi().values()[0:]
   r[row1:,1] = results.algebraic()['outputs/minus_V'].values()[0:]

   # Save the simulation result
   np.savetxt(filename, vars, fmt='%s',delimiter=",")
   with open(filename, "ab") as f:
       np.savetxt(f, r, delimiter=",")
   f.close
   # clear the results
   simulation.clear_results()