Skip to content

Latest commit

 

History

History
190 lines (130 loc) · 4.43 KB

OpenCOR_Python.rst

File metadata and controls

190 lines (130 loc) · 4.43 KB
Raw

Python in OpenCOR

IPython console window

A dockable window running IPython that includes NumPy, Scipy library and matplotlib.

If not visible then enable it from OpenCOR's menu: View/Windows/PythonQtConsole.

An IPython tutorial is here. Some keys are different than in the tutorial:

Control-Enter

start multi-line input.

Shift-Enter

execute code block.

Escape

clear input.

Command history:

Up/down keys

History filtered with currently typed characters.

%history

All commands in current session.

Change font size in Python console:

Linux and Windows

Control +/-

MacOS

Command +/-

OpenCOR Python module

import OpenCOR as oc

Methods:

openSimulation()

Open a model. Corresponds to File/Open.

Returns the simulation.

openRemoteSimulation()

Open a remote model. Corresponds to File/Open Remote.

Returns the simulation.

simulation()

Returns the current simulation.

simulations()

Returns a dictionary of open simulations.

Objects:

Simulation

DataStore

DataStoreVariable

Sample code

# Change the current directory
%cd ~/biosignalml/workspace/cellml/workspaces/dbrooks/noble62

# Import OpenCOR's Python module
import OpenCOR as oc

# Current (visible) simulation
s = oc.simulation()

# Dictionary of all open simulations
s = oc.simulations()

# Open a simulation (Name of CellML or SEDML file)
s = oc.openSimulation('noble_1962.cellml')

# Open a remote simulation (URL of a CellML or SEDML file)
s = oc.openRemoteSimulation('https://models.physiomeproject.org/workspace/25d/rawfile/f0e63e292ebb1d6798da08914b4217aec434af96/lorenz.sedml')

# Access a simulation's initial conditions
d = s.data()

# Time constraints
d.startingPoint()
d.endingPoint()
d.pointInterval()

# Changing values
d.setStartingPoint(1)
d.setEndingPoint(2)
d.setPointInterval(0.1)

# Parameters (as Python dictionaries)
d.constants()
d.states()
d.rates()

# Changing values
d.constants()['main/k'] = 1.0

# Run a simulation
s.run()

# Reset a simulation's parameters
s.resetParameters()

# Clear a simulation's results
s.clearResults()

# Access a simultaion's results
r = s.results()

# Time points
t = r.points()
t.values()

# Dictionaries of DataStore variables
r.constants()
r.states()
r.rates()
r.algebraic()

# Individual variables
x = r.states()['main/k']
x.values()

# Data also accessible via the simulation's DataStore
ds = r.dataStore()

# Variable of integration (r.points())
ds.voi()

# Dictionaries of DataStore variables
ds.variables()
ds.voiAndVariables()

# Individual variables
x = ds.variables()['main/k']

matplotlib example

A matplotlib tutorial is available here.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# The following assumes a simulation of the Lorenz model has been run
states = s.results().states()

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(states['main/x'].values(), states['main/y'].values(), states['main/z'].values(), linewidth=0.5)
ax.set_xlabel(states['main/x'].label())
ax.set_ylabel(states['main/y'].label())
ax.set_zlabel(states['main/z'].label())
plt.show()