CellMLToNektar.py

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#!/usr/bin/env python

"""Copyright (c) 2005-2016, University of Oxford.
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"""

"""
This part of PyCml deals with converting CellML models into programming language code.
It is a thin executable wrapper around translators.py.
"""

import os #importing functions related to operating system
import sys #importing functions related to system (e.g. path directory etc)

# Make sure PyCml is on sys.path
pycml_path = os.path.dirname(os.path.realpath(__file__))
sys.path[0:0] = [pycml_path]

import translators #importing the main translator class
from translators import CellMLTranslator #again, just to make sure
import CellMLToNektarTranslator #importing the nektar sub-class

#This part actually runs the code

if __name__ == '__main__': #this part of the code is only run when CellMLToNektar.py is run directly, not imported
    CellMLTranslator.register(CellMLTranslator, 'C++') #this registers the main translator and it's name when called as an option in modified_translators
    CellMLTranslator.register(CellMLToNektarTranslator.CellMLToNektarTranslator, 'Nektar') #this registers the nektar subclass and it's name when called as an option in modified_translators
    if '--profile' in sys.argv: #sys.argv is the list of command line items passed to the script, this checks if a specific translator is requested
        import time, cProfile
        profile_name = '/tmp/pycml-profile-%f-%d' % (time.time(), os.getpid())
        cProfile.run('modified_translators.run()', profile_name) #the requested translator is run
    else:
        translators.run() #if no specific translator is requested, the default run function in modified_translators is run

#Below here seem to be variables that do some form of testing

    # For use in testing
    def euler(doc, t, nsteps=1000, dt=0.01):
        global tvar, state_vars, exprs #assigns tvar, state_vars and exprs to be global variables, meaning they can be used by any function
        tvar = t.free_vars[0] #takes the t-input and does "free_vars[0]" to it?
        state_vars = t.state_vars #defines state_vars as the state_vars of the t-input?
        for var in state_vars: #cycles through all the entries in "state_vars"
            var.set_value(float(var.initial_value)) #for all the entries in "state_vars" it sets the value to a float of the initial value
        tvar.set_value(0.0) #this sets the value of tvars to (0.0)?
        exprs = [e for e in doc.model.get_assignments() #I don't understand this part
                 if isinstance(e, modified_translators.mathml_apply)] #what is the mathml_apply function? Can't find it in modified_translators
        for _ in range(nsteps): #this is a for-loop through all the values up to the input of nsteps
            for expr in exprs: #this cycles through all the entries in exprs
                expr.evaluate()
            tvar.set_value(tvar.get_value() + dt)
            for var in state_vars:
                var.set_value(var.get_value() +
                              dt * var.get_value(ode=tvar))
        return

    def writefile(doc, outfn='test.cml'):
        # Write out CellML file
        st = modified_translators.open_output_stream(outfn)
        doc.xml(indent=1, stream=st)
        st.close()
        return

    def show_usage(doc):
        for comp in doc.model.component:
            for var in comp.variable:
                print var.fullname(), var._cml_usage_count


    def fix_divide_by_zero(doc):
        """
        Several models have equations of a form that may give rise to
        a divide by zero error on simulation, especially when lookup
        tables are used.  The general form is:
        
        (a * (V - v0)) / (exp(b * (V - v0)) - 1)
        
        When V = v0 this is undefined, however the limit of the
        function as V approaches v0 from either side is well-defined,
        and each limit is the same.  We approximate the limit by
        linear interpolation between values of the expression for
        (V-v0) = +/- 1e-10.
        """
        divides = [d.xml_parent
                   for d in doc.xml_xpath(u'//m:apply/m:divide')]
        for divide in divides:
            pass
        return