"""
PySCeS - Python Simulator for Cellular Systems (http://pysces.sourceforge.net)

Copyright (C) 2004-2012 B.G. Olivier, J.M. Rohwer, J.-H.S Hofmeyr all rights reserved,

Brett G. Olivier (bgoli@users.sourceforge.net)
Triple-J Group for Molecular Cell Physiology
Stellenbosch University, South Africa.

Permission to use, modify, and distribute this software is given under the
terms of the PySceS (BSD style) license. See LICENSE.txt that came with
this distribution for specifics.

NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
Brett G. Olivier
"""

"""
FUTURE TODO:
Parameter elasticities wrt the compartments
"""

from pysces.version import __version__
__doc__ = '''
            PyscesModel
            -----------

            This module contains the core PySCeS classes which
            create the model and associated data objects

            '''
import os, copy, gc, time
import math, operator, re
import pprint, cPickle, cStringIO
import numpy, scipy, scipy.linalg
from getpass import getuser

from pysces import model_dir as MODEL_DIR
from pysces import output_dir as OUTPUT_DIR
from pysces import install_dir as INSTALL_DIR
from pysces import nleq2
from pysces import nleq2_switch
from pysces import pitcon
from pysces import plt, gplt
from pysces import PyscesStoich
from pysces import PyscesParse
from pysces.PyscesScan import Scanner
from pysces.core2.InfixParser import MyInfixParser

interface = None

# Scipy version check
if int(scipy.version.version.split('.')[1]) < 6:
    print '\nINFO: Your version of SciPy (' +  scipy.version.version + ') might be too old\n\tVersion 0.3.x or newer is strongly recommended\n'
else:
    print 'You are using NumPy (%s) with SciPy (%s)' % (numpy.__version__, scipy.__version__)

_HAVE_PYSUNDIALS = False
_PYSUNDIALS_LOAD_ERROR = ''
try:
    from pysundials import cvode
    print 'PySundials available'
    _HAVE_PYSUNDIALS = True
except Exception, ex:
    _PYSUNDIALS_LOAD_ERROR = '%s' % ex
    _HAVE_PYSUNDIALS = False

# We now welcome a prototype StomPy interface to PySCeS which will provide expanding stochastic simulation support.
_HAVE_STOMPY = False
_STOMPY_LOAD_ERROR = ''
try:
    ##  import stompy
    import stochpy as stompy
    _HAVE_STOMPY = True
except Exception, ex:
    _STOMPY_LOAD_ERROR = '%s' % ex
    _HAVE_STOMPY = False

# for future fun
_HAVE_VPYTHON = False
_VPYTHON_LOAD_ERROR = ''
##  try:
    ##  import visual
    ##  _HAVE_VPYTHON = True
    ##  vpyscene = visual.display(x=150, y=50, title='PySCeS '+__version__+' - C Brett G. Olivier, Stellenbosch 2006', width=640, height=480,\
    ##  center=(0,0,0), background=(0,0,0))
    ##  vpyscene.select()
    ##  # vpyscene.autocenter = True
    ##  l1 = visual.label(pos=(0,0,0), text="Welcome to the PySCeS Visualisation Terminal", color=visual.color.red, box=0)
    ##  l2 = visual.label(pos=(0,0.5,0.5), text="It just works!", color=visual.color.green, box=0)
##  except Exception, ex:
    ##  _VPYTHON_LOAD_ERROR = '%s' % ex
    ##  _HAVE_VPYTHON = False

__psyco_active__ = 0
##  try:
    ##  import psyco
    ##  psyco.profile()
    ##  __psyco_active__ = 1
    ##  print 'PySCeS Model module is now PsycoActive!'
##  except:
    ##  __psyco_active__ = 0

# machine specs
mach_spec = numpy.MachAr()
# grab a parser
pscParser = PyscesParse.PySCeSParser(debug=0)

def chkpsc(File):
    """
    chkpsc(File)

    Chekc whether the filename "File" has a '.psc' extension and adds one if not.

    Arguments:

    File: filename string

    """
    try:
        if File[-4:] == '.psc':
            pass
        else:
            print 'Assuming extension is .psc'
            File += '.psc'
    except:
        print 'Chkpsc error'
    return File

def chkmdir():
    """
    chkmdir()

    Import and grab pysces.model_dir

    Arguments:
    None

    """
    ##  from pysces import model_dir as MODEL_DIR
    pass


class StomPyInterface(object):
    """
    StomPy interface to PySCeS this may move to pysces.link in the future
    """
    SSA = None
    SSA_REACTIONS = None
    SSA_SPECIES = None
    stompy = None
    _MOD2PSC = None

    TMP_FNAME = None
    TMP_PATH = None
    MODEL_PATH = None
    OUTPUT_PATH = None

    STP_IS_TIME_SIM = False
    STP_METHOD = 'Direct'
    STP_TIMEEND = 1
    STP_TRAJ = 1
    STP_INTERACTIVE = True
    STP_TRACK_PROPENSITIES = True
    STP_WAITING_TIMES = True
    STP_STEPS = 10
    STP_INITIAL_SPECIES = True
    STP_KEEP_PSC_FILES = False

    def __init__(self, model_path, output_path):
        """
        An interface class to the StomPy stochastic simulator

         - *model_path* the default PySCeS model directory
         - *output_path* the default PySCeS output directory
        """
        self.stompy = stompy
        self.OUTPUT_PATH = output_path
        self.MODEL_PATH = model_path
        self.TMP_PATH = os.path.join(model_path, 'orca')
        self._MOD2PSC = interface.writeMod2PSC


    def setProperty(self, **kwargs):
        """
        Sets a StomPy simulation parameter

         - *method* [default='Direct'] select simulation algorithm
         - *trajectories* [default=1]
         - *interactive* [default=True]
         - *track_propensities* [default=True]
         - *steps* [default=10]

        """
        print kwargs

        if kwargs.has_key('method'):
            self.STP_METHOD = kwargs['method']
            print '%s = %s' % ('method', kwargs['method'])
        if kwargs.has_key('trajectories'):
            self.STP_TRAJ = kwargs['trajectories']
            self.STP_TRAJ = 1 # TODO I need to look into this
            print 'Currently only single trajectories are supported via the PySCeS interface'
            print '%s = %s' % ('trajectories', self.STP_TRAJ)
        if kwargs.has_key('interactive'):
            self.STP_INTERACTIVE = kwargs['interactive']
            print '%s = %s' % ('interactive', kwargs['interactive'])
        if kwargs.has_key('track_propensities'):
            self.STP_TRACK_PROPENSITIES = kwargs['track_propensities']
            print '%s = %s' % ('track_propensities', kwargs['track_propensities'])
        if kwargs.has_key('steps'):
            self.STP_STEPS = kwargs['steps']
            print '%s = %s' % ('steps', kwargs['steps'])
        if kwargs.has_key('species_initial'):
            self.STP_INITIAL_SPECIES = kwargs['initial_species']
            print '%s = %s' % ('initial_species', kwargs['initial_species'])
        if kwargs.has_key('keep_psc_files'):
            self.STP_KEEP_PSC_FILES = kwargs['keep_psc_files']
            print '%s = %s' % ('keep_psc_files', kwargs['keep_psc_files'])

    def initModelFromMod(self, pscmod, iValues=False):
        """
        Initialise a StomPy SSA instance from a PySCeS model.

         - *pscmod* an initialised PySCeS model
         - *iValues* [default=False] use initial values (not current)

        """
        self.TMP_FNAME = str(time.time()).replace('.','')+'.psc'

        if self.STP_INITIAL_SPECIES:
            for s in pscmod.species:
                setattr(pscmod, s, pscmod.__sDict__[s]['initial'])

        self._MOD2PSC(pscmod, self.TMP_FNAME, self.TMP_PATH, iValues=iValues)

        self.SSA = self.stompy.SSA(Method=self.STP_METHOD, File=self.TMP_FNAME, dir=self.TMP_PATH, IsInteractive=self.STP_INTERACTIVE)
        self.SSA.Trajectories(self.STP_TRAJ)
        self.SSA_REACTIONS = self.SSA.SSA.rate_names
        self.SSA_SPECIES = self.SSA.SSA.species

        if self.STP_TRACK_PROPENSITIES:
            self.SSA.TrackPropensities()
        try:
            print os.path.join(self.TMP_PATH, self.TMP_FNAME)
            if not self.STP_KEEP_PSC_FILES and self.TMP_PATH != None and self.TMP_FNAME != None:
                os.remove(os.path.join(self.TMP_PATH, self.TMP_FNAME))
        except:
            print 'Could not delete intermediatery StomPy PSC file: %s' % os.path.join(self.TMP_PATH, self.TMP_FNAME)
        self.TMP_FNAME = None
        print 'StomPy model ... initialised.'

    def runTimeSimulation(self, pscmod, endtime=None, method='Direct', iValues=False):
        """
        Run a stochastic simulation

         - *pscmod* and instanitiated PySCeS model
         - *endtime* [default=1] the end time **Note: this could take a long time i.e. generate ahuge amount of steps**
         - *method* [default='Direct'] select the simulation method, one of:

          - *Direct*
          - *FirstReactionMethod*
          - *NextReactionMethod*
          - *TauLeaping*

         - *iValues* [default=False] use initial values (not current)

        """

        if method not in ['Direct','FirstReactionMethod','NextReactionMethod','TauLeaping']:
            print 'Method: %s does not exist using - Direct' % method
            self.STP_METHOD = 'Direct'
        else:
            self.STP_METHOD = method
        if endtime != None:
            self.STP_TIMEEND = endtime
        self.initModelFromMod(pscmod)

        ##  self.SSA.Timesteps(self.STP_STEPS)
        self.SSA.Endtime(self.STP_TIMEEND)
        self.SSA.Run()
        self.STP_IS_TIME_SIM = True
        ##  self.SSA.PlotTimeSim()

        print 'StomPy time simulation ... done.'

        # TODO STOCHPY

        ##  if self.SSA.SSA.output[0][-1] == '':
            ##  self.SSA.SSA.output[0][-1] = 0.0
        ##  sim_dat = numpy.array(self.SSA.SSA.output, 'd')

        ##  pscmod.data_stochsim = IntegrationStochasticDataObj()
        ##  pscmod.data_stochsim.setTime(sim_dat[:,0])
        ##  pscmod.data_stochsim.setSpecies(sim_dat[:,1:-1], self.SSA_SPECIES)
        
        pscmod.data_stochsim = self.SSA.data_stochsim
        
        if self.STP_WAITING_TIMES:
            wtimes, wt_lbls = self.getWaitingtimesData(reactions=None,lbls=True)
            pscmod.data_stochsim.setWaitingtimes(wtimes, wt_lbls)
        if self.STP_TRACK_PROPENSITIES:
            pscmod.data_stochsim.setPropensities(self.SSA.SSA.propensities_output)
        pscmod.data_stochsim.TYPE_INFO = 'Stochastic'

    def runStepSimulation(self, pscmod, steps=None, method='Direct', iValues=False):
        """
        Run a stochastic simulation

         - *pscmod* and instanitiated PySCeS model
         - *steps* [default=10] the number of steps to simulate
         - *method* [default='Direct'] select the simulation method, one of:

          - *Direct*
          - *FirstReactionMethod*
          - *NextReactionMethod*
          - *TauLeaping*

        - *iValues* [default=False] use initial values (not current)

        """

        if method not in ['Direct','FirstReactionMethod','NextReactionMethod','TauLeaping']:
            print 'Method: %s does not exist using - Direct' % method
            self.STP_METHOD = 'Direct'
        else:
            self.STP_METHOD = method
        if steps != None:
            self.STP_STEPS = steps
        self.initModelFromMod(pscmod, iValues=iValues)

        self.SSA.Timesteps(self.STP_STEPS)
        ##  self.SSA.Endtime(self.STP_TIMEEND)
        self.SSA.Run()
        self.STP_IS_TIME_SIM = False

        print 'StomPy step simulation ... done.'
        ##  print self.SSA.SSA.output[0]
        ##  print self.SSA.SSA.output[1]
        ##  print self.SSA.SSA.output[-1]
        ##  header_line = self.SSA.SSA.output.pop(0)
        ##  if self.SSA.SSA.output[0][-1] == '':
            ##  self.SSA.SSA.output[0][-1] = 0.0
        ##  sim_dat = numpy.array(self.SSA.SSA.output, 'd')

        ##  pscmod.data_stochsim = IntegrationStochasticDataObj()
        ##  pscmod.data_stochsim.setTime(sim_dat[:,0])
        ##  pscmod.data_stochsim.setSpecies(sim_dat[:,1:-1], self.SSA_SPECIES)
        
        pscmod.data_stochsim = self.SSA.data_stochsim
        
        if self.STP_WAITING_TIMES:
            wtimes, wt_lbls = self.getWaitingtimesData(reactions=None,lbls=True)
            pscmod.data_stochsim.setWaitingtimes(wtimes, wt_lbls)
        if self.STP_TRACK_PROPENSITIES:
            pscmod.data_stochsim.setPropensities(self.SSA.SSA.propensities_output)            
        pscmod.data_stochsim.TYPE_INFO = 'Stochastic'


    def getWaitingtimesData(self,reactions=None,lbls=False):
        """
        Plots the waiting times for each reaction in the model. Makes use of ObtainWaitingtimes to derive the waiting times out of the SSA output.

        Input:

         - *reactions* [default=0] a list of reactions to plot defualts to all reactions
         - *traj* [default=0] trajectory to plot (defaults to first one)
         - *lbls* [default=False] if True return (data_array, column_labels) otherwise just data_array

        This method is derived from StomPy 0.9 (http://stompy.sf.net) Analysis.py

        """
        if self.SSA.IsTauLeaping:
            print 'INFO: waiting times not available when method is Tau Leaping'
            if not lbls:
                return None
            else:
                return None, None
        self.SSA.GetWaitingtimes()
        if reactions == None:
            reactions = self.SSA_REACTIONS
        vect = []
        vect_lbls = []
        for r in reactions:
            if r in self.SSA_REACTIONS:
                vect.append(self.SSA_REACTIONS.index(r)+1)
                vect_lbls.append('wt'+str(r))
            else:
                print "INFO: '%s' is not a valid reaction name" % r
        OUTPUT = []
        ##  for t in range(len(self.SSA.data_stochsim.waiting_times)):
        T_OUTPUT = []
        for i in vect:
            if self.SSA.data_stochsim.waiting_times.has_key(i):
                waiting_time = self.SSA.data_stochsim.waiting_times[i]
                if len(waiting_time) > 1:			# At least 2 waiting times are necessary per reaction
                    T_OUTPUT.append(self.stompy.modules.Analysis.LogBin(waiting_time, 1.5)) 	# Create logarithmic bins
                else:
                    T_OUTPUT.append(None)
            else:
                T_OUTPUT.append(None)
        OUTPUT.append(T_OUTPUT)
        if not lbls:
            return OUTPUT
        else:
            return OUTPUT, vect_lbls


class IntegrationStochasticDataObj(object):
    """
    This class is specifically designed to store the
    results of a stochastic time simulation
    It has methods for setting the Time, Labels, Species and Propensity data and
    getting Time, Species and Rate (including time) arrays. However, of more use:

    - getOutput(\*args) feed this method species/rate labels and it will return
      an array of [time, sp1, r1, ....]
    - getDataAtTime(time) the data generated at time point "time".
    - getDataInTimeInterval(time, bounds=None) more intelligent version of the above
      returns an array of all data points where: time-bounds <= time <= time+bounds

    """
    time = None
    waiting_times = None
    species = None
    propensities = None
    xdata = None
    time_label = 'Time'
    waiting_time_labels = None
    species_labels = None
    propensities_labels = None
    xdata_labels = None
    HAS_SPECIES = False
    HAS_WAITING_TIMES = False
    HAS_PROPENSITIES = False
    HAS_TIME = False
    HAS_XDATA = False
    IS_VALID = True
    TYPE_INFO = 'Stochastic'

    def setLabels(self, species):
        """
        Set the species

         - *species* a list of species labels
        """
        self.species_labels = species

    def setTime(self, time, lbl=None):
        """
        Set the time vector

         - *time* a 1d array of time points
         - *lbl* [default=None] is "Time" set as required

        """
        self.time = time.reshape(len(time), 1)
        self.HAS_TIME = True
        if lbl != None:
            self.time_label = lbl

    def setSpecies(self, species, lbls=None):
        """
        Set the species array

         - *species* an array of species vs time data
         - *lbls* [default=None] a list of species labels
        """
        self.species = species
        self.HAS_SPECIES = True
        if lbls != None:
            self.species_labels = lbls

    def setWaitingtimes(self, waiting_times, lbls=None):
        """
        Set the `waiting_times` this data structure is not an array but a nested list of: waiting time log bins per reaction per trajectory::
         
        waiting_times = [traj_1, ..., traj_n]
        traj_1 = [wt_J1, ..., wt_Jn] # in order of SSA_REACTIONS
        wt_J1 = (xval, yval, nbin)
        xval =[x_1, ..., x_n]
        yval =[y_1, ..., y_n]
        nbin = n
        
         - *waiting_times* a list of waiting times
         - *lbls* [default=None] a list of matching reaction names
         
        """
        self.waiting_times = waiting_times
        self.HAS_WAITING_TIMES = True
        if lbls != None:
            self.waiting_time_labels = lbls
            

    def setPropensities(self, propensities, lbls=None):
        """
        Sets an array of propensities.
        
         - *propensities* a list of propensities
         - *lbls* [default=None] a list of matching reaction names       
         
        """
        
        if lbls == None:
            LB = copy.copy(propensities[0])
            lbls = LB[1:]
            lbls = ['p'+str(r) for r in lbls]
        P_ARR = numpy.zeros((len(propensities), len(propensities[0])-1), 'd')
        P_ARR[-1,:] = numpy.NaN
        for r in range(1, P_ARR.shape[0]):
            P_ARR[r, :] = propensities[r][1:]
        self.propensities = P_ARR
        self.HAS_PROPENSITIES = True
        if lbls != None:
            self.propensities_labels = lbls
        ##  print self.propensities_labels
        ##  print self.propensities
            

    def setXData(self, xdata, lbls=None):
        """
        Sets an array of extra simulation data

        - *xdata* an array of xdata vs time
        - *lbls* [default=None] a list of xdata labels

        """
        self.xdata = xdata
        self.HAS_XDATA = True
        if lbls != None:
            self.xdata_labels = lbls

    def getTime(self, lbls=False):
        """
        Return the time vector

         - *lbls* [default=False] return only the time array or optionally both the time array and time label

        """
        output = None
        if self.HAS_TIME:
            output = self.time.reshape(len(self.time),)
        if not lbls:
            return output
        else:
            return output, [self.time_label]

    def getSpecies(self, lbls=False):
        """
        Return an array fo time+species

        - *lbls* [default=False] return only the time+species array or optionally both the data array and a list of column label

        """
        output = None
        if self.HAS_SPECIES:
            output = numpy.hstack((self.time, self.species))
            labels = [self.time_label]+self.species_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getWaitingtimes(self, lbls=False, traj=[]):
        """
        Return waiting times, time+waiting_time array

         - *lbls* [default=False] return only the time+waiting_time array or optionally both the data array and a list of column label
         - *traj* [default=[0]] return the firs or trajectories defined in this list

        """
        output = None
        labels = None
        if self.HAS_WAITING_TIMES:
            output = []
            if len(traj) == 0:
                traj = range(len(self.waiting_times))
            ##  if len(traj) == 1:
                ##  output = self.waiting_times[0]
            ##  else:
            for t in traj:
                output.append(self.waiting_times[t])
            labels = self.waiting_time_labels
        else:
            output = []
            labels = []
        if not lbls:
            return output
        else:
            return output, labels

    def getPropensities(self, lbls=False):
        """
        Return time+propensity array
        
         - *lbls* [default=False] return only the time+propensity array or optionally both the data array and a list of column label
        
        """
        #assert self.propensities != None, "\nNo propensities"
        output = None
        if self.HAS_PROPENSITIES:
            print self.time.shape
            print self.propensities.shape
            output = numpy.hstack((self.time, self.propensities))
            labels = [self.time_label]+self.propensities_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getXData(self, lbls=False):
        """
        Return time+xdata array

        - *lbls* [default=False] return only the time+xdata array or optionally both the data array and a list of column label

        """
        output = None
        if self.HAS_XDATA:
            output = numpy.hstack((self.time, self.xdata))
            labels = [self.time_label]+self.xdata_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getDataAtTime(self, time):
        """
        Return all data generated at "time"

         - *time* the required exact time point

        """
        #TODO add rate rule data
        t = None
        sp = None
        ra = None
        ru = None
        xd = None
        temp_t = self.time.reshape(len(self.time),)
        for tt in range(len(temp_t)):
            if temp_t[tt] == time:
                t = tt
                if self.HAS_SPECIES:
                    sp = self.species.take([tt], axis=0)
                if self.HAS_PROPENSITIES:
                    ru = self.propensities.take([tt], axis=0)
                if self.HAS_XDATA:
                    xd = self.xdata.take([tt], axis=0)
                break

        output = None
        if t is not None:
            output = numpy.array([[temp_t[t]]])
            if sp is not None:
                output = numpy.hstack((output,sp))
            if ra is not None:
                output = numpy.hstack((output,ra))
            if ru is not None:
                output = numpy.hstack((output,ru))
            if xd is not None:
                output = numpy.hstack((output,xd))

        return output

    def getDataInTimeInterval(self, time, bounds=None):
        """
        Returns an array of all data in interval: time-bounds <= time <= time+bounds
        where bound defaults to stepsize

         - *time* the interval midpoint
         - *bounds* [default=None] interval halfspan defaults to stepsize

        """

        temp_t = self.time.reshape(len(self.time),)
        if bounds == None:
            bounds = temp_t[1] - temp_t[0]
        c1 = (temp_t >= time-bounds)
        c2 = (temp_t <= time+bounds)
        print 'Searching (%s:%s:%s)' % (time-bounds, time, time+bounds)

        t = []
        sp = None
        ra = None

        for tt in range(len(c1)):
            if c1[tt] and c2[tt]:
                t.append(tt)
        output = None
        if len(t) > 0:
            output = self.time.take(t)
            output = output.reshape(len(output),1)
            if self.HAS_SPECIES and self.HAS_TIME:
                output = numpy.hstack((output, self.species.take(t, axis=0)))
            if self.HAS_PROPENSITIES:
                output = numpy.hstack((output, self.propensities.take(t, axis=0)))
            if self.HAS_XDATA:
                output = numpy.hstack((output, self.xdata.take(t, axis=0)))
        return output

    def getAllSimData(self,lbls=False):
        """
        Return an array of time + all available simulation data

         - *lbls* [default=False] return only the data array or (data array, list of labels)

        """
        labels = [self.time_label]
        if self.HAS_SPECIES and self.HAS_TIME:
            output = numpy.hstack((self.time, self.species))
            labels += self.species_labels
        if self.HAS_PROPENSITIES:
            output = numpy.hstack((output, self.propensities))
            labels += self.propensities_labels
        if self.HAS_XDATA:
            output = numpy.hstack((output, self.xdata))
            labels += self.xdata_labels
        if not lbls:
            return output
        else:
            return output, labels

    def getSimData(self, *args, **kwargs):
        """
        Feed this method species/xdata labels and it will return an array of [time, sp1, ....]

         - 'speces_l', 'xdatal' ...
         - *lbls* [default=False] return only the data array or (data array, list of labels)

        """
        output = self.time

        if kwargs.has_key('lbls'):
            lbls = kwargs['lbls']
        else:
            lbls = False
        lout = [self.time_label]
        for roc in args:
            if self.HAS_SPECIES and roc in self.species_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.species.take([self.species_labels.index(roc)], axis=-1)))
            if self.HAS_PROPENSITIES and roc in self.propensities_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.propensities.take([self.propensities_labels.index(roc)], axis=-1)))
            if self.HAS_XDATA and roc in self.xdata_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.xdata.take([self.xdata_labels.index(roc)], axis=-1)))
        if not lbls:
            return output
        else:
            return output, lout

class BagOfStuff(object):
    """
    A collection of attributes defined by row and column lists
    used by Response coefficients etc matrix is an array of values
    while row/col are lists of row colummn name strings
    """

    matrix = None
    row = None
    col = None

    def __init__(self, matrix, row, col):
        "Load attributes from arrays"
        assert len(row) == matrix.shape[0], "\nRow dimension mismatch"
        assert len(col) == matrix.shape[1], "\nCol dimension mismatch"
        self.matrix = matrix
        self.row = list(row)
        self.col = list(col)

    def load(self):
        for r in range(self.matrix.shape[0]):
            for c in range(self.matrix.shape[1]):
                setattr(self, str(self.row[r]) + '_' + str(self.col[c]), self.matrix[r,c])

    def get(self, attr1, attr2):
        "Returns a single attribute \"attr1_attr2\" or None"
        try:
            return getattr(self, attr1 + '_' + attr2)
        except Exception, ex:
            print ex
            return None

    def select(self, attr, search='a'):
        """Return a dictionary of <attr>_<name>, <name>_<attr> : val or {} if none
        If attr exists as an index for both left and right attr then:
        search='a' : both left and right attributes (default)
        search='l' : left attributes only
        search='r' : right attributes"""
        output = {}

        if attr in self.row and attr in self.col:
            if search in ['a','l']:
                row = self.row.index(attr)
                for col in range(self.matrix.shape[1]):
                    output.setdefault(attr + '_' + self.col[col], self.matrix[row,col])
            if search in ['a','r']:
                col = self.col.index(attr)
                for row in range(self.matrix.shape[0]):
                    output.setdefault(self.row[row] + '_' + attr, self.matrix[row,col])
        elif attr in self.row:
            row = self.row.index(attr)
            for col in range(self.matrix.shape[1]):
                output.setdefault(attr + '_' + self.col[col], self.matrix[row,col])
        elif attr in self.col:
            col = self.col.index(attr)
            for row in range(self.matrix.shape[0]):
                output.setdefault(self.row[row] + '_' + attr, self.matrix[row,col])
        return output

    def list(self):
        "Return all attributes as a attr:val dictionary"
        output = {}
        for row in range(self.matrix.shape[0]):
            for col in range(self.matrix.shape[1]):
                output.setdefault(self.row[row] + '_' + self.col[col], self.matrix[row,col])
        return output


class ScanDataObj(object):
    """
    New class used to store parameter scan data (uses StateDataObj)
    """
    species = None
    fluxes = None
    rules = None
    xdata = None
    mod_data = None
    flux_labels = None
    species_labels = None
    rules_labels = None
    xdata_labels = None
    mod_data_labels = None
    invalid_states = None
    parameters = None
    parameter_labels = None
    HAS_FLUXES = False
    HAS_SPECIES = False
    HAS_RULES = False
    HAS_XDATA = False
    HAS_MOD_DATA = False
    HAS_SET_LABELS = False
    ALL_VALID = True
    OPEN = True

    def __init__(self, par_label):
        self.species = []
        self.fluxes = []
        self.rules = []
        self.xdata = []
        self.mod_data = []
        self.invalid_states = []
        self.parameters = []
        if isinstance(par_label, list):
            self.parameter_labels = par_label
        else:
            self.parameter_labels = [par_label]


    def setLabels(self, ssdata):
        if ssdata.HAS_SPECIES:
            self.species_labels = ssdata.species_labels
            self.HAS_SPECIES = True
        if ssdata.HAS_FLUXES:
            self.flux_labels = ssdata.flux_labels
            self.HAS_FLUXES = True
        if ssdata.HAS_RULES:
            self.rules_labels = ssdata.rules_labels
            self.HAS_RULES = True
        if ssdata.HAS_XDATA:
            self.xdata_labels = ssdata.xdata_labels
            self.HAS_XDATA = True
        self.HAS_SET_LABELS = True

    def addPoint(self, ipar, ssdata):
        """takes a list/array of input parameter values and the associated ssdata object"""
        assert self.OPEN, '\nScan has been finalised no new data may be added'
        if not self.HAS_SET_LABELS:
            self.setLabels(ssdata)

        if hasattr(ipar, '__iter__'):
            self.parameters.append(ipar)
        else:
            self.parameters.append([ipar])
        if self.HAS_SPECIES:
            self.species.append(ssdata.getSpecies())
        if self.HAS_FLUXES:
            self.fluxes.append(ssdata.getFluxes())
        if self.HAS_RULES:
            self.rules.append(ssdata.getRules())
        if self.HAS_XDATA:
            self.xdata.append(ssdata.getXData())
        if not ssdata.IS_VALID:
            self.ALL_VALID = False
            self.invalid_states.append(ipar)

    def addModData(self, mod, *args):
        if not self.HAS_MOD_DATA:
            self.HAS_MOD_DATA = True
            self.mod_data_labels = [attr for attr in args if hasattr(mod, attr)]
        self.mod_data.append(tuple([getattr(mod, attr) for attr in self.mod_data_labels]))

    def closeScan(self):
        print '\nINFO: closing scan no new data may be added.'
        if self.HAS_SPECIES:
            self.species = numpy.array(self.species)
        if self.HAS_FLUXES:
            self.fluxes = numpy.array(self.fluxes)
        if self.HAS_RULES:
            self.rules = numpy.array(self.rules)
        if self.HAS_XDATA:
            self.xdata = numpy.array(self.xdata)
        if self.HAS_MOD_DATA:
            self.mod_data = numpy.array(self.mod_data)
        self.parameters = numpy.array(self.parameters)
        self.OPEN = False

    def getSpecies(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = None
        labels = None
        if self.HAS_SPECIES:
            output = numpy.hstack((self.parameters, self.species))
            labels = self.parameter_labels+self.species_labels
        if lbls:
            return output, labels
        else:
            return output

    def getFluxes(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = None
        labels = None
        if self.HAS_FLUXES:
            output = numpy.hstack((self.parameters, self.fluxes))
            labels = self.parameter_labels+self.flux_labels
        if lbls:
            return output, labels
        else:
            return output

    def getRules(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = None
        labels = None
        if self.HAS_RULES:
            output = numpy.hstack((self.parameters, self.rules))
            labels = self.parameter_labels+self.rules_labels
        if lbls:
            return output, labels
        else:
            return output

    def getXData(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = None
        labels = None
        if self.HAS_XDATA:
            output = numpy.hstack((self.parameters, self.xdata))
            labels = self.parameter_labels+self.xdata_labels
        if lbls:
            return output, labels
        else:
            return output

    def getModData(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = None
        labels = None
        if self.HAS_MOD_DATA:
            output = numpy.hstack((self.parameters, self.mod_data))
            labels = self.parameter_labels+self.mod_data_labels
        if lbls:
            return output, labels
        else:
            return output

    def getAllScanData(self, lbls=False):
        if self.OPEN:
            self.closeScan()
        output = self.parameters
        labels = self.parameter_labels
        if self.HAS_SPECIES:
            output = numpy.hstack((output, self.species))
            labels = labels+self.species_labels
        if self.HAS_FLUXES:
            output = numpy.hstack((output, self.fluxes))
            labels = labels+self.flux_labels
        if self.HAS_RULES:
            output = numpy.hstack((output, self.rules))
            labels = labels+self.rules_labels
        if self.HAS_XDATA:
            output = numpy.hstack((output, self.xdata))
            labels = labels+self.xdata_labels
        if self.HAS_MOD_DATA:
            output = numpy.hstack((output, self.mod_data))
            labels = labels+self.mod_data_labels
        if lbls:
            return output, labels
        else:
            return output

    def getScanData(self, *args, **kwargs):
        """getScanData(\*args) feed this method species/flux/rule/mod labels and it
        will return an array of [parameter(s), sp1, f1, ....]
        """
        if kwargs.has_key('lbls'):
            lbls = kwargs['lbls']
        else:
            lbls = False

        output = self.parameters
        lout = self.parameter_labels
        for roc in args:
            if self.HAS_SPECIES and roc in self.species_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.species.take([self.species_labels.index(roc)], axis=-1)))
            if self.HAS_FLUXES and roc in self.flux_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.fluxes.take([self.flux_labels.index(roc)], axis=-1)))
            if self.HAS_RULES and roc in self.rules_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.rules.take([self.rules_labels.index(roc)], axis=-1)))
            if self.HAS_XDATA and roc in self.xdata_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.xdata.take([self.xdata_labels.index(roc)], axis=-1)))
            if self.HAS_MOD_DATA and roc in self.mod_data_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.mod_data.take([self.mod_data_labels.index(roc)], axis=-1)))
        if not lbls:
            return output
        else:
            return output, lout


class StateDataObj(object):
    """
    New class used to store steady-state data.
    """
    fluxes = None
    species = None
    rules = None
    xdata = None
    flux_labels = None
    species_labels = None
    rules_labels = None
    xdata_labels = None
    HAS_FLUXES = False
    HAS_SPECIES = False
    HAS_RULES = False
    HAS_XDATA = False
    IS_VALID = True

    ##  def setLabels(self, species=None, fluxes=None, rules=None):
        ##  """set the species, rate and rule label lists"""
        ##  if species != None:
            ##  self.species_labels = species
        ##  if fluxes != None:
            ##  self.flux_labels = fluxes
        ##  if rules != None:
            ##  self.rules_labels = rules

    def setSpecies(self, species, lbls=None):
        """Set the species array"""
        self.species = species
        self.HAS_SPECIES = True
        if lbls != None:
            self.species_labels = lbls
            for s in range(len(self.species_labels)):
                setattr(self, self.species_labels[s], self.species[s])

    def setFluxes(self, fluxes, lbls=None):
        """set the flux array"""
        self.fluxes = fluxes
        self.HAS_FLUXES = True
        if lbls != None:
            self.flux_labels = lbls
            for f in range(len(self.flux_labels)):
                setattr(self, self.flux_labels[f], self.fluxes[f])

    def setRules(self, rules, lbls=None):
        """Set the results of rate rules"""
        self.rules = rules
        self.HAS_RULES = True
        if lbls != None:
            self.rules_labels = lbls
            for r in range(len(self.rules_labels)):
                setattr(self, self.rules_labels[r], self.rules[r])

    def setXData(self, xdata, lbls=None):
        """Sets extra simulation data"""
        self.xdata = xdata
        self.HAS_XDATA = True
        if lbls != None:
            self.xdata_labels = lbls
            for x in range(len(self.xdata_labels)):
                setattr(self, self.xdata_labels[x], self.xdata[x])

    def getSpecies(self, lbls=False):
        """return species array"""
        output = None
        if self.HAS_SPECIES:
            output = self.species
        if not lbls:
            return output
        else:
            return output, self.species_labels

    def getFluxes(self, lbls=False):
        """return flux array"""
        output = None
        if self.HAS_FLUXES:
            output = self.fluxes
        if not lbls:
            return output
        else:
            return output, self.flux_labels

    def getRules(self, lbls=False):
        """Return rule array"""
        output = None
        if self.HAS_RULES:
            output = self.rules
        if not lbls:
            return output
        else:
            return output, self.rules_labels

    def getXData(self, lbls=False):
        """Return xdata array"""
        output = None
        if self.HAS_XDATA:
            output = self.xdata
        if not lbls:
            return output
        else:
            return output, self.xdata_labels

    def getAllStateData(self, lbls=False):
        """
        Return all available data as species+fluxes+rules
        if lbls=True returns (array,labels) else just array
        """
        labels = []
        output = None
        if self.HAS_SPECIES:
            output = self.species
            labels += self.species_labels
        if self.HAS_FLUXES:
            if output == None:
                output = self.fluxes
            else:
                output = numpy.hstack((output, self.fluxes))
            labels += self.flux_labels
        if self.HAS_RULES:
            if output == None:
                output = self.rules
            else:
                output = numpy.hstack((output, self.rules))
            labels += self.rules_labels
        if self.HAS_XDATA:
            if output == None:
                output = self.xdata
            else:
                output = numpy.hstack((output, self.xdata))
            labels += self.xdata_labels
        if not lbls:
            return output
        else:
            return output, labels

    def getStateData(self, *args, **kwargs):
        """getSimData(\*args) feed this method species/rate labels and it
        will return an array of [time, sp1, r1, ....]
        """

        if kwargs.has_key('lbls'):
            lbls = kwargs['lbls']
        else:
            lbls = False
        lout = []
        output = []
        for roc in args:
            if self.HAS_SPECIES and roc in self.species_labels:
                lout.append(roc)
                output.append(self.species[self.species_labels.index(roc)])
            elif self.HAS_FLUXES and roc in self.flux_labels:
                lout.append(roc)
                output.append(self.fluxes[self.flux_labels.index(roc)])
            elif self.HAS_RULES and roc in self.rules_labels:
                lout.append(roc)
                output.append(self.rules[self.rules_labels.index(roc)])
            elif self.HAS_XDATA and roc in self.xdata_labels:
                lout.append(roc)
                output.append(self.xdata[self.xdata_labels.index(roc)])
            else:
                print 'I don\'t have an attribute %s ... ignoring.' % roc
        if not lbls:
            return output
        else:
            return numpy.array(output), lout


class IntegrationDataObj(object):
    """
    This class is specifically designed to store the results of a time simulation
    It has methods for setting the Time, Labels, Species and Rate data and
    getting Time, Species and Rate (including time) arrays. However, of more use:

    - getOutput(\*args) feed this method species/rate labels and it will return
      an array of [time, sp1, r1, ....]
    - getDataAtTime(time) the data generated at time point "time".
    - getDataInTimeInterval(time, bounds=None) more intelligent version of the above
      returns an array of all data points where: time-bounds <= time <= time+bounds
    """
    time = None
    rates = None
    species = None
    rules = None
    xdata = None
    time_label = 'Time'
    rate_labels = None
    species_labels = None
    rules_labels = None
    xdata_labels = None
    HAS_SPECIES = False
    HAS_RATES = False
    HAS_RULES = False
    HAS_TIME = False
    HAS_XDATA = False
    IS_VALID = True
    TYPE_INFO = 'Deterministic'

    def setLabels(self, species=None, rates=None, rules=None):
        """set the species, rate and rule label lists"""
        if species != None:
            self.species_labels = species
        if rates != None:
            self.rate_labels = rates
        if rules != None:
            self.rules_labels = rules

    def setTime(self, time, lbl=None):
        """Set the time vector"""
        self.time = time.reshape(len(time), 1)
        self.HAS_TIME = True
        if lbl != None:
            self.time_label = lbl

    def setSpecies(self, species, lbls=None):
        """Set the species array"""
        self.species = species
        self.HAS_SPECIES = True
        if lbls != None:
            self.species_labels = lbls

    def setRates(self, rates, lbls=None):
        """set the rate array"""
        self.rates = rates
        self.HAS_RATES = True
        if lbls != None:
            self.rate_labels = lbls

    def setRules(self, rules, lbls=None):
        """Set the results of rate rules"""
        self.rules = rules
        self.HAS_RULES = True
        if lbls != None:
            self.rules_labels = lbls

    def setXData(self, xdata, lbls=None):
        """Sets extra simulation data"""
        self.xdata = xdata
        self.HAS_XDATA = True
        if lbls != None:
            self.xdata_labels = lbls

    def getTime(self, lbls=False):
        """return the time vector"""
        output = None
        if self.HAS_TIME:
            output = self.time.reshape(len(self.time),)
        if not lbls:
            return output
        else:
            return output, [self.time_label]

    def getSpecies(self, lbls=False):
        """return time+species array"""
        output = None
        if self.HAS_SPECIES:
            output = numpy.hstack((self.time, self.species))
            labels = [self.time_label]+self.species_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getRates(self, lbls=False):
        """return time+rate array"""
        output = None
        if self.HAS_RATES:
            output = numpy.hstack((self.time, self.rates))
            labels = [self.time_label]+self.rate_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getRules(self, lbls=False):
        """Return time+rule array"""
        ##  assert self.rules != None, "\nNo rules"
        output = None
        if self.HAS_RULES:
            output = numpy.hstack((self.time, self.rules))
            labels = [self.time_label]+self.rules_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getXData(self, lbls=False):
        """Return time+xdata array"""
        ##  assert self.rules != None, "\nNo rules"
        output = None
        if self.HAS_XDATA:
            output = numpy.hstack((self.time, self.xdata))
            labels = [self.time_label]+self.xdata_labels
        else:
            output = self.time
            labels = [self.time_label]
        if not lbls:
            return output
        else:
            return output, labels

    def getDataAtTime(self, time):
        """Return all data generated at "time" """
        #TODO add rate rule data
        t = None
        sp = None
        ra = None
        ru = None
        xd = None
        temp_t = self.time.reshape(len(self.time),)
        for tt in range(len(temp_t)):
            if temp_t[tt] == time:
                t = tt
                if self.HAS_SPECIES:
                    sp = self.species.take([tt], axis=0)
                if self.HAS_RATES:
                    ra = self.rates.take([tt], axis=0)
                if self.HAS_RULES:
                    ru = self.rules.take([tt], axis=0)
                if self.HAS_XDATA:
                    xd = self.xdata.take([tt], axis=0)
                break

        output = None
        if t is not None:
            output = numpy.array([[temp_t[t]]])
            if sp is not None:
                output = numpy.hstack((output,sp))
            if ra is not None:
                output = numpy.hstack((output,ra))
            if ru is not None:
                output = numpy.hstack((output,ru))
            if xd is not None:
                output = numpy.hstack((output,xd))

        return output

    def getDataInTimeInterval(self, time, bounds=None):
        """
         getDataInTimeInterval(time, bounds=None) returns an array of all
         data points where: time-bounds <= time <= time+bounds
         where bound defaults to stepsize
        """
        #TODO add rate rule data
        temp_t = self.time.reshape(len(self.time),)
        if bounds == None:
            bounds = temp_t[1] - temp_t[0]
        c1 = (temp_t >= time-bounds)
        c2 = (temp_t <= time+bounds)
        print 'Searching (%s:%s:%s)' % (time-bounds, time, time+bounds)

        t = []
        sp = None
        ra = None

        for tt in range(len(c1)):
            if c1[tt] and c2[tt]:
                t.append(tt)
        output = None
        if len(t) > 0:
            output = self.time.take(t)
            output = output.reshape(len(output),1)
            if self.HAS_SPECIES and self.HAS_TIME:
                output = numpy.hstack((output, self.species.take(t, axis=0)))
            if self.HAS_RATES:
                output = numpy.hstack((output, self.rates.take(t, axis=0)))
            if self.HAS_RULES:
                output = numpy.hstack((output, self.rules.take(t, axis=0)))
            if self.HAS_XDATA:
                output = numpy.hstack((output, self.xdata.take(t, axis=0)))
        return output

    def getOutput(self, *args):
        """
        Old alias for getSimData()
        getOutput(\*args) feed this method species/rate labels and it
        will return an array of [time, sp1, r1, ....]
        """
        return self.getSimData(*args)

    def getAllSimData(self,lbls=False):
        """
        Return all available data as time+species+rates+rules
        if lbls=True returns (array,lables) else just array
        """
        labels = [self.time_label]
        if self.HAS_SPECIES and self.HAS_TIME:
            output = numpy.hstack((self.time, self.species))
            labels += self.species_labels
        if self.HAS_RATES:
            output = numpy.hstack((output, self.rates))
            labels +=self.rate_labels
        if self.HAS_RULES:
            output = numpy.hstack((output, self.rules))
            labels += self.rules_labels
        if self.HAS_XDATA:
            output = numpy.hstack((output, self.xdata))
            labels += self.xdata_labels
        if not lbls:
            return output
        else:
            return output, labels

    def getSimData(self, *args, **kwargs):
        """getSimData(\*args) feed this method species/rate labels and it
        will return an array of [time, sp1, r1, ....]
        """
        output = self.time
        ##  print argimrgs
        if kwargs.has_key('lbls'):
            lbls = kwargs['lbls']
        else:
            lbls = False
        lout = [self.time_label]
        for roc in args:
            if self.HAS_SPECIES and roc in self.species_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.species.take([self.species_labels.index(roc)], axis=-1)))
            if self.HAS_RATES and roc in self.rate_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.rates.take([self.rate_labels.index(roc)], axis=-1)))
            if self.HAS_RULES and roc in self.rules_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.rules.take([self.rules_labels.index(roc)], axis=-1)))
            if self.HAS_XDATA and roc in self.xdata_labels:
                lout.append(roc)
                output = numpy.hstack((output, self.xdata.take([self.xdata_labels.index(roc)], axis=-1)))
        if not lbls:
            return output
        else:
            return output, lout

# this must stay in sync with core2
class NewCoreBase(object):
    """
    Core2 base class, needed here as we use Core2 derived classes
    in PySCes
    """
    name = None
    __DEBUG__ = False

    def getName(self):
        return self.name

    def setName(self,name):
        self.name = name

    def get(self, attr):
        """Return an attribute whose name is str(attr)"""
        return self.__getattribute__(attr)


# this must stay in sync with core2
class NumberBase(NewCoreBase):
    """
    Derived Core2 number class.
    """
    value = None
    value_initial = None

    def __call__(self):
        return self.value

    def getValue(self):
        return self.value

    def setValue(self, v):
        self.value = v

# Finally killed my lambda functions - brett07
class ReactionObj(NewCoreBase):
    """
    Defines a reaction with a KineticLaw *kl8, *formula* and *name* bound
    to a model instance, *mod*.
    """
    formula = None
    mod = None
    rate = None
    xcode = None
    code_string = None
    symbols = None
    compartment = None
    piecewises = None

    def __init__(self, mod, name, kl, klrepl='self.'):
        """
        mod : model instance
        name = reaction name
        kl = kinetic law
        klrepl = replacement string for KineticLaw
        """
        self.mod = mod
        self.name = name
        self.setKineticLaw(kl, klrepl='self.')

    def __call__(self, *args):
        exec(self.xcode)
        return self.rate

    def setKineticLaw(self, kl, klrepl='self.'):
        InfixParser.setNameStr('self.mod.', '')
        InfixParser.parse(kl.replace(klrepl, ''))
        self.symbols = InfixParser.names
        self.piecewises = InfixParser.piecewises
        formula = InfixParser.output
        # this code has to stay together #
        for pw in InfixParser.piecewises:
            formula = formula.replace('self.mod.%s' % pw, 'self.mod.%s()' % pw)
        # this code has to stay together #
        self.code_string = 'self.rate=%s' % formula
        self.xcode = compile(self.code_string, 'Req: %s' % self.name, 'exec')
        self.formula = kl.replace(klrepl, '').replace('numpy.', '').replace('math.', '').replace('operator.', '')

InfixParser = MyInfixParser()
InfixParser.buildlexer()
InfixParser.buildparser(debug=0, debugfile='infix.dbg', tabmodule='infix_tabmodule')
InfixParser.setNameStr('self.', '')
os.chdir(OUTPUT_DIR)

# adapted from core2
class Function(NewCoreBase):
    """
    Function class ported from Core2 to enable the use of functions in PySCeS.
    """
    formula = None
    code_string = None
    xcode = None
    value = None
    symbols = None
    argsl = None
    mod = None
    piecewises = None
    functions = None

    def __init__(self, name, mod):
        self.name = name
        self.argsl = []
        self.functions = []
        self.mod = mod

    def __call__(self, *args):
        for ar in range(len(args)):
            self.__setattr__(self.argsl[ar], args[ar])
        exec(self.xcode)
        return self.value

    def setArg(self, var, value=None):
        self.__setattr__(var, value)
        self.argsl.append(var)

    def addFormula(self, formula):
        self.formula = formula
        InfixParser.setNameStr('self.', '')
        InfixParser.SymbolReplacements = {'_TIME_':'mod._TIME_'}
        InfixParser.parse(formula)
        self.piecewises = InfixParser.piecewises
        self.symbols = InfixParser.names
        self.functions = InfixParser.functions
        self.code_string = 'self.value=%s' % InfixParser.output
        self.xcode = compile(self.code_string, 'Func: %s' % self.name, 'exec')

# adapted from core2
class EventAssignment(NumberBase):
    """
    Event assignments are actions that are triggered by an event.
    Ported from Core2 to build an event handling framework fro PySCeS
    """
    variable = None
    symbols = None
    formula = None
    code_string = None
    xcode = None
    mod = None
    piecewises = None
    __DEBUG__ = False

    def __call__(self):
        setattr(self.mod, self.variable, self.value)
        if self.__DEBUG__: print '\tAssigning %s = %s' % (self.variable, self.value)
        return True

    def __init__(self, name, mod):
        self.setName(name)
        self.mod = mod

    def setVariable(self, var):
        self.variable = var

    def setFormula(self, formula):
        self.formula = formula
        InfixParser.setNameStr('self.mod.', '')
        InfixParser.SymbolReplacements = {'_TIME_':'self.mod._TIME_'}
        InfixParser.parse(formula)
        self.piecewises = InfixParser.piecewises
        self.symbols = InfixParser.names
        self.code_string = 'self.value=%s' % InfixParser.output
        self.xcode = compile(self.code_string, 'EvAs: %s' % self.name, 'exec')
        ##  print '\t', self.name, self.code_string

    def evaluateAssignment(self):
        exec(self.xcode)

# adapted from core2
class Event(NewCoreBase):
    """
    Event's have triggers and fire EventAssignments when required.
    Ported from Core2.
    """
    trigger = None
    delay = 0.0
    formula = None
    code_string = None
    xcode = None
    state0 = False
    state = False
    assignments = None
    _TIME_ = 0.0
    _ASS_TIME_ = 0.0
    _need_action = False
    symbols = None
    _time_symbol = None
    piecewises = None
    mod = None
    __DEBUG__ = True

    def __init__(self, name, mod):
        self.setName(name)
        self.assignments = []
        self.mod = mod

    def __call__(self, time):
        self._TIME_ = time
        exec(self.xcode)
        ret = False
        if self.state0 and not self.state:
            self.state0 = self.state
        if not self.state0 and self.state:
            for ass in self.assignments:
                ass.evaluateAssignment()
            self.state0 = self.state
            self._need_action = True
            self._ASS_TIME_ = time + self.delay
            if self.__DEBUG__: print '\nevent %s is evaluating at %s' % (self.name, time)
            ret = True
        if self._need_action and self._TIME_ >= self._ASS_TIME_:
            for ass in self.assignments:
                ass()
            if self.__DEBUG__: print 'event %s is assigning at %s (delay=%s)' % (self.name, time, self.delay)
            self._need_action = False
            ret = True
        return ret

    def setTrigger(self, formula, delay=0.0):
        self.formula = formula
        self.delay = delay
        InfixParser.setNameStr('self.mod.', '')
        ##  print formula, delay
        if self._time_symbol != None:
            InfixParser.SymbolReplacements = {self._time_symbol : '_TIME_'}
        else:
            InfixParser.SymbolReplacements = {'_TIME_' : '_TIME_'}
        InfixParser.parse(formula)
        self.piecewises = InfixParser.piecewises
        self.symbols = InfixParser.names
        self.code_string = 'self.state=%s' % InfixParser.output
        self.xcode = compile(self.code_string, 'Ev: %s' % self.name, 'exec')
        ##  print self.name, self.code_string

    def setAssignment(self, var, formula):
        ass = EventAssignment(var, mod=self.mod)
        ass.setVariable(var)
        ass.setFormula(formula)
        self.assignments.append(ass)
        self.__setattr__('_'+var, ass)

    def reset(self):
        self.state0 = False
        self.state = False
        self._TIME_ = 0.0
        self._ASS_TIME_ = 0.0


class PieceWise(NewCoreBase):
    """
    Generic piecewise class adapted from Core2 that generates a compiled
    Python code block that allows evaluation of arbitrary length piecewise
    functions. Piecewise statements should be defined in assignment rules
    as `piecewise(<Piece>, <Conditional>, <OtherValue>)` where there can
    be an arbitrary number of `<Piece>, <Conditional>` pairs.

    - *args* a dictionary of piecewise information generated by the InfixParser as InfixParser.piecewises

    """
    name = None
    value = None
    formula = None
    code_string = None
    xcode = None
    _names = None
    _TIME_ = None

    def __init__(self, pwd, mod):
        pwd = pwd.copy()
        self.mod = mod
        if pwd['other'] != None:
            other = 'self.value = %s' % pwd.pop('other').replace('self.','self.mod.')
        else:
            other = 'pass'
            pwd.pop('other')

        InfixParser.setNameStr('self.mod.', '')
        self._names = []
        if len(pwd.keys()) == 1:
            formula = pwd[0][0]
            InfixParser.parse(formula)
            for n in InfixParser.names:
                if n not in self._names and n != '_TIME_':
                    self._names.append(n)
            formula = InfixParser.output
            thenStat = pwd[0][1].replace('self.','self.mod.')
            ##  thenStat = pwd[0][1]
            ##  InfixParser.setNameStr('self.mod.', '')
            ##  InfixParser.parse(thenStat)
            ##  thenStat = InfixParser.output
            self.code_string = 'if %s:\n    self.value = %s\nelse:\n    %s' %\
            (formula, thenStat, other)
            self.formula = self.code_string.replace('self.','')
        else:
            formula = pwd[0][0]
            InfixParser.parse(formula)
            for n in InfixParser.names:
                if n not in self._names and n != '_TIME_':
                    self._names.append(n)
            formula = InfixParser.output
            thenStat = pwd[0][1].replace('self.','self.mod.')
            ##  thenStat = pwd[0][1]
            ##  InfixParser.setNameStr('self.mod.', '')
            ##  InfixParser.parse(thenStat)
            ##  thenStat = InfixParser.output
            self.code_string = 'if %s:\n    self.value = %s\n' % (formula, thenStat)
            pwd.pop(0)
            for p in pwd:
                formula = pwd[p][0]
                InfixParser.parse(formula)
                for n in InfixParser.names:
                    if n not in self._names and n != '_TIME_':
                        self._names.append(n)

                formula = InfixParser.output
                thenStat = pwd[p][1].replace('self.','self.mod.')
                ##  thenStat = pwd[p][1]
                ##  InfixParser.setNameStr('self.mod.', '')
                ##  InfixParser.parse(thenStat)
                ##  thenStat = InfixParser.output
                self.code_string += 'elif %s:\n    self.value = %s\n' % (formula, thenStat)
            self.code_string += 'else:\n    %s' % other
            self.formula = self.code_string.replace('self.','')
        self.xcode = compile(self.code_string, 'PieceWise','exec')

    def __call__(self):
        exec(self.xcode)
        return self.value


class PysMod:
    """
    Create a model object and instantiate a PySCeS model so that it can be used for further analyses. PySCeS
    model descriptions can be loaded from files or strings (see the *loader* argument for details).

    - *File* the name of the PySCeS input file if not explicit a \*.psc extension is assumed.
    - *dir* if specified, the path to the input file otherwise the default PyscesModel directory (defined in the pys_config.ini file) is assumed.
    - *autoload* autoload the model, pre 0.7.1 call mod.doLoad(). (default=True) **new**
    - *loader* the default behaviour is to load PSC file, however, if this argument is set to 'string' an input file can be supplied as the *fString* argument (default='file')
    - *fString* a string containing a PySCeS model file (use with *loader='string'*) the *File* argument now sepcifies the new input file name.

    """

    __version__ = __version__
    __pysces_directory__ = INSTALL_DIR
    __settings__ = None
    __STOMPY__ = None

    def __init__(self,File=None,dir=None,loader='file',fString=None,autoload=True):
        """
        Create a model object and instantiate a PySCeS model so that it can be used for further analyses. PySCeS
        model descriptions can be loaded from files or strings (see the *loader* argument for details).

        - *File* the name of the PySCeS input file if not explicit a \*.psc extension is assumed.
        - *dir* if specified, the path to the input file otherwise the default PyscesModel directory (defined in the pys_config.ini file) is assumed.
        - *autoload* autoload the model, pre 0.7.1 call mod.doLoad(). (default=True) **new**
        - *loader* the default behaviour is to load PSC file, however, if this argument is set to 'string' an input file can be supplied as the *fString* argument (default='file')
        - *fString* a string containing a PySCeS model file (use with *loader='string'*) the *File* argument now sepcifies the new input file name.

        """

        if _HAVE_STOMPY:
            self.__STOMPY__ = StomPyInterface(MODEL_DIR, OUTPUT_DIR)
            print 'PySCeS/StomPy interface active'
        else:
            self.__STOMPY__ = None

        self.__settings__ = {}
        self.__settings__.update({'enable_deprecated_attr' : True})

        if loader == 'file':
            self.LoadFromFile(File,dir)
        elif loader == 'string':
            self.LoadFromString(File,fString)
        else:
            self.LoadFromFile(File,dir)
        # stuff that needs to be done before initmodel
        self.__settings__['mode_substitute_assignment_rules'] = False
        self.__settings__['display_compartment_warnings'] = True
        self._TIME_ = 0.0 # this will be the built-in time
        self.piecewise_functions = []
        self.__piecewises__ = {}
        self.__HAS_PIECEWISE__ = False
        if autoload:
            self.ModelLoad()
            self.__PSC_auto_load = True
        else:
            self.__PSC_auto_load = False

    def ModelLoad(self,stoich_load=0):
        """
        Load and instantiate a PySCeS model so that it can be used for further analyses. This function
        replaces the pre-0.7.1 doLoad() method.

        - *stoich_load* try to load a structural analysis saved with Stoichiometry_Save_Serial() (default=0)

        """
        self.InitialiseInputFile()
        assert self.__parseOK, '\nError in input file, parsing could not complete'
        self.Stoichiometry_Analyse(override=0,load=stoich_load)
        # add new Style functions to model
        self.InitialiseFunctions()
        self.InitialiseCompartments()
        self.InitialiseRules()
        self.InitialiseEvents()
        self.InitialiseOldFunctions() # TODO replace this with initialisation functions
        self.InitialiseModel()
        self.InitialiseRuleChecks()

    def doLoad(self,stoich_load=0):
        """
        Load and instantiate a PySCeS model so that it can be used for further analyses. This function is
        being replaced by the ModelLoad() method.

        - *stoich_load* try to load a structural analysis saved with Stoichiometry_Save_Serial() (default=0)

        """
        if not self.__PSC_auto_load:
            self.ModelLoad(stoich_load=stoich_load)
        else:
            print 'PySCeS now automatically loads the model on model object instantiation. If you do not want this behaviour pass the autoload=False argument to the constructor, if you really want to reload the model, run doLoad() again.\n'
            print 'Further calls to doLoad() will work as normal.'
        self.__PSC_auto_load = False

    def LoadFromString(self,File=None,fString=None):
        """
        Docstring required
        """

        #grab model directory
        chkmdir()
        dir = MODEL_DIR

        # check for .psc extension
        File = chkpsc(File)

        print 'Using model directory: ' + dir

        if not os.path.isdir(os.path.join(MODEL_DIR,"orca")):
            os.mkdir(os.path.join(MODEL_DIR,"orca"))

        dir = os.path.join(MODEL_DIR,"orca")

        # write string to file
        try:
            outFile = file(os.path.join(dir,File),'w')
            outFile.write(fString)
            outFile.close()
            print 'Using file: ' + File

            if os.path.exists(os.path.join(dir,File)):
                print os.path.join(dir,File) + ' loading .....',
                self.ModelDir = dir
                self.ModelFile = File
            else:
                print os.path.join(dir,File) + ' does not exist'
                print 'Please set with ModelDir and ModelFile .....',
                self.ModelFile = 'None'
                self.ModelDir = dir
        except Exception, e:
            print e
            print os.path.join(dir,File) + ' does not exist please re-instantiate model .....',
        self.__settings__['display_debug'] = 0
        self.ModelOutput = OUTPUT_DIR


        ##  # Initialise elementary modes
        ##  if os.sys.platform == 'win32':
            ##  self.eModeExe_int = os.path.join(self.__metatool,'meta43_int.exe')
            ##  self.eModeExe_dbl = os.path.join(self.__metatool,'meta43_double.exe')
        ##  else:
            ##  self.eModeExe_int = os.path.join(self.__metatool,'meta43_int')
            ##  self.eModeExe_dbl = os.path.join(self.__metatool,'meta43_double')
        ##  print 'Done.'

        # Initialize serial directory
        self.__settings__['serial_dir'] = os.path.join(self.ModelOutput,'pscdat')
        # Initialize stoichiometric precision
        self.__settings__['stoichiometric_analysis_fp_zero'] = mach_spec.eps*2.0e4
        self.__settings__['stoichiometric_analysis_lu_precision'] = self.__settings__['stoichiometric_analysis_fp_zero']
        self.__settings__['stoichiometric_analysis_gj_precision'] = self.__settings__['stoichiometric_analysis_lu_precision']*10.0


    def LoadFromFile(self,File=None,dir=None):
        """
        __init__(File=None,dir=None)

        Initialise a PySCeS model object with PSC file that can be found in optional directory.
        If a a filename is not supplied the pysces.model_dir directory contents is displayed and
        the model name can be entered at the promp (<ctrl>+C exits the loading process).

        Arguments:

        File [default=None]: the name of the PySCeS input file
        dir [default=pysces.model_dir]: the optional directory where the PSC file can be found

        """
        if dir != None:
            if os.path.isdir(dir):
                pass
            else:
                chkmdir()
                dir = MODEL_DIR
        else:
            chkmdir()
            dir = MODEL_DIR

        mfgo = 0
        if File == None:
            mfgo = 1
        try:
            if File != None:
                File = chkpsc(File)
            if not os.path.exists(os.path.join(dir,File)):
                mfgo = 1
        except:
            mfgo = 1

        while mfgo == 1:
            print 'Models available in your model_dir: \n************'
            cntr = 0
            namelen = 0
            while len(os.listdir(dir)) == 0:
                print 'No models available in model directory, please set using pys_usercfg.ini or call\
                with pysces.model(\'file.psc\',dir=\'path\\to\\models\')'
                dir = raw_input('\nPlease enter full path to models <CTRL+C> exits: ')

            dirList = os.listdir(dir)
            for x in range(len(dirList)-1,-1,-1):
                if dirList[x][-4:] != '.psc':
                    a = dirList.pop(x)

            for x in dirList:
                if len(x) > namelen:
                    namelen = len(x)
            for x in dirList:
                if cntr < 2:
                    print x + ' '*(namelen-len(x)),
                    cntr += 1
                else:
                    print x
                    cntr = 0
            print '\n************\n'
            print '\nYou need to specify a valid model file ...\n'

            File = raw_input('\nPlease enter filename: ')
            try:
                File = chkpsc(File)
                if os.path.exists(os.path.join(dir,File)):
                    mfgo = 0
            except:
                mfgo = 1

        print 'Using model directory: ' + dir

        try:
            if os.path.exists(os.path.join(dir,File)):
                print os.path.join(dir,File) + ' loading .....',
                self.ModelDir = dir
                self.ModelFile = File
            else:
                print os.path.join(dir,File) + ' does not exist'
                print 'Please set with ModelDir and ModelFile .....',
                self.ModelFile = 'None'
                self.ModelDir = dir
        except:
            print os.path.join(dir,File) + ' does not exist please re-instantiate model .....',
        self.__settings__['display_debug'] = 0
        self.ModelOutput = OUTPUT_DIR


        ##  # Initialise elementary modes
        ##  if os.sys.platform == 'win32':
            ##  self.eModeExe_int = os.path.join(self.__metatool,'meta43_int.exe')
            ##  self.eModeExe_dbl = os.path.join(self.__metatool,'meta43_double.exe')
        ##  else:
            ##  self.eModeExe_int = os.path.join(self.__metatool,'meta43_int')
            ##  self.eModeExe_dbl = os.path.join(self.__metatool,'meta43_double')
        ##  print 'Done.'

        # Initialize serial directory
        self.__settings__['serial_dir'] = os.path.join(self.ModelOutput,'pscdat')
        # Initialize stoichiometric precision
        self.__settings__['stoichiometric_analysis_fp_zero'] = mach_spec.eps*2.0e4
        self.__settings__['stoichiometric_analysis_lu_precision'] = self.__settings__['stoichiometric_analysis_fp_zero']
        self.__settings__['stoichiometric_analysis_gj_precision'] = self.__settings__['stoichiometric_analysis_lu_precision']*10.0

    def __LoadStomPyInterface__(self):
        """
        Load the StomPy Stochastic simulation interface
        """
        if _HAVE_STOMPY and self.__STOMPY__ == None:
            self.__STOMPY__ = StomPyInterface(MODEL_DIR, OUTPUT_DIR)
            print 'PySCeS/StomPy interface active'

    def __ParsePiecewiseFunctions__(self, piecewises):
        """
        THIS IS HIGHLY EXPERIMENTAL! Takes the a piecewises dictionary
        and creates a piecewise object while substituting the object name
        in the formula string.

        - *piecewises* a piecewise dictionary created by the InfixParser

        """
        if piecewises != None and len(piecewises.keys()) > 0:
            self.__HAS_PIECEWISE__ = True
            for p in piecewises:
                print 'Info: adding piecewise object: %s' % p
                #if len(piecewises[p].keys()) == 2:
                    # piecewises[p][0].reverse() # only for libsbml generated infix
                self.__piecewises__.update({p : piecewises[p]})
                P = PieceWise(piecewises[p], self)
                P.setName(p)
                self.piecewise_functions.append(P)
                setattr(self, p, P)

    def InitialiseInputFile(self):
        """
        InitialiseInputFile()

        Parse the input file associated with the PySCeS model instance and assign the basic model attributes

        Arguments:
        None

        """
        self.__parseOK = 1 # check that model has parsed ok?
        try:
            if os.path.exists(os.path.join(self.ModelDir,self.ModelFile)):
                pass
            else:
                print '\nInvalid self.ModelFile: ' + os.path.join(self.ModelDir,self.ModelFile)
        except:
            print 'WARNING: Problem verifying: ' + os.path.join(self.ModelDir,self.ModelFile)

        if self.ModelFile[-4:] == '.psc':
            pass
        else:
            print 'Assuming extension is .psc'
            self.ModelFile += '.psc'

        print '\nParsing file: %s' % os.path.join(self.ModelDir, self.ModelFile)

        pscParser.ParsePSC(self.ModelFile,self.ModelDir,self.ModelOutput)
        print ' '

        badlist = pscParser.KeywordCheck(pscParser.ReactionIDs)
        badlist = pscParser.KeywordCheck(pscParser.Inits,badlist)

        if len(badlist) != 0:
            print '\n******************************\nPSC input file contains PySCeS keywords please rename them and reload:'
            for item in badlist:
                print '   --> ' + item
            print '******************************\n'
            self.__parseOK = 0
            #assert len(badlist) != 0, 'Keyword error, please check input file'

        if self.__parseOK:
            # brett 2008
            InfixParser.__pwcntr__ = 0
            self.__nDict__ = pscParser.nDict.copy()
            self.__sDict__ = pscParser.sDict.copy()
            self.__pDict__ = pscParser.pDict.copy()
            self.__uDict__ = pscParser.uDict.copy()

            # model attributes are now initialised here brett2008
            self.__InitDict__ = {}
            # set parameters and add to __InitDict__
            for p in self.__pDict__.keys():
                setattr(self, self.__pDict__[p]['name'], self.__pDict__[p]['initial'])
                self.__InitDict__.update({self.__pDict__[p]['name'] : self.__pDict__[p]['initial']})
            # set species and add to __InitDict__ and set mod.Xi_init
            for s in self.__sDict__.keys():
                setattr(self, self.__sDict__[s]['name'], self.__sDict__[s]['initial'])
                if not self.__sDict__[s]['fixed']:
                    setattr(self, self.__sDict__[s]['name']+'_init', self.__sDict__[s]['initial'])
                self.__InitDict__.update({self.__sDict__[s]['name'] : self.__sDict__[s]['initial']})

            # setup keywords
            self.__KeyWords__ = pscParser.KeyWords.copy()
            if self.__KeyWords__['Modelname'] == None:
                self.__KeyWords__['Modelname'] = self.ModelFile.replace('.psc','')
            if self.__KeyWords__['Description'] == None:
                self.__KeyWords__['Description'] = self.ModelFile.replace('.psc','')
            # if SpeciesTypes undefined assume []
            if self.__KeyWords__['Species_In_Conc'] == None:
                self.__KeyWords__['Species_In_Conc'] = True
            # if OutputType is undefined assume it is the same as SpeciesType
            if self.__KeyWords__['Output_In_Conc'] == None:
                if self.__KeyWords__['Species_In_Conc']:
                    self.__KeyWords__['Output_In_Conc'] = True
                else:
                    self.__KeyWords__['Output_In_Conc'] = False
            if self.__KeyWords__['ModelType'] == None:
                self.__KeyWords__['ModelType'] = ['Deterministic']
            else:
                self.__KeyWords__['ModelType'] = tuple([t.strip() for t in self.__KeyWords__['ModelType'].split(',')])

            # set the species type in sDict according to 'Species_In_Conc'
            for s in self.__sDict__.keys():
                if not self.__KeyWords__['Species_In_Conc']:
                    self.__sDict__[s]['isamount'] = True
                else:
                    self.__sDict__[s]['isamount'] = False

            # setup compartments
            self.__compartments__ = pscParser.compartments.copy()
            if len(self.__compartments__.keys()) > 0:
                self.__HAS_COMPARTMENTS__ = True
            else:
                self.__HAS_COMPARTMENTS__ = False

            # no (self.)
            self.__fixed_species__ = copy.copy(pscParser.fixed_species)
            self.__species__ = copy.copy(pscParser.species)
            self.__parameters__ = copy.copy(pscParser.parameters)
            self.__reactions__ = copy.copy(pscParser.reactions)
            self.__modifiers__ = copy.copy(pscParser.modifiers)
            # Initialize exposed stuff
            self.fixed_species = tuple(pscParser.fixed_species)
            self.species = tuple(pscParser.species)
            self.parameters = tuple(pscParser.parameters)
            self.reactions = tuple(pscParser.reactions)
            self.modifiers = tuple(pscParser.modifiers)

            # Add input file defined fuctions - brett 200500621
            # TODO deprecated
            # self._Function_time = copy.copy(pscParser.TimeFunc)
            self._Function_user = copy.copy(pscParser.UserFunc)
            self._Function_init = pscParser.InitFunc

            self.__functions__ = pscParser.Functions.copy()
            self.__rules__ = pscParser.AssignmentRules.copy()
            self.__InitFuncs__ = pscParser.ModelInit.copy()
            self.__userfuncs__ = pscParser.UserFuncs.copy()
            self.__eDict__ = pscParser.Events.copy()
            self.__cbm_fluxbounds__ = pscParser.cbm_FluxBounds.copy()
            self.__cbm_objfuncs__ = pscParser.cbm_ObjectiveFunctions.copy()
            self.__cbm_userfluxconstraints__ = pscParser.cbm_UserFluxConstraints.copy()

            ##  if pscParser.ModelUsesNumpyFuncs:
                ##  print 'Numpy functions detected in kinetic laws.\n'
        else:
            print '\nERROR: model parsing error, please check input file.\n'
        # added in a check for model correctness and human error reporting (1=ok, 0=error)
        if len(pscParser.SymbolErrors) != 0:
            print '\nUndefined symbols:\n%s' % self.SymbolErrors
        if not pscParser.ParseOK:
            print '\n\n*****\nModel parsing errors detected in input file '+ self.ModelFile +'\n*****'
            print '\nInput file errors'
            for error in pscParser.LexErrors:
                print error
                ##  try:
                    ##  print error[0] + 'in line:\t' + str(error[1]) + ' ('+ error[2][:20] +' ...)'
                ##  except IndexError:
                    ##  print 'Illegal character:', error.__repr__(), error.__str__()
            print '\nParser errors'
            for error in pscParser.ParseErrors:
                print error
                ##  try:
                    ##  print error[0] + '- ' + error[2][:20]
                ##  except IndexError:
                    ##  print 'Illegal character:', error
            assert pscParser.ParseOK == 1, 'Input File Error'

    def InitialiseRules(self):
        # we need to detect different types of rules etc
        #defmod
        self.__HAS_FORCED_FUNCS__ = False
        self.__HAS_RATE_RULES__ = False
        rate_rules = {}
        assignment_rules = {}
        for ar in self.__rules__:
            if self.__rules__[ar]['type'] == 'assignment':
                self.__HAS_FORCED_FUNCS__ = True
                assignment_rules.update({ar : self.__rules__[ar]})
            elif self.__rules__[ar]['type'] == 'rate':
                self.__HAS_RATE_RULES__ = True
                rate_rules.update({ar : self.__rules__[ar]})

         # THE NEW WAY (adds formula parsing for numpy functions etc)
        InfixParser.setNameStr('self.', '')
        self._NewRuleXCode = {}
        if self.__HAS_FORCED_FUNCS__ and not self.__settings__['mode_substitute_assignment_rules']:
            code_string = ''
            all_names = []
            for ar in assignment_rules:
                name = assignment_rules[ar]['name']
                InfixParser.setNameStr('self.', '')
                InfixParser.parse(assignment_rules[ar]['formula'])
                assignment_rules[ar]['symbols'] = InfixParser.names
                formula = InfixParser.output
                # this code has to stay together #
                for pw in InfixParser.piecewises:
                    formula = formula.replace('self.%s' % pw, 'self.%s()' % pw)
                self.__ParsePiecewiseFunctions__(InfixParser.piecewises)
                # this code has to stay together #
                assignment_rules[ar]['code_string'] = formula
                all_names += InfixParser.names

            os.chdir(self.ModelOutput)
            keep = []
            rules = assignment_rules.keys()
            dep = rules
            while len(dep) > 0:
                dep = []
                indep = []
                for ar in rules:
                    if ar in all_names:
                        indep.append(ar)
                    else:
                        dep.append(ar)
                keep += dep
                rules = indep
            for ar in indep+keep:
                evalCode = 'self.%s = %s\n' % (assignment_rules[ar]['name'],\
                                            assignment_rules[ar]['code_string'])
                self._NewRuleXCode.update({assignment_rules[ar]['name'] : evalCode})
                code_string += evalCode

            print '\nAssignment rule(s) detected.'
            self._Function_forced  = code_string
        elif self.__HAS_FORCED_FUNCS__ and self.__settings__['mode_substitute_assignment_rules']:
            # here we substitute nested assignment rules
            InfixParser.setNameStr('self.', '')
            symbR = {}
            for ass in assignment_rules:
                InfixParser.setNameStr('self.', '')
                InfixParser.parse(assignment_rules[ass]['formula'])
                formula = InfixParser.output
                # this code has to stay together #
                for pw in InfixParser.piecewises:
                    formula = formula.replace('self.%s' % pw, 'self.%s()' % pw)
                self.__ParsePiecewiseFunctions__(InfixParser.piecewises)
                # this code has to stay together #
                symbR.update({assignment_rules[ass]['name']  : formula})
            for ass in assignment_rules:
                InfixParser.setNameStr('self.', '')
                InfixParser.FunctionReplacements = symbR
                InfixParser.parse(assignment_rules[ass]['formula'])
                assignment_rules[ass]['code_string'] = InfixParser.output
            self._Function_forced = 'pass\n'
            print 'Assignment rule(s) detected and substituted.'
        else:
            self._Function_forced = 'pass\n'

        self.__CODE_forced = compile(self._Function_forced,'AssignRules','exec')
        # tested in InitialiseRuleChecks()


        # defmod
        self.__rate_rules__ = []
        self.__rrule__ = None
        rr_code_block = ''
        rr_map_block = ''
        self.__CODE_raterule = None
        self._NewRateRuleXCode = {}
        if self.__HAS_RATE_RULES__:
            # create a rr vector
            self.__rrule__ = numpy.ones(len(rate_rules.keys()),'d')
            # brett2008 debug stuff doesn't do any harm
            cntr = 0
            rr_keys = rate_rules.keys()
            rr_keys.sort()
            for ar in rr_keys:
                name = rate_rules[ar]['name']
                InfixParser.setNameStr('self.', '')
                InfixParser.parse(rate_rules[ar]['formula'])
                formula = InfixParser.output
                rate_rules[ar]['symbols'] = InfixParser.names
                # this code has to stay together #
                for pw in InfixParser.piecewises:
                    formula = formula.replace('self.%s' % pw, 'self.%s()' % pw)
                self.__ParsePiecewiseFunctions__(InfixParser.piecewises)
                # this code has to stay together #
                rate_rules[ar]['code_string'] = formula
                self.__rate_rules__.append(name)
                rr_code_block += 'self.__rrule__[%s] = %s\n' % (cntr, formula)
                ##  rr_code_block += 'self.%s = self.__rrule__[%s]\n' % (name, cntr)
                rr_map_block += 'self.%s = self.__rrule__[%s]\n' % (name, cntr)
                cntr += 1
                # create mod.<rule name>_init attributes
                setattr(self, '%s_init' % name, getattr(self, name))
            os.chdir(self.ModelOutput)
            print 'Rate rule(s) detected.'
        else:
            rr_code_block = 'pass\n'
            rr_map_block = 'pass\n'
        #TODO consider putting this in self.__HAS_RATE_RULES__
        self.__CODE_raterule = compile(rr_code_block,'RateRules','exec')
        self.__CODE_raterule_map = compile(rr_map_block,'RateRuleMap','exec')

        del rate_rules, assignment_rules

    def InitialiseRuleChecks(self):
        try:
            exec(self.__CODE_raterule)
        except ZeroDivisionError:
            print 'WARNING: Assignment RateRule ZeroDivision on initialisation (continuing)'
        except Exception, ex:
            print 'WARNING: RateRule initialisation error\n', ex

        zeroDivErr = []
        for k in self._NewRuleXCode:
            try:
                exec(compile(self._NewRuleXCode[k],'NewRuleXCode','exec'))
            except Exception, ex:
                zeroDivErr.append(k)
        exit = len(self._NewRuleXCode.keys())
        while len(zeroDivErr) > 0 and exit > 0:
            zeroDivErr2 = []
            for kk in zeroDivErr:
                try:
                    exec(compile(self._NewRuleXCode[kk],'NewRuleXCode','exec'))
                    if self.__HAS_RATE_RULES__:
                        exec(self.__CODE_raterule)
                        exec(self.__CODE_raterule_map)
                except Exception, ex:
                    zeroDivErr2.append(kk)
            exit -= 1
            zeroDivErr = zeroDivErr2
            if exit < 0:
                print 'WARNING: ZeroDivision elimination failed'

        try:
            exec(self.__CODE_forced)
            #print 'done.'
        except ZeroDivisionError:
            print 'WARNING: Assignment rule ZeroDivision on intialisation'
        except Exception, ex:
            print 'WARNING: Assignment rule error (disabling all rules)\n', ex
            self.__CODE_forced = compile('pass\n','AssignRules','exec')

    def Stoichiometry_Init(self,nmatrix,load=0):
        """
        Stoichiometry_Init(nmatrix,load=0)

        Initialize the model stoichiometry. Given a stoichiometric matrix N, this method will return an instantiated PyscesStoich instance and status flag. Alternatively, if load is enabled, PySCeS will
        attempt to load a previously saved stoichiometric analysis (saved with Stoichiometry_Save_Serial)
        and test it's correctness. The status flag indicates 0 = reanalyse stoichiometry or
        1 = complete structural analysis preloaded.

        Arguments:

        nmatrix: The input stoichiometric matrix, N
        load [default=0]: try to load a saved stoichiometry (1)

        """
        if load:
            print 'Loading saved stoichiometry ...'
            try:
                stc = self.Stoichiometry_Load_Serial()
                go = 1
            except Exception, slx:
                print slx
                go = 0

            row,col = nmatrix.shape
            if go:
                badList = []
                for x in range(row):
                    if stc.__species__[x] != self.__species__[x]:
                        badList.append((stc.__species__[x],self.__species__[x]))
                        go = 0
                    for y in range(col):
                        if stc.__reactions__[y] != self.__reactions__[y]:
                            badList.append((stc.__reactions__[y],self.__reactions__[y]))
                            go = 0
                        if abs(nmatrix[x,y] - stc.nmatrix[x,y]) > stc.stoichiometric_analysis_fp_zero:
                            badList.append(((x,y),nmatrix[x,y],stc.nmatrix[x,y]))
                            go = 0
            if not go:
                ##  print 'Stoichiometry mismatch'
                ##  for x in badList:
                    ##  print x,
                print '\nProblem loading stoichiometry, reanalysing ...'
                stc = PyscesStoich.Stoich(nmatrix)
                status = 0
            else:
                print 'Stoichiometry verified ... we have liftoff'
                status = 1
        else:
            #print 'Instantiating new stoichiometry ...'
            stc = PyscesStoich.Stoich(nmatrix)
            status = 0
        return stc,status


    def Stoichiometry_Save_Serial(self):
        """Serialize and save a Stoichiometric instance to binary pickle""""""
        Stoichiometry_Save_Serial()

        Serilaise and save the current model stoichiometry to a file with name <model>_stoichiometry.pscdat
        in the mod.__settings__['serial_dir'] directory (default: mod.model_output/pscdat)

        Arguments:
        None

        """
        # new plan, I introduce species and reaction arrays into the stoich instance for verification purposes
        # brett - 20050831
        self.__structural__.__species__ = copy.copy(self.__species__)
        self.__structural__.__reactions__ = copy.copy(self.__reactions__)
        self.SerialEncode(self.STOICH,self.ModelFile[:-4]+'_stoichiometry')


    def Stoichiometry_Load_Serial(self):
        """
        Stoichiometry_Load_Serial()

        Load a saved stoichiometry saved with mod.Stoichiometry_Save_Serial() and return
        a stoichiometry instance.

        Arguments:
        None

        """
        stc = self.SerialDecode(self.ModelFile[:-4]+'_stoichiometry')
        return stc

    # new powerslave method - brett 20050825
    def Stoichiometry_Analyse(self,override=0,load=0):
        """
        Stoichiometry_Analyse(override=0,load=0)

        Perform a structural analyses. The default behaviour is to construct and analyse the model
        from the parsed model information. Overriding this behaviour analyses the stoichiometry
        based on the current stoichiometric matrix. If load is specified PySCeS tries to load a
        saved stoichiometry, otherwise the stoichiometric analysis is run. The results of
        the analysis are checked for floating point error and nullspace rank consistancy.

        Arguments:

        override [default=0]: override stoichiometric analysis intialisation from parsed data
        load [default=0]: load a presaved stoichiometry

        """
        if not override:
            self.__nmatrix__ = self.__initmodel__()            #Creates the model N
            #print '\nintializing N\n'
        else:
            print '\nStoichiometric override active\n'

        assert len(self.__nmatrix__) > 0, '\nUnable to generate Stoichiometric Matrix! model has:\n%s reactions\n%s species\nwhat did you have in mind?\n' % (len(self.__reactions__), len(self.__species__))


        self.__Nshape__ = self.__nmatrix__.shape               #Get the shape of N
        ##  self.__Vtemp__ = numpy.zeros((self.__Nshape__[1])) # going going ....

        # get stoich instance and whether it was analysed or loaded - brett 20050830
        self.__structural__, stc_load = self.Stoichiometry_Init(self.__nmatrix__,load=load)

        # if not loaded analyze - brett 20050830
        if not stc_load:
            # technically this means we can define this on the fly - brett #20051013
            self.__structural__.stoichiometric_analysis_fp_zero = self.__settings__['stoichiometric_analysis_fp_zero']
            self.__structural__.stoichiometric_analysis_lu_precision = self.__settings__['stoichiometric_analysis_lu_precision']
            self.__structural__.stoichiometric_analysis_gj_precision = self.__settings__['stoichiometric_analysis_gj_precision']
            self.__structural__.AnalyseL()           #Get all L related stuff
            self.__structural__.AnalyseK()           #Get all K related stuff


        #test matrix values against __settings__['stoichiometric_analysis_lu_precision']
        lsmall,lbig = self.__structural__.MatrixValueCompare(self.__structural__.lzeromatrix)
        ksmall,kbig = self.__structural__.MatrixValueCompare(self.__structural__.kzeromatrix)
        SmallValueError = 0
        if abs(lsmall) < self.__structural__.stoichiometric_analysis_lu_precision*10.0:
            print '\nWARNING: values in L0matrix are close to stoichiometric precision!'
            print 'Stoichiometric LU precision:', self.__structural__.stoichiometric_analysis_lu_precision
            print 'L0 smallest abs(value)', abs(lsmall)
            print 'Machine precision:', mach_spec.eps
            SmallValueError = 1
        if abs(ksmall) < self.__structural__.stoichiometric_analysis_lu_precision*10.0:
            print '\nWARNING: values in K0matrix are close to stoichiometric precision!'
            print 'Stoichiometric precision:', self.__structural__.stoichiometric_analysis_lu_precision
            print 'K0 smallest abs(value)', abs(ksmall)
            print 'Machine precision:', mach_spec.eps
            SmallValueError = 1
        if SmallValueError:
            raw_input('\nStructural Analysis results may not be reliable!!!.\n\nTry change <mod>.__settings__["stoichiometric_analysis_lu_precision"] (see reference manual for details)\n\n\t press any key to continue: ')

        # cross check that rank is consistant between K0 and L0
        if self.__structural__.kzeromatrix.shape[0] != self.__structural__.lzeromatrix.shape[1]:
            print '\nWARNING: the rank calculated by the Kand L analysis methods are not the same!'
            print '\tK analysis calculates the rank as: ' + `self.__structural__.kzeromatrix.shape[0]`
            print '\tL analysis calculates the rank as: ' + `self.__structural__.lzeromatrix.shape[1]`
            print 'This is not good! Structural Analysis results are not reliable!!!\n'
            assert self.__structural__.kzeromatrix.shape[0] == self.__structural__.lzeromatrix.shape[1], '\nStructuralAnalysis Error: rank mismatch'


        self.__HAS_FLUX_CONSERVATION__ = self.__structural__.info_flux_conserve
        self.__HAS_MOIETY_CONSERVATION__ = self.__structural__.info_moiety_conserve

        if self.__settings__['enable_deprecated_attr']:
            ##  self.__nmatrix__ = copy.copy(self.nmatrix)
            self.nmatrix = self.__nmatrix__ # done with caution brett2008
            self.nmatrix_row = self.__structural__.nmatrix_row
            self.nmatrix_col = self.__structural__.nmatrix_col

            self.kmatrix = self.__structural__.kmatrix
            self.kmatrix_row = self.__structural__.kmatrix_row
            self.kmatrix_col = self.__structural__.kmatrix_col
            self.kzeromatrix = self.__structural__.kzeromatrix
            self.kzeromatrix_row = self.__structural__.kzeromatrix_row
            self.kzeromatrix_col = self.__structural__.kzeromatrix_col


            self.lmatrix = self.__structural__.lmatrix
            self.lmatrix_row = self.__structural__.lmatrix_row
            self.lmatrix_col = self.__structural__.lmatrix_col
            self.lzeromatrix = self.__structural__.lzeromatrix
            self.lzeromatrix_row = self.__structural__.lzeromatrix_row
            self.lzeromatrix_col = self.__structural__.lzeromatrix_col
            self.conservation_matrix = self.__structural__.conservation_matrix
            self.conservation_matrix_row = self.__structural__.conservation_matrix_row
            self.conservation_matrix_col = self.__structural__.conservation_matrix_col
            self.nrmatrix = self.__structural__.nrmatrix
            self.nrmatrix_row = self.__structural__.nrmatrix_row
            self.nrmatrix_col = self.__structural__.nrmatrix_col

            self.__kmatrix__ = copy.copy(self.kmatrix)
            self.__kzeromatrix__ = copy.copy(self.kzeromatrix)
            self.__lmatrix__ = copy.copy(self.lmatrix)
            self.__lzeromatrix__ = copy.copy(self.lzeromatrix)
            self.__nrmatrix__ = copy.copy(self.nrmatrix)
        # switch that is set if the stoichiometric analysis is up to date

        self.__structural__.species = self.species
        self.__structural__.reactions = self.reactions
        self.Nmatrix = PyscesStoich.StructMatrix(self.__structural__.nmatrix, self.__structural__.nmatrix_row, self.__structural__.nmatrix_col)
        self.Nmatrix.setRow(self.species)
        self.Nmatrix.setCol(self.reactions)

        self.Nrmatrix = PyscesStoich.StructMatrix(self.__structural__.nrmatrix, self.__structural__.nrmatrix_row, self.__structural__.nrmatrix_col)
        self.Nrmatrix.setRow(self.species)
        self.Nrmatrix.setCol(self.reactions)

        self.Kmatrix = PyscesStoich.StructMatrix(self.__structural__.kmatrix, self.__structural__.kmatrix_row, self.__structural__.kmatrix_col)
        self.Kmatrix.setRow(self.reactions)
        self.Kmatrix.setCol(self.reactions)

        self.K0matrix = PyscesStoich.StructMatrix(self.__structural__.kzeromatrix, self.__structural__.kzeromatrix_row, self.__structural__.kzeromatrix_col)
        self.K0matrix.setRow(self.reactions)
        self.K0matrix.setCol(self.reactions)

        self.Lmatrix = PyscesStoich.StructMatrix(self.__structural__.lmatrix, self.__structural__.lmatrix_row, self.__structural__.lmatrix_col)
        self.Lmatrix.setRow(self.species)
        self.Lmatrix.setCol(self.species)

        self.L0matrix = PyscesStoich.StructMatrix(self.__structural__.lzeromatrix, self.__structural__.lzeromatrix_row, self.__structural__.lzeromatrix_col)
        self.L0matrix.setRow(self.species)
        self.L0matrix.setCol(self.species)

        if self.__structural__.info_moiety_conserve:
            self.Consmatrix = PyscesStoich.StructMatrix(self.__structural__.conservation_matrix, self.__structural__.conservation_matrix_row, self.__structural__.conservation_matrix_col)
            self.Consmatrix.setRow(self.species)
            self.Consmatrix.setCol(self.species)
        else:
            self.Consmatrix = None
        self.__StoichOK = 1
        print ' '

    def Stoichiometry_ReAnalyse(self):
        """
        Stoichiometry_ReAnalyse()

        Reanalyse the stoichiometry using the current N matrix ie override=1
        (for use with mod.Stoich_matrix_SetValue)

        Arguments:
        None

        """
        self.Stoichiometry_Analyse(override=1)
        self.InitialiseConservationArrays()

    def Stoich_nmatrix_SetValue(self,species,reaction,value):
        """
        Stoich_nmatrix_SetValue(species,reaction,value)

        Change a stoichiometric coefficient's value in the N matrix. Only a coefficients magnitude may be set, in other words a
        a coefficient's value must remain negative, positive or zero. After changing a coefficient
        it is necessary to Reanalyse the stoichiometry.

        Arguments:

        species: species name (s0)
        reaction: reaction name (R4)
        value: new coefficient value

        """
        index = self.__Stoich_nmatrix_FindIndex__(species,reaction)
        if self.__Stoich_nmatrix_CheckValue__(index,value):
            self.__Stoich_nmatrix_UpdateValue__(index,value)
            self.__StoichOK = 0

    def __Stoich_nmatrix_FindIndex__(self,species,reaction):
        """
        __Stoich_nmatrix_FindIndex__(species,reaction)

        Return the N matrix co-ordinates of the coefficient referenced by species and reaction name.

        Arguments:

        species: species name
        reaction: reaction name

        """
        rval = None
        cval = None
        for x in enumerate(self.species):
            if species == x[1]:
                rval = x[0]
        for y in enumerate(self.reactions):
            if reaction == y[1]:
                cval = y[0]
        return (rval,cval)

    def __Stoich_nmatrix_UpdateValue__(self,(x,y),val):
        """
        __Stoich_nmatrix_UpdateValue__((x,y), val)

        Update N matrix co-ordinates (x,y) with val

        Arguments:
        (x,y): row, column coordinates
        val: value

        """
        self.nmatrix[x,y] = val
        self.__nmatrix__[x,y] = val
        self.Nmatrix.array[x,y] = val

    def __Stoich_nmatrix_CheckValue__(self,(x,y),val):
        """
        __Stoich_nmatrix_CheckValue__((x,y),val)

        Check validity of new coefficient value against existing N matrix coefficient (x,y) for existance and/or sign.
        Returns 1 (true) or 0.

        Arguments:
        (x,y): N matrix coordinates
        val: new value

        """
        go = 1
        if x == None or y == None:
            print '\nSpecies (nmatrix) index  =', x
            print 'Reaction (nmatrix) index =', y
            print '\nI\'m confused, perhaps you entered an incorrect species or reaction name'
            print 'or they are in the wrong order?'
            go = 0
        elif abs(self.__nmatrix__[x,y]) == 0.0:
            if val != 0.0:
                go = 0
                print '\nZero coefficient violation'
                print '  nmatrix['+`x`+','+`y`+'] can only be = 0.0 (input '+str(val)+')'
        elif self.__nmatrix__[x,y] > 0.0:
            if val <= 0.0:
                go = 0
                print '\nPositive coefficient violation'
                print '  nmatrix['+`x`+','+`y`+'] can only be > 0.0 (input '+str(val)+')'
        elif self.__nmatrix__[x,y] < 0.0:
            if val >= 0.0:
                go = 0
                print '\nNegative coefficient violation'
                print '  nmatrix['+`x`+','+`y`+'] can only be < 0.0 (input '+str(val)+')'
        if go:
            return 1
        else:
            return 0

    def SerialEncode(self,data,filename):
        """
        SerialEncode(data,filename)

        Serialise and save a Python object using a binary pickle to file. The serialised object
        is saved as <filename>.pscdat in the directory defined by mod.model_serial.

        Arguments:

        data: pickleable Python object
        filename: the ouput filename

        """
        filename = str(filename)+'.pscdat'
        if os.path.exists(self.__settings__['serial_dir']):
            pass
        else:
            os.mkdir(self.__settings__['serial_dir'])
        print '\ncPickle data stored in: ' + self.__settings__['serial_dir']

        File = open(os.path.join(self.__settings__['serial_dir'], filename),'wb')
        try:
            cPickle.dump(data,File,2)
            print 'Serialization complete'
        except Exception, E:
            print 'Serialize error:\n',E
        File.flush()
        File.close()
        del data


    def SerialDecode(self,filename):
        """
        SerialDecode(filename)

        Decode and return a serialised object saved with SerialEncode.

        Arguments:

        filename: the filename (.pscdat is assumed)

        """
        filename = str(filename)+'.pscdat'
        if not os.path.exists(os.path.join(self.__settings__['serial_dir'],filename)):
            raise RuntimeError, 'Serialized data '+os.path.join(self.__settings__['serial_dir'],filename)+' does not exist'
        data = None
        try:
            File = open(os.path.join(self.__settings__['serial_dir'], filename),'rb')
            data = cPickle.load(File)
            File.close()
            print 'Serial decoding complete'
        except Exception, E:
            print 'Serial decode error:\n',E

        return data

    def InitialiseCompartments(self):
        # the name should say it all
        self.__settings__['compartment_fudge_factor'] = 1.0
        startFudging = 1.0e-8
        I_AM_FUDGING = False
        if self.__HAS_COMPARTMENTS__:
            tmp = min([abs(self.__compartments__[c]['size']) for c in self.__compartments__.keys()])
            if tmp < startFudging:
                ##  self.__settings__['compartment_fudge_factor'] = 10.0**round(numpy.log10(tmp))
                self.__settings__['compartment_fudge_factor'] = tmp # sneaky b%*))_)%$^&*@rds
                ##  print 'compartment_fudge_factor', self.__settings__['compartment_fudge_factor']
            for c in self.__compartments__.keys():
                if self.__settings__['compartment_fudge_factor'] < startFudging:
                    newsize = self.__compartments__[c]['size']/self.__settings__['compartment_fudge_factor']
                    print 'INFO: Rescaling compartment with size %s to %s' % (self.__compartments__[c]['size'], newsize)
                    self.__compartments__[c]['size'] = newsize
                    self.__compartments__[c].update({'scale' : self.__settings__['compartment_fudge_factor']})
                    I_AM_FUDGING = True
                setattr(self, self.__compartments__[c]['name'], self.__compartments__[c]['size'])
                setattr(self, '%s_init' % self.__compartments__[c]['name'], self.__compartments__[c]['size'])
        if self.__HAS_COMPARTMENTS__:
            for sp in self.__sDict__.keys():
                if self.__sDict__[sp]['compartment'] == None and len(self.__compartments__.keys()) == 1:
                    self.__sDict__[sp]['compartment'] = self.__compartments__[self.__compartments__.keys()[0]]['name']
                    print 'COMPARTMENT WARNING: this model has a compartment defined %s but \"%s\" is not in it ... I\'ll try it for now' %\
                    ([self.__compartments__[c]['name'] for c in self.__compartments__.keys()], sp)
                    ##  print self.__sDict__[sp]
                elif self.__sDict__[sp]['compartment'] == None and len(self.__compartments__.keys()) > 1:
                    assert self.__sDict__[sp]['compartment'] != None,\
                    '\nCOMPARTMENT ERROR: this model has multiple compartments defined %s but \"%s\" is not in one!' %\
                    ([self.__compartments__[c]['name'] for c in self.__compartments__.keys()], sp)
                # brett 2008 fudge this!
                if not self.__KeyWords__['Species_In_Conc'] and I_AM_FUDGING:
                    self.__sDict__[sp]['initial'] = self.__sDict__[sp]['initial']/self.__settings__['compartment_fudge_factor']
                    setattr(self, sp, self.__sDict__[sp]['initial'])
                    setattr(self, '%s_init' % sp, self.__sDict__[sp]['initial'])
                    print 'INFO: Rescaling species (%s) to size %s.' % (sp, self.__sDict__[sp]['initial'])

        self.__CsizeAllIdx__ = []
        self.__null_compartment__ = 1.0
        uses_null_compartments = False
        for s in self.__species__:
            if self.__HAS_COMPARTMENTS__:
                self.__CsizeAllIdx__.append(self.__sDict__[s]['compartment'])
            else:
                self.__CsizeAllIdx__.append('__null_compartment__')
                uses_null_compartments = True
        if uses_null_compartments:
            setattr(self, '__null_compartment___init', 1.0)
        ##  self.__CsizeFSIdx__ = []
        ##  for s in self.__fixed_species__:
            ##  if self.__HAS_COMPARTMENTS__:
                ##  self.__CsizeFSIdx__.append(self.__sDict__[s]['compartment'])
            ##  else:
                ##  self.__CsizeFSIdx__.append('__null_compartment__')

        # Init compartment divisions vector - brett 2008
        ##  self.__CsizeAll__ = numpy.ones(len(self.__CsizeAllIdx__), 'd')
        if self.__HAS_COMPARTMENTS__:
            self.__CsizeAll__ = numpy.array([self.__compartments__[c]['size'] for c in self.__CsizeAllIdx__], 'd')
        else:
            self.__CsizeAll__ = numpy.ones(len(self.__CsizeAllIdx__), 'd')
        ##  self.__CsizeFS__ = numpy.array([self.__compartments__[c]['size'] for c in self.__CsizeFSIdx__], 'd')


    # add new function types to model
    def InitialiseFunctions(self):
        for func in self.__functions__:
            F = Function(func, self)
            for arg in self.__functions__[func]['args']:
                F.setArg(arg.strip())
            F.addFormula(self.__functions__[func]['formula'])
            setattr(self, func, F)
        os.chdir(self.ModelOutput)
        for func in self.__functions__:
            fobj = getattr(self, func)
            for f in fobj.functions:
                if f in self.__functions__:
                    setattr(fobj, f, getattr(self, f))

    # add event types to model
    def InitialiseEvents(self):
        self.__events__ = []
        # for each event
        for e in self.__eDict__:
            ev = Event(e, self)
            ev._time_symbol = self.__eDict__[e]['tsymb']
            ev.setTrigger(self.__eDict__[e]['trigger'], self.__eDict__[e]['delay'])
            # for each assignment
            for ass in self.__eDict__[e]['assignments']:
                ev.setAssignment(ass, self.__eDict__[e]['assignments'][ass])
            self.__events__.append(ev)
            setattr(self, ev.name, ev)
        os.chdir(self.ModelOutput)
        if len(self.__events__) > 0:
            self.__HAS_EVENTS__ = True
            print 'Event(s) detected.'
        else:
            self.__HAS_EVENTS__ = False

    def InitialiseModel(self):
        """
        InitialiseModel()

        Initialise and set up dynamic model attributes and methods using the model defined in
        the associated PSC file

        Arguments:
        None

        """
        #TODO killkillkill
        self.__inspec__ = numpy.zeros((len(self.__species__)),'d')
        self.__D_s_Order, DvarUpString = self.__initvar__() #Initialise s[] array


        self.__CDvarUpString__ = compile(DvarUpString,'DvarUpString','exec')

        self.state_species = None
        self.state_flux = None
        ##  self.sim_res = None
        self.elas_var_u = None
        self.elas_var = None
        self.__vvec__ = numpy.zeros((self.__Nshape__[1]),'d')
        self.__tvec_a__ = None
        self.__tvec_c__ = None

        self.__CVODE_Vtemp = numpy.zeros((self.__Nshape__[1]),'d')

        #  #debug
        #  self.display_debugcount = 0
        #  self.cntr = 50

        par_map2store, par_remap = self.__initfixed__()         #Initialise fixed species

        #self.__par_map2storeC =  par_map2store
        #self.__par_remapC =  par_remap
        self.__par_map2storeC =  compile(par_map2store,'par_map2store','exec')
        self.__par_remapC =  compile(par_remap,'par_remap','exec')

        ##  self.__xMake = compile(xString,'xString','exec')
        ##  exec(self.__xMake)

        # initialise rate equations
        mapString,mapString_R,vString = self.__initREQ__()

        self.__mapFunc__ = compile(mapString,'mapString','exec')
        self.__mapFunc_R__ = compile(mapString_R,'mapString_R','exec')
        self.__vFunc__ = compile(vString,'vString','exec')
        ##  exec(lambdaFuncsC)

        # Machine specific values (for IEEE compliant FP machines this is around 2.e-16)
        # FutureNote: investigate arbitrary precision FP in Python, probably GNU-GMP based - brett 20040326
        self.__settings__['mach_floateps'] = mach_spec.eps

        # PySCeS mode switches
        self.__settings__['mode_number_format'] = '%2.4e' # this affects number output formatting in PySCeS)
        self.__settings__['mode_sim_init'] = 0         # 0:initval, 1:zeroval, 2:lastss 3:sim_res[-1]
        self.__settings__['mode_sim_max_iter'] = 3     # maximum number of auto-stepsize adjustments

        #TODO UPGRADE
        self.mode_state_init = 0       # 0:initval, 1:zeroval, 2:sim, 3:%state
        self.mode_solver = 'HYBRD'              # 0:HYBRD, 1:FINTSLV, 2:NLEQ2
        self.mode_solver_fallback = 1  # 0:Solver failure fails,
                                       # 1:solver failure falls back to NLEQ2 if present then FINTSLV (see next)
        self.STATE_extra_output = []   # extra data added to data_sstate object

        self.__settings__['mode_state_init3_factor'] = 0.1 # factor to multiply the previous ss used as initialiser
        self.__mode_state_init2_array__ = numpy.logspace(0,5,18) # the simulation array used to initialise the steady state
        self.__settings__['mode_state_mesg'] = 1       # happy exit message from State()
        self.__settings__['mode_state_nan_on_fail'] = False # give last best solutions or NaN as solution
        self.__settings__['solver_switch_warning'] = True # the irritating switching to message
        self.__settings__['mode_solver_fallback_integration'] = 1 # 0:no fallback to forward integration 1:fallback to integration
        # this is now initialised in the model parsing sectiomn ParseModel
        self.__settings__['mode_elas_deriv_order'] = 3 # the order of scipy.derivative - 3 seems good for most situations
        self.__settings__['mode_mca_scaled'] = 1  # MCA scaling option 0:unscaled E+C+Eig 1:scaled E+C+Eig
        self.__settings__['mode_eigen_output'] = 0       # 0:normal, 1:extended eigen value + left/right vectors
        # under consideration
        self.__settings__['mode_suppress_info'] = 0    # 0:warnings displayed, 1:warnings suppressed
        # new compartment stuff (using dictionary directly) - brett 2008

        ##  self.Species_In_Conc = False

        # misc settings
        self.__settings__['write_array_header'] = 1 # write an id header before the array
        self.__settings__['write_array_spacer'] = 1 # write a empty line before the array
        self.__settings__['write_array_html_header'] = 1 #write html page header
        self.__settings__['write_array_html_footer'] = 1 #write html page footer
        self.__settings__['write_array_html_format'] = '%2.4f' #html number format
        self.__settings__['write_arr_lflush'] = 5 # lines to write before flushing to disk

        # Hybrd options (zero value means routine decides)
        self.__settings__['hybrd_xtol'] = 1.e-12    # relative error tolerance
        self.__settings__['hybrd_maxfev'] = 0       # Maximum number of calls, zero means then 100*(len(species)+1)
        self.__settings__['hybrd_epsfcn'] = copy.copy(self.__settings__['mach_floateps']) # A suitable step length for the forward-difference approximation of the Jacobian
        self.__settings__['hybrd_factor'] = 100     # A parameter determining the initial step bound in interval (0.1,100)
        self.__settings__['hybrd_mesg'] = 1         # print the exit status message

        # Finstslv options (forward integration solver) - brett (20030331)
        self.__settings__['fintslv_tol'] = 1.e-3   # max allowed deviation between max(sim_res) to be a steady state
        self.__settings__['fintslv_step'] = 5       # threshold number of steps where deviation < atol to be declared a steady state
        # a "saturation" type range - should be ok for most systems
        self.__fintslv_range__ = numpy.array([1,10,100,1000,5000,10000,50000,50100,50200,50300,50400,50500,50600,50700,50800,50850,50900,50950,51000],'d')
        #self.__fintslv_range__ = numpy.array([1,10,100,1000,5000,10000,50000,100000,500000,1000000,1000100, 1000200,1000300,1000400,1000500,1000600,1000700,1000800],'d')
        self.__settings__['fintslv_rmult'] = 1.0

        # NLEQ2 options (optional non-linear solver) - brett (20030331)
        # IOPT
        self.__settings__['nleq2_advanced_mode'] = False # use advanced NLEQ2 features ... may speedup some parameter scans
        self.__settings__['nleq2_growth_factor'] = 10 # nitmax growth factor per nleq2 iteration
        self.__settings__['nleq2_iter']   = 3       # number of itertions to loop the solver through (2 should almost always be sufficient)
        self.__settings__['nleq2_iter_max'] = 10    # maximum numbe rof iterations in advanced mode

        self.__settings__['nleq2_rtol']   = 1.e-8   # automatically calculated by nleq12
        self.__settings__['nleq2_jacgen'] = 2       # 2:numdiff, 3:numdiff+feedback
        self.__settings__['nleq2_iscaln']  = 0       # 0:xscal lower thresholdof scaling vector, 1:always scaling vector
        # Dangerous anything not zero seqfaults
        ## self.__settings__['nleq2_mprerr'] = 1       # 0:no output, 1:error, 2:+warning, 3:+info
        self.__settings__['nleq2_nonlin'] = 4       # 1:linear, 2:mildly non-lin, 3:highly non-lin, 4:extremely non-lin
        self.__settings__['nleq2_qrank1'] = 1       # 0:no Broyden approx. rank-1 updates, 1:Broyden approx. rank-1 updates
        self.__settings__['nleq2_qnscal'] = 0       # 0:Automatic row scaling is active, 1:inactive
        self.__settings__['nleq2_ibdamp'] = 0       # 0:auto damping strategy, 1:damping on, 2:damping off
        self.__settings__['nleq2_nitmax_growth_factor'] = 4 # on non-superlinear convergance nitmax *= this (ierr=5)
        # TODO: chekc this
        self.__settings__['nleq2_iormon'] = 2       # 0:default(2) 1:convergance not checked, 2:+'weak stop', 3:+'hard stop' criterion
        self.__settings__['nleq2_mesg'] = 1         # print the exit status message
        #TODO:
        self.nleq2_nitmax = 50      # optimized and self adjusting :-)

        # Other SteadyState options
        self.__settings__['small_concentration'] = 1.0e-6       # the initial values of 1:zeroval smaller than 1.0e-6 sometimes give problems
        ##  self.__state_set_conserve__ = 1
        self.__StateOK__ = True

        self.data_sstate = None
        self.data_sim = None        # the new integration results data object
        self.data_stochsim = None        # the new stochastic integration results data object
        self.__scan2d_results__ = None # yaaaay gnuplot is back
        self.__scan2d_pars__ = None

        # Simulate/lsoda options (zero value means routine decides)
        self.__settings__['lsoda_atol'] = 1.e-12    # The input parameters rtol and atol determine the error
        self.__settings__['lsoda_rtol'] = 1.e-7     # control performed by the solver. -- johann 20050217 changed from 1e-5
        self.__settings__["lsoda_mxstep"] = 0       # maximum number (internally defined) steps allowed per point. 0: x <= 500
        self.__settings__["lsoda_h0"] = 0.0         # the step size to be attempted on the first step.
        self.__settings__["lsoda_hmax"] = 0.0       # the maximum absolute step size allowed.
        self.__settings__["lsoda_hmin"] = 0.0       # the minimum absolute step size allowed.
        self.__settings__["lsoda_mxordn"] = 12      # maximum order to be allowed for the nonstiff (Adams) method.
        self.__settings__["lsoda_mxords"] = 5       # maximum order to be allowed for the stiff (BDF) method.
        self.__settings__["lsoda_mesg"] = 1         # print the exit status message

        # try to select the best integration algorithm
        self.mode_integrator = 'LSODA' # LSODA/CVODE set the intgration algorithm
        if self.__HAS_EVENTS__:
            if _HAVE_PYSUNDIALS:
                print '\nINFO: events detected and we have PySundials installed, switching to CVODE (mod.mode_integrator=\'CVODE\').'
                self.mode_integrator = 'CVODE'
            else:
                print '\nWARNING: PySCeS needs PySundials installed for event handling, PySCeS will continue with LSODA (NOTE: ALL EVENTS WILL BE IGNORED!).'
                print 'Windows users may install the "pysces_pysundials" package from pysces.sf.net'
                print 'For other OS\'s see pysundials.sf.net for installation details\n'
                self.__events__ = []
        if self.__HAS_RATE_RULES__ or self.__HAS_COMPARTMENTS__:
            if _HAVE_PYSUNDIALS:
                print 'INFO: Compartments andor RateRules detected and PySundials installed, switching to CVODE (mod.mode_integrator=\'CVODE\').\n'
                self.mode_integrator = 'CVODE'
            else:
                print '\nWARNING: Compartments and or RateRules detected! PySCeS prefers CVODE but will continue with LSODA (NOTE: VARIABLE COMPARTMENTS ARE NOT SUPPORTED WITH LSODA!)'
                print 'Windows users may install the "pysces_pysundials" package from pysces.sf.net'
                print 'For other OS\'s see pysundials.sf.net for installation details\n'

        # pysundials
        self.mode_integrate_all_odes = False # only available with CVODE
        self.__settings__["cvode_abstol"] = 1.0e-15     # absolute tolerance
        ##  self.__settings__["cvode_abstol_max"]  = 1.0e-3 # not used anymore
        self.__settings__["cvode_abstol_factor"]  = 1.0e-6
        self.__settings__["cvode_reltol"] = 1.0e-9      # relative tolerance
        self.__settings__["cvode_auto_tol_adjust"] = True # auto reduce tolerances
        self.__settings__["cvode_mxstep"] = 1000        # max step default
        self.__settings__["cvode_stats"] = False         # print some pretty stuff after a simulation

        if self.__HAS_PIECEWISE__ and self.__settings__["cvode_reltol"] <= 1.0e-9 :
            self.__settings__["cvode_reltol"] = 1.0e-6
            print 'INFO: Piecewise functions detected increasing CVODE tolerance slightly (mod.__settings__[\"cvode_reltol\"] = 1.0e-9 ).'

        # Normal simulation options
        self.sim_start = 0.0
        self.sim_end = 10.0
        self.sim_points = 20.0
        sim_steps = (self.sim_end - self.sim_start)/self.sim_points
        self.sim_time = numpy.arange(self.sim_start, self.sim_end + sim_steps, sim_steps)
        self.CVODE_extra_output = []
        self.CVODE_xdata = None

        # elasticity options
        self.__settings__["elas_evar_upsymb"] = 1        # attach elasticities
        self.__settings__["elas_epar_upsymb"] = 1        # attach parameter elasticities
        self.__settings__["elas_evar_remap"] = 1             # remap steady state values ... no if SimElas
        self.__settings__['mode_elas_deriv_factor'] = 0.0001 # used to determine a stepsize dx=So*factor
        self.__settings__['mode_elas_deriv_min'] = 1.0e-12   # minimum value dx is allowed to have
        self.__settings__['elas_zero_flux_fix'] = False # replaces zero fluxes with a very small number
        self.__settings__['elas_zero_conc_fix'] = False # replaces zero concentrations with a very small number
        self.__settings__['elas_scaling_div0_fix'] = False # if Infinite values are created when scaling set to zero

        # MCA options
        self.__settings__["mca_ccj_upsymb"] = 1         # attach the flux control coefficients
        self.__settings__["mca_ccs_upsymb"] = 1         # attach the concentration control coefficients
        self.__settings__["mca_ccall_fluxout"] = 1       # in .showCC() output flux cc's
        self.__settings__["mca_ccall_concout"] = 1       # in .showCC() output conc cc's
        self.__settings__["mca_ccall_altout"] = 0        # in .showCC() all CC's group by reaction

        # gone in 60 seconds brett2008 (Refactored to PyscesLink)
        ##  # Elementary mode options
        ##  self.emode_userout = 0     # write metatool output to file 0:no, 1:yes
        ##  self.emode_intmode = 0  # 0:float metatool, 1:integer metatool
        ##  self.emode_file = self.ModelFile[:-4] + '_emodes'

        # pitcon (pitcon 6.1) continuation options - brett 20040429
        #my interface controls
        self.__settings__["pitcon_fix_small"] = 0    #in the REq evaluation values <1.e-15 = 1.e-15
        #TODO:
        self.pitcon_par_space = numpy.logspace(-1,3,10) #parameter space that pitcon must search in
        self.pitcon_iter = 10        #number of iterations to search for every point in par_space

        self.__settings__["pitcon_allow_badstate"] = 0 #initialize with non steady-state values 0:no,1:yes
        self.__settings__["pitcon_flux_gen"] = 1       #generate fluxes as output
        self.__settings__["pitcon_filter_neg"] = 1       #drop pitcon results containing negative concentrations 0:no,1:yes
        self.__settings__["pitcon_filter_neg_res"] = 0 #drop output results containing negative concentrations 0:no,1:yes
        self.__settings__["pitcon_max_step"] = 30.0        #Maximum stepsize
        #TODO:
        self.pitcon_target_points = [] #list of calculated target points
        self.pitcon_limit_points = []  #list of calculated limit points
        self.pitcon_targ_val = 0.0        #Target value (Seek solution with iwork[4]=)
        
        # moved to InitialiseConservationArrays
        # self.__settings__["pitcon_max_step"] = 10*(len(self.__SI__)+1) #max corrector steps
        
        #pitcon integer options iwork in pitcon/dpcon61.f
        self.__settings__["pitcon_init_par"] = 1        #Use X(1) for initial parameter
        self.__settings__["pitcon_par_opt"] = 0            #Parameterization option 0:allows program
        self.__settings__["pitcon_jac_upd"] = 0            #Update jacobian every newton step
        self.__settings__["pitcon_targ_val_idx"] = 0    #Seek target values for X(n)
        self.__settings__["pitcon_limit_point_idx"] = 0    #Seek limit points in X(n)
        self.__settings__["pitcon_output_lvl"] = 0        #Control amount of output.
        self.__settings__["pitcon_jac_opt"] = 1            #Jacobian choice. 0:supply jacobian,1:use forward difference,2:central difference
        #pitcon float options rwork in pitcon/dpcon61.f
        self.__settings__["pitcon_abs_tol"] = 0.00001    #Absolute error tolerance
        self.__settings__["pitcon_rel_tol"] = 0.00001    #Relative error tolerance
        self.__settings__["pitcon_min_step"] = 0.01        #Minimum stepsize

        self.__settings__["pitcon_start_step"] = 0.3    #Starting stepsize
        self.__settings__["pitcon_start_dir"] = 1.0        #Starting direction +1.0/-1.0
        self.__settings__["pitcon_max_grow"] = 3.0        #maximum growth factor

        # setup conservation matrices (L_switch is set by stoich to 1 if it "detects" conservation)
        self.InitialiseConservationArrays()

        # scan option - removed from initsimscan
        self.scan_in = ''
        self.scan_out = []
        self.scan_res = None
        self.__settings__["scan1_mca_mode"] = 0 # should scan1 run mca analysis 0:no,1:elas,2:cc
        self.__settings__["scan1_dropbad"] = 0  # should scan1 drop invalid steady states (rare)?
        self.__settings__["scan1_nan_on_bad"] = True  # invalid steady states are returned as NaN
        self.__scan_errors_par__ = None # collect errors, parameters values
        self.__scan_errors_idx__ = None # collect errors, indexes


    def InitialiseConservationArrays(self):
        """
        Initialise conservation related vectors/array was in InitialiseModel but has been
        moved out so is can be called by when the stoichiometry is reanalysed
        
        """
        # Init and Sx vectors # these vectors are multiplying all by themselves - brett
        self.__SI__ = numpy.zeros((self.__lzeromatrix__.shape[1]),'d')
        self.__SALL__ = numpy.zeros((len(self.__species__)),'d')
        self.__settings__["pitcon_max_step"] = 10*(len(self.__SI__)+1) #max corrector steps        
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            # reorder the conservation matrix so that it is compatible with L
            idx = [list(self.conservation_matrix_col).index(n) for n in range(len(self.conservation_matrix_col))]
            self.__reordered_lcons = self.conservation_matrix.take(idx,1)
            if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Species_In_Conc']:
                self.__Build_Tvec__(amounts=False)
            else:
                self.__Build_Tvec__(amounts=True)
            self.showConserved(screenwrite=0)
        

    def InitialiseOldFunctions(self):
        """
        InitialiseOldFunctions()

        Parse and initialise user defined functions specified by !T !U in the PSC input file

        Arguments:
        None

        """
        self.__CODE_user = compile(self._Function_user,'_Function_user','exec')
        try:
            exec(self.__CODE_user)
            #print 'done.'
        except Exception, e:
            print 'WARNING: User function error\n', e
            print 'This might be due to non-instantiated (e.g. MCA/state) attributes'
            print 'Make sure the attributes that are used in your function exist ...'
            print 'and manually load using the self.ReloadUserFunc() method'

        #print '\nInitializing init function ...'
        #print self._Function_init
        try:
            self.ReloadInitFunc()
        except Exception, e:
            print 'WARNING: Init function error\n', e
            print 'This function is meant to be used exclusively for the initialization'
            print 'of PySCeS properties and expressions based on model attributes defined in the input file.'
            print 'Reinitialize with ReloadInitFunc()'

    def ReloadUserFunc(self):
        """
        ReloadUserFunc()

        Recompile and execute the user function (!U) from the input file.

        Arguments:
        None

        """
        ##  print "User functions disabled ..."
        self.__CODE_user = compile(self._Function_user,'_Function_user','exec')
        try:
            exec(self.__CODE_user)
        except Exception, e:
            print 'WARNING: User function load error\n', e

    def ReloadInitFunc(self):
        """
        ReloadInitFunc()

        Recompile and execute the user initialisations (!I) as defined in the PSC input file.
        and in mod.__InitFuncs__

        Arguments:
        None

        """
        ##  print "User functions disabled"
        for init in self.__InitFuncs__:
            if hasattr(self, init):
                if type(self.__InitFuncs__[init]) == str:
                    if self.__InitFuncs__[init].isdigit():
                        self.__InitFuncs__[init] = eval(self.__InitFuncs__[init])
                        setattr(self, init, self.__InitFuncs__[init])
                    else:
                        setattr(self, init, self.__InitFuncs__[init])
                else:
                    setattr(self, init, self.__InitFuncs__[init])
            else:
                assert hasattr(self, init), '\nModelInit error'


    def __initREQ__(self):
        """
        __initREQ__()

        Compile and initialise the model rate equations and associated arrays. Rate equations are
        generated as lambda functions callable as mod.R1(mod)

        Arguments:
        None

        """
        # create work arrays for Reactions and variable species
        # s = numpy.zeros(len(self.__species__),'d')
        # self.Vtemp = numpy.zeros(len(self.__reactions__),'d')
        # This appears to be redundant leftover code - brett 20031215

        if self.__settings__['display_debug'] == 1:
            pass
            #print 'Vtemp'

        # create the map string by taking the VarReagent[] and assigning it to an s[index]
        # a reverse map function mapping self.sXi back to self.init[x] for simulation initiation
        mapString = ''
        mapString_R = ''
        for x in range(0,len(self.__species__)):
            mapString += 'self.%s = s[%s]\n' % (self.__species__[x], x)
            mapString_R += 'self.__inspec__[%s] = self.%s_init\n' % (x, self.__species__[x])
        ##  print mapString_R
            # this creates the mapping string for the derivative functions

        if self.__settings__['display_debug'] == 1:
            print 'mapString'
            print mapString
            print 'mapString_R'
            print mapString_R

        # create the REq string in ReactionIDs order as a single multiline definition
        # using indexes: vString (Vtemp[x] =)
        # or a list of individual compile definitions: vArray (v + ReactionID = )
        vString = ''
        ##  vString2 = ''
        ##  DvOrder = []
        EStat = []
        ##  dispREq = ''

        symbR = {}
        if len(self.__rules__.keys()) > 0 and self.__settings__['mode_substitute_assignment_rules']:
            # create substitution dictionary
            for ass in self.__rules__:
                symbR.update({self.__rules__[ass]['name'] : self.__rules__[ass]['code_string']})

        for x in range(0,len(self.__reactions__)):
            req1 = self.__nDict__[self.__reactions__[x]]['RateEq']
            ##  DvOrder.append(self.__reactions__[x])
            vString += 'Vtemp[%s] = self.%s()\n' % (x, self.__reactions__[x])

            # Core update inspired by Core2, lambda functions replaced by Reaction instances

            if len(self.__rules__.keys()) > 0 and self.__settings__['mode_substitute_assignment_rules']:
                # substitute assignment rules
                InfixParser.setNameStr('self.', '')
                InfixParser.FunctionReplacements = symbR
                InfixParser.parse(req1.replace('self.', ''))
                req2 = InfixParser.output
                ##  req1_names = InfixParser.names
                rObj = ReactionObj(self, self.__reactions__[x], req2, 'self.')
            else:
                rObj = ReactionObj(self, self.__reactions__[x], req1, 'self.')
            self.__ParsePiecewiseFunctions__(rObj.piecewises)
            rObj.compartment = self.__nDict__[rObj.name]['compartment']

            setattr(self, self.__reactions__[x], rObj)

        # this is to check that if a model defines compartments then REq's MUST be in amounts brett2008
        # brett2008
        if self.__HAS_COMPARTMENTS__:
            cnames = [self.__compartments__[c]['name'] for c in self.__compartments__]
            warnings = ''
            for rr in self.__reactions__:
                rrobj = getattr(self, rr)
                warn = False
                if rrobj.compartment == None and self.__settings__['display_compartment_warnings']:
                    warnings += "# %s is not located in a compartment.\n" % rrobj.name
                else:
                    for comp in cnames:
                        if comp in rrobj.symbols:
                            warn = False
                            break
                        else:
                            warn = True
                    if warn:
                        warnings += "# %s: %s\n#  assuming kinetic constants are flow constants.\n" % (rr, rrobj.formula)
            if warnings != '' and self.__settings__['display_compartment_warnings']:
                print '\n# -- COMPARTMENT WARNINGS --'
                print warnings
        os.chdir(self.ModelOutput)
        if self.__settings__['display_debug'] == 1:
            print 'vString'
            print vString
            ##  print 'vString2'
            ##  print vString2
            ##  print 'DvOrder'
            ##  print DvOrder
        return mapString,mapString_R,vString

    def __initmodel__(self):
        """
        __initmodel__()

        Generate the stoichiometric matrix N from the parsed model description.
        Returns a stoichiometric matrix (N)

        Arguments:
        None

        """
        VarReagents = ['self.'+s for s in self.__species__]
        StoicMatrix = numpy.zeros((len(VarReagents),len(self.__reactions__)),'d')
        for reag in VarReagents:
            for id in self.__reactions__:
                if reag in self.__nDict__[id]['Reagents'].keys():
                    StoicMatrix[VarReagents.index(reag)][self.__reactions__.index(id)] = self.__nDict__[id]['Reagents'][reag]
        return StoicMatrix

    def __initvar__(self):
        """
        __initvar__()

        Compile and initialise the model variable species and derivatives and associated mapping arrays.

        Arguments:
        None

        """
        mvarString = ''
        sOrder = []
        self.__remaps = ''
        DvarUpString = ''

        #self.__inspec__ = numpy.zeros((len(self.__species__)),'d') moved to ParseModel
        for x in range(0,len(self.__species__)):
            key = self.__species__[x]
            ##  self.__inspec__[x] = eval(key)
            self.__inspec__[x] = getattr(self, key)
            mvarString += 'self.__inspec__[%s] = float(self.%s)\n' % (x, key)
            self.__remaps += 'self.%s = self.%s_ss\n' % (key, key)
            sOrder.append('self.'+key)
            DvarUpString += 'self.%s = input[%s]\n' % (self.__species__[x], x)

        if self.__settings__['display_debug'] == 1:
            print 's_initDeriv'
            print s_initDeriv
            print '\n__remaps'
            print self.__remaps
            print '\nDvarUpString'
            print DvarUpString

        mvarFunc = compile(mvarString,'mvarString','exec')
        return sOrder,DvarUpString

    def __initfixed__(self):
        """
        __initfixed__()

        Compile and initialise the fixed species and associated mapping arrays

        Arguments:
        None

        """
        runmapString = ''
        if self.__settings__['display_debug'] == 1:
            print 'InitParams2'
            print self.__parameters__
            print 'InitStrings2'
            ##  print self.__InitStrings

        # Initialise parameter elasticities
        par_map2store = ''
        par_remap = ''
        for x in range(len(self.__parameters__)):
            par_map2store += 'parVal_hold[%s] = self.%s\n' % (x, self.__parameters__[x])
            par_remap += 'self.%s = parVal_hold[%s]\n' % (self.__parameters__[x], x)

        if self.__settings__['display_debug'] == 1:
            print 'par_map2store'
            print par_map2store
            print 'par_remap'
            print par_remap
        return (par_map2store,par_remap)

#pysces core - the steady-state solver and integration routines
    def _SpeciesAmountToConc(self, s):
        """takes and returns s"""
        for x in range(len(self.__CsizeAllIdx__)):
            self.__CsizeAll__[x] = getattr(self, self.__CsizeAllIdx__[x])
        ##  print '__CALL__', self.__CsizeAll__
        return s/self.__CsizeAll__

    def _SpeciesConcToAmount(self, s):
        """takes and returns s"""
        for x in range(len(self.__CsizeAllIdx__)):
            self.__CsizeAll__[x] = getattr(self, self.__CsizeAllIdx__[x])
        ##  print '__CALL__', self.__CsizeAll__
        return s*self.__CsizeAll__

    def _FixedSpeciesAmountToConc(self):
        for x in range(len(self.__fixed_species__)):
            am = getattr(self, self.__fixed_species__[x])
            self.__CsizeFS__[x] = getattr(self, self.__CsizeFSIdx__[x])
            setattr(self, self.__fixed_species__[x], am/self.__CsizeFS__[x])

    def _FixedSpeciesConcToAmount(self):
        for x in range(len(self.__fixed_species__)):
            cn = getattr(self, self.__fixed_species__[x])
            self.__CsizeFS__[x] = getattr(self, self.__CsizeFSIdx__[x])
            setattr(self, self.__fixed_species__[x], cn*self.__CsizeFS__[x])

    # extract SI and "correct" SD in s solve for v
    # Vtemp is passed through the function to avoid an irritating bug
    # that appears if it isn't (ie defined as a global) - brett
    def _EvalREq2_alt(self,s,Vtemp):
        """
        _EvalREq2_alt(s,Vtemp)

        Evaluate the rate equations correcting for mass conservation.
        Takes full [s] returns full [s] --> moiety aware

        Arguments:

        s: a vector of species cosnservations
        Vtemp: the rate vector

        """
        #form an SI vector
        for x in range(0,len(self.lzeromatrix_col)):
            self.__SI__[x] = s[self.lzeromatrix_col[x]]

        #replace SD with SI calculated values
        for x in range(0,len(self.lzeromatrix_row)):
            s[self.lzeromatrix_row[x]] = self.__tvec_a__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*self.__SI__)
        return self._EvalREq(s,Vtemp)


    def _EvalREq(self,s,Vtemp):
        if self.__HAS_RATE_RULES__:
            exec(self.__CODE_raterule_map)
        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Species_In_Conc']:
            s = self._SpeciesAmountToConc(s)
        exec(self.__mapFunc__)
        try:
            self.Forcing_Function()
        except Exception, de:
            print 'INFO: forcing function failure', de
        try:
            exec(self.__vFunc__)
        except (ArithmeticError,AttributeError,NameError,ZeroDivisionError,ValueError), detail:
            print 'INFO: REq evaluation failure:', detail
            Vtemp[:] = self.__settings__['mach_floateps']
        if self.__HAS_RATE_RULES__:
            try:
                exec(self.__CODE_raterule)
            except (ArithmeticError,AttributeError,NameError,ZeroDivisionError,ValueError), detail:
                print 'INFO: RateRule evaluation failure:', detail
        return Vtemp

    def _EvalREq2(self,s,Vtemp):
        """
        _EvalREq2(s,Vtemp)

        Evaluate the rate equations, as PySCeS uses the reduced set of ODE's for its core operations, this method
        takes mass conservation into account and regenerates the full species vector
        takes [si] returns full [s]

        Arguments:

        s: species vector
        Vtemp: rate vector

        """
        #stick SI into s using Nrrow order
        for x in range(len(s)):
            self.__SALL__[self.nrmatrix_row[x]] = s[x]
        #stick SD into s using lzerorow order
        for x in range(len(self.lzeromatrix_row)):
            self.__SALL__[self.lzeromatrix_row[x]] = self.__tvec_a__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*s)
        return self._EvalREq(self.__SALL__,Vtemp)

    def _EvalODE(self,s,Vtemp):
        """
        _EvalODE(s,Vtemp)

        Core ODE evaluation routine evaluating the reduced set of ODE's. Depending on whether mass conservation is present or not either N*v (_EvalREq) or Nr*v (_EvalREq2) is used to automatically generate the ODE's.
        Returns sdot.

        Arguments:

        s: species vector
        Vtemp: rate vector

        """
        if self.__HAS_RATE_RULES__:
            s, self.__rrule__ = numpy.split(s,[self.Nrmatrix.shape[0]])
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            for x in range(len(s)):
                self.__SALL__[self.nrmatrix_row[x]] = s[x]
            for x in range(len(self.lzeromatrix_row)):
                self.__SALL__[self.lzeromatrix_row[x]] = self.__tvec_a__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*s)
            self.__vvec__ = self._EvalREq(self.__SALL__,Vtemp)
            s = numpy.add.reduce(self.__nrmatrix__*self.__vvec__,1)
        else:
            self.__vvec__ = self._EvalREq(s,Vtemp)
            s = numpy.add.reduce(self.__nmatrix__*self.__vvec__,1)
        if self.__HAS_RATE_RULES__:
            s = numpy.concatenate([s, self.__rrule__]).copy()
        return s

    # the following are user functions defined in the input file or assigned after instantiated
    def _EvalODE_CVODE(self,s,Vtemp):
        """
        _EvalODE_CVODE(s,Vtemp)

        Evaluate the full set of ODE's. Depending on whether mass conservation is present or not
        either N*v (_EvalREq) or Nr*v (_EvalREq2_alt) is used.

        Arguments:

        s: species vector
        Vtemp: rate vector

        """
        if self.__HAS_RATE_RULES__:
            s, self.__rrule__ = numpy.split(s,[self.Nmatrix.shape[0]])
        self.__vvec__ = self._EvalREq(s,Vtemp)
        s = numpy.add.reduce(self.__nmatrix__*self.__vvec__,1)
        if self.__HAS_RATE_RULES__:
            s = numpy.concatenate([s, self.__rrule__]).copy()
        return s

    def Forcing_Function(self):
        """
        Forcing_Function()

        User defined forcing function either defined in the PSC input file as !F or by overwriting this method.
        This method is evaluated prior to every rate equation evaluation.

        Arguments:
        None

        """
        # initialized in __initFunction__() - brett 20050621
        exec(self.__CODE_forced)

    def User_Function(self):
        """
        **Deprecated**
        """
        exec(self.__CODE_user)

    # pysundials

    def _EvalExtraData(self, xdata):
        """
        Takes a list of model attributes and returns an array of values
        """
        return numpy.array([getattr(self, d) for d in xdata])

    __CVODE_initialise__ = True
    CVODE_continuous_result = None
    __CVODE_initial_num__ = None
    def CVODE_continue(self, tvec):
        """
        **Experimental:** continues a simulation over a new time vector, the
        CVODE memobj is reused and not reinitialised and model parameters can be
        changed between calls to this method. The mod.data_sim objects from
        the initial simulation and all calls to this method are stored in the list
        *mod.CVODE_continuous_result*.

         - *tvec* a numpy array of time points

        """
        assert self.mode_integrator == 'CVODE', "\nFor what should be rather obvious reasons, this method requires CVODE to be used as the default integration algorithm.\n"
        if self.CVODE_continuous_result == None:
            self.CVODE_continuous_result = [self.data_sim]

        self.__CVODE_initialise__ = False
        self.sim_time = tvec

        Tsim0 = time.time()
        sim_res, rates, simOK = self.CVODE(None)
        Tsim1 = time.time()
        print "%s time for %s points: %s" % (self.mode_integrator, len(self.sim_time), Tsim1-Tsim0)

        if self.__HAS_RATE_RULES__:
            sim_res, rrules = numpy.split(sim_res,[len(self.__species__)],axis=1)
            print 'RateRules evaluated and added to mod.data_sim.'

        # TODO: split this off into a method shared by this and Simulate()
        self.data_sim = IntegrationDataObj()
        self.IS_VALID = simOK
        self.data_sim.setTime(self.sim_time)
        self.data_sim.setSpecies(sim_res, self.__species__)
        self.data_sim.setRates(rates, self.__reactions__)
        if self.__HAS_RATE_RULES__:
            self.data_sim.setRules(rrules, self.__rate_rules__)
        if len(self.CVODE_extra_output) > 0:
            self.data_sim.setXData(self.CVODE_xdata, lbls=self.CVODE_extra_output)
            self.CVODE_xdata = None
        if not simOK:
            print 'Simulation failure'
        del sim_res

        self.CVODE_continuous_result.append(self.data_sim)
        self.__CVODE_initialise__ = True

    def CVODE(self, initial):
        """
        CVODE(initial)

        PySCeS interface to the CVODE integration algorithm.

        Arguments:
        initial: vector containing initial species concentrations

        """
        assert _HAVE_PYSUNDIALS, '\nPySundials is not installed or did not import correctly\n%s' % _PYSUNDIALS_LOAD_ERROR
        Vtemp = numpy.zeros((self.__Nshape__[1]))
        def findi(t, y, ydot, f_data):
            self._TIME_ = t
            ydota = self._EvalODE(numpy.array(y), Vtemp)
            ydot[:] = ydota[:]
            return 0 #non-zero return indicates error state

        def ffull(t, y, ydot, f_data):
            self._TIME_ = t
            ##  ya = numpy.array(y)
            ydota = self._EvalODE_CVODE(numpy.array(y), Vtemp) # unreduced ODE's
            ydot[:] = ydota[:]
            return 0

        func = None
        if self.mode_integrate_all_odes:
            func = ffull
        else:
            func = findi

        if self.__CVODE_initialise__:
            tZero = initial.copy()
            if self.__HAS_RATE_RULES__:
                initial, rrules = numpy.split(initial,[self.Nrmatrix.shape[0]])
                tZero = initial.copy()
            if self.__HAS_MOIETY_CONSERVATION__ and self.mode_integrate_all_odes:
                initial = self.Fix_S_indinput(initial, amounts=True)
                tZero = initial.copy()
            elif self.__HAS_MOIETY_CONSERVATION__:
                tZero = self.Fix_S_indinput(tZero, amounts=True)
            if self.__HAS_RATE_RULES__:
                initial = numpy.concatenate([initial, rrules])
                tZero = numpy.concatenate([tZero, rrules])
        else:
            tZero = None

        #the following block initialises the cvode integrator, and sets various options
        if self.__CVODE_initialise__:
            self.__CVODE_y__ = cvode.NVector(initial.tolist())
            self.__CVODE_mem__ = cvode.CVodeCreate(cvode.CV_BDF, cvode.CV_NEWTON) #initialisation with basic options, newtonian solver etc...
            self.__CVODE_initial_num__ = len(initial)
        del initial
        t = cvode.realtype(0.0)
        rates = numpy.zeros((len(self.sim_time), len(self.__reactions__)))
        output = None
        if self.__HAS_RATE_RULES__:
            output = numpy.zeros((len(self.sim_time), len(self.__rrule__)+len(self.__species__)))
        else:
            output = numpy.zeros((len(self.sim_time), len(self.__species__)))

        CVODE_XOUT = False
        if len(self.CVODE_extra_output) > 0:
            out = []
            for d in self.CVODE_extra_output:
                if hasattr(self, d) and d not in self.__species__ + self.__reactions__ + self.__rate_rules__:
                    out.append(d)
                else:
                    print '\nWarning: CVODE is ignoring extra data (%s), it either doesn\'t exist or it\'s a species or rate.\n' % d
            if len(out) > 0:
                self.CVODE_extra_output = out
                CVODE_XOUT = True
            del out

        if CVODE_XOUT:
            self.CVODE_xdata = numpy.zeros((len(self.sim_time), len(self.CVODE_extra_output)))

        sim_st = 0
        if self.sim_time[0] == 0.0:

            if self.__CVODE_initialise__:
                output[0,:] = tZero[:].copy()
            else:
                output[0,:] = numpy.array(self.__CVODE_y__[:])

            if CVODE_XOUT:
                self.CVODE_xdata[0,:] = self._EvalExtraData(self.CVODE_extra_output)
            sim_st = 1
        del tZero

        var_store = {}
        if self.__HAS_EVENTS__:
            for ev in self.__events__:
                ev.reset()
                for ass in ev.assignments:
                    var_store.update({ass.variable : getattr(self, ass.variable)})
        TOL_ADJUSTER = 0
        MAX_TOL_CNT = 5
        MAX_REL_TOLERANCE = 1.0e-3
        RELTOL_ADJUST_FACTOR = 1.0e3
        MIN_ABS_TOL = self.__settings__["cvode_abstol"] # 1.0e-15
        ##  MAX_ABS_TOL = self.__settings__["cvode_abstol_max"] #1.0e-3 not used anymore
        ABSTOL_ADJUST_FACTOR = self.__settings__["cvode_abstol_factor"] # 1.0e-6
        cvode_sim_range = range(sim_st, len(self.sim_time))
        cvode_scale_range = range(sim_st, len(self.sim_time), len(self.sim_time)/4 or len(self.sim_time))
        cvode_scale_range = cvode_scale_range[1:]
        reltol = cvode.realtype(self.__settings__["cvode_reltol"]) #relative tolerance must be a realtype
        abstol = cvode.NVector(self.__CVODE_initial_num__*[self.__settings__["cvode_abstol"]])
        for s in range(len(self.__CVODE_y__)):
            newVal = abs(self.__CVODE_y__[s])*ABSTOL_ADJUST_FACTOR
            if newVal < MIN_ABS_TOL:
                abstol[s] = MIN_ABS_TOL
            else:
                abstol[s] = newVal
        if self.__CVODE_initialise__:
            cvode.CVodeMalloc(self.__CVODE_mem__, func, 0.0, self.__CVODE_y__, cvode.CV_SV, reltol, abstol) #set tolerances, specify vector of initial conditions, set integrtion function etc...
            cvode.CVDense(self.__CVODE_mem__, self.__CVODE_initial_num__) #set dense option, specify dimension of problem (3)
        if self.__HAS_EVENTS__:
            cvode.CVodeRootInit(self.__CVODE_mem__, len(self.__events__), self.CVODE_EVENTS, None) #specify to 'root' conditions, and function that calculates them


        for st in cvode_sim_range:
            tout = self.sim_time[st]
            errcount = 0
            ##  print TOL_ADJUSTER, MAX_TOL_CNT, abstol, MAX_REL_TOLERANCE
            if self.__settings__["cvode_auto_tol_adjust"] and TOL_ADJUSTER >= MAX_TOL_CNT and reltol.value < MAX_REL_TOLERANCE:
                reltol.value = reltol.value*RELTOL_ADJUST_FACTOR
                cvode.CVodeSetTolerances(self.__CVODE_mem__, cvode.CV_SV, reltol, abstol)
                ##  print '\nCVODE: new tolerance set:\nreltol=%s' % reltol.value
                ##  print '\nAbs tolerance:\n%s' % abstol
                TOL_ADJUSTER = 0
            if (st in cvode_scale_range) and self.__settings__["cvode_auto_tol_adjust"]:
                for s in range(len(self.__CVODE_y__)):
                    newVal = abs(self.__CVODE_y__[s])*ABSTOL_ADJUST_FACTOR
                    if newVal < MIN_ABS_TOL:
                        abstol[s] = MIN_ABS_TOL
                    else:
                        abstol[s] = newVal
                ##  print '\nCVODE: new tolerance set, abstol:\n%s' % abstol
                cvode.CVodeSetTolerances(self.__CVODE_mem__, cvode.CV_SV, reltol, abstol)
                ##  cvode.CVodeReInit(self.__CVODE_mem__, func, self.sim_time[st-1], self.__CVODE_y__, cvode.CV_SV, reltol, abstol)
            while True:
                try:
                    flag = cvode.CVode(self.__CVODE_mem__, tout, self.__CVODE_y__, cvode.ctypes.byref(t), cvode.CV_NORMAL)
                except AssertionError, ex:
                    print 'cvode error1', ex
                    flag = None
                self._TIME_ = tout
                if flag == cvode.CV_ROOT_RETURN: #if a root was found before desired time point, output it
                    ya = numpy.array(self.__CVODE_y__)
                    rootsfound = cvode.CVodeGetRootInfo(self.__CVODE_mem__, len(self.__events__))
                    reInit = False
                    for ev in range(len(self.__events__)):
                        if rootsfound[ev] == 1:
                            for ass in self.__events__[ev].assignments:
                                # only can assign to independent species vector
                                if ass.variable in self.L0matrix.getLabels()[1] or\
                                (self.mode_integrate_all_odes and ass.variable in self.__species__):
                                    assVal = ass.getValue()
                                    assIdx = self.__species__.index(ass.variable)
                                    if self.__KeyWords__['Species_In_Conc']:
                                        ##  print self.__CVODE_y__
                                        self.__CVODE_y__[assIdx] = assVal*getattr(self, self.__CsizeAllIdx__[assIdx])
                                        ##  raw_input(self.__CVODE_y__)
                                    else:
                                        self.__CVODE_y__[assIdx] = assVal
                                    reInit = True
                                elif not self.mode_integrate_all_odes and ass.variable in self.L0matrix.getLabels()[0]:
                                    print 'Event assignment to dependent species consider setting \"mod.mode_integrate_all_odes = True\"'
                                elif self.__HAS_RATE_RULES__ and ass.variable in self.__rate_rules__:
                                    ##  print 'Event is assigning to rate rule'
                                    assVal = ass.getValue()
                                    rrIdx = self.__rate_rules__.index(ass.variable)
                                    ##  print ass.variable, assVal
                                    self.__rrule__[rrIdx] = assVal
                                    ##  print self.L0matrix.shape[1], rrIdx, len(self.__CVODE_y__)
                                    self.__CVODE_y__[self.L0matrix.shape[1]+rrIdx] = assVal
                                    setattr(self, ass.variable, assVal)
                                    reInit = True

                    if reInit:
                        cvode.CVodeReInit(self.__CVODE_mem__, func, tout, self.__CVODE_y__, cvode.CV_SV, reltol, abstol)

                    # this gets everything into the current tout state
                    tmp = None
                    if not self.mode_integrate_all_odes:
                        tmp = self._EvalODE(ya.copy(),self.__CVODE_Vtemp)
                    else:
                        tmp = self._EvalODE_CVODE(ya.copy(),self.__CVODE_Vtemp)
                    del tmp

                    # here we regenerate Sd's and fix concentrations
                    rrules = None
                    if self.__HAS_MOIETY_CONSERVATION__ and not self.mode_integrate_all_odes:
                        if self.__HAS_RATE_RULES__:
                            ya, rrules = numpy.split(ya,[self.Nrmatrix.shape[0]])
                        ya = self.Fix_S_indinput(ya, amounts=True)
                        # convert to concentrations
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            ya = self._SpeciesAmountToConc(ya)
                        if self.__HAS_RATE_RULES__:
                            ya = numpy.concatenate([ya, rrules])
                    else:
                        if self.__HAS_RATE_RULES__:
                            ya, rrules = numpy.split(ya,[self.Nmatrix.shape[0]])
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            ya = self._SpeciesAmountToConc(ya)
                        if self.__HAS_RATE_RULES__:
                            ya = numpy.concatenate([ya, rrules])

                    output[st] = ya
                    # set with self._EvalODE above
                    rates[st] = self.__vvec__

                    if CVODE_XOUT:
                        self.CVODE_xdata[st,:] = self._EvalExtraData(self.CVODE_extra_output)
                    # this should adjust the expected time to the new output time time
                    self.sim_time[st] = float(tout)
                    # dont need anymore i think
                    if _HAVE_VPYTHON:
                        self.CVODE_VPYTHON(ya)
                    del ya, rrules
                    break
                if flag == cvode.CV_SUCCESS:
                    ya = numpy.array(self.__CVODE_y__)
                    # this gets everything into the current tout state
                    tmp = None
                    if not self.mode_integrate_all_odes:
                        tmp = self._EvalODE(ya.copy(),self.__CVODE_Vtemp)
                    else:
                        tmp = self._EvalODE_CVODE(ya.copy(),self.__CVODE_Vtemp)
                    del tmp
                    # here we regenerate Sd's and fix concentrations
                    rrules = None
                    if self.__HAS_MOIETY_CONSERVATION__ and not self.mode_integrate_all_odes:
                        if self.__HAS_RATE_RULES__:
                            ya, rrules = numpy.split(ya,[self.Nrmatrix.shape[0]])
                        ya = self.Fix_S_indinput(ya, amounts=True)
                        # convert to concentrations
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            ya = self._SpeciesAmountToConc(ya)
                        if self.__HAS_RATE_RULES__:
                            ya = numpy.concatenate([ya, rrules])
                    else:
                        if self.__HAS_RATE_RULES__:
                            ya, rrules = numpy.split(ya,[self.Nmatrix.shape[0]])
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            ya = self._SpeciesAmountToConc(ya)
                        if self.__HAS_RATE_RULES__:
                            ya = numpy.concatenate([ya, rrules])

                    output[st] = ya
                    # set with self._EvalODE above
                    rates[st] = self.__vvec__

                    if CVODE_XOUT:
                        self.CVODE_xdata[st,:] = self._EvalExtraData(self.CVODE_extra_output)
                    if _HAVE_VPYTHON:
                        self.CVODE_VPYTHON(ya)
                    del ya, rrules
                    break
                elif flag == -1:
                    if self.__settings__["cvode_mxstep"] == 1000:
                        self.__settings__["cvode_mxstep"] = 3000
                        TOL_ADJUSTER += 1
                    elif self.__settings__["cvode_mxstep"] == 3000:
                        self.__settings__["cvode_mxstep"] = 10000
                        TOL_ADJUSTER += 2
                    elif self.__settings__["cvode_mxstep"] == 10000:
                        ##  TOL_ADJUSTER += 1
                        output[st] = numpy.NaN
                        break
                    print 'mxstep warning (%s) mxstep set to %s' % (flag, self.__settings__["cvode_mxstep"])
                    cvode.CVodeSetMaxNumSteps(self.__CVODE_mem__, self.__settings__["cvode_mxstep"])
                elif flag < -3:
                    print 'CVODE error:', flag
                    print 'At ', tout
                    output[st] = numpy.NaN
                    rates[st] = numpy.NaN
                    if CVODE_XOUT:
                        self.CVODE_xdata[st] = numpy.NaN
                    break
            self.__settings__["cvode_mxstep"] = 1000
            cvode.CVodeSetMaxNumSteps(self.__CVODE_mem__, self.__settings__["cvode_mxstep"])


        if self.__HAS_EVENTS__:
            for ass in var_store.keys():
                ##  print 'old value', getattr(self, ass)
                setattr(self, ass, var_store[ass])
                ##  print 'new value', getattr(self, ass)

        if self.__settings__["cvode_stats"]:
            #print some stats from the intgrator
            nst = cvode.CVodeGetNumSteps(self.__CVODE_mem__)
            nfe = cvode.CVodeGetNumRhsEvals(self.__CVODE_mem__)
            nsetups = cvode.CVodeGetNumLinSolvSetups(self.__CVODE_mem__)
            netf = cvode.CVodeGetNumErrTestFails(self.__CVODE_mem__)
            nni = cvode.CVodeGetNumNonlinSolvIters(self.__CVODE_mem__)
            ncfn = cvode.CVodeGetNumNonlinSolvConvFails(self.__CVODE_mem__)
            nje = cvode.CVDenseGetNumJacEvals(self.__CVODE_mem__)
            nfeLS = cvode.CVDenseGetNumRhsEvals(self.__CVODE_mem__)
            nge = cvode.CVodeGetNumGEvals(self.__CVODE_mem__)

            print "\nFinal Statistics:"
            print "nst = %-6i nfe  = %-6i nsetups = %-6i nfeLS = %-6i nje = %i"%(nst, nfe, nsetups, nfeLS, nje)
            print "nni = %-6ld ncfn = %-6ld netf = %-6ld nge = %ld\n "%(nni, ncfn, netf, nge)
            print 'reltol = %s' % reltol
            print 'abstol:\n%s' % abstol

        if cvode.CV_SUCCESS >= 0:
            return output, rates, True
        else:
            return output, rates, False

    def CVODE_EVENTS(self, t, svec, eout, f_data):
        svec = numpy.array(svec)
        self._TIME_ = t
        rrules = []
        tmp = None
        if not self.mode_integrate_all_odes:
            tmp = self._EvalODE(svec.copy(),self.__CVODE_Vtemp)
        else:
            tmp = self._EvalODE_CVODE(svec.copy(),self.__CVODE_Vtemp)
        del tmp
        eventFired = False
        for ev in range(len(self.__events__)):
            if self.__events__[ev](t):
                eout[ev] = 0
                eventFired = True
            else:
                eout[ev] = 1
        if self.__HAS_RATE_RULES__:
            exec(self.__CODE_raterule)
        return 0

    def CVODE_VPYTHON(self, s):
        """Future VPython hook for CVODE"""
        pass

    def LSODA(self,initial):
        """
        LSODA(initial)

        PySCeS interface to the LSODA integration algorithm. Given a set of initial
        conditions LSODA returns an array of species concentrations and a status flag.
        LSODA controls are accessible as mod.lsoda_<control>

        Arguments:

        initial: vector containing initial species concentrations

        """
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        def function_sim(s,t):
            self._TIME_ = t
            return self._EvalODE(s,Vtemp)

        iter = 0
        go = True

        status = 0
        while go:
            sim_res,infodict = scipy.integrate.odeint(function_sim,initial,self.sim_time,\
                                                           atol=self.__settings__['lsoda_atol'],\
                                                           rtol=self.__settings__['lsoda_rtol'],\
                                                           mxstep=self.__settings__["lsoda_mxstep"],\
                                                           h0=self.__settings__["lsoda_h0"],\
                                                           hmax=self.__settings__["lsoda_hmax"],\
                                                           hmin=self.__settings__["lsoda_hmin"],\
                                                           mxordn=self.__settings__["lsoda_mxordn"],\
                                                           mxords=self.__settings__["lsoda_mxords"],\
                                                           printmessg=self.__settings__["lsoda_mesg"],\
                                                           full_output=1)
            if infodict['message'] == 'Integration successful.':
                status = 0
            else:
                status = 1
            if status > 0 and iter < self.__settings__['mode_sim_max_iter']:
                if self.__settings__["lsoda_mxstep"] == 0:
                    print '\nIntegration error\n\nSetting self.__settings__["lsoda_mxstep"] = 1000 and reSimulating ...'
                    self.__settings__["lsoda_mxstep"] = 1000
                else:
                    print 'Integration error\n\nSetting self.__settings__["lsoda_mxstep"] = ' + `self.__settings__["lsoda_mxstep"]*3` + ' and reSimulating ...'
                    self.__settings__["lsoda_mxstep"] = self.__settings__["lsoda_mxstep"]*3
                iter += 1
            elif status > 0 and iter == self.__settings__['mode_sim_max_iter']:
                print '\nThis simulation is going nowhere fast\nConsider trying CVODE (mod.mode_integrator = \'CVODE\')\n'
                print 'self.__settings__["lsoda_mxstep"] = ' + `self.__settings__["lsoda_mxstep"]`
                print '__settings__[\'mode_sim_max_iter\'] = ' + `iter`
                go = False
            else:
                go = False
        self.__settings__["lsoda_mxstep"] = 0

        rates = numpy.zeros((sim_res.shape[0], len(self.__reactions__)))
        if status == 0:

            tmp = None
            for r in range(sim_res.shape[0]):
                tmp = self._EvalODE(sim_res[r].copy(), self.__CVODE_Vtemp)
                # set with self._EvalODE above
                rates[r] = self.__vvec__
            del tmp

            # regenerate dependent variables
            if self.__HAS_RATE_RULES__:
                sim_res, rrules = numpy.split(sim_res,[self.Nrmatrix.shape[0]],axis=1)
            if self.__HAS_MOIETY_CONSERVATION__ == True:
                res = numpy.zeros((sim_res.shape[0], len(self.__species__)))
                for x in range(0,sim_res.shape[0]):
                    res[x,:] = self.Fix_S_indinput(sim_res[x,:], amounts=True)
                sim_res = res
                del res
            if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                for x in range(0,sim_res.shape[0]):
                    sim_res[x] = self._SpeciesAmountToConc(sim_res[x])

            if self.__HAS_RATE_RULES__:
                sim_res = numpy.concatenate([sim_res, rrules],axis=1)
            return sim_res, rates, True
        else:
            if self.__HAS_MOIETY_CONSERVATION__ == True and self.__HAS_RATE_RULES__:
                sim_res = numpy.zeros((sim_res.shape[0],len(self.__species__)+len(self.__rrule__)),'d')
            elif self.__HAS_MOIETY_CONSERVATION__ == True:
                sim_res = numpy.zeros((sim_res.shape[0],len(self.__species__)),'d')
            sim_res[:] = scipy.NaN
            return sim_res, rates, False

    def HYBRD(self,initial):
        """
        HYBRD(initial)

        PySCeS interface to the HYBRD solver. Returns a steady-state solution and
        error flag. Good general purpose solver.
        Algorithm controls are available as mod.hybrd_<control>

        Arguments:

        initial: vector of initial species concentrations

        """
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        def function_state(s):
            return self._EvalODE(s,Vtemp)

        state_out = scipy.optimize.fsolve(function_state,initial,args=(),\
                                          xtol=self.__settings__['hybrd_xtol'],\
                                          maxfev=self.__settings__['hybrd_maxfev'],\
                                          epsfcn=self.__settings__['hybrd_epsfcn'],\
                                          factor=self.__settings__['hybrd_factor'],\
                                          col_deriv=0,\
                                          full_output=1)
        if state_out[2] == 1:
            if self.__settings__['hybrd_mesg'] == 1:
                print '(hybrd)', state_out[3]
            return state_out[0], True
        else:
            if self.__settings__['hybrd_mesg']:
                print 'INFO: (hybrd) Invalid steady state:'
            if self.__settings__['hybrd_mesg'] == 1:
                print '(hybrd)', state_out[3]
            return state_out[0], False

    def FINTSLV(self,initial):
        """
        FINTSLV(initial)

        Forward integration steady-state solver. Finds a steady state when the
        maximum change in species concentration falls within a specified tolerance.
        Returns the steady-state solution and a error flag.
        Algorithm controls are available as mod.fintslv_<control>

        Arguments:

        initial: vector of initial concentrations

        """
        sim_time = self.__fintslv_range__*self.__settings__['fintslv_rmult']
        #print sim_time
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        def function_sim(s,t):
            return self._EvalODE(s,Vtemp)

        res,infodict = scipy.integrate.odeint(function_sim,initial.copy(),sim_time,\
                                                  atol=self.__settings__['lsoda_atol'],\
                                                  rtol=self.__settings__['lsoda_rtol'],\
                                                  ##  mxstep=self.__settings__["lsoda_mxstep"],\
                                                  mxstep=10000,\
                                                  h0=self.__settings__["lsoda_h0"],\
                                                  hmax=self.__settings__["lsoda_hmax"],\
                                                  hmin=self.__settings__["lsoda_hmin"],\
                                                  mxordn=self.__settings__["lsoda_mxordn"],\
                                                  mxords=self.__settings__["lsoda_mxords"],\
                                                  printmessg=self.__settings__["lsoda_mesg"],\
                                                  full_output=1)
        if infodict['message'] == 'Integration successful.':
            status = True
        else:
            status = False

        # run through results if max(abs([x]-[x-1])) < self.__settings__['fintslv_tol'] score +1
        # if you get 5 points by seq end ... happiness
        OK = 0
        if status:
            for x in range(len(res)):
                if OK >= self.__settings__['fintslv_step']:
                    break
                if x > 0 and OK < self.__settings__['fintslv_step']:
                    dif = abs(res[x] - res[x-1])
                    if max(dif) < self.__settings__['fintslv_tol']:
                        OK += 1
                    else:
                        pass
        if OK >= 5:
            return res[-1], True
        else:
            return res[-1], False

    def NLEQ2(self,initial):
        """
        NLEQ2(initial)

        PySCeS interface to the (optional) NLEQ2 algorithm. This is a powerful steady-state
        solver that can usually find a solution for when HYBRD() fails. Algorithm
        controls are available as: mod.nleq2_<control>
        Returns as steady-state solution and error flag.

        Arguments:

        initial: vector of initial species concentrations

        """
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        initial0 = initial.copy()
        s_scale = initial.copy()

        N = len(initial)
        iwk = numpy.zeros((N+52),'i')
        rwk = numpy.zeros(((N+max(N,10)+15)*N + 61),'d')
        iopt = numpy.zeros((50),'i')

        if self.__settings__['nleq2_jacgen'] == 1:
            print '(nleq2)User supplied Jacobian not supported yet ... setting __settings__[\'nleq2_jacgen\'] = 0'
            self.__settings__['nleq2_jacgen'] = 0

        rtol = mach_spec.eps*10.0*N
        iopt[2] =  self.__settings__['nleq2_jacgen'] #2
        iopt[8] =  self.__settings__['nleq2_iscaln']  #0
        iopt[10] = 0
        iopt[11] = 6
        iopt[12] = 0
        iopt[13] = 6
        iopt[14] = 0
        iopt[15] = 6
        iopt[30] = self.__settings__['nleq2_nonlin'] #4
        iopt[31] = self.__settings__['nleq2_qrank1'] #1
        iopt[34] = self.__settings__['nleq2_qnscal'] #0
        iopt[37] = self.__settings__['nleq2_ibdamp'] #0
        iopt[38] = self.__settings__['nleq2_iormon'] #2

        iwk[30] = self.nleq2_nitmax

        def func(s, ifail):
            s = self._EvalODE(s,Vtemp)
            if numpy.isnan(s).any():
                ifail = -1
            elif (numpy.abs(s)>1.0e150).any():
                ifail = -1
            return s, ifail

        def jacfunc(s):
            return s

        ierr = 0

        BRETT_DEBUG_MODE = False
        ADVANCED_MODE = self.__settings__['nleq2_advanced_mode']
        if ADVANCED_MODE:
            max_iter = self.__settings__['nleq2_iter'] # 3
            max_iter_ceiling = self.__settings__['nleq2_iter_max'] # 10
        else:
            max_iter_ceiling = self.__settings__['nleq2_iter']
            max_iter = self.__settings__['nleq2_iter']

        iter = 1
        GO = True
        while GO:
            if BRETT_DEBUG_MODE:
                print 's_scale', s_scale
                print 'ierr(%s) = %s' % (iter,ierr)
                print 'rtol(%s) = %s' % (iter,rtol)
                print 'nitmax(%s) = %s' % (iter,iwk[30])
                print 's_scale(%s) = %s' % (iter,s_scale)
                print 'max_iter(%s) = %s' % (iter,max_iter)

            res,s_scale,rtol,iopt,ierr = nleq2.nleq2(func,jacfunc,initial,s_scale,rtol,iopt,iwk,rwk)


            if ierr == 0:
                # success
                GO = False
            elif ierr == 21:
                # negative rtol
                GO = False
                ##  rtol = abs(rtol)
                ##  ierr = 0
            elif ierr == 2:
                # nitmax reached
                iwk[30] = iwk[30]*self.__settings__['nleq2_growth_factor'] # 10

            if iter >= max_iter and iter >= max_iter_ceiling:
                GO = False
            iter += 1

        if BRETT_DEBUG_MODE and ierr > 0:
            print 'ierr = %s' % (ierr)
            print res
            time.sleep(5)

        if ierr > 0:
            if self.__settings__['nleq2_mesg']:
                print '(nleq2) exits with ierr = %s' % ierr
        else:
            if self.__settings__['nleq2_mesg']:
                print '(nleq2) The solution converged'
        if ierr > 0:
            return res, False
        else:
            return res, True

    def PITCON(self,scanpar,scanpar3d=None):
        """
        PITCON(scanpar,scanpar3d=None)

        PySCeS interface to the PITCON continuation algorithm. Single parameter
        continuation has been implemented as a "scan" with the continuation
        being initialised in mod.pitcon_par_space. The second argument does not
        affect the continuation but can be used to insert a third axis parameter into
        the results. Returns an array containing the results.
        Algorithm controls are available as mod.pitcon_<control>

        Arguments:

        scanpar: the model parameter to scan (x5)
        scanpar3d [default=None]: additional output parameter for 3D plots

        """
        if self.__HAS_RATE_RULES__:
            raise NotImplementedError, '\nBifurcation analysis not currently available for models containing RateRules'

        assert type(scanpar) == str, '\nscanpar must be a <string> representing a model parameter'
        modpar = list(self.__parameters__)
        try:
            a = modpar.index(scanpar)
        except:
            raise NameError, `scanpar` + ' is not a parameter of this model'
        if scanpar3d != None:
            if type(scanpar3d) == str:
                scanpar3d = float(scanpar3d)
                assert type(scanpar3d) == float, 'scanpar3d must be a <float>'


        par_hold = getattr(self, scanpar)

        if self.__settings__["pitcon_jac_opt"] < 1:
            self.__settings__["pitcon_jac_opt"] = 1
            print '\nINFO: .__settings__["pitcon_jac_opt"] set to 1 - user defined jacobian function not yet supported'

        # DONE!
        def fx(s):
            setattr(self, scanpar, s[-1])
            try:
                sdot[:-1] = self._EvalODE(s[:-1], Vtemp)
            except Exception, ex:
                print 'PITCON EXCEPTION 1', ex
                sdot[:-1] = 0.0
            sdot[-1] = s[-1]
            return sdot

        parm = len(self.__SI__)+1
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        xr2 = numpy.zeros((parm),'d')
        sdot = numpy.zeros((parm),'d')
        iwork = numpy.zeros((30 + parm),'i')
        rwork = numpy.zeros((30 + (6*(parm))*(parm)),'d')
        ipar = numpy.zeros((parm),'i')
        fpar = numpy.zeros((parm),'d')

        res = []
        for xscan in self.pitcon_par_space:
            Vtemp[:] = 0.0
            xr2[:] = 0.0
            sdot[:] = 0.0
            ipar[:] = 0
            fpar[:] = 0.0

            iwork[0] = 0 #This is a startup
            iwork[1] = self.__settings__["pitcon_init_par"] #Use X(1) for initial parameter
            iwork[2] = self.__settings__["pitcon_par_opt"] #Parameterization option 0:allows program
            iwork[3] = self.__settings__["pitcon_jac_upd"] #Update jacobian every newton step
            iwork[4] = self.__settings__["pitcon_targ_val_idx"] #Seek target values for X(n)
            iwork[5] = self.__settings__["pitcon_limit_point_idx"] #Seek limit points in X(n)
            iwork[6] = self.__settings__["pitcon_output_lvl"] #Control amount of output.
            iwork[7] = 6 #Output unit 6=PC
            iwork[8] = self.__settings__["pitcon_jac_opt"] #Jacobian choice. 0:supply jacobian,1:use forward difference,2:central difference
            iwork[9] = 0
            iwork[10] = 0
            iwork[11] = 0
            iwork[12] = 30
            iwork[13] = len(iwork)
            iwork[14] = 30 + (4*parm)
            iwork[15] = len(rwork)
            iwork[16] = self.__settings__["pitcon_max_step"] #max corrector steps
            iwork[17] = 0
            iwork[18] = 0
            iwork[19] = 0
            iwork[20] = 0
            iwork[21] = 0
            iwork[22] = 0
            iwork[23] = 0
            iwork[24] = 0
            iwork[25] = 0
            iwork[26] = 0
            iwork[27] = 0

            rwork[0] = self.__settings__["pitcon_abs_tol"]      #Absolute error tolerance
            rwork[1] = self.__settings__["pitcon_rel_tol"]      #Relative error tolerance
            rwork[2] = self.__settings__["pitcon_min_step"]        #Minimum stepsize
            rwork[3] = self.__settings__["pitcon_max_step"]        #Maximum stepsize
            rwork[4] = self.__settings__["pitcon_start_step"]   #Starting stepsize
            rwork[5] = self.__settings__["pitcon_start_dir"]    #Starting direction +1.0/-1.0
            rwork[6] = self.pitcon_targ_val     #Target value (Seek solution with iwork[4]=)
            rwork[7] = 0.0
            rwork[8] = 0.0
            rwork[9] = 0.0
            rwork[10] = 0.0
            rwork[11] = 0.0
            rwork[12] = 0.0
            rwork[13] = 0.0
            rwork[14] = 0.0
            rwork[15] = 0.0
            rwork[16] = 0.0
            rwork[17] = 0.0
            rwork[18] = 0.0
            rwork[19] = self.__settings__["pitcon_max_grow"] #maximum growth factor
            rwork[20] = 0.0
            rwork[21] = 0.0
            rwork[22] = 0.0
            rwork[23] = 0.0
            rwork[24] = 0.0
            rwork[25] = 0.0
            rwork[26] = 0.0
            rwork[27] = 0.0
            rwork[28] = 0.0

            setattr(self, scanpar, xscan)
            self.State()

            if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
            ##  if self.__KeyWords__['Species_In_Conc']:
                self.__inspec__ = copy.copy(self._SpeciesConcToAmount(self.state_species))
            else:
                self.__inspec__ = copy.copy(self.state_species)

            go = False
            if self.__StateOK__:
                go = True
            elif not self.__StateOK__ and self.__settings__["pitcon_allow_badstate"]:
                go = True
            else:
                go = False

            if go:
                if self.__HAS_MOIETY_CONSERVATION__ == True:
                    temp = numpy.zeros((len(self.__SI__)),'d')
                    #sI0_sim_init
                    for x in range(0,len(self.lzeromatrix_col)):
                        xr2[x] = self.__inspec__[self.nrmatrix_row[x]]
                else:
                    xr2[:-1] = copy.copy(self.state_species[:])
                xr2[-1] = copy.copy(xscan)
                for x in range(int(self.pitcon_iter)):
                    ierror,iwork,rwork,xr2 = pitcon.pitcon1(fx,fpar,fx,ipar,iwork,rwork,xr2)
                    if iwork[0] == 2:
                        if self.__settings__["pitcon_flux_gen"]:
                            if min(xr2[:-1]) < 0.0 and self.__settings__["pitcon_filter_neg"]:
                                pass
                            else:
                                xout = xr2.tolist()
                                a = xout.pop(-1)
                                if self.__HAS_MOIETY_CONSERVATION__ == True:
                                    xout = self.Fix_S_indinput(xout, amounts=True)
                                else:
                                    xout = numpy.array(xout)
                                if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                                    xout = self._SpeciesAmountToConc(xout)
                                xout2 = (self.__FluxGen__(xout)).tolist()
                                xout = xout.tolist()
                                xout.insert(0,a)
                                if scanpar3d != None:
                                    xout.insert(0,scanpar3d)
                                xout2 = xout+xout2
                                res.append(xout2)
                        else:
                            if min(xr2[:-1]) < 0.0 and self.__settings__["pitcon_filter_neg"]:
                                pass
                            else:
                                xout = xr2.tolist()
                                a = xout.pop(-1)
                                if self.__HAS_MOIETY_CONSERVATION__ == True:
                                    xout = self.Fix_S_indinput(xout, amounts=True)
                                else:
                                    xout = numpy.array(xout)
                                if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                                    xout = self._SpeciesAmountToConc(xout)
                                xout = xout.tolist()
                                xout.insert(0,a)
                                if scanpar3d != None:
                                    xout.insert(0,scanpar3d)
                                res.append(xout)
                    elif iwork[0] == 3:
                        print '\nTarget point:'
                        xout = xr2.tolist()
                        a = xout.pop(-1)

                        if self.__HAS_MOIETY_CONSERVATION__ == True:
                            xout = self.Fix_S_indinput(xout, amounts=True)
                        else:
                            xout = numpy.array(xout)
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            xout = self._SpeciesAmountToConc(xout)
                        xout = xout.tolist()
                        xout.insert(0,a)

                        print xout
                        self.pitcon_target_points.append(xout)
                    elif iwork[0] == 4:
                        print '\nLimit point'
                        xout = xr2.tolist()
                        a = xout.pop(-1)

                        if self.__HAS_MOIETY_CONSERVATION__ == True:
                            xout = self.Fix_S_indinput(xout, amounts=True)
                        else:
                            xout = numpy.array(xout)
                        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                            xout = self._SpeciesAmountToConc(xout)
                        xout = xout.tolist()
                        xout.insert(0,a)

                        print xout
                        self.pitcon_limit_points.append(xout)
                    elif iwork[0] == 1:
                        pass
                    else:
                        print iwork[0]
                        #raw_input()
            else:
                print '\nInvalid steady state, skipping ...'

        if self.__settings__["pitcon_filter_neg_res"]:
            for result in range(len(res)-1,-1,-1):
                if min(res[result][:len(self.species)+1]) < 0.0:
                    res.pop(result)
        setattr(self, scanpar, par_hold)
        return numpy.array(res)

    def Simulate(self,userinit=0):
        """
        PySCeS integration driver routine that evolves the system over the time.
        Resulting array of species concentrations is stored in the **mod.data_sim** object
        Initial concentrations can be selected using *mod.__settings__['mode_sim_init']*
        (default=0):

        - 0  initialise with intial concentrations
        - 1  initialise with a very small (close to zero) value
        - 2  initialise with results of previously calculated steady state
        - 3  initialise with final point of previous simulation

        *userinit* values can be (default=0):

        - 0: initial species concentrations intitialised from (mod.S_init), time array calculated from sim_start/sim_end/sim_points
        - 1: intial species concentrations intitialised from (mod.S_init) existing "mod.sim_time" used directly
        - 2: initial species concentrations read from "mod.__inspec__", "mod.sim_time" used directly
        """
        # check if the stoichiometry has been adjusted using Stoich_nmatrix_SetValue - brett 20050719
        if not self.__StoichOK:
            self.Stoichiometry_ReAnalyse()

        # initialises self.__inspec__[x] with self.sXi
        if userinit == 1:
            eval(self.__mapFunc_R__)
        elif userinit == 2:
            try:
                assert len(self.__inspec__) == len(self.__species__)
            except:
                print '\nINFO: sim_sinit is the incorrect length initialising with .sX_init'
                self.__inspec__ = numpy.zeros(len(self.__species__))
                eval(self.__mapFunc_R__)
        else:
            self.sim_start = float(self.sim_start)
            self.sim_end = float(self.sim_end)
            self.sim_points = float(self.sim_points)
            if self.sim_points == 1.0:
                print '*****\nWARNING: simulations require a minimum of 2 points, setting sim_points = 2.0\n*****'
                self.sim_points = 2.0
            self.sim_time = numpy.linspace(self.sim_start, self.sim_end, self.sim_points, endpoint=1, retstep=0)
            eval(self.__mapFunc_R__)

        # initialise __rrule__ to mod.<rule>_init
        if self.__HAS_RATE_RULES__:
            for r in range(len(self.__rate_rules__)):
                self.__rrule__[r] = getattr(self, '%s_init' % self.__rate_rules__[r])
        for c in range(len(self.__CsizeAllIdx__)):
            cval = getattr(self, '%s_init' % self.__CsizeAllIdx__[c])
            setattr(self, self.__CsizeAllIdx__[c], cval)
            self.__CsizeAll__[c] = cval
        # set initialisation array to amounts
        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Species_In_Conc']:
            self.__inspec__ = self._SpeciesConcToAmount(self.__inspec__)

        # set mod.<species> to corrected mod.<species>_init value
        for s in range(len(self.__species__)):
            setattr(self, self.__species__[s], self.__inspec__[s])

        #This should work ok __Build_Tvec__ uses .self.__inspec__ to create Tvec
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            self.__Build_Tvec__(amounts=True)
            self.showConserved(screenwrite=0)
            if self.__settings__['display_debug'] == 1:
                print self.conserved_sums

        # Initialise the simulation ...
        if self.__settings__['mode_sim_init'] == 0:
        # Start with s[x] at their initial values
            s0_sim_init = copy.copy(self.__inspec__)
        elif self.__settings__['mode_sim_init'] == 1:
        # Start with s[x] at zero ... well close to it anyway
            s0_sim_init = copy.copy(self.__inspec__)
            s0_sim_init[:] = self.__settings__['small_concentration']
        elif self.__settings__['mode_sim_init'] == 2:
        # Start with s[x] at the previous steady state if it exists and check if s[x] < 0.0
        # if so set that value to 1.0e-10
            if self.state_species != None:
                s0_sim_init = copy.copy(self.state_species)*1.0
                for x in range(len(s0_sim_init)):
                    if s0_sim_init[x] < 0.0:
                        s0_sim_init[x] = self.__settings__['small_concentration']
                        print 'Negative concentration detected in SimInit: s['+`x`+'] set to ' + `self.__settings__['small_concentration']`
            else:
                s0_sim_init = copy.copy(self.__inspec__)
        elif self.__settings__['mode_sim_init'] == 3:
        # Start with s[x] at the final point of the previous simulation if exists -- johann 20050220
            try:
                s0_sim_init = copy.copy(self.data_sim.species[-1])
            except:
                s0_sim_init = copy.copy(self.__inspec__)
        else:
            s0_sim_init = copy.copy(self.__inspec__)

        if self.__HAS_MOIETY_CONSERVATION__ == True:
            temp = numpy.zeros((len(self.__SI__)),'d')
            for x in range(0,len(self.lzeromatrix_col)):
                temp[x] = s0_sim_init[self.nrmatrix_row[x]]
            s0_sim_init = temp
            del temp

        # ok so now we add RateRules to the initialisation_vec and see what happens
        if self.__HAS_RATE_RULES__:
            s0_sim_init = numpy.concatenate([s0_sim_init, self.__rrule__])

        # real pluggable integration routines - brett 2007
        # the array copy is important ... brett leave it alone!
        Tsim0 = time.time()
        if self.mode_integrator == 'LSODA':
            sim_res, rates, simOK = self.LSODA(copy.copy(s0_sim_init))
        elif self.mode_integrator == 'CVODE':
            sim_res, rates, simOK = self.CVODE(copy.copy(s0_sim_init))
        Tsim1 = time.time()
        print "%s time for %s points: %s" % (self.mode_integrator, len(self.sim_time), Tsim1-Tsim0)

        if self.__HAS_RATE_RULES__:
            sim_res, rrules = numpy.split(sim_res,[len(self.__species__)],axis=1)
            print 'RateRules evaluated and added to mod.data_sim.'

        self.data_sim = IntegrationDataObj()
        self.IS_VALID = simOK
        self.data_sim.setTime(self.sim_time)
        self.data_sim.setSpecies(sim_res, self.__species__)
        self.data_sim.setRates(rates, self.__reactions__)
        if self.__HAS_RATE_RULES__:
            self.data_sim.setRules(rrules, self.__rate_rules__)
        if len(self.CVODE_extra_output) > 0:
            self.data_sim.setXData(self.CVODE_xdata, lbls=self.CVODE_extra_output)
            self.CVODE_xdata = None
        if not simOK:
            print 'Simulation failure'
        del sim_res


    def State(self):
        """
        State()

        PySCeS non-linear solver driver routine. Solve for a steady state using HYBRD/NLEQ2/FINTSLV
        algorithms. Results are stored in mod.state_species and mod.state_flux. The results
        of a steady-state analysis can be viewed with the mod.showState() method.

        The solver can be initialised in 3 ways using the mode_state_init switch.
        mod.mode_state_init = 0 initialize with species initial values
        mod.mode_state_init = 1 initialize with small values
        mod.mode_state_init = 2 initialize with the final value of a 10-logstep simulation numpy.logspace(0,5,18)

        Arguments:
        None

        """
        # check if the stoichiometry has been adjusted using Stoich_nmatrix_SetValue - brett 20050719
        if not self.__StoichOK:
            self.Stoichiometry_ReAnalyse()

        self.__StateOK__ = True

        # function to feed the simulation routine if used to initialise the solver
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        def function_sim(s,t):
            return self._EvalODE(s,Vtemp)

        #set self.__inspec__ to current Xi values
        eval(self.__mapFunc_R__)

        # initialise __rrule__ to mod.<rule>_init
        if self.__HAS_RATE_RULES__:
            for r in range(len(self.__rate_rules__)):
                self.__rrule__[r] = getattr(self, '%s_init' % self.__rate_rules__[r])
        # set compartment values to initial values
        for c in range(len(self.__CsizeAllIdx__)):
            cval = getattr(self, '%s_init' % self.__CsizeAllIdx__[c])
            setattr(self, self.__CsizeAllIdx__[c], cval)
            self.__CsizeAll__[c] = cval
        # set initialisation array to amounts
        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Species_In_Conc']:
            self.__inspec__ = self._SpeciesConcToAmount(self.__inspec__)
        # set mod.<species> to corrected mod.<species>_init value
        for s in range(len(self.__species__)):
            setattr(self, self.__species__[s], self.__inspec__[s])
        #This should work ok __Build_Tvec__ uses .self.__inspec__ to create Tvec
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            self.__Build_Tvec__(amounts=True)
            self.showConserved(screenwrite=0)
            if self.__settings__['display_debug'] == 1:
                print self.conserved_sums

        #clear the solver initialisation array
        s0_ss_init = None
        # save the __settings__['hybrd_factor']
        hybrd_factor_temp = copy.copy(self.__settings__['hybrd_factor'])

        # use StateInit to initialise the solver with either 0:initval 1:zeroval 2:sim 3:1%state
        # in all cases fallback to init
        # initialising to previous ss seems problematic so I use |10%| of previous - brett
        if self.mode_state_init == 0:
        # Use initial S values
            s0_ss_init = self.__inspec__.copy()
        elif self.mode_state_init == 1:
        # Use almost zero values 1.0e-8 any smaller seems to cause a problem
        # this is user defineable option --> model.ZeroVal - brett 20040121
            s0_ss_init = self.__inspec__.copy()
            s0_ss_init[:] = self.__settings__['small_concentration']
        elif self.mode_state_init == 2:
            print 'This initialisation mode has been disabled, using initial values.'
            s0_ss_init = self.__inspec__.copy()
        ##  # Perform a 10-logstep simulation numpy.logspace(0,5,18) and initialise solver with final value
        ##  # This is guesstimate ... if anyone has a better idea please let me know
        ##  # it should and be a user defineable array (min/max/len)- brett 20031215
            ##  self.__settings__['hybrd_factor'] = 10
            ##  try:
                ##  s0_ss_init = scipy.integrate.odeint(function_sim,self.__inspec__.tolist(),self.__mode_state_init2_array__,10000,full_output=0)
                ##  if self.__HAS_MOIETY_CONSERVATION__ == True:
                    ##  s0_ss_init = self.Fix_S_fullinput(s0_ss_init[-1], amounts=True)
                ##  else:
                    ##  s0_ss_init = s0_ss_init[-1]
            ##  except:
                ##  s0_ss_init = copy.copy(self.__inspec__)
        elif self.mode_state_init == 3:
        # Use |10%| of previous steady-state - seems to be safe, needs more testing and probably professional help
        # My rationale for this is "a set of approximate values where all variables are relatively close"
        # without having the overhead of a simulation ...
        # will eventually be a user option so that it can be +/- x% previous steadystate - brett 20031215
        # Note: hybrid doesn't seem to like to start too close to its solution more than 10% is dodgy
            if  self.state_species != None and self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                s0_ss_init = self._SpeciesConcToAmount(abs(copy.copy(self.state_species))*float(self.__settings__['mode_state_init3_factor']))
            else:
                s0_ss_init = copy.copy(self.__inspec__)
        else:
            s0_ss_init = copy.copy(self.__inspec__)
        # reset __settings__['hybrd_factor']
        self.__settings__['hybrd_factor'] = hybrd_factor_temp

        # form an initialisation vector of independent species
        temp = numpy.zeros((len(self.__SI__)),'d')
        for x in range(0,len(self.lzeromatrix_col)):
            temp[x] = s0_ss_init[self.nrmatrix_row[x]]
        # add RateRules to the initialisation_vec and see what happens
        if self.__HAS_RATE_RULES__:
            temp = numpy.concatenate([temp, self.__rrule__])
        s0_ss_init = temp
        del temp

        available_solvers = ['HYBRD']

        # set mode_solver to new syntax for backwards compatibility only
        if self.mode_solver == 0:
            self.mode_solver = 'HYBRD'
        elif self.mode_solver == 1:
            self.mode_solver = 'FINTSLV'
        elif self.mode_solver == 2:
            self.mode_solver = 'NLEQ2'

        # check for nleq2 and add if available this should be first
        if nleq2_switch == 1:
            available_solvers.append('NLEQ2')
        else:
            if self.mode_solver == 'NLEQ2':
                self.mode_solver = 'HYBRD'
            print 'INFO: switching to HYBRD.\nNleq2 solver not available see /nleq/readme.txt for details'

        # ******* OTHER SOLVERS GO IN HERE *******

        # ******* OTHER SOLVERS GO IN HERE *******

        # shall we add HYBRD to fallback? this should be last
        if self.__settings__['mode_solver_fallback_integration']:
            available_solvers.append('FINTSLV')

        if not self.mode_solver_fallback == 1:
            assert self.mode_solver in available_solvers, '\nERROR: %s is not a valid (%s) solver!' % (solver, str(available_solvers))
            available_solvers = [self.mode_solver]


        STATE_XOUT = False
        STATE_xdata = None
        if len(self.STATE_extra_output) > 0:
            out = []
            for d in self.STATE_extra_output:
                if hasattr(self, d) and d not in self.__species__ + self.__reactions__ + self.__rate_rules__:
                    out.append(d)
                else:
                    print '\nWARNING: STATE is ignoring extra data (%s), it either doesn\'t exist or it\'s a species, rate or rule.\n' % d
            if len(out) > 0:
                self.STATE_extra_output = out
                STATE_XOUT = True
            del out

        self.__StateOK__ = True
        state_species = None
        rrules = None
        state_flux = None
        for solver in available_solvers:
            if solver == 'HYBRD':
                state_species, self.__StateOK__ = self.HYBRD(s0_ss_init.copy())
            elif solver == 'NLEQ2':
                state_species, self.__StateOK__ = self.NLEQ2(s0_ss_init.copy())
            elif solver == 'FINTSLV':
                state_species, self.__StateOK__ = self.FINTSLV(s0_ss_init.copy())
            #In case of a number (scalar) output from the solver
            if numpy.isscalar(state_species):
                state_species = numpy.array([state_species],'d')

            # this gets everything into the current tout state
            tmp = self._EvalODE(state_species.copy(), Vtemp)
            state_flux = self.__vvec__

            if self.__HAS_RATE_RULES__:
                state_species, rrules = numpy.split(state_species,[self.Nrmatrix.shape[0]])
            # test for negative concentrations
            if (state_species < 0.0).any():
                self.__StateOK__ = False
            if self.__StateOK__:
                break
            else:
                if self.mode_solver_fallback and self.__settings__['solver_switch_warning']:
                    slv_idx  = available_solvers.index(solver)
                    if slv_idx != len(available_solvers)-1:
                        print 'INFO: STATE is switching to %s solver.' % available_solvers[slv_idx+1]
                    else:
                        print 'INFO: STATE calculation failed!'
        if STATE_XOUT:
            STATE_xdata = self._EvalExtraData(self.STATE_extra_output)

        # the current status quo is all state algorithms will use and produce results
        # in amounts and autoconversion to and from species takes place in the calling
        # algorithms -- brett2008
        # THIS IS OPPOSITE TO THE SIMULATE METHOD brett - again

        if self.__HAS_MOIETY_CONSERVATION__ == True:
            state_species = self.Fix_S_indinput(state_species, amounts=True)
        if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
            self.state_species = self._SpeciesAmountToConc(state_species.copy())
        else:
            self.state_species = state_species

        self.state_flux = state_flux

        del state_species, state_flux

        # final check for a bad state set check if fluxes are == mach_eps
        # this is almost never going to be true
        if (self.state_flux == self.__settings__['mach_floateps']).any():
            print '\nWARNING: extremely small flux detected! proceed with caution:'
            print self.state_flux
            ##  self.__StateOK__ = False

        if not self.__StateOK__:
            print '\n***\nWARNING: invalid steady state solution (species concentrations and fluxes)\n***\n'
            if self.__settings__['mode_state_nan_on_fail']:
                self.state_species[:] = numpy.NaN
                self.state_flux[:] = numpy.NaN

        # set the instance steady state flux and species attributes
        self.SetStateSymb(self.state_flux,self.state_species)

        # coming soon to a terminal near you
        self.data_sstate = StateDataObj()
        self.data_sstate.setSpecies(self.state_species, self.__species__)
        self.data_sstate.setFluxes(self.state_flux, self.__reactions__)
        if self.__HAS_RATE_RULES__:
            self.data_sstate.setRules(rrules, self.__rate_rules__)
        if STATE_XOUT:
            self.data_sstate.setXData(STATE_xdata, lbls=self.STATE_extra_output)
            del STATE_xdata

        self.data_sstate.IS_VALID = self.__StateOK__

        if self.__settings__['display_debug'] == 1:
            print 'self.state_species'
            print self.state_species
            print 'self.state_flux'
            print self.state_flux

#driver routines that support the core routines
#   core support

    # takes the flux and species arrays and
    # assigns them as instances variable self.Jx and self.Xss
    # I'm not sure if anyone wants to use this in real life so it might be hidden at some point - brett 20040122
    # gone - brett 20040506 ... back brett 20040720
    def SetStateSymb(self,flux,metab):
        """
        SetStateSymb(flux,metab)

        Sets the individual steady-state flux and concentration attributes as
        mod.J_<reaction> and mod.<species>_ss

        Arguments:

        flux: the steady-state flux array
        metab: the steady-state concentration array

        """
        for x in range(0,len(self.state_species)):
            setattr(self, self.__species__[x]+'_ss', metab[x])

        for x in range(0,len(self.state_flux)):
            setattr(self, 'J_' + self.__reactions__[x], flux[x])

    # uses __inspec__ to build Tvec
    def __Build_Tvec__(self, amounts=True):
        """
        __Build_Tvec_(concs=False)

        Creates vectors of conserved moiety totals from __inspec__
        self.__tvec_a__ = amounts
        self.__tvec_c__ = concentrations

        Arguments:
        None

        """
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            if amounts:
                self.__tvec_a__ = numpy.add.reduce(copy.copy(self.__reordered_lcons)*self.__inspec__,1)
                self.__tvec_c__ = numpy.add.reduce(copy.copy(self.__reordered_lcons)*self._SpeciesAmountToConc(self.__inspec__),1)
            else:
                self.__tvec_c__ = numpy.add.reduce(copy.copy(self.__reordered_lcons)*self.__inspec__,1)
                self.__tvec_a__ = numpy.add.reduce(copy.copy(self.__reordered_lcons)*self._SpeciesConcToAmount(self.__inspec__),1)

    def showConserved(self,File=None,screenwrite=1,fmt='%2.3f'):
        """
        showConserved(File=None,screenwrite=1,fmt='%2.3f')

        Print the moiety conserved cycles present in the system.

        Arguments:

        File [default=None]: an open writable Python file object
        screenwrite [default=1]: write results to console (0 means no reponse)
        fmt [default='%2.3f']: the output number format string

        """
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            Tlist = range(0,len(self.__tvec_a__))
            if Tlist != []:
                ConSumPstr = ''
                for x in range(0,len(Tlist)):
                    for y in range (0,len(self.conservation_matrix_col)):
                        if self.conservation_matrix[Tlist[x],y] > 0.0:
                            #coeff = self.__settings__['mode_number_format'] % (s_init[y]*abs(conservation_matrix[Tlist[x],y]))
                            coeff = fmt % abs(self.conservation_matrix[Tlist[x],y])
                            met = self.__D_s_Order[self.conservation_matrix_col[y]]
                            ConSumPstr += ' + {' + coeff + '}' + met.replace('self.','')
                        elif self.conservation_matrix[Tlist[x],y] < 0.0:
                            #coeff = self.__settings__['mode_number_format'] % (s_init[y]*abs(conservation_matrix[Tlist[x],y]))
                            coeff = fmt % abs(self.conservation_matrix[Tlist[x],y])
                            met = self.__D_s_Order[self.conservation_matrix_col[y]]
                            ConSumPstr += ' - {' + coeff + '}' + met.replace('self.','')
                    if self.__HAS_COMPARTMENTS__ and self.__KeyWords__['Output_In_Conc']:
                        ConSumPstr += ' = ' + self.__settings__['mode_number_format'] % self.__tvec_c__[Tlist[x]] + '\n'
                    else:
                        ConSumPstr += ' = ' + self.__settings__['mode_number_format'] % self.__tvec_a__[Tlist[x]] + '\n'
            self.conserved_sums = ConSumPstr
        else:
            self.conserved_sums = 'No moiety conservation'

        if File != None:
            print '\nConserved relationships'
            #assert type(File) == file, 'showConserved() needs an open file object'
            File.write('\n## Conserved relationships\n')
            File.write(self.conserved_sums)
        elif screenwrite:
            print '\nConserved relationships'
            print self.conserved_sums


    def showFluxRelationships(self,File=None):
        """
        showConserved(File=None)

        Print the flux relationships present in the system.

        Arguments:

        File [default=None]: an open writable Python file object

        """
        Ostr = ''
        for row in range(self.__kzeromatrix__.shape[0]):
            Ostr += "%s =" % self.reactions[self.kzeromatrix_row[row]]
            for col in range(self.__kzeromatrix__.shape[1]):
                if self.__kzeromatrix__[row,col]  != 0.0:
                    if self.__kzeromatrix__[row,col]  > 0.0:
                        Ostr += " + {%2.2f}%s" % (abs(self.__kzeromatrix__[row,col]), self.reactions[self.kzeromatrix_col[col]])
                    else:
                        Ostr += " - {%2.2f}%s" % (abs(self.__kzeromatrix__[row,col]), self.reactions[self.kzeromatrix_col[col]])
            Ostr += '\n'

        if File != None:
            print '\nFlux relationships'
            #assert type(File) == file, 'showConserved() needs an open file object'
            File.write('\n## Flux relationships\n')
            File.write(Ostr)
        else:
            print '\nFlux relationships'
            print Ostr

    # Calculate dependant variables done directly in EvalREq2 this is a utility version
    def Fix_S_fullinput(self, s_vec, amounts=True):
        """
        Fix_S_fullinput(s_vec)

        Using the full concentration vector evaluate the dependent species

        Arguments:

        s_vec: a full length concentration vector

        """
        # s_vec = copy.copy(s)
        for x in range(0,len(self.lzeromatrix_col)):
            self.__SI__[x] = s_vec[self.lzeromatrix_col[x]]

        for x in range(0,len(self.lzeromatrix_row)):
            if amounts:
                s_vec[self.lzeromatrix_row[x]] = self.__tvec_a__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*self.__SI__)  #there might be a way to reduce the 2 for loops
            else:
                s_vec[self.lzeromatrix_row[x]] = self.__tvec_c__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*self.__SI__)  #there might be a way to reduce the 2 for loops

        return s_vec

    # Calculate dependant variables done directly in EvalREq2B this is a utility version
    def Fix_S_indinput(self, s_vec, amounts=True):
        """
        Fix_S_indinput(s_vec, amounts=True)
        whether to use self.__tvec_a__ (default)
        or self.__tvec_c__

        Given a vector of independent species evaluate and return a full concentration vector.

        Arguments:

        s_vec: vector of independent species

        """
        #stick SI into s using Nrrow order
        for x in range(len(s_vec)):
            self.__SALL__[self.nrmatrix_row[x]] = s_vec[x]
        #stick SD into s using lzerorow order
        for x in range(len(self.lzeromatrix_row)):
            if amounts:
                self.__SALL__[self.lzeromatrix_row[x]] = self.__tvec_a__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*s_vec)
            else:
                self.__SALL__[self.lzeromatrix_row[x]] = self.__tvec_c__[x] + numpy.add.reduce(self.__lzeromatrix__[x,:]*s_vec)
        return copy.copy(self.__SALL__)

#   output support routines

    # Quick and dirty flux regeneration uses the Rx form of the RE's to cater for potential conservation
    # caters for both vectors and arrays and is conservation aware
    def __FluxGen__(self,s):
        """
        **Deprecating** only used by **PITCON**

        s: species vector/array
        """
        Vtemp = numpy.zeros((self.__Nshape__[1]),'d')
        try:
            s.shape[0]
            Vout = numpy.zeros((len(s),len(self.__vvec__)),'d')
            for x in range(len(s)):
                if self.__HAS_MOIETY_CONSERVATION__ == True:  #depending if there is conservation -- N.L_switch is: 0 for none or 1 for present
                    Vout[x,:] = self._EvalREq2_alt(s[x,:],Vtemp)
                elif self.__HAS_MOIETY_CONSERVATION__ == False:
                    Vout[x,:] = self._EvalREq(s[x,:],Vtemp)
        except:
            if self.__HAS_MOIETY_CONSERVATION__ == True:  #depending if there is conservation -- N.L_switch is: 0 for none or 1 for present
                Vout = self._EvalREq2_alt(s,Vtemp)
            elif self.__HAS_MOIETY_CONSERVATION__ == False:
                Vout = self._EvalREq(s,Vtemp)
        return Vout

    def FluxGenSim(self,s):
        """
        **Deprecated**
        """
        pass

    def ParGenSim(self):
        """
        **Deprecated**
        """
        pass

    def Fix_Sim(self,metab,flux=0,par=0):
        """
        **Deprecated**
        """
        pass

# MCA routines
#   elasticity routines

    def EvalEvar(self,input=None,input2=None):
        """
        EvalEvar(input=None,input2=None)

        Calculate reaction elasticities towards the variable species.

        Both inputs (input1=species,input2=rates) should be valid (steady state for MCA) solutions and given in the correct order for them to be used. If either or both are missing the last state values are used automatically.
        Elasticities are scaled using input 1 and 2.

        Arguments::

         - input [default=None]:  species concentration vector
         - input2 [default=None]: reaction rate vector

        Settings, set in mod.__settings__::

         - elas_evar_upsymb   [default = 1] attach individual elasticity symbols to model instance
         - elas_zero_conc_fix [default=False] if zero concentrations are detected in a steady-state solution make it a very small number
         - elas_zero_flux_fix [default=False] if zero fluxes are detected in a steady-state solution make it a very small number
         - elas_scaling_div0_fix [default=False] if INf's are detected after scaling set to zero

        """
        if input == None or input2 == None:
            input = self.state_species
            input2 = self.state_flux
            #print 'INFO: Using state_species and state_flux as input'
        else:
            assert len(input) == len(self.species), 'length error this array must have ' + str(len(self.species)) + ' elements'
            assert len(input2) == len(self.reactions), 'length error this array must have ' + str(len(self.reactions)) + ' elements'
            # not really necessary but in here just in case - brett 20040930
            # map input back to self.Sx
            exec(self.__CDvarUpString__)

        if self.__settings__['elas_zero_flux_fix']:
            val = self.__settings__['mach_floateps']**2
            for x in range(len(input2)):
                if abs(input2[x]) <= val:
                    print 'Info: zero flux detected: J_%s set to %s' % (self.reactions[x], val)
                    input2[x] = val
        if self.__settings__['elas_zero_conc_fix']:
            val = self.__settings__['mach_floateps']**2
            for x in range(len(input)):
                if abs(input[x]) <= val:
                    print 'Info: zero concentration detected: %s set to %s' % (self.species[x], val)
                    input[x] = val

        if self.__settings__['display_debug'] == 1:
            print '\nVarinput = ' + `input` + '\n'

        self.__evmatrix__ = numpy.zeros((len(self.__reactions__),len(self.__species__)),'d')

        # scale KL (future refactoring allow user input)
        self.ScaleKL(input,input2)

        # create tuples of the rows and columns for the Ev matrix
        self.elas_var_row = tuple(copy.copy(self.__reactions__))
        col = copy.copy(self.__D_s_Order)
        for x in range(len(self.__D_s_Order)):
            col[x] = col[x].replace('self.','')
        self.elas_var_col = tuple(col)
        del col

        # attempt to evaluate every variable against every flux: E's > mach_eps are assumed to exist
        for react in range(len(self.__reactions__)):
            if self.__settings__['display_debug'] == 1:
                print '\nReaction: ' + self.__reactions__[react]
            for met in range(len(self.__species__)):
                countV = 0
                countMet = 0
                # this modification should make this independant of a steady state - finally implimented july2004
                # eval('self.'+ self.__species__[met] + '_ss'),order=self.__settings__['mode_elas_deriv_order'],dx=hstep,n=1,\
                try:
                    """ I got the idea of scaling the stepsize to So from Herbert Sauro's Jarnac TModel.uEEOp function

                    brett - 20040818"""

                    hstep = input[met]*self.__settings__['mode_elas_deriv_factor']  # self.__settings__['mode_elas_deriv_factor'] = 0.0001
                    if abs(hstep) < self.__settings__['mode_elas_deriv_min']:         # self.__settings__['mode_elas_deriv_min'] = 1.0e-12
                        hstep = self.__settings__['mode_elas_deriv_min']

                    a = scipy.derivative(self.__num_deriv_function__,input[met],order=self.__settings__['mode_elas_deriv_order'],dx=hstep,n=1,\
                        args=('v=self.' + self.__reactions__[react] + '()','self.' + self.__species__[met] + '=x'))
                except Exception, ex:
                    print ex
                    print '\nINFO: Elasticity evaluation failure in ', self.__reactions__[react], self.__species__[met]
                    print 'Elasticity has been set to zero'
                    print 'A stepsize that is too large might cause this ... try decreasing the factor and or min stepsize'
                    print 'Keep in mind machine precision, is', self.__settings__['mach_floateps'], ' and if min stepsize'
                    print 'becomes too small numeric error can become significant'
                    a = 0.0
                if abs(a) >= self.__settings__['mach_floateps']:
                    if self.__settings__['display_debug'] == 1:
                        print 'species: ' + self.__species__[met]
                        print '--> d(' + self.__reactions__[react] + ')d(' + self.__species__[met] + ') = ' + str(a)
                    self.__evmatrix__[react,met] = a

       # restore variables to ss values only if steady state used
        if self.__settings__['elas_evar_remap']:
            eval(compile(self.__remaps,'__remaps','exec'))
            #print "\nREMAPPING\n"

        self.elas_var_u = self.__evmatrix__

        # If scaled mca is requested
        if self.__settings__['mode_mca_scaled'] == 1:
            Ds = numpy.zeros((len(input),len(input)),'d')
            Dj = numpy.zeros((len(input2),len(input2)),'d')

            for x in range(0,len(input)):
                Ds[x,x] = input[x]
            #print Ds

            for x in range(0,len(input2)):
                Dj[x,x] = 1.0/input2[x]
                if self.__settings__['elas_scaling_div0_fix'] and numpy.isinf(Dj[x,x]):
                    print 'Infinite elasticity detected during scaling setting to zero (%s)' % self.reactions[x]
                    Dj[x,x] = 1.0e-16
            #print Dj

            Dj_e = numpy.dot(Dj,self.__evmatrix__)
            self.__evmatrix__ = numpy.dot(Dj_e, Ds)
            self.elas_var = self.__evmatrix__
        else:
            self.elas_var = None

        if self.__settings__["elas_evar_upsymb"] == 1:
            # Upload elasticity symbols into namespace
            r,c = self.__evmatrix__.shape
            output2 = ''
            for x in range(0,r):
                react = self.__reactions__[x]
                for y in range (0,c):
                    met = self.__D_s_Order[y]
                    if self.__settings__['mode_mca_scaled']:
                        setattr(self, 'ec' + react + '_' + met.replace('self.',''), self.__evmatrix__[x, y])
                    else:
                        setattr(self, 'uec' + react + '_' + met.replace('self.',''), self.__evmatrix__[x, y])

            # the new way of doing things (test phase) brett - 2007
            self.ec = BagOfStuff(self.__evmatrix__, self.elas_var_row, self.elas_var_col)
            self.ec.load()
            if self.__settings__['mode_mca_scaled'] == 1:
                self.ec.scaled = True
            else:
                self.ec.scaled = False

            if self.__settings__['display_debug'] == 1:
                print '\n\n********************************\n'
                print output2
                print '\n********************************\n'
        else:
            pass
            #print 'INFO: variable elasticity symbols not attached - .__settings__["elas_evar_upsymb"] = ' + `self.__settings__["elas_evar_upsymb"]`

        if self.__settings__['display_debug'] == 1:
            print '\ne_vmatrix'
            print `self.__D_s_Order`.replace('self.','')
            print self.__reactions__
            print self.__evmatrix__

    def CleanNaNsFromArray(self, arr, replace_val=0.0):
        """
        Scan a matrix for NaN's and replace with zeros:

         - *arr* the array to be cleaned

        """
        nantest = numpy.isnan(arr)
        if nantest.any():
            for r in range(nantest.shape[0]):
                if nantest[r].any():
                    for c in range(nantest.shape[1]):
                        if numpy.isnan(arr[r,c]):
                            arr[r,c] = replace_val

    def EvalEpar(self,input=None,input2=None):
        """
        EvalEpar(input=None,input2=None)

        Calculate reaction elasticities towards the parameters.

        Both inputs (input1=species,input2=rates) should be valid (steady state for MCA) solutions and given in the correct order for them to be used. If either or both are missing the last state values are used automatically. Elasticities are scaled using input 1 and 2.

        Arguments:

         - input [default=None]: species concentration vector
         - input2 [default=None]: reaction rate vector

        Settings, set in mod.__settings__::

         - elas_epar_upsymb   [default = 1] attach individual elasticity symbols to model instance
         - elas_scaling_div0_fix [default=False] if NaN's are detected in the variable and parameter elasticity matrix replace with zero

        """
        if input == None or input2 == None:
            input = self.state_species
            input2 = self.state_flux
        else:
            assert len(input) == len(self.species), 'length error this array must have ' + str(len(self.species)) + ' elements'
            assert len(input2) == len(self.reactions), 'length error this array must have ' + str(len(self.reactions)) + ' elements'
            # put in to fix the juicy bug - brett 20040930
            # iow automatic derivatives use the current values of self.Sx to operate
            exec(self.__CDvarUpString__)

        # create parameter holding array
        parVal_hold = numpy.zeros((len(self.__parameters__)),'d')
        if self.__settings__['display_debug'] == 1:
            print '\nParinput = ' + `input` + '\n'
            print '\nparVal_hold1'
            print parVal_hold

        #Store parameter values into the storage array and copy them to the working array (parVal2)
        exec(self.__par_map2storeC)
        parVal2 = copy.copy(parVal_hold)

        # create the matrix of parameter elasticities
        self.__epmatrix__ = numpy.zeros((len(self.__reactions__),len(self.__parameters__)),'d')

        # create tuples of the rows and columns for the Ep matrix
        self.elas_par_row = tuple(copy.copy(self.__reactions__))
        self.elas_par_col = tuple(self.__parameters__)

        for react in range(len(self.__reactions__)):
            if self.__settings__['display_debug'] == 1:
                print '\nReaction: ' + self.__reactions__[react]
            for par in range(len(self.__parameters__)):
                countV = 0
                countPar = 0
                try:
                    """ I got the idea of scaling the stepsize to So from Herbert Sauro's Jarnac TModel.uEEOp function

                    brett - 20040818"""

                    hstep = getattr(self,self.__parameters__[par])*self.__settings__['mode_elas_deriv_factor']  # self.__settings__['mode_elas_deriv_factor'] = 0.0001
                    if abs(hstep) < self.__settings__['mode_elas_deriv_min']:         # self.__settings__['mode_elas_deriv_min'] = 1.0e-12
                        hstep = self.__settings__['mode_elas_deriv_min']

                    a = scipy.derivative(self.__num_deriv_function__,getattr(self,self.__parameters__[par]),order=self.__settings__['mode_elas_deriv_order'],dx=hstep,n=1,\
                        args=('v=self.' + self.__reactions__[react] + '()','self.' + self.__parameters__[par] + '=x'))
                except Exception, ex:
                    print '\nNumeric derivative evaluation failure in ', self.__reactions__[react], self.__parameters__[par]
                    print 'Elasticity has been set to NaN'
                    print 'A stepsize that is too large might cause this ... try decreasing the factor and or min stepsize'
                    print 'Keep in mind machine precision, is', self.__settings__['mach_floateps'], ' and if min stepsize'
                    print 'becomes too small numeric error can become significant'
                    print ex
                    a = numpy.NaN

                if numpy.isnan(a) or abs(a) > self.__settings__['mach_floateps']:
                    if self.__settings__['display_debug'] == 1:
                        print 'parameter: ' + self.__parameters__[par]
                        print '--> d(' + self.__reactions__[react] + ')d(' + self.__parameters__[par] + ') = ' + `a`
                    self.__epmatrix__[react,par] = a

        self.elas_par_u = self.__epmatrix__

        #Retrieve parameter values from the storage array
        exec(self.__par_remapC)

        if self.__settings__['display_debug'] == 1:
            print '\nparVal_hold2'
            print parVal_hold

        # Parameters are scaled by [1/J]*[Ep]*[P]
        # If scaled mca is requested scale self.__epmatrix__
        if self.__settings__['mode_mca_scaled'] == 1:
            Dp = numpy.zeros((len(self.__parameters__),len(self.__parameters__)),'d')
            Dj = numpy.zeros((len(input2),len(input2)),'d')

            for x in range(0,len(self.__parameters__)):
                Dp[x,x] = parVal_hold[x]
            #print Dp

            for x in range(0,len(input2)):
                Dj[x,x] = 1.0/input2[x]
                if self.__settings__['elas_scaling_div0_fix'] and numpy.isinf(Dj[x,x]):
                    print 'Infinite elasticity detected during scaling setting to zero (%s)' % self.reactions[x]
                    Dj[x,x] = 1.0e-16
            #print Dj

            Dj_e = numpy.dot(Dj,self.__epmatrix__)
            self.__epmatrix__ = numpy.dot(Dj_e, Dp)
            self.elas_par = self.__epmatrix__
        else:
            self.elas_par = None

        del parVal_hold

        if self.__settings__["elas_epar_upsymb"] == 1:
            # Upload elasticity symbols into namespace
            r,c = self.__epmatrix__.shape
            output2 = ''
            for x in range(0,r):
                react = self.__reactions__[x]
                for y in range (0,c):
                    met = self.__parameters__[y]
                    if self.__settings__['mode_mca_scaled']:
                        setattr(self, 'ec' + react + '_' + met.replace('self.',''), self.__epmatrix__[x, y])
                    else:
                        setattr(self, 'uec' + react + '_' + met.replace('self.',''), self.__epmatrix__[x, y])

            # the new way of doing things (test phase) brett - 2007
            self.ecp = BagOfStuff(self.__epmatrix__, self.elas_par_row, self.elas_par_col)
            self.ecp.load()
            if self.__settings__['mode_mca_scaled'] == 1:
                self.ecp.scaled = True
            else:
                self.ecp.scaled = False

            if self.__settings__['display_debug'] == 1:
                print '\n\n********************************\n'
                print output2
                print '\n********************************\n'

        if self.__settings__['display_debug'] == 1:
            print '\ne_pmatrix'
            print self.__parameters__
            print self.__reactions__
            print self.__epmatrix__

    def showEvar(self,File=None):
        """
        showEvar(File=None)

        Write out all variable elasticities as \'LaTeX\' formatted strings, alternatively write results to a file.

        Arguments:

        File [default=None]: an open writable Python file object

        """
        # The Variable elasticities
        try:
            r,c = self.__evmatrix__.shape
            evar_output = ''
            for x in range(0,r):
                react = self.__reactions__[x]
                evar_output += '\n' + `self.__reactions__[x]` + '\n'
                for y in range (0,c):
                    rtemp = self.__settings__['mode_number_format'] % self.__evmatrix__[x,y]
                    met = self.__D_s_Order[y]
                    if self.__evmatrix__[x,y] != 0.0:
                        if self.__settings__['mode_mca_scaled']:
                            elas = '\\ec{' + react + '}{' + met + '} = ' + rtemp
                        else:
                            elas = '\\uec{' + react + '}{' + met + '} = ' + rtemp
                        evar_output += elas + '\n'
            evar_output = evar_output.replace('self.','')
        except:
            evar_output = 'No variable elasticities - run EvalEvar() to calculate'

        if File != None:
            print '\nspecies elasticities'
            File.write('\n## species elasticities\n')
            File.write(evar_output)
        else:
            print '\nspecies elasticities'
            print evar_output


    def showEpar(self,File=None):
        """
        showEpar(File=None)

        Write out all nonzero parameter elasticities as \'LaTeX\' formatted strings, alternatively write to file.

        Arguments:

        File [default=None]: an open writable Python file object

        """
        # The parameter elasticities
        try:
            r,c = self.__epmatrix__.shape
            epar_output = ''
            for x in range(0,r):
                react = self.__reactions__[x]
                epar_output += '\n' + `self.__reactions__[x]` + '\n'
                for y in range (0,c):
                    rtemp = self.__settings__['mode_number_format'] % self.__epmatrix__[x,y]
                    met = self.__parameters__[y]
                    # brett (paranoia removal) October 2004
                    #if abs(self.__epmatrix__[x,y]) >= 1.e-15:
                    if self.__settings__['mode_mca_scaled']:
                        elas = '\\ec{' + react + '}{' + met + '} = ' + rtemp
                    else:
                        elas = '\\uec{' + react + '}{' + met + '} = ' + rtemp
                        #print elas
                    epar_output += elas + '\n'
            epar_output = epar_output.replace('self.','')
        except:
            epar_output = 'No parameter elasticities - run EvalEpar() to calculate'

        if File != None:
            print '\nParameter elasticities'
            File.write('\n## Parameter elasticities\n')
            File.write(epar_output)
        else:
            print '\nParameter elasticities'
            print epar_output

    def showElas(self,File=None):
        """
        showElas(File=None)

        Print all elasticities to screen or file as \'LaTeX\' compatible strings.
        Calls showEvar() and showEpar()

        Arguments:

        File [default=None]: an open writable Python file object

        """
        if File != None:
            self.showEvar(File)
            self.showEpar(File)
        else:
            self.showEvar()
            self.showEpar()


    def ScaleKL(self,input,input2):
        """
        ScaleKL(input,input2)

        Scale the K and L matrices with current steady state (if either input1 or 2 == None) or user input.

        Arguments:

        input: vector of species concentrations
        input2: vector of reaction rates

        """
        # called by EvalEvar
        # Scale L matrix
        lmat = copy.copy(self.__lmatrix__)

        if input == None or input2 == None:
            input = self.state_species
            input2 = self.state_flux
            #print 'INFO: Using state_species and state_flux as input'
        else:
            assert len(input) == len(self.species), 'length error this array must have ' + str(len(self.species)) + ' elements'
            assert len(input2) == len(self.reactions), 'length error this array must have ' + str(len(self.reactions)) + ' elements'

        s_1_scale = numpy.zeros((len(input),len(input)),'d')

        for x in range(0,len(input)):
            try:
                s_1_scale[x,x] = 1.0/input[self.lmatrix_row[x]] #create 1/D Using the steady-state met's ordered to the rows
            except:
                print 'Zero species detected: ' + self.__species__[self.lmatrix_row[x]] + '_ss = ' + `input[self.lmatrix_row[x]]`
                s_1_scale[x,x] = 0.0

        Si_order = numpy.zeros(len(self.lmatrix_col)) #check
        Si_scale = numpy.zeros((len(self.lmatrix_col),len(self.lmatrix_col)),'d')

        for x in range (0,len(self.lmatrix_col)):
            Si_order[x] = self.lmatrix_col[x] #check
            Si_scale[x,x] = input[self.lmatrix_col[x]]

        L_Si = numpy.dot(lmat, Si_scale)
        L_scaled = numpy.dot(s_1_scale, L_Si)

        self.lmatrix_scaled = L_scaled

        #Scale K matrix
        kmat = copy.copy(self.__kmatrix__)

        j_1_scale = numpy.zeros((len(input2),len(input2)),'d')

        for x in range(0,len(input2)):
            try:
                j_1_scale[x,x] = 1.0/input2[self.kmatrix_row[x]]
            except:
                print '\nNull flux detected: ' + self.__reactions__[self.kmatrix_row[x]] + '_ss = ' + `input2[self.kmatrix_row[x]]`
                j_1_scale[x,x] = 0.0

        Ji_order = numpy.zeros(len(self.kmatrix_col)) #check
        Ji_scale = numpy.zeros((len(self.kmatrix_col),len(self.kmatrix_col)),'d')

        for x in range (0,len(self.kmatrix_col)):
            Ji_order[x] = self.kmatrix_col[x] #check
            Ji_scale[x,x] = input2[self.kmatrix_col[x]]

        K_Ji = numpy.dot(kmat, Ji_scale)
        K_scaled = numpy.dot(j_1_scale, K_Ji)

        self.kmatrix_scaled = K_scaled

    def __num_deriv_function__(self,x,react,met):
        """
        __num_deriv_function__(x,react,met)

        System function that evaluates the rate equation, used by numeric perturbation methods to derive
        elasticities. It uses a specific format assigning x to met and evaluating react for v and is tailored for the scipy.derivative() function using precompiled function strings.

        Arguments:

        x: value to assign to met
        react: reaction
        met: species

        """
        exec(met)
        self.Forcing_Function()
        exec(react)
        return v

#   Control coefficient routines
    def EvalCC(self):
        """
        EvalCC()

        Calculate the MCA control coefficients using the current steady-state solution.

        mod.__settings__["mca_ccj_upsymb"] = 1  attach the flux control coefficients to the model instance
        mod.__settings__["mca_ccs_upsymb"] = 1 attach the concentration control coefficients to the model instance

        Arguments:
        None

        """
        #sort E to match K and L
        e2 = numpy.zeros((len(self.kmatrix_row), len(self.lmatrix_row)), 'd')
        #print e2

        for x in range (0,len(self.kmatrix_row)):
            for y in range (0,len(self.lmatrix_row)):
                e2[x,y]  = self.elas_var_u[self.kmatrix_row[x],self.lmatrix_row[y]]

        if self.__settings__['display_debug'] == 1:
            print self.lmatrix_row
            print self.kmatrix_row
            print '---'
            print self.__nmatrix__.shape
            print self.nmatrix_row
            print self.nmatrix_col
            print self.nmatrix
            print '---'
            print self.__lmatrix__.shape
            print self.__lmatrix__
            print '---'
            print self.__kmatrix__.shape
            print self.__kmatrix__
            print '---'
            print e2.shape
            print e2

        EL = -1.0*numpy.dot(e2,self.__lmatrix__)
        KEL = numpy.concatenate((self.__kmatrix__, EL),1)
        self.kel_unscaled = KEL

        go = 0
        try:
            Ci_u = scipy.linalg.inv(KEL)
            # fortran has a very fp idea of zero I use abs(val)<1.0e-15
            self.__structural__.MatrixFloatFix(Ci_u,val=1.e-15)
            go = 1
        except Exception, ex:
            print ex
            print '\nINFO: K-EL matrix inversion failed this is possibly due to NaN values in the Elasticity matrix'
            print 'NaN elasticities can be caused by zero fluxes or concentrations look at the settings (in mod.__settings__)'
            print  '\'elas_scaling_div0_fix\' and \'elas_zero_flux_fix\''
            go = 0

        if go == 1 and not self.__settings__['mode_mca_scaled']:
            self.mca_ci = Ci_u
            self.__mca_CCi_unscaled = Ci_u
            del Ci_u
        elif go == 1 and self.__settings__['mode_mca_scaled']:
            Vi_l = self.__kmatrix__.shape[1] #Ind fluxes
            Vd_l = abs(self.__kmatrix__.shape[0] - self.__kmatrix__.shape[1]) # Can never be the case but b.paranoid
            Si_l = self.__lmatrix__.shape[1]  #Ind species
            Sd_l = abs(self.__lmatrix__.shape[0] - self.__lmatrix__.shape[1]) # Can never be the case but b.paranoid

            scale1_l = Vi_l + Si_l
            scale1 = numpy.zeros((scale1_l,scale1_l),'d')

            for x in range(0,Vi_l):
                scale1[x,x] = 1.0/self.state_flux[self.kmatrix_row[x]]
            for x in range(Vi_l,scale1_l):
                scale1[x,x] = 1.0/self.state_species[self.lmatrix_row[x - Vi_l]]

            scale2 = numpy.zeros((Vi_l + Vd_l, Vi_l + Vd_l),'d')

            for x in range(0,len(self.state_flux)):
                scale2[x,x] = self.state_flux[self.kmatrix_row[x]]

            left = numpy.dot(scale1, Ci_u)
            Ci = numpy.dot(left, scale2)

            self.mca_ci = Ci
            del scale1_l, scale1, scale2, left, Ci, Ci_u

        Cirow = []
        Cicol = []

        # the wierdness continues
        for x in range (0,len(self.kmatrix_row)):
            Cicol.append(self.__reactions__[self.kmatrix_row[x]])

        for x in range (0,len(self.kmatrix_col)):
            Cirow.append(self.__reactions__[self.kmatrix_col[x]])

        for x in range (0,len(self.lmatrix_col)):
            Cirow.append(self.__D_s_Order[self.lmatrix_col[x]].replace('self.',''))

        self.mca_ci_row = Cirow
        self.mca_ci_col = Cicol

        del e2, EL, KEL

        if self.__settings__['display_debug'] == 1:
            print 'print self.mca_ci_row'
            print self.mca_ci_row
            print 'print self.mca_ci_col'
            print self.mca_ci_col
            print 'print self.mca_ci'
            print self.mca_ci

        CJi = numpy.zeros((len(self.kmatrix_col),self.mca_ci.shape[1]),'d')
        CSi = numpy.zeros((self.mca_ci.shape[0] - len(self.kmatrix_col),self.mca_ci.shape[1]),'d')

        for x in range (0, self.mca_ci.shape[0]):
            for y in range (0, self.mca_ci.shape[1]):
                if x < len(self.kmatrix_col):
                    CJi[x,y] = self.mca_ci[x,y]
                else:
                    CSi[x - len(self.kmatrix_col),y] = self.mca_ci[x,y]

        Ko_row = []
        Ko_col = []
        sKo = numpy.zeros(self.__kzeromatrix__.shape,'d')
        xFactor = self.__kmatrix__.shape[0] - self.__kzeromatrix__.shape[0]

        #we need the scaled k/l matrices to calculate the dependent cc's
        #self.ScaleKL()

        for x in range (self.__kzeromatrix__.shape[0]-1,-1,-1):
            Ko_row.append(self.__reactions__[self.kzeromatrix_row[x]])
            for y in range (self.__kzeromatrix__.shape[1]-1,-1,-1): #this can be replaced by a row slice operation
                sKo[x,y] = self.kmatrix_scaled[x+xFactor, y]
                if x == 0:
                    Ko_col.append(self.__reactions__[self.kzeromatrix_col[y]])

        Ko_row.reverse()
        Ko_col.reverse()

        if self.__settings__['display_debug'] == 1:
            print 'CJi'
            print CJi
            print 'sKo'
            print sKo
            print 'self.kzeromatrix'
            print self.__kzeromatrix__

        #new
        if self.__settings__['mode_mca_scaled']:
            self.mca_cjd = numpy.dot(sKo, CJi)
        else:
            self.mca_cjd = numpy.dot(self.__kzeromatrix__, CJi)

        self.mca_cjd_row = Ko_row
        self.mca_cjd_col = copy.copy(self.mca_ci_col)

        del sKo, CJi

        if self.__settings__['display_debug'] == 1:
            print 'self.mca_cjd_row'
            print self.mca_cjd_row
            print 'self.mca_cjd_col'
            print self.mca_cjd_col
            print 'self.mca_cjd'
            print self.mca_cjd

        Lo_row = []
        Lo_col = []
        sLo = numpy.zeros(self.__lzeromatrix__.shape,'d')
        xFactor = self.__lmatrix__.shape[0] - self.__lzeromatrix__.shape[0]
        for x in range (self.__lzeromatrix__.shape[0]-1,-1,-1):
            Lo_row.append(self.__D_s_Order[self.lzeromatrix_row[x]].replace('self.',''))
            for y in range (self.__lzeromatrix__.shape[1]-1,-1,-1): #this can be replaced by a row slice operation
                sLo[x,y] = self.lmatrix_scaled[x+xFactor,y]
                if x == 0:
                    Lo_col.append(self.__D_s_Order[self.lzeromatrix_col[y]])

        Lo_row.reverse()
        Lo_col.reverse()

        if self.__HAS_MOIETY_CONSERVATION__ == True: #Only do this if there is S dependency
            if self.__settings__['mode_mca_scaled']:
                self.mca_csd = numpy.dot(sLo, CSi)
            else:
                self.mca_csd = numpy.dot(self.__lzeromatrix__, CSi)
            self.mca_csd_row = Lo_row
            self.mca_csd_col = copy.copy(self.mca_ci_col)
        else:
            self.mca_csd = None
            self.mca_csd_row = None
            self.mca_csd_col = None

        del CSi, sLo

        if self.__settings__['display_debug'] == 1:
            print 'self.mca_csd'
            print self.mca_csd
            print 'self.mca_csd_row'
            print self.mca_csd_row
            print 'self.mca_csd_col'
            print self.mca_csd_col

        if self.__HAS_FLUX_CONSERVATION__ and self.__HAS_MOIETY_CONSERVATION__:
            self.cc_flux = numpy.concatenate((self.mca_ci[:self.__kmatrix__.shape[1],:],self.mca_cjd))
            self.cc_flux_row = copy.copy(self.mca_ci_row[:self.__kmatrix__.shape[1]] + self.mca_cjd_row)
            self.cc_flux_col = copy.copy(self.mca_ci_col)
            self.cc_conc = numpy.concatenate((self.mca_ci[self.__kmatrix__.shape[1]:,:],self.mca_csd))
            self.cc_conc_row = copy.copy(self.mca_ci_row[self.__kmatrix__.shape[1]:] + self.mca_csd_row)
            self.cc_conc_col = copy.copy(self.mca_ci_col)
        elif self.__HAS_FLUX_CONSERVATION__:
            self.cc_flux = numpy.concatenate((self.mca_ci[:self.__kmatrix__.shape[1],:],self.mca_cjd))
            self.cc_flux_row = copy.copy(self.mca_ci_row[:self.__kmatrix__.shape[1]] + self.mca_cjd_row)
            self.cc_flux_col = copy.copy(self.mca_ci_col)
            self.cc_conc = copy.copy(self.mca_ci[self.__kmatrix__.shape[1]:,:])
            self.cc_conc_row = copy.copy(self.mca_ci_row[self.__kmatrix__.shape[1]:])
            self.cc_conc_col = copy.copy(self.mca_ci_col)
        elif self.__HAS_MOIETY_CONSERVATION__:
            print 'INFO: this is interesting no dependent flux cc\'s only dependent conc cc\'s!'
            self.cc_flux = copy.copy(self.mca_ci[:self.__kmatrix__.shape[1],:])
            self.cc_flux_row = copy.copy(self.mca_ci_row[:self.__kmatrix__.shape[1]])
            self.cc_flux_col = copy.copy(self.mca_ci_col)
            self.cc_conc = numpy.concatenate((self.mca_ci[self.__kmatrix__.shape[1]:,:],self.mca_csd))
            self.cc_conc_row = copy.copy(self.mca_ci_row[self.__kmatrix__.shape[1]:] + self.mca_csd_row)
            self.cc_conc_col = copy.copy(self.mca_ci_col)
        else:
            print 'INFO: this is interesting no dependent flux/conc coefficients!'
            self.cc_flux = copy.copy(self.mca_ci[:self.__kmatrix__.shape[1],:])
            self.cc_flux_row = copy.copy(self.mca_ci_row[:self.__kmatrix__.shape[1]])
            self.cc_flux_col = copy.copy(self.mca_ci_col)
            self.cc_conc = copy.copy(self.mca_ci[self.__kmatrix__.shape[1]:,:])
            self.cc_conc_row = copy.copy(self.mca_ci_row[self.__kmatrix__.shape[1]:])
            self.cc_conc_col = copy.copy(self.mca_ci_col)

        self.cc_all = numpy.concatenate((self.cc_flux,self.cc_conc))
        self.cc_all_row = self.cc_flux_row + self.cc_conc_row
        self.cc_all_col = copy.copy(self.mca_ci_col)

        # the new way of doing things (test phase) brett - 2007
        self.cc = BagOfStuff(self.cc_all, self.cc_all_row, self.cc_all_col)
        self.cc.load()
        if self.__settings__['mode_mca_scaled'] == 1:
            self.cc.scaled = True
        else:
            self.cc.scaled = False

        if self.__settings__['display_debug'] == 1:
            print 'self.cc_flux'
            print self.cc_flux_row
            print self.cc_flux_col
            print self.cc_flux.shape

            print 'self.cc_conc'
            print self.cc_conc_row
            print self.cc_conc_col
            print self.cc_conc.shape

            print 'self.cc_all'
            print self.cc_all_row
            print self.cc_all_col
            print self.cc_all.shape

        if self.__settings__["mca_ccj_upsymb"]:
            #CJ
            r,c = self.cc_all.shape
            CJoutput = ''
            for x in range(0,len(self.__reactions__)):
                for y in range (0,c):
                    if self.__settings__['mode_mca_scaled']:
                        setattr(self, 'ccJ' + self.cc_all_row[x] + '_' + self.cc_all_col[y], self.cc_all[x,y])
                    else:
                        setattr(self, 'uccJ' + self.cc_all_row[x] + '_' + self.cc_all_col[y], self.cc_all[x,y])

        if self.__settings__["mca_ccs_upsymb"]:
            #CS
            CSoutput = ''
            for x in range(len(self.__reactions__),r):
                for y in range (0,c):
                    if self.__settings__['mode_mca_scaled']:
                        setattr(self, 'cc' + self.cc_all_row[x] + '_' + self.cc_all_col[y], self.cc_all[x,y])
                    else:
                        setattr(self, 'ucc' + self.cc_all_row[x] + '_' + self.cc_all_col[y], self.cc_all[x,y])

        if self.__settings__['display_debug'] == 1:
            print 'CJoutput'
            print CJoutput
            print 'CSoutput'
            print CSoutput

    def showCC(self,File=None):
        """
        showCC(File=None)

        Print all control coefficients as \'LaTex\' formatted strings to the screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        r,c = self.cc_all.shape
        if self.__settings__["mca_ccall_altout"]:
            CAltoutput = ''
            for x in range(0,c):
                col = self.cc_all_col[x]
                CAltoutput += '\n`Reaction ' + col + '\'\n'
                for y in range(0,r):
                    if y == 0:
                        CAltoutput += '\"Flux control coefficients\"\n'
                    if y == len(self.__reactions__):
                        CAltoutput += '\"Concentration control coefficients\"\n'
                    row = self.cc_all_row[y]
                    rtemp = self.__settings__['mode_number_format'] % self.cc_all[y,x]
                    if y < len(self.__reactions__):
                        if self.__settings__['mode_mca_scaled']:
                            cc = '\\cc{J' + row + '}{' + col + '} = ' + rtemp
                        else:
                            cc = '\\ucc{J' + row + '}{' + col + '} = ' + rtemp
                    else:
                        if self.__settings__['mode_mca_scaled']:
                            cc = '\\cc{' + row + '}{' + col + '} = ' + rtemp
                        else:
                            cc = '\\ucc{' + row + '}{' + col + '} = ' + rtemp
                    CAltoutput += cc + '\n'
        else:
            if self.__settings__["mca_ccall_fluxout"]:
                #CJ
                CJoutput = ''
                for x in range(0,len(self.__reactions__)):
                    row = self.cc_all_row[x]
                    CJoutput += '\n`J' + row + '\'\n'
                    for y in range (0,c):
                        col = self.cc_all_col[y]
                        rtemp = self.__settings__['mode_number_format'] % self.cc_all[x,y]
                        if self.__settings__['mode_mca_scaled']:
                            cc = '\\cc{J' + row + '}{' + col + '} = ' + rtemp
                        else:
                            cc = '\\ucc{J' + row + '}{' + col + '} = ' + rtemp
                        CJoutput += cc + '\n'
            if self.__settings__["mca_ccall_concout"]:
                #CS
                CSoutput = ''
                for x in range(len(self.__reactions__),r):
                    row = self.cc_all_row[x]
                    CSoutput += '\n`' + row + '\'\n'
                    for y in range (0,c):
                        col = self.cc_all_col[y]
                        rtemp = self.__settings__['mode_number_format'] % self.cc_all[x,y]
                        if self.__settings__['mode_mca_scaled']:
                            cc = '\\cc{' + row + '}{' + col + '} = ' + rtemp
                        else:
                            cc = '\\ucc{' + row + '}{' + col + '} = ' + rtemp
                        CSoutput += cc + '\n'
        if File != None:
            #assert type(File) == file, 'showCC() needs an open file object'
            if self.__settings__["mca_ccall_altout"]:
                print '\nControl coefficients grouped by reaction'
                File.write('\n## Control coefficients grouped by reaction\n')
                File.write(CAltoutput)
            else:
                if self.__settings__["mca_ccall_fluxout"]:
                    print '\nFlux control coefficients'
                    File.write('\n## Flux control coefficients\n')
                    File.write(CJoutput)
                if self.__settings__["mca_ccall_concout"]:
                    print '\nConcentration control coefficients'
                    File.write('\n## Concentration control coefficients\n')
                    File.write(CSoutput)
        else:
            if self.__settings__["mca_ccall_altout"]:
                print '\nControl coefficients grouped by reaction'
                print CAltoutput
            else:
                if self.__settings__["mca_ccall_fluxout"]:
                    print '\nFlux control coefficients'
                    print CJoutput
                if self.__settings__["mca_ccall_concout"]:
                    print '\nConcentration control coefficients'
                    print CSoutput

    def EvalRC(self):
        """
        EvalRC()

        Calculate the MCA response coefficients using the current steady-state solution.

        Arguments:
        None

        """

        reordered_cc_all = numpy.zeros(self.cc_all.shape,'d')
        for reac in range(len(self.elas_par_row)):
            reordered_cc_all[:,reac] = self.cc_all[:,list(self.cc_all_col).index(self.elas_par_row[reac])]

        self.mca_rc_par = numpy.dot(reordered_cc_all,self.elas_par)
        self.mca_rc_par_row = self.cc_all_row
        self.mca_rc_par_col = self.elas_par_col
        del reordered_cc_all
        self.__structural__.MatrixFloatFix(self.mca_rc_par)
        self.rc = BagOfStuff(self.mca_rc_par, self.mca_rc_par_row, self.mca_rc_par_col)
        self.rc.load()
        if self.__settings__['mode_mca_scaled'] == 1:
            self.rc.scaled = True
        else:
            self.rc.scaled = False


    def EvalEigen(self):
        """
        EvalEigen()

        Calculate the eigenvalues or vectors of the unscaled Jacobian matrix and thereby
        analyse the stability of a system

        Arguments:
        None

        """
        Nr = copy.copy(self.__nrmatrix__)

        #sort E to match K and L
        Es = numpy.zeros((self.elas_var_u.shape), 'd')
        #print e2

        for x in range (0,len(self.nmatrix_col)):
            for y in range (0,len(self.lmatrix_row)):
                Es[x,y]  = self.elas_var_u[self.nmatrix_col[x],self.lmatrix_row[y]]
        if self.__HAS_MOIETY_CONSERVATION__ == True:
            jac1 = numpy.dot(Nr,Es)
            jacobian = numpy.dot(jac1,copy.copy(self.__lmatrix__))
        else:
            jacobian = numpy.dot(Nr,Es)
        Si = []
        for x in range(0,len(self.lmatrix_col)):
            Si.append(self.__species__[self.lmatrix_col[x]])

            self.jacobian = tuple(jacobian)
            self.jacobian_row = tuple(Si)
            self.jacobian_col = tuple(Si)
            if self.__settings__['mode_eigen_output']:
                #returns eigenvalues as well as left and right eigenvectors
                eigenval,self.eigen_vecleft,self.eigen_vecright = scipy.linalg.eig(jacobian,numpy.identity(jacobian.shape[0],'d'),left=1,right=1)
                if self.__settings__['display_debug'] == 1:
                    print '\nEigenvalues'
                    print eigenval
                    print '\nLeft Eigenvector'
                    print self.eigen_vecleft
                    print '\nRight Eigenvector'
                    print self.eigen_vecright
            else:
                #returns eigenvalues
                eigenval = scipy.linalg.eigvals(jacobian)
                if self.__settings__['display_debug'] == 1:
                    print '\nEigenvalues'
                    print eigenval

        self.eigen_values = eigenval
        #self.eigen_order = tuple(eigenorder)

        for x in range(len(self.eigen_values)):
            setattr(self, 'lambda' + str(x+1), self.eigen_values[x])

        if self.__settings__['display_debug'] == 1:
            print '\nEigenvalues attached as lambda1 ... lambda' + `len(eigenorder)` + '\n'


    def showEigen(self,File=None):
        """
        showEigen(File=None)

        Print the eigenvalues and stability analysis of a system generated with EvalEigen()
        to the screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        eigenstats = ''
        eigenstats += 'Max real part: ' + self.__settings__['mode_number_format'] % max(self.eigen_values.real) + '\n'
        eigenstats += 'Min real part: ' + self.__settings__['mode_number_format'] % min(self.eigen_values.real) + '\n'
        eigenstats += 'Max absolute imaginary part: ' + self.__settings__['mode_number_format'] % max(abs(self.eigen_values.imag)) + '\n'
        eigenstats += 'Min absolute imaginary part: ' + self.__settings__['mode_number_format'] % min(abs(self.eigen_values.imag)) + '\n'
        eigenstats += 'Stiffness: ' + self.__settings__['mode_number_format'] % (max(abs(self.eigen_values.real))/min(abs(self.eigen_values.real))) + '\n'

        pure_real = 0
        pure_imag = 0
        negcplx = 0
        pure_cplx = 0
        pure_zero = 0
        pos_real  = 0
        neg_real  = 0
        for x in self.eigen_values:
            if x.real != 0.0 and x.imag == 0.0:
                pure_real += 1
            if x.real == 0.0 and x.imag != 0.0:
                pure_imag += 1
            if x.real == 0.0 and x.imag == 0.0:
                pure_zero += 1
            if x.real > 0.0:
                pos_real += 1
            if x.real < 0.0:
                neg_real += 1
            if x.real < 0.0 and x.imag != 0.0:
                negcplx += 1

        eigenstats += '\n## Stability'
        eiglen = len(self.eigen_values)
        if neg_real == eiglen:
            eigenstats += ' --> Stable state'
        elif pure_zero > 0:
            eigenstats += ' --> Undetermined'
        elif pos_real == eiglen:
            eigenstats += ' --> Unstable state'

        eigenstats += '\nPurely real: ' + `pure_real`
        eigenstats += '\nPurely imaginary: ' + `pure_imag`
        eigenstats += '\nZero: ' + `pure_zero`
        eigenstats += '\nPositive real part: ' + `pos_real`
        eigenstats += '\nNegative real part: ' + `neg_real`

        if File != None:
            #assert type(File) == file, 'showEigen() needs an open file object'
            print '\nEigen values'
            File.write('\n## Eigen values\n')
            scipy.io.write_array(File,self.eigen_values,precision=2,keep_open=1)
            print '\nEigen statistics'
            File.write('\n## Eigen statistics\n')
            File.write(eigenstats)
            if self.__settings__['mode_eigen_output']:
                print '\nLeft eigen vector'
                File.write('\n## Left eigen vector\n')
                scipy.io.write_array(File,self.eigen_vecleft,precision=2,keep_open=1)
                print '\nRight eigen vector'
                File.write('\n## Right eigen vector\n')
                scipy.io.write_array(File,self.eigen_vecright,precision=2,keep_open=1)
        else:
            print '\nEigen values'
            print self.eigen_values
            print '\nEigen statistics'
            print eigenstats
            if self.__settings__['mode_eigen_output']:
                print '\nLeft eigen vector'
                print self.eigen_vecleft
                print '\nRight eigen vector'
                print self.eigen_vecright

#Utility functions
#new generation metafunctions
    ##  def doLoad(self,stoich_load=0):
        ##  """
        ##  doLoad(stoich_load=0)

        ##  Load and instantiate a PySCeS model so that it can be used for further analyses.

        ##  Calls model loading subroutines:
        ##  Stoichiometry_Analyse() [override=0,load=stoich_load]
        ##  InitialiseModel()

        ##  Arguments:

        ##  stoich_load [default=0]: try to load a stoichiometry saved with Stoichiometry_Save_Serial()

        ##  """
        ##  self.InitialiseInputFile()
        ##  assert self.__parseOK, '\nError in input file, parsing could not complete'
        ##  self.Stoichiometry_Analyse(override=0,load=stoich_load)
        ##  # add new Style functions to model
        ##  self.InitialiseFunctions()
        ##  self.InitialiseCompartments()
        ##  self.InitialiseRules()
        ##  self.InitialiseEvents()
        ##  self.InitialiseOldFunctions() # TODO replace this with initialisation functions
        ##  self.InitialiseModel()
        ##  self.InitialiseRuleChecks()

    def doSim(self,end=10.0,points=21):
        """
        doSim(end=10.0,points=20.0)

        Run a time simulation from t=0 to t=sim_end with sim_points.

        Calls:
        Simulate()

        Arguments:

        end [default=10.0]: simulation end time
        points [default=20.0]: number of points in the simulation

        """
        self.sim_end = end
        self.sim_points = points
        self.Simulate()

    def doSimPlot(self, end=10.0, points=21, plot='species', fmt='lines', filename=None):
        """
        Run a time simulation from t=0 to t=sim_end with sim_points and plot the results.
        The required output data and format can be set:

        - *end* the end time (default=10.0)
        - *points* the number of points in the simulation (default=20.0)
        - *plot* (default='species') select output data

         - 'species'
         - 'rates'
         - 'all' both species and rates

        - *fmt* plot format, UPI backend dependent (default='') or the *CommonStyle* 'lines' or 'points'.
        - *filename* if not None (default) then the plot is exported as *filename*.png

        Calls:
        - **Simulate()**
        - **SimPlot()**
        """
        self.sim_end = end
        self.sim_points = points
        self.Simulate()
        self.SimPlot(plot=plot, format=fmt, filename=filename)

    def doSimPerturb(self,pl,end):
        """
        **Deprecated**: use events instead
        """
        pass

    def doState(self):
        """
        doState()

        Calculate the steady-state solution of the system.

        Calls:
        State()

        Arguments:
        None

        """
        self.State()

    def doStateShow(self):
        """
        doStateShow()

        Calculate the steady-state solution of a system and show the results.

        Calls:
        State()
        showState()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state: run mod.doState() and check for errors'
        self.showState()

    def doElas(self):
        """
        doElas()

        Calculate the model elasticities, this method automatically calculates a steady state.

        Calls:
        State()
        EvalEvar()
        EvalEpar()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state: run mod.doState() and check for errors'
        self.EvalEvar()
        self.EvalEpar()

    def doEigen(self):
        """
        doEigen()

        Calculate the eigenvalues, automatically performs a steady state and elasticity analysis.

        Calls:
        State()
        EvalEvar()
        Evaleigen()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state: run mod.doState() and check for errors'
        self.__settings__["elas_evar_upsymb"] = 1
        self.EvalEvar()
        self.__settings__["elas_evar_upsymb"] = 1
        self.EvalEigen()

    def doEigenShow(self):
        """
        doEigenShow()

        Calculate the eigenvalues, automatically performs a steady state and elasticity analysis
        and displays the results.

        Calls:
        doEigen()
        showEigen()

        Arguments:
        None

        """
        self.doEigen()
        self.showEigen()


    def doEigenMca(self):
        """
        doEigenMca()

        Calculate a full Control Analysis and eigenvalues, automatically performs a steady state, elasticity, control analysis.

        Calls:
        State()
        EvalEvar()
        EvalCC()
        Evaleigen()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state: run mod.doState() and check for errors'
        self.EvalEvar()
        self.EvalCC()
        self.EvalEigen()


    def doMca(self):
        """
        doMca()

        Perform a complete Metabolic Control Analysis on the model, automatically calculates a steady state.

        Calls:
        State()
        EvalEvar()
        EvalEpar()
        EvalCC()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state run: mod.doState() and check for errors'
        self.EvalEvar()
        self.EvalEpar()
        self.EvalCC()

    def doMcaRC(self):
        """
        doMca()

        Perform a complete Metabolic Control Analysis on the model, automatically calculates a steady state.

        Calls:
        State()
        EvalEvar()
        EvalEpar()
        EvalCC()
        EvalRC()

        Arguments:
        None

        """
        self.State()
        assert self.__StateOK__ == True, '\n\nINFO: Invalid steady state run: mod.doState() and check for errors'
        self.EvalEvar()
        self.EvalEpar()
        self.EvalCC()
        self.EvalRC()


#show/save function prototypes
    def showSpecies(self,File=None):
        """
        showSpecies(File=None)

        Prints the current value of the model's variable species (mod.X) to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        out_list = []

        print '\nSpecies values'
        out_list.append('\n## species values\n')
        for x in range(len(self.__species__)):
            if File == None:
                ##  print self.__species__[x] + ' = ' +  self.__settings__['mode_number_format'] % eval('self.' + self.__species__[x])
                print self.__species__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__species__[x])
            else:
                ##  out_list.append(self.__species__[x] + ' = ' +  self.__settings__['mode_number_format'] % eval('self.' + self.__species__[x]) + '\n')
                out_list.append(self.__species__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__species__[x]) + '\n')
        if File != None:
            for x in out_list:
                File.write(x)

    def showSpeciesI(self,File=None):
        """
        showSpeciesI(File=None)

        Prints the current value of the model's variable species initial values (mod.X_init) to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        out_list = []

        print '\nSpecies initial values'
        out_list.append('\n## species initial values\n')
        for x in range(len(self.__species__)):
            if File == None:
                print self.__species__[x] + '_init = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__species__[x]+'_init')
            else:
                out_list.append(self.__species__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__species__[x]+'_init') + '\n')
        if File != None:
            for x in out_list:
                File.write(x)

    def showSpeciesFixed(self,File=None):
        """
        showSpeciesFixed(File=None)

        Prints the current value of the model's fixed species values (mod.X) to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        out_list = []

        print '\nFixed species'
        out_list.append('\n## fixed species\n')
        for x in range(len(self.__fixed_species__)):
            if File == None:
                print self.__fixed_species__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__fixed_species__[x])
            else:
                out_list.append(self.__fixed_species__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__fixed_species__[x]) + '\n')
        if File != None:
            for x in out_list:
                File.write(x)

    def showPar(self,File=None):
        """
        showPar(File=None)

        Prints the current value of the model's parameter values (mod.P) to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        out_list = []

        print '\nParameters'
        out_list.append('\n## parameters\n')
        for x in range(len(self.__parameters__)):
            if File == None:
                print self.__parameters__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__parameters__[x])
            else:
                out_list.append(self.__parameters__[x] + ' = ' +  self.__settings__['mode_number_format'] % getattr(self, self.__parameters__[x]) + '\n')
        if File != None:
            for x in out_list:
                File.write(x)

    def showModifiers(self,File=None):
        """
        showModifiers(File=None)

        Prints the current value of the model's modifiers per reaction to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        noMod = []
        out_list = []

        print '\nModifiers per reaction:'
        out_list.append('\n## modifiers per reaction\n')
        for reac in self.__modifiers__:
            rstr = ''
            if len(reac[1]) == 1:
                if File == None: print reac[0] +' has modifier:',
                rstr = rstr + reac[0] +' has modifier: '
                for x in reac[1]:
                    if File == None: print x,
                    rstr = rstr + x + ' '
                if File == None: print ' '
                rstr += '\n'
            elif len(reac[1]) > 1:
                if File == None: print reac[0] +' has modifiers: ',
                rstr = rstr + reac[0] +' has modifiers: '
                for x in reac[1]:
                    if File == None: print x,
                    rstr = rstr + x + ' '
                if File == None: print ' '
                rstr += '\n'
            else:
                noMod.append(reac[0])

            out_list.append(rstr)
        if len(noMod) > 0:
            print '\nReactions with no modifiers:'
            out_list.append('\n## reactions with no modifiers\n')
            cntr = 0
            rstr = ''
            for n in range(len(noMod)):
                cntr += 1
                if cntr > 6:
                    if File == None: print ' '
                    rstr += '\n'
                    cntr = 0
                if File == None: print noMod[n],
                rstr = rstr + noMod[n] + ' '
            if File == None: print ' '
            rstr += '\n'
            out_list.append(rstr)

            if File != None:
                for x in out_list:
                    File.write(x)


    def showState(self,File=None):
        """
        showState(File=None)

        Prints the result of the last steady-state analyses. Both steady-state flux's and species concentrations are shown.

        Arguments:

        File [default=None]: an open, writable Python file object

        """
        out_list = []

        print '\nSteady-state species concentrations'
        out_list.append('\n## Current steady-state species concentrations\n')
        if self.__StateOK__:
            for x in range(len(self.state_species)):
                if File == None:
                    print self.__species__[x] + '_ss = ' + self.__settings__['mode_number_format'] % self.state_species[x]
                else:
                    out_list.append(self.__species__[x] + '_ss = ' + self.__settings__['mode_number_format'] % self.state_species[x] + '\n')
        else:
            print 'No valid steady state found'
            out_list.append('No valid steady state found.\n')

        print '\nSteady-state fluxes'
        out_list.append('\n## Steady-state fluxes\n')
        if self.__StateOK__:
            for x in range(len(self.state_flux)):
                if File == None:
                    print 'J_' + self.__reactions__[x] + ' = ' + self.__settings__['mode_number_format'] % self.state_flux[x]
                else:
                    out_list.append('J_' + self.__reactions__[x] + ' = ' + self.__settings__['mode_number_format'] % self.state_flux[x] + '\n')
        else:
            print 'No valid steady state found'
            out_list.append('No valid steady state found.\n')

        if File != None:
            for x in out_list:
                File.write(x)

    def showRate(self,File=None):
        """
        Prints the current rates of all the reactions using the current parameter values and species concentrations

        - *File* an open, writable Python file object (default=None)

        """
        Vtemp = [r() for r in getattr(self, self.__reactions__[r])]
        outrate = ''
        for x in range(len(self.__reactions__)):
            outrate += self.__reactions__[x] + ' = ' + self.__settings__['mode_number_format'] % Vtemp[x] + '\n'
        del s,Vtemp

        if File != None:
            print '\nReaction rates'
            File.write('\n##Reaction rates\n')
            File.write(outrate)
        else:
            print '\nReaction rates'
            print outrate


    def showRateEq(self,File=None):
        """
        showRateEq(File=None)

        Prints the reaction stoichiometry and rate equations to screen or File.

        Arguments:

        File [default=None]: an open, writable Python file object
        """
        out_list = []

        tnform = self.__settings__['mode_number_format']

        self.__settings__['mode_number_format'] = '%2.5f'

        print '\nReaction stoichiometry and rate equations'
        out_list.append('\n## Reaction stoichiometry and rate equations\n')

        # writes these out in a better order
        for key in self.Kmatrix.row:
            if File == None:
                print key + ':'
            else:
                out_list.append(key + ':\n')
            reagL = []
            reagR = []
            for reagent in self.__nDict__[key]['Reagents']:
                if self.__nDict__[key]['Reagents'][reagent] > 0:
                    if self.__nDict__[key]['Reagents'][reagent] == 1.0:
                        reagR.append(reagent.replace('self.',''))
                    else:
                        reagR.append('{' + self.__settings__['mode_number_format'] % abs(self.__nDict__[key]['Reagents'][reagent]) + '}' + reagent.replace('self.',''))
                elif self.__nDict__[key]['Reagents'][reagent] < 0:
                    if self.__nDict__[key]['Reagents'][reagent] == -1.0:
                        reagL.append(reagent.replace('self.',''))
                    else:
                        reagL.append('{' + self.__settings__['mode_number_format'] % abs(self.__nDict__[key]['Reagents'][reagent]) + '}' + reagent.replace('self.',''))

            substring = ''
            count = 0
            for x in reagL:
                if count != 0:
                    substring += ' + '
                substring += x.replace(' ','')
                count += 1
            prodstring = ''
            count = 0
            for x in reagR:
                if count != 0:
                    prodstring += ' + '
                prodstring += x.replace(' ','')
                count += 1

            if self.__nDict__[key]['Type'] == 'Rever':
                symbol = ' = '
            else:
                symbol = ' > '
            if File == None:
                print '\t' + substring + symbol + prodstring
                print '\t' + self.__nDict__[key]['RateEq'].replace('self.','')
            else:
                out_list.append('\t' + substring + symbol + prodstring + '\n')
                out_list.append('\t' + self.__nDict__[key]['RateEq'].replace('self.','') + '\n\n')
        if len(self.__rules__.keys()) > 0:
            out_list.append('\n# Assignment rules\n')
            for ass in self.__rules__:
                out_list.append('!F %s = %s\n' % (self.__rules__[ass]['name'],\
                                self.__rules__[ass]['formula']))
            out_list.append('\n')
        if File != None:
            for x in out_list:
                File.write(x)
        self.__settings__['mode_number_format'] = tnform


    def showODE(self,File=None,fmt='%2.3f'):
        """
        showODE(File=None,fmt='%2.3f')

        Print a representation of the full set of ODE's generated by PySCeS to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format

        """
        maxmetlen = 0
        for x in self.__species__:
            if len(x) > maxmetlen:
                maxmetlen = len(x)

        maxreaclen = 0
        for x in self.__reactions__:
            if len(x) > maxreaclen:
                maxreaclen = len(x)
        odes = '\n## ODE\'s (unreduced)\n'
        for x in range(self.__nmatrix__.shape[0]):
            odes +=  'd' + self.__species__[x] + (maxmetlen-len(self.__species__[x]))*' ' + '|'
            beginline = 0
            for y in range(self.__nmatrix__.shape[1]):
                if abs(self.__nmatrix__[x,y]) > 0.0:
                    if self.__nmatrix__[x,y] > 0.0:
                        if beginline == 0:
                            odes +=  '  ' + fmt % abs(self.__nmatrix__[x,y]) + '*' +  self.__reactions__[y] + (maxreaclen-len(self.__reactions__[y]))*' '
                            beginline = 1
                        else:
                            odes +=  ' + ' + fmt % abs(self.__nmatrix__[x,y]) + '*' +  self.__reactions__[y] + (maxreaclen-len(self.__reactions__[y]))*' '
                    else:
                        if beginline == 0:
                            odes +=  ' -' + fmt % abs(self.__nmatrix__[x,y]) + '*' +  self.__reactions__[y] + (maxreaclen-len(self.__reactions__[y]))*' '
                        else:
                            odes +=  ' - ' + fmt % abs(self.__nmatrix__[x,y]) + '*' +  self.__reactions__[y] + (maxreaclen-len(self.__reactions__[y]))*' '
                        beginline = 1
            odes +=  '\n'

        if self.__HAS_RATE_RULES__:
            odes += "\n## Rate Rules:\n"
            for rule in self.__rate_rules__:
                odes += "d%s | %s\n" % (rule, self.__rules__[rule]['formula'].replace('()',''))
            odes += '\n'

        if File != None:
            print '\nODE\'s (unreduced)\n'
            File.write(odes)
        else:
            print odes

    def showODEr(self,File=None,fmt='%2.3f'):
        """
        showODEr(File=None,fmt='%2.3f')

        Print a representation of the reduced set of ODE's generated by PySCeS to screen or file.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format

        """
        maxmetlen = 0
        for x in self.__species__:
            if len(x) > maxmetlen:
                maxmetlen = len(x)
        maxreaclen = 0
        for x in self.__reactions__:
            if len(x) > maxreaclen:
                maxreaclen = len(x)

        odes = '\n## ODE\'s (reduced)\n'
        for x in range(self.nrmatrix.shape[0]):
            odes +=  'd' + self.__species__[self.nrmatrix_row[x]] + (maxmetlen-len(self.__species__[self.nrmatrix_row[x]]))*' ' + '|'
            beginline = 0
            for y in range(self.__nrmatrix__.shape[1]):
                if abs(self.__nrmatrix__[x,y]) > 0.0:
                    if self.__nrmatrix__[x,y] > 0.0:
                        if beginline == 0:
                            odes +=  '  ' + fmt % abs(self.__nrmatrix__[x,y]) + '*' +  self.__reactions__[self.nrmatrix_col[y]] + (maxreaclen-len(self.__reactions__[self.nrmatrix_col[y]]))*' '
                            beginline = 1
                        else:
                            odes +=  ' + ' + fmt % abs(self.__nrmatrix__[x,y]) + '*' +  self.__reactions__[self.nrmatrix_col[y]] + (maxreaclen-len(self.__reactions__[self.nrmatrix_col[y]]))*' '
                    else:
                        if beginline == 0:
                            odes +=  ' -' + fmt % abs(self.__nrmatrix__[x,y]) + '*' +  self.__reactions__[self.nrmatrix_col[y]] + (maxreaclen-len(self.__reactions__[self.nrmatrix_col[y]]))*' '
                        else:
                            odes +=  ' - ' + fmt % abs(self.__nrmatrix__[x,y]) + '*' +  self.__reactions__[self.nrmatrix_col[y]] + (maxreaclen-len(self.__reactions__[self.nrmatrix_col[y]]))*' '
                        beginline = 1
            odes +=  '\n'

        if self.__HAS_RATE_RULES__:
            odes += "\n## Rate Rules:\n"
            for rule in self.__rate_rules__:
                odes += "d%s | %s\n" % (rule, self.__rules__[rule]['formula'].replace('()',''))
            odes += '\n'

        if File != None:
            print '\nODE\'s (reduced)\n'
            File.write(odes)
            self.showConserved(File)
        else:
            print odes
            self.showConserved()

    def showModel(self,filename=None,filepath=None,skipcheck=0):
        """
        showModel(filename=None,filepath=None,skipcheck=0)

        The PySCeS 'save' command, prints the entire model to screen or File in a PSC format.
        (Currently this only applies to basic model attributes, ! functions are not saved).

        Arguments:

        filename [default=None]: the output PSC file
        filepath [default=None]: the output directory
        skipcheck [default=0]: skip check to see if the file exists (1) auto-averwrite

        """
        if filepath == None:
            filepath = self.ModelDir

        if filename == None:
            print '\nFixed species'
            if len(self.__fixed_species__) == 0:
                print '<none>'
            else:
                for x in self.__fixed_species__:
                    print x,
            print ' '

            self.showRateEq()
            self.showSpeciesI()
            self.showPar()
        else:
            #assert type(filename) == str, 'showModel() takes a string filename argument'
            FBuf = 0
            if type(filename) == str:
                #filename = str(filename)
                filename = chkpsc(filename)
                go = 0
                loop = 0
                if skipcheck != 0:
                    loop = 1
                    go = 1
                while loop == 0:
                    try:
                        filex = os.path.join(filepath,filename)
                        f = open(filex,'r')
                        f.close()
                        input = raw_input('\nfile "' + filex + '" exists.\nOverwrite? ([y]/n) ')
                        if input == 'y' or input == '':
                            go = 1
                            loop = 1
                        elif input == 'n':
                            filename = raw_input('\nfile "' + filename + '" exists. Enter a new filename: ')
                            go = 1
                            filex = os.path.join(filepath,filename)
                            filename = chkpsc(filename)
                        else:
                            print '\nInvalid input'
                    except:
                        print '\nfile "' + filex + '" does not exist, proceeding'
                        loop = 1
                        go = 1
            else:
                print '\nI hope we have a filebuffer'
                if type(filename)==file or type(filename)==cStringIO.OutputType: # --johann 20070615
                    go = 1
                    FBuf = 1
                    print 'Seems like it'
                else:
                    go = 0
                    print 'Are you sure you know what ur doing'

            if go == 1:
                if not FBuf:
                    if filex[-4:] == '.psc':
                        pass
                    else:
                        print 'Assuming extension is .psc'
                        filex += '.psc'
                    outFile = open(filex,'w')
                else:
                    outFile = filename

                #header =   '# \n'
                header = '# PySCeS (' + __version__ + ') model input file \n'
                #header += '# Copyright Brett G. Olivier, 2004 (bgoli at sun dot ac dot za)  \n'
                header += '# http://pysces.sourceforge.net/ \n'
                header += '# \n'
                header += '# Original input file: ' + self.ModelFile + '\n'
                header += '# This file generated: ' + time.strftime("%a, %d %b %Y %H:%M:%S") + '\n\n'
                outFile.write(header)

                outFile.write('## Fixed species\n')
                if len(self.__fixed_species__) == 0:
                    outFile.write('#  <none>')
                else:
                    outFile.write('FIX: ')
                    for x in self.__fixed_species__:
                        outFile.write(x + ' ')
                outFile.write('\n')
                self.showRateEq(File=outFile)
                self.showSpeciesI(File=outFile)
                self.showPar(File=outFile)
                if type(filename)==str or type(filename)==file:  # don't close cStringIO objects -- johann 20070615
                    outFile.close()

    def showN(self,File=None,fmt='%2.3f'):
        """
        showN(File=None,fmt='%2.3f')

        Print the stoichiometric matrix (N), including row and column labels to screen or File.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format
        """
        km_trunc = 0
        km_trunc2 = 0
        km = ''
        rtr = []
        ctr = []
        for a in range(len(self.nmatrix_col)):
            if a == 0:
                km += 9*' '
            if len(self.__reactions__[self.nmatrix_col[a]]) > 7:
                km += self.__reactions__[self.nmatrix_col[a]][:6]+'* '
                km_trunc = 1
                ctr.append(a)
            else:
                km += self.__reactions__[a] + (9-len(self.__reactions__[a])-1)*' '
        for x in range(len(self.nmatrix_row)):
            if len(self.__species__[x]) > 6:
                km += '\n' + self.__species__[x][:5]+'*'+1*' '
                km_trunc2 = 1
                rtr.append(x)
            else:
                km += '\n' + self.__species__[x] + (8-len(self.__species__[x])-1)*' '
                #km += '\n' + self.__reactions__[self.nmatrix_row[x]] + 4*' '
            for y in range(len(self.nmatrix_col)):
                if y != 0:
                    km += 3*' '
                if self.__nmatrix__[x,y] >= 10.0:
                    km += ' ' + fmt % self.__nmatrix__[x,y]
                elif self.__nmatrix__[x,y] >= 0.0:
                    km += '  ' + fmt % self.__nmatrix__[x,y]
                else:
                    if abs(self.__nmatrix__[x,y]) >= 10.0:
                        km += fmt % self.__nmatrix__[x,y]
                    else:
                        km += ' ' + fmt % self.__nmatrix__[x,y]
        if km_trunc:
            km += '\n\n' + '* indicates truncated (col) reaction\n('
            for x in range(len(self.nmatrix_col)):
                try:
                    ctr.index(x)
                    km += self.__reactions__[x] + ', '
                except:
                    pass
            km += ')'
        if km_trunc2:
            km += '\n\n' + '* indicates truncated (row) species:\n('
            for x in range(len(self.nmatrix_row)):
                try:
                    rtr.index(x)
                    km += self.__species__[x] + ', '
                except:
                    pass
            km += ')'

        if File != None:
            print '\nStoichiometric matrix (N)'
            File.write('\n\n## Stoichiometric matrix (N)\n')
            File.write(km)
            File.write('\n')
        else:
            print '\nStoichiometric matrix (N)'
            print km

    def showNr(self,File=None,fmt='%2.3f'):
        """
        showNr(File=None,fmt='%2.3f')

        Print the reduced stoichiometric matrix (Nr), including row and column labels to screen or File.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format
        """
        km_trunc = 0
        km_trunc2 = 0
        km = ''
        ctr = []
        rtr = []
        for a in range(len(self.nrmatrix_col)):
            if a == 0:
                km += 9*' '
            if len(self.__reactions__[self.nrmatrix_col[a]]) > 7:
                km += self.__reactions__[self.nrmatrix_col[a]][:6]+'* '
                km_trunc = 1
                ctr.append(a)
            else:
                km += self.__reactions__[a] + (9-len(self.__reactions__[a])-1)*' '
        for x in range(len(self.nrmatrix_row)):
            if len(self.__species__[self.nrmatrix_row[x]]) > 6:
                km += '\n' + self.__species__[self.nrmatrix_row[x]][:5]+'*'+1*' '
                km_trunc2 = 1
                rtr.append(x)
            else:
                km += '\n' + self.__species__[self.nrmatrix_row[x]] + (8-len(self.__species__[self.nrmatrix_row[x]])-1)*' '
                #km += '\n' + self.__reactions__[self.nrmatrix_row[x]] + 4*' '
            for y in range(len(self.nrmatrix_col)):
                if y != 0:
                    km += 3*' '
                if self.__nrmatrix__[x,y] >= 10.0:
                    km += ' ' + fmt % self.__nrmatrix__[x,y]
                elif self.__nrmatrix__[x,y] >= 0.0:
                    km += '  ' + fmt % self.__nrmatrix__[x,y]
                else:
                    if abs(self.__nrmatrix__[x,y]) >= 10.0:
                        km += fmt % self.__nrmatrix__[x,y]
                    else:
                        km += ' ' + fmt % self.__nrmatrix__[x,y]
        if km_trunc:
            km += '\n\n' + '* indicates truncated (col) reaction:\n('
            for x in range(len(self.nrmatrix_col)):
                try:
                    ctr.index(x)
                    km += self.__reactions__[x] + ', '
                except:
                    pass
            km += ')'
        if km_trunc2:
            km += '\n\n' + '* indicates truncated (row) species:\n('
            for x in range(len(self.nrmatrix_row)):
                try:
                    rtr.index(x)
                    km += self.__species__[self.nrmatrix_row[x]] + ', '
                except:
                    pass
            km += ')'
        if File != None:
            print '\nReduced stoichiometric matrix (Nr)'
            File.write('\n\n## Reduced stoichiometric matrix (Nr)\n')
            File.write(km)
            File.write('\n')
        else:
            print '\nReduced stoichiometric matrix (Nr)'
            print km

    def showK(self,File=None,fmt='%2.3f'):
        """
        showK(File=None,fmt='%2.3f')

        Print the Kernel matrix (K), including row and column labels to screen or File.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format
        """
        km_trunc = 0
        km_trunc2 = 0
        km = ''
        ctr = []
        rtr = []
        for a in range(len(self.kmatrix_col)):
            if a == 0:
                km += 8*' '
            if len(self.__reactions__[self.kmatrix_col[a]]) > 7:
                km += self.__reactions__[self.kmatrix_col[a]][:6]+'* '
                km_trunc = 1
                ctr.append(a)
            else:
                km += self.__reactions__[self.kmatrix_col[a]] + (9-len(self.__reactions__[self.kmatrix_col[a]])-1)*' '

        for x in range(len(self.kmatrix_row)):
            if len(self.__reactions__[self.kmatrix_row[x]]) > 6:
                km += '\n' + self.__reactions__[self.kmatrix_row[x]][:5]+'*'+1*' '
                km_trunc2 = 1
                rtr.append(x)
            else:
                km += '\n' + self.__reactions__[self.kmatrix_row[x]] + (8-len(self.__reactions__[self.kmatrix_row[x]])-1)*' '
                #km += '\n' + self.__reactions__[self.kmatrix_row[x]] + 4*' '
            for y in range(len(self.kmatrix_col)):
                if y != 0:
                    km += 4*' '
                if abs(self.__kmatrix__[x,y]) == 0.0:
                    km += ' ' + fmt % abs(self.__kmatrix__[x,y])
                elif self.__kmatrix__[x,y] < 0.0:
                    km += fmt % self.__kmatrix__[x,y]
                else:
                    km += ' ' + fmt % self.__kmatrix__[x,y]

        if km_trunc:
            km += '\n\n' + '* indicates truncated (col) reaction:\n('
            for x in range(len(self.kmatrix_col)):
                try:
                    ctr.index(x)
                    km += self.__reactions__[self.kmatrix_col[x]] + ', '
                except:
                    pass
            km += ')'
        if km_trunc2:
            km += '\n\n' + '* indicates truncated (row) reaction:\n('
            for x in range(len(self.kmatrix_row)):
                try:
                    rtr.index(x)
                    km += self.__reactions__[self.kmatrix_row[x]] + ', '
                except:
                    pass
            km += ')'

        if File != None:
            print '\nKernel matrix (K)'
            File.write('\n\n## Kernel matrix (K)\n')
            if not self.__HAS_FLUX_CONSERVATION__:
                File.write('No flux conservation\n')
            else:
                File.write(km)
                File.write('\n')
        else:
            print '\nKernel matrix (K)'
            if not self.__HAS_FLUX_CONSERVATION__:
                print 'No flux conservation'
            else:
                print km

    def showL(self,File=None,fmt='%2.3f'):
        """
        showL(File=None,fmt='%2.3f')

        Print the Link matrix (L), including row and column labels to screen or File.

        Arguments:

        File [default=None]: an open, writable Python file object
        fmt [default='%2.3f']: output number format
        """
        km_trunc = 0
        km_trunc2 = 0
        km = ''
        ctr = []
        rtr = []
        for a in range(len(self.lmatrix_col)):
            if a == 0:
                km += 8*' '
            if len(self.__species__[self.lmatrix_col[a]]) > 7:
                km += self.__species__[self.lmatrix_col[a]][:6]+'* '
                km_trunc = 1
                ctr.append(a)
            else:
                km += self.__species__[self.lmatrix_col[a]] + (9-len(self.__species__[self.lmatrix_col[a]])-1)*' '

        for x in range(len(self.lmatrix_row)):
            if len(self.__species__[self.lmatrix_row[x]]) > 6:
                km += '\n' + self.__species__[self.lmatrix_row[x]][:5]+'* '
                km_trunc2 = 1
                rtr.append(x)
            else:
                km += '\n' + self.__species__[self.lmatrix_row[x]] + (8-len(self.__species__[self.lmatrix_row[x]])-1)*' '
            for y in range(len(self.lmatrix_col)):
                if y != 0:
                    km += 4*' '
                if abs(self.__lmatrix__[x,y]) == 0.0:
                    km += ' ' + fmt % abs(self.__lmatrix__[x,y])
                elif self.__lmatrix__[x,y] < 0.0:
                    km += fmt % self.__lmatrix__[x,y]
                else:
                    km += ' ' + fmt % self.__lmatrix__[x,y]

        if km_trunc:
            km += '\n\n' + '* indicates truncated (col) species:\n('
            for x in range(len(self.lmatrix_col)):
                try:
                    ctr.index(x)
                    km += self.__species__[self.lmatrix_col[x]] + ', '
                except:
                    pass
            km += ')'
        if km_trunc2:
            km += '\n\n' + '* indicates truncated (row) species:\n('
            for x in range(len(self.lmatrix_row)):
                try:
                    rtr.index(x)
                    km += self.__species__[self.lmatrix_row[x]] + ', '
                except:
                    pass
            km += ')'


        if File != None:
            print '\nLink matrix (L)'
            File.write('\n\n## Link matrix (L)\n')
            if not self.__HAS_MOIETY_CONSERVATION__:
                File.write('No moiety conservation\n')
                File.write(km)
                File.write('\n')
            else:
                File.write(km)
                File.write('\n')
        else:
            print '\nLink matrix (L)'
            if not self.__HAS_MOIETY_CONSERVATION__:
                print '\"No moiety conservation\"\n'
                print km
            else:
                print km

#Internal test functions
    def TestSimState(self,endTime=10000,points=101,diff=1.0e-5):
        """
        **Deprecated**
        """
        pass

    def ResetNumberFormat(self):
        """
        ResetNumberFormat()

        Reset PySCeS default number format stored as mod.mode_number format to %2.4e

        Arguments:
        None

        """
        self.__settings__['mode_number_format'] = '%2.4e'


    def Write_array(self,input,File=None,Row=None,Col=None,close_file=0,separator='  '):
        """
        Write_array(input,File=None,Row=None,Col=None,close_file=0,separator='  ')

        Write an array to File with optional row/col labels. A ',' separator can be specified to create
        a CSV style file.

        mod.__settings__['write_array_header']: add <filename> as a header line (1 = yes, 0 = no)
        mod.__settings__['write_array_spacer']: add a space after the header line (1 = yes, 0 = no)
        mod.__settings__['write_arr_lflush']: set the flush rate for large file writes

        Arguments:

        input: the array to be written
        File [default=None]: an open, writable Python file object
        Row [default=None]: a list of row labels
        Col [default=None]: a list of column labels
        close_file [default=0]: close the file after write (1) or leave open (0)
        separator [default='  ']: the column separator to use

        """
        fname = 'Write_array_' + self.ModelFile.replace('.psc','')  + '_' + time.strftime("%H:%M:%S")

        # seeing as row indexes are now tuples and not arrays - brett 20050713
        if type(input) == tuple or type(input) == list:
            input = numpy.array(input)

        if Row != None:
            assert len(Row) == input.shape[0], 'len(Row) must be equal to len(input_row)'
        if Col != None:
            assert len(Col) == input.shape[1], 'len(Col) must be equal to len(input_col)'

        if File != None:
            #assert File != file, 'WriteArray(input,File=None,Row=None,Col=None,close_file=0)'
            if self.__settings__['write_array_header']:
                File.write('\n## ' + fname + '\n')
            if self.__settings__['write_array_spacer']:
                File.write('\n')
            if Col != None:
                print 'Writing column'
                File.write('#')
                try:
                    input_width = len(self.__settings__['mode_number_format'] % input[0,0])+ 1 + len(str(separator))
                except:
                    input_width = len(self.__settings__['mode_number_format'] % input[0])+ 1 + len(str(separator))
                for x in Col:
                    if len(str(x)) <= len(str(input[0,0])):
                        spacer  = (input_width-len(str(x)))*' '
                        File.write(str(x) + spacer)
                    else:
                        spacer = (len(str(separator)))*' '
                        File.write(str(x[:input_width]) + spacer)
                File.write('\n')
            try:
                print '\nWriting array (normal) to file'
                #scipy.io.write_array(File,input,separator=' ',linesep='\n',precision=3,keep_open=1)
                flush_count = 0
                if self.__settings__['write_arr_lflush'] < 2:
                    print 'INFO: LineFlush must be >= 2'
                    self.__settings__['write_arr_lflush'] = 2
                for x in range(input.shape[0]):
                    flush_count += 1
                    for y in range(input.shape[1]):
                        if input[x,y] < 0.0:
                            File.write(self.__settings__['mode_number_format'] % input[x,y])
                        else:
                            File.write(' ' + self.__settings__['mode_number_format'] % input[x,y])
                        if y < input.shape[1]-1:
                            File.write(separator)
                    File.write('\n')
                    if flush_count == self.__settings__['write_arr_lflush']:
                        File.flush()
                        flush_count = 0
                        #print 'flushing'
                #File.write('\n')
            except IndexError, e:
                print '\nWriting vector (normal) to file'
                for x in range(len(input)):
                    File.write(self.__settings__['mode_number_format'] % input[x])
                    if x < len(input)-1:
                        File.write(separator)
                File.write('\n')
            if Row != None:
                print 'Writing row'
                File.write('# Row: ')
                for x in Row:
                    File.write(str(x)+separator)
                File.write('\n')
            if close_file:
                File.close()
        else:
            print 'INFO: You need to supply an open writable file as the 2nd argument to this function'


    def Write_array_latex(self,input,File=None,Row=None,Col=None,close_file=0):
        """
        Write_array_latex(input,File=None,Row=None,Col=None,close_file=0)

        Write an array to an open file as a \'LaTeX\' {array}

        Arguments:

        input: the array to be written
        File [default=None]: an open, writable Python file object
        Row [default=None]: a list of row labels
        Col [default=None]: a list of column labels
        close_file [default=0]: close the file after write (1) or leave open (0)

        """
        # seeing as row indexes are now tuples and not arrays - brett 20050713
        if type(input) == tuple or type(input) == list:
            input = numpy.array(input)

        try:
            a = input.shape[1]
        except:
            input.shape = (1,input.shape[0])

        if Row != None:
            assert len(Row) == input.shape[0], 'len(Row) must be equal to len(input_row)'
            print 'Writing row'
        if Col != None:
            assert len(Col) == input.shape[1], 'len(Col) must be equal to len(input_col)'
            print 'Writing column'

        if self.__settings__['write_arr_lflush'] < 2:
            print 'INFO: LineFlush must be >= 2'
            self.__settings__['write_arr_lflush'] = 2

        fname = 'Write_array_latex_' + self.ModelFile.replace('.psc','')  + '_' + time.strftime("%H:%M:%S")
        if File != None:
            #assert File != file, 'WriteArray(input,File=None,Row=None,Col=None,close_file=0)'
            File.write('\n%% ' + fname + '\n')
            try:
                a = input.shape
                print '\nWriting array (LaTeX) to file'
                File.write('\\[\n')
                File.write('\\begin{array}{')
                if Row != None:
                    File.write('r|')
                for x in range(input.shape[1]):
                    File.write('r')

                File.write('}\n ')
                flush_count = 0
                for x in range(input.shape[0]):
                    if Col != None and x == 0:
                        for el in range(len(Col)):
                            elx = str(Col[el]).replace('_','\_')
                            if Row != None:
                                File.write(' & $\\small{' + elx + '}$')
                            else:
                                if el == 0:
                                    File.write(' $\\small{' + elx + '}$')
                                else:
                                    File.write(' & $\\small{' + elx + '}$')

                        File.write(' \\\\ \\hline\n ')
                    flush_count += 1
                    if Row != None:
                        el2 = str(Row[x]).replace('_','\\_')
                        File.write('$\\small{' + el2 + '}$ &')

                    for y in range(input.shape[1]):
                        if input[x,y] < 0.0:
                            val = '%2.4f' % input[x,y]
                        else:
                            val = ' ' + '%2.4f' % input[x,y]

                        File.write(val)
                        if y < input.shape[1]-1:
                            File.write(' &')
                    File.write(' \\\\\n ')
                    if flush_count == self.__settings__['write_arr_lflush']:
                        File.flush()
                        flush_count = 0
                        #print 'flushing'
                #File.write('\n')
                File.write('\\end{array}\n')
                File.write('\\]\n\n')
            except:
                print '\nINFO: Only arrays can currently be processed with this method.\n'
            if close_file:
                File.close()
        else:
            print 'INFO: You need to supply an open writable file as the 2nd argument to this method'


    def Write_array_html(self,input,File=None,Row=None,Col=None,name=None,close_file=0):
        """
        Write_array_html(input,File=None,Row=None,Col=None,name=None,close_file=0)

        Write an array as an HTML table (no header/footer) or complete document. Tables
        are formatted with coloured columns if they exceed a specified size.

        mod.__settings__['write_array_html_header']: write the HTML document header
        mod.__settings__['write_array_html_footer']: write the HTML document footer

        Arguments:

        input: the array to be written
        File [default=None]: an open, writable Python file object
        Row [default=None]: a list of row labels
        Col [default=None]: a list of column labels
        name [default=None]: an HTML table description line
        close_file [default=0]: close the file after write (1) or leave open (0)

        """
        # seeing as row indexes are now tuples and not arrays - brett 20050713
        if type(input) == tuple or type(input) == list:
            input = numpy.array(input)

        try:
            a = input.shape[1]
        except:
            input.shape = (1,input.shape[0])

        if Row != None:
            assert len(Row) == input.shape[0], 'len(Row) must be equal to len(input_row)'
            print 'Writing row'
        if Col != None:
            assert len(Col) == input.shape[1], 'len(Col) must be equal to len(input_col)'
            print 'Writing column'
        if self.__settings__['write_arr_lflush'] < 2:
            print 'INFO: LineFlush must be >= 2'
            self.__settings__['write_arr_lflush'] = 2
        if name != None:
            fname = 'PySCeS data "'+name+'" generated from model file: '+self.ModelFile+ ' ' + time.strftime("%H:%M:%S")
        else:
            fname = 'PySCeS data generated from model file: '+self.ModelFile+' '+time.strftime("%H:%M:%S")
        if File != None:
            #assert File != file, 'WriteArray(input,File=None,Row=None,Col=None,close_file=0)'
            header = '\n'
            header += '<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">\n'
            header += '<html>\n'
            header += '<head>\n'
            header += '<title>PySCeS data generated from: ' + self.ModelFile + ' - ' + time.strftime("%H:%M:%S (%Z)") + '</title>\n'
            header += '<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">\n'
            header += '</head>\n'
            header += '<body>\n\n'
            header += '<h4><a href="http://pysces.sourceforge.net">PySCeS</a> data generated from: ' + self.ModelFile + '</h4>\n\n'

            if self.__settings__['write_array_html_header']:
                File.write(header)
            File.write('<!-- ' + fname + '-->\n')
            if name != None:
                File.write('\n<p>\n<font face="Arial, Helvetica, sans-serif"><strong>' + name + '</strong></font>')
            else:
                File.write('\n<p>\n')
            File.write('\n<table border="1" cellpadding="2" cellspacing="2" bgcolor="#FFFFFF">')
            try:
                a = input.shape
                print 'Writing array (HTML) to file'

                double_index = 15

                if Col != None:
                    File.write('\n<tr>\n')
                    if Row != None:
                        File.write('  <td>&nbsp;</td>\n')
                    for col in Col:
                        File.write('  <td bgcolor="#CCCCCC"><div align="center"><b>' + str(col) + '</b></div></td>\n')
                    if Row != None and input.shape[1]+1 >= double_index:
                        File.write('  <td>&nbsp;</td>\n')
                    File.write('</tr>')

                flush_count = 0
                colour_count_row = 6
                colour_count_col = 6
                rowcntr = 0
                html_colour = '#FFFFCC'
                for x in range(input.shape[0]):
                    rowcntr += 1
                    if rowcntr == colour_count_row and input.shape[0] > 3*colour_count_row:
                        File.write('\n<tr bgcolor="'+html_colour+'">\n')
                        rowcntr = 0
                    else:
                        File.write('\n<tr>\n')
                    if Row != None:
                        File.write('  <td bgcolor="#CCCCCC"><div align="center"><b>' + str(Row[x]) + '</b></div></td>\n')
                    colcntr = 0
                    for y in range(input.shape[1]):
                        colcntr += 1
                        if colcntr == colour_count_col and input.shape[1] > 3*colour_count_row:
                            File.write('  <td nowrap bgcolor="'+html_colour+'">' + self.__settings__['write_array_html_format'] % input[x,y] + '</td>\n')
                            colcntr = 0
                        else:
                            File.write('  <td nowrap>' + self.__settings__['write_array_html_format'] % input[x,y] + '</td>\n')
##                        File.write('  <td nowrap>' + self.__settings__['write_array_html_format'] % input[x,y] + '</td>\n')
                    if Row != None and input.shape[1]+1 >= double_index:
                        File.write('  <td bgcolor="#CCCCCC"><div align="center"><b>' + Row[x] + '</b></div></td>\n')
                    File.write('</tr>')

                    if flush_count == self.__settings__['write_arr_lflush']:
                        File.flush()
                        flush_count = 0
                        #print 'flushing'
                if Col != None and input.shape[0]+1 >= double_index:
                    File.write('\n<tr>\n')
                    if Row != None:
                        File.write('  <td>&nbsp;</td>\n')
                    for col in Col:
                        File.write('  <td bgcolor="#CCCCCC"><div align="center"><b>' + col + '</b></div></td>\n')
                    if Row != None and input.shape[1]+1 >= double_index:
                        File.write('  <td>&nbsp;</td>\n')
                    File.write('</tr>\n')
            except:
                print '\nINFO: Only arrays can currently be processed with this method.\n'

            File.write('\n</table>\n')
            File.write('</p>\n\n')
            if self.__settings__['write_array_html_footer']:
                try:
                    File.write('<p><a href="http://pysces.sourceforge.net"><font size="3">PySCeS '+__version__+\
                               '</font></a><font size="2"> HTML output (model <i>'+self.ModelFile+\
                               '</i> analysed at '+time.strftime("%H:%M:%S")+' by <i>'+getuser()+'</i>)</font></p>\n')
                except:
                    File.write('<p><a href="http://pysces.sourceforge.net"><font size="3">PySCeS '+__version__+\
                               '</font></a><font size="2"> HTML output (model <i>'+self.ModelFile+\
                               '</i> analysed at '+time.strftime("%H:%M:%S - %Z")+')</font></p>\n')
                File.write('</body>\n')
                File.write('</html>\n')
            else:
                File.write('\n')

            if close_file:
                File.close()
        else:
            print 'INFO: You need to supply an open writable file as the 2nd argument to this method'


    def SimPlot(self, plot='species', filename=None, title=None, log=None, format='lines'):
        """
        Plot the simulation results, uses the new UPI pysces.plt interface:

        - *plot*: output to plot (default='species')

         - 'all' rates and species
         - 'species' species
         - 'rates' reaction rates
         - `['S1', 'R1', ]` a list of model attributes (species, rates)

        - *filename* if not None file is exported to filename (default=None)
        - *title* the plot title (default=None)
        - *log* use log axis for 'x', 'y', 'xy' (default=None)
        - *format* line format, backend dependant (default='')
        """
        data = None
        labels = None
        allowedplots = ['all', 'species', 'rates']
        if type(plot) != list and plot not in allowedplots:
            raise RuntimeError, '\nPlot must be one of %s not \"%s\"' % (str(allowedplots), plot)
        if plot == 'all':
            data, labels = self.data_sim.getAllSimData(lbls=True)
        elif plot == 'species':
            data, labels = self.data_sim.getSpecies(lbls=True)
        elif plot == 'rates':
            data, labels = self.data_sim.getRates(lbls=True)
        else:
            plot = [at for at in plot if at in self.__species__+self.__reactions__+[self.data_sim.time_label]]
            kwargs = {'lbls' : True}
            if len(plot) > 0:
                data, labels = self.data_sim.getSimData(*plot, **kwargs)
        del allowedplots
        plt.plotLines(data, 0, range(1, data.shape[1]), titles=labels, formats=[format])
        # set the x-axis range so that it is original range + 0.2*sim_end
        # this is a sceintifcally dtermned amount of space that is needed for the title at the
        # end of the line :-) - brett 20040209
        RngTime = self.data_sim.getTime()
        end = RngTime[-1] + 0.2*RngTime[-1]
        plt.setRange('x', RngTime[0], end)
        del RngTime

        if self.__KeyWords__['Output_In_Conc']:
            M = 'Concentration'
        else:
            M = 'Amount (%(multiplier)s x %(kind)s x 10**%(scale)s)**%(exponent)s' % self.__uDict__['substance']
        xu = 'Time (%(multiplier)s x %(kind)s x 10**%(scale)s)**%(exponent)s' % self.__uDict__['time']
        plt.setAxisLabel('x', xu)

        if plot == 'all':
            yl = 'Rates, %s' % M
        elif plot == 'rates':
            yl = 'Rate'
        elif plot == 'species':
            yl = '%s' % M
        else:
            yl = 'Rates, %s' % M
        plt.setAxisLabel('y', yl)
        if log != None:
            plt.setLogScale(log)
        if title == None:
            plt.setGraphTitle('PySCeS Simulation (' + self.ModelFile + ') ' + time.strftime("%a, %d %b %Y %H:%M:%S"))
        else:
            plt.setGraphTitle(title)
        plt.replot()
        if filename != None:
            plt.export(filename, directory=self.ModelOutput, type='png')



#STOMPY INSERT START
    def StochSimPlot(self, plot='species', filename=None, title=None, log=None, format='lines'):
        """
        Plot the Stochastic simulation results, uses the new UPI pysces.plt interface:

        - *plot* [default='species'] output to plot, can be one of:

         - 'all' species and propensities
         - 'species' species
         - 'waiting_times' waiting_times
         - 'propensities' propensities
         - `['S1', 'R1', ]` a list of model attributes ('species')

        - *filename* [default=None] if defined file is exported to filename
        - *title* [default=None] the plot title
        - *log* [default=None] use log axis for axis 'x', 'y', 'xy'
        - *format* [default='lines'] use UPI or backend specific keys
        """
        data = None
        labels = None
        allowedplots = ['all', 'species', 'propensities','waiting_times']
        ##  allowedplots = ['all', 'species', 'waiting_times']
        if type(plot) != list and plot not in allowedplots:
            raise RuntimeError, '\nPlot must be one of %s not \"%s\"' % (str(allowedplots), plot)
        if plot == 'all':
            ##  data, labels = self.data_stochsim.getSpecies(lbls=True)
            data, labels = self.data_stochsim.getAllSimData(lbls=True)
        elif plot == 'species':
            data, labels = self.data_stochsim.getSpecies(lbls=True)
        elif plot == 'propensities':
            data, labels = self.data_stochsim.getPropensities(lbls=True)
            ##  data, labels = self.data_stochsim.getRates(lbls=True)
        elif plot == 'waiting_times':
            dataLst, labels = self.data_stochsim.getWaitingtimes(lbls=True)
            format='points'
            ##  data, labels = self.data_stochsim.getRates(lbls=True)            
        else:
            plot = [at for at in plot if at in self.__species__+[self.data_stochsim.time_label]+self.data_stochsim.propensities_labels]
            kwargs = {'lbls' : True}
            print plot
            if len(plot) > 0:
                data, labels = self.data_stochsim.getSimData(*plot, **kwargs)
        del allowedplots
        
        xu = 'Time (%(multiplier)s x %(kind)s x 10**%(scale)s)**%(exponent)s' % self.__uDict__['time']
        
        if plot == 'waiting_times':
            xu = 'Inter-arrival time (%s)' % xu

            xrng_start = 0.1
            xrng_end = 0.1
            yrng_start = 0.1
            yrng_end = 0.1
            for wt in range(len(dataLst)):
                for d in range(len(dataLst[wt])):
                    D = dataLst[wt][d]
                    if plt.__USE_MATPLOTLIB__ and d > 0:
                        plt.m.hold(True)                         
                    if D != None and len(D[0]) > 0 and len(D[1]) > 0:
                        data = numpy.vstack([D[0], D[1]]).transpose()
                        if min(D[0]) < xrng_start and min(D[0]) > 0.0:
                            xrng_start = min(D[0])
                        if max(D[0]) > xrng_end:
                            xrng_end = max(D[0])
                        if min(D[1]) < yrng_start and min(D[1]) > 0.0:
                            yrng_start = min(D[1])
                        if max(D[1]) > yrng_end:
                            yrng_end = max(D[1])                    
                        plt.plotLines(data, 0, [1], titles=['Time']+[labels[d]], formats=[format])
            plt.setRange('x', xrng_start*0.8, xrng_end*1.2)
            plt.setRange('y', yrng_start*0.8, yrng_end*1.2)
            if plt.__USE_MATPLOTLIB__:
                plt.m.hold(False)
        else:
            plt.plotLines(data, 0, range(1, data.shape[1]), titles=labels, formats=[format])
            # set the x-axis range so that it is original range + 0.2*sim_end
            # this is a sceintifcally dtermned amount of space that is needed for the title at the
            # end of the line :-) - brett 20040209
            RngTime = self.data_stochsim.getTime()
            end = RngTime[-1] + 0.2*RngTime[-1]
            plt.setRange('x', RngTime[0], end)
            del RngTime
            
        if self.__KeyWords__['Output_In_Conc']:
            M = 'Concentration'
        else:
            M = 'Amount (%(multiplier)s x %(kind)s x 10**%(scale)s)**%(exponent)s' % self.__uDict__['substance']

        if plot == 'all':
            yl = 'Concentration, propensities'
        elif plot == 'propensities':
            yl = 'Propensities'
        elif plot == 'waiting_times':
            yl = 'Frequency'
            if log == None:
                log = 'xy'
        elif plot == 'species':
            yl = '%s' % M
        else:
            yl = 'Concentration, propensities'
        plt.setAxisLabel('x', xu)
        plt.setAxisLabel('y', yl)
        if log != None:
            plt.setLogScale(log)
        if title == None:
            plt.setGraphTitle('PySCeS/StochPy simulation (' + self.ModelFile + ') ' + time.strftime("%a, %d %b %Y %H:%M:%S"))
        else:
            plt.setGraphTitle(title)
        plt.replot()
        if filename != None:
            plt.export(filename, directory=self.ModelOutput, type='png')


    def doStochSim(self,end=10,mode='steps',method='Direct',trajectories=1):
        """
        doStochSim(end=10, mode='steps', method='Direct')

        Run a stochastic simulation for until `end` is reached. This can be either steps or end time (which could be a *HUGE* number of steps).

        Arguments:

         - *end* [default=10] simulation end (steps or time)
         - *mode* [default='steps'] simulation mode, can be one of:

          - *steps* total number of steps to simulate
          - *time* simulate until time is reached

         - *method* [default='Direct'] stochastic algorithm, can be one of:

          - Direct
          - FirstReactionMethod
          - NextReactionMethod
          - TauLeaping

        """

        if method not in ['Direct', 'FirstReactionMethod','NextReactionMethod','TauLeaping']:
            print 'Method "%s" not recognised using: "Direct"' % method
            method = 'Direct'
        if mode not in ['steps','time']:
            print 'Mode "%s" not recognised using: "steps"' % mode
            mode = 'steps'

        stompy_track_propensities = True
        stompy_keep_psc_files = True
        self.__STOMPY__.setProperty(method=method, trajectories=trajectories, interactive=True, track_propensities=stompy_track_propensities, keep_psc_files=stompy_keep_psc_files)

        if mode == 'time':
            self.__STOMPY__.runTimeSimulation(self, endtime=end, method=method)
        else:
            self.__STOMPY__.runStepSimulation(self, steps=end, method=method)


    def doStochSimPlot(self, end=10.0, points=21, plot='species', fmt='lines', filename=None):
        """
        Run a time simulation from t=0 to t=sim_end with sim_points and plot the results.
        The required output data and format can be set:

        - *end* the end time (default=10.0)
        - *points* the number of points in the simulation (default=20.0)
        - *plot* (default='species') select output data

         - 'species'
         - 'rates'
         - 'all' both species and rates

        - *fmt* plot format, UPI backend dependent (default='') or the *CommonStyle* 'lines' or 'points'.
        - *filename* if not None (default) then the plot is exported as *filename*.png

        Calls:
        - **Simulate()**
        - **SimPlot()**
        """
        self.sim_end = end
        self.sim_points = points
        self.Simulate()
        self.SimPlot(plot=plot, format=fmt, filename=filename)
#STOMPY INSERT START

    def Scan1(self,range1=[],runUF=0):
        """
        Scan1(range1=[],runUF=0)

        Perform a single dimension parameter scan using the steady-state solvers. The parameter to be scanned is defined (as a model attribute "P") in mod.scan_in while the required output is entered into the list mod.scan_out.
        Results of a parameter scan can be easilly viewed with Scan1Plot().

        mod.scan_in - a model attribute written as in the input file (eg. P, Vmax1 etc)
        mod.scan_out -  a list of required output ['A','T2', ...]
        mod.scan_res -  the results of a parameter scan
        mod.__settings__["scan1_mca_mode"] - force the scan algorithm to evaluate the elasticities (1) and control coefficients (2)
        (this should also be auto-detected by the Scan1 method).

        Arguments:

        range1 [default=[]]: a predefined range over which to scan.
        runUF [default=0]: run (1) the user defined function mod.User_Function (!U) before evaluating the steady state.

        """
        if self.__settings__["scan1_dropbad"] != 0:
            print '\n****\nINFO: Dropping invalid steady states can mask interesting behaviour\n****\n'

        #check the legitimacy of the input and output lists
        #self.__settings__["scan1_mca_mode"] = scanMCA
        run = 1
        # we now accept parameters and intial species values as scanable values - brett 20060519
        parameters = list(self.__parameters__) + [a+'_init' for a in self.__species__]
        scanpar = []

        scanIn = [self.scan_in]


        for x in scanIn:
            try:
                a = parameters.index(x)
                scanpar.append(x)
            except:
                print x, ' is not a parameter'
        if len(scanpar) < 1:
            print 'mod.scan_in should contain something'
            run = 0
        elif len(scanpar) > 1:
            print 'Only 1D scans possible - first element will be used'
            scanpar = scanpar[0]
            print'self.scan_in = ', scanpar
        else:
            scanpar = scanpar[0]

        self.scan_in = scanpar

        wrong = []

        for x in self.scan_out:
            if x[:2] == 'ec' and self.__settings__["scan1_mca_mode"] < 2:
                self.__settings__["scan1_mca_mode"] = 1
            elif x[:2] == 'cc':
                self.__settings__["scan1_mca_mode"] = 2
            #elif x[:2] == 'J_' or x[-3:] == '_ss':
            #    self.__settings__["scan1_mca_mode"] = 3

        if self.__settings__["scan1_mca_mode"] == 1:
            self.doElas()
        elif self.__settings__["scan1_mca_mode"] == 2:
            self.doMca()
        else:
            #elif self.__settings__["scan1_mca_mode"] == 3:
            self.doState() # we are going to run a whole bunch anyway

        for x in self.scan_out:
            try:
                ##  c = eval('self.' + x)
                c = getattr(self, x)
            except:
                print x + ' is not a valid attribute'
                wrong.append(x)
            if x == self.scan_in:
                wrong.append(x)
                print x, ' is mod.scan_in '

        if len(wrong) != 0:
            try:
                for x in wrong:
                    print self.scan_out
                    self.scan_out.remove(x)
                    print self.scan_out
            except:
                print 'No valid output'
                run = 0

        assert len(self.scan_out) != 0, "Output parameter list (mod.scan_out) empty - do the model attributes exist ... see manual for details."

        #print run, self.scan_in, self.scan_out

        result = []
        cntr = 0
        cntr2 = 1
        cntr3 = 0

        # reset scan error controls
        self.__scan_errors_par__ = None
        self.__scan_errors_idx__ = None

        print '\nScanning ...'
        if len(self.scan_in) > 0 and run == 1:
            badList = []
            badList_idx = []
            print len(range1)-(cntr*cntr2),
            for xi in range(len(range1)):
                x = range1[xi]
                setattr(self, self.scan_in, x)
                ##  exec('self.' + self.scan_in + ' = ' + `x`)
                if self.__settings__["scan1_mca_mode"] == 1:
                    self.doElas()
                if self.__settings__["scan1_mca_mode"] == 2:
                    self.doMca()
                else:
                    self.State()
                rawres = [x]
                # these two lines are going to be terminated - brett
                #rawres.append(x)
                #rawres.insert(0,x)
                if runUF:
                    self.User_Function()
                for res in self.scan_out:
                    ##  rawres.append(eval('self.' + res))
                    rawres.append(getattr(self, res))

                # The following is for user friendly reporting:
                # next we check if the state is ok : if bad report it and add it : if good add it
                if not self.__StateOK__ and self.__settings__["scan1_dropbad"] != 0:
                    pass
                elif not self.__StateOK__:
                    if self.__settings__["scan1_nan_on_bad"]:
                        result.append([numpy.NaN]*len(rawres))
                    else:
                        result.append(rawres)
                    badList.append(x)
                    badList_idx.append(xi)
                else:
                    result.append(rawres)
                cntr += 1
                cntr3 += 1
                if cntr == 20:
                    print len(range1)-(cntr*cntr2),
                    cntr = 0
                    cntr2 += 1
                if cntr3 == 101:
                    print ' '
                    cntr3 = 0
        print '\ndone.\n'
        if len(badList) != 0:
            self.__scan_errors_par__ = badList
            self.__scan_errors_idx__ = badList_idx
            print '\nINFO: ' + str(len(badList)) + ' invalid steady states detected at ' + self.scan_in + ' values:'
            print badList
        if len(result) == 0:
            self.scan_res = numpy.zeros((len(range1),len(self.scan_out)+1))
        else:
            self.scan_res = numpy.array(result)

    def Scan1Plot(self, plot=[], title=None, log=None, format='lines', filename=None):
        """
        Plot the results of a parameter scan generated with **Scan1()**

        - *plot* if empty mod.scan_out is used, otherwise any subset of mod.scan_out (default=[])
        - *filename* the filename of the PNG file (default=None, no export)
        - *title* the plot title (default=None)
        - *log* if None a linear axis is assumed otherwise one of ['x','xy','xyz'] (default=None)
        - *format* the backend dependent line format (default='lines')  or the *CommonStyle* 'lines' or 'points'.
        """
        data = self.scan_res
        labels = None
        if type(plot) == str:
            plot = [plot]
        if len(plot) == 0:
            plot = self.scan_out
        else:
            plot = [at for at in plot if hasattr(self, at)]
        if len(plot) == 0:
            print 'No plottable output specified using self.scan_out'
            plot = self.scan_out

        plotidx = [self.scan_out.index(c)+1 for c in plot]
        labels = [self.scan_in] + self.scan_out
        plt.plotLines(data, 0, plotidx, titles=labels, formats=[format])

        end = data[-1,0] + 0.2*data[-1,0]
        plt.setRange('x', data[0,0], end)
        plt.setAxisLabel('x', self.scan_in)

        yl = 'Steady-state variable'
        plt.setAxisLabel('y', yl)
        if log != None:
            plt.setLogScale(log)
        if title == None:
            plt.setGraphTitle('PySCeS Scan1 (' + self.ModelFile + ') ' + time.strftime("%a, %d %b %Y %H:%M:%S"))
        else:
            plt.setGraphTitle(title)
        plt.replot()
        if filename != None:
            plt.export(filename, directory=self.ModelOutput, type='png')

    def Scan2D(self, p1, p2, output, log=False):
        """
        Generate a 2 dimensional parameter scan using the steady-state solvers.

        - *p1* is a list of [parameter1, start, end, points]
        - *p2* is a list of [parameter2, start, end, points]
        - *output* steady-state variable/properties e.g. 'J_R1', 'A_ss', 'ecR1_s1'
        - *log* scan using log ranges for both axes
        """

        for p in [p1, p2]:
            if not hasattr(self, p[0]):
                raise RuntimeError, '\"%s\" is not a valid model attribute' % p[0]

        p1 = list(p1) + [log, False]
        p2 = list(p2) + [log, False]

        sc1 = Scanner(self)
        sc1.quietRun=True
        sc1.addScanParameter(*p1)
        sc1.addScanParameter(*p2)
        sc1.addUserOutput(output)
        sc1.Run()

        self.__scan2d_pars__ = [p1[0], p2[0], output]
        self.__scan2d_results__ = sc1.getResultMatrix()
        del sc1

    def Scan2DPlot(self, title=None, log=None, format='lines', filename=None):
        """
        Plot the results of a 2D scan generated with Scan2D

        - *filename* the filename of the PNG file (default=None, no export)
        - *title* the plot title (default=None)
        - *log* if None a linear axis is assumed otherwise one of ['x','xy','xyz'] (default=None)
        - *format* the backend dependent line format (default='lines')  or the *CommonStyle* 'lines' or 'points'.
        """

        plt.splot(self.__scan2d_results__, 0, 1, 2, titles=self.__scan2d_pars__, format=format)
        plt.setRange('xyz')
        plt.setAxisLabel('x', self.__scan2d_pars__[0])
        plt.setAxisLabel('y', self.__scan2d_pars__[1])
        plt.setAxisLabel('z', 'Steady-state variable')
        if log != None:
            plt.setLogScale(log)
        if title == None:
            plt.setGraphTitle('PySCeS Scan2D (' + self.ModelFile + ') ' + time.strftime("%a, %d %b %Y %H:%M:%S"))
        else:
            plt.setGraphTitle(title)
        plt.replot()
        if filename != None:
            plt.export(filename, directory=self.ModelOutput, type='png')

    def SetQuiet(self):
        """
        SetQuiet()

        Turn off as much solver reporting noise as possible:
        mod.__settings__['hybrd_mesg'] = 0
        mod.__settings__['nleq2_mesg'] = 0
        mod.__settings__["lsoda_mesg"] = 0
        mod.__settings__['mode_state_mesg'] = 0
        mod.__settings__['solver_switch_warning'] = False

        Arguments:
        None

        """
        self.__settings__['hybrd_mesg'] = 0
        self.__settings__['nleq2_mesg'] = 0
        self.__settings__["lsoda_mesg"] = 0
        self.__settings__['mode_state_mesg'] = 0
        self.__settings__['solver_switch_warning'] = False

    def SetLoud(self):
        """
        SetLoud()

        Turn on as much solver reporting noise as possible:
        mod.__settings__['hybrd_mesg'] = 1
        mod.__settings__['nleq2_mesg'] = 1
        mod.__settings__["lsoda_mesg"] = 1
        mod.__settings__['mode_state_mesg'] = 1
        mod.__settings__['solver_switch_warning'] = True

        Arguments:
        None

        """
        self.__settings__['hybrd_mesg'] = 1
        self.__settings__['nleq2_mesg'] = 1
        self.__settings__["lsoda_mesg"] = 1
        self.__settings__['mode_state_mesg'] = 1
        self.__settings__['solver_switch_warning'] = True

if not _HAVE_STOMPY:
    def nofunc(self, *args, **kwargs):
        print '\nStochastic simulation not available, please download/install *StomPy* from: http://stompy.sf.net\n'
    PysMod.doStochSim = nofunc
    PysMod.doStochSimPlot = nofunc
    PysMod.StochSimPlot = nofunc

class WasteManagement(object):
    _gc_delay_hack = gplt # dont f$%^&*)ing even ask ... only 5 hrs to hack this workaround
__waste_manager = WasteManagement()


if __psyco_active__:
    psyco.bind(PysMod)

if __name__ == '__main__':
    print '\nTo use PySCeS import it from a Python Shell: \n\timport pysces\n'
    raw_input('Press <enter> to continue')
