def checkScaledConcentrationControlMatrix(rrInstance, testId):
# Unscaled Concentration Control matrix
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE,
Config.ROADRUNNER_JACOBIAN_MODE_CONCENTRATIONS)
print(("Check " + testId).ljust(rpadding), end="")
st = rrInstance.getScaledConcentrationControlCoefficientMatrix()
checkMatrixVsUpcomingText(st)
def checkJacobian(rrInstance, testId):
# TODO need to update python 2.x printing
print string.ljust("Check " + testId, rpadding),
from roadrunner import Config
import numpy as n
errors = False
# max difference between reduced and full
maxDiff = 2e-10
# save the old value
saved = Config.getValue(Config.ROADRUNNER_JACOBIAN_MODE)
# set to amounts mode
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE,
Config.ROADRUNNER_JACOBIAN_MODE_AMOUNTS)
full = rrInstance.getFullJacobian()
reduced = rrInstance.getReducedJacobian()
m = n.max(n.abs(reduced - full))
if (m > maxDiff):
errors = True
print("Jacobians different in amounts mode, max difference: " + str(m))
# set to conc mode
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE,
Config.ROADRUNNER_JACOBIAN_MODE_CONCENTRATIONS)
full = rrInstance.getFullJacobian()
reduced = rrInstance.getReducedJacobian()
m = n.max(n.abs(reduced - full))
if (m > maxDiff):
errors = True
print("Jacobians different in concentrations mode, max difference: " +
str(m))
# restore previous value
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE, saved)
print passMsg(errors)
def checkJacobian(rrInstance, testId):
# TODO need to update python 2.x printing
print string.ljust ("Check " + testId, rpadding),
from roadrunner import Config
import numpy as n
errors = False
# max difference between reduced and full
maxDiff = 2e-10
# save the old value
saved = Config.getValue(Config.ROADRUNNER_JACOBIAN_MODE)
# set to amounts mode
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE, Config.ROADRUNNER_JACOBIAN_MODE_AMOUNTS)
full = rrInstance.getFullJacobian()
reduced = rrInstance.getReducedJacobian()
m = n.max(n.abs(reduced-full))
if (m > maxDiff):
errors = True
print("Jacobians different in amounts mode, max difference: " + str(m))
# set to conc mode
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE, Config.ROADRUNNER_JACOBIAN_MODE_CONCENTRATIONS)
full = rrInstance.getFullJacobian()
reduced = rrInstance.getReducedJacobian()
m = n.max(n.abs(reduced-full))
if (m > maxDiff):
errors = True
print("Jacobians different in concentrations mode, max difference: " + str(m))
# restore previous value
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE, saved)
print passMsg(errors)
#------------------------------------------------------------------------------- # Purpose: Example demonstrating how to access and plot various statistics from # a fitting session, e.g. RESIDUALS, STANDARDIZED RESIDUALS and # NORMAL PROBABILITY OF RESIDUALS (Q-Q plot) # # Author: Totte Karlsson ([email protected]) #------------------------------------------------------------------------------- import roadrunner import numpy as np import matplotlib.pyplot as plt import teplugins as tel from roadrunner import Config try: Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, False) Config.setValue(Config.SIMULATEOPTIONS_STRUCTURED_RESULT, True) chiPlugin = tel.Plugin("tel_chisquare") #Retrieve a SBML model from plugin modelPlugin = tel.Plugin("tel_test_model") test_model = modelPlugin.Model # Create a roadrunner instance and create some data rr = roadrunner.RoadRunner() rr.load(test_model) data = rr.simulate(0, 10, 15000) #Add noise to the data noisePlugin = tel.Plugin("tel_add_noise") # Get the dataseries from data returned by roadrunner d = tel.getDataSeries(data)
def checkScaledConcentrationControlMatrix(rrInstance, testId):
# Unscaled Concentration Control matrix
Config.setValue(Config.ROADRUNNER_JACOBIAN_MODE, Config.ROADRUNNER_JACOBIAN_MODE_CONCENTRATIONS)
print(string.ljust ("Check " + testId, rpadding), end="")
st = rrInstance.getScaledConcentrationControlCoefficientMatrix();
checkMatrixVsUpcomingText(st)
def chiFun(k1,k2):
rr.reset()
rr.setValue("k1", k1)
rr.setValue("k2", k2)
data = rr.simulate(timeStart, timeEnd, nrPoints - 1)
chiSquare.ModelData = tel.getDataSeries(data)
if not chiSquare.execute():
raise Exception( tel.getLastError() )
return chiSquare.ReducedChiSquare
try:
Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, False)
Config.setValue(Config.SIMULATEOPTIONS_STRUCTURED_RESULT, True)
#Read some 'experimental' data
expData = tel.DataSeries()
#This does not look right.. but it works..
expData = expData.readDataSeries('TwoParametersData.dat')
test_model = 'two_parameters.xml'
# Create a roadrunner instance and create some MODEL data
rr = roadrunner.RoadRunner()
rr.load(test_model)
#Get chi square plugin and set it up
chiSquare = tel.Plugin("tel_chisquare")
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 11 15:06:33 2014
@author: mgaldzic
"""
import tellurium as te
from roadrunner import Config
Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, True)
model = '''
model pathway()
$Xo -> S1; k1*Xo - k2*S1
S1 -> S2; k3*S2
S2 -> $X1; k4*S2
Xo = 1; X1 = 0
S1 = 0; S2 = 0
k1 = 0.1; k2 = 0.56
k3 = 1.2; k4 = 0.9
end
'''
r = te.loadAntimonyModel(model)
# Compute the steady state
r.getSteadyStateValues()
Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, False)
print "S1 =", r.S1, ", S2 =", r.S2
"""
Steady state scan.
"""
from __future__ import print_function, division
import tellurium as te
from roadrunner import Config
Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, True)
model = '''
$Xo -> S1; vo;
S1 -> S2; k1*S1 - k2*S2;
S2 -> $X1; k3*S2;
vo = 1
k1 = 2; k2 = 0; k3 = 3;
'''
rr = te.loada(model)
p = te.SteadyStateScan(rr,
value='k3',
startValue=2,
endValue=3,
numberOfPoints=20,
selection=['S1', 'S2'])
p.plotArray()
Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, False)
verbose = 2 #Verbose level: 0 = garnicht, 1 = Statistik 1x je outerLoop, 2 = Statistik nach jedem N, 3 = +Schleifenzaehler (Haengt sich die Simulation auf?)
Nscale = 100
#Produkt der naechsten 3 Variablen ist die Anzahl der insg. simulierten Pfade
outerLoop = 1 #Anzahl Wiederholungen festlegen (multipliziert sich mit AnzahlPfade zur Anzahl der simulierten Pfade je N)
Nrange = range(
3
) #Bereich von N festlegen. range(5) == 0,1,2,3,4. Daher Multiplikator N = (N+1)*Nscale
AnzahlPfade = 40 #Anzahl Wiederholungen festlegen (multipliziert sich mit outerLoop). Fuer schoene Plots hier Wert 40-100. Fuer grosse N: doplot=False, hier 1, dafuer bei outerLoop gewuenschte Werte eintragen.
r = te.loadAntimonyModel(
antStr) #Modell laden, kann auch URL sein z.B. von www.biomodels.net
r.setIntegrator(
'gillespie') #Integrator auswaehlen. Andere unterstuetzen z.B. ODEs
r.getIntegrator().setValue(
'variable_step_size', True
) #Parameter fuer Integrator setzen, anderer Integrator unterstuetzt andere Parameter
Config.setValue(Config.MAX_OUTPUT_ROWS, pow(
10, 7)) #bei 64-bit pow(10,11) oder weniger bei wenig Arbeitsspeicher
#----------------------------
Reactions = 0
start_time = time.time()
selectToSave = ['N'] + selectToPlot
form = (1, len(selectToSave))
r.selections = selectToSave
simuliertePfade = 0
PfadeGesamt = outerLoop * AnzahlPfade * len(Nrange)
if os.path.isfile(
path + name
): #Header in CSV-Datei automatisch erzeugen, falls Datei noch nicht existiert
f = open(path + name, 'ab')
else:
f = open(path + name, 'ab')
head = ",".join(selectToSave)
Nrange= range(3) #Bereich von N festlegen. range(5) == 0,1,2,3,4. Daher Multiplikator N = (N+1)*Nscale
AnzahlPfade = 40 #Anzahl Wiederholungen festlegen (multipliziert sich mit outerLoop). Fuer schoene Plots hier Wert 40-100. Fuer grosse N: doplot=False, hier 1, dafuer bei outerLoop gewuenschte Werte eintragen.
#--------------stay-above-this-line----- #Modell laden, kann auch URL sein z.B. von www.biomodels.net---------
if os.path.isfile(antStr): #----------- start copy&paste from tellurium.py
code = antimony.loadAntimonyFile(antStr)
else:
code = antimony.loadAntimonyString(antStr)
if code < 0:
raise Exception('Antimony: {}'.format(antimony.getLastError()))
mid = antimony.getMainModuleName()
sbml = antimony.getSBMLString(mid)
r = roadrunner.RoadRunner(sbml) #----------- end copy&paste from tellurium.py
#----------------the-next-three-lines-should-be-user-adapted--------
r.setIntegrator('gillespie') #Integrator auswaehlen. Andere unterstuetzen z.B. ODEs
r.getIntegrator().setValue('variable_step_size', True) #Parameter fuer Integrator setzen, anderer Integrator unterstuetzt andere Parameter
Config.setValue(Config.MAX_OUTPUT_ROWS,pow(10,7)) #bei 64-bit pow(10,11) oder weniger bei wenig Arbeitsspeicher
#-----------end-user-parameters-----------
Reactions = 0
start_time = time.time()
selectToSave = ['N'] + selectToPlot
form=(1,len(selectToSave))
r.selections = selectToSave
simuliertePfade = 0
PfadeGesamt=outerLoop*AnzahlPfade*len(Nrange)
if os.path.isfile(path+name): #Header in CSV-Datei automatisch erzeugen, falls Datei noch nicht existiert
f=open(path+name,'ab')
else:
f=open(path+name,'ab')
head=",".join(selectToSave)
f.write(head)
f.write("\n")
