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
chiSquare.ExperimentalData = expData
chiSquare.NrOfModelParameters = 1
ptn = 0
kStart = 0.3
kEnd = 1.5
kStep = .01
for k in np.arange(kStart, kEnd, kStep):
rr.setValue("k1", k)
rr.reset()
timeStart = 0
timeEnd = 1.5
nrPoints = 15
data = rr.simulate(timeStart, timeEnd, nrPoints - 1)
chiSquare.ModelData = tel.getDataSeries(data)
if not chiSquare.execute():
raise Exception(tel.getLastError())
redChi = chiSquare.ReducedChiSquare
print 'ptn, k, chisquare = ' + ` ptn ` + ', ' + ` k ` + ', ' + ` redChi `
x = np.append(x, k)
y = np.append(y, redChi)
ptn = ptn + 1
plt.subplot(1, 3, 1)
plt.plot(x, y, '-o', label="Reduced ChiSquare")
plt.legend()
plt.xlabel("k1")
plt.ylabel("ChiSquare")
kEnd = 2.5
kStep = .01
#Sweep the k value
for k in np.arange(kStart, kEnd, kStep):
rr.setValue("k1", k)
rr.reset()
#Simulate using the same numbers as in the 'Experimental' data
data = rr.simulate(0,10,14)
#roadrunner.plot(data)
#Get chi square plugin and set it up
chiSquare = tel.Plugin("tel_chisquare")
chiSquare.ExperimentalData = expData
chiSquare.ModelData = tel.getDataSeries(data)
chiSquare.NrOfModelParameters = 1
#Calculate the Chi Square
chiSquare.execute()
redChi = chiSquare.ReducedChiSquare
print 'k, Reduced Chisquare = ' + `k` +', '+ `redChi`
x = np.append(x, k)
y = np.append(y, redChi)
plt.plot(x,y, '-o', label="Reduced ChiSquare")
plt.legend()
plt.xlabel("k1")
plt.show()
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)
# Assign the dataseries to the plugin inputdata
noisePlugin.InputData = d
# Set parameter for the 'size' of the noise
noisePlugin.Sigma = 3.e-6
# Add the noise
noisePlugin.execute()
fName = 'testData3.dat'
noisePlugin.InputData.writeDataSeries(fName)
#===================================================================
lmPlugin = tel.Plugin("tel_levenberg_marquardt")
experimentalData = tel.DataSeries.readDataSeries(fName)
chiPlugin = tel.Plugin("tel_chisquare")
#Retrieve a SBML model from plugin
modelPlugin= tel.Plugin("tel_test_model")
sbmlModel= modelPlugin.Model
# Create a roadrunner instance and create some data
rr = roadrunner.RoadRunner()
rr.load(sbmlModel)
data = rr.simulate(0, 10, 15)
#Add noise to the data
noisePlugin = tel.Plugin ("tel_add_noise")
# Get the dataseries from data returned by roadrunner
d = tel.getDataSeries (data)
# Assign the dataseries to the plugin inputdata
noisePlugin.InputData = d
# Set parameter for the 'size' of the noise
noisePlugin.Sigma = 8.e-6
# Add the noise
noisePlugin.execute()
fName = 'testData3.dat'
noisePlugin.InputData.writeDataSeries (fName)
#=========== FIT DATA =============================================
lm = tel.Plugin ("tel_levenberg_marquardt")
experimentalData = tel.DataSeries.readDataSeries (fName)