import teplugins as tel
p = tel.Plugin("tel_add_noise")
data = tel.DataSeries.readDataSeries("testData.dat")
data.plot()
p.InputData = data
p.Sigma = 0.00005
print 'Sigma is now:' + ` p.Sigma `
p.execute()
p.InputData.plot()
try:
#Read some 'experimental' data
expData = tel.DataSeries()
#This does not look right.. but it works..
expData = expData.readDataSeries('ExperimentalData.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")
chiSquare.ExperimentalData = expData
chiSquare.NrOfModelParameters = 2
k1 = 1.3; k2 = 2.5
timeStart = 0; timeEnd = 1.5 ; nrPoints = 15
eta = 6.0e-6
H = np.matrix('0.0 0.0; 0.0 0.0')
#Diagonal elements
H[0,0] = getHessElement(1, 1, k1, k2, eta)
H[0,1] = getHessElement(1, 2, k1, k2, eta)
H[1,0] = getHessElement(2, 1, k1, k2, eta)
H[1,1] = getHessElement(2, 2, k1, k2, eta)
#------------------------------------------------------------------------------- # Purpose: Example demonstrating how to calculate the ChiSquare, using the # ChiSquare plugin, as a function of a model parameter. # Author: Totte Karlsson ([email protected]) #------------------------------------------------------------------------------- import roadrunner import numpy as np import matplotlib.pyplot as plt import teplugins as tel try: # Config.setValue(Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, False) # Config.setValue(Config.SIMULATEOPTIONS_STRUCTURED_RESULT, True) noisePlugin = tel.Plugin("tel_add_noise") modelPlugin = tel.Plugin("tel_test_model") test_model = modelPlugin.Model modelPlugin.execute() #Use the testModel plugins property TestDataWithNoise as the 'Experimental' data expData = modelPlugin.TestDataWithNoise # Create a roadrunner instance and create some MODEL data rr = roadrunner.RoadRunner() rr.load(test_model) #Create variables to be used in the loop x = np.array([]) y = np.array([]) kStart = 0.1
#------------------------------------------------------------------------------- # 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)
import ctypes
import teplugins as tel
#Get a lmfit plugin object
chiPlugin = tel.Plugin("tel_chisquare")
lm = tel.Plugin("tel_levenberg_marquardt")
#========== EVENT FUNCTION SETUP ===========================
def pluginIsProgressing(lmP):
# The plugin don't know what a python object is.
# We need to cast it here, to a proper python object
lmObject = ctypes.cast(lmP, ctypes.py_object).value
print 'Iterations = ' + `lmObject.getProperty("NrOfIter")` \
+ '\tNorm = ' + `lmObject.getProperty("Norm")`
try:
progressEvent = tel.NotifyEventEx(pluginIsProgressing)
#The ID of the plugin is passed as the last argument in the assignOnProgressEvent.
#The plugin ID is later on retrieved in the plugin Event handler, see above
theId = id(lm)
tel.assignOnProgressEvent(lm.plugin, progressEvent, theId)
#============================================================
#Retrieve a SBML model from plugin
test_model = tel.readAllText('two_parameters.xml')
#Setup lmfit properties.
lm.SBML = test_model
import numpy as np
import matplotlib.pyplot as plt
import teplugins as tel
def firstDerivative(p2, p1, h):
return (p2 - p1) / (2.0*h)
try:
#Read some 'experimental' data
expData = tel.DataSeries()
#This does not look right.. but it works..
expData = expData.readDataSeries('ExperimentalData.dat')
#Get a model
modelPlugin = tel.Plugin("tel_test_model")
test_model = modelPlugin.Model
# Create a roadrunner instance and create some MODEL data
rr = roadrunner.RoadRunner()
rr.load(test_model)
#Simulate using the same numbers as in the 'Experimental data
x = np.array([])
y = np.array([])
#Get chi square plugin and set it up
chiSquare = tel.Plugin("tel_chisquare")
chiSquare.ExperimentalData = expData
chiSquare.NrOfModelParameters = 1
#------------------------------------------------------------------------------- # Purpose: Example demonstrating how to setup and use the TestModel plugin # This example also shows how to plot data and how to view a plugins # embedded manual # # Author: Totte Karlsson ([email protected]) #------------------------------------------------------------------------------- import teplugins as tel try: modelPlugin = tel.Plugin("tel_test_model") #Test model plugin depends on the add_noise plugin noisePlugin = tel.Plugin("tel_add_noise") #Generate internal test data modelPlugin.execute() test_data = modelPlugin.TestData test_data_with_noise = modelPlugin.TestDataWithNoise test_data.plot() test_data_with_noise.plot() #modelPlugin.viewManual() print 'Plugin version: ' + ` modelPlugin.getVersion() ` except Exception as e: print 'Problem: ' + ` e `
import ctypes
import teplugins as tel
#Get a lmfit plugin object
chiPlugin = tel.Plugin("tel_chisquare")
lm = tel.Plugin("tel_levenberg_marquardt")
#========== EVENT FUNCTION SETUP ===========================
def pluginIsProgressing(lmP):
# The plugin don't know what a python object is.
# We need to cast it here, to a proper python object
lmObject = ctypes.cast(lmP, ctypes.py_object).value
print 'Iterations = ' + `lmObject.getProperty("NrOfIter")` \
+ '\tNorm = ' + `lmObject.getProperty("Norm")`
try:
progressEvent = tel.NotifyEventEx(pluginIsProgressing)
#The ID of the plugin is passed as the last argument in the assignOnProgressEvent.
#The plugin ID is later on retrieved in the plugin Event handler, see above
theId = id(lm)
tel.assignOnProgressEvent(lm.plugin, progressEvent, theId)
#============================================================
#Retrieve a SBML model from plugin
modelPlugin = tel.Plugin("tel_test_model")
test_model = modelPlugin.Model
#Setup lmfit properties.
lm.SBML = test_model
import teplugins as tel
try:
p = tel.Plugin('tel_AddPlugin')
p.x = 1.2
p.y = 3.6
print p.execute()
print p.z
except Exception as e:
print e
import roadrunner
import teplugins as tel
try:
#Retrieve test 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, 511)
roadrunner.plot(data)
print "done"
except Exception as e:
print 'Problem: ' + ` e `
import ctypes
import teplugins as tel
#Get a nmfit plugin object
chiPlugin = tel.Plugin("tel_chisquare")
nm = tel.Plugin("tel_nelder_mead")
#========== EVENT FUNCTION SETUP ===========================
def pluginIsProgressing(nmP):
# The plugin don't know what a python object is.
# We need to cast it here, to a proper python object
nmObject = ctypes.cast(nmP, ctypes.py_object).value
print 'Iterations = ' + `nmObject.getProperty("NrOfIter")` \
+ 'FuncIterations = ' + `nmObject.getProperty("NrOfFuncIter")` \
+ '\tNorm = ' + `nmObject.getProperty("Norm")`
try:
progressEvent = tel.NotifyEventEx(pluginIsProgressing)
#The ID of the plugin is passed as the last argument in the assignOnProgressEvent.
#The plugin ID is later on retrieved in the plugin Event handler, see above
theId = id(nm)
tel.assignOnProgressEvent(nm.plugin, progressEvent, theId)
#============================================================
#Retrieve a SBML model from plugin
modelPlugin = tel.Plugin("tel_test_model")
test_model = modelPlugin.Model
#Setup nmfit properties.
