C0 = readAttribute(experiment_path, ["C0"])["C0"]
rescaleFactor = readAttribute(experiment_path,
["rescaleFactor"])["rescaleFactor"]
output = results[modes.index("outputEqui")]
output = np.reshape(output, (len(X1), len(X2)))
X1 = X1 / (C0 * rescaleFactor)
X2 = X2 / (C0 * rescaleFactor)
otherActivInitialC = otherActivInitialC / (C0 * rescaleFactor)
otherInhibInitialC = otherInhibInitialC / (C0 * rescaleFactor)
# colorDiagram(X1,X2,output,"Initial concentration of X1","Initial concentration of X2","Equilibrium concentration of the output",figname=os.path.join(experiment_path, "neuralDiagramm.png"),equiPotential=False)
neuronPlot(X1,
X2,
output,
figname=os.path.join(experiment_path,
"activationLogX1.png"),
figname2=os.path.join(experiment_path,
"activationLogX2.png"),
useLogX=True,
doShow=False)
neuronPlot(X1,
X2,
output,
figname=os.path.join(experiment_path, "activationX1.png"),
figname2=os.path.join(experiment_path, "activationX2.png"),
useLogX=False,
doShow=False)
df = pandas.DataFrame(X1)
df.to_csv(os.path.join(experiment_path, "inputX1.csv"))
df2 = pandas.DataFrame(X2)
df2.to_csv(os.path.join(experiment_path, "inputX2.csv"))
for layer in range(1,len(masks)): ## the first layer need not to be initiliazed
for node in range(masks[layer].shape[0]):
initialization_dic["X_"+str(layer)+"_"+str(node)] = layerInit
initialization_dic["E"] = enzymeInit
initialization_dic["E2"] = enzymeInit
results = executeODESimulation(f, name, x_test, initialization_dic, ["X_1_0"], leak, endTime=endTime, sparse=False, modes=modes)
if("outputPlot" in modes):
import matplotlib.pyplot as plt
shapeP = len(X1)*len(X2)
nameDic = results[-1]
outArrayFullPlot = results[modes.index("output")]
toObserve=range(0,shapeP,max(int(shapeP/100),1))
time = np.arange(0,endTime,0.1)
for t in toObserve:
plt.figure()
for idx,k in enumerate(nameDic.keys()):
if idx in [nameDic["X_1_0"]]:
plt.plot(time,outArrayFullPlot[t,:,idx],label=k)
print("the observed min value is "+str(np.min(outArrayFullPlot[t,:,idx]))+" for "+k)
plt.legend()
plt.show()
if("outputEqui" in modes):
experiment_path = name
C0=readAttribute(experiment_path,["C0"])["C0"]
output = results[modes.index("output")]
colorDiagram(X1,X2,output,"Initial concentration of X1","Initial concentration of X2","Equilibrium concentration of the output",figname=os.path.join(experiment_path, "neuralDiagramm.png"),equiPotential=False)
neuronPlot(X1/C0,X2/C0,output,figname=os.path.join(experiment_path, "activationX1.png"),figname2=os.path.join(experiment_path, "activationX2.png"))
def simulModelIndicator(name,
nameFig,
enzymeInit=10**(-6),
activInit=10**(-8),
inhibInit=10**(-8),
x2val=None,
x1val=None,
indexSplit=10):
"""
Generate an indicator using the indicator function after simulating the model over a range of inputs.
:param name: directory where the network is located
:param nameFig: directory to store figure
:param enzymeInit: initial concentration of the enzyme
:param activInit: initial concentration of the activators
:param inhibInit: initial concentration of the inhibitors
:param x2val: if not none, define fix initial concentration value for inhibitors
:param x1val: if not none, define fix initial concentration value for activators
:param indexSplit: the size of the test will be two time indexSplit.
:return:
"""
endTime = 10000
timeStep = 0.1
modes = ["verbose", "outputEqui"]
leak = 10**(-10)
layerInit = 10**(-13) #initial concentation value for species in layers
initValue = 10**(-13) #initial concentration value for all species.
endoInit = 10**(-5) #only used if useEndo == True
#generate the first layer concentration:
if x1val is not None:
assert x2val is None
X1 = np.array([x1val])
X2 = np.concatenate(
(np.logspace(-8, -6, indexSplit), np.logspace(-6, -4, indexSplit)))
else:
assert x2val is not None
X2 = np.array([x2val])
X1 = np.concatenate(
(np.logspace(-8, -6, indexSplit), np.logspace(-6, -4, indexSplit)))
# generate concentration for all different experiments:
x_test = []
for x1 in X1:
for x2 in X2:
x_test += [[x1, x2]]
x_test = np.array(x_test)
initialization_dic = {}
for layer in range(
0, len(masks)): ## the first layer need not to be initiliazed
for node in range(masks[layer].shape[0]):
initialization_dic["X_" + str(layer + 1) + "_" +
str(node)] = layerInit
inhibTemplateNames = obtainTemplateArray(masks=masks, activ=False)
for k in inhibTemplateNames:
initialization_dic[k] = inhibInit
activTemplateNames = obtainTemplateArray(masks=masks, activ=True)
for k in activTemplateNames:
initialization_dic[k] = activInit
initialization_dic["E"] = enzymeInit
if complexity != "simple":
initialization_dic["E2"] = enzymeInit
if complexity != None and useEndo:
initialization_dic["Endo"] = endoInit
if useDerivativeLeak:
results = executeODESimulation(f,
name,
x_test,
initialization_dic,
outputList=outputList,
leak=leak,
endTime=endTime,
sparse=False,
modes=modes,
timeStep=timeStep,
initValue=initValue)
else:
results = executeODESimulation(f,
name,
x_test,
initialization_dic,
outputList=outputList,
leak=0,
endTime=endTime,
sparse=False,
modes=modes,
timeStep=timeStep,
initValue=initValue)
if ("outputEqui" in modes):
experiment_path = name
C0 = readAttribute(experiment_path, ["C0"])["C0"]
rescaleFactor = readAttribute(experiment_path,
["rescaleFactor"])["rescaleFactor"]
output = results[modes.index("outputEqui")]
output = np.reshape(output, (len(X1), len(X2)))
X1 = X1 / (C0 * rescaleFactor)
X2 = X2 / (C0 * rescaleFactor)
X1log = np.log(X1)
X2log = np.log(X2)
experiment_path = nameFig
colorDiagram(X1,
X2,
output,
"Initial concentration of X1",
"Initial concentration of X2",
"Equilibrium concentration of the output",
figname=os.path.join(experiment_path,
"neuralDiagramm.png"),
equiPotential=False)
neuronPlot(X1,
X2,
output,
figname=os.path.join(experiment_path, "activationX1.png"),
figname2=os.path.join(experiment_path, "activationX2.png"),
doShow=False)
df = pandas.DataFrame(X1)
df.to_csv(os.path.join(experiment_path, "inputX1.csv"))
df2 = pandas.DataFrame(X2)
df2.to_csv(os.path.join(experiment_path, "inputX2.csv"))
if x1val is None:
indicatorValue = indicator(X1log, X1, output[:, 0], indexSplit)
else:
indicatorValue = indicator(X2log,
X2,
output[0, :],
indexSplit,
sens=-1,
Xactiv=x1val / (C0 * rescaleFactor))
else:
raise Exception("outputEqui should be in outputModes")
return indicatorValue
df=pandas.read_csv(os.path.join(experiment_path, "neural_equilibrium.csv"))
output = df.values[:,1:]
df=pandas.read_csv(os.path.join(experiment_path, "neural_X1.csv"))
X1 = np.transpose(df.values[:,1:])[0]
df=pandas.read_csv(os.path.join(experiment_path, "neural_X2.csv"))
X2 = np.transpose(df.values[:,1:])[0]
C0 = 8.086075400626399e-07
#separate from bad values
X1=X1[:164]/C0
output=output[:164]
X2=X2[:164]/C0
colorDiagram(X1,X2,output,"Concentration of X1","Concentration of X2","Equilibrium concentration of the output",figname=os.path.join(experiment_path, "neuralDiagramm2.png"),equiPotential=False)
# neuronPlot(X1/(8.086075400626399e-07),X2/(8.086075400626399e-07),output,figname=os.path.join(experiment_path, "activation2.png"))
neuronPlot(X1,X2,output,figname=os.path.join(experiment_path, "activationX1.png"),figname2=os.path.join(experiment_path, "activationX2.png"))
# Plotting time:
colorDiagram(X1,X2,np.exp(output),"Concentration of X1","Concentration of X2","Equilibrium concentration of the output",figname=os.path.join(experiment_path, "neuralDiagramm2log.png"),equiPotential=False)
# neuronPlot(X1/(8.086075400626399e-07),X2/(8.086075400626399e-07),output,figname=os.path.join(experiment_path, "activation2.png"))
neuronPlot(X1,X2,np.exp(output),figname=os.path.join(experiment_path, "activationX1log.png"),figname2=os.path.join(experiment_path, "activationX2log.png"))
## Let us plot the frontier:
coords=[]
Xfrontier=[]
X2frontier=[]
for y in range(output.shape[1]):
for x in range(output.shape[0]-1):
if(output[x,y]<1 and 2<output[x+1,y]): #we have a discontinuity --> we are on the frontier
coords+=[[X1[x],X2[y]]]
Xfrontier+=[[X1[x],output[x+1,y]]]
X2frontier+=[[X2[y], output[x + 1, y]]]
break