name,
os.path.join(os.path.join(name, "activation"),
str(idx) + "_" + str(idx2)),
enzymeInit=e,
activInit=t,
inhibInit=t,
x2val=x2Val,
x1val=None,
indexSplit=indexSplit)
df = pandas.DataFrame(indicatorMatrixActiv)
df.to_csv(os.path.join(name, "indicatorMatrixActiv.csv"))
colorDiagram(enzyme,
templateInit,
indicatorMatrixActiv,
"Initial concentration of E",
"Initial concentration of template",
"Indicator of the model",
figname=os.path.join(name,
"modelIndicatorDiagrammActivation.png"),
equiPotential=False)
# second: generate indicator matrix for fixed activation
indicatorMatrixInhib = np.zeros((enzyme.shape[0], templateInit.shape[0]))
x1Val = 10**(-6)
for idx, e in enumerate(enzyme):
for idx2, t in enumerate(templateInit):
if not os.path.exists(
os.path.join(os.path.join(name, "inhibition"),
str(idx) + "_" + str(idx2))):
os.makedirs(
os.path.join(os.path.join(name, "inhibition"),
str(idx) + "_" + str(idx2)))
df.to_csv(os.path.join(experiment_path, "Y1.csv"))
df = pandas.DataFrame(outputArrayY2)
df.to_csv(os.path.join(experiment_path, "Y2.csv"))
df = pandas.DataFrame(outputY1)
df.to_csv(os.path.join(experiment_path, "Y1equi.csv"))
df = pandas.DataFrame(outputY2)
df.to_csv(os.path.join(experiment_path, "Y2equi.csv"))
df2 = pandas.DataFrame(A2onA1)
df2.to_csv(os.path.join(experiment_path, "a2on1.csv"))
df2 = pandas.DataFrame(C)
df2.to_csv(os.path.join(experiment_path, "c.csv"))
colorDiagram(A2onA1,
C,
outputY1 / outputY2,
"Ratio A2/A1",
"Concentration of cooperative species, arbitrary unit",
'Ratio: Y2/Y1',
os.path.join(experiment_path, "diagrammRatio.png"),
equiPotential=False)
colorDiagram(A2onA1,
C,
outputY1,
"Ratio A2/A1",
"Concentration of cooperative species, arbitrary unit",
'output concentration: Y1',
os.path.join(experiment_path, "diagrammY1.png"),
equiPotential=False)
colorDiagram(A2onA1,
C,
outputY2,
"Ratio A2/A1",
displayOther=True)
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)
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,
import numpy as np
import pandas
from simulOfBioNN.plotUtils.adaptivePlotUtils import colorDiagram
import matplotlib.pyplot as plt
dfoutput = pandas.read_csv("equilibriumTest.csv")
output = dfoutput.values[:, 1:]
dfA2onA1 = pandas.read_csv("a2on1Test.csv")
A2onA1 = np.reshape(dfA2onA1.values[:, 1:], (dfA2onA1.values[:, 1:].shape[0]))
dfC = pandas.read_csv("cTest.csv")
C = np.reshape(dfC.values[:, 1:], (dfC.values[:, 1:].shape[0]))
colorDiagram(A2onA1, C, output, "Ratio A2/A1",
"Concentration of cooperative species, arbitrary unit",
'Ratio: Y2/Y1', "diagrammTest.png")
plt.show()
activInit = np.logspace(-8, -4, 4)
inhibInit = np.logspace(-8,-4, 4)
indexSplit = 10
indicatorMatrixActiv = np.zeros((inhibInit.shape[0], activInit.shape[0]))
#first: generate indicator matrix for fixed inhibition
x2Val = 10**(-6)
for idx,cactiv in enumerate(activInit):
for idx2,cinhib in enumerate(inhibInit):
if not os.path.exists(os.path.join(os.path.join(name2,"activation"),str(idx)+"_"+str(idx2))):
os.makedirs(os.path.join(os.path.join(name2,"activation"),str(idx)+"_"+str(idx2)))
indicatorMatrixActiv[idx,idx2] = simulModelIndicator(name,os.path.join(os.path.join(name2,"activation"),str(idx)+"_"+str(idx2)),enzymeInit = e,activInit = cactiv,inhibInit = cinhib, x2val = x2Val, x1val = None, indexSplit = indexSplit)
df = pandas.DataFrame(indicatorMatrixActiv)
df.to_csv(os.path.join(name2,"indicatorMatrixActiv.csv"))
# second: generate indicator matrix for fixed activation
indicatorMatrixInhib = np.zeros((enzyme.shape[0], activInit.shape[0]))
x1Val = 10**(-6)
for idx,cactiv in enumerate(activInit):
for idx2,cinhib in enumerate(inhibInit):
if not os.path.exists(os.path.join(os.path.join(name2,"inhibition"),str(idx)+"_"+str(idx2))):
os.makedirs(os.path.join(os.path.join(name2,"inhibition"),str(idx)+"_"+str(idx2)))
indicatorMatrixInhib[idx,idx2] = simulModelIndicator(name,os.path.join(os.path.join(name2,"inhibition"),str(idx)+"_"+str(idx2)),enzymeInit = e,activInit = cactiv,inhibInit = cinhib, x2val = None, x1val = x1Val, indexSplit = indexSplit)
df2 = pandas.DataFrame(indicatorMatrixInhib)
df2.to_csv(os.path.join(name2,"indicatorMatrixInhib.csv"))
colorDiagram(activInit,inhibInit,indicatorMatrixInhib,"Initial concentration of Activ","Initial concentration of Inhib","Average Indicator of the model",figname=os.path.join(name2, "indicatorMatrixInhib.png"),equiPotential=False,uselog=True)
colorDiagram(activInit,inhibInit,indicatorMatrixActiv,"Initial concentration of Activ","Initial concentration of Inhib","Average Indicator of the model",figname=os.path.join(name2, "indicatorMatrixActiv.png"),equiPotential=False,uselog=True)
indicatorAvg = (indicatorMatrixActiv + indicatorMatrixInhib)/2
colorDiagram(activInit,inhibInit,indicatorAvg,"Initial concentration of Activ","Initial concentration of Inhib","Average Indicator of the model",figname=os.path.join(name2, "modelIndicatorAverage.png"),equiPotential=False,uselog=True)
experiment_path=os.path.join(sys.path[0],"")
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]]]