https://github.com/QTB-HHU/ModelHeatShock
Tip revision: 7ead643f56ee9bfa3077d5f0c05f0912cbf9a4ee authored by StefanoMagni on 05 April 2018, 15:38:57 UTC
Delete .DS_Store
Delete .DS_Store
Tip revision: 7ead643
HSM_FunctsOfSimulateClass.py
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
from HSM_ODEsSystem10or9eqs import *
from HSM_HSModelClass import *
from HSM_PlottingFunctions import *
import time
def SolveODEsystem(self, DirectControlnuPp, ModifyKset="No", ParamsToBeModif={}, TimeOfMod=vorl, Add3rdmRNA_ARS="No"):
""" Set the system of Ordinary Differential Equations and solve it """
#start_time = time.time()
#print("!!! In SolveODEsystem " + str(DirectControlnuPp))
# Put the initial conditions into an array (keep the right ordering!)
y0 = np.zeros((12, 1))
if Add3rdmRNA_ARS == "Yes":
y0 = np.zeros((14, 1))
i = 0
for key in ["Pin", "Phin", "Sin", "Ssin", "Fin", "Fsin", "Gin", "FsGin", "FGin", "RFin", "RHPin", "HPin"]:
y0[i] = self.model.ParamsSetIC.CurrentParams[key]
i = i + 1
if Add3rdmRNA_ARS == "Yes":
y0[12] = 0. # 0.00360 # (microM) mRNA_HP_ARS, mRNA for ARS enzime of which gene has been attached to HSP promot
y0[13] = 0. # 1. # (microM) ARS enzime of which the gene has been attached to the HSP promoter
# Copy the set of k (rates) parameters and that off params for reactions
kset = self.model.ParamsSetRATES.CurrentParams.copy()
ksetMOD = self.model.ParamsSetRATES.CurrentParams.copy()
ParSetREAC = self.model.ParamsSetForREAC.CurrentParams.copy()
# Specify integrator ('lsoda' choses automatically between 'bdf' and 'Adams'):
ODEs = ode(f).set_integrator('lsoda', nsteps=1000, max_step = 60.0) # max_step is in seconds.
# IMPORTANT: "nsteps" tells the maximum number of steps allowed. if lower the integrator might crash.
# IMPORTANT: "max_step" tells the maximal lenght of a step in the integration. If not specified or too big,
# the integrator can "miss" an heat shock if it is too short (like few minutes) compared to vorl.
# Thus, max_step should be of same order (or smaller, which is even better!) than the minimal duration of an HS.
# Set initial conditions for the integrating variable and time
ODEs.set_initial_value(y0, self.timeset.CurrentParams["t_start"]).set_f_params(kset, self.TparamSet, ParSetREAC,
DirectControlnuPp, Add3rdmRNA_ARS)
# Integrate the ODEs across each delta_t timestep (i.e. compute y(t) for every t = n * delta_t)
i = 1
while ODEs.successful() and i < self.StepsNum:
if ModifyKset == "Yes" and ODEs.t + self.timeset.CurrentParams["delta_t"] >= TimeOfMod:
for key in ParamsToBeModif.keys():
ksetMOD[key] = ParamsToBeModif[key]
ODEs.set_initial_value(ODEs.y, ODEs.t).set_f_params(ksetMOD, self.TparamSet, ParSetREAC, DirectControlnuPp, Add3rdmRNA_ARS)
ModifyKset = "No"
ODEs.integrate(ODEs.t + self.timeset.CurrentParams["delta_t"]) # , step=True
# Store the results to plot later
self.t[i] = (ODEs.t - vorl) / 60. # (min)
self.Tplot[i] = T(i * self.timeset.CurrentParams["delta_t"], self.TparamSet) # (°C)
self.P[i] = ODEs.y[0] / 1000. # (mM)
self.Ph[i] = ODEs.y[1] / 1000. # (mM)
self.S[i] = ODEs.y[2] * 1000. # (nM)
self.Ss[i] = ODEs.y[3] * 1000. # (nM)
self.F[i] = ODEs.y[4] # (microM)
self.Fs[i] = ODEs.y[5] # (microM)
self.G[i] = ODEs.y[6] * 1000. # (nM)
self.FsG[i] = ODEs.y[7] * 1000. # (nM)
self.FG[i] = ODEs.y[8] * 1000. # (nM)
self.RF[i] = ODEs.y[9] # (microM)
self.RHP[i] = ODEs.y[10] # (microM)
self.HP[i] = ODEs.y[11] / 1000. # (mM)
if Add3rdmRNA_ARS == "Yes":
self.RHP_ARS[i] = ODEs.y[12] # (microM)
self.HP_ARS[i] = ODEs.y[13] # (microM)
i += 1
# end_time = time.time()
# print("Elapsed time was %g seconds" % (end_time - start_time))
def SolveODEsystem10eqs(self, DirectControlnuPp):
""" Set the system of Ordinary Differential Equations, with only 10 ODEs, and solve it """
# Put the initial conditions into an array (keep the right ordering!)
y0 = np.zeros((10, 1))
i = 0
for key in ["Phin", "Ssin", "Fin", "Fsin", "Gin", "FsGin", "FGin", "RFin", "RHPin", "HPin"]:
y0[i] = self.model.ParamsSetIC.CurrentParams[key]
i = i + 1
# Copy the set of k (rates) parameters and that off params for reactions
kset = self.model.ParamsSetRATES.CurrentParams.copy()
ksetMOD = self.model.ParamsSetRATES.CurrentParams.copy()
ParSetREAC = self.model.ParamsSetForREAC.CurrentParams.copy()
# Specify integrator ('lsoda' choses automatically between 'bdf' and 'Adams'):
ODEs = ode(f10eqs).set_integrator('lsoda', nsteps=1000, max_step = 60.0) # max_step is in seconds.
IC_PplusPp = self.model.ParamsSetIC.CurrentParams["Pin"] + self.model.ParamsSetIC.CurrentParams["Phin"]
IC_SplusSs = self.model.ParamsSetIC.CurrentParams["Sin"] + self.model.ParamsSetIC.CurrentParams["Ssin"]
# Set initial conditions for the integrating variable and time
ODEs.set_initial_value(y0, self.timeset.CurrentParams["t_start"]).set_f_params(kset, self.TparamSet, ParSetREAC,
DirectControlnuPp, IC_PplusPp, IC_SplusSs)
# Integrate the ODEs across each delta_t timestep (i.e. compute y(t) for every t = n * delta_t)
i = 1
while ODEs.successful() and i < self.StepsNum:
ODEs.integrate(ODEs.t + self.timeset.CurrentParams["delta_t"])
# Store the results to plot later
self.t[i] = (ODEs.t - vorl) / 60. # (min)
self.Tplot[i] = T(i * self.timeset.CurrentParams["delta_t"], self.TparamSet) # (°C)
self.Ph[i] = ODEs.y[0] / 1000. # (mM)
self.Ss[i] = ODEs.y[1] * 1000. # (nM)
self.F[i] = ODEs.y[2] # (microM)
self.Fs[i] = ODEs.y[3] # (microM)
self.G[i] = ODEs.y[4] * 1000. # (nM)
self.FsG[i] = ODEs.y[5] * 1000. # (nM)
self.FG[i] = ODEs.y[6] * 1000. # (nM)
self.RF[i] = ODEs.y[7] # (microM)
self.RHP[i] = ODEs.y[8] # (microM)
self.HP[i] = ODEs.y[9] / 1000. # (mM)
self.P[i] = ( self.P[0] + self.Ph[0] ) - self.Ph[i] # (mM)
self.S[i] = ( self.S[0] + self.Ss[0] ) - self.Ss[i] # (nM)
i += 1
def SolveODEsystem9eqs(self, DirectControlnuPp):
""" Set the system of Ordinary Differential Equations, with only 10 ODEs, and solve it """
# Put the initial conditions into an array (keep the right ordering!)
y0 = np.zeros((9, 1))
i = 0
for key in ["Phin", "Ssin", "Fin", "Fsin", "FsGin", "FGin", "RFin", "RHPin", "HPin"]:
y0[i] = self.model.ParamsSetIC.CurrentParams[key]
i = i + 1
# Copy the set of k (rates) parameters and that off params for reactions
kset = self.model.ParamsSetRATES.CurrentParams.copy()
ksetMOD = self.model.ParamsSetRATES.CurrentParams.copy()
ParSetREAC = self.model.ParamsSetForREAC.CurrentParams.copy()
# Specify integrator ('lsoda' choses automatically between 'bdf' and 'Adams'):
ODEs = ode(f9eqs).set_integrator('lsoda', nsteps=1000, max_step = 60.0) # max_step is in seconds.
IC_PplusPp = self.model.ParamsSetIC.CurrentParams["Pin"] + self.model.ParamsSetIC.CurrentParams["Phin"]
IC_SplusSs = self.model.ParamsSetIC.CurrentParams["Sin"] + self.model.ParamsSetIC.CurrentParams["Ssin"]
IC_GplusFsGplusFG = self.model.ParamsSetIC.CurrentParams["Gin"] + self.model.ParamsSetIC.CurrentParams["FsGin"] + self.model.ParamsSetIC.CurrentParams["FGin"]
# Set initial conditions for the integrating variable and time
ODEs.set_initial_value(y0, self.timeset.CurrentParams["t_start"]).set_f_params(kset, self.TparamSet, ParSetREAC,
DirectControlnuPp, IC_PplusPp, IC_SplusSs, IC_GplusFsGplusFG)
# Integrate the ODEs across each delta_t timestep (i.e. compute y(t) for every t = n * delta_t)
i = 1
while ODEs.successful() and i < self.StepsNum:
ODEs.integrate(ODEs.t + self.timeset.CurrentParams["delta_t"])
# Store the results to plot later
self.t[i] = (ODEs.t - vorl) / 60. # (min)
self.Tplot[i] = T(i * self.timeset.CurrentParams["delta_t"], self.TparamSet) # (°C)
self.Ph[i] = ODEs.y[0] / 1000. # (mM)
self.Ss[i] = ODEs.y[1] * 1000. # (nM)
self.F[i] = ODEs.y[2] # (microM)
self.Fs[i] = ODEs.y[3] # (microM)
self.FsG[i] = ODEs.y[4] * 1000. # (nM)
self.FG[i] = ODEs.y[5] * 1000. # (nM)
self.RF[i] = ODEs.y[6] # (microM)
self.RHP[i] = ODEs.y[7] # (microM)
self.HP[i] = ODEs.y[8] / 1000. # (mM)
self.P[i] = ( self.P[0] + self.Ph[0] ) - self.Ph[i] # (mM)
self.S[i] = ( self.S[0] + self.Ss[0] ) - self.Ss[i] # (nM)
self.G[i] = ( self.G[0] + self.FsG[0] + self.FG[0] ) - self.FsG[i] - self.FG[i] # (nM)
i += 1
def FuncPlotTemperature(self, tminMANUAL=("No",0.)):
""" Plot the temperature T(t) """
fig0 = figure()
plt.xlabel('Time (min)', fontsize="x-large")
if self.TparamSet.CurrentParams["Ttype"] == 6 or (
self.TparamSet.CurrentParams["Ttype"] == 2 and self.TparamSet.CurrentParams["tb"] >= 12 * 60. * 60.):
plt.xticks([0., 6. * 60., 12. * 60., 18. * 60., 24 * 60., (24. + 8.) * 60.],
["0", "6", "12", "18", "24", "24+8"])
plt.xlabel('Time (h)', fontsize="x-large")
#plt.xlim(0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
#if tminMANUAL[0] == "Yes":
#print("Sbleught")
#print(str(tminMANUAL[1]/60.))
#print(str((self.timeset.CurrentParams["t_stop"] - vorl) / 60.))
plt.xlim(0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
plt.ylim(16., 44.)
plt.ylabel(r'Temperature ($^\circ$C)', fontsize="x-large")
plt.plot(self.t, self.Tplot, 'g', linewidth=1.)
if self.TparamSet.CurrentParams["Ttype"] == 7:
plt.xticks([0., 3. * 60., 6. * 60., 9. * 60., 12. * 60., 15. * 60., 18. * 60., 21 * 60., 24 * 60.],
["3:00", "6:00", "9:00", "12:00", "15:00", "18:00", "21:00", "24:00", "3:00"])
plt.xlabel('Time (h)', fontsize="x-large")
if self.TparamSet.CurrentParams["Ttype"] == 7 and tminMANUAL[0] == "Yes":
plt.xticks([0.+tminMANUAL[1]/60., 3. * 60.+tminMANUAL[1]/60., 6. * 60.+tminMANUAL[1]/60., 9. * 60.+tminMANUAL[1]/60., 12. * 60.+tminMANUAL[1]/60., 15. * 60.+tminMANUAL[1]/60., 18. * 60.+tminMANUAL[1]/60., 21 * 60.+tminMANUAL[1]/60., 24 * 60.+tminMANUAL[1]/60.],
["3:00", "6:00", "9:00", "12:00", "15:00", "18:00", "21:00", "24:00", "3:00"])
plt.xlim(tminMANUAL[1]/60., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
plt.xlabel('Time (h)', fontsize="x-large")
#plt.xlim(0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
plt.ylim(16., 44.)
plt.ylabel(r'Temperature ($^\circ$C)', fontsize="x-large")
plt.plot(self.t, self.Tplot, 'g', linewidth=1.)
PlotAndSave(fig0, "Temperature_" + self.name, "PS", 0, 1)
def PlotTrajectories(self, ModifyKset="No", ParamsToBeModif={}, TimeOfMod=vorl, ZoomInPanelA="No", tminMANUAL=("No",123456789)):
""" Plot the trajectories in 6 separated subplots """
tmax = (self.timeset.CurrentParams["t_stop"] - vorl) / 60.
tmin = 0.
if tminMANUAL[0] == "Yes":
tmin = 0. + tminMANUAL[1]
#print("\ntminMANUAL[0] = " + str(tminMANUAL[0]) + " tmin = " + str(tmin) + "\n")
indexoft0 = int(vorl/self.timeset.CurrentParams["delta_t"])
fig1 = figure()
# RESCALING FOR PLOTTING
Prescaled = []
Phrescaled = []
Srescaled = []
Ssrescaled = []
Frescaled = []
Fsrescaled = []
Grescaled = []
FsGrescaled = []
FGrescaled = []
RFrescaled = []
RHPrescaled = []
HPrescaled = []
i = 0
for element in self.t:
Prescaled.append(self.P[i]/SetOfReferenceValuesForPlotting["Ptot"])
Phrescaled.append(self.Ph[i]/SetOfReferenceValuesForPlotting["Ptot"])
Srescaled.append(self.S[i]/SetOfReferenceValuesForPlotting["SK"])
Ssrescaled.append(self.Ss[i]/SetOfReferenceValuesForPlotting["SK"])
Frescaled.append(self.F[i]/SetOfReferenceValuesForPlotting["HSF"])
Fsrescaled.append(self.Fs[i]/SetOfReferenceValuesForPlotting["HSF"])
Grescaled.append(self.G[i]/SetOfReferenceValuesForPlotting["Genes"])
FsGrescaled.append(self.FsG[i]/SetOfReferenceValuesForPlotting["Genes"])
FGrescaled.append(self.FG[i]/SetOfReferenceValuesForPlotting["Genes"])
RFrescaled.append(self.RF[i]/SetOfReferenceValuesForPlotting["mRNA"])
RHPrescaled.append(self.RHP[i]/SetOfReferenceValuesForPlotting["mRNA"])
HPrescaled.append(self.HP[i]/SetOfReferenceValuesForPlotting["HSP"])
i=i+1
if ZoomInPanelA=="No":
ax1 = plt.subplot(321)
ax1.set_title("Protein", fontsize = "small")
#SubPlot(ax1, self.t, [["P", self.P], [r"P$^\#$", self.Ph]],
# 'Time (min)', tmin, tmax, 'Concentration (mM)', 0., 100.5, "center right", "A", LineStyleListInverted = "Yes")
SubPlot(ax1, self.t, [["P", Prescaled], [r"P$^\#$", Phrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(fraction of total)', 0., 100.5/SetOfReferenceValuesForPlotting["Ptot"], "center right", "A", LineStyleListInverted = "Yes")
elif ZoomInPanelA=="Yes" or ZoomInPanelA=="Yes2" or ZoomInPanelA=="Yes3" :
CutY = []
#for i in range(size(self.Ph)-indexoft0):
# CutY.append(self.Ph[i+indexoft0])
for i in range(size(Phrescaled)-indexoft0):
CutY.append(Phrescaled[i+indexoft0])
MaxY = 1.4 * max(CutY)
ax1 = plt.subplot(321)
ax1.set_title("Protein", fontsize = "small")
#SubPlot(ax1, self.t, [[r"P$^\#$", self.Ph]],
# 'Time (min)', tmin, tmax, 'Concentration (mM)', 0., MaxY, "center right", "A", LineStyleListInverted = "No")
SubPlot(ax1, self.t, [[r"P$^\#$", Phrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(fraction of total)', 0., MaxY, "center right", "A", LineStyleListInverted = "No")
#elif ZoomInPanelA=="Yes":
# ax1 = plt.subplot(321)
# ax1.set_title("Protein", fontsize = "small")
# SubPlot(ax1, self.t, [[r"P$^\#$", self.Ph]],
# 'Time (min)', tmin, tmax, 'Concentration (mM)', 0., 1.2, "center right", "A", LineStyleListInverted = "Yes")
# #AddLetterToSubplot(ax1, "A", -0.2, 1.065)
#
#elif ZoomInPanelA=="Yes2":
# ax1 = plt.subplot(321)
# ax1.set_title("Protein", fontsize = "small")
# SubPlot(ax1, self.t, [[r"P$^\#$", self.Ph]],
# 'Time (min)', tmin, tmax, 'Concentration (mM)', 0., 4.5, "center right", "A", LineStyleListInverted = "Yes")
# #AddLetterToSubplot(ax1, "A", -0.2, 1.065)
ax2 = plt.subplot(322)
ax2.set_title("Stress Kinases", fontsize = "small")
#SubPlot(ax2, self.t, [[r"SK", self.S], [r"SK$^*$", self.Ss]],
# 'Time (min)', tmin, tmax, 'Concentration (nM)', 0., 120., "center right", "B", LineStyleListInverted = "Yes")
SubPlot(ax2, self.t, [[r"SK", Srescaled], [r"SK$^*$", Ssrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(fraction of total)', 0., 120./SetOfReferenceValuesForPlotting["SK"], "center right", "B", LineStyleListInverted = "Yes")
ax3 = plt.subplot(323)
ax3.set_title("Heat Shock Factor", fontsize = "small")
SubPlot(ax3, self.t, [[r"HSF", Frescaled], [r"HSF$^*$", Fsrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(a.u.)', 0, 0, "center right", "C", LineStyleListInverted = "Yes")
ax4 = plt.subplot(324)
ax4.set_title("Gene", fontsize = "small")
SubPlot(ax4, self.t, [[r"G", Grescaled], [r"HSF$^*$G", FsGrescaled], [r"HSFG", FGrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(fraction of total)', 0, 0, "center right", "D", LineStyleListInverted = "Yes")
ax5 = plt.subplot(325)
ax5.set_title("mRNA", fontsize = "small")
SubPlot(ax5, self.t, [[r"mR$_{F}$", RFrescaled], [r"mR$_{HP}$", RHPrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(a.u.)', 0, 0, "center right", "E", LineStyleListInverted = "Yes")
ax6 = plt.subplot(326)
ax6.set_title("Heat Shock Protein", fontsize = "small")
SubPlot(ax6, self.t, [[r"HP", HPrescaled]],
'Time (min)', tmin, tmax, 'Concentration\n(a.u.)', 0, 0, "center right", "F", LineStyleListInverted = "No")
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
MakeGrayBackgroundTemperature(ax, self.timeset, self.TparamSet)
# Make the time measured in hours for certain plots
if self.TparamSet.CurrentParams["Ttype"] == 2 and self.TparamSet.CurrentParams["tb"] >= 12 * 60. * 60. + vorl:
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.set_xticks([0., 6. * 60., 12. * 60., 18. * 60., 24 * 60., (24. + 8.) * 60.])
ax.set_xticklabels(["0", "6", "12", "18", "24", "24+8"])
ax.set_xlabel('Time (h)', fontsize="small")
ax.set_xlim(0. - 60., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
if self.TparamSet.CurrentParams["Ttype"] == 7:
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.set_xticks([0., 3. * 60., 6. * 60., 9. * 60., 12. * 60., 15. * 60., 18. * 60., 21 * 60., 24 * 60.])
if tminMANUAL[0] == "Yes":
ax.set_xticks([0.+tminMANUAL[1]/60., 3. * 60.+tminMANUAL[1]/60., 6. * 60.+tminMANUAL[1]/60., 9. * 60.+tminMANUAL[1]/60., 12. * 60.+tminMANUAL[1]/60., 15. * 60.+tminMANUAL[1]/60., 18. * 60.+tminMANUAL[1]/60., 21 * 60.+tminMANUAL[1]/60., 24 * 60.+tminMANUAL[1]/60.])
ax.set_xticklabels(["3:00", "6:00", "9:00", "12:00", "15:00", "18:00", "21:00", "24:00", "3:00"],
fontsize="x-small")
ax.set_xlabel('Time (h)', fontsize="small")
ax.set_xlim(tmin / 60., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
if ModifyKset == "No":
PlotAndSave(fig1, "HeatShockResponse_" + self.name, "PS", 1, 0)
elif ModifyKset == "Yes":
MyString = ""
for key in ParamsToBeModif:
MyString = MyString + key + str(ParamsToBeModif[key])
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.axvline(x=((TimeOfMod - vorl) / 60.), linestyle='--', color='black')
PlotAndSave(fig1, "HeatShockResponse_" + MyString + "_" + self.name, "PS", 1, 0)
def FuncTimeRun(self, DirectControlnuPp, AvoidPlots, ModifyKset="No", ParamsToBeModif={}, TimeOfMod=vorl, Add3rdmRNA_ARS="No", ZoomInPanelA="No", tminMANUAL=("No",123456789)):
"""Solve ODEs system for given parameter values, plot time behaviour of T and concentrations"""
#print("!!! In FuncTimeRun " + str(DirectControlnuPp))
#print("Using 12 equations!")
SolveODEsystem(self, DirectControlnuPp, ModifyKset, ParamsToBeModif, TimeOfMod, Add3rdmRNA_ARS)
if AvoidPlots == "No":
self.PlotTemperature(tminMANUAL)
PlotTrajectories(self, ModifyKset, ParamsToBeModif, TimeOfMod, ZoomInPanelA, tminMANUAL)
elif AvoidPlots == "Yes":
pass
else:
print("Error in FuncTimeRun")
def FuncTimeRun10eqs(self, DirectControlnuPp, AvoidPlots):
"""Solve ODEs system for given parameter values, plot time behaviour of T and concentrations"""
#print("!!! In FuncTimeRun " + str(DirectControlnuPp))
#print("Using 10 equations!")
SolveODEsystem10eqs(self, DirectControlnuPp)
if AvoidPlots == "No":
self.PlotTemperature()
PlotTrajectories(self)
elif AvoidPlots == "Yes":
pass
else:
print("Error in FuncTimeRun10eqs")
def FuncTimeRun9eqs(self, DirectControlnuPp, AvoidPlots):
"""Solve ODEs system for given parameter values, plot time behaviour of T and concentrations"""
#print("!!! In FuncTimeRun " + str(DirectControlnuPp))
#print("Using 9 equations!")
SolveODEsystem9eqs(self, DirectControlnuPp)
if AvoidPlots == "No":
self.PlotTemperature()
PlotTrajectories(self)
elif AvoidPlots == "Yes":
pass
else:
print("Error in FuncTimeRun10eqs")
def FuncFeedingExperimentStaur2Dplots(self, ParamName, ListOf3Values):
""" Plots mRNAf and Fs for different values of ParamName, reproduces staurosporine plots """
ListRF, ListFs = [], []
i = 0
for value in ListOf3Values:
self.model.ParamsSetRATES.UpdatePar({ParamName: ListOf3Values[i]})
SolveODEsystem(self, ("No",0.))
ListRF.append(np.copy(self.RF))
ListFs.append(np.copy(self.Fs))
i = i + 1
self.PlotTemperature()
fig2 = figure()
plt.plot(self.t, ListRF[0], 'r', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[0])
plt.plot(self.t, ListRF[1], 'b', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[1])
plt.plot(self.t, ListRF[2], 'black', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[2])
plt.xlim(0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
plt.xlabel('Time (min)', fontsize=18)
plt.ylabel(r'mRNA$_{F}$ ($\mu$M)', fontsize=18)
plt.legend(loc="upper right", fontsize="medium", fancybox=True)
MakeGrayBackgroundTemperature(plt, self.timeset, self.TparamSet)
PlotAndSave(fig2, "FeedingExperimentRF_" + self.name, "PS", 1, 0)
fig3 = figure()
plt.plot(self.t, ListFs[0], 'r', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[0])
plt.plot(self.t, ListFs[1], 'b', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[1])
plt.plot(self.t, ListFs[2], 'black', linewidth=1., label=r"k$_S$' = %r s$^{-1}$" % ListOf3Values[2])
plt.xlim(0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
plt.xlabel('Time (min)', fontsize=18)
plt.ylabel(r'phosphorylated heatshock factor ($\mu$M)', fontsize=18)
plt.legend(loc="upper right", fontsize="medium", fancybox=True)
MakeGrayBackgroundTemperature(plt, self.timeset, self.TparamSet)
PlotAndSave(fig3, "FeedingExperimentF_" + self.name, "PS", 1, 0)
self.model.ParamsSetRATES.RestoreDefaultParams()
def FuncFeedingExperiment3Dplots(self, switchHPmRNAF, ParamName, ParamLabel, ParMin, ParMAX, ParStepsNumber, Camera):
""" Make a 3D plot of some concentrations as functions of time while variating ParamName between ParMin and ParMAX """
# switchHPmRNAF = 0 --> HP, switchHPmRNAF = 1 --> RF
ParStep = (ParMAX - ParMin) / ParStepsNumber
ListOfNValues = []
for i in range(0, ParStepsNumber + 1):
ListOfNValues.append(ParMin + i * ParStep)
ZvalList = []
tis0index = 123456789
for val in ListOfNValues:
self.model.ParamsSetRATES.UpdatePar({ParamName: val})
SolveODEsystem(self, ("No",0.))
if switchHPmRNAF == 0: # Selects which variable to plot on Z
Zval = np.copy(self.HP)
ConcentrationLabel = "HP (mM)"
elif switchHPmRNAF == 1:
Zval = np.copy(self.RF)
ConcentrationLabel = r"mR$_{F}$ ($\mu$M)"
else:
print("Index error in FeedingExperiment3Dplots!")
if tis0index == 123456789: # Cut Z vector to have only times >= 0 for plot
for i in range(1, len(self.t)):
if self.t[i] >= 0:
tis0index = i
break
ZvalCUT = Zval[tis0index:].copy()
ZvalList.append(ZvalCUT)
# Cut the time vector to have only times >= 0 for plot
tCUT = self.t[tis0index:].copy()
fig4 = plt.figure()
axf4 = fig4.add_subplot(111, projection='3d')
# ---> x (obtain oriantation of x vector required)
a_list_of_lists = tCUT.tolist()
a_flattened = [val for sublist in a_list_of_lists for val in sublist]
X_timeList = []
for val in ListOfNValues:
X_timeList.append(a_flattened)
X_time = np.array(X_timeList)
# |
# V y (obtain oriantation of y vector required)
ListOfNLists = []
for j in range(0, len(ListOfNValues)):
ListOfNLists.append([])
i = 0
while i < len(a_flattened):
for j in range(0, len(ListOfNValues)):
ListOfNLists[j].append(ListOfNValues[j])
i = i + 1
Y_k = np.array(ListOfNLists)
# ---> x
# |
# V y (obtain oriantation of x and y in a matrix to finally add Z)
a_list_of_lists = []
a_flattened = []
for k in range(0, len(ListOfNValues)):
a_list_of_lists.append(ZvalList[k].tolist())
a_flattened.append([val for sublist in a_list_of_lists[k] for val in sublist])
Z_Concentration = np.array(a_flattened)
# axf4.plot_wireframe(X_time, Y_k, Z_Concentration, rstride=1, cstride=25)
axf4.plot_surface(X_time, Y_k, Z_Concentration, cmap=cm.jet, rstride=1, cstride=5, linewidth=0.2)
axf4.set_xlabel("time (min)")
axf4.set_ylabel(ParamLabel)
axf4.set_zlabel(ConcentrationLabel)
axf4.view_init(elev=Camera[0], azim=Camera[1]) # Set camera angle for optimal view
PlotAndSave(fig4, "FeedingExperiment3D_F_" + self.name, "PS", 0, 0)
self.model.ParamsSetRATES.RestoreDefaultParams()
def FuncFeedingExperimentPlotsVsData(self, ParamName, ListOfValues,
ModelLegend, YVariabToPlot, DataFileName, DataLegend, Ylabel, NameOfFigure,
LegendPosition, ColumnNumber, MultipleT="No", TParamsToUpdate={}):
""" Simulate feeding experiment by variating a k between two values, and compare with literature data """
Yval, YvalNORM, ListForPlotting = [], [], []
i = 0
for val in ListOfValues:
if MultipleT == "No":
self.model.ParamsSetRATES.UpdatePar({ParamName: ListOfValues[i]})
SolveODEsystem(self, ("No",0.))
Yval.append(np.copy(YVariabToPlot))
if MultipleT == "Yes":
SolveODEsystem(self, ("No",0.), ModifyKset="Yes", ParamsToBeModif={ParamName: ListOfValues[i]}, TimeOfMod=vorl)
Yval.append(np.copy(YVariabToPlot))
self.TparamSet.UpdatePar(TParamsToUpdate) # Update T settings for HS
SolveODEsystem(self, ("No",0.), ModifyKset="Yes", ParamsToBeModif={ParamName: ListOfValues[i]}, TimeOfMod=vorl)
Yval.append(np.copy(YVariabToPlot))
self.TparamSet.RestoreDefaultParams() # T settings for HS back to default
i = i + 1
self.PlotTemperature()
Max = max(np.max(Yval[i]) for i in range(len(Yval)))
for j in range(len(Yval)):
YvalNORM.append(np.asarray(Yval[j]) * 100. / Max)
ListForPlotting.append([ModelLegend[j], YvalNORM[j]]) ### ListForPlotting.append([ModelLegend[j],YvalNORM[j]])
ListOfOutputArrays = []
FromDataFileToArrays(DataFileName, ColumnNumber, ListOfOutputArrays) # Read data file, put in list of arrays
DataTimeArMOD = ListOfOutputArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig6 = figure()
print()
print(DataTimeArMOD)
print()
print(ListOfOutputArrays)
print()
ax1 = plt.subplot(121)
SubPlot(ax1, self.t, ListForPlotting, 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl) / 60., Ylabel, 0, 0, LegendPosition, "A",
Legendfontsize="small", Legendfancybox=True)
ax2 = plt.subplot(122)
DataSubPlot(ax2, DataTimeArMOD, ListOfOutputArrays, 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl) / 60., Ylabel, 0., 100., LegendPosition, DataLegend, "B",
Legendfontsize="small", Legendfancybox=True)
for ax in [ax1, ax2]:
MakeGrayBackgroundTemperature(ax, self.timeset, self.TparamSet)
PlotAndSave(fig6, "FeedingExperimentVsData_" + NameOfFigure + '_' + self.name, "PS", 1, 0)
self.model.ParamsSetRATES.RestoreDefaultParams()
def FuncFeedingExperimentPlotsVsDataALLinONE(self, ParamName, ListOfValues,
ModelLegend, YVariabToPlot, DataFileName, DataLegend, Ylabel, NameOfFigure,
LegendPosition, ColumnNumber, MultipleT="No", TParamsToUpdate={}):
""" Simulate feeding experiment by variating a k between two values, and compare with literature data """
Yval, YvalNORM, ListForPlotting = [], [], []
i = 0
for val in ListOfValues:
if MultipleT == "No":
self.model.ParamsSetRATES.UpdatePar({ParamName: ListOfValues[i]})
SolveODEsystem(self, ("No",0.))
Yval.append(np.copy(YVariabToPlot))
if MultipleT == "Yes":
SolveODEsystem(self, ("No",0.), ModifyKset="Yes", ParamsToBeModif={ParamName: ListOfValues[i]}, TimeOfMod=vorl)
Yval.append(np.copy(YVariabToPlot))
self.TparamSet.UpdatePar(TParamsToUpdate) # Update T settings for HS
SolveODEsystem(self, ("No",0.), ModifyKset="Yes", ParamsToBeModif={ParamName: ListOfValues[i]}, TimeOfMod=vorl)
Yval.append(np.copy(YVariabToPlot))
self.TparamSet.RestoreDefaultParams() # T settings for HS back to default
i = i + 1
self.PlotTemperature()
Max = max(np.max(Yval[i]) for i in range(len(Yval)))
for j in range(len(Yval)):
YvalNORM.append(np.asarray(Yval[j]) * 100. / Max)
ListForPlotting.append([ModelLegend[j], YvalNORM[j]]) ### ListForPlotting.append([ModelLegend[j],YvalNORM[j]])
ListOfOutputArrays = []
FromDataFileToArrays(DataFileName, ColumnNumber, ListOfOutputArrays) # Read data file, put in list of arrays
DataTimeArMOD = ListOfOutputArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig6 = figure()
print()
print(DataTimeArMOD)
print()
print(ListOfOutputArrays)
print()
ax = plt.subplot(111)
time = self.t
ArraysToPlot = ListForPlotting
xLabel = 'Time (min)'
xMin = 0.
xMax = (self.timeset.CurrentParams["t_stop"] - vorl) / 60.
yLabel = Ylabel
yMin = 0
yMax = 0
LegendPosition = LegendPosition
SubPlotLabel = "A"
Legendfontsize = "medium"
Legendfancybox = True
#def SubPlot(ax, time, ArraysToPlot, xLabel, xMin, xMax, yLabel, yMin, yMax, LegendPosition, SubPlotLabel,
Legendfancybox=True
UsePointMarker="No"
LineStyleListInverted ="No"
ColorsList = ['red', 'blue', 'green', 'black', 'orange', 'gray', 'violet', 'yellow', 'cyan', 'chartreuse']
LinestylesList = ['-', '-.', '--', ':', '-', '--', '-.', ':']
MarkersList = ["o", "s", "^", "v", ">", "<", ",",]
for i in range(len(ArraysToPlot)):
ax.plot(time, ArraysToPlot[i][1], color=ColorsList[i], linewidth=2, linestyle=LinestylesList[i], label=ArraysToPlot[i][0])
for k in [1,2,3]:
ax.plot(DataTimeArMOD, ListOfOutputArrays[k], marker=MarkersList[k], color=ColorsList[k-1], label=DataLegend[k-1],linestyle='None', markersize = 10)
ax.set_xlim(xMin, xMax)
if (yMin - yMax) != 0:
ax.set_ylim(yMin, yMax)
ax.set_xlabel(xLabel, fontsize="medium")
ax.set_ylabel(yLabel, fontsize="medium")
#AddLetterToSubplot(ax, SubPlotLabel, -0.2, 1.065)
ax.legend(loc=LegendPosition, numpoints=1, fontsize=Legendfontsize, fancybox=Legendfancybox)
self.model.ParamsSetRATES.RestoreDefaultParams()
PlotAndSave(fig6, "FeedingExperimentVsDataALLinONE_" + NameOfFigure + '_' + self.name, "PS", 1, 0)
def FuncTimeCourseVsDataPlot(self):
""" Compare time course with literature data """
SolveODEsystem(self, ("No",0.))
#Y = np.copy(self.HP)
# RESCALING FOR PLOTTING
Y = np.copy(self.HP)
Yrescaled = []
i = 0
for element in self.t:
Yrescaled.append(self.HP[i]/SetOfReferenceValuesForPlotting["HSP"])
i=i+1
self.PlotTemperature()
ListOfDataArrays = []
FromDataFileToArrays("DataFiles/DataMuehlhaus2011Fig3ABC.dat", 19, ListOfDataArrays) # Charge data file
DataTimeMOD = ListOfDataArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig7 = figure()
ax1 = plt.subplot(221)
#ax1.plot(self.t, Y, 'blue', linewidth=1., label="HP")
ax1.plot(self.t, Yrescaled, 'blue', linewidth=1., label="HP")
ax1.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax1.set_xlabel('Time (min)', fontsize="small")
ax1.set_ylim(0., 6.5/SetOfReferenceValuesForPlotting["HSP"]) # 5.5
#ax1.set_ylabel(r'Heatshock protein (mM)', fontsize="small")
ax1.set_ylabel(r'Heatshock protein (a.u.)', fontsize="small")
ax1.legend(loc='lower right', fontsize="small", fancybox=True)
AddLetterToSubplot(ax1, "A", -0.25, 1.055)
ax2 = plt.subplot(222)
ax2.errorbar(DataTimeMOD, ListOfDataArrays[1], marker="s", yerr=[ListOfDataArrays[2], ListOfDataArrays[3]],
color='green', label="HSP70A, western blot")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[4], marker="s", yerr=[ListOfDataArrays[5], ListOfDataArrays[6]],
color='red', label="HSP70A, mass spectrometry")
ax2.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax2.set_ylim(-1.6, 1.6)
ax2.set_xlabel('Time (min)', fontsize="small")
ax2.set_ylabel('z-score', fontsize="small")
ax2.legend(loc='lower right', numpoints=1, fontsize="small", fancybox=True)
AddLetterToSubplot(ax2, "B", -0.25, 1.055)
ax3 = plt.subplot(223)
ax3.errorbar(DataTimeMOD, ListOfDataArrays[7], marker="s", yerr=[ListOfDataArrays[8], ListOfDataArrays[9]],
color='green', label="HSP70B, western blot")
ax3.errorbar(DataTimeMOD, ListOfDataArrays[10], marker="s", yerr=[ListOfDataArrays[11], ListOfDataArrays[12]],
color='red', label="HSP70B, mass spectrometry")
ax3.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax3.set_ylim(-1.6, 1.6)
ax3.set_xlabel('Time (min)', fontsize="small")
ax3.set_ylabel('z-score', fontsize="small")
ax3.legend(loc='lower right', numpoints=1, fontsize="small", fancybox=True)
AddLetterToSubplot(ax3, "C", -0.25, 1.055)
ax4 = plt.subplot(224)
ax4.errorbar(DataTimeMOD, ListOfDataArrays[13], marker="s", yerr=[ListOfDataArrays[14], ListOfDataArrays[15]],
color='green', label="HSP90A, western blot")
ax4.errorbar(DataTimeMOD, ListOfDataArrays[16], marker="s", yerr=[ListOfDataArrays[17], ListOfDataArrays[18]],
color='red', label="HSP90A, mass spectrometry")
ax4.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax4.set_ylim(-1.6, 1.6)
ax4.set_xlabel('Time (min)', fontsize="small")
ax4.set_ylabel('z-score', fontsize="small")
ax4.legend(loc='lower right', numpoints=1, fontsize="small", fancybox=True)
AddLetterToSubplot(ax4, "D", -0.25, 1.055)
for ax in [ax1, ax2, ax3, ax4]:
MakeGrayBackgroundTemperature(ax, self.timeset, self.TparamSet)
PlotAndSave(fig7, "TimeCoruseData_" + self.name, "PS", 1, 0)
self.model.ParamsSetRATES.RestoreDefaultParams()
def FuncTimeCourseVsDataPlotAllInOne(self):
""" Compare time course with literature data """
SolveODEsystem(self, ("No",0.))
#Y = np.copy(self.HP)
# RESCALING FOR PLOTTING
Y = np.copy(self.HP)
Yrescaled = []
i = 0
for element in self.t:
Yrescaled.append(self.HP[i]/SetOfReferenceValuesForPlotting["HSP"])
i=i+1
self.PlotTemperature()
ListOfDataArrays = []
FromDataFileToArrays("DataFiles/DataMuehlhaus2011Fig3ABC.dat", 19, ListOfDataArrays) # Charge data file
DataTimeMOD = ListOfDataArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig7 = figure()
#print(Yrescaled)
#temp = self.t
#print(temp.where(temp==0))
print(np.average(Yrescaled))
ax1 = plt.subplot()#plt.subplot(121)
#ax1.plot(self.t, Y, 'blue', linewidth=1., label="HP")
ax1.plot(self.t, Yrescaled, 'black', linewidth=2., label="HP, simulation")
ax1.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax1.set_xlabel('Time (min)', fontsize="medium")
ax1.set_ylim(0., 7.0/SetOfReferenceValuesForPlotting["HSP"]) # 5.5
#ax1.set_ylabel(r'Heatshock protein (mM)', fontsize="small")
ax1.set_ylabel(r'Concentration of Heatshock protein (a.u.)', fontsize="medium")
ax1.legend(loc='upper left', fontsize="medium", fancybox=True)
AddLetterToSubplot(ax1, "A", -0.25, 1.055)
ax2 = ax2 = ax1.twinx()#plt.subplot(122)
ax2.errorbar(DataTimeMOD, ListOfDataArrays[1], marker="v", yerr=[ListOfDataArrays[2], ListOfDataArrays[3]],
color='green', linestyle='',label="HSP70A, western blot")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[4], marker="s", yerr=[ListOfDataArrays[5], ListOfDataArrays[6]],
color='green', linestyle='', label="HSP70A, mass spectrometry")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[7], marker="v", yerr=[ListOfDataArrays[8], ListOfDataArrays[9]],
color='red', linestyle='',label="HSP70B, western blot")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[10], marker="s", yerr=[ListOfDataArrays[11], ListOfDataArrays[12]],
color='red', linestyle='', label="HSP70B, mass spectrometry")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[13], marker="v", yerr=[ListOfDataArrays[14], ListOfDataArrays[15]],
color='blue', linestyle='', label="HSP90A, western blot")
ax2.errorbar(DataTimeMOD, ListOfDataArrays[16], marker="s", yerr=[ListOfDataArrays[17], ListOfDataArrays[18]],
color='blue', linestyle='', label="HSP90A, mass spectrometry")
ax2.set_xlim(0. - 5., (self.timeset.CurrentParams["t_stop"] - vorl) / 60.)
ax2.set_ylim(-1.6, 1.6)
ax2.set_xlabel('Time (min)', fontsize="medium")
ax2.set_ylabel('z-score', fontsize="medium")
ax2.legend(loc='lower right', numpoints=1, fontsize="medium", fancybox=True)
AddLetterToSubplot(ax2, "B", -0.25, 1.055)
PlotAndSave(fig7, "TimeCoruseData_" + self.name, "PS", 1, 0)
self.model.ParamsSetRATES.RestoreDefaultParams()
def FuncTimeRunPlusARS(self):
""" Compare with data for single HS experiment (using ARS enzyme) """
SolveODEsystem(self, ("No",0.), Add3rdmRNA_ARS="Yes")
self.PlotTemperature()
ListOfOutputArrays = []
FromDataFileToArrays("DataFiles/DataShroda2000ARSFig6b.dat", 3,
ListOfOutputArrays) # Read data file, put in list of arrays
DataTimeArMOD = ListOfOutputArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
Ylabel = ["ARS mRNA levels (arbitrary)", r"ARS enzyme activity"]
#DataLegend = ["ARS mRNA", "ARS activity"]
DataLegend = ["", ""]
# RESCALING FOR PLOTTING
print("started ARS...")
RHP_ARS_rescaled, HP_ARS_rescaled = [], []
i = 0
Max_RHP_ARS = max(self.RHP_ARS)
Max_HP_ARS = max(self.HP_ARS)
print("started for loop...")
for element in self.t:
RHP_ARS_rescaled.append(self.RHP_ARS[i]/Max_RHP_ARS)
HP_ARS_rescaled.append(self.HP_ARS[i]/Max_HP_ARS)
i=i+1
print("ended for loop...")
fig8 = figure()
ax1 = plt.subplot(221)
SubPlot(ax1, self.t, [["", RHP_ARS_rescaled]],
'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'Concentration of mRNA for ARS enzyme ($\mu$M)', 0.,
0., "center right", "A")
#SubPlot(ax1, self.t, [["", self.RHP_ARS]],
# 'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'mRNA for ARS enzyme ($\mu$M)', 0.,
# 0., "center right", "A")
ax2 = plt.subplot(222)
#SubPlot(ax2, self.t, [[r"", self.HP_ARS]],
# 'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'ARS enzyme ($\mu$M)', 0., 0.,
# "center right", "B")
SubPlot(ax2, self.t, [[r"", HP_ARS_rescaled]],
'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'ARS enzyme ($\mu$M)', 0., 0.,
"center right", "B")
ax3 = plt.subplot(223)
DataSubPlot(ax3, DataTimeArMOD, [DataTimeArMOD, ListOfOutputArrays[1]], 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl) / 60., Ylabel[0], 0., 1200., "center right",
[DataLegend[0]], "C", Legendfontsize="x-small")
ax4 = plt.subplot(224)
DataSubPlot(ax4, DataTimeArMOD, [DataTimeArMOD, ListOfOutputArrays[2]], 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl) / 60., Ylabel[1], 0., 2000., "center right",
[DataLegend[1]], "D", Legendfontsize="x-small")
for ax in [ax1, ax2, ax3, ax4]:
MakeGrayBackgroundTemperature(ax, self.timeset, self.TparamSet)
PlotAndSave(fig8, "HeatShockARSExp_" + self.name, "PS", 1, 1)
PlotTrajectories(self)
def FuncTimeRunPlusARSdoubleHS(self, EmptyListToBeFilled, AvoidPlots):
""" Compare with data for double HS experiments (using ARS enzyme) """
Yval, ListForPlotting = [], []
Legend = ["single HS", "2nd HS 2 h after 1st", "2nd HS 3 h after 1st", "2nd HS 4 h after 1st",
"2nd HS 5 h after 1st"]
# Solve ODEs system with no 2nd HS
SolveODEsystem(self, ("No",0.), Add3rdmRNA_ARS="Yes")
Yval.append(np.copy(self.HP_ARS))
# Solve ODEs system multiple times with 2nd HS after, 2h, 3h, 4h, 5h.
HSduration = self.TparamSet.CurrentParams["tb"] - self.TparamSet.CurrentParams["ta"]
for val in [2., 3., 4., 5.]:
self.TparamSet.UpdatePar({"Ttype": 4, "tc": val * 3600. + HSduration + vorl,
"td": (val * 3600. + HSduration) + HSduration + vorl}) # Update T
SolveODEsystem(self, ("No",0.), Add3rdmRNA_ARS="Yes")
Yval.append(deepcopy(self.HP_ARS))
for j in range(len(Yval)):
ListForPlotting.append(deepcopy([Legend[j], Yval[j]]))
if AvoidPlots == "Yes":
pass
elif AvoidPlots == "No":
# Commands to plot from data file
ListOfOutputArrays = []
FromDataFileToArrays("DataFiles/DataShroda2000ARSFig7b.dat", 6,
ListOfOutputArrays) # Read data file, put in list of arrays
DataTimeArMOD = ListOfOutputArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig9 = figure()
ax1 = plt.subplot(121)
SubPlot(ax1, self.t, ListForPlotting,
'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'ARS enzyme concentration (a.u.)', 0.,
0., "lower right", "A")
#'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'ARS enzyme concentration ($\mu$M)', 0.,
#0., "lower right", "A")
ax2 = plt.subplot(122)
DataSubPlot(ax2, DataTimeArMOD, ListOfOutputArrays, 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl)/60.,r"ARS enzyme activity (nmol $\alpha$-naphtol/mg protein $\cdot$ h)", 0., 3500., "lower right",
Legend, "B", Legendfontsize="x-small")
PlotAndSave(fig9, "HeatShockARSExpDoubleHS_" + self.name, "PS", 1, 1)
self.PlotTemperature()
PlotTrajectories(self)
else:
print("Error in AvoidPlots ARS time run!")
# In case in addition to the plots you also would like the numbers, take them out as imput of the function
OutputList = []
OutputList.append(deepcopy(self.t))
OutputList.append(deepcopy(ListForPlotting))
#print("HERE\n" + str(OutputList) + "\n")
EmptyListToBeFilled.append(deepcopy(OutputList))
def FuncTimeRunPlusARSdoubleHSMOD(self, EmptyListToBeFilled, AvoidPlots, MyLinestyle):
""" Compare with data for double HS experiments (using ARS enzyme) """
Yval, ListForPlotting = [], []
Legend = ["single HS", "2nd HS 2 h after 1st", "2nd HS 3 h after 1st", "2nd HS 4 h after 1st",
"2nd HS 5 h after 1st"]
# Solve ODEs system with no 2nd HS
SolveODEsystem(self, ("No",0.), Add3rdmRNA_ARS="Yes")
Yval.append(np.copy(self.HP_ARS))
# Solve ODEs system multiple times with 2nd HS after, 2h, 3h, 4h, 5h.
HSduration = self.TparamSet.CurrentParams["tb"] - self.TparamSet.CurrentParams["ta"]
for val in [2., 3., 4., 5.]:
self.TparamSet.UpdatePar({"Ttype": 4, "tc": val * 3600. + HSduration + vorl,
"td": (val * 3600. + HSduration) + HSduration + vorl}) # Update T
SolveODEsystem(self, ("No",0.), Add3rdmRNA_ARS="Yes")
Yval.append(deepcopy(self.HP_ARS))
YvalMAX = max(max(Yval[0]),max(Yval[1]),max(Yval[2]),max(Yval[3]),max(Yval[4]))
for j in range(len(Yval)):
ListForPlotting.append(deepcopy([Legend[j], Yval[j]/YvalMAX]))
if AvoidPlots == "Yes":
pass
elif AvoidPlots == "No":
# Commands to plot from data file
ListOfOutputArrays = []
FromDataFileToArrays("DataFiles/DataShroda2000ARSFig7b.dat", 6,
ListOfOutputArrays) # Read data file, put in list of arrays
DataTimeArMOD = ListOfOutputArrays[0] + ((self.TparamSet.CurrentParams["ta"] - vorl) / 60.) # Translate time values
fig9 = figure()
ax = plt.subplot(111)
SubPlot(ax, self.t, ListForPlotting,
'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'Normalized ARS enzyme activity', 0.,
1., "lower right", "A")
#'Time (min)', 0., (self.timeset.CurrentParams["t_stop"] - vorl) / 60., 'ARS enzyme concentration ($\mu$M)', 0.,
#0., "lower right", "A")
MAX = max(max(ListOfOutputArrays[1]),max(ListOfOutputArrays[2]),max(ListOfOutputArrays[3]),max(ListOfOutputArrays[4]),max(ListOfOutputArrays[5]))
#ax2 = plt.subplot(122)
DataSubPlot(ax, DataTimeArMOD, ListOfOutputArrays/MAX, 'Time (min)', 0.,
(self.timeset.CurrentParams["t_stop"] - vorl)/60.,r"Normalized ARS enzyme activity", 0., 1., "lower right",
Legend, "B", Legendfontsize="medium", MyLinestyle='None', ncolumns=2)
# r"ARS enzyme activity (nmol $\alpha$-naphtol/mg protein $\cdot$ h)"
PlotAndSave(fig9, "HeatShockARSExpDoubleHS_" + self.name, "PS", 1, 1)
self.PlotTemperature()
PlotTrajectories(self)
else:
print("Error in AvoidPlots ARS time run!")
# In case in addition to the plots you also would like the numbers, take them out as imput of the function
OutputList = []
OutputList.append(deepcopy(self.t))
OutputList.append(deepcopy(ListForPlotting))
#print("HERE\n" + str(OutputList) + "\n")
EmptyListToBeFilled.append(deepcopy(OutputList))
