def mySolve(xf,boltz_eqs,rtol,atol,verbosity=50):
"""Sets the main options for the ODE solver and solve the equations. Returns the
array of x,y points for all components.
If numerical instabilities are found, re-do the problematic part of the evolution with smaller steps"""
boltz_solver = CVode(boltz_eqs) #Define solver method
boltz_solver.rtol = rtol
boltz_solver.atol = atol
boltz_solver.verbosity = verbosity
boltz_solver.linear_solver = 'SPGMR'
boltz_solver.maxh = xf/300.
xfinal = xf
xres = []
yres = []
sw = boltz_solver.sw[:]
while xfinal <= xf:
try:
boltz_solver.re_init(boltz_eqs.t0,boltz_eqs.y0)
boltz_solver.sw = sw[:]
x,y = boltz_solver.simulate(xfinal)
xres += x
for ypt in y: yres.append(ypt)
if xfinal == xf: break #Evolution has been performed until xf -> exit
except Exception,e:
print e
if not e.t or 'first call' in e.msg[e.value]:
logger.error("Error solving equations:\n "+str(e))
return False
xfinal = max(e.t*random.uniform(0.85,0.95),boltz_eqs.t0+boltz_solver.maxh) #Try again, but now only until the error
logger.warning("Numerical instability found. Restarting evolution from x = "
+str(boltz_eqs.t0)+" to x = "+str(xfinal))
continue
xfinal = xf #In the next step try to evolve from xfinal -> xf
sw = boltz_solver.sw[:]
x0 = float(x[-1])
y0 = [float(yval) for yval in y[-1]]
boltz_eqs.updateValues(x0,y0,sw)
def RunModel(flagD,th,STIM,xoutS,xoutG,dataS,dataG,kTCleak,kTCmaxs, inds_to_watch = []):
# going to return [tout_all,xoutG_all,xoutS_all]
# This function runs the model and outputs timecourse simulation results.
# Required Inputs:
# flagD: 1 for deterministic simulations, 0 for stochastic simulations.
# th: simulation time (hours)
# STIM: stimulus vector
#
# Outputs:
# tout_all: n-by-1 vector of time values (seconds)
# xoutG_all: n-by-g matrix of species (g) through time (n) (g indices lines up to gm tab in Names.xls sheet)
# xoutS_all: n-by-p matrix of speices (p) through time (n) (p indices lines up to PARCDL tab in Names.xls sheet)
#
# %% RUN
ts=dataS.ts;
ts_up=ts;
N_STEPS=th*3600/ts;
N_STEPS = int(N_STEPS)
# % IMPORT INITIALIZED PARAMETERS
pathi='initialized/';
# % for PARCDL
kbR0 = float(open(pathi + "i_kbR0.txt").read())
kTL = []
with open(pathi + 'i_kTLF.txt') as f:
for line in f:
kTL.append(float(line))
kTL = np.array(kTL)
kC173 = float(open(pathi + "i_kC173.txt").read())
kC82 = float(open(pathi + "i_kC82.txt").read())
kA77 = float(open(pathi + "i_kA77.txt").read())*5
# ^ forgot to add the *5 to this line and spent sooooo long looking for this mistake lol
kA87 = float(open(pathi + "i_kA87.txt").read())
Rt = float(open(pathi + "i_Rt.txt").read())
EIF4Efree = float(open(pathi + "i_EIF4Efree.txt").read())
kDDbasal = float(open(pathi + "i_kDDbasal.txt").read())
Vc = dataS.kS[2]
# % for gm
if len(kTCleak)==0:
for line in open(pathi + "i_kTCleakF.txt").readlines():
kTCleak.append(float(line))
kTCleak = np.matrix.transpose(np.matrix(kTCleak))
if len(kTCmaxs)==0:
for line in open(pathi + "i_kTCmaxsF.txt").readlines():
kTCmaxs.append(float(line))
kTCmaxs = np.matrix.transpose(np.matrix(kTCmaxs))
kTCmaxs = np.array(kTCmaxs)
# % modifying data.S structure
dataS.kS[0]=Rt;
dataS.kS[1]=EIF4Efree;
dataS.kS[11]=kbR0;
dataS.kS[16:157]=kTL;
dataS.kS[631]=kC173;
dataS.kS[540]=kC82;
dataS.kS[708]=kA77;
dataS.kS[718]=kA87;
dataS.kS[449]=kDDbasal;
# % modifying data.G structure
dataG.kTCleak=kTCleak;
dataG.kTCmaxs=kTCmaxs;
# %species
if len(xoutS) == 0:
xoutS = []
with open(pathi + 'i_xoutF.csv', newline='') as csvfile:
spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spamreader:
x = ', '.join(row)
x = x.split(',')
to_append = []
for item in x:
to_append.append(float(item))
xoutS.append(to_append)
xoutS = np.matrix(xoutS)
xoutS = xoutS[24,:]
if len(xoutG) == 0:
if flagD:
xoutG = dataG.x0gm_mpc_D
else:
xoutG = dataG.x0gm_mpc
indsD=dataG.indsD
xoutG[indsD] = dataG.x0gm_mpc_D[indsD]
xoutG[indsD+141] = dataG.x0gm_mpc_D[indsD+141]
xoutG[indsD+141*2] = dataG.x0gm_mpc_D[indsD+141*2]
# % Apply STIM
Etop = STIM[len(STIM)-1]
STIM = STIM[0:len(STIM)-1]
# code for logical
if np.any(STIM):
xoutS[0,STIM.astype(bool)] = STIM[STIM.astype(bool)]
dataS.kS[452] = Etop
# NOTE - matlab code
# % Instantiation
# t0 = 0;
# optionscvodes = CVodeSetOptions('UserData', dataS,...
# 'RelTol',1.e-3,...
# 'LinearSolver','Dense',...
# 'JacobianFn',@Jeval774);
# CVodeInit(@createODEs, 'BDF', 'Newton', t0, xoutS', optionscvodes);
#
# %ODE15s options
# %optionsode15s=odeset('RelTol',1e-3,'Jacobian',@Jeval774ode15s);
#
tout_all = np.zeros(shape=(N_STEPS+1))
xoutG_all = np.zeros(shape=(N_STEPS+1,len(xoutG)))
xoutS_all = np.zeros(shape=(N_STEPS+1,xoutS.shape[1]))
tout_all[0] = 0
xoutG_all[0,:] = np.matrix.transpose(xoutG)
xoutS_all[0,:] = xoutS
# % Starting simulations
print("... Starting Sims")
start_time = time.time()
for i in range(0,int(N_STEPS)+1):
# gm
[xginN,xgacN,AllGenesVecN,xmN,vTC] = gm(flagD,dataG,ts,xoutG,xoutS);
xoutG = np.append(np.append(np.squeeze(np.asarray(xgacN)),np.squeeze(np.asarray(xginN))),np.squeeze(np.asarray(xmN)))
# NOTE - matrix to array syntax
xoutG = np.matrix.transpose(np.matrix(xoutG))
dataS.mMod=xmN*(1E9/(Vc*6.023E+23)); #convert mRNAs from mpc to nM
dataG.AllGenesVec=AllGenesVecN;
xoutG_all[i,:] = np.matrix.transpose(xoutG)
try:
xoutS_all[i,:] = np.squeeze(np.asarray(xoutS))
except:
xoutS_all[i,:] = np.squeeze(np.asarray(xoutS[1]))
if xoutS[0,103]<xoutS[0,105]:
print("Apoptosis happened")
tout_all = tout_all[0:i+1]
xoutG_all = xoutG_all[0:i+1]
xoutS_all = xoutS_all[0:i+1]
return [tout_all, xoutG_all, xoutS_all]
# scipy.odeint -- takes forever
# xoutS = odeint(createODEs, xoutS_all[i,:],np.array([ts_up-ts, ts_up]), args=(dataS.kS,dataS.VvPARCDL,dataS.VxPARCDL,dataS.S_PARCDL,dataS.mExp_nM.as_matrix(),dataS.mMod,dataS.flagE))
# assimulo -- much faster
ode_start_time = time.time()
exp_mod = MyProblem(y0=xoutS_all[i,:],dataS=dataS, Jeval774 = Jeval774)
exp_sim = CVode(exp_mod)
exp_sim.verbosity=50
exp_sim.re_init(ts_up-ts,xoutS_all[i,:] )
t1, xoutS = exp_sim.simulate(ts_up, 1)
try:
print(xoutS[1,inds_to_watch])
except:
print(xoutS)
print("--- %s seconds ---" % (time.time() - ode_start_time))
print("Percent complete: " + str(i/N_STEPS))
# xoutG_all[i,:] = np.matrix.transpose(xoutG);
try:
tout_all[i+1] = ts_up
except:
pass
ts_up = ts_up + ts
print("ODEs done")
print("--- %s seconds ---" % (time.time() - start_time))
return [tout_all, xoutG_all, xoutS_all]
