def Baserun(pdict):
model = stochpy.SSA()
ignored = pdict
model.Model(model_file='protective_decolV04.psc', dir=workingdir)
model.Endtime(end_time)
model.DoStochSim()
model.ShowSpecies()
def Metapop_exp(iteration):
model = stochpy.SSA()
model.Model(model_file='1dr_3nurseratio.psc.txt', dir=workingdir)
model.Endtime(end_time)
model.DoStochSim()
model.ShowSpecies()
def stoch_simulate(self, amounts, t=20):
"""
Stochastic discrete simulation of the CRN until time `t` with initial
molecule count `amounts`. The species that are omitted from the
dictionary of initial concentrations are assumed to have an initial
count of 0. The simulation runs until time `t` unless the
propensities all reach zero. If that's the case, the system has
reached a steady state, and the simulation stops.
args:
amount: Dict[Species, int]
A map describing each species' initial count.
t: Union[float, int]
The upper bound of the time to run the simulation to.
"""
# Write psc file
pscfile = utils.datadir(f"{self.name}.psc")
self.write_pscfile(pscfile, amounts)
# invoke stochpy
smod = stochpy.SSA()
smod.Model(pscfile)
smod.DoStochSim(mode="time", end=t)
# return data
data = {}
for sp in self.species:
if sp.name != "nothing":
data[sp] = smod.data_stochsim.getSimData(sp.name)[:, 1]
if "time" not in data:
data["time"] = smod.data_stochsim.getSimData(sp.name)[:, 0]
return Simulation(data, stochastic=True)
def DoExample5(self):
""" Protein turnover example with and without cell division (available at http://stochpy.sourceforge.net/examples.html) """
smod = _stochpy_.SSA(model_file='CellDivision.psc',
dir=_stochpy_.model_dir
) # start SSA module with CellDivision model
### do protein synthesis without cell division ###
smod.DoStochSim(end=10, mode='time')
smod.PlotSpeciesTimeSeries(species2plot='Protein')
smod.DoStochSim(end=300, mode='time')
smod.PlotSpeciesDistributions(species2plot='Protein')
### do protein synthesis with cell division ###
n_generations = 10
cell_division_times = abs(
np.random.gamma(scale=0.1, shape=3, size=n_generations)
) # cell division times with a gamma distribution
smod.DoStochSim(end=cell_division_times[0],
mode='time',
trajectories=1)
smod = doSequentialSim(smod, n_generations, cell_division_times)
smod.PlotSpeciesTimeSeries(species2plot='Protein')
n_generations = 500
cell_division_times = abs(
np.random.gamma(scale=0.1, shape=3, size=n_generations)
) # cell division times with a gamma distribution
smod.DoStochSim(end=cell_division_times[0],
mode='time',
trajectories=1)
smod = doSequentialSim(smod, n_generations, cell_division_times)
smod.PlotSpeciesDistributions(species2plot='Protein')
def get_sim(filename, filedir=None, outdir=None, argDict=dict(), paramDict=dict(), interval=0.5,
suffix='_TS_data.txt'):
smod = stochpy.SSA()
smod.Model(filename, filedir)
if paramDict:
for param in paramDict:
smod.ChangeParameter(param, paramDict[param])
val = paramDict[param]
if val != 0:
exp = int(np.log10(val))
new_val = int(val * 1.0 / 10**exp)
suffix += '_' + param + '_' + str(new_val) + 'e' + str(exp)
else:
suffix += '_' + param + '_' + str(val) + 'e0'
suffix += '.txt'
smod.DoStochSim(**argDict)
smod.Export2File(analysis='timeseries', datatype='species', directory=outdir)
import csv
with open(outdir + filename + '_species_timeseries1.txt', 'rU') as csvfile:
csvfile.readline()
reader = csv.DictReader(csvfile, delimiter='\t')
dictToVector = collections.OrderedDict()
for field in reader.fieldnames:
dictToVector[field] = []
for row in reader:
for field in dictToVector:
dictToVector[field].append(eval(row[field]))
for field in dictToVector:
dictToVector[field] = np.array(dictToVector[field])
#print "dictToVector is ", dictToVector
max_time = max(dictToVector['Time'])
times = np.arange(0, max_time, interval)
# indices = [min([x for x in range(len(dictToVector['Time'])) if dictToVector['Time'][x] > t])
# for t in times]
indices = [np.nonzero(dictToVector['Time'] > t)[0][0] for t in times]
#print "Indices are ", indices[0:10]
newdictToVector = collections.OrderedDict()
for key in dictToVector:
newdictToVector[key] = dictToVector[key][np.ix_(indices)]
out_file = outdir + filename + suffix
with open(out_file, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([key for key in newdictToVector])
for i in range(len(indices)):
writer.writerow([newdictToVector[key][i] for key in newdictToVector])
print "Data written to ", out_file
return smod, newdictToVector, times
def __init__(
self,
n_init_settings,
n_trajs,
obs_state_bounds,
param_space_bounds,
model_name,
time_step,
T,
obs_state_dim,
non_obs_state_dim,
):
self.n_init_settings = n_init_settings
self.n_trajs = n_trajs
self.n_training_points = n_init_settings * n_trajs
self.obs_state_bounds = obs_state_bounds
self.obs_state_dim = obs_state_dim
self.non_obs_state_dim = non_obs_state_dim
self.global_state_space_dim = obs_state_dim + non_obs_state_dim
self.stoch_mod = stochpy.SSA(IsInteractive=False)
self.stoch_mod.Model(model_name + '.psc')
self.directory_name = model_name
self.time_step = time_step
self.T = T # end time
self.param_space_bounds = param_space_bounds
self.param_space_dim = param_space_bounds.shape[0]
def fitness(self, verbose=True):
time = 1000
if not self.cached_fitness is None:
return self.cached_fitness
with open(
"/home/calebsimmons/Hungry_Monsters/hungry_monsters/template.psc"
) as f:
template = "".join(f.readlines())
model = (template % tuple(self.genotype))\
.replace("alpha",str(Chromosome.mrna_cost))\
.replace("beta",str(Chromosome.protein_cost))\
.replace("gamma",str(Chromosome.sugar_benefit))\
.replace("delta",str(Chromosome.enzyme_mrna_cost))\
.replace('zeta',str(Chromosome.enzyme_cost))
filename = "/home/calebsimmons/Hungry_Monsters/hungry_monsters/model%s.psc" % self.serial_no
with open(filename, 'w') as f:
f.write(model)
mod = stochpy.SSA(Method="TauLeaping", File=filename, dir='.')
mod.DoStochSim(epsilon=1, mode="steps", end=time)
mod.PlotTimeSim(species2plot=["ATP"])
atp_label = mod.data_stochsim.species_labels.index('ATP')
self.cached_fitness = mod.data_stochsim.species[-1][atp_label]
#mod2 = Parser(filename)
#atp_label2 = getATP(stochsimm(mod2.parse()))
#self.cached_fitness2 = atp_label2[-1]
mod3 = pysces.model(
filename, dir='/home/calebsimmons/Hungry_Monsters/hungry_monsters')
mod3.doSim(end=60, points=3000)
mod3.SimPlot(plot=["ATP"])
self.cached_fitness = mod3.data_sim.getSimData("ATP")[-1][1]
if verbose:
print self.cached_fitness
return self.cached_fitness
def Baseline(iteration):
model = stochpy.SSA()
model.Model(model_file='MRSA_Colonization.psc', dir=workingdir)
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[4][0][-1]
acquisitions[iteration, 0] = Incident
def Run(filename,k):
model = stochpy.SSA()
model.Model(model_file=filename, dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('psi',k)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
acquisitions = outcomes[4][0][-1]
return acquisitions
def Football(beta_draw):
model = stochpy.SSA()
model.Model(model_file='football_beta_sweep.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('betaF', beta_draw)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
return cases
def Efficient(iteration):
model = stochpy.SSA()
model.Model(model_file='MRSA_Colonization.psc', dir=workingdir)
model.ChangeParameter('tau', (2.389 * 0.823))
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[4][0][-1]
acquisitions[iteration, 2] = Incident
def FirstIt(iteration):
model = stochpy.SSA()
model.Model(model_file="2dr3nurseratiov2.psc",
dir='C:/Users/ssjoh/Documents/Lofgren/Iterations')
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[16][0][-1]
acquisitions[iteration, 1] = Incident
def RedHH(iteration):
model = stochpy.SSA()
model.Model(model_file='MRSA_Colonization.psc', dir=workingdir)
model.ChangeParameter('rho', (4.154 * 0.823))
model.ChangeParameter('iota', (6.520))
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[4][0][-1]
acquisitions[iteration, 3] = Incident
def fitness(self,verbose=True):
time = 100
if not self.cached_fitness is None:
return self.cached_fitness
# with open ("template.psc") as f:
# template = f.read()
# model = template % tuple(self.genotype)
# replace = {
# 'alpha': Chromosome.mrna_cost,
# 'beta': Chromosome.protein_cost,
# 'gamma': Chromosome.sugar_benefit,
# 'delta': Chromosome.enzyme_mrna_cost,
# 'zeta': Chromosome.enzyme_cost
# }
# for k, v in replace.iteritems():
# model.replace (k, str(v))
# filename = "model%s.psc" % self.serial_no
# with open (filename, 'w') as f:
# f.write (model)
with open("template.psc") as f:
template = "".join(f.readlines())
model = (template % tuple(self.genotype))\
.replace("alpha",str(Chromosome.mrna_cost))\
.replace("beta",str(Chromosome.protein_cost))\
.replace("gamma",str(Chromosome.sugar_benefit))\
.replace("delta",str(Chromosome.enzyme_mrna_cost))\
.replace('zeta',str(Chromosome.enzyme_cost))
filename = "model%s.psc" % self.serial_no
with open(filename,'w') as f:
f.write(model)
while self.cached_fitness == None:
try:
mod = stochpy.SSA(Method="Direct", File=filename,dir='.')
mod.DoStochSim(epsilon=.01,mode="time",end=time)
atp_gained_label = mod.data_stochsim.species_labels.index('ATP_gained')
atp_spent_label = mod.data_stochsim.species_labels.index('ATP_spent')
self.cached_fitness = mod.data_stochsim.species[-1][atp_gained_label] - mod.data_stochsim.species[-1][atp_spent_label]
except AssertionError:
continue
mod.PlotTimeSim(species2plot=["ATP_gained",'ATP_spent'])
#mod2 = Parser(filename)
#atp_label2 = getATP(stochsimm(mod2.parse()))
#self.cached_fitness2 = atp_label2[-1]
#mod3 = pysces.model(filename,dir='.')
#mod3.doSim(end = 1200 , points = 1000)
#mod3.SimPlot(plot=["ATP",'S'])
#self.cached_fitness3 = mod3.data_sim.getSimData("ATP")[-1][1]
if verbose:
print self.cached_fitness
return self.cached_fitness
def Reduced(iteration):
model = stochpy.SSA()
model.Model(model_file='MRSA_Colonization.psc', dir=workingdir)
percentchange = random.uniform(0.50, 1)
model.ChangeParameter('rho', (4.154 * percentchange))
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[4][0][-1]
acquisitions[iteration, 0] = (1 - percentchange) * 100
acquisitions[iteration, 1] = Incident
def StochProcess3():
#This uses the stochpy module
smod = stochpy.SSA() #Instance of a Stochastic Simulation Algorithm
smod.DoStochSim(IsTrackPropensities=True)
smod.PlotSpeciesTimeSeries()
smod.PlotPropensitiesTimeSeries()
#Somemore functions
smod.DoStochSim(end=10**5)
smod.GetSpeciesAutocorrelations()
smod.PlotSpeciesAutocorrelations(nlags=25)
smod.GetWaitingtimes()
smod.PlotWaitingtimesDistributions()
stochpy.plt.xlim([10**-4, 10**0])
def LowBetaHighPrevHighMix(iteration, scen):
model = stochpy.SSA()
model.Model(model_file='football_highmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta', 0.25)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration + (n_runs * scen), 0] = 5
results[iteration + (n_runs * scen), 1] = cases
results[iteration + (n_runs * scen), 2] = symptom
def HighControl(iteration, scen):
model = stochpy.SSA()
model.Model(model_file='no_football.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta', 0.30)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
symptom = outcomes[4][0][-1]
results[iteration + (n_runs * scen), 0] = 1
results[iteration + (n_runs * scen), 1] = cases
results[iteration + (n_runs * scen), 2] = symptom
def __init__(self, n_init_states, n_trajs, state_space_bounds, model_name,
time_step, T):
self.n_init_states = n_init_states
self.n_trajs = n_trajs
self.n_training_points = n_init_states * n_trajs
self.state_space_bounds = state_space_bounds
self.state_space_dim = state_space_bounds.shape[0]
self.stoch_mod = stochpy.SSA(IsInteractive=False)
self.stoch_mod.Model(model_name + '.psc')
self.directory_name = model_name
self.time_step = time_step
self.T = T # end time
self.conc_flag = False
def Football(pdict):
model = stochpy.SSA()
model.Model(model_file='football_highmix_highprev.psc', dir=workingdir)
model.Endtime(end_time)
model.ChangeParameter('beta', 0.25)
model.ChangeParameter('sigma', pdict['sigma'] * 0.5)
model.ChangeParameter('gamma', pdict['gamma'] * 0.1960784)
model.ChangeParameter('alpha', pdict['alpha'] * 0.80)
model.ChangeParameter('delta_I', pdict['delta_I'] * 0.20)
model.ChangeParameter('delta_A', pdict['delta_A'] * 0.20)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases = outcomes[2][0][-1]
return cases
def __init__(self, n_init_states, n_trajs, state_space_bounds, model_name,
time_step, T):
# state_space_bounds : shape = (state_space_dim,2)
# param_space_bounds : shape = (param_space_dim,2)
self.n_init_states = n_init_states
self.n_trajs = n_trajs
self.n_training_points = n_init_states * n_trajs
self.state_space_bounds = state_space_bounds
self.state_space_dim = state_space_bounds.shape[0]
self.stoch_mod = stochpy.SSA(IsInteractive=False)
self.stoch_mod.Model(model_name + '.psc')
self.directory_name = model_name
self.time_step = time_step
self.T = T # end time
self.N = None # population size
def simulate(mu, initmRNA):
smod = stochpy.SSA()
smod.Model('Transc-Transl.psc')
smod.ChangeInitialSpeciesCopyNumber("S1", initmRNA)
traj = max(10, int(round(1e6 * (poisson(mu, initmRNA)))))
print "Simulating " + str(traj) + " trajectories"
smod.DoStochSim(end=5, mode='time', trajectories=traj)
smod.GetRegularGrid()
fp = open("poisssim" + str(mu) + "_" + str(initmRNA) + ".pkl", "wb")
pickle.dump(smod.data_stochsim_grid, fp)
fp.close()
del smod
return
def benchmark(fdir, method, tfinal, nsaves, nreal, seed, n_sample):
# initialize
smod = stochpy.SSA()
times = []
smod.Model(model_file='stochpy.psc', dir=fdir)
random.seed(seed)
for i in range(n_sample):
print(f"sample {i+1} / {n_sample}")
smod.DoStochSim(trajectories=nreal,
end=tfinal,
method=method,
mode="time")
times.append(smod.simulation_time)
return times
def Efficient(iteration):
model = stochpy.SSA()
model.Model(model_file='MRSA_Colonization.psc', dir=workingdir)
model.ChangeParameter('rho',random.uniform(3.3232,4.9848))
model.ChangeParameter('sigma',random.uniform(0.0432,0.0648))
model.ChangeParameter('psi',random.uniform(0.0745144,0.1117716))
model.ChangeParameter('theta',random.uniform(0.007592,0.011388))
colonized_admits = random.uniform(0.06232,0.09348)
model.ChangeParameter('nu_C',colonized_admits)
model.ChangeParameter('nu_U',1-colonized_admits)
model.ChangeParameter('iota',random.uniform(4.592,6.888))
model.ChangeParameter('tau',random.uniform(1.572918,2.359376))
model.Endtime(end_time)
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
Incident = outcomes[4][0][-1]
acquisitions[iteration,2] = Incident
def Baserun(iterations):
for k in range(0, iterations):
pdict = {
'epsilon1': random.uniform(0.015, 0.417),
'epsilon2': random.uniform(0.1, 96)
}
model = stochpy.SSA()
model.Model(model_file='protective_decol.psc', dir=workingdir)
model.ChangeParameter('epsilon1', pdict['epsilon1'])
model.Endtime(end_time)
model.ChangeParameter('epsilon2', 1 / pdict['epsilon2'])
model.DoStochSim()
model.GetRegularGrid(n_samples=end_time)
outcomes = model.data_stochsim_grid.species
cases[k, 0] = outcomes[16][0][-1]
cases[k, 1] = outcomes[34][0][-1]
cases[k, 2] = outcomes[40][0][-1]
cases[k, 3] = pdict['epsilon1']
cases[k, 4] = pdict['epsilon2']
return cases
def Baserun(iterations):
for k in range(0, iterations):
#pdict = {'epsilon1':random.uniform(0.01,0.39),
#'epsilon2':random.uniform(0.1,48)
#}
model = stochpy.SSA()
model.Model(model_file='protective_decolV04.psc', dir=workingdir)
model.Endtime(end_time)
#model.ChangeParameter('epsilon1',pdict['epsilon1'])
#model.ChangeParameter('epsilon2',1/pdict['epsilon2'])
model.DoStochSim()
model.GetRegularGrid(n_samples)
outcomes = model.data_stochsim_grid.species
cases[k, 0] = outcomes[16][0][-1]
cases[k, 1] = outcomes[17][0][-1]
cases[k, 2] = outcomes[28][0][-1]
cases[k, 3] = outcomes[36][0][-1]
cases[k, 4] = outcomes[42][0][-1]
for t in range(0, n_samples):
acq_traj[t, k] = outcomes[16][0][t]
#con_traj[t,k]=outcomes[16][0][t]+outcomes[19][0][t]+outcomes[21][0][t]+outcomes[23][0][t]+outcomes[25][0][t]+outcomes[27][0][t]+outcomes[35][0][t]+outcomes[37][0][t]+outcomes[38][0][t]+outcomes[39][0][t]+outcomes[40][0][t]+outcomes[41][0][t]
return cases
def get_sim(filename,
filedir=None,
outdir=None,
argDict=dict(),
paramDict=dict(),
interval=0.5,
suffix='_TS_data.txt'):
"""
:param filename: name of .psc file (specifying diff eq's, values)
:param filedir: directory of .psc file
:param outdir: directory of output file
:param argDict: dict containing keyword args for the smod.Model() call
:param paramDict: dict containing parameter values. This can be empty;
otherwise the default values are used
:param interval: interval to keep in our array. For example, if the simulation
produces a value for each second (which we can't control), setting interval to 5
will result in a vector spaced at 5 seconds.
:param suffix: any suffix to append
:return: smod, newdictToVector, times
smod: the original stochpy simulation model
newdictToVector: a dictionary of form:
"A" -> vector of "A"'s values
"time" -> vector of time
times: the timepoints of our simulation
"""
smod = stochpy.SSA()
smod.Model(filename, filedir)
if paramDict:
# suffix will note the parameters that were set
for param in paramDict:
smod.ChangeParameter(param, paramDict[param])
val = paramDict[param]
if val != 0:
exp = int(np.log10(val))
new_val = int(val * 1.0 / 10**exp)
suffix += '_' + param + '_' + str(new_val) + 'e' + str(exp)
else:
suffix += '_' + param + '_' + str(val) + 'e0'
suffix += '.txt'
smod.DoStochSim(**argDict)
# species was simulated
smod.Export2File(analysis='timeseries',
datatype='species',
directory=outdir)
# now load it into dictionary vector
import csv
with open(os.path.join(outdir, filename + '_species_timeseries1.txt'),
'rU') as csvfile:
csvfile.readline()
reader = csv.DictReader(csvfile, delimiter='\t')
dictToVector = collections.OrderedDict()
for field in reader.fieldnames:
dictToVector[field] = []
for row in reader:
for field in dictToVector:
dictToVector[field].append(eval(row[field]))
for field in dictToVector:
dictToVector[field] = np.array(dictToVector[field])
max_time = max(dictToVector['Time'])
# get the spaced intervals we want
times = np.arange(0, max_time, interval)
indices = [np.nonzero(dictToVector['Time'] > t)[0][0] for t in times]
newdictToVector = collections.OrderedDict()
for key in dictToVector:
newdictToVector[key] = dictToVector[key][np.ix_(indices)]
# Save
out_file = os.path.join(outdir, filename + suffix)
with open(out_file, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([key for key in newdictToVector])
for i in range(len(indices)):
writer.writerow(
[newdictToVector[key][i] for key in newdictToVector])
print "Data written to ", out_file
return smod, newdictToVector, times
import stochpy
from scipy import *
from numpy import *
import matplotlib.pyplot as plt
smod = stochpy.SSA(model_file='ge.psc', dir='data')
smod.DoStochSim(end=1.0e3, mode='time')
smod.PlotSpeciesTimeSeries()
stochpy.plt.title("Your own title")
stochpy.plt.show()
#stochpy.plt.savefig("stochpy_plot.pdf")
S = smod.data_stochsim
t = reshape(S.time, len(S.time))
#print(S.species[:,0])
#plt.plot(t, S.species[:,1], label='1')
#plt.legend()
#plt.xlabel('Time (hours)')
#plt.ylabel('Species')
##plt.yscale('log')
#plt.axis([-5,250,1.0e-4,1.0e10])
#plt.tick_params( axis='both',labelsize=12)
#plt.show()
import stochpy
from scipy import *
from numpy import *
import matplotlib.pyplot as plt
smod = stochpy.SSA(model_file='bact_phage_model.psc', dir='data')
smod.DoStochSim(end=13.0, mode='time')
smod.PlotSpeciesTimeSeries()
stochpy.plt.yscale('log')
#S = smod.data_stochsim
#t= reshape(S.time, len(S.time))
#print(S.species[:,0])
#plt.plot(t, S.species[:,1], label='1')
#plt.legend()
#plt.xlabel('Time (hours)')
#plt.ylabel('Species')
##plt.yscale('log')
#plt.axis([-5,250,1.0e-4,1.0e10])
#plt.tick_params( axis='both',labelsize=12)
#plt.show()
def playgame(currentdir,stochastic,parameter1,parameter2):
#parameter 1 determines final level
#parameter 2 determines
#type should be 0 or 1 -> toggle between stochastic and deterministic
if stochastic:
#stochastic
#change directory
smod = stochpy.SSA()
smod.Model(os.path.join(currentdir, 'mRNAproteinIulia.psc'))
#change parameters
smod.ChangeParameter("Ksynmrna",parameter1)
smod.ChangeParameter("Kdeg2",parameter2)
#smod.ChangeInitialSpeciesCopyNumber("Kdeg1",parameter2)
#smod.data_stochsim.simulation_endtime
#smod.data_stochsim.simulation_timesteps = 51.0
smod.Timesteps(50)
smod.Endtime(50)
smod.DoStochSim(end=50)
#Only plot as a test
#smod.PlotSpeciesTimeSeries()
#pysces.plt.setAxisLabel('x', label='time')
#pysces.plt.setAxisLabel('y', label='expression levels')
#plt.savefig(currentdir+'/stoch.png')
simvalues_stoch = smod.data_stochsim.getSpecies()
x_values = range(5,51,5)
#arrange results
time_val = []
mRNA_val = []
protein_val =[]
for list in simvalues_stoch:
time_val.append(list[0])
mRNA_val.append(list[1])
protein_val.append(list[2])
time_val_rounded = []
for i in time_val:
time_val_rounded.append(int(round(i)))
count = 0
mRNA_final = []
protein_final = []
for val in range(0,len(mRNA_val)-1):
mRNA_final.append([mRNA_val[val]] * ((time_val_rounded[count+1] - time_val_rounded[count])))
protein_final.append([protein_val[val]] * ((time_val_rounded[count+1] - time_val_rounded[count])))
count = count + 1
time_final = range(1,51)
mRNA_final = flatten(mRNA_final)
protein_final = flatten(protein_final)
simvalues = zip(time_final,mRNA_final,protein_final)
else:
#deterministic
print(currentdir+'pysces_determ.psc')
mod = pysces.model('pysces_determ', dir=os.getcwd())
#change params
mod.Ksynmrna = parameter1
mod.Kdeg2 = parameter2
mod.doSim(end=50.0, points=50.0)
mod.Simulate()
#print os.getcwd(), '\n\n\n'
#mod.doSimPlot()
#plot here to check it works but don't actually plot
mod.sim_start = 1.0
mod.doSimPlot(end=50.0, points=51, plot='species', fmt='lines', filename=None)
#pysces.plt.p_activateInterface('matplotlib')
#pysces.plt.setAxisLabel('x', label='time')
#pysces.plt.setAxisLabel('y', label='expression levels')
#plt.savefig(currentdir+'/simpledeterm.png')
#plt.close()
#get simulation values
simvalues = mod.data_sim.getSpecies()
# print(mod.data_sim.getSpecies())
os.chdir(currentdir)
with open("simvalues.csv","wb") as f:
writer = csv.writer(f)
writer.writerows(simvalues)
return simvalues
