def __init__(self,
psc_file,
variable_names,
v_name,
ranges,
psc_string=None):
self.psc_file = psc_file
self.psc_string = psc_string
self.variable_names = variable_names
self.v_name = 'J_{0}'.format(v_name) if v_name[0:2] != 'J_' else v_name
self.ranges = ranges
import pysces
if self.psc_string is None:
self.mod = pysces.model(self.psc_file)
else:
self.mod = pysces.model(self.psc_file,
loader='string',
fString=self.psc_string)
self.mod.SetQuiet()
self.variables = None
self.v = numpy.array([])
self.scanner = pysces.Scanner(self.mod)
for n, r in zip(self.variable_names, self.ranges):
self.scanner.addScanParameter(n, r[0], r[1], r[2])
self.scanner.addUserOutput(self.v_name)
self.scanner.Run()
results = self.scanner.getResultMatrix()
self.variables = results[:, 0:-1]
self.v = results[:, -1]
def __init__(self, psc_file, variable_names, v_name, ranges, psc_string=None):
self.psc_file = psc_file
self.psc_string = psc_string
self.variable_names = variable_names
self.v_name = 'J_{0}'.format(v_name) if v_name[0:2] != 'J_' else v_name
self.ranges = ranges
import pysces
if self.psc_string is None:
self.mod = pysces.model(self.psc_file)
else:
self.mod = pysces.model(self.psc_file, loader='string', fString=self.psc_string)
self.mod.SetQuiet()
self.variables = None
self.v = numpy.array([])
self.scanner = pysces.Scanner(self.mod)
for n, r in zip(self.variable_names, self.ranges):
self.scanner.addScanParameter(n, r[0], r[1], r[2])
self.scanner.addUserOutput(self.v_name)
self.scanner.Run()
results = self.scanner.getResultMatrix()
self.variables = results[:, 0:-1]
self.v = results[:, -1]
# self.v = self.v.reshape(len(self.v), 1)
def _init(self, parameters):
import pysces
if self.psc_string is None:
self.mod = pysces.model(self.psc_file)
else:
self.mod = pysces.model(self.psc_file, loader='string', fString=self.psc_string)
self.mod.SetQuiet()
self.parameter_names = [i.name for i in parameters]
def test_statemetab_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
linmet = array([23.1025641, 38.61538462, 38.94871795], 'd')
assert_array_almost_equal(lin.state_species, linmet)
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 fix_metabolite(mod, fix, model_name=None):
"""
Fix a metabolite in a model and return a new model with the fixed
metabolite.
Parameters
----------
mod : PysMod
The original model.
fix : str
The metabolite to fix.
model_name : str, optional (Default : none)
The file name to use when saving the model (in psc/orca).
If None it defaults to original_model_name_fix.
Returns
-------
PysMod
A new model instance with an additional fixed species.
"""
assert fix in mod.species, "\nInvalid fixed species."
if model_name is None:
model_name = get_model_name(mod) + '_' + fix
mod_str = mod_to_str(mod)
fix_head, mod_str_sans_fix = strip_fixed(mod_str)
new_fix_head = augment_fix_sting(fix_head, fix)
new_mod = model(model_name, loader="string", fString=new_fix_head
+ '\n' + mod_str_sans_fix)
return new_mod
def test_cc_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bra.EvalEvar()
bra.EvalCC()
bracc=[0.25338970963029361000,-0.13797075277724413000,\
-0.21457061516904127000,0.32372624345386269000,0.39406892242342095000,0.38135649243870806000,\
-0.13797075277724413000,0.25338970963029356000,0.39406892242342095000,0.32372624345386258000,\
-0.21457061516904130000,0.38135649243870812000,-0.13797075277724413000,0.25338970963029356000,\
0.39406892242342095000,0.32372624345386258000,-0.21457061516904130000,0.38135649243870812000,\
0.05770947842652475500,0.05770947842652472700,0.08974915362718985400,0.32372624345386269000,\
0.08974915362718984000,0.38135649243870817000,0.25338970963029361000,-0.13797075277724413000,\
-0.21457061516904127000,0.32372624345386269000,0.39406892242342095000,0.38135649243870806000,\
0.05770947842652474100,0.05770947842652471300,0.08974915362718984000,0.32372624345386264000,\
0.08974915362718982600,0.38135649243870806000,-0.23751396180748979000,-0.23751396180748979000,\
-0.36937913195668803000,0.55728841856739109000,-0.36937913195668803000,0.65649776896096446000,\
-0.08622262758262820600,-0.08622262758262820600,-0.13409249329650016000,-0.48367318660804903000,\
-0.13409249329650016000,0.92430342836630586000,-0.79249085140642661000,-0.14644930661681688000,\
-0.22775636995026044000,0.34361981023636490000,0.41828517483934929000,0.40479154289778968000,\
-0.14644930661681685000,-0.79249085140642661000,0.41828517483934929000,0.34361981023636484000,\
-0.22775636995026050000,0.40479154289778979000]
bracc_new = []
for x in range(bra.cc_all.shape[0]):
for y in range(bra.cc_all.shape[1]):
bracc_new.append(round(bra.cc_all[x,y],2))
for x in range(len(bracc)):
bracc[x] = round(bracc[x],2)
#os.sys.stderr.write(str(bracc[x]) + ' --> ' + str(bracc_new[x])+'\n')
assert_array_almost_equal(bracc,bracc_new)
def test_statemetab_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bramet = array([4.8583996, 1.88547254, 1.49124431, 1.49124431], 'd')
assert_array_almost_equal(bra.state_species, bramet)
def test_stateflux_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
linflux = array([76.8974359, 76.8974359, 76.8974359, 76.8974359], 'd')
assert_array_almost_equal(lin.state_flux, linflux)
def test_statemetab_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bramet = array([4.8583996,1.88547254,1.49124431,1.49124431],'d')
assert_array_almost_equal(bra.state_species,bramet)
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 test_statemetab_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moimet = array([3.6886875,16.25569882,7.3113125,4.39229787,41.02504596,2.60770213,0.42718994,2.57281006,2.44791155,17.0012171],'d')
assert_array_almost_equal(moi.state_species,moimet)
def test_stateflux_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
linflux = array([76.8974359, 76.8974359, 76.8974359, 76.8974359],'d')
assert_array_almost_equal(lin.state_flux,linflux)
def test_cc_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bra.EvalEvar()
bra.EvalCC()
bracc=[0.25338970963029361000,-0.13797075277724413000,\
-0.21457061516904127000,0.32372624345386269000,0.39406892242342095000,0.38135649243870806000,\
-0.13797075277724413000,0.25338970963029356000,0.39406892242342095000,0.32372624345386258000,\
-0.21457061516904130000,0.38135649243870812000,-0.13797075277724413000,0.25338970963029356000,\
0.39406892242342095000,0.32372624345386258000,-0.21457061516904130000,0.38135649243870812000,\
0.05770947842652475500,0.05770947842652472700,0.08974915362718985400,0.32372624345386269000,\
0.08974915362718984000,0.38135649243870817000,0.25338970963029361000,-0.13797075277724413000,\
-0.21457061516904127000,0.32372624345386269000,0.39406892242342095000,0.38135649243870806000,\
0.05770947842652474100,0.05770947842652471300,0.08974915362718984000,0.32372624345386264000,\
0.08974915362718982600,0.38135649243870806000,-0.23751396180748979000,-0.23751396180748979000,\
-0.36937913195668803000,0.55728841856739109000,-0.36937913195668803000,0.65649776896096446000,\
-0.08622262758262820600,-0.08622262758262820600,-0.13409249329650016000,-0.48367318660804903000,\
-0.13409249329650016000,0.92430342836630586000,-0.79249085140642661000,-0.14644930661681688000,\
-0.22775636995026044000,0.34361981023636490000,0.41828517483934929000,0.40479154289778968000,\
-0.14644930661681685000,-0.79249085140642661000,0.41828517483934929000,0.34361981023636484000,\
-0.22775636995026050000,0.40479154289778979000]
bracc_new = []
for x in range(bra.cc_all.shape[0]):
for y in range(bra.cc_all.shape[1]):
bracc_new.append(round(bra.cc_all[x, y], 2))
for x in range(len(bracc)):
bracc[x] = round(bracc[x], 2)
#os.sys.stderr.write(str(bracc[x]) + ' --> ' + str(bracc_new[x])+'\n')
assert_array_almost_equal(bracc, bracc_new)
def test_stateflux_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moiflux = array([250.01825652,250.01825652,250.01825652,250.01825652,250.01825652,250.01825652,250.01825652],'d')
assert_array_almost_equal(moi.state_flux,moiflux)
def test_stateflux_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
braflux = array([2.42139889,2.42139889,1.21069945,1.21069945,1.21069945,1.21069945],'d')
assert_array_almost_equal(bra.state_flux,braflux)
def test_statemetab_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
linmet = array([23.1025641, 38.61538462, 38.94871795],'d')
assert_array_almost_equal(lin.state_species,linmet)
def sbml2pysces(self,model_xml):
'''
Use pysces to read the SBML file
'''
current_dir=os.getcwd()
pysces_filename=os.path.join(current_dir,model_xml[-4]+'.psc')
pysces.interface.convertSBML2PSC(model_xml,pscfile=pysces_filename)
return pysces.model(pysces_filename)
def test_stateflux_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
braflux = array([
2.42139889, 2.42139889, 1.21069945, 1.21069945, 1.21069945,
1.21069945
], 'd')
assert_array_almost_equal(bra.state_flux, braflux)
def test_stateflux_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moiflux = array([
250.01825652, 250.01825652, 250.01825652, 250.01825652,
250.01825652, 250.01825652, 250.01825652
], 'd')
assert_array_almost_equal(moi.state_flux, moiflux)
def test_statemetab_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moimet = array([
3.6886875, 16.25569882, 7.3113125, 4.39229787, 41.02504596,
2.60770213, 0.42718994, 2.57281006, 2.44791155, 17.0012171
], 'd')
assert_array_almost_equal(moi.state_species, moimet)
def test_elas_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
lin.EvalEvar()
line = [-0.30043348,0.,0., 1.50216739,-0.50216739,0., 0.,1.50650217,-0.50650217, 0.,0.,1.01300433]
line_new = []
for x in range(lin.elas_var.shape[0]):
for y in range(lin.elas_var.shape[1]):
line_new.append(round(lin.elas_var[x,y],2))
for x in range(len(line)):
line[x] = round(line[x],2)
assert_array_almost_equal(line,line_new)
def get_pysces_model(filename, target='Vode'):
path, fname = os.path.split(filename)
m = pysces.model(fname, dir=path)
max_t = np.Inf
parlist = m.__fixed_species__ + m.__parameters__
pardict = dict([(pname, p['initial']) for pname, p in m.__pDict__.items()])
varlist = m.__species__ # list ['s0', 's1', 's2']
icdict = dict([(vname, v['initial']) for vname, v in m.__sDict__.items()
if not v['fixed']])
fixed_species = dict([(pname, p['initial'])
for pname, p in m.__sDict__.items() if p['fixed']])
pardict.update(fixed_species)
fnspecs = {}
for R in m.__reactions__: # list ['R1', 'R2', 'R3', 'R4']
R_info = m.__nDict__[R]
#assert R_info['Modifiers'] == []
assert R_info['Type'] == 'Rever'
arglist = []
for reagent in R_info['Reagents']:
r = reagent.replace('self.', '')
if r in varlist:
arglist.append(r)
arglist.sort()
fnspecs[R] = (arglist, R_info['RateEq'].replace('self.', ''))
varspecs = make_varspecs(m, fnspecs)
for fname, fspec in m.__userfuncs__.items():
# Don't know how these are implemented yet
fnspec[fname] = fspec
dsargs = args(name=fname[:-3],
varspecs=varspecs,
fnspecs=fnspecs,
pars=pardict,
ics=icdict,
tdata=[0, max_t])
genclassname = target + '_ODEsystem'
try:
genclass = getattr(Generator, genclassname)
except AttributeError:
raise TypeError("Invalid ODE solver type")
return genclass(dsargs)
def get_pysces_model(filename, target='Vode'):
path, fname = os.path.split(filename)
m = pysces.model(fname, dir=path)
max_t = np.Inf
parlist = m.__fixed_species__ + m.__parameters__
pardict = dict([(pname, p['initial']) for pname, p in m.__pDict__.items()])
varlist = m.__species__ # list ['s0', 's1', 's2']
icdict = dict([(vname, v['initial']) for vname, v in m.__sDict__.items() if not v['fixed']])
fixed_species = dict([(pname, p['initial']) for pname, p in m.__sDict__.items() if p['fixed']])
pardict.update(fixed_species)
fnspecs = {}
for R in m.__reactions__: # list ['R1', 'R2', 'R3', 'R4']
R_info = m.__nDict__[R]
#assert R_info['Modifiers'] == []
assert R_info['Type'] == 'Rever'
arglist = []
for reagent in R_info['Reagents']:
r = reagent.replace('self.','')
if r in varlist:
arglist.append(r)
arglist.sort()
fnspecs[R] = (arglist, R_info['RateEq'].replace('self.',''))
varspecs = make_varspecs(m, fnspecs)
for fname, fspec in m.__userfuncs__.items():
# Don't know how these are implemented yet
fnspec[fname] = fspec
dsargs = args(name=fname[:-3],
varspecs=varspecs,
fnspecs=fnspecs,
pars=pardict,
ics=icdict,
tdata=[0, max_t])
genclassname = target + '_ODEsystem'
try:
genclass = getattr(Generator, genclassname)
except AttributeError:
raise TypeError("Invalid ODE solver type")
return genclass(dsargs)
def test_cc_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moi.EvalEvar()
moi.EvalCC()
moicc = moicc=[0.01114694087227875000,0.07381787576415140000,\
0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,0.04329618737852313600,\
0.39140460844743269000,0.01114694087227862500,0.07381787576415056700,0.18139104475836440000,\
0.04685617079784004000,0.25208717198140518000,0.04329618737852265100,0.39140460844742830000,\
0.01114694087227874000,0.07381787576415133100,0.18139104475836623000,0.04685617079784051800,\
0.25208717198140773000,0.04329618737852309500,0.39140460844743230000,0.01114694087227875000,\
0.07381787576415140000,0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,\
0.04329618737852313600,0.39140460844743269000,0.01114694087227875000,0.07381787576415140000,\
0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,0.04329618737852313600,\
0.39140460844743269000,0.01114694087227876100,0.07381787576415146900,0.18139104475836659000,\
0.04685617079784060900,0.25208717198140823000,0.04329618737852317800,0.39140460844743302000,\
0.01114694087227875200,0.07381787576415141400,0.18139104475836645000,0.04685617079784057400,\
0.25208717198140806000,0.04329618737852314300,0.39140460844743280000,-0.96946517151898726000,\
0.07237057005414701500,0.17783461222620814000,0.04593748812318156800,0.24714464011293155000,\
0.04244730330314599300,0.38373055769937375000,-0.00493043463469603270,-0.03265059135931840800,\
-0.08023158100034956400,0.14913606725290945000,\
0.02650472034900546900,0.11529508789995771000,-0.17312326850750914000,-0.00258941341724495350,\
-0.01714775382110165000,-0.04213679882643606200,0.25950964810000904000,-0.07785637143685617000,\
-0.02885675558547331700,-0.09092255501289707400,-0.00651179467430067560,-0.04312275948862360300,\
-0.10596460973080281000,-0.02003169777867189200,0.40783108111031047000,-0.00355036168876400080,\
-0.22864985774914801000,-0.07520202175595115700,-0.49800690277268156000,1.11329060303376930000,\
0.28758054022385904000,1.54718927875470390000,0.26573108181778565000,-2.64058257930148920000,\
0.08511194652599511600,-0.37976718223865702000,-0.93319355560031703000,-0.24105862936563618000,\
-1.29690043219020780000,-0.22274375836758617000,2.98855161123640300000,-0.01413200623441426100,\
0.06305662607990611400,0.15494766227242857000,0.04002542759392844300,0.21533763168639128000,\
0.03698442240380646300,-0.49621976380204558000,0.01332315621836589900,0.08822932693215754200,\
0.21680398717628285000,-0.25121007495788511000,0.32322678367369212000,-0.85819164176559659000,\
0.46781846272298522000,0.01096817827331699600,0.07263406439629339900,0.17848209108569948000,\
0.03374050370795465800,-0.68693259335574453000,0.00598007183654026410,0.38512768405594056000,\
0.00513245176028354240,0.03398840011328187900,0.08351894906745090100,-0.51437161070192172000,\
0.15431837495286327000,0.05719670138973560700,0.18021673341830685000]
moicc_new = []
for x in range(moi.cc_all.shape[0]):
for y in range(moi.cc_all.shape[1]):
moicc_new.append(round(moi.cc_all[x, y], 2))
for x in range(len(moicc)):
moicc[x] = round(moicc[x], 2)
#os.sys.stderr.write(str(moicc[x]) + ' --> ' + str(moicc_new[x])+'\n')
assert_array_almost_equal(moicc, moicc_new)
def load_data2d(file_name, mod=None, ltxe=None):
"""
Loads a gzipped cPickle file containing a Data2D object. Optionally
a model can be provided (which is useful when loading data that
reference the same model. For the same reason a LatexExpr object
can be supplied.
"""
with gzip.open(file_name, 'rb') as f:
data_2dobj = pickle.load(f)
if not mod:
data_2dobj.mod = pysces.model(data_2dobj.mod)
else:
data_2dobj.mod = mod
if ltxe:
del data_2dobj._ltxe
data_2dobj._ltxe = ltxe
return data_2dobj
def test_cc_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moi.EvalEvar()
moi.EvalCC()
moicc = moicc=[0.01114694087227875000,0.07381787576415140000,\
0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,0.04329618737852313600,\
0.39140460844743269000,0.01114694087227862500,0.07381787576415056700,0.18139104475836440000,\
0.04685617079784004000,0.25208717198140518000,0.04329618737852265100,0.39140460844742830000,\
0.01114694087227874000,0.07381787576415133100,0.18139104475836623000,0.04685617079784051800,\
0.25208717198140773000,0.04329618737852309500,0.39140460844743230000,0.01114694087227875000,\
0.07381787576415140000,0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,\
0.04329618737852313600,0.39140460844743269000,0.01114694087227875000,0.07381787576415140000,\
0.18139104475836643000,0.04685617079784056700,0.25208717198140801000,0.04329618737852313600,\
0.39140460844743269000,0.01114694087227876100,0.07381787576415146900,0.18139104475836659000,\
0.04685617079784060900,0.25208717198140823000,0.04329618737852317800,0.39140460844743302000,\
0.01114694087227875200,0.07381787576415141400,0.18139104475836645000,0.04685617079784057400,\
0.25208717198140806000,0.04329618737852314300,0.39140460844743280000,-0.96946517151898726000,\
0.07237057005414701500,0.17783461222620814000,0.04593748812318156800,0.24714464011293155000,\
0.04244730330314599300,0.38373055769937375000,-0.00493043463469603270,-0.03265059135931840800,\
-0.08023158100034956400,0.14913606725290945000,\
0.02650472034900546900,0.11529508789995771000,-0.17312326850750914000,-0.00258941341724495350,\
-0.01714775382110165000,-0.04213679882643606200,0.25950964810000904000,-0.07785637143685617000,\
-0.02885675558547331700,-0.09092255501289707400,-0.00651179467430067560,-0.04312275948862360300,\
-0.10596460973080281000,-0.02003169777867189200,0.40783108111031047000,-0.00355036168876400080,\
-0.22864985774914801000,-0.07520202175595115700,-0.49800690277268156000,1.11329060303376930000,\
0.28758054022385904000,1.54718927875470390000,0.26573108181778565000,-2.64058257930148920000,\
0.08511194652599511600,-0.37976718223865702000,-0.93319355560031703000,-0.24105862936563618000,\
-1.29690043219020780000,-0.22274375836758617000,2.98855161123640300000,-0.01413200623441426100,\
0.06305662607990611400,0.15494766227242857000,0.04002542759392844300,0.21533763168639128000,\
0.03698442240380646300,-0.49621976380204558000,0.01332315621836589900,0.08822932693215754200,\
0.21680398717628285000,-0.25121007495788511000,0.32322678367369212000,-0.85819164176559659000,\
0.46781846272298522000,0.01096817827331699600,0.07263406439629339900,0.17848209108569948000,\
0.03374050370795465800,-0.68693259335574453000,0.00598007183654026410,0.38512768405594056000,\
0.00513245176028354240,0.03398840011328187900,0.08351894906745090100,-0.51437161070192172000,\
0.15431837495286327000,0.05719670138973560700,0.18021673341830685000]
moicc_new = []
for x in range(moi.cc_all.shape[0]):
for y in range(moi.cc_all.shape[1]):
moicc_new.append(round(moi.cc_all[x,y],2))
for x in range(len(moicc)):
moicc[x] = round(moicc[x],2)
#os.sys.stderr.write(str(moicc[x]) + ' --> ' + str(moicc_new[x])+'\n')
assert_array_almost_equal(moicc,moicc_new)
def test_elas_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
lin.EvalEvar()
line = [
-0.30043348, 0., 0., 1.50216739, -0.50216739, 0., 0., 1.50650217,
-0.50650217, 0., 0., 1.01300433
]
line_new = []
for x in range(lin.elas_var.shape[0]):
for y in range(lin.elas_var.shape[1]):
line_new.append(round(lin.elas_var[x, y], 2))
for x in range(len(line)):
line[x] = round(line[x], 2)
assert_array_almost_equal(line, line_new)
def test_elas_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bra.EvalEvar()
brae = [-0.66930781448548349, 0.0, 0.0, 0.0, 0.78845903183743249,\
-0.52920041809870422, 0.0, 0.0, 0.0, 0.95441603882382564,\
-0.60578219088834051, 0.0, 0.0, 0.95441603882382564, 0.0,\
-0.60578219088834051, 0.0, 0.0, 0.9421058792599355, 0.0, 0.0,\
0.0, 0.0, 0.9421058792599355]
brae_new = []
for x in range(bra.elas_var.shape[0]):
for y in range(bra.elas_var.shape[1]):
brae_new.append(round(bra.elas_var[x,y],2))
for x in range(len(brae)):
brae[x] = round(brae[x],2)
assert_array_almost_equal(brae,brae_new)
def load_model(fpath):
cdir = os.getcwd()
d, fname = os.path.split(fpath)
mod = pysces.model(fname, d)
mod.Simulate()
S = np.copy(mod.Nmatrix.array)
pre = np.copy(S)
pre[pre == 1] = 0
pre[pre == -1] = 1
places, transitions = mod.Nmatrix.getLabels()
init = list(getattr(mod, place+"_init") for place in places)
rate_getter = attrgetter("rate")
rates = list(rate_getter(getattr(mod, transition)) for transition in transitions)
os.chdir(cdir)
return StochasticPetriNet._make([pre, np.array(init), S, np.array(rates),
places, transitions])
def test_elas_branch1(self):
bra = pysces.model('pysces_test_branch1.psc', self.model_dir)
bra.doLoad()
bra.hybrd_mesg = 0
bra.State()
bra.EvalEvar()
brae = [-0.66930781448548349, 0.0, 0.0, 0.0, 0.78845903183743249,\
-0.52920041809870422, 0.0, 0.0, 0.0, 0.95441603882382564,\
-0.60578219088834051, 0.0, 0.0, 0.95441603882382564, 0.0,\
-0.60578219088834051, 0.0, 0.0, 0.9421058792599355, 0.0, 0.0,\
0.0, 0.0, 0.9421058792599355]
brae_new = []
for x in range(bra.elas_var.shape[0]):
for y in range(bra.elas_var.shape[1]):
brae_new.append(round(bra.elas_var[x, y], 2))
for x in range(len(brae)):
brae[x] = round(brae[x], 2)
assert_array_almost_equal(brae, brae_new)
def test_cc_linear1(self):
lin = pysces.model("pysces_test_linear1.psc", self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
lin.EvalEvar()
lin.EvalCC()
lincc = [
0.02564102564102570300,
0.05128205128205126600,
0.15384615384615383000,
0.76923076923076916000,
0.02564102564102571000,
0.05128205128205128000,
0.15384615384615385000,
0.76923076923076938000,
0.02564102564102571000,
0.05128205128205128000,
0.15384615384615385000,
0.76923076923076938000,
0.02564102564102570300,
0.05128205128205126600,
0.15384615384615383000,
0.76923076923076916000,
-0.30636428644396779000,
-0.61272857288793536000,
0.15318214322198387000,
0.76591071610991934000,
-0.08534676570192656400,
-0.17069353140385327000,
-0.51208059421155983000,
0.76812089131733974000,
-0.96185074526088343000,
0.05062372343478333000,
0.15187117030435002000,
0.75935585152175000000,
]
lincc_new = []
for x in range(lin.cc_all.shape[0]):
for y in range(lin.cc_all.shape[1]):
lincc_new.append(round(lin.cc_all[x, y], 2))
for x in range(len(lincc)):
lincc[x] = round(lincc[x], 2)
assert_array_almost_equal(lincc, lincc_new)
def P_INIT(self, *args):
args = args[0]
print 'Args', args
self.RESULT = None
self.setStatus('INITIALISING')
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect((self.client_address[0], CFSERVE_PORT))
client.send('GET')
GO = True
model = ''
while GO:
data = client.recv(self.block_size)
model += data
if data == '':
GO = False
self.model_name = args[0]
self.model = pysces.model(self.model_name, loader="string", fString=model)
if self.debug: print 'P_INIT ... ok.'
self.setStatus('DONE_INITIALISE')
return True
def P_INIT(self, *args):
args = args[0]
print 'Args', args
self.RESULT = None
self.setStatus('INITIALISING')
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect((self.client_address[0], CFSERVE_PORT))
client.send('GET')
GO = True
model = ''
while GO:
data = client.recv(self.block_size)
model += data
if data == '':
GO = False
self.model_name = args[0]
self.model = pysces.model(self.model_name, loader="string", fString=model)
if self.debug: print 'P_INIT ... ok.'
self.setStatus('DONE_INITIALISE')
return True
def load_model(fpath):
cdir = os.getcwd()
d, fname = os.path.split(fpath)
mod = pysces.model(fname, d)
mod.Simulate()
S = np.copy(mod.Nmatrix.array)
pre = np.copy(S)
pre[pre == 1] = 0
pre[pre == -1] = 1
places, transitions = mod.Nmatrix.getLabels()
init = list(getattr(mod, place + "_init") for place in places)
rate_getter = attrgetter("rate")
rates = list(
rate_getter(getattr(mod, transition)) for transition in transitions)
os.chdir(cdir)
return StochasticPetriNet._make(
[pre, np.array(init), S,
np.array(rates), places, transitions])
def main ():
# Check for file
if len (sys.argv) < 2:
s.write ("Error! Please supply a filename.\n")
sys.exit (1)
# Load model and convert to SBML
m = pysces.model (sys.argv [1], dir=CWD)
m.doLoad()
sbml = pysces.interface.writeMod2SBML (m, getstrbuf=True).getvalue()
# Output to 'model_' + current time + '.sbml'
name = CWD + "/model_" + time.strftime ("%m%d%H%M%S") + ".sbml"
out = open (name, 'w')
out.write (sbml)
out.flush ()
out.close ()
# Finished
s.write ("Success! {} printed.\n".format (name))
def test_cc_linear1(self):
lin = pysces.model('pysces_test_linear1.psc', self.model_dir)
lin.doLoad()
lin.hybrd_mesg = 0
lin.State()
lin.EvalEvar()
lin.EvalCC()
lincc=[0.02564102564102570300,0.05128205128205126600,\
0.15384615384615383000,0.76923076923076916000,0.02564102564102571000,0.05128205128205128000,\
0.15384615384615385000,0.76923076923076938000,0.02564102564102571000,0.05128205128205128000,\
0.15384615384615385000,0.76923076923076938000,0.02564102564102570300,0.05128205128205126600,\
0.15384615384615383000,0.76923076923076916000,-0.30636428644396779000,-0.61272857288793536000,\
0.15318214322198387000,0.76591071610991934000,-0.08534676570192656400,-0.17069353140385327000,\
-0.51208059421155983000,0.76812089131733974000,-0.96185074526088343000,0.05062372343478333000,\
0.15187117030435002000,0.75935585152175000000]
lincc_new = []
for x in range(lin.cc_all.shape[0]):
for y in range(lin.cc_all.shape[1]):
lincc_new.append(round(lin.cc_all[x, y], 2))
for x in range(len(lincc)):
lincc[x] = round(lincc[x], 2)
assert_array_almost_equal(lincc, lincc_new)
def test_elas_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moi.EvalEvar()
moie = [1.4753672613878632,-0.47536726138786306,-0.47536726138786312,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4278931518740323,0.0,\
1.4278931518740325,-0.42789315187403271,-0.42789315187403271,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0514524437327983,0.0,\
1.0514524437327983,-0.051452443732798468,0.0,0.0,0.0,0.0,\
0.0,-0.04300468618304179,0.0,1.0430046861830418,0.0,0.0,\
-0.043004686183041797,0.0,-0.073768363069393092,0.0,\
1.0737683630693931,0.0,0.0,0.0,0.0,0.0,1.0737683630693931,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.029048874655981143,\
1.0290488746559812,0.0,-0.029048874655981143,0.0,0.0,0.0,\
0.0,0.0,0.0,0.0,0.0,0.0, 1.0199985395848277]
moie_new = []
for x in range(moi.elas_var.shape[0]):
for y in range(moi.elas_var.shape[1]):
moie_new.append(round(moi.elas_var[x,y],2))
for x in range(len(moie)):
moie[x] = round(moie[x],2)
assert_array_almost_equal(moie,moie_new)
def test_elas_moiety1(self):
moi = pysces.model('pysces_test_moiety1.psc', self.model_dir)
moi.doLoad()
moi.hybrd_mesg = 0
moi.State()
moi.EvalEvar()
moie = [1.4753672613878632,-0.47536726138786306,-0.47536726138786312,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4278931518740323,0.0,\
1.4278931518740325,-0.42789315187403271,-0.42789315187403271,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0514524437327983,0.0,\
1.0514524437327983,-0.051452443732798468,0.0,0.0,0.0,0.0,\
0.0,-0.04300468618304179,0.0,1.0430046861830418,0.0,0.0,\
-0.043004686183041797,0.0,-0.073768363069393092,0.0,\
1.0737683630693931,0.0,0.0,0.0,0.0,0.0,1.0737683630693931,\
0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.029048874655981143,\
1.0290488746559812,0.0,-0.029048874655981143,0.0,0.0,0.0,\
0.0,0.0,0.0,0.0,0.0,0.0, 1.0199985395848277]
moie_new = []
for x in range(moi.elas_var.shape[0]):
for y in range(moi.elas_var.shape[1]):
moie_new.append(round(moi.elas_var[x, y], 2))
for x in range(len(moie)):
moie[x] = round(moie[x], 2)
assert_array_almost_equal(moie, moie_new)
graphs generated will be both displayed and saved as PNG format files.
NO WARRANTY OF FITNESS OF ANY KIND FOR ANYTHING ... SERIOUSLY!
(C) Brett G. Olivier Amsterdam 2011. All rights reserved
"""
# First we need to import PySCeS 0.8.0 or newer and store the current directory in CDIR
import os, time
CDIR = os.path.dirname(os.path.abspath(os.sys.argv[0]))
import numpy, pysces
# Next we instantiate a PySCeS model object (load the input file from the current directory)
# and set the simulation endtime/endsteps. Note how in this input file we specify that this
# model should be simulated stochastically and that the output should be displayed in amounts.
mod = pysces.model('Burstmodel.psc', CDIR)
MODEL_NAME = 'Burstmodel'
ENDTIME = 100
ENDSTEPS = 10000
# First let's assume this model represents a determinisitic system and do a continuous
# simulation to endtime with 2*endtime time points with LSODA or CVODE.
mod.doSimPlot(ENDTIME, 2*ENDTIME)
# Save the results to a file using the PySCeS plotting library (pysces.plt.*)
pysces.plt.export('%s_par_set_1_deterministic' % MODEL_NAME, CDIR)
# Typical continuous result. Now let's assume this is a stochastic system and do a discrete
# simulation to the same end time using the Direct method
mod.doStochSim(end=ENDTIME, mode='time', method='Direct')
# we can plot the results with this function and save the results
mod.StochSimPlot()
outcomes[10][0][t])
if QueueNTrajectories[-1,iteration] == 0:
QueueExtinction[iteration,0] = 1
else:
QueueExtinction[iteration,0] = 0
for i in range(0,n_runs):
print "CDI Queue Iteration %i of %i" % (i+1,n_runs)
CDIQueueRun(CDIQueue,i)
numpy.savetxt('CDIQueueOutcomes.csv',QueueOutcomes,delimiter=','
,header="Incident,Recur,N",comments='')
numpy.savetxt('CDIQueueDTrajectories.csv',QueueDTrajectories,delimiter=','
,header="D",comments='')
numpy.savetxt('CDIQueueNTrajectories.csv',QueueNTrajectories,delimiter=','
,header="N",comments='')
numpy.savetxt('CDIQueueExtinction.csv',QueueExtinction,delimiter=',',header="Extinction",comments='')
print "C. difficile Queue Model - Runs Complete"
#######################
# Deterministic Model #
#######################
import pysces
detCDI = pysces.model('CDIpool.psc', dir=workingdir)
detCDI.doSim(end=end_time,points=end_time*10)
detTS = detCDI.data_sim.getSpecies()
detHead = "Time," + ','.join(detCDI.species)
os.chdir(workingdir)
numpy.savetxt('CDIDeterministic.csv',detTS,delimiter=',',header=detHead,comments='')
self.name = name
self.value = value
self.components = []
def setComponent(self, comp):
self.components.append(comp)
setattr(self, comp.name, comp)
def hasComponents(self):
return MapList([a.name for a in self.components])
if __name__ == '__main__':
import pysces
M = pysces.model('pysces_model_linear1')
M.doLoad()
print '\nModel', M.ModelFile
print '============='
modmap = ModelMap(M)
print 'Reactions\n', modmap.hasReactions()
print 'Species\n', modmap.hasSpecies()
print 'FixedSpecies\n', modmap.hasFixedSpecies()
print ' '
print 'R1 has reagents\n', modmap.R1.hasReagents()
print 'R1 has sub\n', modmap.R1.hasSubstrates()
print 'R1 has prod\n', modmap.R1.hasProducts()
print 'R1 has mod\n', modmap.R1.hasModifiers()
print ' '
def run(dir_method,
json_method,
dir_model,
json_model,
dir_results,
dir_solver,
json_solver,
dir_data=None,
file_data=None,
exp_sign='_exp_'):
config_method = Config(os.path.join(dir_method, json_method))
config_model = Config(os.path.join(dir_model, json_model))
config_solver = Config(os.path.join(dir_solver, json_solver))
# Experiment name
exp_name = exp_sign + config_method.config['method_name'] + '_'
# Load PySCES model
mod = pysces.model(config_model.config['mod_name'], dir=dir_model)
# Solver settings
mod.__settings__["mode_sim_max_iter"] = config_solver.config[
"mode_sim_max_iter"]
mod.__settings__['lsoda_atol'] = config_solver.config['lsoda_atol']
mod.__settings__['lsoda_rtol'] = config_solver.config['lsoda_rtol']
mod.__settings__['lsoda_mxordn'] = config_solver.config['lsoda_mxordn']
mod.__settings__['lsoda_mxords'] = config_solver.config['lsoda_mxords']
mod.__settings__['lsoda_mxstep'] = config_solver.config['lsoda_mxstep']
# =====REAL DATA PREPARATION=====
# Remove fixed_species from params. We do it only once.
params = remove_fixed(mod.parameters,
mod.fixed_species,
compartment=config_model.config['compartment'])
if dir_data is not None:
config_model.config['real_data'] = True
mod.sim_start = config_model.config['sim_start']
mod.sim_end = config_model.config['sim_end']
mod.sim_points = config_model.config['sim_points']
x_obs = np.load(os.path.join(dir_data, file_data))
else:
config_model.config['real_data'] = False
x_obs, t = generate_data(mod,
params,
sim_start=config_model.config['sim_start'],
sim_end=config_model.config['sim_end'],
sim_points=config_model.config['sim_points'],
noise=config_model.config['noise'])
real_params = read_real_parameters(mod, params)
real_params_array = dict_to_array(real_params, params)
np.save(os.path.join(dir_results, exp_name + 'x_obs.npy'), x_obs)
np.save(os.path.join(dir_results, exp_name + 't.npy'), t)
np.save(os.path.join(dir_results, exp_name + 'real_params_array.npy'),
real_params_array)
json.dump(
real_params,
open(os.path.join(dir_results, exp_name + 'real_params.json'),
"w"))
json.dump(
params,
open(os.path.join(dir_results, exp_name + 'params.json'), "w"))
pickle.dump(mod, open(os.path.join(dir_results, exp_name + 'mod.pkl'),
"wb"))
# =======EXPERIMENT=======
# dump, just in case, configs
pickle.dump(
config_method.config,
open(os.path.join(dir_results, exp_name + 'config_method.pkl'), "wb"))
pickle.dump(
config_model.config,
open(os.path.join(dir_results, exp_name + 'config_model.pkl'), "wb"))
# Init method
# -get all classes in the file
classes = [x for x in dir(EA) if isclass(getattr(EA, x))]
# -check whether the provided name is available
assert config_method.config[
'method_name'] in classes, 'Wrong name of the method! Please pick one of the following methods: {}'.format(
classes)
# -initialize the appropriate class
module = __import__("algorithms.population_optimization_algorithms",
fromlist=[config_method.config['method_name']])
my_class = getattr(module, config_method.config['method_name'])
method = my_class(config_method.config, config_model.config)
# Init parameters
theta = np.random.uniform(low=config_model.config['low'],
high=config_model.config['high'],
size=(config_method.config['pop_size'],
len(params)))
theta = np.clip(theta,
a_min=config_method.config['clip_min'],
a_max=config_method.config['clip_max'])
# Calcute their energy
E = calculate_fitness(x_obs,
theta,
mod,
params,
dist=method.dist,
config_model=config_model.config,
config_method=config_method.config)
# -=Start experiment=-
best_E = [np.min(E)]
all_E = E
all_theta = theta
clock_start = time.time()
print('START ~~~~~~>')
g = config_method.config['generations']
for i in range(g):
print(f'========> Generation {i+1}/{g}')
theta, E = method.step(theta, E, x_obs, mod, params)
if np.min(E) < best_E[-1]:
best_E.append(np.min(E))
else:
best_E.append(best_E[-1])
all_theta = np.concatenate((all_theta, theta), 0)
all_E = np.concatenate((all_E, E), 0)
# SAVING
np.save(os.path.join(dir_results, exp_name + 'all_theta.npy'),
all_theta)
np.save(os.path.join(dir_results, exp_name + 'all_E.npy'), all_E)
np.save(os.path.join(dir_results, exp_name + 'best_E.npy'),
np.asarray(best_E))
# early stopping
if i > config_method.config['patience']:
if best_E[-config_method.config['patience']] == best_E[-1]:
break
print('~~~~~~> END')
clock_stop = time.time()
print('Time elapsed: {}'.format(clock_stop - clock_start))
np.save(os.path.join(dir_results, exp_name + 'time.npy'),
np.asarray(clock_stop - clock_start))
#!/usr/bin/env python
# Testing the new parallel scanner class
import os
backupdir = os.getcwd()
import numpy as np
import pysces
tbox = pysces.PyscesUtils.TimerBox()
import time
m = pysces.model('isola2a')
print("\n\nParallel execution...using RunScatter")
par2 = pysces.ParScanner(m, engine='ipcluster')
t5 = time.time()
par2.addScanParameter('V4', 60, 100, 11)
par2.addScanParameter('V1', 100, 130, 16)
par2.addScanParameter('V2', 100, 130, 16, follower=True)
par2.addScanParameter('V3', 80, 90, 6)
par2.addUserOutput('J_R1', 'A', 'ecR4_X', 'ccJR1_R1')
# par2.addUserOutput('J_R1', 'A')
par2.RunScatter()
t6 = time.time()
print("Duration: %.2f seconds" % (t6 - t5))
par2.statespersecond = par2.Tsteps / (t6 - t5)
print("States per second: %.1f" % par2.statespersecond)
os.chdir(backupdir)
#!/usr/bin/env python
import os
dir = os.getcwd()
import numpy as np
import matplotlib.pyplot as plt
import pysces
models = [("PT test", "Wajima2009_PTtest.xml", 0, 7), \
("aPTT test", "Wajima2009_aPTTtest.xml", 2, 6)]
figure = "Wajima2009_tests.png"
for model in models:
pysces.PyscesInterfaces.Core2interfaces().convertSBML2PSC(sbmlfile=model[1], sbmldir=dir, pscfile=model[1], pscdir=dir)
mod = pysces.model(model[1], dir=dir)
os.chdir(dir)
mod.doSim(end=0.025, points=500)
x = mod.data_sim.getSpecies()
x[0] = x[1] # we don't want pre-dilution concentration in the time course plot
x[0,-1] = 0 # start the time at 0
x = x.transpose()
data = lambda name: x[mod.data_sim.species_labels.index(name)+1]
time = mod.data_sim.getTime() * 3600 # we want seconds here, not hours
plot = lambda species, norm: plt.plot(time, data(species)/max(data(norm))*100)
plt.subplot(221 + model[2]) # rows, cols, num
pdict = {"Fg":"F", "II":"IIa", "X":"Xa"}
for key in pdict.keys():
plot(pdict[key], key)
def getPyscesModel():
A = tc_allItems();
N = fromMatrix( tc_getStoichiometry(A), True );
rates0 = fromTC( tc_getRates(A) );
params = fromTC( tc_getParameters(A) );
fixed = fromTC( tc_getFixedVariables(A) );
inits = fromTC( tc_getInitialValues(A) );
funcsNames = fromTC( tc_getForcingFunctionNames(A) );
funcsAssign0 = fromTC( tc_getForcingFunctionAssignments(A) );
triggers = fromTC( tc_getEventTriggers() );
events0 = fromTC( tc_getEventResponses() );
#tc_deleteItemsArray(A);
emptyExists = False;
modelString = '';
rates = [];
funcsAssign = [];
events = [];
p = re.compile('\^');
for i in rates0:
rates.append(p.sub('**',i));
for i in funcsAssign0:
funcsAssign.append(p.sub('**',i));
for i in events0:
events.append(p.sub('**',i));
reacs = len(rates);
species = len(N[0]);
for i in range(0,reacs): #for each reaction
lhs = [];
rhs = [];
for j in range(0,species): #get reactants and products
n = N[2][j][i];
if n > 0:
if n != 1.0:
rhs.append("{" + str(n) + "}" + str(N[0][j])); #product
else:
rhs.append(str(N[0][j]));
elif n < 0:
n = -n;
if n != 1.0:
lhs.append("{" + str(n) + "}" + str(N[0][j])); #reactants
else:
lhs.append(str(N[0][j]));
#full reaction and its rate
if len(lhs) > 0 or len(rhs) > 0:
modelString += N[1][i] + ":\n"; #print its name
if len(lhs) == 0:
lhs.append("EMPTY");
emptyExists = True;
if len(rhs) == 0:
rhs.append("EMPTY");
emptyExists = True;
modelString += " " + "+".join(lhs) + " > " + "+".join(rhs) + "\n";
modelString += " " + rates[i] + "\n\n";
#we are done with reactions. moving on to params, events, functions, etc.
fix = '';
if emptyExists:
fix = "FIX: EMPTY";
n = len(fixed[0]);
if n > 0 and len(fixed[0]) == len(fixed[2][0]):
if not emptyExists:
fix += "FIX:";
for i in range(0,n):
fix += " " + fixed[0][0][i];
modelString = fix + "\n\n" + modelString;
modelString += "# Init ext\n";
#fixed variables
if n > 0 and len(fixed[0]) == len(fixed[2][0]):
for i in range(0,n):
modelString += fixed[0][i] + " = " + str(fixed[2][0][i]) + "\n";
#initial variables
hashInits = {};
n = len(inits[0]);
if n > 0 and len(inits[0]) == len(inits[2][0]):
modelString += "\n# Init vars\n";
for i in range(0,n):
hashInits[ inits[0][i] ] = inits[2][0][i];
modelString += inits[0][i] + " = " + str(inits[2][0][i]) + "\n";
for j in N[0]:
if not hashInits.has_key(j):
modelString += j + " = 0.0\n";
#parameters -- remove unused parameters
n = len(params[0]);
if n > 0 and len(params[0]) == len(params[2][0]):
modelString += "\n# Init params\n";
for i in range(0,n): #for each parameter
modelString += params[0][i] + " = " + str(params[2][0][i]) + "\n";
n = len(funcsNames);
if n > 0 and len(funcsNames) == len(funcsAssign):
modelString += "\n# Forcing functions\n";
for i in range(0,n):
modelString += "!F " + funcsNames[i] + " = " + funcsAssign[i] + "\n";
n = len(triggers);
if n > 0 and len(triggers) == len(events):
modelString += "\n# Events\n";
for i in range(0,n):
modelString += "Event: event" + str(i) + "," + triggers[i] + " , 0 {" + events[i] + "}\n";
p = re.compile("\^");
p.sub("**",modelString);
#return modelString;
mod = pysces.model("model",loader="string",fString=modelString);
mod.doLoad();
return mod;
QueueMedian[iteration,1] = numpy.median(QueuePredator[:,iteration])
QueueMedian[iteration,2] = numpy.median(QueueTotal[:,iteration])
QueueMean[iteration,0] = numpy.mean(QueuePrey[:,iteration])
QueueMean[iteration,1] = numpy.mean(QueuePredator[:,iteration])
QueueMean[iteration,2] = numpy.mean(QueueTotal[:,iteration])
for i in range(0,n_runs):
LVQueueRun(LVqueue,i)
print "LV Queue Iteration %i of %i" % (i+1,n_runs)
numpy.savetxt('QueuePrey.csv',QueuePrey,delimiter=',',header="Prey",comments='')
numpy.savetxt('QueuePredator.csv',QueuePredator,delimiter=',',header="Predator",comments='')
numpy.savetxt('QueueTotal.csv',QueueTotal,delimiter=',',header="TotalN",comments='')
numpy.savetxt('LVqueueExtinction.csv',QueueExtinction,delimiter=',',header="Prey,Predator,N",comments='')
numpy.savetxt('LVqueueMedian.csv',QueueMedian,delimiter=',', header="Prey,Predator,N",comments='')
numpy.savetxt('LVqueueMean.csv',QueueMean,delimiter=',',header="Prey,Predator,N",comments='')
print "LV Queue Model - Runs Complete"
#######################
# Deterministic Model #
#######################
import pysces
detLV = pysces.model('LVpool.psc', dir=workingdir)
detLV.doSim(end=end_time,points=end_time*10)
detLVTS = detLV.data_sim.getSpecies()
detLVHead = "Time," + ','.join(detLV.species)
os.chdir(workingdir)
numpy.savetxt('LVDeterministic.csv',detLVTS,delimiter=',',header=detLVHead,comments='')
def __init__(self, name, value=None):
self.name = name
self.value = value
self.components = []
def setComponent(self, comp):
self.components.append(comp)
setattr(self, comp.name, comp)
def hasComponents(self):
return MapList([a.name for a in self.components])
if __name__ == '__main__':
import pysces
M = pysces.model('pysces_model_linear1')
M.doLoad()
print '\nModel', M.ModelFile
print '============='
modmap = ModelMap(M)
print 'Reactions\n', modmap.hasReactions()
print 'Species\n', modmap.hasSpecies()
print 'FixedSpecies\n', modmap.hasFixedSpecies()
print ' '
print 'R1 has reagents\n', modmap.R1.hasReagents()
print 'R1 has sub\n', modmap.R1.hasSubstrates()
print 'R1 has prod\n', modmap.R1.hasProducts()
print 'R1 has mod\n', modmap.R1.hasModifiers()
print ' '
#!/usr/bin/env python
# Simulation D'alcantara with pysces
import pysces
#mod = pysces.model('/home/mattions/Work/model/diff_eq/Fernandez/BIOMD0000000152.xml')
mod = pysces.model('/home/mattions/Work/model/diff_eq/Dalcantara_model/dAlcantara2003.xml')
mod.doLoad()
mod.doSimPlot(end=600.00)
#!/usr/bin/env python
# Testing the new parallel scanner class
import os
backupdir = os.getcwd()
import numpy as np
import pysces
tbox=pysces.PyscesUtils.TimerBox()
import time
m=pysces.model('isola2a')
ser = pysces.Scanner(m)
print("Serial execution...")
print("Start: ", tbox.normal_timer('SER'))
print(next(tbox.SER))
t1=time.time()
ser.quietRun = True
ser.addScanParameter('V4',60,100,11)
ser.addScanParameter('V1',100,160,16)
ser.addScanParameter('V2',100,130,16,slave=True)
ser.addScanParameter('V3',80,90,6)
ser.addUserOutput('J_R1', 'A', 'ecR4_X','ccJR1_R1')
#ser.addUserOutput('J_R1', 'A')
ser.Run()
print("Done: ", next(tbox.SER))
t2=time.time()
print("Duration: %.2f seconds" % (t2-t1))
graphs generated will be both displayed and saved as PNG format files.
NO WARRANTY OF FITNESS OF ANY KIND FOR ANYTHING ... SERIOUSLY!
(C) Brett G. Olivier Amsterdam 2011. All rights reserved
"""
# First we need to import PySCeS 0.8.0 or newer and store the current directory in CDIR
import os, time
CDIR = os.path.dirname(os.path.abspath(os.sys.argv[0]))
import numpy, pysces
# Next we instantiate a PySCeS model object (load the input file from the current directory)
# and set the simulation endtime/endsteps. Note how in this input file we specify that this
# model should be simulated stochastically and that the output should be displayed in amounts.
mod = pysces.model('Burstmodel.psc', CDIR)
MODEL_NAME = 'Burstmodel'
ENDTIME = 100
ENDSTEPS = 10000
# First let's assume this model represents a determinisitic system and do a continuous
# simulation to endtime with 2*endtime time points with LSODA or CVODE.
mod.doSimPlot(ENDTIME, 2 * ENDTIME)
# Save the results to a file using the PySCeS plotting library (pysces.plt.*)
pysces.plt.export('%s_par_set_1_deterministic' % MODEL_NAME, CDIR)
# Typical continuous result. Now let's assume this is a stochastic system and do a discrete
# simulation to the same end time using the Direct method
mod.doStochSim(end=ENDTIME, mode='time', method='Direct')
# we can plot the results with this function and save the results
mod.StochSimPlot()
self.name = name
self.value = value
self.components = []
def setComponent(self, comp):
self.components.append(comp)
setattr(self, comp.name, comp)
def hasComponents(self):
return MapList([a.name for a in self.components])
if __name__ == "__main__":
import pysces
M = pysces.model("pysces_model_linear1")
M.doLoad()
print "\nModel", M.ModelFile
print "============="
modmap = ModelMap(M)
print "Reactions\n", modmap.hasReactions()
print "Species\n", modmap.hasSpecies()
print "FixedSpecies\n", modmap.hasFixedSpecies()
print " "
print "R1 has reagents\n", modmap.R1.hasReagents()
print "R1 has sub\n", modmap.R1.hasSubstrates()
print "R1 has prod\n", modmap.R1.hasProducts()
print "R1 has mod\n", modmap.R1.hasModifiers()
print " "
S = self.pcMod.data_sim.getSpecies()[:, 1:]
# print 'called!'
return S
if __name__ == "__main__":
#nr samples to generate
N = 10000
burn = 5000
thin = 5
force = False
fname = 'testLogInverseModel1.pkl'
#initialize the pysces model
pcMod = pysces.model('pysces_test_linear2')
pcMod.sim_end = 5
pcMod.sim_points = 60
#initialize "data"
pcMod.Simulate()
D = pcMod.data_sim.getSpecies()[:, 1:]
D += sigmaExp * randn(D.shape[0], D.shape[1])
#get initial parameters
nK = 0
for p in pcMod.parameters:
if p[0] == 'k':
nK += 1
k0 = zeros(nK)
for p in pcMod.parameters:
def bounceSpecies(S, fname='speciesBounce.spc'):
with open(fname, 'w') as f:
for i, s in enumerate(S):
f.write('s%d = %.3e\n' % (i + 1, s[-1]))
def spc(pcMod):
return pcMod.data_sim.getSpecies()[:, 1:].T
if __name__ == '__main__':
#-------------------------------------------------------
# initialize the pysces model
#-------------------------------------------------------
modName = 'R-ERK_s.psc'
pcMod = pysces.model(modName)
pcMod.__settings__["lsoda_mxstep"] = 2000
# pcMod.sim_end = 5e2
# pcMod.Simulate()
# bounceSpecies(spc(pcMod))
# pcMod.__settings__['mode_sim_init'] = 3
pcMod.sim_end = 1e5
pcMod.sim_points = 200
idxDic, nmDic, sDic = loadSpeciesDict(modName[:-4] +
'_speciesDictionary.txt')
#-------------------------------------------------------
#u,sv,vh = linalg.svd(pcMod.nmatrix)
#figure()
#plot(sv)
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
model_file = 'BIOMD0000000860.xml'
# environment
import os
cDir = os.path.dirname(os.path.abspath(os.sys.argv[0]))
import pysces
import numpy as np
import pickle
model_dir = os.path.join(cDir, 'models')
out_dir = os.path.join(cDir, 'out', model_file)
data_dir = os.path.join(cDir, 'ref_data', model_file)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
pysces.output_dir = out_dir
# code
pysces.interface.convertSBML2PSC(model_file, sbmldir=model_dir, pscdir=out_dir)
mod = pysces.model(model_file + '.psc', dir=out_dir)
mod.doSim(4 * 3600, 4 * 3600 + 1)
mod.SimPlot()
pysces.plt.export(model_file, directory=out_dir, outtype='png')
ref_sim_data = pickle.load(open(data_dir + '/simdata.pkl', 'rb'))
assert np.allclose(mod.data_sim.getAllSimData(), ref_sim_data), \
"Data doesn't match reference!"
print('\nOutput path: \"{}\"\n\nDone.\n'.format(out_dir))
def __load_sbml__(self):
psc_file_name = self.sbml_file_name + ".psc"
pysces.interface.convertSBML2PSC(self.sbml_file_name)
self.pyscesModel = pysces.model(psc_file_name)
self.pyscesModel.doLoad()
def run(mod_name='wolf1',
sim_start=0.0,
sim_end=30.,
sim_points=30,
exp_sign='exp_1_',
method_name='DE',
generations=5,
pop_size=500,
clip_min=0.,
clip_max=15.,
a=-100.,
b=100.,
scale=1.,
p=-1.,
std=0.1,
gamma=0.75,
CR=0.9,
best=False,
dist_name='truncnorm',
low=0.,
high=100.,
indices=None,
compartment=True,
patience=100,
noise=0.1,
dir_model='C:\\Dev\\github\\abcde\\',
slash='\\'):
# Experiment name
exp_name = exp_sign + method_name + '_'
# Load PySCES model
mod = pysces.model(mod_name, dir=dir_model)
# Solver settings
mod.__settings__["mode_sim_max_iter"] = 0
mod.__settings__['lsoda_atol'] = 1.0e-012
mod.__settings__['lsoda_rtol'] = 1.0e-007
mod.__settings__['lsoda_mxordn'] = 12
mod.__settings__['lsoda_mxords'] = 5
mod.__settings__['lsoda_mxstep'] = 0
# =====REAL DATA PREPARATION=====
# Remove fixed_species from params. Do it only once
params = remove_fixed(mod.parameters,
mod.fixed_species,
compartment=compartment)
x_obs, t = generate_data(mod,
params,
sim_start=sim_start,
sim_end=sim_end,
sim_points=sim_points,
noise=noise)
real_params = read_real_parameters(mod, params)
real_params_array = dict_to_array(real_params, params)
np.save(dir_model + 'results' + slash + exp_name + 'x_obs.npy', x_obs)
np.save(dir_model + 'results' + slash + exp_name + 't.npy', t)
np.save(dir_model + 'results' + slash + exp_name + 'real_params_array.npy',
real_params_array)
json.dump(
real_params,
open(dir_model + 'results' + slash + exp_name + 'real_params.json',
"w"))
json.dump(
params,
open(dir_model + 'results' + slash + exp_name + 'params.json', "w"))
pickle.dump(
mod, open(dir_model + 'results' + slash + exp_name + 'mod.pkl', "wb"))
# =======EXPERIMENT=======
# config
conf = Config(method_name=method_name,
generations=generations,
pop_size=pop_size,
clip_min=clip_min,
clip_max=clip_max,
a=a,
b=b,
scale=scale,
p=p,
std=std,
gamma=gamma,
CR=CR,
best=best,
dist_name=dist_name,
indices=indices,
patience=patience)
pickle.dump(
conf.config,
open(dir_model + 'results' + slash + exp_name + 'config.pkl', "wb"))
# Init method
if method_name in ['DE']:
method = DE(conf.config)
elif method_name in ['RevDE']:
method = RevDE(conf.config)
elif method_name in ['ES']:
method = ES(conf.config)
elif method_name in ['EDA']:
method = EDA(conf.config)
elif method_name in ['RevDEknn']:
method = RevDEknn(conf.config)
elif method_name in ['EDAknn']:
method = RevDEknn(conf.config)
else:
raise ValueError('Wrong method! Only DE, ABC_DE and ABC_MH.')
# Init parameters
theta = np.random.uniform(low=low,
high=high,
size=(conf.config['pop_size'], len(params)))
theta = np.clip(theta,
a_min=conf.config['clip_min'],
a_max=conf.config['clip_max'])
# Calcute their energy
E = calculate_fitness(x_obs,
theta,
mod,
params,
dist=method.dist,
conf=conf.config)
# Start experiment
best_E = [np.min(E)]
all_E = E
all_theta = theta
clock_start = time.time()
print('START ~~~~~~>')
g = conf.config['generations']
for i in range(conf.config['generations']):
print(f'========> Generation {i+1}/{g}')
theta, E = method.step(theta, E, x_obs, mod, params)
if np.min(E) < best_E[-1]:
best_E.append(np.min(E))
else:
best_E.append(best_E[-1])
all_theta = np.concatenate((all_theta, theta), 0)
all_E = np.concatenate((all_E, E), 0)
# SAVING
np.save(dir_model + 'results' + slash + exp_name + 'all_theta.npy',
all_theta)
np.save(dir_model + 'results' + slash + exp_name + 'all_E.npy', all_E)
np.save(dir_model + 'results' + slash + exp_name + 'best_E.npy',
np.asarray(best_E))
# early stopping
if i > patience:
if best_E[-patience] == best_E[-1]:
break
print('~~~~~~> END')
clock_stop = time.time()
print('Time elapsed: {}'.format(clock_stop - clock_start))
np.save(dir_model + 'results' + slash + exp_name + 'time.npy',
np.asarray(clock_stop - clock_start))
