def plot_checked(self):
import pylab as pl
pl.ioff()
from statistics import expectation
exp = []
# The expectation plotter
if len(self._stored_t) != 0:
pl.figure(2)
pl.title(" Method %s" % ("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Expectation")
for i in range(len(self._stored_t)):
exp.append(
expectation(
(self._stored_domain_states[i], self._stored_p[i])))
EXP = np.array(exp).T
for i in range(EXP.shape[0]):
pl.plot(self._stored_t,
EXP[i, :],
'x-',
label=self.model.species[i])
pl.legend()
# The probability plotter
if len(self._probed_t) != 0:
pl.figure(3)
pl.title(" Method %s | Probing States over Time " % ("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Probability")
probs = np.array(self._probed_probs).T
for i in range(probs.shape[0]):
pl.plot(self._probed_t,
probs[i, :],
'x-',
label=str(self._probed_states[0][:, i]))
pl.legend()
pl.show()
def set_initial_values(self, domain_states, p, t=0.0):
"""
initialise the hybrid solver with the OFSP state space and probabiltiy. The function will automatically do the projection.
Parameters
----------------------
domain_states : numpy.ndarray
shape = (num of species x num of states)
p : numpy.ndarray
shape = (num of states,)
t : numpy.ndarray
Default = 0.0
"""
from Hybrid.Support_Expander import positions
# We project them down
from Hybrid.proj import project_to_Hybrid as POH
from statistics import expectation
clean = p > 1e-9
domain_states = domain_states[:, clean]
p = p[clean]
self.t = t
self._X, self._w = POH(domain_states, p, self.stoc_vector)
if self.model_name == "HL":
self._X[self.deter_vector, :] = expectation(
(domain_states, p))[self.deter_vector, np.newaxis]
# These are the key hashing components needed to find adjacent states.
self.domain_enum = Hybrid.state_enum.StateEnum(self._X,
self.stoc_vector)
self._valid, self._position = positions(self._X, self._V_s,
self.stoc_vector,
self.domain_enum)
def set_initial_values(self,domain_states,p,t = 0.0): """ initialise the hybrid solver with the OFSP state space and probabiltiy. The function will automatically do the projection. Parameters ---------------------- domain_states : numpy.ndarray shape = (num of species x num of states) p : numpy.ndarray shape = (num of states,) t : numpy.ndarray Default = 0.0 """ from Hybrid.Support_Expander import positions # We project them down from Hybrid.proj import project_to_Hybrid as POH from statistics import expectation clean = p > 1e-9 domain_states = domain_states[:,clean] p = p[clean] self.t = t self._X, self._w = POH(domain_states,p,self.stoc_vector) if self.model_name == "HL": self._X[self.deter_vector,:] = expectation((domain_states,p))[self.deter_vector,np.newaxis] # These are the key hashing components needed to find adjacent states. self.domain_enum = Hybrid.state_enum.StateEnum(self._X,self.stoc_vector) self._valid, self._position = positions(self._X, self._V_s, self.stoc_vector, self.domain_enum)
def plot_checked(self):
import pylab as pl
pl.ioff()
from statistics import expectation
exp = []
# The expectation plotter
if len(self._stored_t) != 0:
pl.figure(2)
pl.title(" Method %s"%("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Expectation")
for i in range(len(self._stored_t)):
exp.append(expectation((self._stored_domain_states[i],self._stored_p[i])))
EXP = np.array(exp).T
for i in range(EXP.shape[0]):
pl.plot(self._stored_t,EXP[i,:],'x-',label=self.model.species[i])
pl.legend()
# The probability plotter
if len(self._probed_t) != 0:
pl.figure(3)
pl.title(" Method %s | Probing States over Time "%("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Probability")
probs = np.array(self._probed_probs).T
for i in range(probs.shape[0]):
pl.plot(self._probed_t,probs[i,:],'x-',label=str(self._probed_states[0][:,i]))
pl.legend()
pl.show()
def plot_checked(self):
import pylab as pl
pl.ioff()
from statistics import expectation
exp = []
# The expectation plotter
if len(self._stored_t) != 0:
pl.figure(2)
pl.title(" Method %s" % ("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Expectation")
pl.grid(True)
for i in range(len(self._stored_t)):
exp.append(
expectation(
(self._stored_domain_states[i], self._stored_p[i])))
EXP = np.array(exp).T
expect_value_data = []
for i in range(EXP.shape[0]):
expect_value_data.append(EXP[i, :])
pl.plot(self._stored_t,
EXP[i, :],
'x-',
label=self.model.species[i])
pl.legend()
pl.savefig("figureExpectation.png", dpi=180, bbox_width="tight")
pl.clf()
pl.close()
expectation_data = []
expectation_data.append(self._stored_t)
for data in expect_value_data:
expectation_data.append(data.tolist())
np.savetxt('dataexpectation.csv',
np.column_stack(expectation_data),
delimiter=',')
# The probability plotter
if len(self._probed_t) != 0:
pl.figure(3)
pl.title(" Method %s | Probing States over Time " % ("OFSP"))
pl.xlabel("Time, t")
pl.ylabel("Probability")
pl.grid(True)
probs = np.array(self._probed_probs).T
probabilities_data = []
for i in range(probs.shape[0]):
probabilities_data.append(probs[i, :])
pl.plot(self._probed_t,
probs[i, :],
'x-',
label=str(self._probed_states[0][:, i]))
pl.legend()
pl.savefig("figureStatesExaming.png", dpi=180, bbox_width="tight")
pl.clf()
pl.close()
isp_position_data = []
isp_position_data.append(self._stored_t)
for data in probabilities_data:
isp_position_data.append(data.tolist())
np.savetxt('dataStatesExaming.csv',
np.column_stack(isp_position_data),
delimiter=',')