def plot(self, **kwargs):
if len(self.all_vars) == 1 and len(self.free_rvs) == 1:
pfun = FunDistr(self.nddistr, breakPoints = self.nddistr.Vars[0].range())
pfun.plot(label = str(self.nddistr.Vars[0].getSymname()), **kwargs)
legend()
elif len(self.all_vars) == 2 and len(self.free_rvs) == 2:
plot_2d_distr(self.nddistr, **kwargs)
elif len(self.all_vars) == 2 and len(self.free_rvs) == 1:
a, b = self.free_rvs[0].range()
freesym = self.free_rvs[0].getSym()
fun = my_lambdify(freesym, self.rv_to_equation[self.dep_rvs[0]], "numpy")
ax = plot_1d1d_distr(self.nddistr, a, b, fun)
ax.set_xlabel(self.free_rvs[0].getSymname())
ax.set_ylabel(self.dep_rvs[0].getSymname())
else:
raise RuntimeError("Too many variables.")
def plot(self, **kwargs):
if len(self.all_vars) == 1 and len(self.free_rvs) == 1:
pfun = FunDistr(self.nddistr,
breakPoints=self.nddistr.Vars[0].range())
pfun.plot(label=str(self.nddistr.Vars[0].getSymname()), **kwargs)
legend()
elif len(self.all_vars) == 2 and len(self.free_rvs) == 2:
plot_2d_distr(self.nddistr, **kwargs)
elif len(self.all_vars) == 2 and len(self.free_rvs) == 1:
a, b = self.free_rvs[0].range()
freesym = self.free_rvs[0].getSymname()
fun = my_lambdify([freesym], self.rv_to_equation[self.dep_rvs[0]],
"numpy")
ax = plot_1d1d_distr(self.nddistr, a, b, fun)
ax.set_xlabel(self.free_rvs[0].getSymname())
ax.set_ylabel(self.dep_rvs[0].getSymname())
elif len(self.all_vars) == 1 and len(self.free_rvs) == 0:
DiracSegment(self.as_const(), 1).plot(**kwargs)
else:
raise RuntimeError("Too many variables.")
# funi.get_piecewise_cdf().plot(color="r")
# funi.summary()
# print "==============="
# V= X-Y
# cp = PiCopula(marginals=[X, Y])
# m = TwoVarsModel(cp, V)
# fun = m.eval()
# fun.summary()
# fun.plot()
# show()
# 0/0
cij = IJthOrderStatsNDDistr(X, 8, 2, 7)
X1, X2 = cij.Vars
plot_2d_distr(cij)
figure()
X1.plot(color="r")
X1.summary()
X2.plot(color="g")
X2.summary()
V=X2-X1
print("p=", V.parents[1].getSym())
mR = TwoVarsModel(cij, V)
funR = mR.eval()
funR.summary()
funR.plot(color="k")
# funi = Mi.eval()
# funi.get_piecewise_cdf().plot(color="r")
# funi.summary()
# print "==============="
# V= X-Y
# cp = PiCopula(marginals=[X, Y])
# m = TwoVarsModel(cp, V)
# fun = m.eval()
# fun.summary()
# fun.plot()
# show()
# 0/0
cij = IJthOrderStatsNDDistr(X, 8, 2, 7)
X1, X2 = cij.Vars
plot_2d_distr(cij)
figure()
X1.plot(color="r")
X1.summary()
X2.plot(color="g")
X2.summary()
V = X2 - X1
print "p=", V.parents[1].getSym()
mR = TwoVarsModel(cij, V)
funR = mR.eval()
funR.summary()
funR.plot(color="k")
