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plot_inf_set.py
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Tue, Jun 4, 06:14

plot_inf_set.py
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import warnings
import numpy as np
from matplotlib import pyplot as plt
def plotInfSet1FromData(mus, Z, T, E, murange, approx, mu0, exp = 2.):
if hasattr(approx, "mus"):
mu2x = approx.mus(0) ** exp
else:
mu2x = mu0[0] ** exp
murangeExp = [[murange[0][0] ** exp], [murange[1][0] ** exp]]
mu1 = np.power(mus, exp)
ZTmin, ZTmax = min(np.min(Z), np.min(T)), max(np.max(Z), np.max(T))
Emin, Emax = np.min(E), np.max(E)
plt.figure(figsize = (15, 7))
plt.jet()
plt.semilogy(mu1, Z)
plt.semilogy(mu1, T, '--')
for l_ in approx.trainedModel.getPoles():
plt.plot([np.real(l_ ** exp)] * 2, [ZTmin, ZTmax], 'b:')
plt.plot(mu2x, [ZTmin] * len(mu2x), 'kx')
plt.plot([murangeExp[0][0]] * 2, [ZTmin, ZTmax], 'm:')
plt.plot([murangeExp[1][0]] * 2, [ZTmin, ZTmax], 'm:')
plt.xlim(mu1[0], mu1[-1])
plt.title("|u(mu)|, |u_app(mu)|")
plt.grid()
plt.show()
plt.figure(figsize = (15, 7))
plt.jet()
plt.semilogy(mu1, E)
for l_ in approx.trainedModel.getPoles():
plt.plot([np.real(l_ ** exp)] * 2, [Emin, Emax], 'b:')
plt.plot(mu2x, [Emax] * len(mu2x), 'kx')
plt.plot([murangeExp[0][0]] * 2, [Emin, Emax], 'm:')
plt.plot([murangeExp[1][0]] * 2, [Emin, Emax], 'm:')
plt.xlim(mu1[0], mu1[-1])
plt.title("|err(mu)|")
plt.grid()
plt.show()
def plotInfSet1(murange, murangeEff, approx, mu0, nSamples = 20, exp = 2.):
mu1 = np.linspace(murangeEff[0][0] ** exp, murangeEff[1][0] ** exp,
nSamples)
mus = np.power(mu1, 1. / exp)
Z = approx.normHF(mus)
T = approx.normApprox(mus)
E = approx.normErr(mus) / Z
plotInfSet1FromData(mus, Z, T, E, murange, approx, mu0, exp)
return mus, Z, T, E
def plotInfSet2FromData(mus, Ze, Te, Ee, murange, approx, mu0,
exps = [2., 2.], clip = -1):
if hasattr(approx, "mus"):
mu2x, mu2y = approx.mus(0) ** exps[0], approx.mus(1) ** exps[1]
else:
mu2x, mu2y = mu0[0] ** exps[0], mu0[1] ** exps[1]
murangeExp = [[murange[0][0] ** exps[0], murange[0][1] ** exps[1]],
[murange[1][0] ** exps[0], murange[1][1] ** exps[1]]]
mu1s = np.unique([m[0] for m in mus])
mu2s = np.unique([m[1] for m in mus])
mu1 = np.power(mu1s, exps[0])
mu2 = np.power(mu2s, exps[1])
Mu1, Mu2 = np.meshgrid(np.real(mu1), np.real(mu2))
Z = np.log(Ze)
T = np.log(Te)
E = np.log(Ee)
ZTmin, ZTmax = min(np.min(Z), np.min(T)), max(np.max(Z), np.max(T))
Emin, Emax = np.min(E), np.max(E)
if clip > 0:
ZTmax -= clip * (ZTmax - ZTmin)
cmap = plt.cm.bone
else:
cmap = plt.cm.jet
warnings.simplefilter("ignore", category = UserWarning)
plt.figure(figsize = (15, 7))
plt.jet()
p = plt.contourf(Mu1, Mu2, Z, cmap = cmap,
levels = np.linspace(ZTmin, ZTmax, 50))
if clip > 0:
plt.contour(Mu1, Mu2, Z, [ZTmin])
plt.plot(mu2x, mu2y, 'kx')
plt.plot([murangeExp[0][0]] * 2,
[murangeExp[0][1], murangeExp[1][1]], 'm:')
plt.plot([murangeExp[0][0], murangeExp[1][0]],
[murangeExp[1][1]] * 2, 'm:')
plt.plot([murangeExp[1][0]] * 2,
[murangeExp[1][1], murangeExp[0][1]], 'm:')
plt.plot([murangeExp[1][0], murangeExp[0][0]],
[murangeExp[0][1]] * 2, 'm:')
plt.colorbar(p)
plt.title("log|u(mu)|")
plt.grid()
plt.show()
plt.figure(figsize = (15, 7))
plt.jet()
p = plt.contourf(Mu1, Mu2, T, cmap = cmap,
levels = np.linspace(ZTmin, ZTmax, 50))
if clip > 0:
plt.contour(Mu1, Mu2, T, [ZTmin])
plt.plot(mu2x, mu2y, 'kx')
plt.plot([murangeExp[0][0]] * 2,
[murangeExp[0][1], murangeExp[1][1]], 'm:')
plt.plot([murangeExp[0][0], murangeExp[1][0]],
[murangeExp[1][1]] * 2, 'm:')
plt.plot([murangeExp[1][0]] * 2,
[murangeExp[1][1], murangeExp[0][1]], 'm:')
plt.plot([murangeExp[1][0], murangeExp[0][0]],
[murangeExp[0][1]] * 2, 'm:')
plt.title("log|u_app(mu)|")
plt.colorbar(p)
plt.grid()
plt.show()
plt.figure(figsize = (15, 7))
plt.jet()
p = plt.contourf(Mu1, Mu2, E, levels = np.linspace(Emin, Emax, 50))
plt.plot(mu2x, mu2y, 'kx')
plt.plot([murangeExp[0][0]] * 2,
[murangeExp[0][1], murangeExp[1][1]], 'm:')
plt.plot([murangeExp[0][0], murangeExp[1][0]],
[murangeExp[1][1]] * 2, 'm:')
plt.plot([murangeExp[1][0]] * 2,
[murangeExp[1][1], murangeExp[0][1]], 'm:')
plt.plot([murangeExp[1][0], murangeExp[0][0]],
[murangeExp[0][1]] * 2, 'm:')
plt.title("log|err(mu)|")
plt.colorbar(p)
plt.grid()
plt.show()
def plotInfSet2(murange, murangeEff, approx, mu0, nSamples = 200,
exps = [2., 2.], clip = -1):
mu1 = np.linspace(murangeEff[0][0] ** exps[0], murangeEff[1][0] ** exps[0],
nSamples)
mu2 = np.linspace(murangeEff[0][1] ** exps[1], murangeEff[1][1] ** exps[1],
nSamples)
mu1s = np.power(mu1, 1. / exps[0])
mu2s = np.power(mu2, 1. / exps[1])
mus = [(m1, m2) for m2 in mu2s for m1 in mu1s]
Ze = approx.normHF(mus).reshape((nSamples, nSamples))
Te = approx.normApprox(mus).reshape((nSamples, nSamples))
Ee = approx.normErr(mus).reshape((nSamples, nSamples)) / Ze
plotInfSet2FromData(mus, Ze, Te, Ee, murange, approx, mu0, exps, clip)
return mus, Ze, Te, Ee

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