def empiricalDistributionPlot(self, sample, bounds=None):
if bounds:
lower = bounds[0]
upper = bounds[1]
else:
lower = min(sample)
upper = max(sample)
plotDomain = linspace(lower, upper, len(sample))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(sample))])
empiricalCDF.observations = sample
obs = []
for j in range(len(sample)):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample = array(obs)
plt.plot(plotDomain, ecdf_sample)
plt.show()
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_ad_pf(initial_land, costshocks[t]) - initial_land)
defosequence_ad = array(defosequence) * landtoemissions / 1e6
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_pf(initial_land, costshocks[t]) - initial_land)
defosequence_defo = array(defosequence) * landtoemissions / 1e6
lower = min(min(defosequence_defo), min(defosequence_ad))
upper = max(max(defosequence_defo), max(defosequence_ad))
plotDomain = linspace(lower, upper, len(costshocks))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(costshocks))])
empiricalCDF.observations = defosequence_ad
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_ad = array(obs)
plt.plot(plotDomain, ecdf_sample_ad, label='AD')
empiricalCDF.observations = defosequence_defo
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_defo = array(obs)
plt.plot(plotDomain, ecdf_sample_defo, label='Defo')
plt.legend()
plt.show()
]) # calculate sum E-fields at obsevation points
Emags2 = abs(numpy.array([numpy.dot(a, a) for a in Es]))
av = sum(Emags2) / N_obs_points # the average of |E|**2
ma = max(Emags2) # the maximum of |E|**2
D = ma / av # directivity
#print mc, ma, av, D
Ds.append(D)
Eslist.append(Es)
D_max_list.append(numpy.mean(Ds))
D_max_var_list.append(numpy.var(Ds))
#ED=EDmax_hansen(ka, mu=1., Ns=Ns)
# print
#print f, ka, sum(Ds)/N_MC, ED
print f, N_obs_points
#sys.stdout.flush()
ecdfD = ECDF(Ds)
#pylab.plot(deval, [FD_hertz_one_cut(d) for d in deval], label="Theoretical CDF (a=0 m)")
#pylab.plot(deval, [FD_hertz_one_cut_costheta(d) for d in deval], label="Theoretical CDF cos(theta)(a=0 m)")
pylab.figure(1)
pylab.plot(N_obs_points_list,
D_max_list,
'%s+-' % clr,
label="f=%dGHz" % (f / 1e9))
output_data1.append(D_max_list)
pylab.figure(2)
pylab.plot(N_obs_points_list,
D_max_var_list,
'%s+-' % clr,
label="f=%dGHz" % (f / 1e9))
output_data2.append(D_max_var_list)
Es = numpy.array([
E_hertz_far(r, p, R, pha, f, t=0, epsr=1.) for r in rs
]) # calculate sum E-fields at obsevation points
Emags2 = abs(numpy.array([numpy.dot(a, a) for a in Es]))
av = sum(Emags2) / N_obs_points # the average of |E|**2
ma = max(Emags2) # the maximum of |E|**2
D = ma / av # directivity
#print mc, ma, av, D
Ds_K.append(D)
Es2_list_av_K.append(av)
print "K", mc
Es2_ratio = numpy.divide(Es2_list_max_R, Es2_list_av_K)
ecdfD = ECDF(Es2_ratio)
pylab.figure(1)
pylab.plot(deval, ecdfD(deval), '%s+-' % clr, label="ECDF (Dipoles)")
output_data.append(remove_nan(ecdfD(deval)))
#pylab.plot(deval, [FD_hertz_one_cut(d) for d in deval], label="Theoretical CDF (a=0 m)")
#pylab.plot(deval, [FD_hertz_one_cut_costheta(d) for d in deval], label="Theoretical CDF cos(theta)(a=0 m)")
pylab.axis([deval[0], deval[-1], 0, 1])
pylab.grid()
pylab.legend(loc=4)
pylab.xlabel(r"$\frac{E^{R^{2}}_{max}}{E^{K^{2}}}$")
pylab.ylabel(r"F($\frac{E^{R^{2}}_{max}}{E^{K^{2}}}$)")
pylab.title("$N_{dipoles}=%d$, MC runs=%d, $Frequency=%dGHz$, $R=%dm$" %
(N_dipole, N_MC, f / 1e9, distance))
#pylab.show()
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_ad_pf(initial_land, costshocks[t]) - initial_land)
defosequence_ad = array(defosequence) * landtoemissions / 1e6
defosequence = []
for t in range(sampleSize):
defosequence.append(defo_pf(initial_land, costshocks[t]) - initial_land)
defosequence_defo = array(defosequence) * landtoemissions / 1e6
lower = min(min(defosequence_defo), min(defosequence_ad))
upper = max(max(defosequence_defo), max(defosequence_ad))
plotDomain = linspace(lower, upper, len(costshocks))
empiricalCDF = ECDF([uniform(0, 1) for i in range(len(costshocks))])
empiricalCDF.observations = defosequence_ad
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_ad = array(obs)
plt.plot(plotDomain, ecdf_sample_ad, label='AD')
empiricalCDF.observations = defosequence_defo
obs = []
for j in range(sampleSize):
obs.append(empiricalCDF(plotDomain[j]))
ecdf_sample_defo = array(obs)
plt.plot(plotDomain, ecdf_sample_defo, label='Defo')
plt.legend()
plt.show()
