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()
def quantile_mapping(obs_cube, mod_cube, sce_cubes, *args, **kwargs):
"""
Quantile Mapping
apply quantile mapping to all scenario cubes using the distributions
of obs_cube and mod_cube
Args:
* obs_cube (:class:`iris.cube.Cube`):
the observational data
* mod_cube (:class:`iris.cube.Cube`):
the model data at the reference period
* sce_cubes (:class:`iris.cube.CubeList`):
the scenario data that shall be corrected
"""
from ecdf import ECDF
cell_iterator = np.nditer(obs_cube.data[0], flags=['multi_index'])
while not cell_iterator.finished:
index_list = list(cell_iterator.multi_index)
cell_iterator.iternext()
index_list.insert(0, 0)
index = tuple(index_list)
if isinstance(obs_cube.data, np.ma.MaskedArray) \
and obs_cube.data.mask[index]:
continue
index_list[0] = slice(0, None, 1)
index = tuple(index_list)
obs_data = obs_cube.data[index]
mod_data = mod_cube.data[index]
mod_ecdf = ECDF(mod_data)
for sce_cube in sce_cubes:
sce_data = sce_cube[index].data
p = mod_ecdf(sce_data) * 100
corr = np.percentile(obs_data, p) - \
np.percentile(mod_data, p)
sce_cube.data[index] += corr
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.append(D)
Eslist.append(Es)
D_max_list.append(numpy.mean(Ds))
#ED=EDmax_hansen(ka, mu=1., Ns=Ns)
# print
#print f, ka, sum(Ds)/N_MC, ED
print f
#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))
pylab.axis([N_obs_points_list[0], N_obs_points_list[-1], 1, 4])
pylab.grid()
pylab.legend(loc=4)
pylab.xlabel("$N_{dipoles}$")
pylab.ylabel("$<D_{max}^{R}>$")
pylab.title(" MC runs=%d, $a_{EUT}=%.4fm$, $R=%dm$,$N_{dipole}=%d$" %
(N_MC, a_EUT, distance, N_dipole))
pha=2*pi*numpy.random.random(N_dipole) # generate random phases
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.append(D)
Eslist.append(Es)
ED=EDmax_hansen(ka, mu=1., Ns=Ns)
print
print f, ka, sum(Ds)/N_MC, ED
print
sys.stdout.flush()
ecdfD=ECDF(Ds)
pylab.figure(1)
pylab.plot(deval,ecdfD(deval), '%s+-'%clr, label="$D_{max}^{R}")
output_data.append(remove_nan(ecdfD(deval)))
#pylab.plot(deval, [FD_hertz_one_cut(d) for d in deval], label="Theoretical CDF (a=0 m)")
#pylab.plot(deval, [FD_hertz_one_cut_costheta(d) for d in deval], label="Theoretical CDF cos(theta)(a=0 m)")
distance = 10 # measurement distance
a_EUT=0.27# radius of EUT
N_dipole = 50 # number of random dipoles
N_obs_points=50 #number of observation points (randomly distributed) on Ring around EUT
N_MC=1000 # number of MC runs -> average over different random configurations
freqs=numpy.array([30,150,300])*1e6#[30,50,80,100,150, 200,250, 300,350, 400,450, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500])*1e6#numpy.array(range(30,301,30))*1000000#numpy.logspace(10,11,3) # generate frequencies
#kas=a_EUTs*2*pi*freqs/c # vector with k*a values (a: EUT radius)
deval=numpy.linspace(1,12,100)
Es = numpy.array([
E_hertz_far(r, p, R, pha, f, t=0, epsr=1.) for r in rs
]) # calculate sum E-fields at obsevation points
Emags2 = abs(numpy.array([numpy.dot(a, a) for a in Es]))
av = sum(Emags2) / N_obs_points # the average of |E|**2
ma = max(Emags2) # the maximum of |E|**2
D = ma / av # directivity
#print mc, ma, av, D
Ds_K.append(D)
Es2_list_av_K.append(av)
print "K", mc
Es2_ratio = numpy.divide(Es2_list_max_R, Es2_list_av_K)
ecdfD = ECDF(Es2_ratio)
pylab.figure(1)
pylab.plot(deval, ecdfD(deval), '%s+-' % clr, label="ECDF (Dipoles)")
output_data.append(remove_nan(ecdfD(deval)))
#pylab.plot(deval, [FD_hertz_one_cut(d) for d in deval], label="Theoretical CDF (a=0 m)")
#pylab.plot(deval, [FD_hertz_one_cut_costheta(d) for d in deval], label="Theoretical CDF cos(theta)(a=0 m)")
pylab.axis([deval[0], deval[-1], 0, 1])
pylab.grid()
pylab.legend(loc=4)
pylab.xlabel(r"$\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()
