R = N - len(runs_edges)
# expected value of R
e_R = ((2.0 * m * n) / N) + 1
# variance of R is _numer/_denom
_numer = 2 * m * n * (2 * m * n - N)
_denom = N ** 2 * (N - 1)
# see Eq. 1 in Friedman 1979
# W approaches a standard normal distribution
W = (R - e_R) / np.sqrt(_numer/_denom)
return W, R
data = read_prepare()
limithour = 6.0
dataspin = data[data['runtime'] < 3600.0*limithour]
datagood = data[data['runtime'] > 3600.0*limithour]
#dataspin = data[(data['runtime'] < 3600.0*13)*(data['runtime'] > 3600.0*12)]
#datagood = data[(data['runtime'] < 3600.0*14)*(data['runtime'] > 3600.0*13)]
droplabels = ['runtime', 'Hgt', 'P', 'T', 'RH', 'DWRxk', 'DWRkw']
dataspin.drop(droplabels, axis=1, inplace=True)
datagood.drop(droplabels, axis=1, inplace=True)
Nsamples = 5000
X = dataspin.values[random.sample(range(len(dataspin)), Nsamples),:]
Y = datagood.values[random.sample(range(len(datagood)), Nsamples),:]
@author: dori
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from statistics import hist_and_plot
from READ import read_prepare
plt.close('all')
addlabel = ''
addlabel = 'run'
#addlabel = 'spin'
campaign = 'tripex'
hydroset = campaign + '_all_hydro_'
data = read_prepare(hydroset, maxhour=46.0, minhour=6.0)
lognorm = True
h, x, y = hist_and_plot(data,
'CFAD X-band SNR',
'Hgt',
'N10',
'SNR X-band [dBZ]',
'Height [m]',
xlim=[-15, 70],
ylim=[0, 10000],
inverty=False,
savename='CFAD/CFAD_snrX_Hgt' + campaign + addlabel +
'.png',
lognorm=lognorm)
Vr = V[2, 0, :, :, 0, 0]
Vs = V[3, 0, :, :, 0, 0]
Vg = V[4, 0, :, :, 0, 0]
Vh = V[5, 0, :, :, 0, 0]
Sc = S[0, 0, :, :, 0, 0]
Si = S[1, 0, :, :, 0, 0]
Sr = S[2, 0, :, :, 0, 0]
Ss = S[3, 0, :, :, 0, 0]
Sg = S[4, 0, :, :, 0, 0]
Sh = S[5, 0, :, :, 0, 0]
campaign = 'tripex'
minhour = 6.0
pamtra = read_prepare(hydroset=campaign + '_all_hydro_', minhour=minhour)
pamtraTl0 = slice_data(pamtra, 'T', maxvalue=0)
lognormrule = True
#%% CFAD Sensitivity
xlim = [-60, 50]
ylim = [0, 12000]
h1, x1, y1 = hist_and_plot(pamtra,
'sensitivity cone X',
yvar='Hgt',
xvar='Z10',
xlabel='Zx [dBZ]',
ylabel='Hgt [m]',
xlim=xlim,
ylim=ylim,
lognorm=lognormrule,
Sr = S[2, 0, :, :, 0, 0]
Ss = S[3, 0, :, :, 0, 0]
Sg = S[4, 0, :, :, 0, 0]
Sh = S[5, 0, :, :, 0, 0]
campaign = 'tripex'
minhour = 6.0
hydroset = 'only_ice'
#hydroset = 'only_snow'
#hydroset = 'no_snow'
#hydroset = 'only_graupel_hail'
#hydroset = 'only_liquid'
#hydroset = 'all_hydro'
pamtra = read_prepare(hydroset=campaign + '_' + hydroset + '_',
minhour=minhour)
pamtraTl0 = slice_data(pamtra, 'T', maxvalue=0)
#%% CFAD Sensitivity
xlim = [-60, 50]
ylim = [0, 12000]
hist_and_plot(pamtra,
'sensitivity cone X',
yvar='Hgt',
xvar='Z10',
xlabel='Zx [dBZ]',
ylabel='Hgt [m]',
xlim=xlim,
ylim=ylim,
lognorm=True,
savename='tripex/' + hydroset + '/pamtraCFAD_Zx_Hgt.png',