def statplot(cor, err, Z):
from pcc import get_my_cmap
plt.errorbar(range(0, len(cor)),
cor,
yerr=err,
fmt='o',
zorder=1,
color='grey')
plt.scatter(range(0, len(cor)),
cor,
c=Z,
vmin=100,
vmax=1000,
cmap=get_my_cmap(),
zorder=2)
cb = plt.colorbar(shrink=0.5)
cb.set_label("Hits in #", fontsize=ff)
cb.ax.tick_params(labelsize=ff)
#plt.show()
plt.ylim(-1, 1)
plt.xlim(0, len(cor))
plt.grid()
plt.ylabel('Correlation', fontsize=ff)
plt.xlabel('overpasses with time', fontsize=ff)
plt.title('Overpass statistics betwen DPR and RADOLAN', fontsize=ff)
def statplot(cor, err, Z):
from pcc import get_my_cmap
plt.errorbar(range(0,len(cor)),cor, yerr=err, fmt='o', zorder=1, color='grey')
plt.scatter(range(0,len(cor)),cor,c=Z, vmin=100, vmax=1000, cmap=get_my_cmap(), zorder=2)
cb = plt.colorbar(shrink=0.5)
cb.set_label("Hits in #",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
#plt.show()
plt.ylim(-1,1)
plt.xlim(0,len(cor))
plt.grid()
plt.ylabel('Correlation',fontsize=ff)
plt.xlabel('overpasses with time',fontsize=ff)
plt.title('Overpass statistics betwen DPR and RADOLAN',fontsize=ff)
cbar.set_label('#'+ str(TH), rotation=270)
plt.grid()
plt.ylabel('y')
plt.xlabel('Ref')
plt.title('RADOLAN REF Hist')
plt.show()
maskr = ~np.isnan(r_sat) & ~np.isnan(r_rad)
from pcc import get_my_cmap
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(111, aspect='auto')
plt.hist2d(r_sat[maskr],r_rad[maskr],bins=bbb, cmap=get_my_cmap(), vmin=0.1)
cbar = plt.colorbar()
cbar.set_label('#'+ str(TH), rotation=270)
plt.grid()
plt.xlabel('x')
plt.ylabel('Ref')
plt.title('GPM NS REF Hist')
plt.show()
A, B = r_rad[maskr],r_sat[maskr]
"""
extrema = 40
popo = np.where((A>extrema)&(B>extrema))
fig = plt.figure(figsize=(12,12))
from pcc import get_time_of_gpm
from pcc import cut_the_swath
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
from pcc import get_miub_cmap
my_cmap = get_miub_cmap()
from pcc import get_my_cmap
my_cmap2 = get_my_cmap()
GGG = []
RRR = []
# Ref.Threshold nach RADOLAN_Goudenhoofdt_2016
TH_ref = 12#18#7
'''
zz = np.array([20140609, 20140610, 20140629, 20140826, 20140921, 20141007,
20141016, 20150128, 20150227, 20150402, 20150427, 20160405,
20160607, 20160805, 20160904, 20160917, 20161001, 20161024,
20170113, 20170203,20170223])
'''
ZP = '20141007'
"""
import numpy as np
import h5py
import matplotlib.pyplot as plt
import wradlib
import pandas as pd
import pcc as pcc
from pcc import boxpol_pos
from pcc import plot_radar
from pcc import plot_borders
from time import *
my_cmap = pcc.get_my_cmap()
cmap2 = pcc.get_miub_cmap()
from pcc import get_radar_locations
radar = get_radar_locations()
from pcc import zeitschleife as zt
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
blat, blon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
import os
#kometar
t1 = clock()
zeit = zt(2013,05,28,00,00,0,
2013,05,28,23,55,0,
gn.reshape(gn.shape[0] * gn.shape[1], 1),
wrl.ipol.Idw,
nnearest=4)
res_gpm = res_gpm.reshape(xx_new.shape)
res_gpm[res_gpm != 0] = 1 # Randkorrektur
from pcc import get_my_cmap
#result_gpm[np.where(xx_new < gpm_x[:,0])] = np.nan
#result_gpm[np.where(yy_new < gpm_y[:,-1])] = np.nan
plt.subplot(2, 3, 1)
plt.pcolormesh(gpm_x,
gpm_y,
np.ma.masked_invalid(rrr),
cmap=get_my_cmap(),
vmin=0.01,
vmax=50)
plt.plot(gpm_x[:, 0], gpm_y[:, 0], color='black', lw=1)
plt.plot(gpm_x[:, -1], gpm_y[:, -1], color='black', lw=1)
plt.subplot(2, 3, 2)
plt.pcolormesh(gpm_x,
gpm_y,
np.ma.masked_invalid(bpp),
cmap=get_my_cmap(),
vmin=0.01,
vmax=50)
plt.plot(gpm_x[:, 0], gpm_y[:, 0], color='black', lw=1)
plt.plot(gpm_x[:, -1], gpm_y[:, -1], color='black', lw=1)
plt.subplot(2, 3, 4)
res_gpm = wrl.ipol.interpolate(grid_gpm_xy, xy_new, gn.reshape(gn.shape[0]*gn.shape[1],1),
wrl.ipol.Idw, nnearest=4)
res_gpm = res_gpm.reshape(xx_new.shape)
res_gpm[res_gpm!=0]= 1 # Randkorrektur
from pcc import get_my_cmap
#result_gpm[np.where(xx_new < gpm_x[:,0])] = np.nan
#result_gpm[np.where(yy_new < gpm_y[:,-1])] = np.nan
plt.subplot(2,3,1)
plt.pcolormesh(gpm_x, gpm_y, np.ma.masked_invalid(rrr), cmap=get_my_cmap(), vmin=0.01, vmax=50)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
plt.subplot(2,3,2)
plt.pcolormesh(gpm_x, gpm_y, np.ma.masked_invalid(bpp), cmap=get_my_cmap(), vmin=0.01, vmax=50)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
plt.subplot(2,3,4)
plt.pcolormesh(xx_new, yy_new, result_rad, cmap=get_my_cmap(), vmin=0.01, vmax=50)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
plt.subplot(2,3,5)
plt.pcolormesh(xx_new, yy_new, np.ma.masked_invalid(result_gpm*res_gpm), cmap=get_my_cmap(), vmin=0.01, vmax=50)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plot_radar(bonnlon, bonnlat, ax1, reproject=False, cband=False,col='black')
#ax1 = plt.scatter(lon_ppi, lat_ppi, c=50 ,s=50, color='red')
plt.scatter(k1,l1, c=50 ,s=50, color='red')
plt.scatter(k2,l1, c=50 ,s=50, color='red')
plt.scatter(k1,l2, c=50 ,s=50, color='red')
plt.scatter(k2,l2, c=50 ,s=50, color='red')
plt.grid()
plt.xlim(-420,390)
plt.ylim(-4700, -3700)
##################
ax2 = fig.add_subplot(222, aspect='auto')
plt.hist2d(ppp[maske],hhh[maske], bins=30, cmap=get_my_cmap(), vmin=0.1)
plt.ylim(0,5000)
plt.xlim(-10,10)
print pp.shape
plt.plot(np.nanmean(pp[:,:],axis=0),hdpr, color='red', lw=2)
plt.plot(np.nanmedian(pp[:,:],axis=0),hdpr, color='green', lw=2)
#plt.plot(np.nanmax(pp[:,:],axis=0),hdpr, color='red', lw=2)
#plt.plot(np.nanmin(pp[:,:],axis=0),hdpr, color='red', lw=2)
#plt.plot(np.nanmean(pp[:,:],axis=0),hdpr, color='red', lw=2)
#plt.plot(np.nanmedian(pp[:,:],axis=0),hdpr, color='green', lw=2)
cbar = plt.colorbar()
cbar.set_label('#')
color='black',
lw=1,
ls='--')
plt.plot(dpr_lon[:, cut1], dpr_lat[:, cut1], color='red', lw=2, ls='--')
plt.plot(dpr_lon[cut2, :], dpr_lat[cut2, :], color='green', lw=2, ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50, s=50, color='red')
plt.grid()
plt.xlim(-350, -100)
plt.ylim(-4350, -4100)
##################exit()
hhh = hhh / 1000.
ax2 = fig.add_subplot(222, aspect='auto')
ax2.hist2d(ppp[maske], hhh[maske], bins=30, cmap=get_my_cmap(), vmin=0.1)
#plt.plot(np.nanmax(pp[:,:],axis=0),hdpr, color='red', lw=2)
ax2.plot(np.nanmean(pp[:, :, :], axis=(0, 1)), hdpr / 1000., color='red', lw=2)
plt.plot(np.nanmedian(pp[:, :, :], axis=(0, 1)),
hdpr / 1000.,
color='green',
lw=2)
cbar = plt.colorbar()
cbar.set_label('number of samples')
#plt.title('DPR Ref. in Box')
plt.xlabel('Reflectivity (dBZ)')
plt.ylabel('Height (km)')
plt.grid()
import scipy as sp
import wradlib as wrl
from osgeo import osr
from pcc import get_time_of_gpm
from pcc import cut_the_swath
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
from pcc import get_my_cmap
my_cmap = get_my_cmap()
from pcc import get_my_cmap
my_cmap2 = get_my_cmap()
GGG = []
RRR = []
from pcc import get_radar_locations
from pcc import plot_radar2
def plot_all_cband(ax):
for i in get_radar_locations().keys():
plot_radar2(get_radar_locations()[i]['lon'],
cbar = plt.colorbar()
cbar.set_label('#' + str(TH), rotation=270)
plt.grid()
plt.ylabel('y')
plt.xlabel('Ref')
plt.title('RADOLAN REF Hist')
plt.show()
maskr = ~np.isnan(r_sat) & ~np.isnan(r_rad)
from pcc import get_my_cmap
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(111, aspect='auto')
plt.hist2d(r_sat[maskr], r_rad[maskr], bins=bbb, cmap=get_my_cmap(), vmin=0.1)
cbar = plt.colorbar()
cbar.set_label('#' + str(TH), rotation=270)
plt.grid()
plt.xlabel('x')
plt.ylabel('Ref')
plt.title('GPM NS REF Hist')
plt.show()
A, B = r_rad[maskr], r_sat[maskr]
"""
extrema = 40
popo = np.where((A>extrema)&(B>extrema))
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(111, aspect='auto')
plt.plot(dpr_lon[:,0],dpr_lat[:,0], color='black',lw=1)
plt.plot(dpr_lon[:,-1],dpr_lat[:,-1], color='black',lw=1)
plt.plot(dpr_lon[:,dpr_lon.shape[1]/2],dpr_lat[:,dpr_lon.shape[1]/2], color='black',lw=1, ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50 ,s=50, color='red')
plt.grid()
plt.xlim(-420,390)
plt.ylim(-4700, -3700)
##################exit()
hhh = hhh/1000.
ax2 = fig.add_subplot(222, aspect='auto')
ax2.hist2d(ppp[maske],hhh[maske], bins=30, cmap=get_my_cmap(), vmin=0.1)
print pp.shape
print ppp.shape
#plt.plot(np.nanmax(pp[:,:],axis=0),hdpr, color='red', lw=2)
ax2.plot(np.nanmean(pp[:,:,:],axis=(0,1)),hdpr/1000., color='red', lw=2)
plt.plot(np.nanmedian(pp[:,:,:],axis=(0,1)),hdpr/1000., color='green', lw=2)
cbar = plt.colorbar()
cbar.set_label('number of samples')
#plt.title('DPR Ref. in Box')
plt.xlabel('Reflectivity (dBZ)')
plt.ylabel('Height (km)')
plt.grid()
t1, t2 = "20170307024500", "20170307025000"
x, y, z, bz = read_rado(t1, 'rx')
x1, y1, z1, bz1 = read_rado(t2, 'rx')
from pcc import get_my_cmap
import pcc
from scipy import stats, linspace
ff = 15
cc = 0.5
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(221, aspect='equal')#------------------------------------
pm1 = plt.pcolormesh(x, y, z, cmap=get_my_cmap(), vmin=0.01, vmax=50, zorder=2)
cb = plt.colorbar(shrink=cc)
cb.set_label("Reflectivity [dBZ]",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
pcc.plot_borders(ax1)
plt.title('RADOLAN Reflectivity:\n '+ t1+' UTC',fontsize=ff)
plt.grid(color='r')
plt.tick_params(
axis='both',
which='both',
bottom='off',
top='off',
labelbottom='off',
right='off',
def gpm_bb(dates, pn=0):
zt = dates
pfad = ('/automount/ags/velibor/gpmdata/dpr/2A.GPM.DPR.V6-20160118.' + zt +
'*.HDF5')
dpr_pfad = sorted(glob.glob(pfad))[pn]
print dpr_pfad
scan = 'NS' #or MS
# Einlesen
dpr = h5py.File(dpr_pfad, 'r')
dpr_lat = np.array(dpr[scan]['Latitude'])
dpr_lon = np.array(dpr[scan]['Longitude'])
dpr_pp = np.array(dpr[scan]['SLV']['zFactorCorrected'])
dpr_pp[dpr_pp < 0] = np.nan
dpr_pp_surf = np.array(dpr[scan]['SLV']['zFactorCorrectedNearSurface'])
dpr_pp_surf[dpr_pp_surf < 0] = np.nan
dpr_bbh = np.array(dpr[scan]['CSF']['heightBB'], dtype=float)
dpr_bbh[dpr_bbh < 0] = np.nan
dpr_bbw = np.array(dpr[scan]['CSF']['widthBB'], dtype=float)
dpr_bbw[dpr_bbw < 0] = np.nan
dpr_time = dpr['NS']['ScanTime']
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
blat, blon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
dpr_lon, dpr_lat = wradlib.georef.reproject(dpr_lon,
dpr_lat,
projection_target=proj_stereo,
projection_source=proj_wgs)
bonnlon, bonnlat = wradlib.georef.reproject(bonnlon,
bonnlat,
projection_target=proj_stereo,
projection_source=proj_wgs)
print '-------->', bonnlon, bonnlat
lon0, lat0, radius = bonnlon, bonnlat, 100
r = np.sqrt((dpr_lat - lat0)**2 + (dpr_lon - lon0)**2)
position = r < radius
lat = dpr_lat[position]
lon = dpr_lon[position]
dpr_pp[np.where(r > radius)] = np.nan
pp = dpr_pp
dpr_pp_surf[np.where(r > radius)] = np.nan
dpr_bbw[np.where(r > radius)] = np.nan
dpr_bbh[np.where(r > radius)] = np.nan
# Zeitstempel erstellen
l2, l1 = -190, -250
k2, k1 = -4210, -4270
# BoxPol
#l2, l1 = -110, -320
#k2, k1 = -4130, -4340
#
pos = np.where((dpr_lat < k2) & (dpr_lat > k1) & (dpr_lon < l2)
& (dpr_lon > l1))
stunde = np.array(dpr_time['Hour'])[pos[0]][0]
minute = np.array(dpr_time['Minute'])[pos[0]][0]
sekunde = np.array(dpr_time['Second'])[pos[0]][0]
jahr = np.array(dpr_time['Year'])[pos[0]][0]
monat = np.array(dpr_time['Month'])[pos[0]][0]
tag = np.array(dpr_time['DayOfMonth'])[pos[0]][0]
zeit = (str(jahr) + '.' + str(monat) + '.' + str(tag) + ' -- ' +
str(stunde) + ':' + str(minute) + ':' + str(sekunde))
print zeit
h = np.arange(150, 4800, 150)
if scan == 'HS':
hdpr = 1000 * (np.arange(88, 0, -1) * 0.250)
else:
hdpr = 1000 * (np.arange(176, 0, -1) * 0.125)
hhh = np.array(pp.shape[0] * pp.shape[1] * list(hdpr))
ppp = pp.reshape(pp.shape[0] * pp.shape[1] * pp.shape[2])
maske = ~np.isnan(hhh) & ~np.isnan(ppp)
fig = plt.figure(figsize=(14, 12))
zzz = str(jahr) + '-' + str(monat) + '-' + str(tag) + '--' + str(
stunde) + ':' + str(minute) + ' UTC'
fig.suptitle(zzz + ' UTC')
###################
ax1 = fig.add_subplot(221, aspect='auto')
#plt.subplot(2,2,1)
plt.pcolormesh(dpr_lon,
dpr_lat,
np.ma.masked_invalid(dpr_pp_surf),
vmin=np.nanmin(dpr_pp_surf),
vmax=np.nanmax(dpr_pp_surf),
cmap=get_miub_cmap())
cbar = plt.colorbar()
cbar.set_label('Ref. in dbz')
plot_borders(ax1)
plot_radar(blon, blat, ax1, reproject=True, cband=False, col='black')
plt.plot(dpr_lon[:, 0], dpr_lat[:, 0], color='black', lw=1)
plt.plot(dpr_lon[:, -1], dpr_lat[:, -1], color='black', lw=1)
plt.plot(dpr_lon[:, dpr_lon.shape[1] / 2],
dpr_lat[:, dpr_lon.shape[1] / 2],
color='black',
lw=1,
ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50, s=50, color='red')
plt.grid()
plt.xlim(-420, 390)
plt.ylim(-4700, -3700)
##################
ax2 = fig.add_subplot(222, aspect='auto')
plt.hist2d(ppp[maske], hhh[maske], bins=30, cmap=get_my_cmap(), vmin=0.1)
print pp.shape
#plt.plot(np.nanmax(pp[:,:],axis=0),hdpr, color='red', lw=2)
plt.plot(np.nanmean(pp[:, :, :], axis=(0, 1)), hdpr, color='red', lw=2)
plt.plot(np.nanmedian(pp[:, :, :], axis=(0, 1)), hdpr, color='green', lw=2)
cbar = plt.colorbar()
cbar.set_label('#')
plt.title('DPR Ref. in Box')
plt.xlabel('Reflectivity in dBZ')
plt.grid()
plt.xticks()
plt.yticks()
#plt.ylim(0,6000)
#plt.xlim(0,50)
##################
#print np.uniforn(bbh)
#mini = np.nanmin(bbh[bbh>0])
ax3 = fig.add_subplot(223, aspect='auto')
plt.pcolormesh(dpr_lon,
dpr_lat,
np.ma.masked_invalid(dpr_bbh),
vmin=np.nanmin(dpr_bbh[dpr_bbh > 0]),
vmax=np.nanmax(dpr_bbh),
cmap='jet')
cbar = plt.colorbar()
cbar.set_label('BB Hight in m')
plot_borders(ax3)
plot_radar(blon, blat, ax3, reproject=True, cband=False, col='black')
plt.plot(dpr_lon[:, 0], dpr_lat[:, 0], color='black', lw=1)
plt.plot(dpr_lon[:, -1], dpr_lat[:, -1], color='black', lw=1)
plt.plot(dpr_lon[:, dpr_lon.shape[1] / 2],
dpr_lat[:, dpr_lon.shape[1] / 2],
color='black',
lw=1,
ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50, s=50, color='red')
plt.grid()
#plt.title('BB Hight')
plt.xlim(-420, 390)
plt.ylim(-4700, -3700)
##################
ax4 = fig.add_subplot(224, aspect='auto')
plt.pcolormesh(dpr_lon,
dpr_lat,
np.ma.masked_invalid(dpr_bbw),
vmin=np.nanmin(dpr_bbw[dpr_bbh > 0]),
vmax=np.nanmax(dpr_bbw),
cmap='jet')
cbar = plt.colorbar()
cbar.set_label('BB Width in m')
plot_borders(ax4)
plot_radar(blon, blat, ax4, reproject=True, cband=False, col='black')
plt.plot(dpr_lon[:, 0], dpr_lat[:, 0], color='black', lw=1)
plt.plot(dpr_lon[:, -1], dpr_lat[:, -1], color='black', lw=1)
plt.plot(dpr_lon[:, dpr_lon.shape[1] / 2],
dpr_lat[:, dpr_lon.shape[1] / 2],
color='black',
lw=1,
ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50, s=50, color='red')
plt.grid()
#plt.title('BB Width')
plt.xlim(-420, 390)
plt.ylim(-4700, -3700)
plt.tight_layout()
plt.show()
from pcc import get_time_of_gpm
from pcc import cut_the_swath
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
#from pcc import get_miub_cmap
#my_cmap = get_miub_cmap()
from pcc import get_my_cmap
my_cmap = get_my_cmap()
GGG = []
RRR = []
#Alle Zeitpunkte
#'''
zz = np.array([20140826])#,
#20141016, 20150128, 20150227, 20150402, 20150427, 20160405,
#20160607, 20160805, 20160904, 20160917, 20161001, 20161024,
#20170113, 20170203,20170223])
#'''
#Alle rz rx zeitpunkte
#zz = np.array([20140921, 20141007,20140826,
# 20141016, 20150128, 20150227, 20150402, 20150427])
#zz = np.array([20170223])
from pcc import get_time_of_gpm from pcc import cut_the_swath ## Landgrenzenfunktion ## ------------------- from pcc import boxpol_pos bonn_pos = boxpol_pos() bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi'] bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi'] from pcc import plot_borders from pcc import plot_radar #from pcc import get_miub_cmap #my_cmap = get_miub_cmap() from pcc import get_my_cmap my_cmap = get_my_cmap() GGG = [] RRR = [] #Alle Zeitpunkte #''' zz = np.array([20140826]) #, #20141016, 20150128, 20150227, 20150402, 20150427, 20160405, #20160607, 20160805, 20160904, 20160917, 20161001, 20161024, #20170113, 20170203,20170223]) #''' #Alle rz rx zeitpunkte #zz = np.array([20140921, 20141007,20140826, # 20141016, 20150128, 20150227, 20150402, 20150427]) #zz = np.array([20170223])
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
from pcc import get_miub_cmap
my_cmap = get_miub_cmap()
from pcc import get_my_cmap
my_cmap2 = get_my_cmap()
ZP = '20141007'
year, m, d = ZP[0:4], ZP[4:6], ZP[6:8]
ye = ZP[2:4]
## Read GPM Data
## -------------
pfad2 = ('/home/velibor/shkgpm/data/' + str(year) + str(m) + str(d) +
'/dpr/*.HDF5')
pfad_gpm = glob.glob(pfad2)
pfad_gpm_g = pfad_gpm[0]
# GPM Lage und Zeit
gpmdpr = h5py.File(pfad_gpm_g, 'r')
import scipy as sp
import wradlib as wrl
from osgeo import osr
from pcc import get_time_of_gpm
from pcc import cut_the_swath
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
from pcc import get_my_cmap
my_cmap = get_my_cmap()
from pcc import get_my_cmap
my_cmap2 = get_my_cmap()
GGG = []
RRR = []
from pcc import get_radar_locations
from pcc import plot_radar2
def plot_all_cband(ax):
for i in get_radar_locations().keys():
plot_radar2(get_radar_locations()[i]['lon'],
plt.plot(dpr_lon[:, dpr_lon.shape[1] / 2],
dpr_lat[:, dpr_lon.shape[1] / 2],
color='black',
lw=1,
ls='--')
ax1 = plt.scatter(bonnlon, bonnlat, c=50, s=50, color='red')
plt.grid()
plt.xlim(-420, 390)
plt.ylim(-4700, -3700)
##################exit()
hhh = hhh / 1000.
ax2 = fig.add_subplot(222, aspect='auto')
ax2.hist2d(ppp[maske], hhh[maske], bins=30, cmap=get_my_cmap(), vmin=0.1)
print pp.shape
print ppp.shape
#plt.plot(np.nanmax(pp[:,:],axis=0),hdpr, color='red', lw=2)
ax2.plot(np.nanmean(pp[:, :, :], axis=(0, 1)), hdpr / 1000., color='red', lw=2)
plt.plot(np.nanmedian(pp[:, :, :], axis=(0, 1)),
hdpr / 1000.,
color='green',
lw=2)
cbar = plt.colorbar()
cbar.set_label('number of samples')
#plt.title('DPR Ref. in Box')
#df[np.where(df['N']<300)] = np.nan#
A = df['r_value'].values.copy()
B = df['std_err'].values.copy()
C = df['H'].values.copy()
T = 100
A[np.where(C<T)] = np.nan
B[np.where(C<T)] = np.nan
C[np.where(C<T)] = np.nan
from pcc import get_my_cmap
plt.errorbar(range(0,len(A)),A, B, fmt='o', zorder=1, color='grey')
plt.scatter(range(0,len(A)),A,c=C,s=300,linewidths=0.001, vmin=T, vmax=1000, cmap=get_my_cmap(), zorder=2)
cb = plt.colorbar(shrink=0.5)
cb.set_label("Hits in #",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
plt.ylim(-1,1)
plt.xlim(0,len(A))
plt.grid()
plt.ylabel('Correlation',fontsize=ff)
plt.xlabel('overpasses with time',fontsize=ff)
plt.title('Overpass statistics betwen DPR and RADOLAN, \n Threshold: N = '+str(T),fontsize=ff)
plt.show()
dataframes = [df, df2]
names = ['RADOLAN','BoXPol']
for j in range(2):
