import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from io import StringIO
import wradlib
import satlib as sl
import wradlib as wrl
from osgeo import osr
import h5py
import glob
from pcc import get_my_cmap
from pcc import get_miub_cmap
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']
from pcc import plot_borders
from pcc import plot_radar
dates = '20140729'
#dates ='20151216'
#def gpm_bb(dates, pn=0):
zt = dates
pfad = ('/automount/ags/velibor/gpmdata/dpr/2A.GPM.DPR.V6-20160118.' + zt +
'*.HDF5')
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from io import StringIO
import satlib as sl
import h5py
import glob
import wradlib
from pcc import get_my_cmap
from pcc import get_miub_cmap
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
dates ='20141007'
pfad = ('/automount/ags/velibor/gpmdata/dpr/2A.GPM.DPR.V6-20160118.'+dates+'*.HDF5')
dpr_pfad = sorted(glob.glob(pfad))[0]
print dpr_pfad
scan = 'MS' #or MS
import pandas as pd
import wradlib
from scipy import stats
import matplotlib.cm as cm
my_cmap = cm.get_cmap('jet',40)
my_cmap.set_under('lightgrey')
my_cmap.set_over('darkred')
from pcc import get_miub_cmap as my_cmap
from pcc import plot_radar
from pcc import boxpol_pos
from pcc import plot_borders
import wradlib as wrl
from osgeo import osr
from satlib import ipoli_radi_stat
from satlib import corcor
Pos = boxpol_pos()
blon0, blat0 = Pos['lon_ppi'], Pos['lat_ppi']
bbx, bby = Pos['gkx_ppi'], Pos['gky_ppi']
from time import *
from pcc import cut_the_swath
from satlib import good_overpasses_dpr_boxpol as overpasses_dpr_boxpol
print ("_")
tstart = clock()
#print (overpasses_dpr_boxpol)
for ii in overpasses_dpr_boxpol.keys()[0]:
import math
import pandas as pd
from scipy import stats
# ftp://ftp.meteo.uni-bonn.de/pub/pablosaa/gpmdata/
import matplotlib.cm as cm
my_cmap = cm.get_cmap('jet',40)
my_cmap.set_under('lightgrey')
my_cmap.set_over('darkred')
from pcc import get_miub_cmap as my_cmap
from pcc import plot_radar
from pcc import boxpol_pos
import wradlib as wrl
from osgeo import osr
Pos = boxpol_pos()
bblon, bblat = Pos['lon_ppi'], Pos['lat_ppi']
gkx, gky = Pos['gkx_ppi'], Pos['gky_ppi']
def get_frist_and_last_valid(data,attributes,thr):
'''
This function enables you to find valid pixels with preciptitation. To choose correct and uncluttered bins different
radii are treatet differently:
< 5 km all pixel must be larger than rhohv and zh threshold within a radius of +- 3 pixel
>=5 km but < 20 km all pixel must be larger than rhohv and zh threshold within a radius of +- 2 pixel
>=20 km but < 50 km all pixel must be larger than rhohv and zh threshold within a radius of +- 1 pixel
>=50 km but < 100 km all pixel must be larger than rhohv and zh threshold within a bin_radius in equal azi of +- 1 pixel
The first valid and the last valid radii are choosen as the ones where all substractions are fullfilled.
'''
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()
