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pcc_gprof_rado.py
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pcc_gprof_rado.py
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"""
Einlesen und darstellen von GPM DPR und Radolan Dateien
Radolanpfad:
"""
import h5py
import numpy as np
import matplotlib.pyplot as plt
import wradlib
import glob
from scipy import stats, linspace
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_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])
for i in range(len(zz)):
ZP = str(zz[i])
#year, m, d, ht, mt, st = ZP[0:4], ZP[4:6], ZP[6:8], ZP[8:10], ZP[10:12], ZP[12:14]
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)+'/gprof/*.HDF5')
pfad_gprof = glob.glob(pfad2)
pfad_gprof_g = pfad_gprof[0]
gpmdprs = h5py.File(pfad_gprof_g, 'r')
gprof_lat=np.array(gpmdprs['S1']['Latitude'])
gprof_lon=np.array(gpmdprs['S1']['Longitude'])
gprof_pp=np.array(gpmdprs['S1']['surfacePrecipitation'])
gpm_time = gpmdprs['S1']['ScanTime']
gpm_zeit = get_time_of_gpm(gprof_lon, gprof_lat, gpm_time)
print gpm_zeit
gprof_pp[gprof_pp==-9999.9]= np.nan
ht, mt = gpm_zeit[14:16], str(int(round(float(gpm_zeit[17:19])/5.0)*5.0))
if mt == '0':
mt = '00'
elif mt == '5':
mt = '05'
print mt
## Read RADOLAN Data
## -----------------
r_pro = 'rx' #rz oder rx###############
pfad = ('/automount/radar/dwd/'+ r_pro +'/'+str(year)+'/'+str(year)+'-'+
str(m)+'/'+ str(year)+'-'+str(m)+'-'+str(d)+'/raa01-'+r_pro+'_10000-'+
str(ye)+str(m)+ str(d)+str(ht)+str(mt)+'-dwd---bin.gz')
pfad_radolan = pfad[:-3]
try:
rw_filename = wradlib.util.get_wradlib_data_file(pfad)
except EnvironmentError:
rw_filename = wradlib.util.get_wradlib_data_file(pfad_radolan)
rwdata, rwattrs = wradlib.io.read_RADOLAN_composite(rw_filename)
radolan_zeit = rwattrs['datetime'].strftime("%Y.%m.%d -- %H:%M:%S")
#Binaere Grid
rn = rwdata.copy()
rn[rn != -9999] = 1
rn[rn == -9999] = 0
radolan_grid_xy = wradlib.georef.get_radolan_grid(900,900)
x = radolan_grid_xy[:,:,0]
y = radolan_grid_xy[:,:,1]
if r_pro=='rz':
rwdata = np.ma.masked_equal(rwdata, -9999) *8 # Einheit 1/100mm pro 5min
if r_pro=='rx':
rwdata = np.ma.masked_equal(rwdata, -9999) / 2 - 32.5
#rwdata[rwdata < 0] = np.nan
## Cut the GPM Swath
## ------------------
from pcc import cut_the_swath
blon, blat, gprof_pp_b = cut_the_swath(gprof_lon,gprof_lat,gprof_pp, eu=True)
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
gpm_x, gpm_y = wradlib.georef.reproject(blon, blat, projection_target=proj_stereo , projection_source=proj_wgs)
grid_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
## INTERLOLATION
## --------------
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
xy = np.vstack((x.ravel(), y.ravel())).transpose()
mask = ~np.isnan(rwdata)
result = wrl.ipol.interpolate(xy, grid_gpm_xy, rwdata[mask].reshape(900*900,1), wrl.ipol.Idw, nnearest=4)
result = np.ma.masked_invalid(result)
rrr = result.reshape(gpm_x.shape)
## Interpolation of the binary Grid
## ------------------------------
res_bin = wrl.ipol.interpolate(xy, grid_gpm_xy, rn.reshape(900*900,1), wrl.ipol.Idw, nnearest=4)
res_bin = res_bin.reshape(gpm_x.shape)
res_bin[res_bin!=0]= 1 # Randkorrektur
rand_y_unten = -4658.6447242655722
rand_y_oben = -3759.6447242655722
rand_x_rechts = 375.5378330781441
rrr[np.where(gpm_y < rand_y_unten)] = np.nan
rrr[np.where(gpm_y > rand_y_oben)] = np.nan
rrr[np.where(gpm_x > rand_x_rechts)] = np.nan
res_bin[np.where(gpm_y < rand_y_unten)] = np.nan
res_bin[np.where(gpm_y > rand_y_oben)] = np.nan
res_bin[np.where(gpm_x > rand_x_rechts)] = np.nan
res_bin[res_bin == 0] = np.nan #check nur 1 un NaN
ggg = gprof_pp_b * res_bin
if r_pro =='rx':
print 'es ist rx'
from pcc import z2r2
Z = wradlib.trafo.idecibel(rwdata)
rwdata = wradlib.zr.z2r(Z, a=200., b=1.6)
#rwdata = z2r2(Z)
#z2rEnhanced
Z2 = wradlib.trafo.idecibel(rrr)
rrr = wradlib.zr.z2r(Z2, a=200., b=1.6)
#rrr = z2r2(Z2)
if r_pro=='rz':
print 'es ist rz'
## Nur Niederschlagsrelevante
rrr[rrr <= 0.1] = np.nan
ggg[ggg <= 0.1] = np.nan
rwdata[rwdata <= 0.1] = np.nan
########### Todo: GPROFRAND entfernen!
#rrr = rrr * (ggg / ggg) # Rand vom GPROF entfernen !
ff = 15
cc = 0.5
vmini=0
maxi = np.nanmax([ggg,rrr])+2
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(221, aspect='equal')#------------------------------------
pm1 = plt.pcolormesh(x, y, rwdata, cmap=my_cmap, vmin=vmini, vmax=10, zorder=2)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
cb = plt.colorbar(shrink=cc)
cb.set_label("RR [mm/h]",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
plot_borders(ax1)
plot_radar(bonnlon, bonnlat, ax1, reproject=True, cband=False,col='black')
plt.title('RADOLAN: \n'+ radolan_zeit + ' UTC',fontsize=ff)
plt.grid(color='r')
plt.tick_params(
axis='both',
which='both',
bottom='off',
top='off',
labelbottom='off',
right='off',
left='off',
labelleft='off')
plt.xlim(-420,390)
plt.ylim(-4700, -3700)
ax2 = fig.add_subplot(222, aspect='equal')#------------------------------------
pm2 = plt.pcolormesh(gpm_x, gpm_y,np.ma.masked_invalid(ggg),
cmap=my_cmap, vmin=vmini, vmax=10, zorder=2)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
cb = plt.colorbar(shrink=cc)
cb.set_label("RR [mm/h]",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
plt.title('GPM GPROF: \n'+'20' + gpm_zeit + ' UTC',fontsize=ff)
plot_borders(ax2)
plot_radar(bonnlon, bonnlat, ax1, reproject=True, cband=False,col='black')
plt.grid(color='r')
#plt.tick_params(
# axis='both',
# which='both',
# bottom='off',
# top='off',
# labelbottom='off',
# right='off',
# left='off',
# labelleft='off')
plt.xlim(-420,390)
plt.ylim(-4700, -3700)
ax2 = fig.add_subplot(223, aspect='equal')#------------------------------------
pm3 = plt.pcolormesh(gpm_x, gpm_y,np.ma.masked_invalid(rrr),
cmap=my_cmap, vmin=vmini, vmax=10,zorder=2)
plt.plot(gpm_x[:,0],gpm_y[:,0], color='black',lw=1)
plt.plot(gpm_x[:,-1],gpm_y[:,-1], color='black',lw=1)
cb = plt.colorbar(shrink=cc)
cb.set_label("RR [mm/h]",fontsize=ff)
cb.ax.tick_params(labelsize=ff)
plt.title('RADOLAN Interpoliert: \n'+ radolan_zeit + ' UTC',fontsize=ff) #RW Product Polar Stereo
plot_borders(ax2)
plot_radar(bonnlon, bonnlat, ax1, reproject=True, cband=False,col='black')
plt.grid(color='r')
plt.tick_params(
axis='both',
which='both',
bottom='off',
top='off',
labelbottom='off',
right='off',
left='off',
labelleft='off')
plt.xlim(-420,390)
plt.ylim(-4700, -3700)
ax4 = fig.add_subplot(224, aspect='equal')#------------------------------------
maske = ~np.isnan(ggg) & ~np.isnan(rrr)
slope, intercept, r_value, p_value, std_err = stats.linregress(ggg[maske], rrr[maske])
line = slope * ggg +intercept
from pcc import skill_score
SS = skill_score(ggg,rrr,th=0)
plt.scatter(ggg, rrr, label='RR [mm/h]', color='grey', alpha=0.6)
text = ('f(x) = ' + str(round(slope,3)) + 'x + ' + str(round(intercept,3)) +
'\nCorr: ' + str(round(r_value,3)) + r'$\pm$: '+ str(round(std_err,3))+
'\nN: '+ str(int(SS['N']))+
'\nHit: ' + str(SS['H'])+
'\nMiss: ' + str(SS['M'])+
'\nFalse: ' + str(SS['F'])+
'\nCnegative: ' + str(SS['C'])+
'\nHR: ' + str(round(SS['HR'],3))+
'\nPOD: ' + str(round(SS['POD'],3))+
'\nFAR: ' + str(round(SS['FAR'],3))+
'\nBID: ' + str(round(SS['BID'],3))+
'\nHSS: ' + str(round(SS['HSS'],3))+
'\nBias: '+ str(round(SS['bias'],3))+
'\nRMSE: '+ str(round(SS['RMSE'],3))
)
ax4.annotate(text, xy=(0.01, 0.99), xycoords='axes fraction', fontsize=10,
horizontalalignment='left', verticalalignment='top', color='blue')
t1 = linspace(0,maxi,maxi)
plt.plot(t1,t1,'k-')
#plt.plot(t1,t1 + 5,'k-.')
#plt.plot(t1,t1 - 5,'k-.')
plt.plot(t1, t1*slope + intercept, 'r-', lw=3 ,label='Reg')
plt.plot(t1, t1*slope + (intercept+5), 'r-.', lw=1.5 ,label=r'Reg $\pm$ 5 mm/h')
plt.plot(t1, t1*slope + (intercept-5), 'r-.', lw=1.5 )
plt.plot(np.nanmean(ggg),np.nanmean(rrr), 'ob', lw = 4,label='Mean')
#plt.plot(np.nanmedian(ggg),np.nanmedian(rrr), 'vb', lw = 4,label='Median')
import matplotlib as mpl
mean = [ np.nanmean(ggg),np.nanmean(rrr)]
width = np.nanstd(ggg)
height = np.nanstd(rrr)
print width, height
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax4.add_patch(ell)
plt.xlim(0,maxi)
plt.ylim(0,maxi)
plt.legend(loc='lower right', fontsize=10, scatterpoints= 1, numpoints=1, shadow=True, ncol=1)
plt.xlabel('GPM GPROF RR [mm/h]',fontsize=ff)
plt.ylabel('RADOLAN RR [mm/h]',fontsize=ff)
plt.xticks(fontsize=ff)
plt.yticks(fontsize=ff)
plt.grid(color='r')
plt.tight_layout()
#plt.savefig('/home/velibor/shkgpm/plot/gpm_gprof_radolan_'+r_pro+'_'+ZP + '.png' )
#plt.close()
plt.show()
GGG.append(ggg.reshape(ggg.shape[0]*ggg.shape[1]))
RRR.append(rrr.reshape(rrr.shape[0]*rrr.shape[1]))
'''
G_all = np.concatenate(GGG,axis=0)
R_all = np.concatenate(RRR,axis=0)
from pcc import plot_scatter
fig = plt.figure(figsize=(12,12))
ax11 = fig.add_subplot(111, aspect='equal')
plot_scatter(G_all, R_all)
import matplotlib as mpl
mean = [ np.nanmean(G_all),np.nanmean(R_all)]
width = np.nanstd(G_all)
height = np.nanstd(R_all)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax11.add_patch(ell)
plt.xlabel(('GPM DPR (dBZ)'))
plt.ylabel(('RADOLAN (dBZ)'))
plt.grid()
plt.savefig('/home/velibor/shkgpm/plot/gprof/all_gpm_gprof_radolan_'+ r_pro + '.png' )
plt.close()
'''