def get_dataframe(cases=None, minutes=None):
data = {"case": [], "bbyp": [], "czdp": [], "echotvar": []}
cprecip = pd.DataFrame(data=data)
for c in cases:
" sprof data"
"*****************************************************"
dbz, vvel, ht, ts, ts2, dayt = sprof.get_arrays(str(c))
" echo top variance"
echotm = sprof.timeserie_echotop(dbz, ht, plot=False, retrieve="km")
" layer-averaged variance"
partition = read_partition.partition(dayt[0].year)
bbht, bbtimeidx = partition.get_bbht(time=dayt)
# center = np.nanmax(bbht)+1.0
# bottom = np.nanmax(bbht)
bottom = np.asarray(0.0)
vvel_mean, _ = sprof.layer_mean(vvel=vvel, height=ht, bottom=bottom)
_, dbz_mean = sprof.layer_mean(dbz=dbz, height=ht, bottom=bottom)
" compute variance "
a = {"echotm": echotm, "vvel_mean": vvel_mean, "dbz_mean": dbz_mean}
df = pd.DataFrame(data=a, index=dayt)
timeg = pd.TimeGrouper(str(minutes) + "T")
dfg = df.groupby(timeg).var()
dfg2 = dfg.ix[:]
dr_sprof = pd.date_range(dfg2.index[0], periods=len(dfg2), freq=str(minutes) + "T")
" sum precip in minutes interval "
"*****************************************************"
bby, czd, frs, _ = precip.get_data(str(c))
bbyp = bby.precip.groupby(timeg).sum()
czdp = czd.precip.groupby(timeg).sum()
frsp = frs.precip.groupby(timeg).sum()
" use time from sprof to select"
inix = bbyp.index.get_loc(dr_sprof[0])
endx = bbyp.index.get_loc(dr_sprof[-1])
bbyp2 = bbyp.ix[inix:endx]
czdp2 = czdp.ix[inix:endx]
frsp2 = frsp.ix[inix:endx]
ratioCB = np.round(czdp2 / bbyp2, 2)
ratioCB[np.isinf(ratioCB.values)] = np.nan
ratioCF = np.round(czdp2 / frsp2, 2)
ratioCF[np.isinf(ratioCF.values)] = np.nan
" windprof data "
"*****************************************************"
wspd, wdir, timestamp, hgt = wprof.make_arrays(case=str(c), surface=True)
dr_wprof = pd.date_range(timestamp[0], timestamp[-1], freq="60T")
idx_time = np.where((dr_wprof >= dr_sprof[0]) & (dr_wprof < dr_sprof[-1]))[0]
" select altitudes "
ws00 = np.round(wspd[0, idx_time], 1)
wd00 = np.round(wdir[0, idx_time], 0)
f = interp1d(hgt, range(0, len(hgt)))
idx = int(np.round(f(0.25), 0))
ws025 = np.round(wspd[idx, idx_time], 1)
wd025 = np.round(wdir[idx, idx_time], 0)
idx = int(np.round(f(1.25), 0))
ws125 = np.round(wspd[idx, idx_time], 1)
wd125 = np.round(wdir[idx, idx_time], 0)
idx = int(np.round(f(1.75), 0))
ws175 = np.round(wspd[idx, idx_time], 1)
wd175 = np.round(wdir[idx, idx_time], 0)
idx = int(np.round(f(2.5), 0))
ws250 = np.round(wspd[idx, idx_time], 1)
wd250 = np.round(wdir[idx, idx_time], 0)
idx = int(np.round(f(3.5), 0))
ws350 = np.round(wspd[idx, idx_time], 1)
wd350 = np.round(wdir[idx, idx_time], 0)
d = {
"case": np.repeat(c, len(bbyp.ix[inix:endx])),
"bbyp": bbyp2,
"czdp": czdp2,
"frsp": frsp2,
"echotvar": dfg2["echotm"][:-1],
"vvelvar": dfg2["vvel_mean"][:-1],
"dbzvar": dfg2["dbz_mean"][:-1],
"wsSfc": ws00,
"wdSfc": wd00,
"ws025": ws025,
"wd025": wd025,
"ws125": ws125,
"wd125": wd125,
"ws175": ws175,
"wd175": wd175,
"ws250": ws250,
"wd250": wd250,
"ws350": ws350,
"wd350": wd350,
"ratioCB": ratioCB,
"ratioCF": ratioCF,
}
df = pd.DataFrame(data=d, index=dfg2[:-1].index)
cprecip = cprecip.append(df)
""" reorder columns """
cprecip = cprecip[
[
"case",
"bbyp",
"czdp",
"frsp",
"ratioCB",
"ratioCF",
"wsSfc",
"wdSfc",
"ws025",
"wd025",
"ws125",
"wd125",
"ws175",
"wd175",
"ws250",
"wd250",
"ws350",
"wd350",
"echotvar",
"vvelvar",
"dbzvar",
]
]
return cprecip
def main():
global extent
global dayt
casenum = raw_input('\nIndicate case number (i.e. 1): ')
dbz,vvel, ht, dayt = read_sprof.retrieve_arrays(base_directory, casenum)
extent=[0, len(dayt), 0, len(ht)]
''' set index for time zooming '''
try:
idx_st=find_index(dayt,reqdates[casenum]['ini'])
idx_end=find_index(dayt,reqdates[casenum]['end'])
except KeyError:
idx_st=0
idx_end=len(dayt)-1
''' Precipitation partition
**************************'''
partition=read_partition.partition(dayt[0].year)
bbht=partition.get_bbht()
rtype=partition.get_rtype()
begdayt=partition.get_begdayt()
enddayt=partition.get_enddayt()
beg_aux = np.asarray([datetime(t.year, t.month, t.day, t.hour,0,0) for t in begdayt])
end_aux = np.asarray([datetime(t.year, t.month, t.day, t.hour,0,0) for t in enddayt])
dayt_aux=np.asarray([datetime(t.year, t.month, t.day, t.hour,0,0) for t in dayt])
beg_index = np.where(beg_aux==dayt_aux[0])[0][0]
end_index = np.where(end_aux==dayt_aux[-1])[0][1]
bbht = bbht[beg_index+1:end_index]
rtype = rtype[beg_index+1:end_index]
begdayt = begdayt[beg_index:end_index]
enddayt = enddayt[beg_index:end_index]
d = [[t.year, t.month, t.day, t.hour,t.minute] for t in begdayt]
idx_be=np.asarray([find_index(dayt,x) for x in d[1:]])
d = [[t.year, t.month, t.day, t.hour,t.minute] for t in enddayt]
idx_en=np.asarray([find_index(dayt,x) for x in d[1:]])
idx_bbht=(idx_be+idx_en)/2
''' Plots
**************'''
fig,ax=plt.subplots(2,1,sharex=True, figsize=(12,8))
plot_reflectivity(ax[0],dbz,ht, cmap='nipy_spectral',vmin=-20,vmax=60)
plot_velocity(ax[1],vvel,ht, cmap='bwr',vmin=-3,vmax=3)
f = interp1d(ht, range(len(ht)))
ax[0].plot(idx_bbht, f(bbht),marker='o',linestyle='--',color='black')
for n, s in enumerate(rtype):
if s[0] != 'NaN':
ax[0].text(idx_bbht[n], -5, s[0][0].upper(), horizontalalignment='center',clip_on=True)
ax[1].plot(idx_bbht, f(bbht),marker='o',linestyle='--',color='black')
' it has to be before set_xlim'
# format_xaxis(ax[1],dayt,2,casenum)
format_xaxis(ax[1],dayt,labels=True,format='%d\n%H')
ax[0].set_xlim([idx_st,idx_end])
ax[0].set_ylim([-10,len(ht)])
ax[1].set_ylim([-10,len(ht)])
fig.subplots_adjust(hspace=.07)
ax[1].invert_xaxis()
ax[1].set_xlabel(r'$\Leftarrow$'+' Time [UTC]')
# ax[1].set_xlabel(' Time [UTC]'+r'$\Rightarrow$')
# ax[0].grid(b=True, which='major', color='b', linestyle='-')
plt.suptitle('SPROF observations. Date: '+dayt[0].strftime('%Y-%b'))
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.1)
plt.draw()
# plt.show()
plt.show(block=False)
def get_dataframe(cases=None,minutes=None):
data={'case':[], 'bbyp':[], 'czdp': [], 'echotvar':[]}
cprecip=pd.DataFrame(data=data)
for c in cases:
' sprof data'
'*****************************************************'
dbz,vvel,ht,ts,ts2,dayt = sprof.get_arrays(str(c))
' echo top variance'
echotm = sprof.timeserie_echotop(dbz,ht,plot=False,retrieve='km')
' layer-averaged variance'
partition=read_partition.partition(dayt[0].year)
bbht,bbtimeidx=partition.get_bbht(time=dayt)
# center = np.nanmax(bbht)+1.0
# bottom = np.nanmax(bbht)
bottom = np.asarray(0.)
vvel_mean,_=sprof.layer_mean(vvel=vvel,height=ht,bottom=bottom)
_,dbz_mean=sprof.layer_mean(dbz=dbz,height=ht,bottom=bottom)
' compute variance '
a={'echotm':echotm, 'vvel_mean':vvel_mean, 'dbz_mean':dbz_mean}
df = pd.DataFrame(data=a,index=dayt)
timeg=pd.TimeGrouper(str(minutes)+'T')
dfg = df.groupby(timeg).var()
dfg2=dfg.ix[:]
dr_sprof=pd.date_range(dfg2.index[0],periods=len(dfg2),freq=str(minutes)+'T')
' sum precip in minutes interval '
'*****************************************************'
bby,czd,frs,_ = precip.get_data(str(c))
bbyp = bby.precip.groupby(timeg).sum()
czdp = czd.precip.groupby(timeg).sum()
frsp = frs.precip.groupby(timeg).sum()
' use time from sprof to select'
inix = bbyp.index.get_loc(dr_sprof[0])
endx = bbyp.index.get_loc(dr_sprof[-1])
bbyp2=bbyp.ix[inix:endx]
czdp2=czdp.ix[inix:endx]
frsp2=frsp.ix[inix:endx]
ratioCB = np.round(czdp2/bbyp2,2)
ratioCB[np.isinf(ratioCB.values)]=np.nan
ratioCF = np.round(czdp2/frsp2,2)
ratioCF[np.isinf(ratioCF.values)]=np.nan
' windprof data '
'*****************************************************'
wspd,wdir,timestamp,hgt = wprof.make_arrays(case=str(c),surface=True)
dr_wprof = pd.date_range(timestamp[0],timestamp[-1], freq='60T')
idx_time = np.where((dr_wprof>=dr_sprof[0]) & (dr_wprof<dr_sprof[-1]))[0]
' select altitudes '
ws00=np.round(wspd[0,idx_time],1)
wd00=np.round(wdir[0,idx_time],0)
f = interp1d(hgt,range(0,len(hgt)))
idx = int(np.round(f(0.25), 0))
ws025=np.round(wspd[idx, idx_time],1)
wd025=np.round(wdir[idx, idx_time],0)
idx = int(np.round(f(1.25), 0))
ws125=np.round(wspd[idx, idx_time],1)
wd125=np.round(wdir[idx, idx_time],0)
idx = int(np.round(f(1.75), 0))
ws175=np.round(wspd[idx, idx_time],1)
wd175=np.round(wdir[idx, idx_time],0)
idx = int(np.round(f(2.5), 0))
ws250=np.round(wspd[idx, idx_time],1)
wd250=np.round(wdir[idx, idx_time],0)
idx = int(np.round(f(3.5), 0))
ws350=np.round(wspd[idx, idx_time],1)
wd350=np.round(wdir[idx, idx_time],0)
d={'case':np.repeat(c, len(bbyp.ix[inix:endx])),
'bbyp':bbyp2,
'czdp': czdp2,
'frsp': frsp2,
'echotvar':dfg2['echotm'][:-1],
'vvelvar':dfg2['vvel_mean'][:-1],
'dbzvar':dfg2['dbz_mean'][:-1],
'wsSfc': ws00, 'wdSfc': wd00,
'ws025': ws025, 'wd025': wd025,
'ws125': ws125, 'wd125': wd125,
'ws175': ws175, 'wd175': wd175,
'ws250': ws250, 'wd250': wd250,
'ws350': ws350, 'wd350': wd350,
'ratioCB': ratioCB,
'ratioCF':ratioCF}
df=pd.DataFrame(data=d, index=dfg2[:-1].index,)
cprecip=cprecip.append(df)
''' reorder columns '''
cprecip=cprecip[['case',
'bbyp', 'czdp','frsp',
'ratioCB', 'ratioCF',
'wsSfc', 'wdSfc',
'ws025','wd025',
'ws125','wd125',
'ws175','wd175',
'ws250','wd250',
'ws350','wd350',
'echotvar','vvelvar','dbzvar']]
return cprecip