コード例 #1
0
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
コード例 #2
0
ファイル: plot_sprof.py プロジェクト: rvalenzuelar/sprof_vis
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)
コード例 #3
0
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