def __init__(self,year=[],wdsurf=None, wdwpro=None,

rainbb=None, raincz=None, nhours=None):

out = processv2(year=year,wdsurf=wdsurf,

wdwpro=wdwpro,rainbb=rainbb,

raincz=raincz, nhours=nhours)

self.spd_hist = out[0]

self.dir_hist = out[1]

self.u_hist = out[2]

self.v_hist = out[3]

self.spd_cfad = out[4]

self.dir_cfad = out[5]

self.u_cfad = out[6]

self.v_cfad = out[7]

self.bins_spd = out[8]

self.bins_dir = out[9]

self.bins_u = out[10]

self.bins_v = out[11]

self.hgts = out[12]

self.wp_hours = out[13]

self.tta_hours = out[14]

self.notta_hours = out[15]

self.spd_average = out[16]

self.dir_average = out[17]

self.u_average = out[18]

self.v_average = out[19]

self.spd_median = out[20]

self.dir_median = out[21]

self.u_median = out[22]

self.v_median = out[23]

self.wdsurf = wdsurf

self.wdwpro = wdwpro

self.rainbb = rainbb

self.raincz = raincz

self.nhours = nhours

self.year = year

def plot(self,target,axes=None,pngsuffix=False, pdfsuffix=False,

contourf=True, add_median=False,add_average=False,

add_title=True, add_cbar=True,cbar_label=None,show=True,

subax_label=True,top_altitude=4000,

orientation=None):

name={'wdir':'Wind Direction',

'wspd':'Wind Speed',

'u':'u-wind',

'v':'v-wind'}

if target == 'wdir':

cfad = self.dir_cfad

median = self.dir_median

average = self.dir_average

bins = self.bins_dir

hist_xticks = np.arange(0,420,60)

hist_xlim = [0,360]

elif target == 'wspd':

cfad = self.spd_cfad

median = self.spd_median

average = self.spd_average

bins = self.bins_spd

hist_xticks = np.arange(0,40,5)

hist_xlim = [0,35]

elif target == 'u':

cfad = self.u_cfad

median = self.u_median

average = self.u_average

bins = self.bins_u

hist_xticks = np.arange(-14,22,4)

hist_xlim = [-14,20]

elif target == 'v':

cfad = self.v_cfad

median = self.v_median

average = self.v_average

bins = self.bins_v

hist_xticks = np.arange(-14,24,4)

hist_xlim = [-14,20]

if axes is None:

fig,axs = plt.subplots(1,3,sharey=True,figsize=(10,8))

ax1 = axs[0]

ax2 = axs[1]

ax3 = axs[2]

else:

ax1 = axes[0]

ax2 = axes[1]

ax3 = axes[2]

hist_wp = np.squeeze(cfad[:,:,0])

hist_wptta = np.squeeze(cfad[:,:,1])

hist_wpnotta = np.squeeze(cfad[:,:,2])

x = bins

y = self.hgts

if contourf:

X,Y = np.meshgrid(x,y)

nancol = np.zeros((40,1))+np.nan

hist_wp = np.hstack((hist_wp,nancol))

hist_wptta = np.hstack((hist_wptta,nancol))

hist_wpnotta = np.hstack((hist_wpnotta,nancol))

vmax=15

nlevels = 6

delta = int(vmax/nlevels)

v = np.arange(2,vmax+delta,delta)

cmap = cm.get_cmap('plasma')

ax1.contourf(X,Y,hist_wp,v,cmap=cmap)

p = ax2.contourf(X,Y,hist_wptta,v,cmap=cmap)

ax3.contourf(X,Y,hist_wpnotta,v,cmap=cmap)

lcolor = (0.6,0.6,0.6)

lw = 3

ax1.vlines(0,0,4000,linestyle='--',color=lcolor,lw=lw)

ax2.vlines(0,0,4000,linestyle='--',color=lcolor,lw=lw)

ax3.vlines(0,0,4000,linestyle='--',color=lcolor,lw=lw)

lw=3

if add_median:

ax1.plot(median[:,0],self.hgts,color='w',lw=lw)

ax2.plot(median[:,1],self.hgts,color='w',lw=lw)

ax3.plot(median[:,2],self.hgts,color='w',lw=lw)

if add_average:

ax1.plot(average[:,0],self.hgts,color='w',lw=lw)

ax2.plot(average[:,1],self.hgts,color='w',lw=lw)

ax3.plot(average[:,2],self.hgts,color='w',lw=lw)

else:

p = ax1.pcolormesh(x,y,hist_wp,cmap='viridis')

ax2.pcolormesh(x,y,hist_wptta,cmap='viridis')

ax3.pcolormesh(x,y,hist_wpnotta,cmap='viridis')

''' add color bar '''

if add_cbar is True:

add_colorbar(ax3,p,size='4%',loc='right',

label=cbar_label)

' --- setup ax1 --- '

ax1.set_xticks(hist_xticks)

ax1.set_xlim(hist_xlim)

ax1.set_ylim([0,top_altitude])

ax1.set_ylabel('Altitude [m] MSL')

' --- setup ax2 --- '

ax2.set_xticks(hist_xticks)

ax2.set_xlim(hist_xlim)

ax2.set_ylim([0,top_altitude])

ax2.set_xlabel(name[target])

' --- setup ax3 --- '

ax3.set_xticks(hist_xticks)

ax3.set_xlim(hist_xlim)

ax3.set_ylim([0,top_altitude])

''' add subaxis label '''

if orientation == 'horizontal':

vpos=1.05

if subax_label is True:

txt = 'All profiles (n={})'.format(self.wp_hours)

ax1.text(0.5,vpos,txt,fontsize=15,weight='bold',

transform=ax1.transAxes,va='center',ha='center')

txt = 'TTA (n={})'.format(self.tta_hours)

ax2.text(0.5,vpos,txt,fontsize=15,weight='bold',

transform=ax2.transAxes,va='center',ha='center')

txt = 'NO-TTA (n={})'.format(self.notta_hours)

ax3.text(0.5,vpos,txt,fontsize=15, weight='bold',

transform=ax3.transAxes,va='center',ha='center')

elif orientation == 'vertical':

hpos=1.05

if subax_label is True:

txt = 'All profiles (n={})'.format(self.wp_hours)

ax1.text(hpos,0.5,txt,fontsize=15,weight='bold',

transform=ax1.transAxes,va='center',

ha='center',rotation=-90)

txt = 'TTA (n={})'.format(self.tta_hours)

ax2.text(hpos,0.5,txt,fontsize=15,weight='bold',

transform=ax2.transAxes,va='center',

ha='center',rotation=-90)

txt = 'NO-TTA (n={})'.format(self.notta_hours)

ax3.text(hpos,0.5,txt,fontsize=15, weight='bold',

transform=ax3.transAxes,va='center',

ha='center',rotation=-90)

''' add title '''

if add_title is True:

title = 'Normalized frequencies of BBY wind profiles {} \n'

title += 'TTA wdir_surf:{}, wdir_wp:{}, '

title += 'rain_bby:{}, rain_czd:{}, nhours:{}'

if len(self.year) == 1:

yy = 'year {}'.format(self.year[0])

else:

yy = 'year {} to {}'.format(self.year[0],self.year[-1])

plt.suptitle(title.format(yy, self.wdsurf,

self.wdwpro, self.rainbb, self.raincz, self.nhours),

fontsize=15)

if pngsuffix:

out_name = 'wprof_{}_cfad{}.png'

plt.savefig(out_name.format(target,pngsuffix))

plt.close()

elif pdfsuffix:

out_name = 'wprof_{}_cfad{}.pdf'

plt.savefig(out_name.format(target,pdfsuffix))

plt.close()

if show is True:

plt.show()

wdwpro=None,rainbb=None,

raincz=None, nhours=None):

''' v2: target loop moved into year loop '''

binss={'wdir': np.arange(0,370,10),

'wspd': np.arange(0,36,1),

'u': np.arange(-15,21,1),

'v': np.arange(-14,21,1),

}

target = ['wdir','wspd']

arrays = {}

wsp = np.empty((40,1))

wsp_tta = np.empty((40,1))

wsp_notta = np.empty((40,1))

wdr = np.empty((40,1))

wdr_tta = np.empty((40,1))

wdr_notta = np.empty((40,1))

for y in year:

print('Processing year {}'.format(y))

' tta analysis '

tta = tta_analysis(y)

tta.start_df(wdir_surf = wdsurf,

wdir_wprof = wdwpro,

rain_bby = rainbb,

rain_czd = raincz,

nhours = nhours)

' retrieve dates '

include_dates = tta.include_dates

tta_dates = tta.tta_dates

notta_dates = tta.notta_dates

' read wprof '

wprof_df = parse_data.windprof(y)

for n,t in enumerate(target):

wprof = wprof_df.dframe[t]

' wprof partition '

wprof = wprof.loc[include_dates] # all included

wprof_tta = wprof.loc[tta_dates] # only tta

wprof_notta = wprof.loc[notta_dates]# only notta

s1 = np.squeeze(pandas2stack(wprof))

if wprof_tta.size > 0:

s2 = np.squeeze(pandas2stack(wprof_tta))

ttaok = True

else:

ttaok =False

s3 = np.squeeze(pandas2stack(wprof_notta))

if t == 'wdir':

wdr = np.hstack((wdr,s1))

if ttaok is True:

if s2.ndim == 1:

s2=np.expand_dims(s2,axis=1)

wdr_tta = np.hstack((wdr_tta,s2))

wdr_notta = np.hstack((wdr_notta, s3))

else:

wsp = np.hstack((wsp,s1))

if ttaok is True:

if s2.ndim == 1:

s2=np.expand_dims(s2,axis=1)

wsp_tta = np.hstack((wsp_tta,s2))

wsp_notta = np.hstack((wsp_notta, s3))

arrays['wdir']=[wdr,wdr_tta,wdr_notta]

arrays['wspd']=[wsp,wsp_tta,wsp_notta]

uw = -wsp*np.sin(np.radians(wdr))

uw_tta = -wsp_tta*np.sin(np.radians(wdr_tta))

uw_notta = -wsp_notta*np.sin(np.radians(wdr_notta))

vw = -wsp*np.cos(np.radians(wdr))

vw_tta = -wsp_tta*np.cos(np.radians(wdr_tta))

vw_notta = -wsp_notta*np.cos(np.radians(wdr_notta))

arrays['u']=[uw,uw_tta,uw_notta]

arrays['v']=[vw,vw_tta,vw_notta]

''' total hours, first rows are empty '''

_,wp_hours = wsp.shape

_,tta_hours = wsp_tta.shape

_,notta_hours = wsp_notta.shape

wp_hours -= 1

tta_hours-= 1

notta_hours -= 1

' initialize arrays '

hist_array_spd = np.empty((40,len(binss['wspd'])-1,3))

hist_array_dir = np.empty((40,len(binss['wdir'])-1,3))

cfad_array_spd = np.empty((40,len(binss['wspd'])-1,3))

cfad_array_dir = np.empty((40,len(binss['wdir'])-1,3))

average_spd = np.empty((40,3))

average_dir = np.empty((40,3))

median_spd = np.empty((40,3))

median_dir = np.empty((40,3))

' loop for variable (wdir,wspd) '

for k,v in arrays.iteritems():

hist_array = np.empty((40,len(binss[k])-1,3))

cfad_array = np.empty((40,len(binss[k])-1,3))

average = np.empty((40,3))

median = np.empty((40,3))

' extract value'

wp = v[0]

wp_tta = v[1]

wp_notta = v[2]

' makes CFAD '

for hgt in range(wp.shape[0]):

row1 = wp[hgt,:]

row2 = wp_tta[hgt,:]

row3 = wp_notta[hgt,:]

for n,r in enumerate([row1,row2,row3]):

' following CFAD Yuter et al (1995) '

freq,bins=np.histogram(r[~np.isnan(r)],

bins=binss[k])

hist_array[hgt,:,n] = freq

cfad_array[hgt,:,n] = 100.*(freq/float(freq.sum()))

bin_middle = (bins[1:]+bins[:-1])/2.

average[hgt,n] = np.sum(freq*bin_middle)/freq.sum()

median[hgt,n] = np.percentile(r[~np.isnan(r)],50)

if k == 'wspd':

hist_array_spd = hist_array

cfad_array_spd = cfad_array

average_spd = average

median_spd = median

elif k == 'wdir':

hist_array_dir = hist_array

cfad_array_dir = cfad_array

average_dir = average

median_dir = median

elif k == 'u':

hist_array_u = hist_array

cfad_array_u = cfad_array

average_u = average

median_u = median

elif k == 'v':

hist_array_v = hist_array

cfad_array_v = cfad_array

average_v = average

median_v = median

return [hist_array_spd,

hist_array_dir,

hist_array_u,

hist_array_v,

cfad_array_spd,

cfad_array_dir,

cfad_array_u,

cfad_array_v,

binss['wspd'],

binss['wdir'],

binss['u'],

binss['v'],

wprof_df.hgt,

wp_hours,

tta_hours,

notta_hours,

average_spd,

average_dir,

average_u,

average_v,

median_spd,

median_dir,

median_u,

median_v,

wdwpro=None,rainbb=None,

raincz=None, nhours=None):

binss={'wdir':np.arange(0,370,10),

'wspd':np.arange(0,36,1)}

target = ['wdir','wspd']

arrays = {}

for t in target:

first = True

for y in year:

print('Processing year {}'.format(y))

' tta analysis '

tta = tta_analysis(y)

tta.start_df(wdir_surf=wdsurf,

wdir_wprof=wdwpro,

rain_bby=rainbb,

rain_czd=raincz,

nhours=nhours)

' retrieve dates '

include_dates = tta.include_dates

tta_dates = tta.tta_dates

notta_dates = tta.notta_dates

' read wprof '

wprof_df = parse_data.windprof(y)

wprof = wprof_df.dframe[t]

' wprof partition '

wprof = wprof.loc[include_dates] # all included

wprof_tta = wprof.loc[tta_dates] # only tta

wprof_notta = wprof.loc[notta_dates]# only notta

s1 = np.squeeze(pandas2stack(wprof))

s2 = np.squeeze(pandas2stack(wprof_tta))

s3 = np.squeeze(pandas2stack(wprof_notta))

if first:

wp = s1

wp_tta = s2

wp_notta = s3

first = False

else:

wp = np.hstack((wp,s1))

wp_tta = np.hstack((wp_tta,s2))

wp_notta = np.hstack((wp_notta, s3))

_,wp_hours = wp.shape

_,tta_hours = wp_tta.shape

_,notta_hours = wp_notta.shape

arrays[t]=[wp,wp_tta,wp_notta]

' makes CFAD '

hist_array_spd = np.empty((40,len(binss['wspd'])-1,3))

hist_array_dir = np.empty((40,len(binss['wdir'])-1,3))

cfad_array_spd = np.empty((40,len(binss['wspd'])-1,3))

cfad_array_dir = np.empty((40,len(binss['wdir'])-1,3))

average_spd = np.empty((40,3))

average_dir = np.empty((40,3))

median_spd = np.empty((40,3))

median_dir = np.empty((40,3))

for k,v in arrays.iteritems():

hist_array = np.empty((40,len(binss[k])-1,3))

cfad_array = np.empty((40,len(binss[k])-1,3))

average = np.empty((40,3))

median = np.empty((40,3))

wp = v[0]

wp_tta = v[1]

wp_notta = v[2]

for hgt in range(wp.shape[0]):

row1 = wp[hgt,:]

row2 = wp_tta[hgt,:]

row3 = wp_notta[hgt,:]

for n,r in enumerate([row1,row2,row3]):

' following CFAD Yuter et al (1995) '

freq,bins=np.histogram(r[~np.isnan(r)],

bins=binss[k])

hist_array[hgt,:,n] = freq

cfad_array[hgt,:,n] = 100.*(freq/float(freq.sum()))

bin_middle = (bins[1:]+bins[:-1])/2.

average[hgt,n] = np.sum(freq*bin_middle)/freq.sum()

median[hgt,n] = np.percentile(r[~np.isnan(r)],50)

if k == 'wspd':

hist_array_spd = hist_array

cfad_array_spd = cfad_array

average_spd = average

median_spd = median

else:

hist_array_dir = hist_array

cfad_array_dir = cfad_array

average_dir = average

median_dir = median

return [hist_array_spd,

hist_array_dir,

cfad_array_spd,

cfad_array_dir,

binss['wspd'],

binss['wdir'],

wprof_df.hgt,

wp_hours,

tta_hours,

notta_hours,

average_spd,

average_dir,

median_spd,