def plot_with_lines(year=None,target=None):
fig = plt.figure(figsize=(10,5))
gs = gsp.GridSpec(1, 2,
width_ratios=[2,1]
)
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
wprof = parse_data.windprof(year)
wp = np.squeeze(pandas2stack(wprof.dframe[target]))
wp_ma = ma.masked_where(np.isnan(wp),wp)
X,Y=wprof.time,wprof.hgt
ax1.pcolormesh(X,Y,wp_ma,vmin=0,vmax=360)
ax1.xaxis.set_major_locator(mdates.MonthLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
ax1.set_xlabel(r'$ Time \rightarrow$')
ax1.set_ylabel('height gate')
for prof in range(wp.shape[1]):
x = wp[:,prof]
y = range(wp.shape[0])
ax2.plot(x,y,color='r',alpha=0.05)
# ax2.scatter(x,y,color='r',alpha=0.05)
ax2.set_yticklabels('')
ax2.set_xlabel(target)
ax1.set_title('BBY Windprof wdir')
plt.tight_layout()
plt.show(block=False)
def plot_with_lines(year=None, target=None):
fig = plt.figure(figsize=(10, 5))
gs = gsp.GridSpec(1, 2, width_ratios=[2, 1])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
wprof = parse_data.windprof(year)
wp = np.squeeze(pandas2stack(wprof.dframe[target]))
wp_ma = ma.masked_where(np.isnan(wp), wp)
X, Y = wprof.time, wprof.hgt
ax1.pcolormesh(X, Y, wp_ma, vmin=0, vmax=360)
ax1.xaxis.set_major_locator(mdates.MonthLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
ax1.set_xlabel(r'$ Time \rightarrow$')
ax1.set_ylabel('height gate')
for prof in range(wp.shape[1]):
x = wp[:, prof]
y = range(wp.shape[0])
ax2.plot(x, y, color='r', alpha=0.05)
# ax2.scatter(x,y,color='r',alpha=0.05)
ax2.set_yticklabels('')
ax2.set_xlabel(target)
ax1.set_title('BBY Windprof wdir')
plt.tight_layout()
plt.show(block=False)
czd = czd.dframe.loc[first:last]
bby = bby.dframe.loc[first:last]
''' select rainy days '''
rain_czd = czd.precip > 0
rain_dates = rain_czd.loc[rain_czd.values].index
# rain_dates = None
if rain_dates is None:
wd = pd.DataFrame(index=wspd.index,columns=range(16))
ws = pd.DataFrame(index=wspd.index,columns=range(16))
wd.iloc[:,0] = bby.wdir
ws.iloc[:,0] = bby.wspd
wdir = pandas2stack(wdir).T
wspd = pandas2stack(wspd).T
wd.iloc[:,1:]=np.squeeze(wdir[:,:15])
ws.iloc[:,1:]=np.squeeze(wspd[:,:15])
else:
wd = pd.DataFrame(index=wspd.loc[rain_dates].index,
columns=range(16))
ws = pd.DataFrame(index=wspd.loc[rain_dates].index,
columns=range(16))
wd.iloc[:,0] = bby.wdir.loc[rain_dates]
ws.iloc[:,0] = bby.wspd.loc[rain_dates]
wdir = pandas2stack(wdir.loc[rain_dates]).T
def process(year=[],wdsurf=None,
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,
median_dir]
def processv2(year=[],wdsurf=None,
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,
]
wdir = wpr.dframe.loc[first:last].wdir
czd = czd.dframe.loc[first:last]
bby = bby.dframe.loc[first:last]
''' select rainy days '''
rain_czd = czd.precip > 0
rain_dates = rain_czd.loc[rain_czd.values].index
# rain_dates = None
if rain_dates is None:
wd = pd.DataFrame(index=wspd.index, columns=range(16))
ws = pd.DataFrame(index=wspd.index, columns=range(16))
wd.iloc[:, 0] = bby.wdir
ws.iloc[:, 0] = bby.wspd
wdir = pandas2stack(wdir).T
wspd = pandas2stack(wspd).T
wd.iloc[:, 1:] = np.squeeze(wdir[:, :15])
ws.iloc[:, 1:] = np.squeeze(wspd[:, :15])
else:
wd = pd.DataFrame(index=wspd.loc[rain_dates].index,
columns=range(16))
ws = pd.DataFrame(index=wspd.loc[rain_dates].index,
columns=range(16))
wd.iloc[:, 0] = bby.wdir.loc[rain_dates]
ws.iloc[:, 0] = bby.wspd.loc[rain_dates]
wdir = pandas2stack(wdir.loc[rain_dates]).T
def plot_with_hist(year=None,target=None,normalized=True,
pngsuffix=None):
name={'wdir':'Wind Direction',
'wspd':'Wind Speed'}
if target == 'wdir':
vmin,vmax = [0,360]
bins = np.arange(0,370,10)
hist_xticks = np.arange(0,400,40)
hist_xlim = [0,360]
elif target == 'wspd':
vmin,vmax = [0,30]
bins = np.arange(0,36,1)
hist_xticks = np.arange(0,40,5)
hist_xlim = [0,35]
fig = plt.figure(figsize=(20,5))
gs = gsp.GridSpec(1, 2,
width_ratios=[3,1]
)
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
wprof = parse_data.windprof(year)
wp = np.squeeze(pandas2stack(wprof.dframe[target]))
wp_ma = ma.masked_where(np.isnan(wp),wp)
X,Y = wprof.time,wprof.hgt
p = ax1.pcolormesh(X,Y,wp_ma,vmin=vmin,vmax=vmax)
add_colorbar(ax1,p)
ax1.xaxis.set_major_locator(mdates.MonthLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
ax1.set_xlabel(r'$ Time \rightarrow$')
ax1.set_ylabel('Altitude [m] MSL')
ax1.set_title('BBY Windprof '+name[target])
array = np.empty((40,len(bins)-1))
for hgt in range(wp.shape[0]):
row = wp[hgt,:]
freq,bins=np.histogram(row[~np.isnan(row)],
bins=bins,
density=normalized)
array[hgt,:]=freq
x = bins
y = wprof.hgt
p = ax2.pcolormesh(x,y,array,cmap='viridis')
amin = np.amin(array)
amax = np.amax(array)
cbar = add_colorbar(ax2,p,size='4%',ticks=[amin,amax])
cbar.ax.set_yticklabels(['low','high'])
ax2.set_xticks(hist_xticks)
ax2.set_yticklabels('')
ax2.set_xlabel(name[target])
ax2.set_xlim(hist_xlim)
ax2.set_title('Normalized frequency')
plt.tight_layout()
if pngsuffix:
out_name = 'wprof_{}_{}.png'
plt.savefig(out_name.format(target,pngsuffix))
else:
plt.show(block=False)
def plot_with_hist(year=None, target=None, normalized=True, pngsuffix=None):
name = {'wdir': 'Wind Direction', 'wspd': 'Wind Speed'}
if target == 'wdir':
vmin, vmax = [0, 360]
bins = np.arange(0, 370, 10)
hist_xticks = np.arange(0, 400, 40)
hist_xlim = [0, 360]
elif target == 'wspd':
vmin, vmax = [0, 30]
bins = np.arange(0, 36, 1)
hist_xticks = np.arange(0, 40, 5)
hist_xlim = [0, 35]
fig = plt.figure(figsize=(20, 5))
gs = gsp.GridSpec(1, 2, width_ratios=[3, 1])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
wprof = parse_data.windprof(year)
wp = np.squeeze(pandas2stack(wprof.dframe[target]))
wp_ma = ma.masked_where(np.isnan(wp), wp)
X, Y = wprof.time, wprof.hgt
p = ax1.pcolormesh(X, Y, wp_ma, vmin=vmin, vmax=vmax)
add_colorbar(ax1, p)
ax1.xaxis.set_major_locator(mdates.MonthLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
ax1.set_xlabel(r'$ Time \rightarrow$')
ax1.set_ylabel('Altitude [m] MSL')
ax1.set_title('BBY Windprof ' + name[target])
array = np.empty((40, len(bins) - 1))
for hgt in range(wp.shape[0]):
row = wp[hgt, :]
freq, bins = np.histogram(row[~np.isnan(row)],
bins=bins,
density=normalized)
array[hgt, :] = freq
x = bins
y = wprof.hgt
p = ax2.pcolormesh(x, y, array, cmap='viridis')
amin = np.amin(array)
amax = np.amax(array)
cbar = add_colorbar(ax2, p, size='4%', ticks=[amin, amax])
cbar.ax.set_yticklabels(['low', 'high'])
ax2.set_xticks(hist_xticks)
ax2.set_yticklabels('')
ax2.set_xlabel(name[target])
ax2.set_xlim(hist_xlim)
ax2.set_title('Normalized frequency')
plt.tight_layout()
if pngsuffix:
out_name = 'wprof_{}_{}.png'
plt.savefig(out_name.format(target, pngsuffix))
else:
plt.show(block=False)
def plot(year=[],target=None,pngsuffix=False, normalized=True,
contourf=True, pdfsuffix=False, wdsurf=None, wdwpro=None,
rainbb=None, raincz=None, nhours=None):
name={'wdir':'Wind Direction',
'wspd':'Wind Speed'}
if target == 'wdir':
bins = np.arange(0,370,10)
hist_xticks = np.arange(0,420,60)
hist_xlim = [0,360]
elif target == 'wspd':
bins = np.arange(0,36,1)
hist_xticks = np.arange(0,40,5)
hist_xlim = [0,35]
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[target]
' 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
' makes CFAD '
hist_array = np.empty((40,len(bins)-1,3))
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=bins)
if normalized:
hist_array[hgt,:,n] = 100.*(freq/float(freq.sum()))
else:
hist_array[hgt,:,n] = freq
fig,axs = plt.subplots(1,3,sharey=True,figsize=(10,8))
ax1 = axs[0]
ax2 = axs[1]
ax3 = axs[2]
hist_wp = np.squeeze(hist_array[:,:,0])
hist_wptta = np.squeeze(hist_array[:,:,1])
hist_wpnotta = np.squeeze(hist_array[:,:,2])
x = bins
y = wprof_df.hgt
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=20
nlevels = 10
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,extend='max')
p.cmap.set_over(cmap(1.0))
ax3.contourf(X,Y,hist_wpnotta,v,cmap=cmap)
cbar = add_colorbar(ax3,p,size='4%')
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')
amin = np.amin(hist_wpnotta)
amax = np.amax(hist_wpnotta)
cbar = add_colorbar(ax3,p,size='4%',ticks=[amin,amax])
cbar.ax.set_yticklabels(['low','high'])
' --- setup ax1 --- '
amin = np.amin(hist_wp)
amax = np.amax(hist_wp)
ax1.set_xticks(hist_xticks)
ax1.set_xlim(hist_xlim)
ax1.set_ylim([0,4000])
txt = 'All profiles (n={})'.format(wp_hours)
ax1.text(0.5,0.95,txt,fontsize=15,
transform=ax1.transAxes,va='bottom',ha='center')
ax1.set_ylabel('Altitude [m] MSL')
' --- setup ax2 --- '
amin = np.amin(hist_wptta)
amax = np.amax(hist_wptta)
ax2.set_xticks(hist_xticks)
ax2.set_xlim(hist_xlim)
ax2.set_ylim([0,4000])
ax2.set_xlabel(name[target])
txt = 'TTA (n={})'.format(tta_hours)
ax2.text(0.5,0.95,txt,fontsize=15,
transform=ax2.transAxes,va='bottom',ha='center')
' --- setup ax3 --- '
ax3.set_xticks(hist_xticks)
ax3.set_xlim(hist_xlim)
ax3.set_ylim([0,4000])
txt = 'NO-TTA (n={})'.format(notta_hours)
ax3.text(0.5,0.95,txt,fontsize=15,
transform=ax3.transAxes,va='bottom',ha='center')
title = 'Normalized frequencies of BBY wind profiles {} \n'
title += 'TTA wdir_surf:{}, wdir_wp:{}, '
title += 'rain_bby:{}, rain_czd:{}, nhours:{}'
if len(year) == 1:
yy = 'year {}'.format(year[0])
else:
yy = 'year {} to {}'.format(year[0],year[-1])
plt.suptitle(title.format(yy, wdsurf, wdwpro, rainbb, raincz, nhours),
fontsize=15)
plt.subplots_adjust(top=0.9,left=0.1,right=0.95,bottom=0.1, wspace=0.1)
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()
else:
plt.show()
target='wdir'
for n, y in enumerate([0, 1998] + range(2001, 2013)):
if n == 0:
ax[n].axis('off')
else:
print y
if n != 1:
ax[n].set_yticklabels('')
if n == 12:
ax[n].set_xlabel(r'$ Time \rightarrow$')
# parse windprof dataframe with wspd and wdir
wprof = parse_data.windprof(y)
wp = np.squeeze(pandas2stack(wprof.dframe[target]))
# plot array
# ax[n].imshow(wp, aspect='auto', origin='lower',
# interpolation='none')
X,Y = wprof.time,wprof.hgt
wp_ma = ma.masked_where(np.isnan(wp),wp)
ax[n].pcolormesh(X,Y,wp_ma)
ax[n].xaxis.set_major_locator(mdates.MonthLocator())
ax[n].xaxis.set_major_formatter(mdates.DateFormatter('%b'))
txt = 'Season: {}/{}'
ax[n].text(0.05, 0.8, txt.format(str(y-1),str(y)),
weight='bold', transform=ax[n].transAxes)
fig.suptitle('BBY Windprof '+target)