def thetaeq(self, filled=True, cmap=None, **kwargs):
self.initialize_plot()
self.level = kwargs['level']*100
clevels = kwargs['clevels']
t_arrays = read_files(self, 'temperature') # [C]
q_arrays = read_files(self, 'sphum') # [kg/kg]
rh_arrays = read_files(self, 'relhumid')
press = np.zeros(rh_arrays[0].shape)+kwargs['level'] # [hPa]
X, Y = np.meshgrid(self.lons, self.lats)
self.series['thetaeq'] = []
for i in range(len(self.dates)):
mixr = thermo.mixing_ratio(specific_humidity=q_arrays[i])
theta = thermo.theta_equiv2(C=t_arrays[i], hPa=press,
mixing_ratio=mixr, relh=rh_arrays[i])
val = get_value_at(-123., 38.5, theta, self) # closest to BBY
self.series['thetaeq'].append(val)
if filled:
cf = self.axes[i].contourf(X, Y, theta, clevels, cmap=cmap)
try:
self.axes[i].cax.colorbar(cf, ticks=clevels[::4])
except AttributeError:
add_colorbar(self.axes[i], cf)
else:
cs = self.axes[i].contour(X, Y, theta, clevels, colors='r',
linewidths=0.6)
clabels = self.axes[i].clabel(cs, clevels[::5],
fontweight='bold',
fontsize=10,
fmt='%1.0f')
[txt.set_color('r') for txt in clabels]
set_limits(self, i)
self.add_date(i)
txt = 'Equivalent Potential Temperature [K] at {} hPa\n'
self.title += txt.format(str(self.level/100))
def plot_vertical_shear(ax=None, wind=None, time=None, height=None):
diff = np.diff(wind, axis=0)
nrows, ncols = diff.shape
# cmap = custom_cmap(17)
# norm = colors.BoundaryNorm(np.arange(-20, 20), cmap.N)
img = ax.imshow(
diff,
interpolation='nearest',
origin='lower',
# cmap=cmap,
cmap='RdBu',
vmin=-20,
vmax=20,
# norm=norm,
extent=[0, ncols, 0, nrows],
aspect='auto')
add_colorbar(img, ax)
format_xaxis(ax, time)
format_yaxis(ax, height)
ax.invert_xaxis()
plt.draw()
for n, f in enumerate(fs):
T, Tc, Hgt, timestamp = mf.parse_rass(f)
if n == 0:
Tstack = T
Hgtstack = Hgt
ts = np.array(timestamp)
else:
Tstack = np.hstack((Tstack, T))
Hgtstack = np.hstack((Hgtstack, Hgt))
ts = np.hstack((ts, np.array(timestamp)))
thetav = get_thetav(case=r, Tv_array=Tstack,
hgt_array=Hgtstack, homedir=homedir)
titletxt = 'Case{} ({})'
fig, ax = plt.subplots(2, 1, sharex=True)
im = ax[0].imshow(Tstack, interpolation='none', origin='lower')
ax[0].set_title(titletxt.format(str(r), ts[0].strftime('%Y-%b')))
ax[0].text(0.05, 0.9, 'Virtual temperature', transform=ax[0].transAxes)
add_colorbar(ax[0], im)
format_xaxis(ax[0], ts)
format_yaxis(ax[0], Hgt[:, 0])
im = ax[1].imshow(thetav, vmin=282, vmax=294,
interpolation='none', origin='lower')
add_colorbar(ax[1], im)
format_yaxis(ax[1], Hgt[:, 0])
ax[1].text(0.05, 0.9, 'Thetav', transform=ax[1].transAxes)
ax[1].invert_xaxis()
plt.tight_layout()
plt.show(block=False)
def plot_shaded_map(ax=None, cmap='gist_earth',saveto=None):
if ax is None:
scale = 0.7
fig,ax = plt.subplots(figsize=(10*scale,10*scale))
# dtmfile = '/home/raul/Github/RadarQC/merged_dem_38-39_123-124_extended.tif'
# dtmfile = '/home/raul/Dropbox/NOCAL_DEM/merged_dem_38-39_123-124_extended.tif'
dtmfile = '/Users/raulvalenzuela/Dropbox/NOCAL_DEM/merged_dem_38-39_123-124_extended.tif'
domain = [-123.4, -122.8, 38.8, 38.1, 8]
elev = elevation(file_name=dtmfile,
domain=domain)
elev.get_elevation()
elev_lims = [0,700]
ls = LightSource(azdeg=15, altdeg=60)
rgb = ls.shade(elev.dtm,
cmap=getattr(plt.cm,cmap),
vmin=elev_lims[0],
vmax=elev_lims[1],
blend_mode='soft',
fraction=0.7)
ax.imshow(rgb)
" interpolate latlon to cartesian coords"
nrows, ncols = elev.dtm.shape
flat = interp1d(np.linspace(domain[3],domain[2],nrows),
range(nrows))
flon = interp1d(np.linspace(domain[0], domain[1], ncols),
range(ncols))
lat, lon = locations['FRS']
ax.scatter(flon(lon), flat(lat), color='r',s=50)
lat, lon = locations['BBY']
ax.scatter(flon(lon), flat(lat), color='r',s=50)
lat, lon = locations['CZD']
ax.scatter(flon(lon), flat(lat), color='r',s=50)
'Use a proxy artist for the colorbar'
im = ax.imshow(elev.dtm,
cmap=cmap,
vmin=elev_lims[0],
vmax=elev_lims[1],
origin='lower',
)
im.remove()
add_colorbar(ax,im,label='Meters')
ax.set_xticklabels('')
ax.set_yticklabels('')
plt.tight_layout()
if saveto is not None:
fname = 'shaded_terrain.png'
plt.savefig(saveto + fname,
dpi=300, format='png', papertype='letter',
bbox_inches='tight')
def plot_elevation_map(self, ax=None, shaded=True,
cmap=None, add_loc=None,
colorbar=True, blend_mode='overlay',
grid=True, gridcolor='w',
altitude_range=[-10,600],
contour_lines=None,
latdelta=None, londelta=None,
homed=None, figsize=None,
addrivers=False,
add_geoline=None):
import os
if homed is None:
homed = os.path.expanduser('~')
' get elevation model '
if self.source is not None:
fname = self.source
dtmfile = homed + '/' + fname
self.file_name = dtmfile
if self.dtm is None:
self.get_dem_matfile()
else:
fname = 'merged_dem_38-39_123-124_extended.tif'
dtmfile = homed + '/Github/RadarQC/' + fname
self.file_name = dtmfile
if self.dtm is None:
self.get_elevation()
if ax is None:
if figsize is None:
fig,ax = plt.subplots(figsize=(10,10))
else:
fig,ax = plt.subplots(figsize=figsize)
' make map axis '
m = Basemap(projection='merc',
llcrnrlat=self.latn,
urcrnrlat=self.latx,
llcrnrlon=self.lonn,
urcrnrlon=self.lonx,
resolution='h',
ax=ax)
vmin, vmax = altitude_range
if shaded:
' make hill shaded image '
ls = LightSource(azdeg=15, altdeg=60)
rgb = ls.shade(self.dtm,
cmap=getattr(plt.cm,cmap),
vmin=vmin,
vmax=vmax,
blend_mode='soft',
fraction=0.7)
m.imshow(rgb)
else:
m.imshow(self.dtm, cmap=cmap, vmin=vmin, vmax=vmax)
' add parallels and meridians '
parallels = np.arange(-90, 90, latdelta)
meridians = np.arange(-180, 180, londelta)
lw = 0
if grid:
lw = 1.
parallels = m.drawparallels(parallels,
labels=[1, 0, 0, 0], fontsize=10,
labelstyle='+/-', fmt='%2.1f',linewidth=lw,
color=gridcolor)
m.drawmeridians(meridians,
labels=[0, 0, 0, 1], fontsize=10,
labelstyle='+/-', fmt='%2.1f', linewidth=lw,
color=gridcolor)
for p in parallels:
try:
parallels[p][1][0].set_rotation(90)
except:
pass
' add locations '
if add_loc:
fsize = 15
psize = 50
ec = (1.0,0,0,1)
fc = (0.5,0,0,1)
if isinstance(add_loc,dict):
for loc, coord in locations.iteritems():
x, y = m(*coord[::-1])
m.scatter(x, y, psize, color='r')
ax.text(x, y, loc, ha='right', va='bottom',
color='r',fontsize=fsize,weight='bold')
elif isinstance(add_loc,list):
for loc in add_loc:
coord = locations[loc]
x, y = m(*coord[::-1])
m.scatter(x, y, psize, facecolor=fc,edgecolor=ec)
ax.text(x, y, loc, ha='right', va='bottom',
color='r',fontsize=fsize,weight='bold')
' add section line '
if add_geoline:
if isinstance(add_geoline,dict):
geolines = list(add_geoline)
elif isinstance(add_geoline,list):
geolines = add_geoline
for line in geolines:
x, y = get_line_ini_end(line)
self.line_x = x
self.line_y = y
x, y = m(*[x, y])
if 'color' in line:
color = line['color']
else:
color='k'
m.plot(x, y, color=color, linewidth=2)
if 'ndiv' in line:
ndiv = line['ndiv']
xp = np.linspace(x[0], x[1], ndiv)
yp = np.linspace(y[0], y[1], ndiv)
m.plot(xp, yp, marker='s',
markersize=5, color=color)
if 'label' in line:
if 'center' in line:
x,y = m(*line['center'][::-1])
ax.text(x,y,line['label']+r'$^\circ$',
color=color,
weight='bold',
fontsize=15,
rotation=90-line['az'])
' add rivers '
if addrivers:
rivers = get_rivers(mmap=m)
ax.add_collection(rivers)
' add colorbar '
if colorbar:
im = m.imshow(self.dtm, cmap=cmap, vmin=vmin, vmax=vmax)
im.remove()
add_colorbar(ax,im,label='Meters')
' add contour line(s) '
if contour_lines is not None:
nlats, nlons = self.dtm.shape
lons, lats = np.meshgrid(
np.linspace(self.lonn, self.lonx, nlons),
np.linspace(self.latn, self.latx, nlats))
x, y = m(lons, lats)
m.contour(x, y, self.dtm, contour_lines, colors='k')
' add coastline'
m.drawcoastlines(color='w')
def plot_shaded_map(ax=None, cmap='gist_earth', saveto=None):
if ax is None:
scale = 0.7
fig, ax = plt.subplots(figsize=(10 * scale, 10 * scale))
# dtmfile = '/home/raul/Github/RadarQC/merged_dem_38-39_123-124_extended.tif'
# dtmfile = '/home/raul/Dropbox/NOCAL_DEM/merged_dem_38-39_123-124_extended.tif'
dtmfile = '/Users/raulvalenzuela/Dropbox/NOCAL_DEM/merged_dem_38-39_123-124_extended.tif'
domain = [-123.4, -122.8, 38.8, 38.1, 8]
elev = elevation(file_name=dtmfile, domain=domain)
elev.get_elevation()
elev_lims = [0, 700]
ls = LightSource(azdeg=15, altdeg=60)
rgb = ls.shade(elev.dtm,
cmap=getattr(plt.cm, cmap),
vmin=elev_lims[0],
vmax=elev_lims[1],
blend_mode='soft',
fraction=0.7)
ax.imshow(rgb)
" interpolate latlon to cartesian coords"
nrows, ncols = elev.dtm.shape
flat = interp1d(np.linspace(domain[3], domain[2], nrows), range(nrows))
flon = interp1d(np.linspace(domain[0], domain[1], ncols), range(ncols))
lat, lon = locations['FRS']
ax.scatter(flon(lon), flat(lat), color='r', s=50)
lat, lon = locations['BBY']
ax.scatter(flon(lon), flat(lat), color='r', s=50)
lat, lon = locations['CZD']
ax.scatter(flon(lon), flat(lat), color='r', s=50)
'Use a proxy artist for the colorbar'
im = ax.imshow(
elev.dtm,
cmap=cmap,
vmin=elev_lims[0],
vmax=elev_lims[1],
origin='lower',
)
im.remove()
add_colorbar(ax, im, label='Meters')
ax.set_xticklabels('')
ax.set_yticklabels('')
plt.tight_layout()
if saveto is not None:
fname = 'shaded_terrain.png'
plt.savefig(saveto + fname,
dpi=300,
format='png',
papertype='letter',
bbox_inches='tight')
def plot_elevation_map(self,
ax=None,
shaded=True,
cmap=None,
add_loc=None,
colorbar=True,
blend_mode='overlay',
grid=True,
gridcolor='w',
altitude_range=[-10, 600],
contour_lines=None,
latdelta=None,
londelta=None,
homed=None,
figsize=None,
addrivers=False,
add_geoline=None):
import os
if homed is None:
homed = os.path.expanduser('~')
' get elevation model '
if self.source is not None:
fname = self.source
dtmfile = homed + '/' + fname
self.file_name = dtmfile
if self.dtm is None:
self.get_dem_matfile()
else:
fname = 'merged_dem_38-39_123-124_extended.tif'
dtmfile = homed + '/Github/RadarQC/' + fname
self.file_name = dtmfile
if self.dtm is None:
self.get_elevation()
if ax is None:
if figsize is None:
fig, ax = plt.subplots(figsize=(10, 10))
else:
fig, ax = plt.subplots(figsize=figsize)
' make map axis '
m = Basemap(projection='merc',
llcrnrlat=self.latn,
urcrnrlat=self.latx,
llcrnrlon=self.lonn,
urcrnrlon=self.lonx,
resolution='h',
ax=ax)
vmin, vmax = altitude_range
if shaded:
' make hill shaded image '
ls = LightSource(azdeg=15, altdeg=60)
rgb = ls.shade(self.dtm,
cmap=getattr(plt.cm, cmap),
vmin=vmin,
vmax=vmax,
blend_mode='soft',
fraction=0.7)
m.imshow(rgb)
else:
m.imshow(self.dtm, cmap=cmap, vmin=vmin, vmax=vmax)
' add parallels and meridians '
parallels = np.arange(-90, 90, latdelta)
meridians = np.arange(-180, 180, londelta)
lw = 0
if grid:
lw = 1.
parallels = m.drawparallels(parallels,
labels=[1, 0, 0, 0],
fontsize=10,
labelstyle='+/-',
fmt='%2.1f',
linewidth=lw,
color=gridcolor)
m.drawmeridians(meridians,
labels=[0, 0, 0, 1],
fontsize=10,
labelstyle='+/-',
fmt='%2.1f',
linewidth=lw,
color=gridcolor)
for p in parallels:
try:
parallels[p][1][0].set_rotation(90)
except:
pass
' add locations '
if add_loc:
fsize = 15
psize = 50
ec = (1.0, 0, 0, 1)
fc = (0.5, 0, 0, 1)
if isinstance(add_loc, dict):
for loc, coord in locations.iteritems():
x, y = m(*coord[::-1])
m.scatter(x, y, psize, color='r')
ax.text(x,
y,
loc,
ha='right',
va='bottom',
color='r',
fontsize=fsize,
weight='bold')
elif isinstance(add_loc, list):
for loc in add_loc:
coord = locations[loc]
x, y = m(*coord[::-1])
m.scatter(x, y, psize, facecolor=fc, edgecolor=ec)
ax.text(x,
y,
loc,
ha='right',
va='bottom',
color='r',
fontsize=fsize,
weight='bold')
' add section line '
if add_geoline:
if isinstance(add_geoline, dict):
geolines = list(add_geoline)
elif isinstance(add_geoline, list):
geolines = add_geoline
for line in geolines:
x, y = get_line_ini_end(line)
self.line_x = x
self.line_y = y
x, y = m(*[x, y])
if 'color' in line:
color = line['color']
else:
color = 'k'
m.plot(x, y, color=color, linewidth=2)
if 'ndiv' in line:
ndiv = line['ndiv']
xp = np.linspace(x[0], x[1], ndiv)
yp = np.linspace(y[0], y[1], ndiv)
m.plot(xp, yp, marker='s', markersize=5, color=color)
if 'label' in line:
if 'center' in line:
x, y = m(*line['center'][::-1])
ax.text(x,
y,
line['label'] + r'$^\circ$',
color=color,
weight='bold',
fontsize=15,
rotation=90 - line['az'])
' add rivers '
if addrivers:
rivers = get_rivers(mmap=m)
ax.add_collection(rivers)
' add colorbar '
if colorbar:
im = m.imshow(self.dtm, cmap=cmap, vmin=vmin, vmax=vmax)
im.remove()
add_colorbar(ax, im, label='Meters')
' add contour line(s) '
if contour_lines is not None:
nlats, nlons = self.dtm.shape
lons, lats = np.meshgrid(np.linspace(self.lonn, self.lonx, nlons),
np.linspace(self.latn, self.latx, nlats))
x, y = m(lons, lats)
m.contour(x, y, self.dtm, contour_lines, colors='k')
' add coastline'
m.drawcoastlines(color='w')
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()
return {'axes':[ax1,ax2,ax3],'im':p}
czdh.values,
'-o',
lw=2,
color=cmap(1),
label='CZD')
axes[1].plot(np.arange(0.5, 24.5),
bbyh.values,
'-o',
lw=2,
color=cmap(0),
label='BBY')
axes[1].legend(numpoints=1, loc=7)
add_colorbar(axes[1], im, invisible=True)
axes[1].text(0.05,
0.88,
'(b)',
ha='right',
weight='bold',
fontsize=15,
transform=axes[1].transAxes)
axes[1].text(0.5,
0.88,
'Rainfall',
ha='center',
weight='bold',
fontsize=15,
def plot_colored_staff(ax=None,
wspd=None,
wdir=None,
time=None,
height=None,
cmap=None,
spd_range=None,
spd_delta=None,
vdensity=None,
hdensity=None,
title=None,
cbar=True):
'''
NOAA wind profiler files after year 2000 indicate
the start time of averaging period; so a timestamp of
13 UTC indicates average between 13 and 14 UTC
There is a bug that plots wrong staff when using
meridional winds only
'''
from matplotlib import rcParams
rcParams['mathtext.default'] = 'sf'
spd_array = wspd
time_array = time
height_array = height
''' make a color map of fixed colors '''
cmap = discrete_cmap(24, base_cmap=cmap, norm_range=[0.1, 0.9])
if len(spd_range) == 2:
bounds = range(spd_range[0], spd_range[1] + spd_delta, spd_delta)
# vmin = spd_range[0]
# vmax = spd_range[1]
else:
bounds = spd_range
# vmin = spd_range[0]
# vmax = spd_range[-1]
norm = colors.BoundaryNorm(bounds, cmap.N)
nrows, ncols = spd_array.shape
''' derive U and V components '''
U = -wspd * np.sin(wdir * np.pi / 180.)
V = -wspd * np.cos(wdir * np.pi / 180.)
x = np.array(range(len(time))) + 0.5 # wind staff in the middle of pixel
# wind staff in the middle of pixel
y = np.array(range(height_array.size)) + 0.5
X = np.tile(x, (y.size, 1)) # repeats x y.size times to make 2D array
Y = np.tile(y, (x.size, 1)).T # repeates y x.size times to make 2D array
Uzero = U - U
Vzero = V - V
''' change arrays density '''
if vdensity == 0 or hdensity == 0:
pass
else:
U = fill2D_with_nans(inarray=U,
start=[3, 0],
size=[vdensity, hdensity])
V = fill2D_with_nans(inarray=V,
start=[3, 0],
size=[vdensity, hdensity])
ax.barbs(X,
Y,
U,
V,
np.sqrt(U * U + V * V),
sizes={'height': 0},
length=5,
linewidth=0.5,
barb_increments={'half': 1},
cmap=cmap,
norm=norm)
barb = ax.barbs(X,
Y,
Uzero,
Vzero,
np.sqrt(U * U + V * V),
sizes={'emptybarb': 0.05},
fill_empty=True,
cmap=cmap,
norm=norm)
if isinstance(cbar, maxes._subplots.Axes):
hcbar = add_colorbar(cbar,
barb,
loc='right',
label='[m s-1]',
labelpad=20,
fontsize=12)
elif isinstance(cbar, bool) and cbar:
hcbar = add_colorbar(ax, barb, loc='right')
else:
hcbar = None
ax.set_xlim([-3.0, len(time_array) + 3.0])
format_xaxis(ax, time_array, delta_hours=1)
ax.invert_xaxis()
ax.set_xlabel(r'$\leftarrow UTC \left[\stackrel{day}{time}\right]$',
fontsize=12)
format_yaxis2(ax, height_array)
ax.set_ylabel('Altitude MSL [km]')
if title is not None:
ax.text(0., 1.01, title, transform=ax.transAxes)
plt.subplots_adjust(left=0.08, right=0.95)
plt.draw()
return [ax, hcbar]
def plot_time_height(ax=None,
wspd=None,
time=None,
height=None,
spd_range=None,
spd_delta=None,
cmap=None,
title=None,
cbar=None,
cbarinvi=False,
timelabstep=None,
kind='pcolormesh',
cbar_label=None):
''' NOAA wind profiler files after year 2000 indicate
the start time of averaging period; so a timestamp of
13 UTC indicates average between 13 and 14 UTC '''
''' make a color map of fixed colors '''
if len(spd_range) == 2:
bounds = range(spd_range[0], spd_range[1] + spd_delta, spd_delta)
else:
bounds = spd_range
cmap = discrete_cmap(len(bounds) + 1,
base_cmap=cmap,
norm_range=[0.2, 1.0])
norm = colors.BoundaryNorm(bounds, cmap.N)
x = np.linspace(0, len(time), len(time) + 1)
y = np.linspace(0, height.size, height.size + 1)
wspdm = ma.masked_where(np.isnan(wspd), wspd)
# wspdm = ma.masked_where(wspdm<0, wspdm)
if kind == 'contourf':
x, y = np.meshgrid(np.linspace(0, len(time), len(time)),
np.linspace(0, height.size, height.size))
img = ax.contourf(x, y, wspdm, cmap=cmap, norm=norm)
else:
img = ax.pcolormesh(x, y, wspdm, cmap=cmap, norm=norm)
hcbar = add_colorbar(cbar,
img,
loc='right',
label=cbar_label,
labelpad=20,
fontsize=12,
invisible=cbarinvi)
ax.set_xlim([-1.0, len(time) + 1.0])
if timelabstep is None:
timelabstep = '1H'
format_xaxis2(ax, time, timelabstep=timelabstep)
ax.invert_xaxis()
ax.set_xlabel(r'$\leftarrow UTC \left[\stackrel{day}{time}\right]$',
fontsize=20)
ax.set_ylabel('Altitude MSL [km]')
if title is not None:
ax.text(0., 1.01, title, transform=ax.transAxes)
plt.subplots_adjust(left=0.08, right=0.95)
return [ax, hcbar, img]
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_sector(filein=None,
ax=None,
latlabel=True,
lonlabel=True,
labels=True,
colorbar=False,
cbarpos='top',
sector=None,
target=None,
boundary=None,
id=None):
latn = sector['latn']
latx = sector['latx']
lonn = sector['lonn']
lonx = sector['lonx']
lw = boundary['lw']
color = boundary['color']
data = Dataset(filein, 'r')
lats = data.variables['lat'][:]
lons = data.variables['lon'][:]
idxlon = np.where((lons > lonn) & (lons < lonx))[0]
idxlat = np.where((lats > latn) & (lats < latx))[0]
array = np.squeeze(data.variables[target][:, idxlat, idxlon]) - 273.15
data.close()
lats2 = lats[idxlat]
lons2 = lons[idxlon]
if target == 'irwin_cdr':
mycmap = goes_cmap(type='ir')
elif target == 'irwvp':
mycmap = goes_cmap(type='wvp')
im = ax.pcolormesh(
lons2,
lats2,
array,
cmap=mycmap,
vmax=20,
vmin=-60,
)
add_boundaries(ax, sector, lw=lw, color=color)
latlabels = ['', '50S', '', '40', '', '30', '']
ax.set_yticklabels(latlabels, rotation=90)
lonlabels = ['123', '100W', '90', '80', '70']
ax.set_xticklabels(lonlabels)
ax.set_xlim([lonn, lonx])
ax.set_ylim([latn, latx])
if labels is False:
ax.set_xticklabels([])
ax.set_yticklabels([])
elif latlabel is False:
ax.set_yticklabels([])
elif lonlabel is False:
ax.set_xticklabels([])
if colorbar:
cbar = add_colorbar(ax,
im,
loc=cbarpos,
size='10%',
label='',
ticks_everyother=True,
fontsize=12)
# cbar.ax.invert_xaxis()
cbar.ax.invert_yaxis()
if id is not None:
ax.text(0.05,
0.9,
id,
fontsize=14,
color='w',
weight='bold',
transform=ax.transAxes)
plt.draw()
newxtl.append(lb)
else:
newxtl.append(lb.get_text())
ax.set_xticklabels(newxtl)
ax.set_ylim([-13, 30])
ax.set_xlim([24, 0])
" line plots "
cmap = discrete_cmap(7, base_cmap='Set1')
axes[1].plot(np.arange(0.5, 24.5), czdh.values, '-o',
lw=2, color=cmap(1), label='CZD')
axes[1].plot(np.arange(0.5, 24.5), bbyh.values, '-o',
lw=2, color=cmap(0), label='BBY')
add_colorbar(axes[1], im, invisible=True)
axes[1].legend(numpoints=1, loc=5)
# ax2 = axes[1].twinx()
# ax2.plot(np.arange(0.5, 24.5), czdh.values/bbyh.values,
# '-o', lw=2, color=(0, 0, 0))
# add_colorbar(ax2, im, invisible=True)
axes[1].text(0.95, 0.85, '16 February 2004',
ha='right', weight='bold', fontsize=15,
transform=axes[1].transAxes)
axes[1].invert_xaxis()
# axes[1].grid(True)
axes[1].set_xticks(range(0, 23, 3))
axes[1].set_xlim([24, 0])
axes[1].set_xticklabels('')
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()
