def map_add_currents(ax,
currents,
coarsen=None,
scale=None,
headwidth=None,
headlength=None,
headaxislength=None):
"""
Add currents to map
:param dsd: dataset
:param sub: amount to downsample by
:return:
"""
scale = scale or 90
headwidth = headwidth or 2.75
headlength = headlength or 2.75
headaxislength = headaxislength or 2.5
coarsen = coarsen or 2
try:
qds = currents.coarsen(lon=coarsen, boundary='pad').mean().coarsen(
lat=coarsen, boundary='pad').mean()
mesh = True
except ValueError:
qds = currents.coarsen(X=coarsen, boundary='pad').mean().coarsen(
Y=coarsen, boundary='pad').mean()
mesh = False
angle, speed = uv2spdir(qds['u'],
qds['v']) # convert u/v to angle and speed
u, v = spdir2uv( # convert angle and speed back to u/v, normalizing the arrow sizes
np.ones_like(speed),
angle,
deg=True)
qargs = {}
qargs['scale'] = scale
qargs['headwidth'] = headwidth
qargs['headlength'] = headlength
qargs['headaxislength'] = headaxislength
qargs['transform'] = ccrs.PlateCarree()
if mesh:
lons, lats = np.meshgrid(qds['lon'], qds['lat'])
q = ax.quiver(lons, lats, u, v, **qargs)
else:
q = ax.quiver(qds.lon.squeeze().data,
qds.lat.squeeze().data, u.squeeze(), v.squeeze(),
**qargs)
return q
fig = plt.figure()
tds = ds.squeeze()
u = tds['u'].data
v = tds['v'].data
lon = tds.coords['lon'].data
lat = tds.coords['lat'].data
# time = tds.coords['time'].data
u = ma.masked_invalid(u)
v = ma.masked_invalid(v)
angle, speed = uv2spdir(u, v)
us, vs = spdir2uv(np.ones_like(speed), angle, deg=True)
lons, lats = np.meshgrid(lon, lat)
speed_clipped = np.clip(speed[::sub, ::sub], velocity_min,
velocity_max).squeeze()
fig, ax = plt.subplots(figsize=(11, 8),
subplot_kw=dict(projection=ccrs.PlateCarree()))
# Plot title
plt.title('{}\n{}'.format(title_str, str(ds.time.values[0])))
# plot arrows over pcolor
h = ax.quiver(lons[::sub, ::sub],
lats[::sub, ::sub],
label=ndbc[0])
ax6.plot(df2['datetime'], df2['wind_speed'], color='tab:blue', label=ndbc[1])
# ax6.plot(df['datetime'], df['wind_gust'], color='tab:red', linestyle='--', alpha=0.3, label=ndbc[0])
# ax6b.plot(df['datetime'], df['wind_direction'], color='tab:red', linestyle='-', alpha=0.3, label=ndbc[0])
# ax6b.plot(df2['datetime'], df2['wind_direction'], color='tab:blue', linestyle='-', alpha=0.3, label=ndbc[1])
ax6.set_ylabel('Wind Speed [m/s]', fontsize=18, fontweight='bold')
# ax6b.set_ylabel('Wind Direction', fontsize=10, fontweight='bold')
ax6.legend(fontsize=16)
ax6.grid(True, linestyle='--', linewidth=0.5)
ax6.tick_params(axis='both', labelsize=18)
ax6.xaxis.set_minor_locator(AutoMinorLocator(12))
# Stick plot
u1, v1 = spdir2uv(df['wind_speed'], df['wind_direction'], deg=True)
u2, v2 = spdir2uv(df2['wind_speed'], df2['wind_direction'], deg=True)
q = stick_plot(df['datetime'], -u1, -v1, ax=ax7)
ref = 10
qk = plt.quiverkey(
q,
0.1,
0.85,
ref,
"{} {}".format(ref, 'm/s'),
labelpos="N",
coordinates="axes",
)
def surface_map_glider_track(ds, region,
bathy=None,
argo=None,
gliders=None,
transform=None,
model=None,
save_dir=None,
dpi=None,
custom_transect=None,
current_glider_loc=None):
"""
Written by Mike Smith
Modified by Lori Garzio
"""
bathy = bathy or None
transform = transform or ccrs.PlateCarree()
argo = argo or False
gliders = gliders or False
save_dir = save_dir or os.getcwd()
model = model or 'rtofs'
dpi = dpi or 150
custom_transect = custom_transect or None
current_glider_loc = current_glider_loc or None
limits = region[1]
extent = limits['lonlat']
save_dir_maps = os.path.join(save_dir, 'surface_maps', region[1]["code"])
os.makedirs(save_dir_maps, exist_ok=True)
glider_name = gliders.deployment_name.split('-')[0]
glidert0 = np.nanmin(gliders.time.values)
glidert1 = np.nanmax(gliders.time.values)
glidert0_str = pd.to_datetime(glidert0).strftime('%Y-%m-%dT%H:%M')
glidert1_str = pd.to_datetime(glidert1).strftime('%Y-%m-%dT%H:%M')
if model in ['gofs', 'cmems']:
t1 = pd.to_datetime(ds.time.data)
elif model == 'rtofs':
t1 = pd.to_datetime(ds.time.data[0])
else:
return 'Incorrect model type. Please enter "gofs", "rtofs" or "cmems"'
for k, v in limits.items():
if k in ['lonlat', 'code', 'currents']:
continue
if k == 'salinity':
var_str = 'Sea Surface Salinity'
elif k == 'temperature':
var_str = 'Sea Surface Temperature'
for item in v:
depth = item['depth']
if depth > 0: # only plot sea surface
continue
try:
dsd = ds.sel(depth=depth)
except KeyError:
dsd = ds.sel(depth=slice(0, 1))
if len(dsd.depth) > 1:
raise ValueError('More than one depth between 0-1m')
title = f'{glider_name} track: {glidert0_str} to {glidert1_str}\n' \
f'{model.upper()} {var_str} at {str(t1)} UTC'
sname = f'{glider_name}_{region[1]["code"]}_{model}_{k}_{t1.strftime("%Y-%m-%dT%H%M%SZ")}'
save_file = os.path.join(save_dir_maps, sname)
vargs = {}
vargs['vmin'] = item['limits'][0]
vargs['vmax'] = item['limits'][1]
vargs['transform'] = transform
vargs['cmap'] = sp.cmaps(ds[k].name)
vargs['extend'] = 'both'
if k == 'sea_surface_height':
vargs['levels'] = np.arange(vargs['vmin'], vargs['vmax'], item['limits'][2])
limits['currents'] = True
else:
vargs['levels'] = np.arange(vargs['vmin'], vargs['vmax'], item['limits'][2])
try:
vargs.pop('vmin'), vargs.pop('vmax')
except KeyError:
pass
fig, ax = plt.subplots(
figsize=(11, 8),
subplot_kw=dict(projection=ccrs.Mercator())
)
h = plt.contourf(dsd['lon'], dsd['lat'], dsd[k].squeeze(), **vargs)
if k == 'sea_surface_height':
sub = 6
qds = dsd.coarsen(lon=sub, boundary='pad').mean().coarsen(lat=sub, boundary='pad').mean()
angle, speed = uv2spdir(qds['u'], qds['v']) # convert u/v to angle and speed
u, v = spdir2uv( # convert angle and speed back to u/v, normalizing the arrow sizes
np.ones_like(speed),
angle,
deg=True
)
qargs = {}
# qargs['norm'] = Normalize(vmin=velocity_min, vmax=velocity_max, clip=True)
qargs['scale'] = 90
# qargs['headwidth'] = 2.5
# qargs['headlength'] = 4
# qargs['headaxislength'] = 4
qargs['headwidth'] = 2.75
qargs['headlength'] = 2.75
qargs['headaxislength'] = 2.5
qargs['transform'] = ccrs.PlateCarree()
# qargs['pivot'] = 'mid'
# qargs['units'] = 'inches'
# sub = 3
lons, lats = np.meshgrid(qds['lon'], qds['lat'])
q = plt.quiver(lons, lats, u, v, **qargs)
if bathy:
levels = np.arange(-100, 0, 50)
bath_lat = bathy.variables['lat'][:]
bath_lon = bathy.variables['lon'][:]
bath_elev = bathy.variables['elevation'][:]
CS = plt.contour(bath_lon, bath_lat, bath_elev, levels, linewidths=.75, alpha=.5, colors='k', transform=ccrs.PlateCarree())
ax.clabel(CS, [-100], inline=True, fontsize=7, fmt=sp.fmt)
# plt.contourf(bath_lon, bath_lat, bath_elev, np.arange(-9000,9100,100), cmap=cmocean.cm.topo, transform=ccrs.PlateCarree())
sp.map_add_features(ax, extent)
sp.map_add_ticks(ax, extent)
if argo:
atimes = []
for i in argo:
ax.plot(i.lon, i.lat, marker='o', markersize=7, color='w', markeredgecolor='black', label=i.name, transform=ccrs.PlateCarree())
atimes.append(pd.to_datetime(i.time))
title = '{}\n Argo floats (circles): {} to {} '.format(title, pd.to_datetime(np.min(atimes)).strftime('%Y-%m-%dT%H:%M'),
pd.to_datetime(np.max(atimes)).strftime('%Y-%m-%dT%H:%M'))
#ax.legend(loc='upper right', fontsize=6)
# plot full glider track
ax.plot(gliders.longitude.values, gliders.latitude.values, color='white', linewidth=1.5,
label='Glider Track', transform=ccrs.PlateCarree())
ax.legend(loc='upper left')
if current_glider_loc:
ax.plot(gliders.longitude.values[-1], gliders.latitude.values[-1], color='white', marker='^',
markeredgecolor='black', markersize=8.5, transform=ccrs.PlateCarree())
if custom_transect:
ax.plot(custom_transect['lon'], custom_transect['lat'], color='magenta', linewidth=1.5,
label='Model Comparison', transform=ccrs.PlateCarree())
ax.legend(loc='upper left')
# Plot title
plt.title(title)
# Set colorbar height equal to plot height
divider = make_axes_locatable(ax)
cax = divider.new_horizontal(size='5%', pad=0.05, axes_class=plt.Axes)
fig.add_axes(cax)
# generate colorbar
cbar = plt.colorbar(h, cax=cax)
cbar.set_label(ds[k].units)
plt.savefig(save_file, dpi=dpi, bbox_inches='tight', pad_inches=0.1)
plt.close()
def plot_radials(dataset, *,
plot_type='velocity',
output_file=None,
extent=None, lon_ticks=None, lat_ticks=None,
scale=True,
sub=2,
cbar_step=10,
velocity_min=None, velocity_max=None,
markers=None,
prim_filter=False,
title='HF Radar'):
"""
param dataset: a file-path to an xarray compatible object or an xarray Dataset object
"""
try:
ds = xr.open_dataset(dataset)
closing = ds.close
except AttributeError:
if isinstance(dataset, xr.Dataset):
ds = dataset
closing = int # dummy func to close nothing
else:
raise
tds = ds.squeeze()
if prim_filter:
if 'PRIM' in list(tds.keys()):
tds = tds.where(tds.PRIM == 1).squeeze()
title = title + ' - QC'
else:
logging.warning('PRIM flag not found. Bypassing quality control filters')
time = ds.time.values[0]
lon = tds.coords['lon'].data
lat = tds.coords['lat'].data
u = tds['u'].data
v = tds['v'].data
u = ma.masked_invalid(u)
v = ma.masked_invalid(v)
if scale:
angle, speed = uv2spdir(u, v) # convert u/v to angle and speed
u, v = spdir2uv( # convert angle and speed back to u/v, normalizing the arrow sizes
np.ones_like(speed),
angle,
deg=True
)
velocity_min = velocity_min or -40
velocity_max = velocity_max or 40
kwargs = dict(extent=extent, lon_ticks=lon_ticks, lat_ticks=lat_ticks,
output_file=output_file,
sub=sub,
markers=markers,
title=title,
meshgrid=False,
scale=120, headwidth=2.5, headlength=4, headaxislength=4)
if 'velocity' in plot_type:
"""
Velocity displays the direction and magnitude of the radials
"""
kwargs['colorbar'] = True
kwargs['cmap'] = cmocean.cm.balance
# Define arrow colors. Limited by velocity_min and velocity_max
kwargs['color_clipped'] = np.clip(
tds.velocity.data[::sub],
velocity_min,
velocity_max
).squeeze()
kwargs['offset'] = Normalize(vmin=velocity_min, vmax=velocity_max, clip=True)
kwargs['ticks'] = np.append(np.arange(velocity_min, velocity_max, cbar_step), velocity_max)
elif 'motion' in plot_type:
"""
Motion displays the direction (towards or away) from radar
"""
kwargs['colorbar'] = False
kwargs['cmap'] = 'bwr'
velocity = tds.velocity
velocity_temp = velocity.where(velocity > 0, other=-1) # Going away from radar
kwargs['color_clipped'] = velocity_temp.where(velocity < 0, other=1).data # Going towards radar
kwargs['offset'] = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
elif 'qc_pass_fail' in plot_type:
kwargs['colorbar'] = False
kwargs['cmap'] = colors.ListedColormap(['limegreen', 'red'])
if prim_filter:
tds = ds.squeeze()
kwargs['color_clipped'] = tds.PRIM.where(tds.PRIM != 1, other=-1).data # PRIM == 1 where vectors pass qc
kwargs['offset'] = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
closing()
plot_common(time, lon, lat, u, v, **kwargs)
def plot_totals(dataset, *,
output_file=None,
extent=None,
scale=True,
sub=2,
cbar_step=20,
velocity_min=None, velocity_max=None,
markers=None,
title='HF Radar'):
"""
param dataset: a file-path to an xarray compatible object or an xarray Dataset object
"""
try:
ds = xr.open_dataset(dataset)
closing = ds.close
except AttributeError:
if isinstance(dataset, xr.Dataset):
ds = dataset
closing = int # dummy func to close nothing
else:
raise
tds = ds.squeeze()
u = tds['u'].data
v = tds['v'].data
lon = tds.coords['lon'].data
lat = tds.coords['lat'].data
try:
time = str(ds.time.values[0])
except IndexError:
time = ds.time.values
closing()
if scale:
angle, speed = uv2spdir(u, v) # convert u/v to angle and speed
u, v = spdir2uv( # convert angle and speed back to u/v, normalizing the arrow sizes
np.ones_like(speed),
angle,
deg=True
)
velocity_min = velocity_min or np.int32(np.nanmin(speed)) or 0
velocity_max = velocity_max or np.int32(np.nanmax(speed)) or 15
kwargs = dict(output_file=output_file,
sub=sub,
markers=markers,
title=title,
meshgrid=True,
scale=60,
headwidth=3,
headlength=5,
headaxislength=4.5)
kwargs['color_clipped'] = np.clip(
speed[::sub],
velocity_min,
velocity_max
).squeeze()
kwargs['offset'] = Normalize(vmin=velocity_min, vmax=velocity_max, clip=True)
kwargs['ticks'] = np.append(np.arange(velocity_min, velocity_max, cbar_step), velocity_max)
kwargs['extent'] = extent
plt = plot_common(
time, lon, lat, u, v,
**kwargs
)
if output_file is None:
return plt
def plot_common(time,
lon,
lat,
u,
v,
*,
output_file=None,
meshgrid=True,
sub=2,
velocity_min=None,
velocity_max=None,
markers=None,
title='HF Radar'):
"""
param markers: a list of 3-tuple/lists containng [lon, lat, marker kwargs] as should be
passed into ax.plot()
eg. [
[-74.6, 38.5, dict(marker='o', markersize=8, color='r')],
[-70.1, 35.2, dict(marker='o', markersize=8, color='b')]
]
"""
markers = markers or []
fig = plt.figure()
u = ma.masked_invalid(u)
v = ma.masked_invalid(v)
angle, speed = uv2spdir(u, v)
us, vs = spdir2uv(np.ones_like(speed), angle, deg=True)
if meshgrid is True:
lons, lats = np.meshgrid(lon, lat)
else:
lons, lats = lon, lat
velocity_min = velocity_min or 0
velocity_max = velocity_max or np.nanmax(speed) or 15
speed_clipped = np.clip(speed[::sub, ::sub], velocity_min,
velocity_max).squeeze()
fig, ax = plt.subplots(figsize=(11, 8),
subplot_kw=dict(projection=ccrs.PlateCarree()))
# Plot title
plt.title('{}\n{}'.format(title, time))
# plot arrows over pcolor
h = ax.quiver(lons[::sub, ::sub],
lats[::sub, ::sub],
us[::sub, ::sub],
vs[::sub, ::sub],
speed_clipped,
cmap='jet',
scale=60)
divider = make_axes_locatable(ax)
cax = divider.new_horizontal(size='5%', pad=0.05, axes_class=plt.Axes)
fig.add_axes(cax)
# generate colorbar
ticks = np.linspace(velocity_min, velocity_max, 5)
cb = plt.colorbar(h, cax=cax, ticks=ticks)
cb.ax.set_yticklabels([f'{s:.2f}' for s in ticks])
cb.set_label('cm/s')
for m in markers:
ax.plot(m[0], m[1], **m[2])
# Gridlines and grid labels
gl = ax.gridlines(draw_labels=True,
linewidth=1,
color='black',
alpha=0.5,
linestyle='--')
gl.xlabels_top = gl.ylabels_right = False
gl.xlabel_style = {'size': 10, 'color': 'gray'}
gl.ylabel_style = {'size': 10, 'color': 'gray'}
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Axes properties and features
ax.set_extent([lon.min() - 1, lon.max() + 1, lat.min() - 1, lat.max() + 1])
ax.add_feature(LAND, zorder=0, edgecolor='black')
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.BORDERS)
ax.add_feature(state_lines, edgecolor='black')
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 12
fig_size[1] = 8.5
plt.rcParams["figure.figsize"] = fig_size
if output_file is not None:
create_dir(str(Path(output_file).parent))
resoluton = 150 # plot resolution in DPI
plt.savefig(output_file, dpi=resoluton)
plt.close('all')
else:
return plt
def plot_ruv(radial_file,
save_path=None,
fname=None,
speed_display='color',
redblue=True,
plotflag=None,
scale=50,
vlims=(-100, 100)):
"""
Main function to plot radial files.
Args:
radial_file (str or Path): Path to radial file or a Radial object
save_path (str or Path): Path to save figures
fname (str): Output file name. If not specified, the radial object filename is used, Defaults to None
speed_display (str, optional): 'color' or 'arrowlength' to specify whether current speed is depicted by color or arrow length, Defaults to color
redblue (bool, optional): If True, colorbar scheme is redblue, Defaults to True
plotflag (str, optional): QARTOD QC test code, fail and suspect flags for that test will be highlighted, Defaults to None
scale (int, optional): Scaling factor for drawing the vectors, Default = 50
vlims (tuple, optional): Velocity limits for the colorbar, Default = (-100,100)
"""
if not isinstance(radial_file, Radial):
r = Radial(radial_file)
else:
r = radial_file
if not r.is_valid():
return
if r._iscorrupt:
return
if fname == None:
fname = r.file_name[0:-4]
# Adjust some standard plotting settings to make them the size of a sheet of paper
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 12
fig_size[1] = 8
plt.rcParams["figure.figsize"] = fig_size
# Set colors of the land.
edgecolor = 'black'
landcolor = 'tan'
LAND = cfeature.NaturalEarthFeature('physical',
'land',
'10m',
edgecolor='face',
facecolor='tan')
state_lines = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
bf = 0.3 #degrees
extent = [
r.data.LOND.min() - bf,
r.data.LOND.max() + bf,
r.data.LATD.min() - bf,
r.data.LATD.max() + bf
]
# Split out everything into seperate variables in order to pass them easier to the plotting functions
time = r.time
lon = r.data.LOND.to_numpy()
lat = r.data.LATD.to_numpy()
u = r.data.VELU.to_numpy()
v = r.data.VELV.to_numpy()
velocity = r.data.VELO.to_numpy()
sitename = r.metadata['Site'][0:4]
ptype = r.metadata['PatternType']
# Mask nans just in case there are any
u = ma.masked_invalid(u)
v = ma.masked_invalid(v)
# convert U and V component velocities to angle and speed
angle, speed = uv2spdir(u, v)
# convert angle and speed right back back to U and V component velocities,
# Passing speed as an array of 1's allows for the normalizing of the arrow sizes
# if we pass the correct
u, v = spdir2uv(np.ones_like(speed), angle, deg=True)
# Get the receiver location
receiver_location = [float(x) for x in r.metadata['Origin'].split(' ')]
receiver_location.reverse()
# Intialize an empty subplot using cartopy
fig, ax = plt.subplots(figsize=(11, 8),
subplot_kw=dict(projection=ccrs.Mercator()))
#plt.quiver(lon, lat, u, v, transform=ccrs.PlateCarree())
plt.plot(receiver_location[0],
receiver_location[1],
'o',
markersize=10,
markeredgecolor='black',
color='red',
transform=ccrs.PlateCarree())
map_features(ax, extent, LAND, edgecolor, landcolor, state_lines)
# The next lines specify the arrow shapes. You can customize this to your preference, usually by trial and error.
# scale = 50
headwidth = 2.5
headlength = 4
headaxislength = 4
sub = 1
# if user requested speed displayed as arrow length
if speed_display == 'arrowlength':
scale_units = 'width'
width = 0.005
if not plotflag == None:
fail = r.data[plotflag] == 4
suspect = r.data[plotflag] == 3
noteval = r.data[plotflag] == 2
away = r.data.VELO > 0
plt.quiver(lon,
lat,
u,
v,
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='lightpink')
plt.quiver(lon[away],
lat[away],
u[away],
v[away],
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='lightblue')
#plt.quiver(lon, lat, u, v, transform=ccrs.PlateCarree(), scale=scale, color='white')
plt.quiver(lon[fail],
lat[fail],
u[fail],
v[fail],
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='red')
plt.quiver(lon[suspect],
lat[suspect],
u[suspect],
v[suspect],
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='gold')
plt.quiver(lon[noteval],
lat[noteval],
u[noteval],
v[noteval],
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='gray')
plt.title(
f'{sitename} {ptype} {plotflag}\nFail(red) Suspect(yellow) Not Evaluated(grey)\n{time}'
)
plt.savefig(save_path + '/' + fname + '_' + plotflag)
plt.close('all')
elif redblue:
away = r.data.VELO > 0
plt.quiver(lon,
lat,
u,
v,
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='red')
plt.quiver(lon[away],
lat[away],
u[away],
v[away],
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='blue')
plt.title(f'{sitename} {ptype}\n{time}')
plt.savefig(save_path + '/' + fname + '_rb')
plt.close('all')
else:
plt.quiver(lon,
lat,
u,
v,
transform=ccrs.PlateCarree(),
scale=scale,
scale_units=scale_units,
width=width,
color='wheat')
plt.title(f'{sitename} {ptype}\n{time}')
plt.savefig(save_path + '/' + fname)
plt.close('all')
# if user requested speed displayed as color
else:
if not plotflag == None:
test = r.data[plotflag]
velocity[np.where(test >= 0)] = 1
velocity[np.where(test == 4)] = -1
color_clipped = np.clip(r.data.VELO[::sub], -1, 1).squeeze()
offset = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
cmap = colors.ListedColormap(['red', 'wheat'])
plt.title(f'{sitename} {ptype} {plotflag} Fail\n{time}')
qargs = dict(cmap=cmap,
scale=scale,
headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength)
qargs['transform'] = ccrs.PlateCarree()
qargs['norm'] = offset
# plot arrows over pcolor
h = ax.quiver(lon[::sub], lat[::sub], u[::sub], v[::sub],
color_clipped, **qargs)
plt.savefig(save_path + '/' + fname + '_' + plotflag + '_fail')
plt.close('all')
elif redblue:
cmap = 'bwr'
# velocity_temp = velocity.where(velocity > 0, other=-1) # Going away from radar
velocity[np.where(velocity < 0)] = -1
velocity[np.where(velocity >= 0)] = 1
# We will create temporary variable of velocities that sets any velocity less than 0 to 1
# color_clipped = velocity_temp.where(velocity < 0, other=1) # Going towards radar
# Define arrow colors. Limited by velocity_min and velocity_max
color_clipped = np.clip(r.data.VELO[::sub], -1, 1).squeeze()
offset = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
plt.title(f'{sitename} {ptype}\n{time}')
qargs = dict(cmap=cmap,
scale=scale,
headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength)
qargs['transform'] = ccrs.PlateCarree()
qargs['norm'] = offset
# plot arrows over pcolor
h = ax.quiver(lon[::sub], lat[::sub], u[::sub], v[::sub],
color_clipped, **qargs)
# map_features(ax, extent, LAND, edgecolor, landcolor, state_lines)
plt.savefig(save_path + '/' + fname + '_rb')
plt.close('all')
else:
plt.title(f'{sitename} {ptype}\n{time}')
cmap = cmocean.cm.balance
# Colorbar options
velocity_min = vlims[
0] # The minimum speed that should be displayed on the colorbar
velocity_max = vlims[
1] # The maximum speed that should be displayed on the colorbar
cbar_step = 10 # The step between each colorbar tick
offset = Normalize(vmin=velocity_min, vmax=velocity_max, clip=True)
# Define arrow colors. Limited by velocity_min and velocity_max
color_clipped = np.clip(r.data.VELO[::sub], velocity_min,
velocity_max).squeeze()
ticks = np.append(np.arange(velocity_min, velocity_max, cbar_step),
velocity_max)
qargs = dict(cmap=cmap,
scale=scale,
headwidth=headwidth,
headlength=headlength,
headaxislength=headaxislength)
qargs['transform'] = ccrs.PlateCarree()
qargs['norm'] = offset
# plot arrows over pcolor
h = ax.quiver(lon[::sub], lat[::sub], u[::sub], v[::sub],
color_clipped, **qargs)
# map_features(ax, extent, LAND, edgecolor, landcolor, state_lines)
# generate colorbar
divider = make_axes_locatable(ax)
cax = divider.new_horizontal(size='5%',
pad=0.05,
axes_class=plt.Axes)
fig.add_axes(cax)
cb = plt.colorbar(h, cax=cax, ticks=ticks)
cb.ax.set_yticklabels([f'{s:d}' for s in ticks])
cb.set_label('cm/s')
plt.savefig(save_path + '/' + fname)
plt.close('all')
