def main(radial_file, save_dir, types=['tabular']):
"""
Main function to parse and qc radial files
:param radial_file: Path to radial file
:param save_path: Path to save quality controlled radial file
"""
save_dir = Path(save_dir)
try:
r = Radial(radial_file)
except Exception:
return
if r.is_valid():
sname = save_dir / r.file_name
try:
for t in types:
r.export(sname, 'netcdf-{}'.format(t), prepend_ext=True)
except ValueError:
pass
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')