def plt_tsk(nc, model, figname):
"""
Create a pcolor surface map of surface skin temperature
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
"""
tsk = nc['TSK']
color_label = 'TSK (\N{DEGREE SIGN}C)'
title = 'Surface Skin Temperature (\N{DEGREE SIGN}C)'
plot_types = ['full_grid',
'bight'] # plot the full grid and just NY Bight area
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
tsk_sub, ax_lims, xticks, yticks = cf.subset_grid(tsk, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
tsk_sub, ax_lims, xticks, yticks = cf.subset_grid(tsk, 'bight')
fig, ax, lat, lon = cf.set_map(tsk_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# convert degrees K to degrees C
tsk_sub_c = tsk_sub.values - 273.15
# add contour lines
contour_list = np.linspace(0, 30, 7)
pf.add_contours(ax, lon, lat, tsk_sub_c, contour_list)
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
vlims = [0, 32]
cmap = cmo.cm.thermal
levels = MaxNLocator(nbins=16).tick_values(
vlims[0], vlims[1]) # levels every 2 degrees C
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = title
kwargs['clab'] = color_label
# kwargs['var_lims'] = vlims
kwargs['norm_clevs'] = norm
kwargs['extend'] = 'both'
kwargs['cmap'] = cmap
pf.plot_pcolormesh(fig, ax, lon, lat, tsk_sub_c, **kwargs)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
plt.savefig(figname, dpi=200)
plt.close()
def plt_radar(nc, model, figname, lease_areas=None):
"""
Create filled contour surface maps of radar reflectivity
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
"""
lease_areas = lease_areas or None
# MDBZ = max radar reflectivity
radar = nc['MDBZ']
plot_types = ['full_grid', 'bight'] # plot the full grid and just NY Bight area
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
radar_sub, ax_lims, xticks, yticks = cf.subset_grid(radar, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]), new_fname)
radar_sub, ax_lims, xticks, yticks = cf.subset_grid(radar, 'bight')
fig, ax, lat, lon = cf.set_map(radar_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start, model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
title = 'Radar Composite Reflectivity ({})'.format(radar.units)
vmin = 0
vmax = 72
levels = np.linspace(vmin, vmax, 145)
ticklevs = np.linspace(vmin, 70, 15)
# If the array is all zeros, turn the zeros to nans. Otherwise the plot will be all teal instead of white.
if np.nanmax(radar_sub) == 0.0:
radar_sub.values[radar_sub == 0] = np.nan
kwargs = dict()
kwargs['ttl'] = title
kwargs['cmap'] = 'pyart_NWSRef'
kwargs['clab'] = title
kwargs['var_lims'] = [vmin, vmax]
kwargs['cbar_ticks'] = ticklevs.tolist()
pf.plot_contourf(fig, ax, lon, lat, radar_sub, levels, **kwargs)
plt.savefig(figname, dpi=200)
plt.close()
def plt_radar(nc, subset_domain, figname):
"""
Create filled contour surface maps of radar reflectivity
:param nc: netcdf file
:param subset_domain: the plotting limit domain, e.g. 3km, 9km, bight (NY Bight), full_grid, mab, nj, snj
:param figname: full file path to save directory and save filename
"""
radar = nc['Reflectivity']
radar_sub, ax_lims, xticks, yticks = cf.subset_grid_wct(radar, subset_domain)
fig, ax, lat, lon = cf.set_map(radar_sub)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
title = 'Radar Reflectivity ({})'.format(radar_sub.units)
kwargs = dict()
kwargs['ttl'] = title
kwargs['cmap'] = 'pyart_NWSRef'
kwargs['clab'] = title
kwargs['var_lims'] = [0, 72]
# If the array is all zeros, turn the zeros to nans. Otherwise the plot will be all teal instead of white.
if np.nanmax(radar_sub) == 0.0:
radar_sub.values[radar_sub == 0] = np.nan
pf.plot_pcolormesh(fig, ax, lon, lat, np.squeeze(radar_sub.values), **kwargs)
plt.savefig(figname, dpi=200)
plt.close()
def plt_rain(nc, model, figname, raintype, lease_areas=None, ncprev=None):
"""
Create filled contour surface maps of hourly and accumulated rain
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
:param raintype: plot type to make, e.g. 'acc' (accumulated) or 'hourly'
:param lease_areas: optional, dictionary containing lat/lon coordinates for wind energy lease area polygon
:param ncprev: optional, netcdf file from the previous model hour to calculate hourly rainfall
"""
lease_areas = lease_areas or None
ncprev = ncprev or None
# RAINNC = total accumulated rainfall
rn = nc['RAINNC']
plot_types = ['full_grid', 'bight']
if raintype == 'acc':
new_fname = 'acc{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
color_label = 'Total Accumulated Precipitation (in)'
title = color_label
slp = None # don't plot sea level pressure for accumulated rain maps
elif raintype == 'hourly':
color_label = 'Hourly Precipitation (in)'
title = '{}, Sea Level Pressure (mb)'.format(color_label)
slp = nc['SLP']
# calculate hourly rainfall for every model hour by subtracting the rainfall for the previous hour from
# the rainfall for the current hour
if ncprev:
preciprev = ncprev['RAINNC']
rn = np.subtract(np.squeeze(rn), np.squeeze(preciprev))
else:
rn = rn
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
if isinstance(slp, xr.DataArray):
slp_sub, _, _, _ = cf.subset_grid(slp, model)
rn_sub, ax_lims, xticks, yticks = cf.subset_grid(rn, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
if isinstance(slp, xr.DataArray):
slp_sub, _, _, _ = cf.subset_grid(slp, 'bight')
rn_sub, ax_lims, xticks, yticks = cf.subset_grid(rn, 'bight')
fig, ax, lat, lon = cf.set_map(rn_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
# convert mm to inches
rn_sub = rn_sub * 0.0394
# modified NWS colormap, from http://jjhelmus.github.io/blog/2013/09/17/plotting-nsw-precipitation-data/
nws_precip_colors = [
"#fdfdfd", # 0.01 - 0.10 inches
"#019ff4", # 0.10 - 0.25 inches
"#0300f4", # 0.25 - 0.50 inches
"#02fd02", # 0.50 - 0.75 inches
"#01c501", # 0.75 - 1.00 inches
"#008e00", # 1.00 - 1.50 inches
"#fdf802", # 1.50 - 2.00 inches
"#e5bc00", # 2.00 - 2.50 inches
"#fd9500", # 2.50 - 3.00 inches
"#fd0000", # 3.00 - 4.00 inches
"#d40000", # 4.00 - 5.00 inches
"#bc0000", # 5.00 - 6.00 inches
"#f800fd", # 6.00 - 8.00 inches
"#9854c6", # 8.00 - 10.00 inches
"#4B0082" # 10.00+
]
precip_colormap = mpl.colors.ListedColormap(nws_precip_colors)
# specify colorbar level demarcations
levels = [
0.01, 0.1, 0.25, 0.50, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0,
6.0, 8.0, 10., 12.
]
kwargs = dict()
kwargs['ttl'] = title
kwargs['cmap'] = precip_colormap
kwargs['clab'] = color_label
kwargs['normalize'] = 'yes'
kwargs['extend'] = 'max'
# plot data
pf.plot_contourf(fig, ax, lon, lat, rn_sub, levels, **kwargs)
# add slp as contours if provided
if isinstance(slp, xr.DataArray):
contour_list = [
940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984,
988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028,
1032, 1036, 1040
]
pf.add_contours(ax, lon, lat, slp_sub, contour_list)
plt.savefig(figname, dpi=200)
plt.close()
def plt_2m_temp(nc, model, figname, lease_areas=None):
"""
Create a pcolor surface map of air temperature at 2m with contours
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
"""
lease_areas = lease_areas or None
t2 = nc['T2']
color_label = 'Air Temperature (\N{DEGREE SIGN}F)'
plot_types = ['full_grid',
'bight'] # plot the full grid and just NY Bight area
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
t2_sub, ax_lims, xticks, yticks = cf.subset_grid(t2, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
t2_sub, ax_lims, xticks, yticks = cf.subset_grid(t2, 'bight')
fig, ax, lat, lon = cf.set_map(t2_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
# convert K to F
d = np.squeeze(t2_sub.values) * 9 / 5 - 459.67
# add contour lines
contour_list = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
pf.add_contours(ax, lon, lat, d, contour_list)
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
vlims = [-20, 110]
cmap = plt.get_cmap('jet')
# levels = MaxNLocator(nbins=30).tick_values(vlims[0], vlims[1]) # levels every 5 degrees F
levels = MaxNLocator(nbins=65).tick_values(
vlims[0], vlims[1]) # levels every 2 degrees F
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = '2m {}'.format(color_label)
kwargs['clab'] = color_label
# kwargs['var_lims'] = vlims
kwargs['norm_clevs'] = norm
kwargs['extend'] = 'both'
pf.plot_pcolormesh(fig, ax, lon, lat, d, **kwargs)
# # contourf: smooths the resolution of the model data, plots are less pixelated
# kwargs = dict()
# kwargs['ttl'] = '2m {}'.format(color_label)
# kwargs['clab'] = color_label
# kwargs['var_lims'] = [-20, 110]
# kwargs['cbar_ticks'] = np.linspace(-20, 100, 7)
#
# levels = np.arange(-20, 110.5, .5)
# pf.plot_contourf(fig, ax, lon, lat, d, levels, **kwargs)
plt.savefig(figname, dpi=200)
plt.close()
def plt_power(nc, model, ht, figname, lease_areas=None):
"""
Create pseudocolor surface maps of estimated wind power at 160m.
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param ht: wind speed height to plot, e.g. 160m
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
"""
power_curve = '/home/lgarzio/rucool/bpu/wrf/wrf_lw15mw_power_15001max.csv' # on server, max is set to 15001
pc = pd.read_csv(power_curve)
turbine = power_curve.split('/')[-1].split('_')[1].split('lw')[-1].upper()
lease_areas = lease_areas or None
if ht == '10m':
u = nc['U10']
v = nc['V10']
else:
u = nc.sel(height=int(ht[0:-1]))['U']
v = nc.sel(height=int(ht[0:-1]))['V']
color_label = f'Estimated {turbine} Wind Power (kW)'
plot_types = ['full_grid', 'bight']
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
u_sub, _, _, _ = cf.subset_grid(u, model)
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
u_sub, _, _, _ = cf.subset_grid(u, 'bight')
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, 'bight')
fig, ax, lat, lon = cf.set_map(u_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
# calculate wind speed from u and v
speed = cf.wind_uv_to_spd(u_sub, v_sub)
# calculate wind power
power = xr.DataArray(np.interp(speed, pc['Wind Speed'], pc['Power']),
coords=speed.coords)
# add contours
contour_list = [15000]
pf.add_contours(ax, lon, lat, power, contour_list)
# set color map
cmap = plt.get_cmap('OrRd')
levels = list(np.arange(0, 15001, 1000))
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = '{} {}'.format(ht, color_label)
kwargs['cmap'] = cmap
kwargs['clab'] = color_label
kwargs['norm_clevs'] = norm
kwargs['extend'] = 'neither'
pf.plot_pcolormesh(fig, ax, lon, lat, power, **kwargs)
# add power values of 15000 as another layer
power_copy = power.copy()
custom_color = ["#67000d"] # dark red
custom_colormap = ListedColormap(custom_color)
mask = np.logical_and(power_copy.values < 15001,
power_copy.values < 15001)
power_copy.values[mask] = np.nan
ax.pcolormesh(lon,
lat,
power_copy,
cmap=custom_colormap,
transform=ccrs.PlateCarree())
plt.savefig(figname, dpi=200)
plt.close()
def plt_solar(nc, model, figname, lease_areas=None):
"""
Create pcolor surface maps of total, diffuse, and direct shortwave flux with contours
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
"""
lease_areas = lease_areas or None
varname = figname.split('/')[-1].split('_')[0]
if varname == 'swdown':
solar = nc['SWDOWN']
title = r'Total Shortwave Flux (W $\rm m^{-2}$)'
elif varname == 'diffuse':
solar = nc['SWDOWN'] * nc['DIFFUSE_FRAC']
title = r'Diffuse Shortwave Flux (W $\rm m^{-2}$)'
elif varname == 'direct':
solar = nc['SWDOWN'] * (1 - nc['DIFFUSE_FRAC'])
title = r'Direct Shortwave Flux (W $\rm m^{-2}$)'
plot_types = ['full_grid',
'bight'] # plot the full grid and just NY Bight area
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
solar_sub, ax_lims, xticks, yticks = cf.subset_grid(solar, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
solar_sub, ax_lims, xticks, yticks = cf.subset_grid(solar, 'bight')
fig, ax, lat, lon = cf.set_map(solar_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
# for diffuse shortwave flux, make state and coastline edgecolor gray, and make wind energy lease area magenta
# for total and direct, change the state and coastline edgecolor to gray, and make wind energy lease area
# magenta if solar radiation is beneath a certain threshold
mingray = 100 # minimum average value for making the state/coastlines gray
if varname == 'diffuse':
kwargs['ecolor'] = '#525252'
cf.add_map_features(ax, ax_lims, **kwargs)
lease_area_color = 'magenta'
else:
if np.nanmean(solar_sub) < mingray:
kwargs['ecolor'] = '#525252'
cf.add_map_features(ax, ax_lims, **kwargs)
lease_area_color = 'magenta'
else:
cf.add_map_features(ax, ax_lims, **kwargs)
lease_area_color = '#252525' # #252525 is very close to black
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, lease_area_color)
color_label = r'Surface Downwelling Shortwave Flux (W $\rm m^{-2}$)' # \rm removes the italics
contour_list = np.linspace(200, 1000, 5)
# add contour lines
pf.add_contours(ax, lon, lat, solar_sub, contour_list)
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
vlims = [0, 1200]
cmap = plt.get_cmap(plt.cm.CMRmap)
#levels = MaxNLocator(nbins=14).tick_values(vlims[0], vlims[1]) # every 100 W m-2
levels = MaxNLocator(nbins=25).tick_values(vlims[0],
vlims[1]) # every 50 W m-2
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = title
kwargs['cmap'] = cmap
kwargs['clab'] = color_label
# kwargs['var_lims'] = vlims
kwargs['norm_clevs'] = norm
pf.plot_pcolormesh(fig, ax, lon, lat, solar_sub, **kwargs)
plt.savefig(figname, dpi=200)
plt.close()
def plt_snow(nc, model, figname, snowtype, lease_areas=None, ncprev=None):
"""
Create filled contour surface maps of hourly and accumulated snowfall
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param figname: full file path to save directory and save filename
:param snowtype: plot type to make, e.g. 'acc' (accumulated) or 'hourly'
:param lease_areas: optional, dictionary containing lat/lon coordinates for wind energy lease area polygon
:param ncprev: optional, netcdf file from the previous model hour to calculate hourly snowfall
"""
lease_areas = lease_areas or None
ncprev = ncprev or None
# SNOWNC = water equivalent of total accumulated snowfall
snow = nc['SNOWNC']
plot_types = ['full_grid', 'bight']
if snowtype == 'acc':
new_fname = 'acc{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
color_label = 'Total Accumulated Snow 10:1 (in)'
# specify colorbar level demarcations and contour levels
levels = [0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 20]
contour_list = [0, 2, 4, 6, 10, 20]
elif snowtype == 'hourly':
color_label = 'Hourly Snowfall 10:1 (in)'
# calculate hourly snowfall for every model hour by subtracting the snowfall for the previous hour from
# the snowfall for the current hour
if ncprev is not None:
snowprev = ncprev['SNOWNC']
snow = np.subtract(np.squeeze(snow), np.squeeze(snowprev))
else:
snow = snow
# specify colorbar level demarcations and contour levels
levels = [0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5]
contour_list = [0, 1, 2, 3, 4, 5]
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
snow_sub, ax_lims, xticks, yticks = cf.subset_grid(snow, model)
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
snow_sub, ax_lims, xticks, yticks = cf.subset_grid(snow, 'bight')
fig, ax, lat, lon = cf.set_map(snow_sub)
# convert mm to in then multiply by 10 since the output is water equivalent
snow_in = snow_sub * 0.0393701 * 10
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
snow_colors = [
"#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#08519c",
"#9e9ac8", "#8c6bb1", "#88419d", "#810f7c", "#4d004b"
]
snow_colormap = mpl.colors.ListedColormap(snow_colors)
# make 0 white instead of blue without showing up on the colorbar
snow_colormap.set_under('white')
levels[0] = 1e-10
kwargs = dict()
kwargs['ttl'] = color_label
kwargs['cmap'] = snow_colormap
kwargs['clab'] = color_label
kwargs['normalize'] = 'yes'
kwargs['cbar_ticks'] = levels
kwargs['extend'] = 'max'
# plot data
pf.plot_contourf(fig, ax, lon, lat, snow_in, levels, **kwargs)
# add contours
pf.add_contours(ax, lon, lat, snow_in, contour_list)
plt.savefig(figname, dpi=200)
plt.close()
def plt_windsp(nc, model, ht, figname, lease_areas=None):
"""
Create pseudocolor surface maps of wind speed with quivers indicating wind direction.
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param ht: wind speed height to plot, e.g. 10m, 80m, 160m
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
"""
lease_areas = lease_areas or None
if ht == '10m':
u = nc['U10']
v = nc['V10']
else:
u = nc.sel(height=int(ht[0:-1]))['U']
v = nc.sel(height=int(ht[0:-1]))['V']
color_label = 'Wind Speed (knots)'
# define the subsetting for the quivers on the map based on model and height
quiver_subset = dict(_3km=dict(_10m=11, _80m=12, _160m=13),
_9km=dict(_10m=4, _80m=5, _160m=6),
bight_3km=dict(_10m=6, _80m=6, _160m=7),
bight_9km=dict(_10m=2, _80m=2, _160m=3))
plot_types = ['full_grid', 'bight']
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
u_sub, _, _, _ = cf.subset_grid(u, model)
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, model)
qs = quiver_subset['_{}'.format(model)]['_{}'.format(ht)]
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
u_sub, _, _, _ = cf.subset_grid(u, 'bight')
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, 'bight')
qs = quiver_subset['bight_{}'.format(model)]['_{}'.format(ht)]
fig, ax, lat, lon = cf.set_map(u_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
# convert wind speeds from m/s to knots
u_sub = np.squeeze(u_sub.values) * 1.94384
v_sub = np.squeeze(v_sub.values) * 1.94384
# standardize the vectors so they only represent direction
u_sub_standardize = u_sub / cf.wind_uv_to_spd(u_sub, v_sub)
v_sub_standardize = v_sub / cf.wind_uv_to_spd(u_sub, v_sub)
# calculate wind speed from u and v
speed = cf.wind_uv_to_spd(u_sub, v_sub)
# mask vectors if wind speed is < 2
mask = speed < 2
u_sub_standardize[mask] = np.nan
v_sub_standardize[mask] = np.nan
# add contours
contour_list = [5, 10, 15, 20]
pf.add_contours(ax, lon, lat, speed, contour_list)
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
cmap = plt.get_cmap('turbo')
vlims = [0, 20]
levels = MaxNLocator(nbins=20).tick_values(vlims[0],
vlims[1]) # every 2 knots
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = 'Wind Speed ({}) Wind Ops'.format(ht)
kwargs['cmap'] = cmap
kwargs['clab'] = color_label
kwargs['norm_clevs'] = norm
kwargs['extend'] = 'neither'
pf.plot_pcolormesh(fig, ax, lon, lat, speed, **kwargs)
ax.quiver(lon[::qs, ::qs],
lat[::qs, ::qs],
u_sub_standardize[::qs, ::qs],
v_sub_standardize[::qs, ::qs],
scale=50,
width=.002,
headlength=4,
transform=ccrs.PlateCarree())
plt.savefig(figname, dpi=200)
plt.close()
def plt_windsp(nc,
model,
ht,
figname,
lease_areas=None,
summary=None,
add_text=None):
"""
Create pseudocolor surface maps of wind speed with quivers indicating wind direction.
:param nc: netcdf file
:param model: the model version that is being plotted, e.g. 3km or 9km
:param ht: wind speed height in mb to plot, e.g. 925
:param figname: full file path to save directory and save filename
:param lease_areas: optional dictionary containing lat/lon coordinates for wind energy lease area polygon
:param summary: optional dictionary containing locations of specific locations for seabreeze classification,
and a list to append windspeeds at specific locations for summary output
:param add_text: optional, add windspeed/direction values at specific locations to the figure
"""
lease_areas = lease_areas or None
summary = summary or None
add_text = add_text or None
u = nc.sel(pressure=ht)['UP']
v = nc.sel(pressure=ht)['VP']
color_label = 'Wind Speed (m/s)'
quiver_subset = dict(_3km=dict(_925=12),
_9km=dict(_925=4),
bight_3km=dict(_925=6),
bight_9km=dict(_925=2))
plot_types = ['full_grid', 'bight']
for pt in plot_types:
if pt == 'full_grid': # subset the entire grid
u_sub, _, _, _ = cf.subset_grid(u, model)
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, model)
qs = quiver_subset['_{}'.format(model)]['_{}'.format(ht)]
else: # subset just NY Bight
new_fname = 'bight_{}'.format(figname.split('/')[-1])
figname = '/{}/{}'.format(os.path.join(*figname.split('/')[0:-1]),
new_fname)
u_sub, _, _, _ = cf.subset_grid(u, 'bight')
v_sub, ax_lims, xticks, yticks = cf.subset_grid(v, 'bight')
qs = quiver_subset['bight_{}'.format(model)]['_{}'.format(ht)]
fig, ax, lat, lon = cf.set_map(u_sub)
# add text to the bottom of the plot
cf.add_text(ax, nc.SIMULATION_START_DATE, nc.time_coverage_start,
model)
# initialize keyword arguments for map features
kwargs = dict()
kwargs['xticks'] = xticks
kwargs['yticks'] = yticks
cf.add_map_features(ax, ax_lims, **kwargs)
if lease_areas:
pf.add_lease_area_polygon(ax, lease_areas, 'magenta')
# convert wind speeds from m/s to knots
# u_sub = xr.DataArray(np.squeeze(u_sub.values) * 1.94384, coords=u_sub.coords)
# v_sub = xr.DataArray(np.squeeze(v_sub.values) * 1.94384, coords=v_sub.coords)
# standardize the vectors so they only represent direction
u_sub_standardize = u_sub / cf.wind_uv_to_spd(u_sub, v_sub)
v_sub_standardize = v_sub / cf.wind_uv_to_spd(u_sub, v_sub)
# calculate wind speed and direction from u and v
speed = cf.wind_uv_to_spd(u_sub, v_sub)
direction = cf.wind_uv_to_dir(u_sub, v_sub)
# write a summary for wind speeds/directions at specified locations for seabreeze classification
# add the windspeeds/directions to the map
map_values = dict()
if summary:
tmstr = pd.to_datetime(
nc.Time.values[0]).strftime('%Y-%m-%dT%H:%M')
for key, coords in summary['locations'].items():
# find the closest model grid point to the location
a = abs(speed.XLAT - coords['lat']) + abs(speed.XLONG -
coords['lon'])
i, j = np.unravel_index(a.argmin(), a.shape)
sp = speed[i, j]
d = direction[i, j]
coords.update(ws=np.round(float(sp.values), 2))
coords.update(direction=np.round(float(d.values), 2))
map_values[key] = coords
if pt == 'full_grid':
wrf_lat = np.round(float(sp.XLAT.values), 4)
wrf_lon = np.round(float(sp.XLONG.values), 4)
summary['rows'].append([
tmstr, key, coords['lat'], coords['lon'], ht, wrf_lat,
wrf_lon,
np.round(float(sp.values), 4),
np.round(float(d.values), 4)
])
# mask vectors if wind speed is < 1 m/s
mask = speed.values < 1
u_sub_standardize.values[mask] = np.nan
v_sub_standardize.values[mask] = np.nan
# add contours
#contour_list = [10, 22, 34, 48, 64]
contour_list = [5, 11, 17, 25, 32]
pf.add_contours(ax, lon, lat, speed, contour_list)
# plot data
# pcolormesh: coarser resolution, shows the actual resolution of the model data
cmap = plt.get_cmap('BuPu')
# vlims = [0, 40]
# levels = MaxNLocator(nbins=20).tick_values(vlims[0], vlims[1]) # every 2 knots
vlims = [0, 25]
levels = MaxNLocator(nbins=25).tick_values(vlims[0],
vlims[1]) # every 1 m/s
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
kwargs = dict()
kwargs['ttl'] = '{}mb {}'.format(ht, color_label)
kwargs['cmap'] = cmap
kwargs['clab'] = color_label
#kwargs['var_lims'] = vlims
kwargs['norm_clevs'] = norm
kwargs['extend'] = 'max'
pf.plot_pcolormesh(fig, ax, lon, lat, speed, **kwargs)
# # contourf: smooths the resolution of the model data, plots are less pixelated
# kwargs = dict()
# kwargs['ttl'] = '{} {}'.format(ht, color_label)
# kwargs['cmap'] = cmap
# kwargs['clab'] = color_label
# kwargs['var_lims'] = [0, 40]
# kwargs['cbar_ticks'] = np.linspace(0, 40, 9)
#
# levels = np.arange(0, 40.1, .1)
# pf.plot_contourf(fig, ax, lon, lat, speed, levels, **kwargs)
# subset the quivers and add as a layer
# ax.quiver(lon[::qs, ::qs], lat[::qs, ::qs], u_sub[::qs, ::qs], v_sub[::qs, ::qs], scale=1000,
# width=.002, headlength=4, transform=ccrs.PlateCarree())
ax.quiver(lon[::qs, ::qs],
lat[::qs, ::qs],
u_sub_standardize.values[::qs, ::qs],
v_sub_standardize.values[::qs, ::qs],
scale=50,
width=.002,
headlength=4,
transform=ccrs.PlateCarree())
# add the seabreeze classification locations to the map
if summary:
if pt == 'full_grid':
offset = 1.75
else:
offset = .85
for key, values in map_values.items():
ax.scatter(values['lon'],
values['lat'],
c='magenta',
s=40,
zorder=15,
transform=ccrs.PlateCarree())
if add_text:
ax.text(values['lon'] - offset,
values['lat'],
'{} {}'.format(values['ws'], values['direction']),
transform=ccrs.PlateCarree(),
bbox=dict(facecolor='lightgray', alpha=1),
fontsize=8,
zorder=15)
plt.savefig(figname, dpi=200)
plt.close()
