def draw_precipitation_nws(ax,
prep,
map_extent=(73, 136, 17, 54),
gridlines=True):
"""
Draw NWS-style precipitation map.
http://jjhelmus.github.io/blog/2013/09/17/plotting-nsw-precipitation-data/
:param ax: `matplotlib.axes.Axes`, the `Axes` instance used for plotting.
:param prep: precipitation, dictionary:
{'lon': 1D array, 'lat': 1D array, 'data': 2D array}
:param map_extent: (lonmin, lonmax, latmin, latmax),
longitude and latitude range.
:param gridlines: bool, draw grid lines or not.
:return: plots dictionary.
:Example:
>>> plt.ioff()
>>> fig = plt.figure(figsize=(8.6, 6.2))
>>> fig.clf()
>>> ax = plt.axes((0.1, 0.08, 0.85, 0.92), projection=ccrs.PlateCarree())
>>> prep = {'lon': lon, 'lat': lat, 'data': rain}
>>> plots = draw_precipitation_nws(ax, prep, map_extent=[73, 136, 17, 54])
>>> ax.set_title('24h Accumulated Precipitation', fontsize=18, loc='left')
>>> cax = fig.add_axes([0.16, 0.08, 0.7, 0.03])
>>> cb = plt.colorbar(plots['prep'], cax=cax, orientation='horizontal',
>>> ticks=plots['prep'].norm.boundaries)
>>> cb.set_label('Precipitation (mm)', fontsize=10)
>>> cb.ax.tick_params(labelsize=10)
>>> plt.savefig('D:/plot.png')
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=1)
# plots container
plots = {}
# draw precipitation map
x, y = np.meshgrid(prep['lon'], prep['lat'])
cmap, norm = cm_precipitation_nws()
plots['prep'] = ax.pcolormesh(x,
y,
np.squeeze(prep['data']),
norm=norm,
cmap=cmap,
transform=datacrs)
# add grid lines
if gridlines:
add_gridlines(ax)
# return
return plots
def draw_qpf_nmc(ax, prep, stations=None, map_extent=(107., 123, 28, 43.)):
"""
Draw filled-contour QPF.
:param ax: ax: `matplotlib.axes.Axes`, the `Axes` instance.
:param prep: precipitation, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'data': 2D array}
optional, {'clevs': 1D array}
:param stations: station locations, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array}
:param map_extent: [lonmin, lonmax, latmin, latmax],
longitude and latitude range.
:return: plots dictionary.
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=1)
# plots container
plots = {}
# draw precipitation map
x, y = np.meshgrid(prep['lon'], prep['lat'])
if prep.get('clevs') is None:
clevs = [0.1, 10, 25, 50, 100, 250, 600]
cmap = plt.get_cmap("YlGnBu")
norm = mpl.colors.BoundaryNorm(clevs, cmap.N)
plots['prep'] = ax.contourf(x,
y,
np.squeeze(prep['data']),
clevs,
norm=norm,
cmap=cmap,
transform=datacrs)
# add station points
if stations is not None:
plots['stations'] = ax.scatter(stations['lon'],
stations['lat'],
transform=ccrs.PlateCarree())
# add grid lines
add_gridlines(ax)
# return
return plots
def fig_cldas_temp(indata,
figsize=12,
map_extent=(100, 125, 25, 45),
gridlines=False,
outfile=None,
title="CLDAS Temperature",
time=None):
"""Produce CLDAS temperature map figure.
Arguments:
indata {dictionary or xarray dataset} --
{'lon': 1D array, 'lat': 1D array, 'data': 2D array}
Keyword Arguments:
figsize {tuple or int} -- figure size (default: {12})
map_extent {tuple} -- (lonmin, lonmax, latmin, latmax) (default: {(100, 125, 25, 45)})
gridlines {bool} -- bool, draw grid lines or not. (default: {False})
outfile {string} -- save figure to outfile (default: {None})
title {string} -- figure title.
time {datetime} -- analysis time.
"""
# check data type
if isinstance(indata, xr.core.dataarray.DataArray):
data = {
'lon': indata.coords['lon'].values,
'lat': indata.coords['lat'].values,
'data': np.squeeze(indata.values)
}
else:
data = indata
# set data projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.0,
central_longitude=(map_extent[0] + map_extent[1]) / 2.0,
standard_parallels=[30., 60.])
# set figure
if isinstance(figsize, int):
ratio = (map_extent[3] - map_extent[2]) / (map_extent[1] -
map_extent[0])
figsize = (figsize, figsize * ratio * 0.8)
fig = plt.figure(figsize=figsize)
gs = mpl.gridspec.GridSpec(1,
2,
width_ratios=[1, .02],
bottom=.07,
top=.99,
hspace=0.01,
wspace=0.01)
ax = plt.subplot(gs[0], projection=plotcrs)
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='darkcyan', lw=1)
# set color maps
pos = np.array(
[-45, -30, -20, -10, -5, 0, 0, 5, 5, 10, 20, 20, 30, 30, 40, 45])
colormap = cm_temperature_nws(pos)
# draw CLDAS temperature
x, y = np.meshgrid(data['lon'], data['lat'])
pm = ax.pcolormesh(x,
y,
np.squeeze(data['data']),
cmap=colormap,
vmin=pos.min(),
vmax=pos.max(),
transform=datacrs)
# add title
plt.title(title, loc='left', fontsize=18)
if time is not None:
plt.title(time.strftime("%Y-%m-%dT%H"), loc='right', fontsize=18)
# add grid lines
if gridlines:
add_gridlines(ax)
# add color bar
cax = plt.subplot(gs[1])
cb = plt.colorbar(pm, cax=cax, orientation='vertical', extendrect='True')
cb.set_label('Temperature', size=12)
# return
if outfile is not None:
fig.savefig(outfile)
plt.close(fig)
return None
