def draw_low_level_wind(uv=None,
wsp=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# draw figure
plt.figure(figsize=(16, 9))
# set data projection
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
plt.title('[' + uv['model'] + '] ' + uv['lev'] + ' 风场, 风速',
loc='left',
fontsize=30)
datacrs = ccrs.PlateCarree()
#adapt to the map ratio
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
#adapt to the map ratio
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=105,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=105)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=105)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=105,
alpha=0.5)
# define return plots
plots = {}
# draw mean sea level pressure
if wsp is not None:
x, y = np.meshgrid(wsp['lon'], wsp['lat'])
z = np.squeeze(wsp['data'])
cmap = dk_ctables.cm_wind_speed_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['wsp'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=90,
transform=datacrs,
alpha=0.5)
# draw -hPa wind bards
if uv is not None:
x, y = np.meshgrid(uv['lon'], uv['lat'])
u = np.squeeze(uv['udata']) * 2.5
v = np.squeeze(uv['vdata']) * 2.5
#plots['uv'] = ax.barbs(
# x, y, u, v, length=6, regrid_shape=regrid_shape,
# transform=datacrs, fill_empty=False, sizes=dict(emptybarb=0.05),
# zorder=100)
x, y = np.meshgrid(uv['lon'], uv['lat'])
lw = 5 * wsp['data'] / wsp['data'].max()
plots['stream'] = ax.streamplot(x,
y,
uv['udata'],
uv['vdata'],
density=2,
color='r',
linewidth=lw,
zorder=100,
transform=datacrs)
spd_rtio = 0.03
for i in range(0, len(uv['lon']) - 1, 10):
for j in range(0, len(uv['lat']) - 1, 10):
ax.arrow(x[j, i],
y[j, i],
u[j, i] * spd_rtio,
v[j, i] * spd_rtio,
color='black',
zorder=100,
transform=datacrs,
width=0.05)
#ax.quiver(x[j,i], y[j,i], x[j,i]+u[j,i]*spd_rtio, y[j,i]+v[j,i]*spd_rtio, color='black', zorder=100, transform=datacrs)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=40)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
#http://earthpy.org/cartopy_backgroung.html
#C:\ProgramData\Anaconda3\Lib\site-packages\cartopy\data\raster\natural_earth
ax.background_img(name='RD', resolution='high')
#forecast information
bax = plt.axes([0.01, 0.835, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initial_time = pd.to_datetime(str(
uv['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=uv['fhour'])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5,
7.5,
'起报时间: ' + initial_time.strftime("%Y年%m月%d日%H时"),
size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 2.5, '预报时效: ' + str(uv['fhour']) + '小时', size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (wsp != None):
cax = plt.axes([0.11, 0.06, .86, .02])
cb = plt.colorbar(plots['wsp'],
cax=cax,
orientation='horizontal',
extend='max',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('(m/s)', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[0.85, 0.13, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=110,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[-0.01, 0.835, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '低层风_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(uv['fhour']) + '小时' + '.png',
dpi=200)
if (output_dir == None):
plt.show()
def draw_mslp_gust10m(gust=None,
mslp=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# draw figure
plt.figure(figsize=(16, 9))
# set data projection
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
plt.title('[' + mslp['model'] + '] ' + '海平面气压, ' + '逐6小时最大阵风 ',
loc='left',
fontsize=30)
datacrs = ccrs.PlateCarree()
#adapt to the map ratio
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
#adapt to the map ratio
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=105,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=105)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=105)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=105,
alpha=0.5)
# define return plots
plots = {}
# draw mean sea level pressure
if gust is not None:
x, y = np.meshgrid(gust['lon'], gust['lat'])
z = np.squeeze(gust['data'])
cmap = dk_ctables.cm_wind_speed_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['gust'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=90,
transform=datacrs,
alpha=0.5)
# draw -hPa geopotential height
if mslp is not None:
x, y = np.meshgrid(mslp['lon'], mslp['lat'])
clevs_mslp = np.arange(900, 1100, 2.5)
z = gaussian_filter(np.squeeze(mslp['data']), 5)
plots['mslp'] = ax.contour(x,
y,
z,
clevs_mslp,
colors='black',
linewidths=1,
transform=datacrs,
zorder=110)
plt.clabel(plots['mslp'],
inline=1,
fontsize=15,
fmt='%.0f',
colors='black')
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=40)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
#http://earthpy.org/cartopy_backgroung.html
#C:\ProgramData\Anaconda3\Lib\site-packages\cartopy\data\raster\natural_earth
ax.background_img(name='RD', resolution='high')
#forecast information
bax = plt.axes([0.01, 0.835, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initial_time = pd.to_datetime(str(
mslp['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=mslp['fhour'])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5,
7.5,
'起报时间: ' + initial_time.strftime("%Y年%m月%d日%H时"),
size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 2.5, '预报时效: ' + str(mslp['fhour']) + '小时', size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (gust != None):
cax = plt.axes([0.11, 0.06, .86, .02])
cb = plt.colorbar(plots['gust'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('风速 (m/s)', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[0.85, 0.13, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=110,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[-0.01, 0.835, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '海平面气压_逐6小时最大风速_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(mslp['fhour']) + '小时' + '.png',
dpi=200)
if (output_dir == None):
plt.show()
def draw_mslp_gust10m(gust=None,
mslp=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
# set font
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# set figure
plt.figure(figsize=(16, 9))
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
datacrs = ccrs.PlateCarree()
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
#draw data
plots = {}
if gust is not None:
x, y = np.meshgrid(gust['lon'], gust['lat'])
z = np.squeeze(gust['data'].values)
cmap = dk_ctables.cm_wind_speed_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
z[z < 7.9] = np.nan
plots['gust'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
vmin=7.9,
vmax=65,
zorder=1,
transform=datacrs,
alpha=0.5)
# draw -hPa geopotential height
if mslp is not None:
x, y = np.meshgrid(mslp['lon'], mslp['lat'])
clevs_mslp = np.arange(900, 1100, 2.5)
z = gaussian_filter(np.squeeze(mslp['data']), 5)
plots['mslp'] = ax.contour(x,
y,
z,
clevs_mslp,
colors='black',
linewidths=1,
transform=datacrs,
zorder=2)
plt.clabel(plots['mslp'],
inline=1,
fontsize=15,
fmt='%.0f',
colors='black')
#additional information
plt.title('[' + mslp.attrs['model'] + '] ' + '海平面气压, ' + '逐' +
'%i' % gust.attrs['t_gap'] + '小时最大阵风 ',
loc='left',
fontsize=30)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=3,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=3)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=3)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=3,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=4)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
#utl.add_cartopy_background(ax,name='RD')
ax.add_feature(cfeature.LAND, color='#F5E19F')
ax.add_feature(cfeature.OCEAN)
l, b, w, h = ax.get_position().bounds
#forecast information
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(mslp.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=mslp.coords['forecast_period'].values[0])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5, 7.5, '起报时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(mslp.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (gust != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['gust'],
cax=cax,
orientation='horizontal',
extend='max',
ticks=[
8.0, 10.8, 13.9, 17.2, 20.8, 24.5, 28.5, 32.7,
37, 41.5, 46.2, 51.0, 56.1, 61.3
])
cb.ax.tick_params(labelsize='x-large')
cb.set_label('风速 (m/s)', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=5,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '海平面气压_逐6小时最大风速_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(mslp.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
def draw_mslp_gust10m_uv10m(gust=None,
mslp=None,
uv10m=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
# set font
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# set figure
plt.figure(figsize=(16, 9))
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
datacrs = ccrs.PlateCarree()
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
#draw data
plots = {}
if gust is not None:
x, y = np.meshgrid(gust['lon'], gust['lat'])
z = np.squeeze(gust['data'].values)
cmap = dk_ctables.cm_wind_speed_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
z[z < 7.9] = np.nan
plots['gust'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
vmin=7.9,
vmax=65,
zorder=1,
transform=datacrs,
alpha=0.5)
ax.quiver(x,
y,
np.squeeze(uv10m['u10m'].values),
np.squeeze(uv10m['v10m'].values),
transform=datacrs,
regrid_shape=40,
width=0.001)
if mslp is not None:
x, y = np.meshgrid(mslp['lon'], mslp['lat'])
clevs_mslp = np.arange(900, 1100, 2.5)
z = gaussian_filter(np.squeeze(mslp['data']), 5)
plots['mslp'] = ax.contour(x,
y,
z,
clevs_mslp,
colors='black',
linewidths=1,
transform=datacrs,
zorder=2,
alpha=0.5)
cl = plt.clabel(plots['mslp'],
inline=1,
fontsize=15,
fmt='%.1f',
colors='black')
for t in cl:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()
])
#+画高低压中心
res = mslp['lon'].values[1] - mslp['lon'].values[0]
nwindow = int(9.5 / res)
mslp_hl = np.ma.masked_invalid(mslp['data'].values).squeeze()
local_min, local_max = utl.extrema(mslp_hl,
mode='wrap',
window=nwindow)
#Get location of extrema on grid
xmin, xmax, ymin, ymax = map_extent2
lons2d, lats2d = x, y
transformed = datacrs.transform_points(datacrs, lons2d, lats2d)
x = transformed[..., 0]
y = transformed[..., 1]
xlows = x[local_min]
xhighs = x[local_max]
ylows = y[local_min]
yhighs = y[local_max]
lowvals = mslp_hl[local_min]
highvals = mslp_hl[local_max]
yoffset = 0.022 * (ymax - ymin)
dmin = yoffset
#Plot low pressures
xyplotted = []
for x, y, p in zip(xlows, ylows, lowvals):
if x < xmax - yoffset and x > xmin + yoffset and y < ymax - yoffset and y > ymin + yoffset:
dist = [
np.sqrt((x - x0)**2 + (y - y0)**2) for x0, y0 in xyplotted
]
if not dist or min(dist) > dmin: #,fontweight='bold'
a = ax.text(x,
y,
'L',
fontsize=28,
ha='center',
va='center',
color='r',
fontweight='normal',
transform=datacrs)
b = ax.text(x,
y - yoffset,
repr(int(p)),
fontsize=14,
ha='center',
va='top',
color='r',
fontweight='normal',
transform=datacrs)
a.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.SimpleLineShadow(),
path_effects.Normal()
])
b.set_path_effects([
path_effects.Stroke(linewidth=1.0, foreground='black'),
path_effects.SimpleLineShadow(),
path_effects.Normal()
])
xyplotted.append((x, y))
#Plot high pressures
xyplotted = []
for x, y, p in zip(xhighs, yhighs, highvals):
if x < xmax - yoffset and x > xmin + yoffset and y < ymax - yoffset and y > ymin + yoffset:
dist = [
np.sqrt((x - x0)**2 + (y - y0)**2) for x0, y0 in xyplotted
]
if not dist or min(dist) > dmin:
a = ax.text(x,
y,
'H',
fontsize=28,
ha='center',
va='center',
color='b',
fontweight='normal',
transform=datacrs)
b = ax.text(x,
y - yoffset,
repr(int(p)),
fontsize=14,
ha='center',
va='top',
color='b',
fontweight='normal',
transform=datacrs)
a.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.SimpleLineShadow(),
path_effects.Normal()
])
b.set_path_effects([
path_effects.Stroke(linewidth=1.0, foreground='black'),
path_effects.SimpleLineShadow(),
path_effects.Normal()
])
xyplotted.append((x, y))
#-画高低压中心
#additional information
plt.title('[' + mslp.attrs['model'] + '] ' + '海平面气压, ' + '过去' +
'%i' % gust.attrs['t_gap'] + '小时10米最大阵风和10米平均风',
loc='left',
fontsize=30)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=3,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=3)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=3)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=3,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=4)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
#utl.add_cartopy_background(ax,name='RD')
ax.add_feature(cfeature.LAND, color='#F5E19F')
ax.add_feature(cfeature.OCEAN)
l, b, w, h = ax.get_position().bounds
#forecast information
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(mslp.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=mslp.coords['forecast_period'].values[0])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5, 7.5, '起报时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(mslp.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (gust != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['gust'],
cax=cax,
orientation='horizontal',
extend='max',
ticks=[
8.0, 10.8, 13.9, 17.2, 20.8, 24.5, 28.5, 32.7,
37, 41.5, 46.2, 51.0, 56.1, 61.3
])
cb.ax.tick_params(labelsize='x-large')
cb.set_label('风速 (m/s)', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=5,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '海平面气压_逐6小时最大风速_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(mslp.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
def draw_low_level_wind(uv=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
# set font
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# set figure
plt.figure(figsize=(16, 9))
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
datacrs = ccrs.PlateCarree()
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
# define return plots
plots = {}
# draw mean sea level pressure
if uv is not None:
x, y = np.meshgrid(uv['lon'], uv['lat'])
wsp = np.squeeze((uv['u'].values**2 + uv['v'].values**2)**0.5)
z = wsp
cmap = dk_ctables.cm_wind_speed_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['wsp'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.5)
# draw -hPa wind bards
if uv is not None:
x, y = np.meshgrid(uv['lon'], uv['lat'])
u = np.squeeze(uv['u'].values) * 2.5
v = np.squeeze(uv['v'].values) * 2.5
x, y = np.meshgrid(uv['lon'], uv['lat'])
lw = 5 * wsp / wsp.max()
plots['stream'] = ax.streamplot(x,
y,
u,
v,
density=2,
color='r',
linewidth=lw,
zorder=100,
transform=datacrs)
spd_rtio = 0.03
for i in range(0, len(uv['lon']) - 1, 10):
for j in range(0, len(uv['lat']) - 1, 10):
ax.arrow(x[j, i],
y[j, i],
u[j, i] * spd_rtio,
v[j, i] * spd_rtio,
color='black',
zorder=100,
transform=datacrs,
width=0.05)
plt.title('[' + uv.attrs['model'] + '] ' + uv.attrs['level'] + ' 风场, 风速',
loc='left',
fontsize=30)
#adapt to the map ratio
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
#adapt to the map ratio
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=2,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=2)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=2)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=2,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=3)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
utl.add_cartopy_background(ax, name='RD')
l, b, w, h = ax.get_position().bounds
#forecast information
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(str(
uv.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=uv.coords['forecast_period'].values[0])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5, 7.5, '起报时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(uv.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (wsp is not None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['wsp'],
cax=cax,
orientation='horizontal',
extend='max',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('(m/s)', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=4,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '低层风_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(uv.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()