def Horizontal_Pallete(figsize=(16, 9),
plotcrs=ccrs.PlateCarree(),
datacrs=ccrs.PlateCarree(),
map_extent=(60, 145, 15, 55),
title='',
forcast_info='',
add_china=True,
add_city=False,
add_background=True,
south_China_sea=True,
info_zorder=10,
title_fontsize=30):
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）
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')
fig = plt.figure(figsize=figsize)
if (plotcrs is None):
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)
#ax.set_extent(map_extent, crs=crs)
map_extent = adjust_map_ratio(ax, map_extent=map_extent, datacrs=datacrs)
plt.title(title, loc='left', fontsize=title_fontsize)
# add grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=info_zorder)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=info_zorder,
alpha=0.5)
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=info_zorder)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=info_zorder)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=info_zorder,
alpha=0.5)
if add_city:
small_city = False
if (map_extent[1] - map_extent[0] < 25):
small_city = True
utl.add_city_on_map(ax,
map_extent=map_extent,
size=12,
transform=datacrs,
zorder=info_zorder + 1,
small_city=small_city)
if add_background:
ax.add_feature(cfeature.OCEAN)
utl.add_cartopy_background(ax, name='RD')
if south_China_sea:
l, b, w, h = ax.get_position().bounds
utl.add_south_China_sea(pos=[l + w - 0.0875, b, .1, .2])
if forcast_info:
l, b, w, h = ax.get_position().bounds
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
bax.text(
2.2,
9.8,
forcast_info,
size=15,
va='top',
ha='left',
)
l, b, w, h = ax.get_position().bounds
utl.add_logo_extra_in_axes(pos=[l - 0.022, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
return fig, ax, map_extent
def draw_gh_uv_VVEL(gh=None,
uv=None,
VVEL=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('[' + gh.attrs['model'] + '] ' +
str(int(gh['level'].values[0])) + 'hPa 位势高度场, ' +
str(int(uv['level'].values[0])) + 'hPa 风场和垂直气压速度',
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=5,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=5,
alpha=0.5)
# define return plots
plots = {}
# draw mean sea level pressure
if VVEL is not None:
x, y = np.meshgrid(VVEL['lon'], VVEL['lat'])
clevs_VVEL = [0.1, 4, 13, 25, 60, 120]
clevs_VVEL = [
-30, -20, -10, -5, -2.5, -1, -0.5, 0.5, 1, 2.5, 5, 10, 20, 30
]
z = np.squeeze(VVEL['data'] / 10.)
cmap, norm = dk_ctables.cm_vertical_velocity_nws(pos=clevs_VVEL)
#cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['VVEL'] = ax.pcolormesh(x,
y,
z,
norm=norm,
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']) * 2.5
v = np.squeeze(uv['v']) * 2.5
plots['uv'] = ax.barbs(x,
y,
u.values,
v.values,
length=6,
regrid_shape=regrid_shape,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.05),
zorder=2)
# draw -hPa geopotential height
if gh is not None:
x, y = np.meshgrid(gh['lon'], gh['lat'])
clevs_gh = np.append(
np.append(
np.arange(0, 480, 4),
np.append(np.arange(480, 584, 8), np.arange(580, 604, 4))),
np.arange(604, 2000, 8))
plots['gh'] = ax.contour(x,
y,
np.squeeze(gh['data']),
clevs_gh,
colors='black',
linewidths=2,
transform=datacrs,
zorder=3)
plt.clabel(plots['gh'],
inline=1,
fontsize=20,
fmt='%.0f',
colors='black')
# 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')
#forecast information
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(
gh.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=gh.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(gh.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (VVEL != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['VVEL'],
cax=cax,
orientation='horizontal',
ticks=clevs_VVEL[:],
extend='max',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Vertical Velocity (0.1Pa/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=110,
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 + '高度场_风场_垂直气压速度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(gh.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
if (output_dir == None):
plt.show()
def draw_cumulated_precip_evo(
rain=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
fig = plt.figure(figsize=(16,9))
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)
# define return plots
plots = {}
# draw mean sea level pressure
if rain is not None:
x, y = np.meshgrid(rain['lon'], rain['lat'])
z=np.squeeze(rain['data'].values)
z[z<0.1]=np.nan
znan=np.zeros(shape=x.shape)
znan[:]=np.nan
cmap,norm=dk_ctables.cm_qpf_nws(atime=24)
cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['rain'] = ax.pcolormesh(
x,y,znan, norm=norm,
cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
#additional information
plt.title('['+rain.attrs['model']+'] '+
str(int(rain.coords['forecast_period'].values[0])-rain.attrs['t_gap'])+
'至'+str(int(rain.coords['forecast_period'].values[-1]))+'时效累积降水预报',
loc='left', fontsize=25)
ax.add_feature(cfeature.OCEAN)
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=1)
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
initTime = pd.to_datetime(
str(rain.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
#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')
# add color bar
cax=plt.axes([l,b-0.04,w,.02])
cb = plt.colorbar(plots['rain'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Precipitation (mm)',size=20)
fcst_time=initTime+timedelta(hours=rain.coords['forecast_period'].values[0])
bax=plt.axes([l,b+h-0.1,.25,.1])
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
fcst_time1=initTime+timedelta(hours=rain.coords['forecast_period'].values[0]-6)
bax.text(2.5, 7.5,'起始时间: '+fcst_time1.strftime("%Y年%m月%d日%H时"),size=15)
bax.text(2.5, 0.5,'www.nmc.cn',size=15)
valid_fhour=bax.text(2.5, 5,'截至时间: ',size=15)
txt_fhour=bax.text(2.5, 2.5,'预报时效: ',size=15)
utl.add_logo_extra_in_axes(pos=[l-0.02,b+h-0.1,.1,.1],which='nmc', size='Xlarge')
# 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=2,size=13,small_city=small_city)
def update(frame_number):
fcst_time2=initTime+timedelta(hours=rain.coords['forecast_period'].values[frame_number])
valid_fhour.set_text('截至时间: '+fcst_time2.strftime("%Y年%m月%d日%H时"))
txt_fhour.set_text('预报时效: '+str(int(rain.coords['forecast_period'].values[frame_number]))+'小时')
return ax.pcolormesh(
x,y,np.squeeze(z[frame_number,:,:]), norm=norm,
cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
nframes=rain['data'].shape[0]
animation1 = FuncAnimation(fig, update, frames=nframes,interval=1000)
# ffmpegpath = os.path.abspath(r"C:\Users\HEYGY\Desktop\ffmpeg-20200824-3477feb-win64-static\bin\ffmpeg.exe")
# import matplotlib
# matplotlib.rcParams["animation.ffmpeg_path"] = ffmpegpath
# writer = animation.FFMpegWriter()
# show figure
plt.subplots_adjust(top=1, bottom=0, right=0.93, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
if(output_dir != None):
animation1.save(output_dir+'累积降水演变_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(rain.coords['forecast_period'].values[-1]))+'小时_'+
'['+rain.attrs['model']+'] '+'.gif',writer='pillow')
if(output_dir == None):
#animation.save('rain.gif', fps=75, writer='imagemagick')
plt.show()
def draw_cumulated_precip(
rain=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
fig = plt.figure(figsize=(9,14))
camera = Camera(fig)
nframe=rain['data'].shape[0]
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)
# define return plots
plots = {}
# draw mean sea level pressure
if rain is not None:
x, y = np.meshgrid(rain['lon'], rain['lat'])
z=np.squeeze(rain['data'].values)
z[z<0.1]=np.nan
znan=np.zeros(shape=x.shape)
znan[:]=np.nan
# cmap,norm=dk_ctables.cm_qpf_nws(atime=None,
# pos=np.concatenate((
# np.array([0, 0.1, 0.5, 1]), np.arange(2.5, 25, 2.5),
# np.arange(25, 50, 5), np.arange(50, 150, 10),
# np.arange(150, 800, 50))))
cmap,norm=dk_ctables.cm_qpf_nws(atime=24)
cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['rain'] = ax.pcolormesh(
x,y,znan, norm=norm,
cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
#additional information
plt.title('['+rain.attrs['model']+'] '+
str(int(rain.coords['forecast_period'].values[0]))+'至'+str(int(rain.coords['forecast_period'].values[-1]))+'时效累积降水预报',
loc='left', fontsize=30)
ax.add_feature(cfeature.OCEAN)
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=1)
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
initTime = pd.to_datetime(
str(rain.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
#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')
# add color bar
cax=plt.axes([l,b-0.04,w,.02])
cb = plt.colorbar(plots['rain'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Precipitation (mm)',size=20)
fcst_time=initTime+timedelta(hours=rain.coords['forecast_period'].values[0])
bax=plt.axes([l,b+h-0.07,.45,.07])
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
fcst_time1=initTime+timedelta(hours=rain.coords['forecast_period'].values[0]-6)
bax.text(2.5, 7.5,'起始时间: '+fcst_time1.strftime("%Y年%m月%d日%H时"),size=15)
bax.text(2.5, 0.5,'www.nmc.cn',size=15)
valid_fhour=bax.text(2.5, 5,'截至时间: ',size=15)
txt_fhour=bax.text(2.5, 2.5,'预报时效: ',size=15)
utl.add_logo_extra_in_axes(pos=[l-0.0,b+h-0.085,.1,.1],which='nmc', size='Xlarge')
# 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=2,size=13,small_city=small_city)
fcst_time2=initTime+timedelta(hours=rain.coords['forecast_period'].values[-1])
valid_fhour.set_text('截至时间: '+fcst_time2.strftime("%Y年%m月%d日%H时"))
txt_fhour.set_text('预报时效: '+str(int(rain.coords['forecast_period'].values[-1]))+'小时')
ax.pcolormesh(
x,y,np.squeeze(z[-1,:,:]), norm=norm,
cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
# plt.draw()
plt.savefig(output_dir+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(rain.coords['forecast_period'].values[-1]))+'小时_'+rain.attrs['model']+'.png', dpi=200,bbox_inches='tight')
def draw_gh_rain(gh=None, rain=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)
# define return plots
plots = {}
# draw mean sea level pressure
if rain is not None:
x, y = np.meshgrid(rain['lon'], rain['lat'])
z=np.squeeze(rain['data'].values)
z[z<0.1]=np.nan
cmap,norm=dk_ctables.cm_qpf_nws(atime=rain.attrs['atime'])
cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['rain'] = ax.pcolormesh(
x,y,z, norm=norm,
cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
# draw -hPa geopotential height
if gh is not None:
x, y = np.meshgrid(gh['lon'], gh['lat'])
clevs_gh = np.append(np.append(np.arange(0, 480, 4),np.append(np.arange(480, 584, 8), np.arange(580, 604, 4))), np.arange(604, 2000, 8))
plots['gh'] = ax.contour(
x, y, np.squeeze(gh['data']), clevs_gh, colors='black',
linewidths=2, transform=datacrs, zorder=3)
plt.clabel(plots['gh'], inline=2, fontsize=20, fmt='%.0f',colors='black')
#additional information
plt.title('['+gh.attrs['model']+'] '+
str(int(gh['level'].values[0]))+'hPa 位势高度场, '+
str(int(rain.attrs['atime']))+'小时降水',
loc='left', fontsize=30)
ax.add_feature(cfeature.OCEAN)
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=1)
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(gh.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
fcst_time=initTime+timedelta(hours=gh.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(gh.coords['forecast_period'].values[0]))+'小时'+'(降水'+str(int(gh.coords['forecast_period'].values[0]+12))+'小时)',size=15)
plt.text(2.5, 0.5,'www.nmc.cn',size=15)
# add color bar
if(rain != None):
cax=plt.axes([l,b-0.04,w,.02])
cb = plt.colorbar(plots['rain'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label(str(int(rain.attrs['atime']))+'h precipitation (mm)',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=2,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+'高度场_降水_预报_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(gh.coords['forecast_period'].values[0]))+'小时'+'.png', dpi=200)
if(output_dir == None):
plt.show()
def draw_Rain_evo(
rain=None,fcs_lvl=4,
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）
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')
# 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.])
ax = plt.axes([0.01,0.1,.98,.84], projection=plotcrs)
datacrs = ccrs.PlateCarree()
map_extent2=utl.adjust_map_ratio(ax,map_extent=map_extent,datacrs=datacrs)
plt.title('['+rain.attrs['model']+'] 预报逐'+
str(rain.attrs['t_gap'])+'小时'+str(fcs_lvl)+'mm降水范围演变',
loc='left', fontsize=30)
#draw data
plots = {}
if rain is not None:
x, y = np.meshgrid(rain['lon'], rain['lat'])
for itime in range(0,len(rain['time'].values)):
z=np.squeeze(rain['data'].values[itime,:,:])
z[z<=0.1]=np.nan
initTime = pd.to_datetime(str(rain.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
labels=(initTime+timedelta(hours=rain.coords['forecast_period'].values[itime])).strftime("%m月%d日%H时")
cmap=mpl.cm.jet
per_color=utl.get_part_clev_and_cmap(cmap=cmap,clev_range=[0,len(rain['time'].values)],clev_slt=itime)
ax.contourf(
x,y,z, levels=[fcs_lvl,800],
colors=per_color, zorder=3,transform=datacrs,
alpha=0.2+itime*((1-0.2)/len(rain['time'].values)))
if(itime == 0):
label_handles = [mpatches.Patch(color=per_color.reshape(4),
alpha=0.2+itime*((1-0.2)/len(rain['time'].values)), label=labels)]
else:
label_handles.append(mpatches.Patch(color=per_color.reshape(4),alpha=0.2+itime*((1-0.2)/len(rain['time'].values)), label=labels))
leg = plt.legend(handles=label_handles, loc=3,framealpha=1)
#additional information
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(
ax, name='coastline', edgecolor='gray', lw=0.8, zorder=1,alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(
ax, name='province', edgecolor='gray', lw=0.5, zorder=1)
utl.add_china_map_2cartopy_public(
ax, name='nation', edgecolor='black', lw=0.8, zorder=1)
utl.add_china_map_2cartopy_public(
ax, name='river', edgecolor='#74b9ff', lw=0.8, zorder=1,alpha=0.5)
# grid lines
gl = ax.gridlines(
crs=datacrs, linewidth=2, color='gray', alpha=0.5, linestyle='--', zorder=1)
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(rain.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
fcst_time=initTime+timedelta(hours=rain.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,'起始时间: '+
(initTime+timedelta(hours=rain.coords['forecast_period'].values[0])).strftime("%Y年%m月%d日%H时"),size=15)
plt.text(2.5, 2.5,'终止时间: '+
(initTime+timedelta(hours=rain.coords['forecast_period'].values[1])).strftime("%Y年%m月%d日%H时"),size=15)
plt.text(2.5, 0.5,'www.nmc.cn',size=15)
# add color bar
# 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=110,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_降水_预报_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(rain.coords['forecast_period'].values[0]))+'小时'+'.png', dpi=200)
if(output_dir == None):
plt.show()
def draw_gh_uv_mslp(gh=None,
uv=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('[' + gh.attrs['model'] + '] ' +
str(int(gh['level'].values[0])) + 'hPa 位势高度场, ' +
str(int(uv['level'].values[0])) + 'hPa 风场, 海平面气压场',
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=5,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=5,
alpha=0.5)
# define return plots
plots = {}
# draw mean sea level pressure
if mslp is not None:
x, y = np.meshgrid(mslp['lon'], mslp['lat'])
clevs_mslp = np.arange(960, 1065, 5)
cmap = guide_cmaps(26)
plots['mslp'] = ax.contourf(x,
y,
np.squeeze(mslp['data']),
clevs_mslp,
cmap=cmap,
alpha=0.8,
zorder=1,
transform=datacrs)
#+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
# draw -hPa wind bards
if uv is not None:
x, y = np.meshgrid(uv['lon'], uv['lat'])
u = np.squeeze(uv['u']) * 2.5
v = np.squeeze(uv['v']) * 2.5
plots['uv'] = ax.barbs(x,
y,
u.values,
v.values,
length=6,
regrid_shape=regrid_shape,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.05),
zorder=2)
# draw -hPa geopotential height
if gh is not None:
x, y = np.meshgrid(gh['lon'], gh['lat'])
clevs_gh = np.append(
np.append(
np.arange(0, 480, 4),
np.append(np.arange(480, 584, 8), np.arange(580, 604, 4))),
np.arange(604, 2000, 8))
linewidths_gh = np.zeros(clevs_gh.shape) + 2
idx_588 = np.where(clevs_gh == 588)
linewidths_gh[idx_588[0]] = 4
plots['gh'] = ax.contour(x,
y,
np.squeeze(gh['data']),
clevs_gh,
colors='purple',
linewidths=linewidths_gh,
transform=datacrs,
zorder=3)
ax.clabel(plots['gh'],
inline=1,
fontsize=20,
fmt='%.0f',
colors='black')
# 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')
#forecast information
l, b, w, h = ax.get_position().bounds
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(
gh.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=gh.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(gh.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['mslp'],
cax=cax,
orientation='horizontal',
ticks=clevs_mslp[:-1],
extend='max',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Mean sea level pressure (hPa)', 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=6,
size=15,
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 + '最高温度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(gh.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
if (output_dir == None):
plt.show()
def draw_Miller_Composite_Chart(fcst_info=None,
u_300=None,
v_300=None,
u_500=None,
v_500=None,
u_850=None,
v_850=None,
pmsl_change=None,
hgt_500_change=None,
Td_dep_700=None,
Td_sfc=None,
pmsl=None,
lifted_index=None,
vort_adv_500_smooth=None,
map_extent=(50, 150, 0, 65),
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 = {}
lons = fcst_info['lon']
lats = fcst_info['lat']
cs1 = ax.contour(lons,
lats,
lifted_index,
range(-8, -2, 2),
transform=ccrs.PlateCarree(),
colors='red',
linewidths=0.75,
linestyles='solid',
zorder=7)
cs1.clabel(fontsize=10,
inline=1,
inline_spacing=7,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# Plot Surface pressure falls
cs2 = ax.contour(lons,
lats,
pmsl_change.to('hPa'),
range(-10, -1, 4),
transform=ccrs.PlateCarree(),
colors='k',
linewidths=0.75,
linestyles='dashed',
zorder=6)
cs2.clabel(fontsize=10,
inline=1,
inline_spacing=7,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# Plot 500-hPa height falls
cs3 = ax.contour(lons,
lats,
hgt_500_change,
range(-60, -29, 15),
transform=ccrs.PlateCarree(),
colors='k',
linewidths=0.75,
linestyles='solid',
zorder=5)
cs3.clabel(fontsize=10,
inline=1,
inline_spacing=7,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# Plot surface pressure
ax.contourf(lons,
lats,
pmsl.to('hPa'),
range(990, 1011, 20),
alpha=0.5,
transform=ccrs.PlateCarree(),
colors='yellow',
zorder=1)
# Plot surface dewpoint
ax.contourf(lons,
lats,
Td_sfc.to('degF'),
range(65, 76, 10),
alpha=0.4,
transform=ccrs.PlateCarree(),
colors=['green'],
zorder=2)
# Plot 700-hPa dewpoint depression
ax.contourf(lons,
lats,
Td_dep_700,
range(15, 46, 30),
alpha=0.5,
transform=ccrs.PlateCarree(),
colors='tan',
zorder=3)
# Plot Vorticity Advection
ax.contourf(lons,
lats,
vort_adv_500_smooth,
range(5, 106, 100),
alpha=0.5,
transform=ccrs.PlateCarree(),
colors='BlueViolet',
zorder=4)
# Define a skip to reduce the barb point density
skip_300 = (slice(None, None, 12), slice(None, None, 12))
skip_500 = (slice(None, None, 10), slice(None, None, 10))
skip_850 = (slice(None, None, 8), slice(None, None, 8))
x, y = np.meshgrid(fcst_info['lon'], fcst_info['lat'])
# 300-hPa wind barbs
jet300 = ax.barbs(x[skip_300],
y[skip_300],
u_300[skip_300].m,
v_300[skip_300].m,
length=6,
transform=ccrs.PlateCarree(),
color='green',
zorder=10,
label='300-hPa Jet Core Winds (m/s)')
# 500-hPa wind barbs
jet500 = ax.barbs(x[skip_500],
y[skip_500],
u_500[skip_500].m,
v_500[skip_500].m,
length=6,
transform=ccrs.PlateCarree(),
color='blue',
zorder=9,
label='500-hPa Jet Core Winds (m/s)')
# 850-hPa wind barbs
jet850 = ax.barbs(x[skip_850],
y[skip_850],
u_850[skip_850].m,
v_850[skip_850].m,
length=6,
transform=ccrs.PlateCarree(),
color='k',
zorder=8,
label='850-hPa Jet Core Winds (m/s)')
#additional information
plt.title('[' + fcst_info['model'] + '] ' + 'Miller 综合分析图',
loc='left',
fontsize=30)
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)
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))
utl.add_cartopy_background(ax, name='RD')
l, b, w, h = ax.get_position().bounds
# Legend
purple = mpatches.Patch(color='BlueViolet',
label='Cyclonic Absolute Vorticity Advection')
yellow = mpatches.Patch(color='yellow', label='Surface MSLP < 1010 hPa')
green = mpatches.Patch(color='green', label='Surface Td > 65 F')
tan = mpatches.Patch(color='tan',
label='700 hPa Dewpoint Depression > 15 C')
red_line = lines.Line2D([], [], color='red', label='Best Lifted Index (C)')
dashed_black_line = lines.Line2D(
[], [],
linestyle='dashed',
color='k',
label='12-hr Surface Pressure Falls (hPa)')
black_line = lines.Line2D([], [],
linestyle='solid',
color='k',
label='12-hr 500-hPa Height Falls (m)')
leg = plt.legend(handles=[
jet300, jet500, jet850, dashed_black_line, black_line, red_line,
purple, tan, green, yellow
],
loc=3,
title='Composite Analysis Valid: ',
framealpha=1)
leg.set_zorder(100)
#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(
fcst_info['forecast_reference_time'])).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(hours=fcst_info['forecast_period'])
#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(fcst_info['forecast_period']) + '小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# 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=110,
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 + 'Miller_综合图_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(fcst_info['forecast_period']) + '小时' + '.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
def draw_PV_Div_uv(pv=None,
uv=None,
div=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=False,
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('[' + pv.attrs['model'] + '] ' + str(int(pv['level'])) +
'hPa 位涡扰动, 风场, 散度',
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=5,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=5)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=5,
alpha=0.5)
# define return plots
plots = {}
# draw mean sea level pressure
if div is not None:
x, y = np.meshgrid(div['lon'], div['lat'])
z = np.squeeze(div['data'])
clevs_div = np.arange(-15, 16, 1)
# plots['div'] = ax.contourf(
# x, y, z*1e5,clevs_div,cmap=plt.cm.PuOr,
# transform=datacrs,alpha=0.5, zorder=1,extend='both')
cmap = dk_ctables2.ncl_cmaps('hotcolr_19lev')
plots['div'] = ax.contourf(x,
y,
z * 1e5,
levels=clevs_div[:],
vmin=-15,
vmax=15,
cmap=cmap,
transform=datacrs,
alpha=0.5,
zorder=1,
extend='both')
# 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
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=2)
# draw -hPa geopotential height
if pv is not None:
x, y = np.meshgrid(pv['lon'], pv['lat'])
clevs_pv = np.arange(4, 25, 1)
plots['pv'] = ax.contour(x,
y,
np.squeeze(pv['data']) * 1e6,
clevs_pv,
colors='black',
linewidths=2,
transform=datacrs,
zorder=3)
plt.clabel(plots['pv'],
inline=1,
fontsize=20,
fmt='%.0f',
colors='black')
# 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')
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(
pv['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(hours=pv['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(pv.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (div != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['div'],
cax=cax,
orientation='horizontal',
ticks=clevs_div[:],
extend='both',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Divergence ($10^5$ s$^{-1}$)', 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=110,
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 + '位涡_风场_散度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(pv.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
def draw_isentropic_uv(isentrh=None,
isentuv=None,
isentprs=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=False,
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))
# 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.])
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 isentrh is not None:
x, y = np.meshgrid(isentrh['lon'], isentrh['lat'])
z = np.squeeze(isentrh['data'])
clevs_rh = range(10, 106, 5)
plots['isentrh'] = ax.contourf(x,
y,
z,
clevs_rh,
cmap=plt.cm.gist_earth_r,
transform=datacrs,
alpha=0.5,
extend='both')
# draw -hPa wind bards
if isentuv is not None:
x, y = np.meshgrid(isentuv['lon'], isentuv['lat'])
u = np.squeeze(isentuv['isentu'].values) * 2.5
v = np.squeeze(isentuv['isentv'].values) * 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=20)
# draw -hPa geopotential height
if isentprs is not None:
x, y = np.meshgrid(isentprs['lon'], isentprs['lat'])
clevs_isentprs = np.arange(0, 1000, 25)
z = gaussian_filter(np.squeeze(isentprs['data']), 5)
plots['isentprs'] = ax.contour(x,
y,
z,
clevs_isentprs,
colors='black',
linewidths=2,
transform=datacrs,
zorder=30)
plt.clabel(plots['isentprs'],
inline=1,
fontsize=20,
fmt='%.0f',
colors='black')
#additional information
plt.title('[' + isentrh.attrs['model'] + '] ' +
str(isentrh['level'].values) + '等熵面 风场 相对湿度 气压',
loc='left',
fontsize=30)
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)
# 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))
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(isentrh.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=isentrh.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(isentrh.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (isentrh != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['isentrh'],
cax=cax,
orientation='horizontal',
ticks=clevs_rh[:],
extend='both',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Relative Humidity (%)', 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=110,
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 + isentrh['lev'] + '等熵面分析_相对湿度_风场_气压_预报_' +
'起报时间_' + initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(isentrh.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200)
if (output_dir == None):
plt.show()
def draw_T2m_mslp_uv10m(t2m=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）
# draw 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)
# define return plots
plots = {}
# draw mean sea level pressure
if t2m is not None:
x, y = np.meshgrid(t2m['lon'], t2m['lat'])
z = np.squeeze(t2m['data'])
cmap = dk_ctables.cm_high_temperature_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['t2m'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.5)
if uv10m is not None:
x, y = np.meshgrid(uv10m['lon'].values, uv10m['lat'].values)
u = np.squeeze(uv10m['u10m'].values) * 2.5
v = np.squeeze(uv10m['v10m'].values) * 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=2,
color='white')
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=3)
cl = ax.clabel(plots['mslp'],
inline=1,
fontsize=20,
fmt='%.0f',
colors='black')
#additional information
plt.title('[' + mslp.attrs['model'] + '] ' + '海平面气压, ' + '10米风场, ' +
'2米 温度',
loc='left',
fontsize=30)
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=4,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=4,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=5)
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(
mslp['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(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 (t2m != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['t2m'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Temperature at 2m Above Ground (' + u'°C' + ')', 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=6,
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_10米风场_2米温度_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_T_2m(T_2m=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.])
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
datacrs = ccrs.PlateCarree()
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
# define return plots
plots = {}
if T_2m is not None:
x, y = np.meshgrid(T_2m['lon'], T_2m['lat'])
z = np.squeeze(T_2m['data'])
cmap = dk_ctables.cm_temp()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['T_2m'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.5,
vmin=-45,
vmax=45)
z = gaussian_filter(z, 5)
plots['T_2m_zero'] = ax.contour(x,
y,
z,
levels=[0],
colors='#232B99',
linewidths=2,
transform=datacrs,
zorder=2)
cl_zero = plt.clabel(plots['T_2m_zero'],
inline=1,
fontsize=15,
fmt='%i',
colors='#232B99')
for t in cl_zero:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()
])
plots['T_2m_35'] = ax.contour(x,
y,
z,
levels=[35, 37, 40],
colors=['#FF8F00', '#FF6200', '#FF0000'],
linewidths=2,
transform=datacrs,
zorder=2)
cl_35 = plt.clabel(plots['T_2m_35'],
inline=1,
fontsize=15,
fmt='%i',
colors='#FF0000')
for t in cl_35:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()
])
plt.title('[' + T_2m.attrs['model'] + ']' + ' ' + T_2m.attrs['title'],
loc='left',
fontsize=30)
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=4,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=4,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=5)
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(T_2m.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=T_2m.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(T_2m.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (T_2m != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['T_2m'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label(u'°C', 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_and_number_on_map(ax,
data=T_2m,
map_extent=map_extent2,
transform=datacrs,
zorder=6,
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 + '最低温度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(T_2m.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()
