def draw_Crosssection_Wind_Theta_e_div(
cross_div3d=None, cross_Theta_e=None, cross_u=None,
cross_v=None,cross_terrain=None,
gh=None,
h_pos=None,st_point=None,ed_point=None,
levels=None,map_extent=(50, 150, 0, 65),lw_ratio=None,
output_dir=None):
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）
initTime = pd.to_datetime(
str(cross_Theta_e['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
fcst_time=initTime+timedelta(hours=gh['forecast_period'].values[0])
fig = plt.figure(1, figsize=(lw_ratio[0], lw_ratio[1]))
ax = plt.axes()
absv_contour = ax.contourf(cross_div3d['lon'], cross_div3d['level'], cross_div3d['data']*1e6,
levels=range(-100, 100, 1), cmap=dk_ctables2.ncl_cmaps('GMT_haxby'),extend='both')
absv_colorbar = fig.colorbar(absv_contour)
# Plot potential temperature using contour, with some custom labeling
Theta_e_contour = ax.contour(cross_Theta_e['lon'], cross_Theta_e['level'],cross_Theta_e.values,
levels=np.arange(250, 450, 5), colors='k', linewidths=2)
Theta_e_contour.clabel(np.arange(250, 450, 5), fontsize=20, colors='k', inline=1,
inline_spacing=8, fmt='%i', rightside_up=True, use_clabeltext=True)
wind_slc_vert = list(range(0, len(levels), 1))
wind_slc_horz = slice(5, 100, 5)
ax.barbs(cross_u['lon'][wind_slc_horz], cross_u['level'][wind_slc_vert],
cross_u['t_wind'][wind_slc_vert, wind_slc_horz]*2.5,
cross_v['n_wind'][wind_slc_vert, wind_slc_horz]*2.5, color='k')
startcolor = '#8B4513' #棕色
endcolor='#DAC2AD' #绿
cmap2 = col.LinearSegmentedColormap.from_list('own3',[endcolor,startcolor])
# extra arguments are N=256, gamma=1.0
cm.register_cmap(cmap=cmap2)
terrain_contour = ax.contourf(cross_terrain['lon'], cross_terrain['level'], cross_terrain.values,
levels=np.arange(0, 500, 1), cmap=cm.get_cmap('own3'),zorder=100)
# Adjust the y-axis to be logarithmic
ax.set_yscale('symlog')
ax.set_yticklabels(np.arange(levels[0], levels[-1], -100))
ax.set_ylim(levels[0], levels[-1])
ax.set_yticks(np.arange(levels[0], levels[-1], -100))
# Define the CRS and inset axes
data_crs = ccrs.PlateCarree()
ax_inset = fig.add_axes(h_pos, projection=data_crs)
ax_inset.set_extent(map_extent, crs=data_crs)
# Plot geopotential height at 500 hPa using xarray's contour wrapper
ax_inset.contour(gh['lon'], gh['lat'], np.squeeze(gh['data']),
levels=np.arange(500, 600, 4), cmap='inferno')
# Plot the path of the cross section
endpoints = data_crs.transform_points(ccrs.Geodetic(),
*np.vstack([st_point, ed_point]).transpose()[::-1])
ax_inset.scatter(endpoints[:, 0], endpoints[:, 1], c='k', zorder=2)
ax_inset.plot(cross_u['lon'], cross_u['lat'], c='k', zorder=2)
# Add geographic features
ax_inset.coastlines()
utl.add_china_map_2cartopy_public(
ax_inset, name='province', edgecolor='black', lw=0.8, zorder=105)
# Set the titles and axes labels
ax_inset.set_title('')
ax.set_title('相当位温, 水平散度, 沿剖面风', loc='right', fontsize=20)
ax.set_ylabel('Pressure (hPa)')
ax.set_xlabel('Longitude')
absv_colorbar.set_label('水平散度 (dimensionless)')
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')
bax=fig.add_axes([0.10,0.88,.25,.07],facecolor='#FFFFFFCC')
bax.axis('off')
#bax.set_yticks([])
#bax.set_xticks([])
bax.axis([0, 10, 0, 10])
plt.text(2.5, 7.5,'起报时间: '+initTime.strftime("%Y年%m月%d日%H时"),size=11)
plt.text(2.5, 5,'预报时间: '+fcst_time.strftime("%Y年%m月%d日%H时"),size=11)
plt.text(2.5, 2.5,'预报时效: '+str(int(gh['forecast_period'].values[0]))+'小时',size=11)
plt.text(2.5, 0.5,'www.nmc.cn',size=11)
utl.add_logo_extra_in_axes(pos=[0.1,0.88,.07,.07],which='nmc', size='Xlarge')
# show figure
if(output_dir != None):
plt.savefig(output_dir+'相当位温_水平散度_沿剖面风_预报_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(gh['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):
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('['+isentrh['model']+'] '+
isentrh['lev']+'等熵面 风场 相对湿度 气压',
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 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']) * 2.5
v = np.squeeze(isentuv['isentv']) * 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')
# 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])
initTime = pd.to_datetime(
str(isentrh['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time=initTime+timedelta(hours=isentrh['fhour'])
#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(isentrh['fhour'])+'小时',size=15)
plt.text(2.5, 0.5,'www.nmc.cn',size=15)
# add color bar
if(isentrh != None):
cax=plt.axes([0.11,0.06,.86,.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=[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+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['fhour'])+'小时'+'.png', dpi=200)
if(output_dir == None):
plt.show()
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_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_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_Crosssection_Wind_Temp_RH(cross_rh=None,
cross_Temp=None,
cross_u=None,
cross_v=None,
cross_terrain=None,
gh=None,
h_pos=None,
st_point=None,
ed_point=None,
levels=None,
map_extent=(50, 150, 0, 65),
model=None,
output_dir=None):
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）
initial_time = pd.to_datetime(
str(cross_Temp['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=gh['forecast_period'].values[0])
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
cross_rh['data'].values[cross_rh['data'].values > 100] = 100
# example 2: use the "fromList() method
startcolor = '#1E90FF' #蓝色
midcolor = '#F1F1F1' #白色
endcolor = '#696969' #灰色
cmap2 = col.LinearSegmentedColormap.from_list(
'own2', [startcolor, midcolor, endcolor])
# extra arguments are N=256, gamma=1.0
cm.register_cmap(cmap=cmap2)
# we can skip name here as it was already defined
rh_contour = ax.contourf(cross_rh['lon'],
cross_rh['level'],
cross_rh['data'],
levels=np.arange(0, 101, 0.5),
cmap=cm.get_cmap('own2'))
rh_colorbar = fig.colorbar(rh_contour, ticks=[20, 40, 60, 80, 100])
# Plot potential temperature using contour, with some custom labeling
Temp_contour = ax.contour(cross_Temp['lon'],
cross_Temp['level'],
cross_Temp['data'].values,
levels=np.arange(-100, 100, 2),
colors='#A0522D',
linewidths=1)
Temp_contour.clabel(np.arange(-100, 100, 2),
fontsize=16,
colors='#A0522D',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
Temp_zero_contour = ax.contour(cross_Temp['lon'],
cross_Temp['level'],
cross_Temp['data'].values,
levels=[0],
colors='k',
linewidths=3)
Temp_zero_contour.clabel([0],
fontsize=22,
colors='k',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
wind_slc_vert = list(range(0, len(levels), 1))
wind_slc_horz = slice(5, 100, 5)
ax.barbs(cross_u['lon'][wind_slc_horz],
cross_u['level'][wind_slc_vert],
cross_u['t_wind'][wind_slc_vert, wind_slc_horz] * 2.5,
cross_v['n_wind'][wind_slc_vert, wind_slc_horz] * 2.5,
color='k')
startcolor = '#8B4513' #棕色
endcolor = '#DAC2AD' #绿
cmap2 = col.LinearSegmentedColormap.from_list('own3',
[endcolor, startcolor])
# extra arguments are N=256, gamma=1.0
cm.register_cmap(cmap=cmap2)
terrain_contour = ax.contourf(cross_terrain['lon'],
cross_terrain['level'],
cross_terrain.values,
levels=np.arange(0, 200, 1),
cmap=cm.get_cmap('own3'),
zorder=100)
# Adjust the y-axis to be logarithmic
ax.set_yscale('symlog')
ax.set_yticklabels(np.arange(levels[0], levels[-1], -100))
ax.set_ylim(levels[0], levels[-1])
ax.set_yticks(np.arange(levels[0], levels[-1], -100))
# Define the CRS and inset axes
data_crs = ccrs.PlateCarree()
ax_inset = fig.add_axes(h_pos, projection=data_crs)
ax_inset.set_extent(map_extent, crs=data_crs)
# Plot geopotential height at 500 hPa using xarray's contour wrapper
ax_inset.contour(gh['lon'],
gh['lat'],
np.squeeze(gh['data']),
levels=np.arange(500, 600, 4),
cmap='inferno')
# Plot the path of the cross section
endpoints = data_crs.transform_points(
ccrs.Geodetic(),
*np.vstack([st_point, ed_point]).transpose()[::-1])
ax_inset.scatter(endpoints[:, 0], endpoints[:, 1], c='k', zorder=2)
ax_inset.plot(cross_u['lon'], cross_u['lat'], c='k', zorder=2)
# Add geographic features
ax_inset.coastlines()
utl.add_china_map_2cartopy_public(ax_inset,
name='province',
edgecolor='black',
lw=0.8,
zorder=105)
# Set the titles and axes labels
ax_inset.set_title('')
ax.set_title('[' + model + '] ' + '温度, 相对湿度, 水平风场',
loc='right',
fontsize=25)
ax.set_ylabel('Pressure (hPa)')
ax.set_xlabel('Longitude')
rh_colorbar.set_label('Relative Humidity (%)')
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')
bax = fig.add_axes([0.10, 0.88, .25, .07], facecolor='#FFFFFFCC')
bax.axis('off')
#bax.set_yticks([])
#bax.set_xticks([])
bax.axis([0, 10, 0, 10])
plt.text(2.5,
7.5,
'起报时间: ' + initial_time.strftime("%Y年%m月%d日%H时"),
size=11)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=11)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(gh['forecast_period'].values[0])) + '小时',
size=11)
plt.text(2.5, 0.5, 'www.nmc.cn', size=11)
utl.add_logo_extra_in_axes(pos=[0.1, 0.88, .07, .07],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '温度_相对湿度_水平风场_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(int(gh['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_sta_skewT(p=None,
T=None,
Td=None,
wind_speed=None,
wind_dir=None,
u=None,
v=None,
fcst_info=None,
output_dir=None):
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
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）
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot.
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
# Calculate LCL height and plot as black dot. Because `p`'s first value is
# ~1000 mb and its last value is ~250 mb, the `0` index is selected for
# `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted,
# i.e. start from low value, 250 mb, to a high value, 1000 mb, the `-1` index
# should be selected.
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
skew.plot(p, prof, 'k', linewidth=2)
# Shade areas of CAPE and CIN
skew.shade_cin(p, T, prof)
skew.shade_cape(p, T, prof)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
#forecast information
bax = plt.axes([0.12, 0.88, .25, .07], facecolor='#FFFFFFCC')
bax.axis('off')
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(str(
fcst_info['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
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=11)
plt.text(2.5,
5.0,
'[' + str(fcst_info.attrs['model']) + '] ' +
str(int(fcst_info['forecast_period'].values[0])) + '小时预报探空',
size=11)
plt.text(2.5,
2.5,
'预报点: ' + str(fcst_info.attrs['points']['lon']) + ', ' +
str(fcst_info.attrs['points']['lat']),
size=11)
plt.text(2.5, 0.5, 'www.nmc.cn', size=11)
utl.add_logo_extra_in_axes(pos=[0.1, 0.88, .07, .07],
which='nmc',
size='Xlarge')
# Show the plot
if (output_dir != None):
plt.savefig(
output_dir + '时间剖面产品_起报时间_' +
str(fcst_info['forecast_reference_time'].values)[0:13] + '_预报时效_' +
str(int(fcst_info.attrs['forecast_period'].values)) + '.png',
dpi=200,
bbox_inches='tight')
else:
plt.show()
def draw_point_wind(U=None,
V=None,
model=None,
output_dir=None,
points=None,
time_info=None,
extra_info=None):
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')
initTime = pd.to_datetime(str(
time_info['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
# draw figure
fig = plt.figure(figsize=(12, 12))
# draw main figure
#10米风——————————————————————————————————————
ax = plt.axes([0.1, 0.2, .8, .7])
utl.add_public_title_sta(title=model + '预报 ' + extra_info['point_name'] +
' [' + str(points['lon'][0]) + ',' +
str(points['lat'][0]) + ']',
initTime=initTime,
fontsize=21)
for ifhour in time_info['forecast_period'].values:
if (ifhour == time_info['forecast_period'].values[0]):
uv_t = (initTime + timedelta(hours=ifhour))
else:
uv_t = np.append(uv_t, (initTime + timedelta(hours=ifhour)))
wsp = (U**2 + V**2)**0.5
ax.plot(uv_t, np.squeeze(wsp), c='#40C4FF', linewidth=3)
if (extra_info['drw_thr'] == True):
ax.plot([uv_t[0], uv_t[-1]], [11, 11],
c='red',
label='警戒风速',
linewidth=1)
ax.barbs(uv_t,
wsp,
U,
V,
fill_empty=True,
color='gray',
barb_increments={
'half': 2,
'full': 4,
'flag': 20
})
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
plt.xlim(uv_t[0], uv_t[-1])
# add legend
ax.legend(fontsize=15, loc='upper right')
ax.tick_params(length=10)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
ax.tick_params(axis='y', labelsize=15)
ax.tick_params(axis='x', labelsize=15)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
ax.set_ylabel('风速 (m/s)', fontsize=15)
utl.add_logo_extra_in_axes(pos=[0.1, 0.8, .1, .1],
which='nmc',
size='Xlarge')
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
isExists = os.path.exists(output_dir)
if not isExists:
os.makedirs(output_dir)
output_dir2 = output_dir + model + '_起报时间_' + initTime.strftime(
"%Y年%m月%d日%H时") + '/'
if (os.path.exists(output_dir2) == False):
os.makedirs(output_dir2)
plt.savefig(output_dir2 + model + '_' + extra_info['point_name'] +
'_' + extra_info['output_head_name'] +
initTime.strftime("%Y%m%d%H") + '00' +
extra_info['output_tail_name'] + '.jpg',
dpi=200,
bbox_inches='tight')
else:
plt.show()
def draw_point_fcst(t2m=None,
u10m=None,
v10m=None,
rn=None,
model=None,
output_dir=None,
points=None,
extra_info=None):
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')
initTime = pd.to_datetime(str(
t2m['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
# draw figure
fig = plt.figure(figsize=(16, 4.5))
ax_t2m = HostAxes(fig, [0.1, 0.28, .8, .62])
ax_rn = ParasiteAxes(ax_t2m, sharex=ax_t2m)
#其他信息
#append axes
ax_t2m.parasites.append(ax_rn)
#invisible right axis of ax
ax_t2m.axis['right'].set_visible(False)
ax_t2m.axis['right'].set_visible(False)
ax_rn.axis['right'].set_visible(True)
ax_rn.axis['right'].major_ticklabels.set_visible(True)
ax_rn.axis['right'].label.set_visible(True)
#set label for axis
ax_t2m.set_ylabel('温度($^\circ$C)', fontsize=100)
ax_rn.set_ylabel('降水(mm)', fontsize=100)
fig.add_axes(ax_t2m)
# draw main figure
#2米温度——————————————————————————————————————
if (model == '中央台指导'):
model = '智能网格'
utl.add_public_title_sta(title=model + '预报 ' + extra_info['point_name'] +
' [' + str(points['lon'][0]) + ',' +
str(points['lat'][0]) + ']',
initTime=initTime,
fontsize=21)
for ifhour in t2m['forecast_period'].values:
if (ifhour == t2m['forecast_period'].values[0]):
t2m_t = (initTime + timedelta(hours=ifhour))
else:
t2m_t = np.append(t2m_t, (initTime + timedelta(hours=ifhour)))
curve_t2m = ax_t2m.plot(t2m_t,
np.squeeze(t2m['data'].values),
c='#FF6600',
linewidth=3,
label='2m温度')
ax_t2m.set_xlim(t2m_t[0], t2m_t[-1])
ax_t2m.set_ylim(
math.floor(t2m['data'].values.min() / 5) * 5 - 2,
math.ceil(t2m['data'].values.max() / 5) * 5)
#降水——————————————————————————————————————
for ifhour in rn['forecast_period'].values:
if (ifhour == rn['forecast_period'].values[0]):
rn_t = (initTime + timedelta(hours=ifhour))
else:
rn_t = np.append(rn_t, (initTime + timedelta(hours=ifhour)))
mask = (rn['data'] < 999)
rn = rn['data'].where(mask)
ax_rn.bar(rn_t,
np.squeeze(rn.values),
width=0.1,
color='#00008B',
label=str(
int(rn['forecast_period'].values[1] -
rn['forecast_period'].values[0])) + '小时降水',
alpha=0.5)
#curve_rn=ax_rn.plot(rn_t, np.squeeze(rn['data'].values), c='#40C4FF',linewidth=3)
ax_rn.set_ylim(0, np.nanmax(np.append(10, np.squeeze(rn.values))))
###
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax_t2m.xaxis.set_major_locator(xaxis_intaval)
# add legend
ax_t2m.legend(fontsize=15, loc='upper right')
ax_t2m.tick_params(length=10)
ax_t2m.tick_params(axis='y', labelsize=100)
ax_t2m.set_xticklabels([' '])
miloc = mpl.dates.HourLocator(byhour=(8, 11, 14, 17, 20, 23, 2, 5)) #单位是小时
ax_t2m.xaxis.set_minor_locator(miloc)
yminorLocator = MultipleLocator(1) #将此y轴次刻度标签设置为1的倍数
ax_t2m.yaxis.set_minor_locator(yminorLocator)
ymajorLocator = MultipleLocator(5) #将此y轴次刻度标签设置为1的倍数
ax_t2m.yaxis.set_major_locator(ymajorLocator)
ax_t2m.grid(axis='x', which='minor', ls='--')
ax_t2m.axis['left'].label.set_fontsize(15)
ax_t2m.axis['left'].major_ticklabels.set_fontsize(15)
ax_rn.axis['right'].label.set_fontsize(15)
ax_rn.axis['right'].major_ticklabels.set_fontsize(15)
#10米风——————————————————————————————————————
ax_uv = plt.axes([0.1, 0.16, .8, .12])
for ifhour in u10m['forecast_period'].values:
if (ifhour == u10m['forecast_period'].values[0]):
uv_t = (initTime + timedelta(hours=ifhour))
else:
uv_t = np.append(uv_t, (initTime + timedelta(hours=ifhour)))
wsp = (u10m**2 + v10m**2)**0.5
#curve_uv=ax_uv.plot(uv_t, np.squeeze(wsp['data'].values), c='#696969',linewidth=3,label='10m风')
ax_uv.barbs(uv_t,
np.zeros(len(uv_t)),
np.squeeze(u10m['data'].values),
np.squeeze(v10m['data'].values),
fill_empty=True,
color='gray',
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=5.8,
linewidth=1.5,
zorder=100)
ax_uv.set_ylim(-1, 1)
ax_uv.set_xlim(uv_t[0], uv_t[-1])
#ax_uv.axis('off')
ax_uv.set_yticklabels([' '])
#logo
utl.add_logo_extra_in_axes(pos=[0.87, 0.00, .1, .1],
which='nmc',
size='Xlarge')
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax_uv.xaxis.set_major_locator(xaxis_intaval)
ax_uv.tick_params(length=5, axis='x')
ax_uv.tick_params(length=0, axis='y')
miloc = mpl.dates.HourLocator(byhour=(8, 11, 14, 17, 20, 23, 2, 5)) #单位是小时
ax_uv.xaxis.set_minor_locator(miloc)
ax_uv.grid(axis='x', which='both', ls='--')
ax_uv.set_ylabel('10m风', fontsize=15)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
for label in ax_uv.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
ax_uv.tick_params(axis='x', labelsize=15)
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
isExists = os.path.exists(output_dir)
if not isExists:
os.makedirs(output_dir)
output_dir2 = output_dir + model + '_起报时间_' + initTime.strftime(
"%Y年%m月%d日%H时") + '/'
if (os.path.exists(output_dir2) == False):
os.makedirs(output_dir2)
plt.savefig(output_dir2 + model + '_' + extra_info['point_name'] +
'_' + extra_info['output_head_name'] +
initTime.strftime("%Y%m%d%H") + '00' +
extra_info['output_tail_name'] + '.jpg',
dpi=200,
bbox_inches='tight')
else:
plt.show()
def draw_Station_Snow_Synthetical_Forecast_From_Cassandra(
TWC=None,
AT=None,
u10m=None,
v10m=None,
u100m=None,
v100m=None,
gust10m=None,
wsp10m=None,
wsp100m=None,
SNOD1=None,
SNOD2=None,
SDEN=None,
SN06=None,
draw_VIS=False,
VIS=None,
drw_thr=False,
time_all=None,
model=None,
points=None,
output_dir=None,
extra_info={
'output_head_name': ' ',
'output_tail_name': ' ',
'point_name': ' '
}):
#if(sys.platform[0:3] == 'win'):
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')
initTime1 = pd.to_datetime(str(
TWC['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
initTime2 = pd.to_datetime(str(
VIS['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
# draw figure
fig = plt.figure(figsize=(12, 16))
# draw main figure
#风寒指数 体感温度————————————————————————————————————————————————
ax = plt.axes([0.05, 0.83, .94, .15])
utl.add_public_title_sta(title=model + '预报 ' + extra_info['point_name'] +
' [' + str(points['lon'][0]) + ',' +
str(points['lat'][0]) + ']',
initTime=initTime1,
fontsize=23)
for ifhour in TWC['forecast_period'].values:
if (ifhour == TWC['forecast_period'].values[0]):
TWC_t = (initTime1 + timedelta(hours=ifhour))
else:
TWC_t = np.append(TWC_t, (initTime1 + timedelta(hours=ifhour)))
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
ax.set_xticklabels([' '])
ax.plot(TWC_t, np.squeeze(TWC), label='风寒指数')
ax.plot(TWC_t, np.squeeze(AT), c='#00796B', label='2米体感温度')
ax.tick_params(length=10)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
plt.xlim(time_all[0], time_all[-1])
plt.ylim(min([AT.values.min(), TWC.values.min()]),
max([AT.values.max(), TWC.values.max()]))
ax.legend(fontsize=10, loc='upper right')
ax.set_ylabel('2米体感温度 风寒指数 ($^\circ$C)', fontsize=15)
#10米风——————————————————————————————————————
ax = plt.axes([0.05, 0.66, .94, .15])
for ifhour in u10m['forecast_period'].values:
if (ifhour == u10m['forecast_period'].values[0]):
uv10m_t = (initTime1 + timedelta(hours=ifhour))
else:
uv10m_t = np.append(uv10m_t, (initTime1 + timedelta(hours=ifhour)))
for ifhour in u100m['forecast_period'].values:
if (ifhour == u100m['forecast_period'].values[0]):
uv100m_t = (initTime1 + timedelta(hours=ifhour))
else:
uv100m_t = np.append(uv100m_t,
(initTime1 + timedelta(hours=ifhour)))
for ifhour in gust10m['forecast_period'].values:
if (ifhour == gust10m['forecast_period'].values[0]):
gust10m_t = (initTime1 + timedelta(hours=ifhour))
else:
gust10m_t = np.append(gust10m_t,
(initTime1 + timedelta(hours=ifhour)))
ax.plot(uv10m_t,
np.squeeze(wsp10m),
c='#40C4FF',
label='10米风',
linewidth=3)
ax.plot(uv100m_t,
np.squeeze(wsp100m),
c='#FF6F00',
label='100米风',
linewidth=3)
ax.plot(gust10m_t,
np.squeeze(gust10m['data']),
c='#7C4DFF',
label='10米阵风',
linewidth=3)
if (drw_thr == True):
ax.plot([uv10m_t[0], uv10m_t[-1]], [5.5, 5.5],
c='#4CAE50',
label='10米平均风一般影响',
linewidth=1)
ax.plot([uv10m_t[0], uv10m_t[-1]], [8, 8],
c='#FFEB3B',
label='10米平均风较大影响',
linewidth=1)
ax.plot([uv10m_t[0], uv10m_t[-1]], [10.8, 10.8],
c='#F44336',
label='10米平均风高影响',
linewidth=1)
ax.plot([gust10m_t[0], gust10m_t[-1]], [10.8, 10.8],
c='#4CAE50',
label='10米阵风一般影响',
dashes=[6, 2],
linewidth=1)
ax.plot([gust10m_t[0], gust10m_t[-1]], [13.9, 13.9],
c='#FFEB3B',
label='10米阵风较大影响',
dashes=[6, 2],
linewidth=1)
ax.plot([gust10m_t[0], gust10m_t[-1]], [17.2, 17.2],
c='#F44336',
label='10米阵风高影响',
dashes=[6, 2],
linewidth=1)
ax.barbs(uv10m_t[0:-1],
wsp10m[0:-1],
np.squeeze(u10m['data'])[0:-1],
np.squeeze(v10m['data'])[0:-1],
fill_empty=True,
color='gray',
barb_increments={
'half': 2,
'full': 4,
'flag': 20
})
ax.barbs(uv100m_t[0:-1],
wsp100m[0:-1],
np.squeeze(u100m['data'])[0:-1],
np.squeeze(v100m['data'])[0:-1],
fill_empty=True,
color='gray',
barb_increments={
'half': 2,
'full': 4,
'flag': 20
})
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
ax.set_xticklabels([' '])
plt.xlim(time_all[0], time_all[-1])
# add legend
ax.legend(fontsize=10, loc='upper right')
ax.tick_params(length=10)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
ax.set_ylabel('10米风 100米 风\n' + '风速 (m/s)', fontsize=15)
#雪密度——————————————————————————————————————
# draw main figure
ax = plt.axes([0.05, 0.49, .94, .15])
for ifhour in SDEN['forecast_period'].values:
if (ifhour == SDEN['forecast_period'].values[0]):
SDEN_t = (initTime1 + timedelta(hours=ifhour))
else:
SDEN_t = np.append(SDEN_t, (initTime1 + timedelta(hours=ifhour)))
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
ax.set_xticklabels([' '])
ax.plot(SDEN_t, np.squeeze(SDEN['data']), color='#1E88E5', label='雪密度')
#ax.bar(SDEN_t,np.squeeze(SDEN['data']),width=0.12,color='#1E88E5')
gap_hour_SDEN = int(SDEN['forecast_period'].values[1] -
SDEN['forecast_period'].values[0])
if (drw_thr == True):
ax.plot([SDEN_t[0], SDEN_t[-1]],
[1 * gap_hour_SDEN, 1 * gap_hour_SDEN],
c='#FFEB3B',
label=str(gap_hour_SDEN) + '小时降水较大影响',
linewidth=1)
ax.plot([SDEN_t[0], SDEN_t[-1]],
[10 * gap_hour_SDEN, 10 * gap_hour_SDEN],
c='#F44336',
label=str(gap_hour_SDEN) + '小时降水高影响',
linewidth=1)
ax.legend(fontsize=10, loc='upper right')
ax.tick_params(length=10)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
plt.xlim(time_all[0], time_all[-1])
plt.ylim([
np.squeeze(SDEN['data']).values.min(),
np.squeeze(SDEN['data'].values.max()) + 2
])
ax.set_ylabel('雪密度 (kg m-3)', fontsize=15)
#积雪深度——————————————————————————————————————
# draw main figure
ax = plt.axes([0.05, 0.32, .94, .15])
for ifhour in SNOD1['forecast_period'].values:
if (ifhour == SNOD1['forecast_period'].values[0]):
SNOD1_t = (initTime1 + timedelta(hours=ifhour))
else:
SNOD1_t = np.append(SNOD1_t, (initTime1 + timedelta(hours=ifhour)))
# draw main figure
for ifhour in SNOD2['forecast_period'].values:
if (ifhour == SNOD2['forecast_period'].values[0]):
SNOD2_t = (initTime1 + timedelta(hours=ifhour))
else:
SNOD2_t = np.append(SNOD2_t, (initTime1 + timedelta(hours=ifhour)))
for ifhour in SN06['forecast_period'].values:
if (ifhour == SN06['forecast_period'].values[0]):
SN06_t = (initTime1 + timedelta(hours=ifhour))
else:
SN06_t = np.append(SN06_t, (initTime1 + timedelta(hours=ifhour)))
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
ax.set_xticklabels([' '])
#ax.bar(SNOD1_t,np.squeeze(SNOD1['data']),width=0.20,color='#82B1FF',label='EC预报积雪深度')
#ax.bar(SNOD2_t,np.squeeze(SNOD2['data']),width=0.125,color='#2962FF',label='NCEP预报积雪深度')
ax.plot(SNOD1_t,
np.squeeze(SNOD1['data']),
dashes=[6, 2],
color='#4B4B4B',
linewidth=3,
label='EC预报积雪深度')
ax.plot(SNOD2_t,
np.squeeze(SNOD2['data']),
dashes=[6, 2],
color='#969696',
linewidth=3,
label='NCEP预报积雪深度')
ax.plot(SN06_t,
np.squeeze(SN06['data']),
color='#82B1FF',
linewidth=2,
label='EC预报降雪量')
ax.tick_params(length=10)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
plt.xlim(time_all[0], time_all[-1])
plt.ylim(0, 100)
ax.legend(fontsize=10, loc='upper right')
plt.ylim(
min([
np.squeeze(SNOD1['data']).values.min(),
np.squeeze(SNOD2['data']).values.min()
]),
max([
np.squeeze(SNOD1['data']).values.max(),
np.squeeze(SNOD2['data']).values.max()
]) + 5)
ax.set_ylabel('积雪深度 (cm)\n' + '6小时降雪量(mm)', fontsize=15)
if (draw_VIS == False):
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
ax.xaxis.set_major_formatter(xstklbls)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
#发布信息————————————————————————————————————————————————
ax = plt.axes([0.05, 0.08, .94, .05])
ax.axis([0, 10, 0, 10])
ax.axis('off')
utl.add_logo_extra_in_axes(pos=[0.7, 0.23, .05, .05],
which='nmc',
size='Xlarge')
ax.text(7.5,
33, (initTime1 - timedelta(hours=2)).strftime("%Y年%m月%d日%H时") +
'发布',
size=15)
if (draw_VIS == True):
#能见度——————————————————————————————————————
# draw main figure
ax = plt.axes([0.05, 0.15, .94, .15])
#VIS=pd.read_csv(dir_all['VIS_SC']+last_file[model])
for ifhour in VIS['forecast_period'].values:
if (ifhour == VIS['forecast_period'].values[0]):
VIS_t = (initTime2 + timedelta(hours=ifhour))
else:
VIS_t = np.append(VIS_t, (initTime2 + timedelta(hours=ifhour)))
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax.xaxis.set_major_locator(xaxis_intaval)
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
-100,
facecolor='#B3E5FC')
ax.fill_between(VIS_t, np.squeeze(VIS['data']), 1, facecolor='#81D4FA')
ax.fill_between(VIS_t, np.squeeze(VIS['data']), 3, facecolor='#4FC3F7')
ax.fill_between(VIS_t, np.squeeze(VIS['data']), 5, facecolor='#29B6F6')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
10,
facecolor='#03A9F4')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
15,
facecolor='#039BE5')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
20,
facecolor='#0288D1')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
25,
facecolor='#0277BD')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
30,
facecolor='#01579B')
ax.fill_between(VIS_t,
np.squeeze(VIS['data']),
100,
facecolor='#FFFFFF')
ax.plot(VIS_t, np.squeeze(VIS['data']))
if (drw_thr == True):
ax.plot([VIS_t[0], VIS_t[-1]], [5, 5],
c='#4CAF50',
label='能见度一般影响',
linewidth=1)
ax.plot([VIS_t[0], VIS_t[-1]], [3, 3],
c='#FFEB3B',
label='能见度较大影响',
linewidth=1)
ax.plot([VIS_t[0], VIS_t[-1]], [1, 1],
c='#F44336',
label='能见度高影响',
linewidth=1)
ax.legend(fontsize=10, loc='upper right')
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
ax.xaxis.set_major_formatter(xstklbls)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
ax.tick_params(length=10)
ax.grid()
ax.grid(axis='x', c='black')
miloc = mpl.dates.HourLocator(byhour=(11, 14, 17, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
ax.grid(axis='x', which='minor')
plt.xlim(time_all[0], time_all[-1])
plt.ylim(0, 25)
ax.set_ylabel('能见度 （km）', fontsize=15)
#发布信息————————————————————————————————————————————————
ax = plt.axes([0.05, 0.08, .94, .05])
ax.axis([0, 10, 0, 10])
ax.axis('off')
utl.add_logo_extra_in_axes(pos=[0.7, 0.06, .05, .05],
which='nmc',
size='Xlarge')
ax.text(7.5,
0.1,
(initTime2 - timedelta(hours=2)).strftime("%Y年%m月%d日%H时") +
'发布',
size=15)
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
isExists = os.path.exists(output_dir)
if not isExists:
os.makedirs(output_dir)
plt.savefig(output_dir + extra_info['output_head_name'] +
initTime1.strftime("%Y%m%d%H") + '00' +
extra_info['output_tail_name'] + '.jpg',
dpi=200,
bbox_inches='tight')
else:
plt.show()
def draw_mslp_rain_snow(
rain=None, snow=None,sleet=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.])
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('['+mslp.attrs['model']+'] '+
'海平面气压, '+
str(rain.attrs['atime'])+'小时降水',
loc='left', fontsize=30)
#draw data
plots = {}
if rain is not None:
x, y = np.meshgrid(rain['lon'], rain['lat'])
z=np.squeeze(rain.values)
cmap,norm=dk_ctables.cm_rain_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=3,transform=datacrs,alpha=0.8)
if snow is not None:
x, y = np.meshgrid(snow['lon'], snow['lat'])
z=np.squeeze(snow.values)
cmap,norm=dk_ctables.cm_snow_nws(atime=rain.attrs['atime'])
#cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['snow'] = ax.pcolormesh(
x,y,z, norm=norm,
cmap=cmap, zorder=3,transform=datacrs,alpha=0.8)
if sleet is not None:
x, y = np.meshgrid(sleet['lon'], sleet['lat'])
z=np.squeeze(sleet.values)
cmap,norm=dk_ctables.cm_sleet_nws(atime=rain.attrs['atime'])
#cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['sleet'] = ax.pcolormesh(
x,y,z, norm=norm,
cmap=cmap, zorder=3,transform=datacrs,alpha=0.8)
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=2, transform=datacrs, zorder=3)
plt.clabel(plots['mslp'], inline=1, fontsize=20, fmt='%.0f',colors='black')
#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')
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(sleet is not None):
cax=plt.axes([l-0.03,b-0.04,w/3,.02])
cb = plt.colorbar(plots['sleet'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('雨夹雪 (mm)',size=20)
if(snow is not None):
# cax=plt.axes([l+0.32,b-0.04,w/4,.02])
cax=plt.axes([l+w/3,b-0.04,w/3,.02])
cb = plt.colorbar(plots['snow'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('雪 (mm)',size=20)
if(rain is not None):
# cax=plt.axes([l+0.65,b-0.04,w/4,.02])
cax=plt.axes([l+w*2/3+0.03,b-0.04,w/3,.02])
cb = plt.colorbar(plots['rain'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('雨 (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=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(mslp.coords['forecast_period'].values[0]))+'小时'+'.png',
dpi=200,bbox_inches='tight')
plt.close()
if(output_dir == None):
plt.show()
def draw_gh_uv_r6(gh=None, uv=None, r6=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,city=True,south_China_sea=True,
output_dir=None,Global=False):
"""
Draw -hPa geopotential height contours, -hPa wind barbs
and mean sea level pressure filled contours.
:param gh: -hPa gh, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'data': 2D array}
optional, {'clevs': 1D array}
:param uv: -hPa u-component and v-component wind, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'udata': 2D array, 'vdata': 2D array}
:param r6: r6, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'data': 2D array}
optional, {'clevs': 1D array}
:param map_extent: [lonmin, lonmax, latmin, latmax],
longitude and latitude range.
:param add_china: add china map or not.
:param regrid_shape: control the wind barbs density.
:return: plots dictionary.
:Examples:
"""
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['model']+'] '+
gh['lev']+'hPa 位势高度场, '+
uv['lev']+'hPa 风场, 6小时降水',
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 r6 is not None:
x, y = np.meshgrid(r6['lon'], r6['lat'])
clevs_r6 = [0.1, 4, 13, 25, 60, 120]
plots['r6'] = ax.contourf(
x, y, np.squeeze(r6['data']), clevs_r6,
colors=["#88F492", "#00A929", "#2AB8FF", "#1202FC", "#FF04F4", "#850C3E"],
alpha=0.8, zorder=10, transform=datacrs,extend='max',extendrect=False)
# 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=20)
# 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=30)
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=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(gh['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time=initial_time+timedelta(hours=gh['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(gh['fhour'])+'小时',size=15)
plt.text(2.5, 0.5,'www.nmc.cn',size=15)
# add color bar
if(r6 != None):
cax=plt.axes([0.11,0.06,.86,.02])
cb = plt.colorbar(plots['r6'], cax=cax, orientation='horizontal',
ticks=clevs_r6[:],
extend='max',extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('6h precipitation (mm)',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+'高度场_风场_降水_预报_'+
'起报时间_'+initial_time.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(gh['fhour'])+'小时'+'.png', dpi=200)
if(output_dir == None):
plt.show()
def draw_Time_Crossection_rh_uv_t(
rh_2D=None, u_2D=None, v_2D=None,TMP_2D=None,
t_range=None,lw_ratio=[16,9],output_dir=None):
#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）
fig = plt.figure(1, figsize=(lw_ratio[0],lw_ratio[1]))
ax = plt.axes()
# example 2: use the "fromList() method
startcolor = '#1E90FF' #蓝色
midcolor = '#F1F1F1' #白色
endcolor = '#696969' #灰色
cmap2 = col.LinearSegmentedColormap.from_list('own2',['#1E90FF','#94D8F6','#F1F1F1','#BFBFBF','#696969'])
rh_contour = ax.contourf(rh_2D['time'].values, rh_2D['level'].values, np.squeeze(rh_2D['data'].values.swapaxes(1,0)),
levels=np.arange(0, 101, 0.5), cmap=cmap2,extend='max')
rh_colorbar = fig.colorbar(rh_contour,ticks=[20,40,60,80,100])
rh_colorbar.set_label('相对湿度（%）',size=15)
ax.barbs(u_2D['time'].values, u_2D['level'].values,
np.squeeze(u_2D['data'].values.swapaxes(1,0))*2.5,
np.squeeze(v_2D['data'].values.swapaxes(1,0))*2.5, color='k')
TMP_contour = ax.contour(TMP_2D['time'].values, TMP_2D['level'].values, np.squeeze(TMP_2D['data'].values.swapaxes(1,0)),
levels=np.arange(-100, 40, 5), colors='#F4511E', linewidths=2)
TMP_contour.clabel(TMP_contour.levels[1::2], fontsize=15, colors='#F4511E', inline=1,
inline_spacing=8, fmt='%i', rightside_up=True, use_clabeltext=True)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
ax.xaxis.set_major_formatter(xstklbls)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_fontsize(15)
label.set_horizontalalignment('right')
for label in ax.get_yticklabels():
label.set_fontsize(15)
ax.set_yscale('symlog')
ax.set_ylabel('高度 （hPa）', fontsize=15)
ax.set_yticklabels(np.arange(1000, 50, -100))
ax.set_yticks(np.arange(1000, 50, -100))
ax.set_ylim(rh_2D['level'].values.max(), rh_2D['level'].values.min())
ax.set_xlim([rh_2D['time'].values[0], rh_2D['time'].values[-1]])
#forecast information
bax=plt.axes([0.10,0.88,.25,.07],facecolor='#FFFFFFCC')
bax.axis('off')
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(rh_2D['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
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=11)
plt.text(2.5, 5.0,'['+str(rh_2D.attrs['model'])+']'+'模式时间剖面',size=11)
plt.text(2.5, 2.5,'预报点: '+str(rh_2D.attrs['points']['lon'])+
', '+str(rh_2D.attrs['points']['lat']),size=11)
plt.text(2.5, 0.5,'www.nmc.cn',size=11)
utl.add_logo_extra_in_axes(pos=[0.1,0.88,.07,.07],which='nmc', size='Xlarge')
ax.set_title('温度, 相对湿度, 水平风', loc='right', fontsize=23)
#出图——————————————————————————————————————————————————————————
if(output_dir != None ):
plt.savefig(output_dir+'时间剖面产品_起报时间_'+
str(rh_2D['forecast_reference_time'].values)[0:13]+
'_预报时效_'+str(t_range[0])+'_至_'+str(t_range[1])
+'.png', dpi=200,bbox_inches='tight')
plt.close()
else:
plt.show()
def draw_Crosssection_Wind_Theta_e_Qv(cross_Qv=None,
cross_Theta_e=None,
cross_u=None,
cross_v=None,
gh=None,
h_pos=None,
st_point=None,
ed_point=None,
levels=None,
map_extent=(50, 150, 0, 65),
output_dir=None):
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）
initial_time = pd.to_datetime(
str(cross_Theta_e['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=gh['forecast_period'].values[0])
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
Qv_contour = ax.contourf(cross_Qv['lon'],
cross_Qv['level'],
cross_Qv.values,
levels=np.arange(0, 20, 2),
cmap='YlGnBu')
Qv_colorbar = fig.colorbar(Qv_contour)
# Plot potential temperature using contour, with some custom labeling
Theta_e_contour = ax.contour(cross_Theta_e['lon'],
cross_Theta_e['level'],
cross_Theta_e.values,
levels=np.arange(250, 450, 5),
colors='k',
linewidths=2)
Theta_e_contour.clabel(np.arange(250, 450, 5),
fontsize=8,
colors='k',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
wind_slc_vert = list(range(0, len(levels), 1))
wind_slc_horz = slice(5, 100, 5)
ax.barbs(cross_u['lon'][wind_slc_horz],
cross_u['level'][wind_slc_vert],
cross_u['t_wind'][wind_slc_vert, wind_slc_horz] * 2.5,
cross_v['n_wind'][wind_slc_vert, wind_slc_horz] * 2.5,
color='k')
# Adjust the y-axis to be logarithmic
ax.set_yscale('symlog')
ax.set_yticklabels(np.arange(levels[0], levels[-1], -100))
ax.set_ylim(levels[0], levels[-1])
ax.set_yticks(np.arange(levels[0], levels[-1], -100))
# Define the CRS and inset axes
data_crs = ccrs.PlateCarree()
ax_inset = fig.add_axes(h_pos, projection=data_crs)
ax_inset.set_extent(map_extent, crs=data_crs)
# Plot geopotential height at 500 hPa using xarray's contour wrapper
ax_inset.contour(gh['lon'],
gh['lat'],
np.squeeze(gh['data']),
levels=np.arange(500, 600, 4),
cmap='inferno')
# Plot the path of the cross section
endpoints = data_crs.transform_points(
ccrs.Geodetic(),
*np.vstack([st_point, ed_point]).transpose()[::-1])
ax_inset.scatter(endpoints[:, 0], endpoints[:, 1], c='k', zorder=2)
ax_inset.plot(cross_u['lon'], cross_u['lat'], c='k', zorder=2)
# Add geographic features
ax_inset.coastlines()
utl.add_china_map_2cartopy_public(ax_inset,
name='province',
edgecolor='black',
lw=0.8,
zorder=105)
# Set the titles and axes labels
ax_inset.set_title('')
ax.set_title('相当位温, 绝对湿度, 水平风场', loc='right', fontsize=25)
ax.set_ylabel('Pressure (hPa)')
ax.set_xlabel('Longitude')
Qv_colorbar.set_label('Specific Humidity (g/kg)')
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')
bax = fig.add_axes([0.10, 0.88, .25, .07], facecolor='#FFFFFFCC')
bax.axis('off')
#bax.set_yticks([])
#bax.set_xticks([])
bax.axis([0, 10, 0, 10])
plt.text(2.5,
7.5,
'起报时间: ' + initial_time.strftime("%Y年%m月%d日%H时"),
size=11)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=11)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(gh['forecast_period'].values[0])) + '小时',
size=11)
plt.text(2.5, 0.5, 'www.nmc.cn', size=11)
utl.add_logo_extra_in_axes(pos=[0.1, 0.88, .07, .07],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '相当位温_绝对湿度_水平风场_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(int(gh['forecast_period'].values[0])) + '小时' + '.png',
dpi=200)
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):
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('[' + T_2m['model'] + ']' + ' ' + T_2m['title'],
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 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_temperature_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['T_2m'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=100,
transform=datacrs,
alpha=0.5,
vmin=-45,
vmax=45)
clevs_zero = 0
z = gaussian_filter(z, 5)
plots['T_2m_zero'] = ax.contour(x,
y,
z,
clevs_zero,
colors='red',
linewidths=2,
transform=datacrs,
zorder=110)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=120)
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(
T_2m['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=T_2m['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(T_2m['fhour']) + '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([0.11, 0.06, .86, .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=[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 + '最低温度_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(T_2m['fhour']) + '小时' + '.png',
dpi=200)
if (output_dir == None):
plt.show()
def draw_Time_Crossection_rh_uv_theta_e(rh_2D=None,
u_2D=None,
v_2D=None,
theta_e_2D=None,
t_range=None,
output_dir=None):
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）
# # 画图
# Define the figure object and primary axes
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
# utl.add_public_title_sta(title=rh_2D.attrs['model']+'模式预报时间剖面',initial_time=rh_2D['forecast_reference_time'].values, fontsize=23)
# Plot RH using contourf
#rh_contour = ax.contourf(rh_2D['time'].values, rh_2D['level'].values, np.squeeze(rh_2D['data'].values.swapaxes(1,0)),
# levels=np.arange(0, 106, 5), cmap='RdBu')
rh_contour = ax.contourf(rh_2D['time'].values,
rh_2D['level'].values,
np.squeeze(rh_2D['data'].values.swapaxes(1, 0)),
levels=np.arange(50, 100, 5),
cmap='YlGnBu',
extend='max',
alpha=0.8)
rh_colorbar = fig.colorbar(rh_contour)
rh_colorbar.set_label('相对湿度（%）', size=15)
ax.barbs(u_2D['time'].values,
u_2D['level'].values,
np.squeeze(u_2D['data'].values.swapaxes(1, 0)) * 2.5,
np.squeeze(v_2D['data'].values.swapaxes(1, 0)) * 2.5,
color='k')
TMP_contour = ax.contour(theta_e_2D['time'].values,
theta_e_2D['level'].values,
np.squeeze(theta_e_2D.values.swapaxes(1, 0)),
levels=np.arange(250, 450, 5),
colors='#F4511E',
linewidths=2)
TMP_contour.clabel(TMP_contour.levels[1::2],
fontsize=15,
colors='#F4511E',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
ax.xaxis.set_major_formatter(xstklbls)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_fontsize(15)
label.set_horizontalalignment('right')
for label in ax.get_yticklabels():
label.set_fontsize(15)
ax.set_yscale('symlog')
ax.set_ylabel('高度 （hPa）', fontsize=15)
ax.set_yticklabels(np.arange(1000, 50, -100))
ax.set_yticks(np.arange(1000, 50, -100))
ax.set_ylim(rh_2D['level'].values.max(), rh_2D['level'].values.min())
ax.set_xlim([rh_2D['time'].values[0], rh_2D['time'].values[-1]])
#forecast information
bax = plt.axes([0.10, 0.88, .25, .07], facecolor='#FFFFFFCC')
bax.axis('off')
bax.axis([0, 10, 0, 10])
initial_time = pd.to_datetime(str(
rh_2D['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
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=11)
plt.text(2.5,
5.0,
'[' + str(rh_2D.attrs['model']) + ']' + '模式时间剖面',
size=11)
plt.text(2.5,
2.5,
'预报点: ' + str(rh_2D.attrs['points']['lon']) + ', ' +
str(rh_2D.attrs['points']['lat']),
size=11)
plt.text(2.5, 0.5, 'www.nmc.cn', size=11)
utl.add_logo_extra_in_axes(pos=[0.1, 0.88, .07, .07],
which='nmc',
size='Xlarge')
ax.set_title('相当位温, 相对湿度, 水平风', loc='right', fontsize=23)
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
plt.savefig(output_dir + '时间剖面产品_起报时间_' +
str(rh_2D['forecast_reference_time'].values)[0:13] +
'_预报时效_' + str(t_range[0]) + '_至_' + str(t_range[1]) +
'.png',
dpi=200,
bbox_inches='tight')
else:
plt.show()
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_Time_Crossection_rh_uv_Temp(rh_2D=None,
u_2D=None,
v_2D=None,
TMP_2D=None,
terrain_2D=None,
t_range=None,
model=None,
output_dir=None):
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）
# # 画图
# Define the figure object and primary axes
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
# utl.add_public_title_sta(title=rh_2D.attrs['model']+'模式预报时间剖面',initial_time=rh_2D['forecast_reference_time'].values, fontsize=23)
# Plot RH using contourf
#rh_contour = ax.contourf(rh_2D['time'].values, rh_2D['level'].values, np.squeeze(rh_2D['data'].values.swapaxes(1,0)),
# levels=np.arange(0, 106, 5), cmap='RdBu')
startcolor = '#1E90FF' #蓝色
midcolor = '#F1F1F1' #白色
endcolor = '#696969' #灰色
cmap2 = col.LinearSegmentedColormap.from_list(
'own2', [startcolor, midcolor, endcolor])
# extra arguments are N=256, gamma=1.0
cm.register_cmap(cmap=cmap2)
rh_2D['data'].values[rh_2D['data'].values > 100] = 100
rh_contour = ax.contourf(rh_2D['time'].values,
rh_2D['level'].values,
np.squeeze(rh_2D['data'].values.swapaxes(1, 0)),
levels=np.arange(0, 101, 0.5),
cmap=cm.get_cmap('own2'),
extend='max')
rh_colorbar = fig.colorbar(rh_contour, ticks=[20, 40, 60, 80, 100])
rh_colorbar.set_label('相对湿度（%）', size=15)
ax.barbs(u_2D['time'].values,
u_2D['level'].values,
np.squeeze(u_2D['data'].values.swapaxes(1, 0)) * 2.5,
np.squeeze(v_2D['data'].values.swapaxes(1, 0)) * 2.5,
color='k')
TMP_contour = ax.contour(TMP_2D['time'].values,
TMP_2D['level'].values,
np.squeeze(TMP_2D['data'].values.swapaxes(1, 0)),
levels=np.arange(-100, 100, 2),
colors='#F4511E',
linewidths=1)
TMP_contour.clabel(TMP_contour.levels[1::2],
fontsize=12,
colors='#F4511E',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
TMP_contour_zero = ax.contour(TMP_2D['time'].values,
TMP_2D['level'].values,
np.squeeze(TMP_2D['data'].values.swapaxes(
1, 0)),
levels=[0],
colors='k',
linewidths=2)
TMP_contour_zero.clabel([0],
fontsize=22,
colors='k',
inline=1,
inline_spacing=8,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
startcolor = '#8B4513' #棕色
endcolor = '#DAC2AD' #绿
cmap2 = col.LinearSegmentedColormap.from_list('own3',
[endcolor, startcolor])
# extra arguments are N=256, gamma=1.0
cm.register_cmap(cmap=cmap2)
if (terrain_2D.values.max() > 0):
terrain_contour = ax.contourf(terrain_2D['time'].values,
terrain_2D['level'].values,
np.squeeze(
terrain_2D.values.swapaxes(1, 0)),
levels=np.arange(0,
terrain_2D.values.max(),
0.1),
cmap=cm.get_cmap('own3'),
zorder=100)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
ax.xaxis.set_major_formatter(xstklbls)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_fontsize(15)
label.set_horizontalalignment('right')
for label in ax.get_yticklabels():
label.set_fontsize(15)
ax.set_yscale('symlog')
ax.set_ylabel('高度 （hPa）', fontsize=15)
ax.set_yticklabels(np.arange(1000, 50, -100))
ax.set_yticks(np.arange(1000, 50, -100))
ax.set_ylim(rh_2D['level'].values.max(), rh_2D['level'].values.min())
ax.set_xlim([rh_2D['time'].values[0], rh_2D['time'].values[-1]])
#forecast information
bax = plt.axes([0.10, 0.88, .25, .07], facecolor='#FFFFFFCC')
bax.axis('off')
bax.axis([0, 10, 0, 10])
initial_time = pd.to_datetime(str(
rh_2D['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
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=11)
plt.text(2.5,
5.0,
'[' + str(rh_2D.attrs['model']) + ']' + '模式时间剖面',
size=11)
plt.text(2.5,
2.5,
'预报点: ' + str(rh_2D.attrs['points']['lon']) + ', ' +
str(rh_2D.attrs['points']['lat']),
size=11)
plt.text(2.5, 0.5, 'www.nmc.cn', size=11)
utl.add_logo_extra_in_axes(pos=[0.1, 0.88, .07, .07],
which='nmc',
size='Xlarge')
ax.set_title('[' + model + '] ' + '温度, 相对湿度, 水平风',
loc='right',
fontsize=23)
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
plt.savefig(output_dir + '时间剖面产品_起报时间_' +
str(rh_2D['forecast_reference_time'].values)[0:13] +
'_预报时效_' + str(t_range[0]) + '_至_' + str(t_range[1]) +
'.png',
dpi=200,
bbox_inches='tight')
else:
plt.show()
def box_line_wsp(wsp=None, output_dir=None, points=None, extra_info=None):
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')
initTime = pd.to_datetime(str(
wsp['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
# draw figure
fig = plt.figure(figsize=(16, 4.5))
# draw main figure
#10米风——————————————————————————————————————
ax = plt.axes([0.1, 0.28, .8, .62])
utl.add_public_title_sta(
title=wsp.attrs['model'] + '预报 ' + extra_info['point_name'] + ' [' +
str(points['lon'][0]) + ',' + str(points['lat'][0]) + ']',
initTime=initTime,
fontsize=21)
for ifhour in wsp['forecast_period'].values:
if (ifhour == wsp['forecast_period'].values[0]):
if (ifhour % 12 == 0):
uv_t = (initTime +
timedelta(hours=ifhour)).strftime('%m月%d日%H时')
else:
uv_t = ' '
else:
if (ifhour % 12 == 0):
uv_t = np.append(
uv_t,
(initTime + timedelta(hours=ifhour)).strftime('%m月%d日%H时'))
else:
uv_t = np.append(uv_t, ' ')
labels = uv_t
ax.boxplot(np.transpose(np.squeeze(wsp['data'].values)),
labels=labels,
meanline=True,
showmeans=True,
showfliers=False,
whis=(0, 100))
warning = np.zeros(len(uv_t)) + 20.
warning_t = np.arange(0, len(uv_t)) + 1
ax.plot(warning_t, warning, c='red', label=' ', linewidth=1)
ax.set_ylim(0, math.ceil(wsp['data'].values.max() / 2) * 2)
#plt.xlim(uv_t[0],uv_t[-1])
# add legend
#ax.legend(fontsize=15,loc='upper right')
ax.tick_params(length=7)
ax.tick_params(axis='y', labelsize=100)
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
ax.tick_params(axis='y', labelsize=15)
ax.tick_params(axis='x', labelsize=10)
miloc = mpl.dates.HourLocator(byhour=(8, 11, 14, 17, 20, 23, 2, 5)) #单位是小时
ax.xaxis.set_minor_locator(miloc)
yminorLocator = MultipleLocator(1) #将此y轴次刻度标签设置为1的倍数
ax.yaxis.set_minor_locator(yminorLocator)
ymajorLocator = MultipleLocator(2) #将此y轴次刻度标签设置为1的倍数
ax.yaxis.set_major_locator(ymajorLocator)
ax.grid(axis='x', ls='--')
for label in ax.get_yticklabels():
label.set_fontsize(15)
#ax.axis['left'].major_ticklabels.set_fontsize(15)
t_gap = wsp['forecast_period'].values[1] - wsp['forecast_period'].values[0]
ax.set_ylabel('风速 (m s$^-$$^1$)', fontsize=15)
utl.add_logo_extra_in_axes(pos=[0.87, 0.00, .1, .1],
which='nmc',
size='Xlarge')
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
isExists = os.path.exists(output_dir)
if not isExists:
os.makedirs(output_dir)
output_dir2 = output_dir + wsp.attrs[
'model'] + '_起报时间_' + initTime.strftime("%Y年%m月%d日%H时") + '/'
if (os.path.exists(output_dir2) == False):
os.makedirs(output_dir2)
plt.savefig(output_dir2 + wsp.attrs['model'] + '_' +
extra_info['point_name'] + '_' +
extra_info['output_head_name'] +
initTime.strftime("%Y%m%d%H") + '00' +
extra_info['output_tail_name'] + '_风速_箱线图' + '.jpg',
dpi=200,
bbox_inches='tight')
else:
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_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_wvfl(gh=None,
uv=None,
wvfl=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['model'] + '] ' + gh['lev'] + 'hPa 位势高度场, ' +
uv['lev'] + 'hPa 风场, ' + wvfl['lev'] + 'hPa 水汽通量',
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 wvfl is not None:
x, y = np.meshgrid(wvfl['lon'], wvfl['lat'])
z = np.squeeze(wvfl['data']) / 10.
#pos=[0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 22]
idx_nan = np.where(z < 5)
z[idx_nan] = np.nan
cmap, norm = gy_ctables.wvfl_ctable()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['wvfl'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
norm=norm,
zorder=100,
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=110)
# 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=110)
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=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(
gh['init_time'])).replace(tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(hours=gh['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(gh['fhour']) + '小时', size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (wvfl != None):
cax = plt.axes([0.11, 0.06, .86, .02])
cb = plt.colorbar(plots['wvfl'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Water Vapor Flux (0.1g/(cm*hPa*s)', size=20)
# cb.set_ticks([0,4,8,12,16,20,24])
# cb.set_ticklabels(['0','4','8','12','16','20','24'])
# 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 + '位势高度场_风场_水汽通量_预报_' + '起报时间_' +
initial_time.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(gh['fhour']) + '小时' + '.png',
dpi=200)
if (output_dir == None):
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 OBS_Sounding_GeopotentialHeight(IR=None,
Sounding=None,
HGT=None,
map_extent=None,
city=True,
south_China_sea=True,
output_dir=None,
Channel='C009'):
#if(sys.platform[0:3] == 'win'):
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))
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.])
# draw main figure
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
datacrs = ccrs.PlateCarree()
map_extent2 = adjust_map_ratio(ax, map_extent=map_extent, datacrs=datacrs)
# plot map background
ax.add_feature(cfeature.OCEAN)
add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=2,
alpha=0.5)
add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=2)
add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=2,
alpha=0.5)
# define return plots
plots = {}
# draw IR
if IR is not None:
x, y = np.meshgrid(IR['lon'], IR['lat'])
z = np.squeeze(IR['image'])
if (Channel == 'C009'):
cmap = dk_ctables.cm_wv_enhancement()
norm, cmap = colortables.get_with_range('WVCIMSS', 160, 300)
title_name = '水汽图像'
plots['IR'] = ax.pcolormesh(x,
y,
z,
norm=norm,
cmap=cmap,
zorder=1,
transform=datacrs)
if (Channel == 'C012'):
cmap = dk_ctables.cm_ir_enhancement1()
title_name = '红外(10.8微米)'
plots['IR'] = ax.pcolormesh(x - 10.,
y,
z,
vmin=105.,
vmax=335.0,
cmap=cmap,
zorder=1,
transform=datacrs)
plt.title('FY4A' + title_name + '观测' + ' 探空观测 高度场',
loc='left',
fontsize=30)
if (Sounding is not None):
x = np.squeeze(Sounding['lon'].values)
y = np.squeeze(Sounding['lat'].values)
idx_vld = np.where((Sounding['Wind_angle'].values != np.nan)
& (Sounding['Wind_speed'].values != np.nan))
u, v = utl.wind2UV(Winddir=Sounding['Wind_angle'].values[idx_vld],
Windsp=Sounding['Wind_speed'].values[idx_vld])
idx_null = np.where((u > 100) | (v > 100))
u[idx_null] = np.nan
v[idx_null] = np.nan
c_barb = {'C009': 'black', 'C012': 'white'}
plots['uv'] = ax.barbs(x,
y,
u,
v,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.0),
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
zorder=2,
color=c_barb[Channel],
alpha=0.7,
lw=1.5,
length=7)
# draw mean sea level pressure
if (HGT is not None):
x, y = np.meshgrid(HGT['lon'], HGT['lat'])
clevs = np.append(np.arange(480, 584, 8), np.arange(580, 604, 4))
plots_HGT = ax.contour(x,
y,
ndimage.gaussian_filter(np.squeeze(
HGT['data']),
sigma=1,
order=0),
levels=clevs,
colors='black',
alpha=1,
zorder=3,
transform=datacrs,
linewidths=3)
ax.clabel(plots_HGT, inline=1, fontsize=20, fmt='%.0f')
ax.contour(x,
y,
ndimage.gaussian_filter(np.squeeze(HGT['data']),
sigma=1,
order=0),
levels=[588],
colors='black',
alpha=1,
zorder=3,
transform=datacrs,
linewidths=6)
#if city:
gl = ax.gridlines(crs=datacrs,
linewidth=1,
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
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')
IR_time = pd.to_datetime(
IR.coords['time'].values[0]).to_pydatetime() + timedelta(hours=8)
Sounding_time = Sounding['time'][0].to_pydatetime()
HGT_initTime = pd.to_datetime(
str(HGT.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
HGT_time = HGT_initTime + timedelta(
hours=HGT.coords['forecast_period'].values[0])
#logo
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
bax.text(2.5,
7.5,
'卫星图像观测时间: ' + IR_time.strftime("%Y年%m月%d日%H时"),
size=12)
bax.text(2.5,
5,
'探空观测时间: ' + Sounding_time.strftime("%Y年%m月%d日%H时"),
size=12)
bax.text(2.5, 2.5, '高度场时间: ' + HGT_time.strftime("%Y年%m月%d日%H时"), size=12)
bax.text(2.5, 0.5, 'www.nmc.cn', size=12)
initial_time = pd.to_datetime(str(
IR['time'].values[0])).replace(tzinfo=None).to_pydatetime()
#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')
# add color bar
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['IR'],
cax=cax,
orientation='horizontal',
extend='both',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('(K)', size=10)
# add south China sea
if south_China_sea:
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:
add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=2,
size=16,
small_city=small_city)
# show figure
if (output_dir != None):
plt.savefig(
output_dir + '卫星' +
#'_'+initial_time.strftime("%Y年%m月%d日%H时")+
'卫星观测时间_' +
pd.to_datetime(IR['time'].values[0]).strftime("%Y年%m月%d日%H时") +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
def draw_wind_temp_according_to_4D_data(sta_fcs_fcst=None,
zd_fcst_obs=None,
fcst_info=None,
map_extent=(50, 150, 0, 65),
bkgd_type='satellite',
bkgd_level=None,
draw_zd=True,
output_dir=None):
# 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))
plotcrs = ccrs.PlateCarree()
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
if (bkgd_type == 'satellite'):
c_city_names = 'black'
c_sta_fcst = 'black'
c_tile_str = '白色'
else:
c_city_names = 'black'
c_sta_fcst = 'black'
c_tile_str = '黑色'
datacrs = ccrs.PlateCarree()
ax.set_extent(map_extent, crs=datacrs)
# draw data
plots = {}
if (bkgd_type == 'satellite'):
request = utl.TDT_img() #卫星图像
if (bkgd_type == 'terrain'):
request = utl.TDT_ter() #卫星图像
if (bkgd_type == 'road'):
request = utl.TDT() #卫星图像
ax.add_image(request, bkgd_level) # level=10 缩放等级
#cmap=dk_ctables.cm_thetae()
plots['t2m'] = ax.scatter(sta_fcs_fcst['lon'],
sta_fcs_fcst['lat'],
s=1900,
c='white',
alpha=1,
zorder=120)
cmap = dk_ctables.cm_temp()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['t2m'] = ax.scatter(sta_fcs_fcst['lon'],
sta_fcs_fcst['lat'],
s=1700,
c=sta_fcs_fcst['TMP'],
vmin=-40,
vmax=40,
cmap=cmap,
alpha=1,
zorder=120)
for ista in range(0, len(sta_fcs_fcst['lon'])):
city_names = sta_fcs_fcst['name'][ista]
ax.text(sta_fcs_fcst['lon'][ista] - 0.001,
sta_fcs_fcst['lat'][ista],
city_names + ' ',
family='SimHei',
ha='right',
va='center',
size=24,
color=c_city_names,
zorder=140)
ax.text(sta_fcs_fcst['lon'][ista],
sta_fcs_fcst['lat'][ista],
'%i' % sta_fcs_fcst['TMP'][ista],
family='SimHei',
size=24,
color='white',
zorder=140,
ha='center',
va='center')
plots['uv'] = ax.barbs(np.array(sta_fcs_fcst['lon']) - 0.001,
np.array(sta_fcs_fcst['lat']),
sta_fcs_fcst['U'],
sta_fcs_fcst['V'],
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=12,
linewidth=6,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=130,
color='white')
plots['uv'] = ax.barbs(np.array(sta_fcs_fcst['lon']) - 0.001,
np.array(sta_fcs_fcst['lat']),
sta_fcs_fcst['U'],
sta_fcs_fcst['V'],
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=12,
linewidth=3,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=130,
color='black')
if (draw_zd is True):
plots['uv'] = ax.barbs(zd_fcst_obs['lon'],
zd_fcst_obs['lat'],
zd_fcst_obs['U'],
zd_fcst_obs['V'],
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=10,
linewidth=2.6,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=100,
color=c_sta_fcst,
alpha=1)
if (zd_fcst_obs['obs_valid'] is True):
plots['uv'] = ax.barbs(zd_fcst_obs['lon'],
zd_fcst_obs['lat'],
zd_fcst_obs['U_obs'],
zd_fcst_obs['V_obs'],
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=10,
linewidth=2,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=100,
color='red',
alpha=1)
#additional information
plt.title('基于EC模式降尺度预报 ' + '10米风（红色实况，' + c_tile_str + '预报）, ' +
'温度(圆形填色及数字)',
loc='left',
fontsize=20)
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])
initial_time = pd.to_datetime(
str(fcst_info['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initial_time + timedelta(
hours=fcst_info['forecast_period'].values[0])
#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(int(fcst_info['forecast_period'].values)) + '小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add logo
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# add color bar
cax = plt.axes([0.25, 0.06, .5, .02])
cb = plt.colorbar(plots['t2m'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('温度 ($^\circ$C)', size=20)
# show figure
if (output_dir != None):
plt.savefig(output_dir + 'BSEP_NMC_RFFC_ECMWF_EME_ASC_LNO_P9_' +
initial_time.strftime("%Y%m%d%H") + '00' +
str(int(fcst_info['forecast_period'].values[0])).zfill(3) +
'03.jpg',
dpi=200,
bbox_inches='tight')
if (output_dir == None):
plt.show()
def draw_cumulative_precip_and_rain_days(cu_rain=None,
days_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))
# 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.])
plotcrs = ccrs.PlateCarree()
datacrs = ccrs.PlateCarree()
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
ax.set_extent(map_extent, crs=datacrs)
#map_extent2=utl.adjust_map_ratio(ax,map_extent=map_extent,datacrs=datacrs)
# define return plots
plots = {}
# draw mean sea level pressure
#if(os.path.exists('L:/Temp/mask.npy')):
# mask=np.load('L:/Temp/mask.npy')
#else:
#mask=dk_mask.grid_mask_china(cu_rain['lon'], cu_rain['lat'])
#np.save('L:/Temp/mask',mask)
# draw -hPa geopotential height
if days_rain is not None:
x, y = np.meshgrid(days_rain['lon'], days_rain['lat'])
clevs_days = np.arange(2, np.nanmax(days_rain.values) + 1)
z = gaussian_filter(np.squeeze(days_rain.values), 5)
#z=z*mask
plots['days_rain'] = ax.contour(x,
y,
z,
clevs_days,
colors='black',
linewidths=2,
transform=datacrs,
zorder=3)
plt_lbs = plt.clabel(plots['days_rain'],
inline=2,
fontsize=20,
fmt='%.0f',
colors='black')
clip_days = utl.gy_China_maskout(plots['days_rain'], ax, plt_lbs)
if cu_rain is not None:
x, y = np.meshgrid(cu_rain['lon'], cu_rain['lat'])
z = np.squeeze(cu_rain.values)
clevs = [50, 100, 200, 300, 400, 500, 600]
colors = [
'#6ab4f1', '#0001f6', '#f405ee', '#ffa900', '#fc6408', '#e80000',
'#9a0001'
]
linewidths = [1, 1, 2, 2, 3, 4, 4]
cmap, norm = mpl.colors.from_levels_and_colors(clevs,
colors,
extend='max')
plots['rain'] = ax.contourf(x,
y,
z,
clevs,
norm=norm,
cmap=cmap,
transform=datacrs,
extend='max',
alpha=0.1)
plots['rain2'] = ax.contour(x,
y,
z,
clevs,
norm=norm,
cmap=cmap,
transform=datacrs,
linewidths=linewidths)
plt.setp(plots['rain2'].collections,
path_effects=[
path_effects.SimpleLineShadow(),
path_effects.Normal()
])
# plots['rain'] = ax.contourf(x,y,z,
# cmap=cmap, zorder=1,transform=datacrs,alpha=0.5)
clip_rain = utl.gy_China_maskout(plots['rain'], ax)
clip_rain2 = utl.gy_China_maskout(plots['rain2'], ax)
#additional information
plt.title('过去' + str(int(days_rain.attrs['rn_ndays'])) + '天降水日数, ' + '过去' +
str(int(cu_rain.attrs['cu_ndays'])) + '天累积降水量',
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='black',
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)
stamen_terrain = cimg.Stamen('terrain-background')
ax.add_image(stamen_terrain, 6)
# 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')
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 - 0.05, .27, .05], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(days_rain.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=days_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(1.5, 5.5, '观测截止时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(1.5, 0.5, 'www.nmc.cn', size=18)
# # add color bar
# if(cu_rain is not None):
# cax=plt.axes([l,b-0.04,w,.02])
# cb = plt.colorbar(plots['rain2'], cax=cax, orientation='horizontal')
# cb.ax.tick_params(labelsize='x-large')
# cb.set_label('累积降水量 (mm)',size=20)
font = FontProperties(family='Microsoft YaHei', size=16)
ax.legend([mpatches.Patch(color=b) for b in colors], [
'50~100 毫米', '100~200 毫米', '200-300 毫米', '300~400 毫米', '400~500 毫米',
'500~600 毫米', '>=600毫米'
],
prop=font,
loc='lower left')
# 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_extent,
transform=datacrs,
zorder=2,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.01, b + h - 0.05, .06, .05],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '总降水_' + '观测时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
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_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()
