def draw_wind_rh_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 = 'w'
c_sta_fcst = 'w'
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=1400,
c='white',
alpha=1,
zorder=120)
cmap, norm = dk_ctables.cm_relative_humidity_nws()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['t2m'] = ax.scatter(sta_fcs_fcst['lon'],
sta_fcs_fcst['lat'],
s=1300,
c=sta_fcs_fcst['RH'],
vmin=0,
vmax=100,
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=20,
color=c_city_names,
zorder=140)
ax.text(sta_fcs_fcst['lon'][ista],
sta_fcs_fcst['lat'][ista],
'%i' % sta_fcs_fcst['RH'][ista],
family='SimHei',
size=20,
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=7,
linewidth=2,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=100,
color=c_sta_fcst,
alpha=0.7)
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=8,
linewidth=1.5,
transform=datacrs,
fill_empty=False,
sizes=dict(emptybarb=0.01),
zorder=100,
color='red',
alpha=0.7)
#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）, ' +
'相对湿度(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('相对湿度 (%)', 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_gh_uv_rh(gh=None, uv=None, rh=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']))+'hPa 位势高度场, '+
str(int(uv['level']))+'hPa 风场, '+
str(int(rh['level']))+'hPa 相对湿度',
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 rh is not None:
x, y = np.meshgrid(rh['lon'], rh['lat'])
z=np.squeeze(rh['data'])
cmap,norm=dk_ctables.cm_relative_humidity_nws()
cmap.set_under(color=[0,0,0,0],alpha=0.0)
plots['rh'] = 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')
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['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
fcst_time=initTime+timedelta(hours=gh['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['forecast_period'].values[0]))+'小时',size=15)
plt.text(2.5, 0.5,'www.nmc.cn',size=15)
# add color bar
if(rh != None):
cax=plt.axes([l,b-0.04,w,.02])
cb = plt.colorbar(plots['rh'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Relative Humidity (%)',size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l+w-0.091,b,.1,.2])
small_city=False
if(map_extent2[1]-map_extent2[0] < 25):
small_city=True
if city:
utl.add_city_on_map(ax,map_extent=map_extent2,transform=datacrs,zorder=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+'位势高度场_风场_相对湿度_预报_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(gh['forecast_period'].values[0])+'小时'+'.png', dpi=200)
if(output_dir == None):
plt.show()