def AQI_scatter(ax,x,y,z,cmap=dk_ctables2.ncl_cmaps('hotcold_18lev'),vmin=0,vmax=100,alpha=0.8,transform=ccrs.PlateCarree(),levels=None,**kwargs):
if(levels is not None):
cmap,norm=cpt.generate_cmap_norm(levels,cm=cmap,extend='both')
img = ax.scatter(x,y,c=z,cmap=cmap,transform=transform, norm=norm,alpha=alpha,**kwargs)
else:
img = ax.scatter(x,y,c=z,cmap=cmap,transform=transform, vmin=vmin, vmax=vmax,alpha=alpha,**kwargs)
return img
def draw_tmp_evo(
tmp=None,thr=-4,
map_extent=(50, 150, 0, 65),
add_china=True,city=True,south_China_sea=True,
output_dir=None,Global=False):
# set font
initTime = pd.to_datetime(tmp.coords['forecast_reference_time'].values).replace(tzinfo=None).to_pydatetime()
fhour = int(tmp['forecast_period'].values[-1])
fcstTime = initTime + timedelta(hours=fhour)
title = '['+tmp.attrs['model']+'] '+str(int(tmp['level'].values[0]))+'hPa '+str(thr)+'℃等温线演变'
forcast_info = '起报时间: {0:%Y}年{0:%m}月{0:%d}日{0:%H}时\n预报时间: {1:%Y}年{1:%m}月{1:%d}日{1:%H}时\n预报时效: {2}小时\nwww.nmc.cn'.format(initTime, fcstTime, fhour)
fig, ax, map_extent = GF.pallete_set.Horizontal_Pallete((18, 9),map_extent=map_extent, title=title, forcast_info=forcast_info,info_zorder=1,plotcrs=None,
add_china=add_china, add_city=city,south_China_sea=south_China_sea,title_fontsize=25)
plots = {}
label_handles=[]
x, y = np.meshgrid(tmp['lon'], tmp['lat'])
for itime in range(0,len(tmp['time'].values)):
z=np.squeeze(tmp['data'].values[itime,:,:])
initTime = pd.to_datetime(str(tmp.coords['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
labels=(initTime+timedelta(hours=tmp.coords['forecast_period'].values[itime])).strftime("%m月%d日%H时")
cmap=dk_ctables2.ncl_cmaps('BlueDarkRed18')
per_color=utl.get_part_clev_and_cmap(cmap=cmap,clev_range=[0,len(tmp['time'].values)],clev_slt=itime)
ax.contour(
x,y,z, levels=[thr],
colors=per_color, zorder=3,linewidths=2,linestyles='solid',
transform=ccrs.PlateCarree(),alpha=1)
label_handles.append(mpatches.Patch(color=per_color.reshape(4),alpha=1, label=labels))
leg = ax.legend(handles=label_handles, loc=3,framealpha=1,fontsize=10)
#additional information
l, b, w, h = ax.get_position().bounds
# show figure
if(output_dir != None):
plt.savefig(output_dir+'['+tmp.attrs['model']+'] '+str(int(tmp['level'].values[0]))+'hPa '+str(thr)+'℃等温线演变_'+
'起报时间_'+initTime.strftime("%Y年%m月%d日%H时")+
'预报时效_'+str(int(tmp.coords['forecast_period'].values[0]))+'小时'+'.png', dpi=200,bbox_inches='tight')
plt.close()
if(output_dir == None):
plt.show()
def tadv_pcolormesh(ax,
x,
y,
z,
cmap=dk_ctables2.ncl_cmaps('cmp_b2r'),
vmin=-50,
vmax=50,
alpha=0.8,
transform=ccrs.PlateCarree(),
**kwargs):
img = ax.pcolormesh(x,
y,
z,
cmap=cmap,
transform=transform,
vmin=vmin,
vmax=vmax,
**kwargs)
return img
def anm_pcolormesh(ax,
x,
y,
z,
cmap=dk_ctables2.ncl_cmaps('GHRSST_anomaly'),
vmin=-50,
vmax=50,
alpha=0.8,
transform=ccrs.PlateCarree(),
**kwargs):
img = ax.pcolormesh(x,
y,
z,
cmap=cmap,
transform=transform,
vmin=vmin,
vmax=vmax,
**kwargs)
return img
def draw_gh_anomaly_uv(gh=None,
uv=None,
gh_anm=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 位势高度场和异常, ' +
str(int(uv['level'].values[0])) + 'hPa 风场',
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 gh_anm is not None:
x, y = np.meshgrid(gh_anm['lon'], gh_anm['lat'])
z = np.squeeze(gh_anm['data'].values)
cmap = dk_ctables2.ncl_cmaps('GHRSST_anomaly')
plots['gh_anm'] = ax.pcolormesh(x,
y,
z,
vmin=-50,
vmax=50,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.7)
# 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.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 (gh_anm is not None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['gh_anm'],
cax=cax,
orientation='horizontal',
ticks=np.arange(-50, 51, 5),
extend='both',
extendrect=False)
cb.ax.tick_params(labelsize='x-large')
cb.set_label('gpm', 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_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_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()