def dT2m_pcolormesh(ax,
x,
y,
z,
cmap=utl.linearized_ncl_cmap('hotcold_18lev'),
vmin=-16,
vmax=16,
transform=ccrs.PlateCarree(),
**kwargs):
img = ax.pcolormesh(x,
y,
z,
cmap=cmap,
transform=transform,
vmin=vmin,
vmax=vmax,
**kwargs)
return img
def ulj_contourf(ax,
x,
y,
z,
cmap=utl.linearized_ncl_cmap('MPL_Oranges'),
levels=np.arange(30, 80, 2),
alpha=0.8,
transform=ccrs.PlateCarree(),
**kwargs):
z[z < levels[0]] = np.nan
img = ax.contourf(x,
y,
z,
cmap=cmap,
levels=levels,
transform=transform,
extend='max',
alpha=alpha,
**kwargs)
return img
def dT2m_contour(ax,
x,
y,
z,
cmap=utl.linearized_ncl_cmap('BlRe'),
levels=[-16, -12, -10, -8, -6, 6, 8, 10, 12, 16],
vmin=-16,
vmax=16,
alpha=0.5,
transform=ccrs.PlateCarree(),
**kwargs):
img = ax.contour(x,
y,
z,
levels=levels,
cmap=cmap,
transform=transform,
alpha=alpha,
vmin=vmin,
vmax=vmax,
**kwargs)
return img
def draw_dT_2m(dT_2m=None,
T_type='Tmx',
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))
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
datacrs = ccrs.PlateCarree()
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
# define return plots
plots = {}
if dT_2m is not None:
x, y = np.meshgrid(dT_2m['lon'], dT_2m['lat'])
z = np.squeeze(dT_2m['data'])
cmap = utl.linearized_ncl_cmap('hotcold_18lev')
plots['T_2m'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=1,
vmin=-16,
vmax=16)
z = gaussian_filter(z, 5)
cmap = utl.linearized_ncl_cmap('BlRe')
clevs = [-16, -12, -10, -8, -6, 6, 8, 10, 12, 16]
plots['T_2m_contour'] = ax.contour(x,
y,
z,
levels=clevs,
cmap=cmap,
zorder=3,
transform=datacrs,
alpha=0.5,
vmin=-16,
vmax=16)
clev_colors = []
for iclev in clevs:
per_color = utl.get_part_clev_and_cmap(cmap=cmap,
clev_range=[-16, 16],
clev_slt=iclev)
clev_colors.append(np.squeeze(per_color[:]))
cl = plt.clabel(plots['T_2m_contour'],
inline=1,
fontsize=15,
fmt='%i',
colors=clev_colors)
for t in cl:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='#D9D9D9'),
path_effects.Normal()
])
'''
z=gaussian_filter(z,5)
plots['T_2m_zero'] = ax.contour(
x, y, z, levels=[0], colors='#232B99',
linewidths=2, transform=datacrs, zorder=2)
cl_zero=plt.clabel(plots['T_2m_zero'], inline=1, fontsize=15, fmt='%i',colors='#232B99')
for t in cl_zero:
t.set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()])
plots['T_2m_35'] = ax.contour(
x, y, z, levels=[35,37,40], colors=['#FF8F00','#FF6200','#FF0000'],
linewidths=2, transform=datacrs, zorder=2)
cl_35=plt.clabel(plots['T_2m_35'], inline=1, fontsize=15, fmt='%i',colors='#FF0000')
for t in cl_35:
t.set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()])
'''
plt.title('[' + dT_2m.attrs['model'] + ']' + ' ' + dT_2m.attrs['title'],
loc='left',
fontsize=30)
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=4,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=4,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=5)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
utl.add_cartopy_background(ax, name='RD')
l, b, w, h = ax.get_position().bounds
#forecast information
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(dT_2m.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=dT_2m.coords['forecast_period'].values[0])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5, 7.5, '起报时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(dT_2m.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (dT_2m != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(
plots['T_2m'],
cax=cax,
orientation='horizontal',
extend='both',
ticks=[-16, -12, -10, -8, -6, -4, 0, 4, 6, 8, 10, 12, 16])
cb.ax.tick_params(labelsize='x-large')
cb.set_label(u'°C', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
#if(map_extent2[1]-map_extent2[0] < 25):
# small_city=True
#if city:
utl.add_city_and_number_on_map(ax,
data=dT_2m,
map_extent=map_extent2,
transform=datacrs,
zorder=6,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '最低温度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(dT_2m.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()