def Tmx_pcolormesh(ax,
x,
y,
z,
cmap=dk_ctables.cm_temp(),
transform=ccrs.PlateCarree(),
vmin=-45,
vmax=45,
alpha=0.5,
**kwargs):
img = ax.pcolormesh(x,
y,
z,
cmap=cmap,
transform=transform,
alpha=0.5,
vmin=-45,
vmax=45,
**kwargs)
return img
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_gh_uv_tmp(gh=None,
uv=None,
tmp=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
# define plots
plots = {}
# draw mean sea level pressure
if tmp is not None:
x, y = np.meshgrid(tmp['lon'], tmp['lat'])
z = np.squeeze(tmp['data'])
cmap = dk_ctables.cm_temp()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['tmp'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.5)
plots['tmp_contour'] = ax.contour(x,
y,
z,
levels=[-4],
colors='red',
linewidth=8,
zorder=4,
transform=datacrs,
alpha=1,
linestyles='solid')
plt.clabel(plots['tmp_contour'],
inline=1,
fontsize=25,
fmt='%.0f',
colors='red')
# 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,
color='white')
# 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')
#additional information
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, ' +
str(int(tmp['level'].values[0])) + 'hPa 温度',
loc='left',
fontsize=30)
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=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)
# 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 (tmp != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['tmp'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label('Temperature (' + u'°C' + ')', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
if (map_extent2[1] - map_extent2[0] < 25):
small_city = True
if city:
utl.add_city_on_map(ax,
map_extent=map_extent2,
transform=datacrs,
zorder=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 + '高度场_风场_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')
plt.close()
if (output_dir == None):
plt.show()
def draw_T_2m(T_2m=None,
map_extent=(50, 150, 0, 65),
regrid_shape=20,
add_china=True,
city=True,
south_China_sea=True,
output_dir=None,
Global=False):
# set font
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
# set figure
plt.figure(figsize=(16, 9))
if (Global == True):
plotcrs = ccrs.Robinson(central_longitude=115.)
else:
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.,
central_longitude=(map_extent[0] + map_extent[1]) / 2.,
standard_parallels=[30., 60.])
ax = plt.axes([0.01, 0.1, .98, .84], projection=plotcrs)
datacrs = ccrs.PlateCarree()
map_extent2 = utl.adjust_map_ratio(ax,
map_extent=map_extent,
datacrs=datacrs)
# define return plots
plots = {}
if T_2m is not None:
x, y = np.meshgrid(T_2m['lon'], T_2m['lat'])
z = np.squeeze(T_2m['data'])
cmap = dk_ctables.cm_temp()
cmap.set_under(color=[0, 0, 0, 0], alpha=0.0)
plots['T_2m'] = ax.pcolormesh(x,
y,
z,
cmap=cmap,
zorder=1,
transform=datacrs,
alpha=0.5,
vmin=-45,
vmax=45)
z = gaussian_filter(z, 5)
plots['T_2m_zero'] = ax.contour(x,
y,
z,
levels=[0],
colors='#232B99',
linewidths=2,
transform=datacrs,
zorder=2)
cl_zero = plt.clabel(plots['T_2m_zero'],
inline=1,
fontsize=15,
fmt='%i',
colors='#232B99')
for t in cl_zero:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()
])
plots['T_2m_35'] = ax.contour(x,
y,
z,
levels=[35, 37, 40],
colors=['#FF8F00', '#FF6200', '#FF0000'],
linewidths=2,
transform=datacrs,
zorder=2)
cl_35 = plt.clabel(plots['T_2m_35'],
inline=1,
fontsize=15,
fmt='%i',
colors='#FF0000')
for t in cl_35:
t.set_path_effects([
path_effects.Stroke(linewidth=3, foreground='white'),
path_effects.Normal()
])
plt.title('[' + T_2m.attrs['model'] + ']' + ' ' + T_2m.attrs['title'],
loc='left',
fontsize=30)
ax.add_feature(cfeature.OCEAN)
utl.add_china_map_2cartopy_public(ax,
name='coastline',
edgecolor='gray',
lw=0.8,
zorder=4,
alpha=0.5)
if add_china:
utl.add_china_map_2cartopy_public(ax,
name='province',
edgecolor='gray',
lw=0.5,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='nation',
edgecolor='black',
lw=0.8,
zorder=4)
utl.add_china_map_2cartopy_public(ax,
name='river',
edgecolor='#74b9ff',
lw=0.8,
zorder=4,
alpha=0.5)
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--',
zorder=5)
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
utl.add_cartopy_background(ax, name='RD')
l, b, w, h = ax.get_position().bounds
#forecast information
bax = plt.axes([l, b + h - 0.1, .25, .1], facecolor='#FFFFFFCC')
bax.set_yticks([])
bax.set_xticks([])
bax.axis([0, 10, 0, 10])
initTime = pd.to_datetime(
str(T_2m.coords['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
fcst_time = initTime + timedelta(
hours=T_2m.coords['forecast_period'].values[0])
#发布时间
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
plt.text(2.5, 7.5, '起报时间: ' + initTime.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5, 5, '预报时间: ' + fcst_time.strftime("%Y年%m月%d日%H时"), size=15)
plt.text(2.5,
2.5,
'预报时效: ' + str(int(T_2m.coords['forecast_period'].values[0])) +
'小时',
size=15)
plt.text(2.5, 0.5, 'www.nmc.cn', size=15)
# add color bar
if (T_2m != None):
cax = plt.axes([l, b - 0.04, w, .02])
cb = plt.colorbar(plots['T_2m'], cax=cax, orientation='horizontal')
cb.ax.tick_params(labelsize='x-large')
cb.set_label(u'°C', size=20)
# add south China sea
if south_China_sea:
utl.add_south_China_sea(pos=[l + w - 0.091, b, .1, .2])
small_city = False
#if(map_extent2[1]-map_extent2[0] < 25):
# small_city=True
#if city:
utl.add_city_and_number_on_map(ax,
data=T_2m,
map_extent=map_extent2,
transform=datacrs,
zorder=6,
size=13,
small_city=small_city)
utl.add_logo_extra_in_axes(pos=[l - 0.02, b + h - 0.1, .1, .1],
which='nmc',
size='Xlarge')
# show figure
if (output_dir != None):
plt.savefig(output_dir + '最低温度_预报_' + '起报时间_' +
initTime.strftime("%Y年%m月%d日%H时") + '预报时效_' +
str(T_2m.coords['forecast_period'].values[0]) + '小时' +
'.png',
dpi=200,
bbox_inches='tight')
plt.close()
if (output_dir == None):
plt.show()
