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_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)
# 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='purple',
linewidths=2,
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 get_plot_attrs(name, clevs=None, min_lev=None, extend='max'):
"""
获得预先设置的各种变量绘图属性.
Args:
name (str): the name of predefined plot attributes.
Returns:
dict: plot attribute dictionary.
"""
# convert to lower
name = name.lower()
if name == 'z_500_contour':
if clevs is None:
clevs = np.concatenate((np.arange(480, 580,
4), np.arange(580, 604, 4)))
clevs = np.asarray(clevs)
linewidths = np.full(len(clevs), 1)
linewidths[clevs == 588] = 2
return {"levels": clevs, "linewidths": linewidths}
elif name == 'qpf_1h_contourf_blues':
if clevs is None:
clevs = [0.1, 4, 13, 25, 60, 120, 250]
clevs = np.asarray(clevs)
cmap = cm.truncate_colormap('Blues', minval=0.1)
norm = mpl.colors.BoundaryNorm(clevs, cmap.N, extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
elif name == 'nmc_accumulated_rainfall':
if clevs is None:
clevs = [0.1, 10, 25, 50, 100, 250, 400, 600, 800, 1000]
clevs = np.asarray(clevs)
_colors = [[161, 241, 141], [61, 186, 61], [96, 184, 255], [0, 0, 255],
[250, 0, 250], [128, 0, 64], [255, 170, 0], [255, 102, 0],
[230, 0, 0], [80, 45, 10]]
_colors = np.asarray(_colors) / 255.0
if min_lev is not None:
idx = np.where(clevs >= min_lev)
clevs = clevs[idx]
_colors = _colors[idx, ]
cmap, norm = mpl.colors.from_levels_and_colors(clevs,
_colors,
extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
elif name == 'ecmf_accumulated_rainfall':
if clevs is None:
clevs = [0.5, 10, 30, 50, 70, 100, 130, 160]
clevs = np.asarray(clevs)
_colors = np.array([
'#a7aaaa', '#5cc8d7', '#3076bc', '#6aaa43', '#f5832a', '#ee2f2d',
'#8350a0', '#231f20'
])
if min_lev is not None:
idx = np.where(clevs >= min_lev)
clevs = clevs[idx]
_colors = _colors[idx]
cmap, norm = mpl.colors.from_levels_and_colors(clevs,
_colors,
extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
elif name == 'probability_forecast':
if clevs is None:
clevs = [1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
clevs = np.asarray(clevs)
cmap = cm.guide_cmaps(44)
cmap = cm.truncate_colormap(cmap, maxval=0.95)
norm = mpl.colors.BoundaryNorm(clevs, cmap.N, extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
elif name == 'ndfd_t_summer':
if clevs is None:
clevs = np.arange(0, 35, 2)
clevs = np.asarray(clevs)
cmap = cm.ndfd_cmaps('T_summer')
norm = mpl.colors.BoundaryNorm(clevs, cmap.N, extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
elif name == '2m_temperature':
if clevs is None:
clevs = [
-45, -30, -20, -10, -5, 0, 0, 5, 5, 10, 20, 20, 30, 30, 40, 45
]
clevs = np.asarray(clevs)
r = np.asarray([
61, 250, 9, 94, 46, 6, 254, 32, 11, 0, 173, 254, 255, 255, 90, 253
])
g = np.asarray([
2, 0, 0, 157, 94, 249, 254, 178, 244, 97, 255, 254, 140, 99, 3, 253
])
b = np.asarray([
57, 252, 121, 248, 127, 251, 254, 170, 11, 3, 47, 0, 0, 61, 3, 253
])
_colors = np.stack((r, g, b), axis=-1)
_colors = np.asarray(_colors) / 255.0
if min_lev is not None:
idx = np.where(clevs >= min_lev)
clevs = clevs[idx]
_colors = _colors[idx, ]
cmap, norm = mpl.colors.from_levels_and_colors(clevs,
_colors,
extend=extend)
return {'clevs': clevs, 'cmap': cmap, 'norm': norm}
else:
raise ValueError('{} is not supported.'.format(name))
def draw_theta_on_pv(ax,
lon,
lat,
theta,
mslp=None,
gh500=None,
map_extent=(73, 136, 18, 54),
theta_clev=np.arange(300, 400, 4),
alpha=0,
mslp_clev=np.arange(960, 1060, 4),
gh500_clev=np.arange(480, 600, 2),
cax=None,
left_title="850hPa wind",
right_title=None,
add_china=True,
coastline_color='black'):
"""
Draw potential temperature on pv surface.
:param ax: matplotlib axes.
:param lon: longitude coordinates.
:param lat: latitude coordinates.
:param theta: potential temperature.
:param mslp: mean sea level pressure.
:param gh500: geopotential height 500hpa.
:param map_extent: map extent.
:param theta_clev: potential temperature.
:param alpha: theta contour transparency.
:param mslp_clev: mean sea level contour levels.
:param gh500_clev: geopotential height 500 contour levels.
:param cax: color bar axes.
:param left_title: left title.
:param right_title: right title.
:param add_china: draw china province map or not.
:param coastline_color: coast lines color.
:return: potential temperature filled contour cf object.
"""
# set data projection, should be longitude and latitude.
datacrs = ccrs.PlateCarree()
# clear figure
ax.clear
# set map extent
ax.set_extent(map_extent)
# add map boundary
ax.coastlines('50m', edgecolor=coastline_color)
if add_china:
add_china_map_2cartopy(ax, name='province', edgecolor='darkcyan', lw=4)
# draw potential temperature
x, y = np.meshgrid(lon, lat)
cmap = guide_cmaps("27")
cf = ax.contourf(x,
y,
theta,
theta_clev,
cmap=cmap,
alpha=alpha,
antialiased=True,
transform=datacrs)
if cax is not None:
cb = plt.colorbar(cf,
cax=cax,
orientation='horizontal',
extendrect=True,
ticks=theta_clev)
cb.set_label('Potential Temperature [K]', size='large', fontsize=16)
cb.ax.tick_params(labelsize=16)
# draw mean sea level pressure
if mslp is not None:
x, y = np.meshgrid(mslp[0], mslp[1])
cs1 = ax.contour(x,
y,
mslp[2],
mslp_clev,
colors='k',
linewidth=1.0,
linestyles='solid',
transform=datacrs)
plt.clabel(cs1,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# draw 500hPa geopotential height
if gh500 is not None:
x, y = np.meshgrid(gh500[0], gh500[1])
cs2 = ax.contour(x,
y,
gh500[2],
gh500_clev,
colors='w',
linewidth=1.0,
linestyles='dashed',
transform=datacrs)
plt.clabel(cs2,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# add grid lines
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 16}
gl.ylabel_style = {'size': 16}
# add title
ax.set_title(left_title, loc='left', fontsize=18)
if right_title is not None:
ax.set_title(right_title, loc='right', fontsize=18)
# return plot
return cf
def draw_wind850(ax,
lon,
lat,
u,
v,
mslp=None,
gh500=None,
thetae850=None,
map_extent=(73, 136, 18, 54),
wspeed_clev=np.arange(4, 40, 4),
mslp_clev=np.arange(960, 1060, 4),
wind_cmap=None,
gh500_clev=np.arange(480, 600, 2),
thetae850_clev=np.arange(280, 360, 4),
draw_barbs=True,
left_title="850hPa wind",
right_title=None,
add_china=True,
coastline_color='black',
title_font=None,
cax=None,
cb_title='850hPa wind speed (m/s)',
cb_font=None):
"""
Draw 850hPa wind field.
:param ax: matplotlib axes.
:param lon: longitude coordinates.
:param lat: latitude coordinates.
:param u: u wind.
:param v: v wind.
:param mslp: mean sea level pressure, [lon, lat, mslp_data]
:param gh500: 500hPa geopotential height, [lon, lat, gh500_data]
:param thetae850: 850hPa equivalent potential temperature,
[lon, lat, thetae850_data]
:param map_extent: map extent
:param wspeed_clev: wind speed filled contour levels.
:param mslp_clev: mean sea level contour levels.
:param wind_cmap: wind filled contour color map.
:param gh500_clev: geopotential height 500 contour levels.
:param thetae850_clev: 850hPa theta contour levels.
:param draw_barbs: flag for drawing wind barbs.
:param cax: color bar axes, if None, no color bar will be draw.
:param left_title: left title.
:param right_title: right title.
:param add_china: draw china province boundary or not.
:param coastline_color: coast line color.
:param title_font: title font properties, like:
title_font = mpl.font_manager.FontProperties(
fname='C:/Windows/Fonts/SIMYOU.TTF')
:param cb_title: color bar title
:param cb_font: color bar title font properties
:return: wind filled contour cf and barbs bb object.
"""
# set data projection, should be longitude and latitude.
datacrs = ccrs.PlateCarree()
# clear figure
ax.clear
# set map extent
ax.set_extent(map_extent)
# add map boundary
ax.coastlines('50m', edgecolor=coastline_color)
if add_china:
add_china_map_2cartopy(ax, name='province', edgecolor='darkcyan')
# draw 850hPa wind speed
x, y = np.meshgrid(lon, lat)
if wind_cmap is None:
wind_cmap = guide_cmaps("2")
cf = ax.contourf(x,
y,
np.sqrt(u * u + v * v),
wspeed_clev,
cmap=wind_cmap,
transform=datacrs)
if cax is not None:
cb = plt.colorbar(cf,
cax=cax,
orientation='horizontal',
extendrect=True,
ticks=wspeed_clev)
cb.set_label(cb_title,
size='large',
fontsize=16,
fontproperties=cb_font)
cb.ax.tick_params(labelsize=16)
# draw wind barbs
if draw_barbs:
bb = ax.barbs(x,
y,
u,
v,
length=7,
regrid_shape=15,
transform=datacrs,
sizes=dict(emptybarb=0.05))
# draw mean sea level pressure
if mslp is not None:
x, y = np.meshgrid(mslp[0], mslp[1])
cs1 = ax.contour(x,
y,
mslp[2],
mslp_clev,
colors='k',
linewidth=1.0,
linestyles='solid',
transform=datacrs)
plt.clabel(cs1,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# draw 500hPa geopotential height
if gh500 is not None:
x, y = np.meshgrid(gh500[0], gh500[1])
cs2 = ax.contour(x,
y,
gh500[2],
gh500_clev,
colors='w',
linewidth=1.0,
linestyles='dashed',
transform=datacrs)
plt.clabel(cs2,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# draw 850hPa equivalent potential temperature
if thetae850 is not None:
x, y = np.meshgrid(thetae850[0], thetae850[1])
cmap = plt.get_cmap("hsv")
cs3 = ax.contour(x,
y,
thetae850[2],
thetae850_clev,
cmap=cmap,
linewidth=0.8,
linestyles='solid',
transform=datacrs)
plt.clabel(cs3,
fontsize=10,
inline=1,
inline_spacing=10,
fmt='%i',
rightside_up=True,
use_clabeltext=True)
# add grid lines
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 16}
gl.ylabel_style = {'size': 16}
# add title
ax.set_title(left_title,
loc='left',
fontsize=18,
fontproperties=title_font)
if right_title is not None:
ax.set_title(right_title,
loc='right',
fontsize=18,
fontproperties=title_font)
# return plot
if draw_barbs:
return cf, bb
else:
return cf
def draw_gh500_uv850_mslp(ax,
gh500=None,
uv850=None,
mslp=None,
map_extent=(50, 150, 0, 65),
add_china=True,
regrid_shape=20):
"""
Draw 500-hPa geopotential height contours, 850-hPa wind barbs
and mean sea level pressure filled contours.
:param ax: `matplotlib.axes.Axes`, the `Axes` instance used for plotting.
:param gh500: 500-hPa gh, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'data': 2D array}
optional, {'clevs': 1D array}
:param uv850: 850-hPa u-component and v-component wind, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'udata': 2D array, 'vdata': 2D array}
:param mslp: MSLP, 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:
See dk_tool_weather_map.synoptic.gh500_uv850_mslp
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
ax.add_feature(cfeature.LAND, facecolor='0.6')
ax.coastlines('50m', edgecolor='black', linewidth=0.75, zorder=100)
if add_china:
add_china_map_2cartopy(ax,
name='province',
edgecolor='darkcyan',
lw=1,
zorder=100)
# define return plots
plots = {}
# draw mean sea level pressure
if mslp is not None:
x, y = np.meshgrid(mslp['lon'], mslp['lat'])
clevs = mslp.get('clevs')
if clevs is None:
clevs = np.arange(960, 1065, 5)
cmap = guide_cmaps(26)
plots['mslp'] = ax.contourf(x,
y,
np.squeeze(mslp['data']),
clevs,
cmap=cmap,
alpha=0.8,
zorder=10,
transform=datacrs)
# draw 850-hPa wind bards
if uv850 is not None:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
u = np.squeeze(uv850['udata']) * 2.5
v = np.squeeze(uv850['vdata']) * 2.5
plots['uv850'] = 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 500-hPa geopotential height
if gh500 is not None:
x, y = np.meshgrid(gh500['lon'], gh500['lat'])
clevs = gh500.get('clevs')
if clevs is None:
clevs = np.append(np.arange(480, 584, 8), np.arange(580, 604, 4))
plots['gh500'] = ax.contour(x,
y,
np.squeeze(gh500['data']),
clevs,
colors='purple',
linewidths=2,
transform=datacrs,
zorder=30)
plt.clabel(plots['gh500'], inline=1, fontsize=16, fmt='%.0f')
# grid lines
gl = ax.gridlines(crs=datacrs,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xlocator = mpl.ticker.FixedLocator(np.arange(0, 360, 15))
gl.ylocator = mpl.ticker.FixedLocator(np.arange(-90, 90, 15))
# return plots
return plots
def draw_uv850(ax,
uv850=None,
gh850=None,
map_extent=(73, 136, 18, 54),
add_china=True,
regrid_shape=15):
"""
Draw 850-hPa wind field.
:param ax: `matplotlib.axes.Axes`, the `Axes` instance used for plotting.
:param uv850: 850-hPa u-component and v-component wind, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'udata': 2D array, 'vdata': 2D array}
optional, {'clevs': 1D array speed contour levels}
:param gh850: 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.
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
ax.add_feature(cfeature.LAND, facecolor='0.6')
ax.coastlines('50m', edgecolor='black', linewidth=0.75, zorder=100)
if add_china:
add_china_map_2cartopy(ax,
name='province',
edgecolor='darkcyan',
lw=1,
zorder=100)
# define return plots
plots = {}
# draw 850hPa wind speed and barbs
if uv850 is not None:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
u = np.squeeze(uv850['udata'])
v = np.squeeze(uv850['vdata'])
clevs = uv850.get('clevs')
if clevs is None:
clevs = np.arange(4, 40, 4)
cmaps = guide_cmaps("2")
plots['uv850_cf'] = ax.contourf(x,
y,
np.sqrt(u * u + v * v),
clevs,
cmap=cmaps,
transform=datacrs)
plots['uv850_bb'] = ax.barbs(x,
y,
u * 2.5,
v * 2.5,
length=7,
regrid_shape=regrid_shape,
transform=datacrs,
sizes=dict(emptybarb=0.05))
# draw 850hPa geopotential height
if gh850 is not None:
x, y = np.meshgrid(gh850['lon'], gh850['lat'])
clevs = gh850.get('clevs')
if clevs is None:
clevs = np.arange(80, 180, 4)
plots['gh850'] = ax.contour(x,
y,
np.squeeze(gh850['data']),
clevs,
colors='purple',
linewidths=2,
transform=datacrs,
zorder=30)
plt.clabel(plots['gh850'], inline=1, fontsize=16, fmt='%.0f')
# add grid lines
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 16}
gl.ylabel_style = {'size': 16}
# return
return plots
