def draw_precipitation_nws(ax,
prep,
map_extent=(73, 136, 17, 54),
gridlines=True):
"""
Draw NWS-style precipitation map.
http://jjhelmus.github.io/blog/2013/09/17/plotting-nsw-precipitation-data/
:param ax: `matplotlib.axes.Axes`, the `Axes` instance used for plotting.
:param prep: precipitation, dictionary:
{'lon': 1D array, 'lat': 1D array, 'data': 2D array}
:param map_extent: (lonmin, lonmax, latmin, latmax),
longitude and latitude range.
:param gridlines: bool, draw grid lines or not.
:return: plots dictionary.
:Example:
>>> plt.ioff()
>>> fig = plt.figure(figsize=(8.6, 6.2))
>>> fig.clf()
>>> ax = plt.axes((0.1, 0.08, 0.85, 0.92), projection=ccrs.PlateCarree())
>>> prep = {'lon': lon, 'lat': lat, 'data': rain}
>>> plots = draw_precipitation_nws(ax, prep, map_extent=[73, 136, 17, 54])
>>> ax.set_title('24h Accumulated Precipitation', fontsize=18, loc='left')
>>> cax = fig.add_axes([0.16, 0.08, 0.7, 0.03])
>>> cb = plt.colorbar(plots['prep'], cax=cax, orientation='horizontal',
>>> ticks=plots['prep'].norm.boundaries)
>>> cb.set_label('Precipitation (mm)', fontsize=10)
>>> cb.ax.tick_params(labelsize=10)
>>> plt.savefig('D:/plot.png')
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=1)
# plots container
plots = {}
# draw precipitation map
x, y = np.meshgrid(prep['lon'], prep['lat'])
cmap, norm = cm_precipitation_nws()
plots['prep'] = ax.pcolormesh(x,
y,
np.squeeze(prep['data']),
norm=norm,
cmap=cmap,
transform=datacrs)
# add grid lines
if gridlines:
add_gridlines(ax)
# return
return plots
def draw_qpf_nmc(ax, prep, stations=None, map_extent=(107., 123, 28, 43.)):
"""
Draw filled-contour QPF.
:param ax: ax: `matplotlib.axes.Axes`, the `Axes` instance.
:param prep: precipitation, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array,
'data': 2D array}
optional, {'clevs': 1D array}
:param stations: station locations, dictionary:
necessary, {'lon': 1D array, 'lat': 1D array}
:param map_extent: [lonmin, lonmax, latmin, latmax],
longitude and latitude range.
:return: plots dictionary.
"""
# set data projection
datacrs = ccrs.PlateCarree()
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=1)
# plots container
plots = {}
# draw precipitation map
x, y = np.meshgrid(prep['lon'], prep['lat'])
if prep.get('clevs') is None:
clevs = [0.1, 10, 25, 50, 100, 250, 600]
cmap = plt.get_cmap("YlGnBu")
norm = mpl.colors.BoundaryNorm(clevs, cmap.N)
plots['prep'] = ax.contourf(x,
y,
np.squeeze(prep['data']),
clevs,
norm=norm,
cmap=cmap,
transform=datacrs)
# add station points
if stations is not None:
plots['stations'] = ax.scatter(stations['lon'],
stations['lat'],
transform=ccrs.PlateCarree())
# add grid lines
add_gridlines(ax)
# return
return plots
def show_obfo_rain_24(grid_fo,sta_ob,filename = None):
fig = plt.figure(figsize=(6,3.6))
# 平面对比图
rect1 = [0.01,0.01,0.98,0.98] # 左下宽高
datacrs = ccrs.PlateCarree()
ax = plt.axes(rect1,projection=datacrs)
# 设置地图背景
map_extent = [grid_fo.slon,grid_fo.elon,grid_fo.slat,grid_fo.elat]
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=0.3) #省界
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=0.3) #河流
#绘制格点预报场
x = np.arange(grid_fo.nlon) * grid_fo.dlon + grid_fo.slon
y = np.arange(grid_fo.nlat) * grid_fo.dlat + grid_fo.slat
clevs = [0.1, 10, 25, 50, 100, 250, 1000]
colors_grid = ["#E0EEFA", "#B4D3E9", "#6FB0D7", "#3787C0", "#105BA4", "#07306B","#07306B"]
plot_grid = ax.contourf(x, y, grid_fo.dat, clevs, colors = colors_grid, transform=datacrs) #填色图
colorbar_position_grid = fig.add_axes([0.035, 0.94, 0.25, 0.015]) # 位置[左,下,宽,高]
plt.colorbar(plot_grid,cax = colorbar_position_grid,orientation='horizontal')
plt.text(0.035, 0.955, "model accumulated precipition(mm)", fontsize=10)
# 绘制填色站点值
sta_ob_in = bt.ssf.get_sta_in_grid(sta_ob, grid=grid_fo.grid)
colors_sta = ['#FFFFFF','#0055FF','#00FFB4','#F4FF00','#FE1B00','#910000','#B800BA']
dat = sta_ob_in.values[:, 2]
dat[dat>1000] = 0
clevs = [0,0.1, 10, 25, 50, 100, 250, 1000]
cleves_name = ["0","0.1-10","10-25","25-50","50-100","100-250",">=250"]
for i in range(len(clevs)-1):
index0 = np.where((dat>=clevs[i])&(dat<clevs[i+1]))
if (len(index0[0]) > 0):
x = np.squeeze(sta_ob_in.values[index0, 0])
y = np.squeeze(sta_ob_in.values[index0, 1])
if (len(index0) == 1):
x = np.array([x])
y = np.array([y])
if(i >0):
ax.scatter(x, y, c=colors_sta[i], transform=ccrs.PlateCarree(), s=3,label=cleves_name[i],linewidths=0.3,edgecolor='k')
else:
ax.scatter(x, y, c=colors_sta[i], transform=ccrs.PlateCarree(), s=1, label=cleves_name[i])
ax.legend(facecolor ='whitesmoke',title = "observation",loc = "lower left",edgecolor = 'whitesmoke')
#图片显示或保存
if(filename is None):
plt.show()
else:
plt.savefig(filename,dpi = 300)
plt.close()
return
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
def rain_24h_comprehensive_sg(sta_ob, grd_fo, filename=None):
'''
#绘制24小时降水实况与预报综合对比检验图，画幅中央为预报实况的对比，左右两侧为各类检验指标
:param grd_fo: 输入的网格数据，包含一个平面的网格场
:param sta_ob: 输入的站点数据，包含一个时刻的站点数据列表
:param filename: 图片输出路径，缺省时会以调试窗口形式弹出
:return:无返回值
'''
grid_fo = nmc_verification.nmc_vf_base.get_grid_of_data(grd_fo)
#通过经纬度范围设置画幅
hight = 5.6
title_hight = 0.3
legend_hight = 0.6
left_plots_width = 3
right_plots_width = 2.3
width = (
hight - title_hight - legend_hight
) * grid_fo.nlon / grid_fo.nlat + left_plots_width + right_plots_width
map_width = width - left_plots_width - right_plots_width
fig = plt.figure(figsize=(width, hight))
# 设置画幅的布局方式，
rect1 = [
left_plots_width / width, 0.12,
(width - right_plots_width - left_plots_width) / width, 0.84
] # 左下宽高,中央对比图
ylabelwidth = 0.52 / width
rect2 = [
ylabelwidth, 0.08, left_plots_width / width - ylabelwidth - 0.005, 0.40
] # 左下宽高，散点回归图
ylabelwidth = 0.65 / width
rect3 = [
ylabelwidth, 0.60, left_plots_width / width - ylabelwidth - 0.005, 0.18
] # 左下宽高，频谱统计柱状图
rect4 = [0.01, 0.79, left_plots_width / width - 0.045, 0.15] # 左下宽高，左侧文字
rect5 = [(width - right_plots_width) / width + 0.005, -0.035,
right_plots_width / width - 0.01, 0.90] # 左下宽高，右侧文字
width_ob_fo_str = 0.3
if (map_width < 3.5):
width_bar = 2.1 #根据中间地图的宽度，来确定预报colorbar的尺寸
sta_legend_size = 5 #根据中间地图的宽度，来确定观测legend的size
else:
sta_legend_size = 7
width_bar = 2.9
rect6 = [(left_plots_width + 0.5 * map_width - 0.5 * width_bar +
0.5 * width_ob_fo_str) / width, 0.04, width_bar / width,
0.02] #预报colorbar
rect7 = [(left_plots_width + 0.5 * map_width - 0.5 * width_bar -
0.5 * width_ob_fo_str) / width, 0.04, width_ob_fo_str / width,
0.3] #观测文字
#rect8 = [left_plots_width / width, 0.04, width_ob_fo_str / width, 0.3] # 预报文字
datacrs = ccrs.PlateCarree()
ax = plt.axes(rect1, projection=datacrs)
# 设置地图背景
map_extent = [grid_fo.slon, grid_fo.elon, grid_fo.slat, grid_fo.elat]
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=0.3) # 河流
# 绘制格点预报场
x = np.arange(grid_fo.nlon) * grid_fo.dlon + grid_fo.slon
y = np.arange(grid_fo.nlat) * grid_fo.dlat + grid_fo.slat
clevs = [0.1, 10, 25, 50, 100, 250, 1000]
colors_grid = [
"#D0DEEA", "#B4D3E9", "#6FB0D7", "#3787C0", "#105BA4", "#07306B",
"#07306B"
]
dat = grd_fo.values.squeeze()
#print(x)
#print(y)
#print(dat)
plot_grid = ax.contourf(x,
y,
dat,
clevs,
colors=colors_grid,
transform=datacrs) # 填色图
time_str = nmc_verification.nmc_vf_base.tool.time_tools.time_to_str(
grid_fo.gtime[0])
dati_str = time_str[0:4] + "年" + time_str[4:6] + "月" + time_str[
6:8] + "日" + time_str[8:10] + "时"
if type(grid_fo.members[0]) == str:
model_name = grid_fo.members[0]
else:
model_name = str(grid_fo.members[0])
if map_width < 3:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=7)
elif map_width < 4:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=10)
else:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=11)
colorbar_position_grid = fig.add_axes(rect6) # 位置[左,下,宽,高]
cb = plt.colorbar(plot_grid,
cax=colorbar_position_grid,
orientation='horizontal')
cb.ax.tick_params(labelsize=8) # 设置色标刻度字体大小。
#plt.text(0, 0, "预报(mm)", fontsize=8)
# 绘制填色站点值
sta_ob_in = nmc_verification.nmc_vf_base.function.get_from_sta_data.sta_in_grid_xy(
sta_ob, grid=grid_fo)
colors_sta = [
'#FFFFFF', '#0055FF', '#00FFB4', '#F4FF00', '#FE1B00', '#910000',
'#B800BA'
]
dat = sta_ob_in.values[:, -1]
dat[dat > 1000] = 0
clevs = [0, 0.1, 10, 25, 50, 100, 250, 1000]
cleves_name = [
"0", "0.1-10", "10-25", "25-50", "50-100", "100-250", ">=250"
]
for i in range(len(clevs) - 1):
index0 = np.where((dat >= clevs[i]) & (dat < clevs[i + 1]))
if (len(index0[0]) > 0):
x = np.squeeze(sta_ob_in["lon"].values[index0])
y = np.squeeze(sta_ob_in["lat"].values[index0])
if (len(index0) == 1):
x = np.array([x])
y = np.array([y])
if (i > 0):
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=3,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=1,
label=cleves_name[i],
linewidths=0.1,
edgecolor="k")
ax.legend(facecolor='whitesmoke',
loc="lower center",
ncol=4,
edgecolor='whitesmoke',
prop={'size': sta_legend_size},
bbox_to_anchor=(0.5 + 0.5 * width_ob_fo_str / map_width, -0.08))
ax7 = plt.axes(rect7)
ax7.axes.set_axis_off()
plt.text(0, 0.00, "观测\n\n预报", fontsize=7)
#ax.legend(loc="lower right", ncol=4, facecolor='whitesmoke', title="观测", edgecolor='whitesmoke', fontsize=9,
# bbox_to_anchor=(0, -0.32))
# 散点回归图
ax2 = plt.axes(rect2)
sta_fo = nmc_verification.nmc_vf_base.function.gxy_sxy.interpolation_linear(
grd_fo, sta_ob_in)
#print(sta_fo)
data_name = nmc_verification.nmc_vf_base.get_data_names(sta_ob_in)
ob = sta_ob_in[data_name[0]].values
data_name = nmc_verification.nmc_vf_base.get_data_names(sta_fo)
fo = sta_fo[data_name[0]].values
ax2.plot(ob, fo, '.', color='k')
# 绘制比例线
rate = np.sum(fo) / (np.sum(ob) + 1e-30)
ob_line = np.arange(0, np.max(ob), np.max(ob) / 30)
fo_rate = ob_line * rate
ax2.plot(ob_line[0:20], fo_rate[0:20], 'r')
# 绘制回归线
X = np.zeros((len(ob), 1))
X[:, 0] = ob[:]
clf = LinearRegression().fit(X, fo)
X = np.zeros((len(ob_line), 1))
X[:, 0] = ob_line[:]
fo_rg = clf.predict(X)
ax2.plot(ob_line, fo_rg, color='b', linestyle='dashed')
cor = np.corrcoef(ob, fo)
rg_text1 = "R = " + '%.2f' % (cor[0, 1])
rg_text2 = "y = " + '%.2f' % (clf.coef_[0]) + "* x + " + '%.2f' % (
clf.intercept_)
maxy = max(np.max(ob), np.max(fo)) + 5
plt.xlim(0, maxy)
plt.ylim(0, maxy)
plt.text(0.05 * maxy, 0.9 * maxy, rg_text1, fontsize=10)
plt.text(0.05 * maxy, 0.8 * maxy, rg_text2, fontsize=10)
maxy = max(np.max(ob), np.max(fo))
ax2.set_xlabel("观测", fontsize=9)
ax2.set_ylabel("预报", fontsize=9)
ax2.set_title("Obs.vs Pred. Scatter plot", fontsize=12)
# 设置次刻度间隔
xmi = 2
if (np.max(ob) > 100): xmi = 5
ymi = 2
if (np.max(fo) > 100): ymi = 5
xmajorLocator = mpl.ticker.MultipleLocator(10 * xmi) # 将x主刻度标签设置为次刻度10倍
ymajorLocator = mpl.ticker.MultipleLocator(10 * ymi) # 将y主刻度标签设置为次刻度10倍
ax2.xaxis.set_major_locator(xmajorLocator)
ax2.yaxis.set_major_locator(ymajorLocator)
xminorLocator = mpl.ticker.MultipleLocator(xmi) # 将x轴次刻度标签设置xmi
yminorLocator = mpl.ticker.MultipleLocator(ymi) # 将y轴次刻度标签设置ymi
ax2.xaxis.set_minor_locator(xminorLocator)
ax2.yaxis.set_minor_locator(yminorLocator)
# 绘制频率柱状图
p_ob = np.zeros(6)
p_fo = np.zeros(6)
x = np.arange(6) + 1
for i in range(1, len(clevs) - 1, 1):
index0 = np.where((ob >= clevs[i]) & (ob < clevs[i + 1]))
p_ob[i - 1] = len(index0[0])
index0 = np.where((fo >= clevs[i]) & (fo < clevs[i + 1]))
p_fo[i - 1] = len(index0[0])
ax3 = plt.axes(rect3)
ax3.bar(x - 0.25, p_ob, width=0.2, facecolor="r", label="Obs")
ax3.bar(x + 0.05, p_fo, width=0.2, facecolor="b", label="Pred")
ax3.legend(loc="upper right")
ax3.set_xlabel("precipitation threshold", fontsize=10)
ax3.set_xticks(x)
ax3.set_xticklabels(
["0.1-10", "10-25", "25-50", "50-100", "100-250", ">=250"], fontsize=9)
ax3.set_ylabel("point number", fontsize=10)
ax3.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(100))
# 绘制降水站点实况预报统计表
ax4 = plt.axes(rect4)
ax4.axes.set_axis_off()
ob_has = ob[ob >= 0.01]
fo_has = fo[fo >= 0.01]
text = "降水站点实况和预报 n=" + str(len(ob)) + "\n"
text += "=============================================\n"
text += " 观测 预报\n"
text += "---------------------------------------------\n"
text += "有降水站点数(>=0.01) " + "%4d" % len(
ob_has) + " %4d" % len(fo_has) + "\n"
text += "有降水站点数百分比% " + "%6.1f" % (len(ob_has) / len(ob)) + "%15.1f" % (
len(fo_has) / len(fo)) + "\n"
text += "平均降水量(排除无降水) " + "%6.1f" % (np.mean(ob_has)) + "%15.1f" % (
np.mean(fo_has)) + "\n"
text += "最大降水量 " + "%6.1f" % (np.max(ob_has)) + "%15.1f" % (
np.max(fo_has)) + "\n"
text += "---------------------------------------------"
plt.text(0, 0, text, fontsize=9)
# 绘制统计检验结果
ax5 = plt.axes(rect5)
ax5.axes.set_axis_off()
mae = nmc_verification.nmc_vf_method.continuous.score.mae(ob, fo)
me = nmc_verification.nmc_vf_method.continuous.score.me(ob, fo)
mse = nmc_verification.nmc_vf_method.continuous.score.mse(ob, fo)
rmse = nmc_verification.nmc_vf_method.continuous.score.rmse(ob, fo)
bias_c = nmc_verification.nmc_vf_method.continuous.score.bias(ob, fo)
cor = nmc_verification.nmc_vf_method.continuous.score.corr(ob, fo)
hit, mis, fal, co = nmc_verification.nmc_vf_method.yes_or_no.score.hmfn(
ob, fo, clevs[1:])
ts = nmc_verification.nmc_vf_method.yes_or_no.score.ts(ob, fo, clevs[1:])
ets = nmc_verification.nmc_vf_method.yes_or_no.score.ets(ob, fo, clevs[1:])
bias = nmc_verification.nmc_vf_method.yes_or_no.score.bias(
ob, fo, clevs[1:])
hit_rate = nmc_verification.nmc_vf_method.yes_or_no.score.hit_rate(
ob, fo, clevs[1:])
mis_rate = nmc_verification.nmc_vf_method.yes_or_no.score.mis_rate(
ob, fo, clevs[1:])
fal_rate = nmc_verification.nmc_vf_method.yes_or_no.score.fal_rate(
ob, fo, clevs[1:])
text = str(len(ob)) + "评分站点预报检验统计量\n"
text += "Mean absolute error:" + "%6.2f" % mae + "\n"
text += "Mean error:" + "%6.2f" % me + "\n"
text += "Mean-squared error:" + "%6.2f" % mse + "\n"
text += "Root mean-squared error:" + "%6.2f" % rmse + "\n"
text += "Bias:" + "%6.2f" % bias_c + "\n"
text += "Correctlation coefficiant:" + "%6.2f" % cor + "\n\n\n"
leves_name = [
"0.1-10-", "10-25--", "25-50--", "50-100-", "100-250", ">=250-"
]
for i in range(len(leves_name)):
text += ":" + leves_name[i] + "---------------------------\n"
text += "正确:" + "%-4d" % hit[i] + " 空报:" + "%-4d" % fal[
i] + " 漏报:" + "%-4d" % mis[i] + "\n"
text += "TS:" + "%5.3f" % ts[
i] + " ETS:" + "%5.3f" % ets[i] + "\n"
text += "Hit rate:" + "%5.3f" % hit_rate[
i] + " Miss rate: " + "%5.3f" % mis_rate[i] + "\n"
text += "False alarm ratio:" + "%5.3f" % fal_rate[
i] + " Bias:" + "%5.3f" % bias[i] + "\n\n"
plt.text(0, 0.00, text, fontsize=9)
# 图片显示或保存
if (filename is None):
plt.show()
print()
else:
plt.savefig(filename, dpi=300)
plt.close()
return
def draw_total_precipitation(prep, map_extent=(107., 112, 23.2, 26.5),
back_image='terrain-background', back_image_zoom=8, title="降水量实况图",
draw_station=True, station_info='cities', station_size=22,
just_contourf=False):
"""
该程序用于显示多日的累积降水量分布特征, 2020/6/7按业务要求制作.
Args:
ax (matplotlib.axes.Axes): the `Axes` instance used for plotting.
prep (dictionary): precipitation, dictionary: {'lon': 1D array, 'lat': 1D array, 'data': 2D array}
map_extent (tuple, optional): (lonmin, lonmax, latmin, latmax),. Defaults to (107., 112, 23.2, 26.5).
back_image (str, opional): the background image name. Default is stamen 'terrain-background', else is
arcgis map server 'World_Physical_Map' (max zoom level is 8)
back_image_zoom (int, optional): the zoom level for background image. Defaults to 8.
draw_station (bool, optional): draw station name. Defaults to True.
station_info (str, optional): station information, 'cities' is 260 city names, or province captial shows.
station_size (int, optional): station font size. Defaults to 22.
title (str, optional): title string. Defaults to "降水量实况图".
Example:
import pandas as pd
from nmc_met_graphics.plot.precipitation import draw_total_precipitation
from nmc_met_io.retrieve_micaps_server import get_model_grids
# read data
times = pd.date_range(start = pd.to_datetime('2020-06-02 08:00'), end = pd.to_datetime('2020-06-07 08:00'), freq='1H')
dataset = get_model_grids("CLDAS/RAIN01_TRI_DATA_SOURCE", times.strftime("%y%m%d%H.000"))
data = dataset.sum(dim="time")
data['data'].values[data['data'].values > 2400.0] = np.nan
prep = {'lon': data['lon'].values, 'lat': data['lat'].values, 'data': data['data'].values}
# draw the figure
draw_total_precipitation(prep);
"""
# set figure size
fig = plt.figure(figsize=(16, 14.5))
# set map projection
datacrs = ccrs.PlateCarree()
mapcrs = ccrs.LambertConformal(
central_longitude=np.mean(map_extent[0:1]), central_latitude=np.mean(map_extent[2:3]),
standard_parallels=(30, 60))
ax = plt.axes((0.1, 0.08, 0.85, 0.92), projection=mapcrs)
ax.set_extent(map_extent, crs=datacrs)
# add map background
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='cyan', lw=1)
if back_image == 'terrain-background':
stamen_terrain = cimg.Stamen('terrain-background')
ax.add_image(stamen_terrain, back_image_zoom)
else:
image = cimg.GoogleTiles(url="https://server.arcgisonline.com/arcgis/rest/services/World_Physical_Map/MapServer/tile/{z}/{y}/{x}.jpg")
ax.add_image(image, back_image_zoom)
# set colors and levels
clevs = [50, 100, 200, 300, 400, 500, 600]
colors = ['#6ab4f1', '#0001f6', '#f405ee', '#ffa900', '#fc6408', '#e80000', '#9a0001']
linewidths = [1, 1, 2, 2, 3, 4, 4]
cmap, norm = mpl.colors.from_levels_and_colors(clevs, colors, extend='max')
# draw precipitation contour map
x, y = np.meshgrid(prep['lon'], prep['lat'])
if just_contourf:
_ = ax.contourf(
x, y, np.squeeze(prep['data']), clevs, norm=norm,
cmap=cmap, transform=datacrs, extend='max', alpha=0.5)
else:
_ = ax.contourf(
x, y, np.squeeze(prep['data']), clevs, norm=norm,
cmap=cmap, transform=datacrs, extend='max', alpha=0.1)
con2 = ax.contour(
x, y, np.squeeze(prep['data']), clevs, norm=norm,
cmap=cmap, transform=datacrs, linewidths=linewidths)
# add path effects
plt.setp(con2.collections, path_effects=[
path_effects.SimpleLineShadow(), path_effects.Normal()])
# add title and legend
font = FontProperties(family='Microsoft YaHei', size=32)
ax.set_title('降水量实况图(累计降水: 6月02日—6月06日)', loc='center', fontproperties=font)
font = FontProperties(family='Microsoft YaHei', size=16)
plt.legend([mpatches.Patch(color=b) for b in colors],[
'50~100 毫米', '100~200 毫米', '200-300 毫米', '300~400 毫米', '400~500 毫米', '500~600 毫米', '>=600毫米'],
prop=font)
# add city information
if draw_station:
if station_info == 'cities':
cities = pd.read_csv(pkg_resources.resource_filename(
'nmc_met_graphics', "resources/stations/cma_city_station_info.dat"), delimiter=r"\s+")
else:
cities = pd.read_csv(pkg_resources.resource_filename(
'nmc_met_graphics', "resources/stations/provincial_capital.csv"))
font = FontProperties(family='SimHei', size=22, weight='bold')
geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax)
for _, row in cities.iterrows():
text_transform = offset_copy(geodetic_transform, units='dots', x=-5)
ax.plot(row['lon'], row['lat'], marker='o', color='white', markersize=8,
alpha=0.7, transform=datacrs)
ax.text(row['lon'], row['lat'], row['city_name'], clip_on=True,
verticalalignment='center', horizontalalignment='right',
transform=text_transform, fontproperties=font, color='white',
path_effects=[
path_effects.Stroke(linewidth=1, foreground='black'),path_effects.Normal()])
return fig
def draw_composite_map(date_obj, t850, u200, v200, u500, v500, mslp, gh500,
u850, v850, pwat):
"""
Draw synoptic composite map.
Args:
map_subset (int, optional): [description]. Defaults to 1.
map_region (list, optional): [description]. Defaults to [70, 140, 20, 60].
"""
#Get lat and lon arrays for this dataset:
lat = t850.lat.values
lon = t850.lon.values
#========================================================================================================
# Create a Basemap plotting figure and add geography
#========================================================================================================
#Create a Plate Carree projection object
proj_ccrs = ccrs.Miller(central_longitude=0.0)
#Create figure and axes for main plot and colorbars
fig = plt.figure(figsize=(18, 12), dpi=125)
gs = gridspec.GridSpec(12, 36, figure=fig) #[ytop:ybot, xleft:xright]
ax = plt.subplot(gs[:, :-1], projection=proj_ccrs) #main plot
ax.set_xticklabels([])
ax.set_yticklabels([])
ax2 = plt.subplot(gs[:4, -1]) #top plot
ax2.set_xticklabels([])
ax2.set_yticklabels([])
ax3 = plt.subplot(gs[4:8, -1]) #bottom plot
ax3.set_xticklabels([])
ax3.set_yticklabels([])
ax4 = plt.subplot(gs[8:, -1]) #bottom plot
ax4.set_xticklabels([])
ax4.set_yticklabels([])
#Add political boundaries and coastlines
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidths=1.2)
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linewidths=1.2)
ax.add_feature(cfeature.STATES.with_scale('50m'), linewidths=0.5)
#Add land/lake/ocean masking
land_mask = cfeature.NaturalEarthFeature('physical',
'land',
'50m',
edgecolor='face',
facecolor='#e6e6e6')
sea_mask = cfeature.NaturalEarthFeature('physical',
'ocean',
'50m',
edgecolor='face',
facecolor='#ffffff')
lake_mask = cfeature.NaturalEarthFeature('physical',
'lakes',
'50m',
edgecolor='face',
facecolor='#ffffff')
ax.add_feature(sea_mask, zorder=0)
ax.add_feature(land_mask, zorder=0)
ax.add_feature(lake_mask, zorder=0)
#========================================================================================================
# Fill contours
#========================================================================================================
#--------------------------------------------------------------------------------------------------------
# 850-hPa temperature
#--------------------------------------------------------------------------------------------------------
#Specify contour settings
clevs = np.arange(-40, 40, 1)
cmap = plt.cm.jet
extend = "both"
#Contour fill this variable
norm = col.BoundaryNorm(clevs, cmap.N)
cs = ax.contourf(lon,
lat,
t850,
clevs,
cmap=cmap,
norm=norm,
extend=extend,
transform=proj_ccrs,
alpha=0.1)
#--------------------------------------------------------------------------------------------------------
# PWAT
#--------------------------------------------------------------------------------------------------------
#Specify contour settings
clevs = np.arange(20, 71, 0.5)
#Define a color gradient for PWAT
pwat_colors = gradient([[(255, 255, 255), 0.0], [(255, 255, 255), 20.0]],
[[(205, 255, 205), 20.0], [(0, 255, 0), 34.0]],
[[(0, 255, 0), 34.0], [(0, 115, 0), 67.0]])
cmap = pwat_colors.get_cmap(clevs)
extend = "max"
#Contour fill this variable
norm = col.BoundaryNorm(clevs, cmap.N)
cs = ax.contourf(lon,
lat,
pwat,
clevs,
cmap=cmap,
norm=norm,
extend=extend,
transform=proj_ccrs,
alpha=0.9)
#Add a color bar
cbar = plt.colorbar(cs,
cax=ax2,
shrink=0.75,
pad=0.01,
ticks=[20, 30, 40, 50, 60, 70])
#--------------------------------------------------------------------------------------------------------
# 250-hPa wind
#--------------------------------------------------------------------------------------------------------
#Get the data for this variable
wind = calc.wind_speed(u200, v200)
#Specify contour settings
clevs = [40, 50, 60, 70, 80, 90, 100, 110]
cmap = col.ListedColormap([
'#99E3FB', '#47B6FB', '#0F77F7', '#AC97F5', '#A267F4', '#9126F5',
'#E118F3', '#E118F3'
])
extend = "max"
#Contour fill this variable
norm = col.BoundaryNorm(clevs, cmap.N)
cs = ax.contourf(lon,
lat,
wind,
clevs,
cmap=cmap,
norm=norm,
extend=extend,
transform=proj_ccrs)
#Add a color bar
cbar = plt.colorbar(cs, cax=ax3, shrink=0.75, pad=0.01, ticks=clevs)
#--------------------------------------------------------------------------------------------------------
# 500-hPa smoothed vorticity
#--------------------------------------------------------------------------------------------------------
#Get the data for this variable
dx, dy = calc.lat_lon_grid_deltas(lon, lat)
vort = calc.vorticity(u500, v500, dx, dy)
smooth_vort = smooth(vort, 5.0) * 10**5
#Specify contour settings
clevs = np.arange(2, 20, 1)
cmap = plt.cm.autumn_r
extend = "max"
#Contour fill this variable
norm = col.BoundaryNorm(clevs, cmap.N)
cs = ax.contourf(lon,
lat,
smooth_vort,
clevs,
cmap=cmap,
norm=norm,
extend=extend,
transform=proj_ccrs,
alpha=0.3)
#Add a color bar
cbar = plt.colorbar(cs, cax=ax4, shrink=0.75, pad=0.01, ticks=clevs[::2])
#========================================================================================================
# Contours
#========================================================================================================
#--------------------------------------------------------------------------------------------------------
# MSLP
#--------------------------------------------------------------------------------------------------------
#Specify contour settings
clevs = np.arange(960, 1040 + 4, 4)
style = 'solid' #Plot solid lines
color = 'red' #Plot lines as gray
width = 0.8 #Width of contours 0.25
#Contour this variable
cs = ax.contour(lon,
lat,
mslp,
clevs,
colors=color,
linewidths=width,
linestyles=style,
transform=proj_ccrs,
alpha=0.9)
#Include value labels
ax.clabel(cs, inline=1, fontsize=9, fmt='%d')
#--------------------------------------------------------------------------------------------------------
# Geopotential heights
#--------------------------------------------------------------------------------------------------------
#Get the data for this variable
gh500 = gh500 / 10.0
#Specify contour settings
clevs = np.arange(480, 612, 4)
style = 'solid' #Plot solid lines
color = 'black' #Plot lines as gray
width = 2.0 #Width of contours
#Contour this variable
cs = ax.contour(lon,
lat,
gh500,
clevs,
colors=color,
linewidths=width,
linestyles=style,
transform=proj_ccrs)
#Include value labels
ax.clabel(cs, inline=1, fontsize=12, fmt='%d')
#--------------------------------------------------------------------------------------------------------
# Surface barbs
#--------------------------------------------------------------------------------------------------------
#Plot wind barbs
quivers = ax.quiver(lon,
lat,
u850.values,
v850.values,
transform=proj_ccrs,
regrid_shape=(38, 30),
scale=820,
alpha=0.5)
#--------------------------------------------------------------------------------------------------------
# Label highs & lows
#--------------------------------------------------------------------------------------------------------
#Label highs and lows
add_mslp_label(ax, proj_ccrs, mslp, lat, lon)
#========================================================================================================
# Step 6. Add map boundary, legend, plot title, then save image and close
#========================================================================================================
#Add china province boundary
add_china_map_2cartopy(ax, name='province')
#Add custom legend
from matplotlib.lines import Line2D
custom_lines = [
Line2D([0], [0], color='#00A123', lw=5),
Line2D([0], [0], color='#0F77F7', lw=5),
Line2D([0], [0], color='#FFC000', lw=5),
Line2D([0], [0], color='k', lw=2),
Line2D([0], [0], color='k', lw=0.1, marker=r'$\rightarrow$', ms=20),
Line2D([0], [0], color='r', lw=0.8),
]
ax.legend(custom_lines, [
'PWAT (mm)', '200-hPa Wind (m/s)', '500-hPa Vorticity',
'500-hPa Height (dam)', '850-hPa Wind (m/s)', 'MSLP (hPa)'
],
loc=2,
prop={'size': 12})
#Format plot title
title = "Synoptic Composite \nValid: " + dt.datetime.strftime(
date_obj, '%Y-%m-%d %H%M UTC')
st = plt.suptitle(title, fontweight='bold', fontsize=16)
st.set_y(0.92)
#Return figuration
return (fig)
def fig_cldas_temp(indata,
figsize=12,
map_extent=(100, 125, 25, 45),
gridlines=False,
outfile=None,
title="CLDAS Temperature",
time=None):
"""Produce CLDAS temperature map figure.
Arguments:
indata {dictionary or xarray dataset} --
{'lon': 1D array, 'lat': 1D array, 'data': 2D array}
Keyword Arguments:
figsize {tuple or int} -- figure size (default: {12})
map_extent {tuple} -- (lonmin, lonmax, latmin, latmax) (default: {(100, 125, 25, 45)})
gridlines {bool} -- bool, draw grid lines or not. (default: {False})
outfile {string} -- save figure to outfile (default: {None})
title {string} -- figure title.
time {datetime} -- analysis time.
"""
# check data type
if isinstance(indata, xr.core.dataarray.DataArray):
data = {
'lon': indata.coords['lon'].values,
'lat': indata.coords['lat'].values,
'data': np.squeeze(indata.values)
}
else:
data = indata
# set data projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=(map_extent[2] + map_extent[3]) / 2.0,
central_longitude=(map_extent[0] + map_extent[1]) / 2.0,
standard_parallels=[30., 60.])
# set figure
if isinstance(figsize, int):
ratio = (map_extent[3] - map_extent[2]) / (map_extent[1] -
map_extent[0])
figsize = (figsize, figsize * ratio * 0.8)
fig = plt.figure(figsize=figsize)
gs = mpl.gridspec.GridSpec(1,
2,
width_ratios=[1, .02],
bottom=.07,
top=.99,
hspace=0.01,
wspace=0.01)
ax = plt.subplot(gs[0], projection=plotcrs)
# plot map background
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=1)
add_china_map_2cartopy(ax, name='river', edgecolor='darkcyan', lw=1)
# set color maps
pos = np.array(
[-45, -30, -20, -10, -5, 0, 0, 5, 5, 10, 20, 20, 30, 30, 40, 45])
colormap = cm_temperature_nws(pos)
# draw CLDAS temperature
x, y = np.meshgrid(data['lon'], data['lat'])
pm = ax.pcolormesh(x,
y,
np.squeeze(data['data']),
cmap=colormap,
vmin=pos.min(),
vmax=pos.max(),
transform=datacrs)
# add title
plt.title(title, loc='left', fontsize=18)
if time is not None:
plt.title(time.strftime("%Y-%m-%dT%H"), loc='right', fontsize=18)
# add grid lines
if gridlines:
add_gridlines(ax)
# add color bar
cax = plt.subplot(gs[1])
cb = plt.colorbar(pm, cax=cax, orientation='vertical', extendrect='True')
cb.set_label('Temperature', size=12)
# return
if outfile is not None:
fig.savefig(outfile)
plt.close(fig)
return None
def cref_uv850(initial_time, fhour=0, model='ShangHai',
map_center=(117, 39), map_width=12, draw_wind=False):
"""
Analysis composite reflectivity and 850hPa wind.
Arguments:
initial_time {string or datetime}} --
initial time, string or datetime ojbect.
like '18042008' or datetime(2018, 4, 20, 8).
Keyword Arguments:
fhour {int} -- forecast hour (default: {0})
model {str} -- model name (default: {'ShangHai'})
map_center {tuple} -- map center (default: {(117, 39)})
map_width {int} -- map width (default: {12})
draw_wind {bool} -- draw 850hPa wind or not (default: {False})
Raises:
ValueError -- [description]
"""
# micaps data directory
data_dirs = {
'SHANGHAI': ['SHANGHAI_HR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'SHANGHAI_HR/UGRD/850', 'SHANGHAI_HR/VGRD/850'],
'BEIJING': ['BEIJING_MR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'BEIJING_MR/UGRD/850', 'BEIJING_MR/VGRD/850'],
'GRAPES_MESO': ['GRAPES_MESO_HR/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_MESO_HR/UGRD/850', 'GRAPES_MESO_HR/VGRD/850'],
'GRAPES_3KM': ['GRAPES_3KM/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_3KM/UGRD/850', 'GRAPES_3KM/VGRD/850']}
try:
data_dir = data_dirs[model.strip().upper()]
except KeyError:
raise ValueError('Unknown model, choose ShangHai, BeiJing, Grapes_meso of Grapes_3km.')
# get filename
filename = model_filename(initial_time, fhour)
# retrieve data from micaps server
cref = get_model_grid(data_dir[0], filename=filename)
if cref is None:
return
init_time = cref.coords['init_time'].values[0]
if draw_wind:
u850 = get_model_grid(data_dir[1], filename=filename)
if u850 is None:
return
v850 = get_model_grid(data_dir[2], filename=filename)
if v850 is None:
return
# prepare data
data = np.ma.masked_array(cref.values)
data[data == 9999] = np.ma.masked
data[data < 10] = np.ma.masked
cref_data = {'lon':cref.coords['lon'].values,
'lat':cref.coords['lat'].values,
'data':np.squeeze(data)}
if draw_wind:
uv850 = {'lon': u850.coords['lon'].values,
'lat': u850.coords['lat'].values,
'udata': np.squeeze(u850.values),
'vdata': np.squeeze(v850.values)}
# set up map projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=map_center[1], central_longitude=map_center[0],
standard_parallels=[30., 60.])
# set up figure
fig = plt.figure(figsize=(12, 9))
gs = mpl.gridspec.GridSpec(
1, 2, width_ratios=[1, .03],
bottom=.01, top=.99, hspace=0.01, wspace=0.01)
ax = plt.subplot(gs[0], projection=plotcrs)
# add model title
add_model_title(
'CREF (dBz), 850-hPa Winds', init_time, model=model,
fhour=fhour, fontsize=18, multilines=True)
# add map background
map_extent = (
map_center[0] - map_width/2.0, map_center[0] + map_width/2.0,
map_center[1] - map_width/3.0, map_center[1] + map_width/3.0)
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(
ax, name='province', edgecolor='black',
lw=2, zorder=100)
# draw composite reflectivity
x, y = np.meshgrid(cref_data['lon'], cref_data['lat'])
norm, cmap = colortables.get_with_steps('NWSReflectivity', 12, 4)
pm = ax.pcolormesh(x, y, cref_data['data'], norm=norm, cmap=cmap, transform=datacrs)
cax = plt.subplot(gs[1])
cb = plt.colorbar(pm, cax=cax, orientation='vertical', extendrect='True')
cb.set_label('Composite reflectivity', size=12)
# draw wind vector
if draw_wind:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
ax.quiver(x, y, uv850['udata'], uv850['vdata'],
transform=datacrs, regrid_shape=25)
# show figure
gs.tight_layout(fig)
plt.show()
def cref_uv850_compare(initial_time, fhour=0, map_center=(117, 39), map_width=12, draw_wind=False):
"""
Compare mesoscale model's composite reflectivity.
Arguments:
initial_time {string or datetime}} --
initial time, string or datetime ojbect.
like '18042008' or datetime(2018, 4, 20, 8).
Keyword Arguments:
fhour {int} -- forecast hour (default: {0})
map_center {tuple} -- map center (default: {(117, 39)})
map_width {int} -- map width (default: {12})
draw_wind {bool} -- draw 850hPa wind or not (default: {False})
"""
# micaps data directory
data_dirs = {'SHANGHAI': ['SHANGHAI_HR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'SHANGHAI_HR/UGRD/850', 'SHANGHAI_HR/VGRD/850'],
'BEIJING': ['BEIJING_MR/COMPOSITE_REFLECTIVITY/ENTIRE_ATMOSPHERE',
'BEIJING_MR/UGRD/850', 'BEIJING_MR/VGRD/850'],
'GRAPES_MESO': ['GRAPES_MESO_HR/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_MESO_HR/UGRD/850', 'GRAPES_MESO_HR/VGRD/850'],
'GRAPES_3KM': ['GRAPES_3KM/RADAR_COMBINATION_REFLECTIVITY',
'GRAPES_3KM/UGRD/850', 'GRAPES_3KM/VGRD/850']}
# get filename
filename = model_filename(initial_time, fhour)
# set up map projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(
central_latitude=map_center[1], central_longitude=map_center[0],
standard_parallels=[30., 60.])
# set up figure
fig = plt.figure(figsize=(16, 12))
axes_class = (GeoAxes, dict(map_projection=plotcrs))
grid = AxesGrid(fig, 111, axes_class=axes_class, nrows_ncols=(2, 2),
axes_pad=0.05, cbar_location='right', cbar_mode='single',
cbar_pad=0.05, label_mode='')
# loop every data directory
for index, key in enumerate(data_dirs):
# get axis and data directory
ax = grid[index]
data_dir = data_dirs[key]
# retrieve data from micaps server
cref = get_model_grid(data_dir[0], filename=filename)
if cref is None:
return
init_time = cref.coords['init_time'].values[0]
if draw_wind:
u850 = get_model_grid(data_dir[1], filename=filename)
if u850 is None:
return
v850 = get_model_grid(data_dir[2], filename=filename)
if v850 is None:
return
# add title
if index == 0:
initial_str, fhour_str, valid_str = get_model_time_stamp(init_time, fhour)
fig.suptitle('CREF (dBz), 850-hPa Winds ' + initial_str + '; ' + fhour_str + '; ' + valid_str,
x=0.5, y=0.9, fontsize=16)
# prepare data
data = np.ma.masked_array(cref.values)
data[data == 9999] = np.ma.masked
data[data < 10] = np.ma.masked
cref_data = {'lon':cref.coords['lon'].values,
'lat':cref.coords['lat'].values,
'data':np.squeeze(data)}
if draw_wind:
uv850 = {'lon': u850.coords['lon'].values,
'lat': u850.coords['lat'].values,
'udata': np.squeeze(u850.values),
'vdata': np.squeeze(v850.values)}
# add map background
map_extent = (
map_center[0] - map_width/2.0, map_center[0] + map_width/2.0,
map_center[1] - map_width/3.0, map_center[1] + map_width/3.0)
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(
ax, name='province', edgecolor='black',
lw=2, zorder=100)
# draw composite reflectivity
x, y = np.meshgrid(cref_data['lon'], cref_data['lat'])
norm, cmap = colortables.get_with_steps('NWSReflectivity', 12, 4)
pm = ax.pcolormesh(x, y, cref_data['data'], norm=norm, cmap=cmap, transform=datacrs)
# draw wind vector
if draw_wind:
x, y = np.meshgrid(uv850['lon'], uv850['lat'])
ax.quiver(x, y, uv850['udata'], uv850['vdata'],
transform=datacrs, regrid_shape=25)
# add title
add_titlebox(ax, key)
# add color bar
cbar = grid.cbar_axes[0].colorbar(pm)
# show figure
plt.show()
def temper_comprehensive_gg(grd_ob, grd_fo, filename=None):
ob_min = np.min(grd_ob.values)
fo_min = np.min(grd_fo.values)
ob_max = np.max(grd_ob.values)
fo_max = np.max(grd_fo.values)
ob_fo_max = max(ob_max, fo_max)
ob_fo_min = min(ob_min, fo_min)
clevs_temp, cmap_temp = nmc_verification.nmc_vf_base.tool.color_tools.get_clev_and_cmap_by_element_name(
"temp")
clevs, cmap = nmc_verification.nmc_vf_base.tool.color_tools.get_part_clev_and_cmap(
clevs_temp, cmap_temp, ob_fo_max, ob_fo_min)
width = 9 #整个画面的宽度
width_colorbar = 0.6
height_title = 0.3
height_veri_plot = 3
grid0 = nmc_verification.nmc_vf_base.get_grid_of_data(grd_fo)
if (grid0.nlon <= grid0.nlat * 0.5):
#采用3*1布局
width_map = (width - 2 * width_colorbar) / 3
height_map = (grid0.nlat / grid0.nlon) * width_map
height = height_map + height_title + height_veri_plot
rect1 = [
width_colorbar / width, height_veri_plot / height,
width_map / width, height_map / height
] # 实况
rect2 = [(1 * width_map + width_colorbar) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 预报
rect3 = [(2 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 误差
ob_fo_colorbar_box = [
0.02, height_veri_plot / height, 0.015, height_map / height
]
error_colorbar_box = [(3 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, 0.015,
height_map / height]
else:
#采用1*2 + 1 布局
width_map = (width - 2 * width_colorbar) / 1.5
height_map = (grid0.nlat / grid0.nlon) * width_map
height = height_map + height_title + height_veri_plot
rect1 = [(width_colorbar - 0.03) / width,
(height_veri_plot + 0.5 * height_map) / height,
0.5 * width_map / width, 0.5 * height_map / height] # 实况
rect2 = [(width_colorbar - 0.03) / width, height_veri_plot / height,
0.5 * width_map / width, 0.5 * height_map / height] # 预报
rect3 = [(0.5 * width_map + width_colorbar) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 误差
ob_fo_colorbar_box = [
0.02, height_veri_plot / height, 0.01, height_map / height
]
error_colorbar_box = [
(1.5 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, 0.01, height_map / height
]
rect4 = [0.05, 0.06, 0.26, height_veri_plot / height - 0.1] # 散点回归图，左下宽高
rect5 = [0.38, 0.06, 0.28, height_veri_plot / height - 0.1] # 频率柱状图， 左下宽高
rect6 = [0.67, 0.01, 0.3, height_veri_plot / height - 0.03] # 左下宽高
rect_title = [
width_colorbar / width, (height_veri_plot + height_map) / height,
1 - 2 * width_colorbar / width, 0.001
]
fig = plt.figure(figsize=(width, height))
# 平面对比图1
datacrs = ccrs.PlateCarree()
ax1 = plt.axes(rect1, projection=datacrs)
ax2 = plt.axes(rect2, projection=datacrs)
ax3 = plt.axes(rect3, projection=datacrs)
# 设置地图背景
map_extent = [grid0.slon, grid0.elon, grid0.slat, grid0.elat]
ax1.set_extent(map_extent, crs=datacrs)
ax2.set_extent(map_extent, crs=datacrs)
ax3.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax1, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax1, name='river', edgecolor='blue', lw=0.3) # 河流
add_china_map_2cartopy(ax2, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax2, name='river', edgecolor='blue', lw=0.3) # 河流
add_china_map_2cartopy(ax3, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax3, name='river', edgecolor='blue', lw=0.3) # 河流
# 绘制格点预报场
x = np.arange(grid0.nlon) * grid0.dlon + grid0.slon
y = np.arange(grid0.nlat) * grid0.dlat + grid0.slat
#clevs = [-10,0,15,20,22,24,26,28,30,32,34,35]
#colors_grid = ["#00AAAA","#009500","#808000", "#BFBF00","#FFFF00","#FFD400","#FFAA00","#FF7F00","#FF0000","#FF002A","#FF0055","#FF0055"]
plot_grid = ax1.contourf(x,
y,
grd_ob.values.squeeze(),
levels=clevs,
cmap=cmap,
transform=datacrs) # 填色图
ax1.set_title("实况", fontsize=12, loc="left", y=0.0)
colorbar_position_grid = fig.add_axes(ob_fo_colorbar_box) # 位置[左,下,宽,高]
plt.colorbar(plot_grid, cax=colorbar_position_grid, orientation='vertical')
plt.title("温度(℃)", fontsize=8, verticalalignment='bottom')
ax2.contourf(x,
y,
grd_fo.values.squeeze(),
levels=clevs,
cmap=cmap,
transform=datacrs) # 填色图
ax2.set_title("预报", fontsize=12, loc="left", y=0.0)
clevs1 = [-5, -4, -3, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 3, 4, 5]
error = grd_fo.values.squeeze() - grd_ob.values.squeeze()
plot_grid1 = ax3.contourf(x,
y,
error,
clevs1,
cmap="bwr",
transform=datacrs) # 填色图
colorbar_position_grid1 = fig.add_axes(error_colorbar_box) # 位置[左,下,宽,高]
ax3.set_title("预报 - 实况", fontsize=12, loc="left", y=0.0)
plt.colorbar(plot_grid1,
cax=colorbar_position_grid1,
orientation='vertical')
plt.title("误差(℃)", fontsize=8, verticalalignment='bottom')
time_str = nmc_verification.nmc_vf_base.tool.time_tools.time_to_str(
grid0.gtime[0])
dati_str = time_str[0:4] + "年" + time_str[4:6] + "月" + time_str[
6:8] + "日" + time_str[8:10] + "时"
if type(grid0.members[0]) == str:
model_name = grid0.members[0]
else:
model_name = str(grid0.members[0])
title = model_name + " " + dati_str + "起报" + str(
grid0.dtimes[0]) + "H时效预报和实况对比及误差"
ax_title = plt.axes(rect_title)
ax_title.axes.set_axis_off()
ax_title.set_title(title)
# 散点回归图
ax2 = plt.axes(rect4)
# 保证这两个值的正确性
ob = grd_ob.values.flatten()
fo = grd_fo.values.flatten()
print(len(ob), len(fo))
ax2.plot(ob, fo, '.', color='k')
print(np.sum(fo), np.sum(ob))
# 绘制比例线
rate = np.sum(fo) / np.sum(ob)
ob_line = np.arange(0, np.max(ob), np.max(ob) / 30)
fo_rate = ob_line * rate
ax2.plot(ob_line[0:20], fo_rate[0:20], 'r')
# 绘制回归线
X = np.zeros((len(ob), 1))
X[:, 0] = ob[:]
#绘制回归线
clf = LinearRegression().fit(X, fo)
X = np.zeros((len(ob_line), 1))
X[:, 0] = ob_line[:]
fo_rg = clf.predict(X)
ax2.plot(ob_line, fo_rg, color='b', linestyle='dashed')
rg_text1 = "R = " + '%.2f' % (np.corrcoef(ob, fo)[0, 1])
rg_text2 = "y = " + '%.2f' % (clf.coef_[0]) + "* x + " + '%.2f' % (
clf.intercept_)
maxy = max(np.max(ob), np.max(fo)) + 5
plt.xlim(0, maxy)
plt.ylim(0, maxy)
plt.text(0.05 * maxy, 0.9 * maxy, rg_text1, fontsize=10)
plt.text(0.05 * maxy, 0.8 * maxy, rg_text2, fontsize=10)
# maxy = max(np.max(ob),np.max(fo))
ax2.set_xlabel("实况", fontsize=10)
ax2.set_ylabel("预报", fontsize=10)
ax2.set_title("散点回归图", fontsize=10)
# 设置次刻度间隔
xmi = 1
if (np.max(ob) > 100): xmi = 2
ymi = 1
if (np.max(fo) > 100): ymi = 2
xmajorLocator = mpl.ticker.MultipleLocator(10 * xmi) # 将x主刻度标签设置为次刻度10倍
ymajorLocator = mpl.ticker.MultipleLocator(10 * ymi) # 将y主刻度标签设置为次刻度10倍
ax2.xaxis.set_major_locator(xmajorLocator)
ax2.yaxis.set_major_locator(ymajorLocator)
xminorLocator = mpl.ticker.MultipleLocator(xmi) # 将x轴次刻度标签设置xmi
yminorLocator = mpl.ticker.MultipleLocator(ymi) # 将y轴次刻度标签设置ymi
ax2.xaxis.set_minor_locator(xminorLocator)
ax2.yaxis.set_minor_locator(yminorLocator)
#折线图
# 绘制频率柱状图
p_ob = np.zeros(len(clevs) - 1)
p_fo = np.zeros(len(clevs) - 1)
x = clevs[0:-1]
for i in range(len(clevs) - 1):
index0 = np.where((ob >= clevs[i]) & (ob < clevs[i + 1]))
p_ob[i] = len(index0[0])
index0 = np.where((fo >= clevs[i]) & (fo < clevs[i + 1]))
p_fo[i] = len(index0[0])
ax5 = plt.axes(rect5)
# ax5.plot(p_ob, color='r',label="Obs")
# ax5.plot(p_fo, color='b',label="Pred")
ax5.bar(x + 0.5, p_ob, width=0.4, color='r', label="实况")
ax5.bar(x + 1.1, p_fo, width=0.4, color="b", label="预报")
ax5.legend(loc="upper right")
ax5.set_xlabel("等级", fontsize=10)
ax5.set_ylabel("站点数", fontsize=10)
ax5.set_title("频率统计图", fontsize=10)
ax5.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(1000))
maxy = max(np.max(p_fo), np.max(p_ob)) * 1.4
ax5.set_ylim(0, maxy)
#检验效果图
# 绘制降水站点实况预报统计表
ax6 = plt.axes(rect6)
ax6.axes.set_axis_off()
ob_mean = np.mean(grd_ob.values)
fo_mean = np.mean(grd_fo.values)
maee = nmc_verification.nmc_vf_method.continuous.score.mae(ob, fo)
mee = nmc_verification.nmc_vf_method.continuous.score.me(ob, fo)
msee = nmc_verification.nmc_vf_method.continuous.score.mse(ob, fo)
rmsee = nmc_verification.nmc_vf_method.continuous.score.rmse(ob, fo)
bias_ce = nmc_verification.nmc_vf_method.continuous.score.bias(ob, fo)
cor = nmc_verification.nmc_vf_method.continuous.score.corr(ob, fo)
ob_has = ob[ob >= 0.01]
fo_has = fo[fo >= 0.01]
text = "格点检验统计量 n=" + str(len(ob)) + "\n"
text += "==============================================\n"
text += " 实况 预报 \n"
text += "----------------------------------------------\n"
text += "平均温度 " + "%8.2f" % (ob_mean) + "%20.2f" % (
fo_mean) + "\n\n"
text += "温度范围 " + "%8.2f" % (ob_min) + "~%6.2f" % (
ob_max) + "%12.2f" % (fo_min) + "~%6.2f" % (fo_max) + "\n\n"
text += "==============================================\n\n"
text += "Mean absolute error:" + "%6.2f" % maee + "\n\n"
text += "Mean error:" + "%6.2f" % mee + "\n\n"
text += "Mean-squared error:" + "%6.2f" % msee + "\n\n"
text += "Root mean-squared error:" + "%6.2f" % rmsee + "\n\n"
text += "Bias:" + "%6.2f" % bias_ce + "\n\n"
text += "Correctlation coefficiant:" + "%6.2f" % cor + "\n"
plt.text(0, 0, text, fontsize=9)
# 图片显示或保存
if (filename is None):
plt.show()
print()
else:
plt.savefig(filename, dpi=300)
plt.close()
def temper_gg(grd_ob, grd_fo, filename=None):
ob_min = np.min(grd_ob.values)
fo_min = np.min(grd_fo.values)
ob_max = np.max(grd_ob.values)
fo_max = np.max(grd_fo.values)
ob_fo_max = max(ob_max, fo_max)
ob_fo_min = min(ob_min, fo_min)
clevs_temp, cmap_temp = nmc_verification.nmc_vf_base.tool.color_tools.get_clev_and_cmap_by_element_name(
"temp")
clevs, cmap = nmc_verification.nmc_vf_base.tool.color_tools.get_part_clev_and_cmap(
clevs_temp, cmap_temp, ob_fo_max, ob_fo_min)
width = 9 #整个画面的宽度
width_colorbar = 0.6
height_title = 0.3
height_veri_plot = 0.5
grid0 = nmc_verification.nmc_vf_base.get_grid_of_data(grd_fo)
if (grid0.nlon <= grid0.nlat * 0.5):
#采用3*1布局
width_map = (width - 2 * width_colorbar) / 3
height_map = (grid0.nlat / grid0.nlon) * width_map
height = height_map + height_title + height_veri_plot
rect1 = [
width_colorbar / width, height_veri_plot / height,
width_map / width, height_map / height
] # 实况
rect2 = [(1 * width_map + width_colorbar) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 预报
rect3 = [(2 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 误差
ob_fo_colorbar_box = [
0.02, height_veri_plot / height, 0.015, height_map / height
]
error_colorbar_box = [(3 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, 0.015,
height_map / height]
else:
#采用1*2 + 1 布局
width_map = (width - 2 * width_colorbar) / 1.5
height_map = (grid0.nlat / grid0.nlon) * width_map
height = height_map + height_title + height_veri_plot
rect1 = [(width_colorbar - 0.03) / width,
(height_veri_plot + 0.5 * height_map) / height,
0.5 * width_map / width, 0.5 * height_map / height] # 实况
rect2 = [(width_colorbar - 0.03) / width, height_veri_plot / height,
0.5 * width_map / width, 0.5 * height_map / height] # 预报
rect3 = [(0.5 * width_map + width_colorbar) / width,
height_veri_plot / height, width_map / width,
height_map / height] # 误差
ob_fo_colorbar_box = [
0.02, height_veri_plot / height, 0.01, height_map / height
]
error_colorbar_box = [
(1.5 * width_map + width_colorbar + 0.05) / width,
height_veri_plot / height, 0.01, height_map / height
]
rect4 = [0.05, 0.06, 0.26, height_veri_plot / height - 0.1] # 散点回归图，左下宽高
rect5 = [0.38, 0.06, 0.28, height_veri_plot / height - 0.1] # 频率柱状图， 左下宽高
rect6 = [0.67, 0.01, 0.3, height_veri_plot / height - 0.03] # 左下宽高
rect_title = [
width_colorbar / width, (height_veri_plot + height_map) / height,
1 - 2 * width_colorbar / width, 0.001
]
fig = plt.figure(figsize=(width, height))
# 平面对比图1
datacrs = ccrs.PlateCarree()
ax1 = plt.axes(rect1, projection=datacrs)
ax2 = plt.axes(rect2, projection=datacrs)
ax3 = plt.axes(rect3, projection=datacrs)
# 设置地图背景
map_extent = [grid0.slon, grid0.elon, grid0.slat, grid0.elat]
ax1.set_extent(map_extent, crs=datacrs)
ax2.set_extent(map_extent, crs=datacrs)
ax3.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax1, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax1, name='river', edgecolor='blue', lw=0.3) # 河流
add_china_map_2cartopy(ax2, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax2, name='river', edgecolor='blue', lw=0.3) # 河流
add_china_map_2cartopy(ax3, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax3, name='river', edgecolor='blue', lw=0.3) # 河流
# 绘制格点预报场
x = np.arange(grid0.nlon) * grid0.dlon + grid0.slon
y = np.arange(grid0.nlat) * grid0.dlat + grid0.slat
#clevs = [-10,0,15,20,22,24,26,28,30,32,34,35]
#colors_grid = ["#00AAAA","#009500","#808000", "#BFBF00","#FFFF00","#FFD400","#FFAA00","#FF7F00","#FF0000","#FF002A","#FF0055","#FF0055"]
plot_grid = ax1.contourf(x,
y,
grd_ob.values.squeeze(),
levels=clevs,
cmap=cmap,
transform=datacrs) # 填色图
ax1.set_title("实况", fontsize=12, loc="left", y=0.0)
colorbar_position_grid = fig.add_axes(ob_fo_colorbar_box) # 位置[左,下,宽,高]
plt.colorbar(plot_grid, cax=colorbar_position_grid, orientation='vertical')
plt.title("温度(℃)", fontsize=8, verticalalignment='bottom')
ax2.contourf(x,
y,
grd_fo.values.squeeze(),
levels=clevs,
cmap=cmap,
transform=datacrs) # 填色图
ax2.set_title("预报", fontsize=12, loc="left", y=0.0)
clevs1 = [-5, -4, -3, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 3, 4, 5]
error = grd_fo.values.squeeze() - grd_ob.values.squeeze()
plot_grid1 = ax3.contourf(x,
y,
error,
clevs1,
cmap="bwr",
transform=datacrs) # 填色图
colorbar_position_grid1 = fig.add_axes(error_colorbar_box) # 位置[左,下,宽,高]
ax3.set_title("预报 - 实况", fontsize=12, loc="left", y=0.0)
plt.colorbar(plot_grid1,
cax=colorbar_position_grid1,
orientation='vertical')
plt.title("误差(℃)", fontsize=8, verticalalignment='bottom')
time_str = nmc_verification.nmc_vf_base.tool.time_tools.time_to_str(
grid0.gtime[0])
dati_str = time_str[0:4] + "年" + time_str[4:6] + "月" + time_str[
6:8] + "日" + time_str[8:10] + "时"
if type(grid0.members[0]) == str:
model_name = grid0.members[0]
else:
model_name = str(grid0.members[0])
title = model_name + " " + dati_str + "起报" + str(
grid0.dtimes[0]) + "H时效预报和实况对比及误差"
ax_title = plt.axes(rect_title)
ax_title.axes.set_axis_off()
ax_title.set_title(title)
# 图片显示或保存
if (filename is None):
plt.show()
print()
else:
plt.savefig(filename, dpi=300)
plt.close()
def pcolormesh_2d_grid(grd, title=None, filename=None, clevs=None, cmap=None):
x = grd['lon'].values
y = grd['lat'].values
rlon = x[-1] - x[0]
rlat = y[-1] - y[0]
height = 5
width = height * rlon / rlat + 1
fig = plt.figure(figsize=(width, height))
datacrs = ccrs.PlateCarree()
ax = plt.axes(projection=datacrs)
map_extent = [x[0], x[-1], y[0], y[-1]]
ax.set_extent(map_extent)
if title is not None:
plt.title(title)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=0.3) #省界
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=0.3) #河流
if clevs is None:
vmax = np.max(grd.values)
vmin = np.min(grd.values)
if vmax - vmin < 1e-10:
vmax = vmin + 1.1
dif = (vmax - vmin) / 10.0
inte = math.pow(10, math.floor(math.log10(dif)))
#用基本间隔，将最大最小值除于间隔后小数点部分去除，最后把间隔也整数化
r = dif / inte
if r < 3 and r >= 1.5:
inte = inte * 2
elif r < 4.5 and r >= 3:
inte = inte * 4
elif r < 5.5 and r >= 4.5:
inte = inte * 5
elif r < 7 and r >= 5.5:
inte = inte * 6
elif r >= 7:
inte = inte * 8
vmin = inte * ((int)(vmin / inte))
vmax = inte * ((int)(vmax / inte) + 1)
clevs = np.arange(vmin, vmax, inte)
if cmap is None:
cmap = plt.get_cmap("rainbow")
norm = BoundaryNorm(clevs, ncolors=cmap.N, clip=True)
im = ax.pcolormesh(x,
y,
np.squeeze(grd.values),
cmap=cmap,
norm=norm,
transform=datacrs)
left_low = (width - 1) / width
colorbar_position = fig.add_axes([left_low, 0.11, 0.03,
0.77]) # 位置[左,下,宽,高]
plt.colorbar(im, cax=colorbar_position)
vmax = x[-1]
vmin = x[0]
r = rlon
if r <= 1:
inte = 0.1
elif r <= 5 and r > 1:
inte = 1
elif r <= 10 and r > 5:
inte = 2
elif r < 20 and r >= 10:
inte = 4
elif r <= 30 and r >= 20:
inte = 5
else:
inte = 10
vmin = inte * ((int)(vmin / inte))
vmax = inte * ((int)(vmax / inte) + 1)
xticks = np.arange(vmin, vmax, inte)
xticks_label = []
for x in range(len(xticks)):
xticks_label.append(str(xticks[x]))
xticks_label[-1] += "°E"
ax.set_xticks(xticks)
ax.set_xticklabels(xticks_label)
vmax = y[-1]
vmin = y[0]
r = rlat
if r <= 1:
inte = 0.1
elif r <= 5 and r > 1:
inte = 1
elif r <= 10 and r > 5:
inte = 2
elif r < 20 and r >= 10:
inte = 4
elif r <= 30 and r >= 20:
inte = 5
else:
inte = 10
vmin = inte * ((int)(vmin / inte))
vmax = inte * ((int)(vmax / inte) + 1)
yticks = np.arange(vmin, vmax, inte)
yticks_label = []
for y in range(len(yticks)):
yticks_label.append(str(yticks[y]))
yticks_label[-1] += "°N"
ax.set_yticks(yticks)
ax.set_yticklabels(yticks_label)
if (filename is None):
plt.show()
else:
file1, extension = os.path.splitext(filename)
extension = extension[1:]
plt.savefig(filename, format=extension)
plt.close()
def qpf_24h(initial_time,
fhour=0,
model='ECMWF',
map_center=(117, 39),
map_width=12):
"""
Draw 24h accumulated QPF.
Arguments:
initial_time {string or datetime object} -- model initital time,
like '18042008' or datetime(2018, 4, 20, 8).
Keyword Arguments:
fhour {int} -- model initial time (default: {0})
model {str} -- model name (default: {'ECMWF'})
"""
# micaps data directory
data_dirs = {'ECMWF': ['ECMWF_HR/RAIN24']}
try:
data_dir = data_dirs[model.strip().upper()]
except KeyError:
raise ValueError('Unknown model, choose ECMWF, GRAPES or NCEP.')
# get file name
filename = model_filename(initial_time, fhour)
# retrieve data from micaps server
rain24 = get_model_grid(data_dir[0], filename=filename)
if rain24 is None:
print('Can not retrieve {} from Micaps server.'.format(filename))
return
init_time = rain24.coords['init_time'].values[0]
rain24 = {
'lon': rain24.coords['lon'].values,
'lat': rain24.coords['lat'].values,
'data': np.squeeze(rain24.values)
}
# set up map projection
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.AlbersEqualArea(central_latitude=map_center[1],
central_longitude=map_center[0],
standard_parallels=[30., 60.])
# set up figure
fig = plt.figure(figsize=(10, 8))
ax = fig.add_axes([0, 0, 1, 1], projection=plotcrs)
# add model title
add_model_title('24h accumulated QPF',
init_time,
model=model,
fhour=fhour,
fontsize=18,
multilines=True,
atime=24)
# add map background
map_extent = (map_center[0] - map_width / 2.0,
map_center[0] + map_width / 2.0,
map_center[1] - map_width / 2.0,
map_center[1] + map_width / 2.0)
ax.set_extent(map_extent, crs=datacrs)
land_50m = cfeature.NaturalEarthFeature('physical',
'land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
ax.add_feature(land_50m)
add_china_map_2cartopy(ax,
name='province',
edgecolor='darkcyan',
lw=1,
zorder=100)
# draw QPF
clevs = [0.1, 10, 25, 50, 100, 250]
colors = ["#88F492", "#00A929", "#2AB8FF", "#1202FC", "#FF04F4", "#850C3E"]
cmap, norm = mpl.colors.from_levels_and_colors(clevs, colors, extend='max')
ax.pcolormesh(rain24['lon'],
rain24['lat'],
rain24['data'],
norm=norm,
cmap=cmap,
transform=datacrs,
zorder=2)
# add custom legend
legend_elements = [
Patch(facecolor=colors[0], label='0.1~10mm'),
Patch(facecolor=colors[1], label='10~25mm'),
Patch(facecolor=colors[2], label='25~50mm'),
Patch(facecolor=colors[3], label='50~100mm'),
Patch(facecolor=colors[4], label='100~250mm'),
Patch(facecolor=colors[5], label='>250mm')
]
ax.legend(handles=legend_elements, loc='lower right', fontsize=16)
# add logo
add_logo(fig, alpha=0.7)
# show figure
ax.set_adjustable('datalim')
plt.show()
def draw_veri_rain_24(grid_fo, sta_ob, filename=None):
fig = plt.figure(figsize=(10, 7))
# 平面对比图
rect1 = [0.00, 0.42, 0.7, 0.55] # 左下宽高
datacrs = ccrs.PlateCarree()
ax = plt.axes(rect1, projection=datacrs)
# 设置地图背景
map_extent = [grid_fo.slon, grid_fo.elon, grid_fo.slat, grid_fo.elat]
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=0.3) # 河流
# 绘制格点预报场
x = np.arange(grid_fo.nlon) * grid_fo.dlon + grid_fo.slon
y = np.arange(grid_fo.nlat) * grid_fo.dlat + grid_fo.slat
clevs = [0.1, 10, 25, 50, 100, 250, 1000]
colors_grid = [
"#E0EEFA", "#B4D3E9", "#6FB0D7", "#3787C0", "#105BA4", "#07306B",
"#07306B"
]
plot_grid = ax.contourf(x,
y,
grid_fo.dat,
clevs,
colors=colors_grid,
transform=datacrs) # 填色图
colorbar_position_grid = fig.add_axes([0.035, 0.94, 0.25,
0.015]) # 位置[左,下,宽,高]
plt.colorbar(plot_grid,
cax=colorbar_position_grid,
orientation='horizontal')
plt.text(0.035, 0.955, "model accumulated precipition(mm)", fontsize=10)
# 绘制填色站点值
sta_ob_in = function.get_from_sta_data.sta_in_grid_xy(sta_ob,
grid=grid_fo.grid)
colors_sta = [
'#FFFFFF', '#0055FF', '#00FFB4', '#F4FF00', '#FE1B00', '#910000',
'#B800BA'
]
dat = sta_ob_in.values[:, 2]
dat[dat > 1000] = 0
clevs = [0, 0.1, 10, 25, 50, 100, 250, 1000]
cleves_name = [
"0", "0.1-10", "10-25", "25-50", "50-100", "100-250", ">=250"
]
for i in range(len(clevs) - 1):
index0 = np.where((dat >= clevs[i]) & (dat < clevs[i + 1]))
if (len(index0[0]) > 0):
x = np.squeeze(sta_ob_in.values[index0, 0])
y = np.squeeze(sta_ob_in.values[index0, 1])
if (len(index0) == 1):
x = np.array([x])
y = np.array([y])
if (i > 0):
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=3,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=1,
label=cleves_name[i])
ax.legend(facecolor='whitesmoke',
title="observation",
loc="lower left",
edgecolor='whitesmoke')
# 散点回归图
rect2 = [0.07, 0.07, 0.21, 0.30] # 左下宽高
ax2 = plt.axes(rect2)
sta_fo = function.gxy_sxy.interpolation_linear(grid_fo, sta_ob_in)
ob = sta_ob_in.values[:, 2]
fo = sta_fo.values[:, 2]
ax2.plot(ob, fo, '.', color='k')
# 绘制比例线
rate = np.sum(fo) / np.sum(ob)
ob_line = np.arange(0, np.max(ob), np.max(ob) / 30)
fo_rate = ob_line * rate
ax2.plot(ob_line[0:20], fo_rate[0:20], 'r')
# 绘制回归线
X = np.zeros((len(ob), 1))
X[:, 0] = ob[:]
clf = LinearRegression().fit(X, fo)
X = np.zeros((len(ob_line), 1))
X[:, 0] = ob_line[:]
fo_rg = clf.predict(X)
ax2.plot(ob_line, fo_rg, color='b', linestyle='dashed')
rg_text1 = "R = " + '%.2f' % (np.corrcoef(ob, fo)[0, 1])
rg_text2 = "y = " + '%.2f' % (clf.coef_[0]) + "* x + " + '%.2f' % (
clf.intercept_)
maxy = max(np.max(ob), np.max(fo)) + 5
plt.xlim(0, maxy)
plt.ylim(0, maxy)
plt.text(0.05 * maxy, 0.9 * maxy, rg_text1, fontsize=10)
plt.text(0.05 * maxy, 0.8 * maxy, rg_text2, fontsize=10)
maxy = max(np.max(ob), np.max(fo))
ax2.set_xlabel("observation", fontsize=12)
ax2.set_ylabel("precipitation", fontsize=12)
ax2.set_title("Obs.vs Pred. Scatter plot", fontsize=12)
# 设置次刻度间隔
xmi = 1
if (np.max(ob) > 100): xmi = 2
ymi = 1
if (np.max(fo) > 100): ymi = 2
xmajorLocator = mpl.ticker.MultipleLocator(10 * xmi) # 将x主刻度标签设置为次刻度10倍
ymajorLocator = mpl.ticker.MultipleLocator(10 * ymi) # 将y主刻度标签设置为次刻度10倍
ax2.xaxis.set_major_locator(xmajorLocator)
ax2.yaxis.set_major_locator(ymajorLocator)
xminorLocator = mpl.ticker.MultipleLocator(xmi) # 将x轴次刻度标签设置xmi
yminorLocator = mpl.ticker.MultipleLocator(ymi) # 将y轴次刻度标签设置ymi
ax2.xaxis.set_minor_locator(xminorLocator)
ax2.yaxis.set_minor_locator(yminorLocator)
# 绘制频率柱状图
p_ob = np.zeros(6)
p_fo = np.zeros(6)
x = np.arange(6) + 1
for i in range(1, len(clevs) - 1, 1):
index0 = np.where((ob >= clevs[i]) & (ob < clevs[i + 1]))
p_ob[i - 1] = len(index0[0])
index0 = np.where((fo >= clevs[i]) & (fo < clevs[i + 1]))
p_fo[i - 1] = len(index0[0])
rect3 = [0.35, 0.07, 0.325, 0.17] # 左下宽高
ax3 = plt.axes(rect3)
ax3.bar(x - 0.25, p_ob, width=0.2, facecolor="r", label="Obs")
ax3.bar(x + 0.05, p_fo, width=0.2, facecolor="b", label="Pred")
ax3.legend(loc="upper right")
ax3.set_xlabel("precipitation threshold", fontsize=10)
ax3.set_xticks(x)
ax3.set_xticklabels(
["0.1-10", "10-25", "25-50", "50-100", "100-250", ">=250"], fontsize=9)
ax3.set_ylabel("point number", fontsize=10)
ax3.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(100))
# 绘制降水站点实况预报统计表
rect4 = [0.325, 0.255, 0.4, 0.10] # 左下宽高
ax4 = plt.axes(rect4)
ax4.axes.set_axis_off()
ob_has = ob[ob >= 0.01]
fo_has = fo[fo >= 0.01]
text = "制降水站点实况预报 n=" + str(len(ob)) + "\n"
text += "=======================================================\n"
text += " observation Predication\n"
text += "-------------------------------------------------------\n"
text += "有降水站点数(>=0.01) " + "%4d" % len(
ob_has) + " %4d" % len(fo_has) + "\n"
text += "有降水站点数百分比% " + "%8.2f" % (
len(ob_has) / len(ob)) + "%20.2f" % (len(fo_has) / len(fo)) + "\n"
text += "平均降水量(排除无降水) " + "%8.2f" % (np.mean(ob_has)) + "%20.2f" % (
np.mean(fo_has)) + "\n"
text += "最大降水量 " + "%8.2f" % (np.max(ob_has)) + "%20.2f" % (
np.max(fo_has))
plt.text(0, 0, text, fontsize=9)
# 绘制统计检验结果
rect5 = [0.705, 0.01, 0.28, 0.97] # 左下宽高
ax5 = plt.axes(rect5)
ax5.axes.set_axis_off()
mae = continuous.score.mae(ob, fo)
me = continuous.score.me(ob, fo)
mse = continuous.score.mse(ob, fo)
rmse = continuous.score.rmse(ob, fo)
bias_c = continuous.score.bias(ob, fo)
cor = continuous.score.corr(ob, fo)
hit, mis, fal, co = yes_or_no.score.hmfn(ob, fo, clevs[1:])
ts = yes_or_no.score.ts(ob, fo, clevs[1:])
ets = yes_or_no.score.ets(ob, fo, clevs[1:])
bias = yes_or_no.score.bias(ob, fo, clevs[1:])
hit_rate = yes_or_no.score.hit_rate(ob, fo, clevs[1:])
mis_rate = yes_or_no.score.mis_rate(ob, fo, clevs[1:])
fal_rate = yes_or_no.score.fal_rate(ob, fo, clevs[1:])
text = str(len(ob)) + "评分站点预报检验统计量\n"
text += "Mean absolute error:" + "%6.2f" % mae + "\n"
text += "Mean error:" + "%6.2f" % me + "\n"
text += "Mean-squared error:" + "%6.2f" % mse + "\n"
text += "Root mean-squared error:" + "%6.2f" % rmse + "\n"
text += "Bias:" + "%6.2f" % bias_c + "\n"
text += "Correctlation coefficiant:" + "%6.2f" % cor + "\n\n\n"
leves_name = [
"0.1-10-", "10-25--", "25-50--", "50-100-", "100-250", ">=250-"
]
for i in range(len(leves_name)):
text += ":" + leves_name[i] + "----------------------------\n"
text += "正确:" + "%-4d" % hit[i] + " 空报:" + "%-4d" % fal[
i] + " 漏报:" + "%-4d" % mis[i] + "\n"
text += "TS:" + "%5.3f" % ts[
i] + " ETS:" + "%5.3f" % ets[i] + "\n"
text += "Hit rate:" + "%5.3f" % hit_rate[
i] + " Miss rate: " + "%5.3f" % mis_rate[i] + "\n"
text += "False alarm ratio:" + "%5.3f" % fal_rate[
i] + " Bias:" + "%5.3f" % bias[i] + "\n\n"
plt.text(0, 0.00, text, fontsize=11)
# 图片显示或保存
if (filename is None):
plt.show()
else:
plt.savefig(filename, dpi=300)
plt.close()
return
def rain_24h_sg(sta_ob, grd_fo, filename=None):
'''
#绘制24小时降水实况与预报对比图
:param grd_fo: 输入的网格数据，包含一个平面的网格场
:param sta_ob: 输入的站点数据，包含一个时刻的站点数据列表
:param filename: 图片输出路径，缺省时会以调试窗口形式弹出
:return: 无返回值
'''
grid_fo = nmc_verification.nmc_vf_base.get_grid_of_data(grd_fo)
# 通过经纬度范围设置画幅
hight = 5.6
title_hight = 0.3
legend_hight = 0.6
left_plots_width = 0
right_plots_width = 0
width = (
hight - title_hight - legend_hight
) * grid_fo.nlon / grid_fo.nlat + left_plots_width + right_plots_width
map_width = width - left_plots_width - right_plots_width
fig = plt.figure(figsize=(width, hight))
# 设置画幅的布局方式，
rect1 = [
left_plots_width / width, 0.12,
(width - right_plots_width - left_plots_width) / width, 0.84
] # 左下宽高,中央对比图
ylabelwidth = 0.52 / width
rect2 = [
ylabelwidth, 0.08, left_plots_width / width - ylabelwidth - 0.005, 0.40
] # 左下宽高，散点回归图
ylabelwidth = 0.65 / width
rect3 = [
ylabelwidth, 0.60, left_plots_width / width - ylabelwidth - 0.005, 0.18
] # 左下宽高，频谱统计柱状图
rect4 = [0.01, 0.79, left_plots_width / width - 0.045, 0.15] # 左下宽高，左侧文字
rect5 = [(width - right_plots_width) / width + 0.005, -0.035,
right_plots_width / width - 0.01, 0.90] # 左下宽高，右侧文字
width_ob_fo_str = 0.3
if (map_width < 3.5):
width_bar = 2.1 # 根据中间地图的宽度，来确定预报colorbar的尺寸
sta_legend_size = 5 # 根据中间地图的宽度，来确定观测legend的size
else:
sta_legend_size = 7
width_bar = 2.9
rect6 = [(left_plots_width + 0.5 * map_width - 0.5 * width_bar +
0.5 * width_ob_fo_str) / width, 0.04, width_bar / width,
0.02] # 预报colorbar
rect7 = [(left_plots_width + 0.5 * map_width - 0.5 * width_bar -
0.5 * width_ob_fo_str) / width, 0.04, width_ob_fo_str / width,
0.3] # 观测文字
datacrs = ccrs.PlateCarree()
ax = plt.axes(rect1, projection=datacrs)
# 设置地图背景
map_extent = [grid_fo.slon, grid_fo.elon, grid_fo.slat, grid_fo.elat]
ax.set_extent(map_extent, crs=datacrs)
add_china_map_2cartopy(ax, name='province', edgecolor='k', lw=0.3) # 省界
add_china_map_2cartopy(ax, name='river', edgecolor='blue', lw=0.3) # 河流
# 绘制格点预报场
x = np.arange(grid_fo.nlon) * grid_fo.dlon + grid_fo.slon
y = np.arange(grid_fo.nlat) * grid_fo.dlat + grid_fo.slat
clevs = [0.1, 10, 25, 50, 100, 250, 1000]
colors_grid = [
"#D0DEEA", "#B4D3E9", "#6FB0D7", "#3787C0", "#105BA4", "#07306B",
"#07306B"
]
dat = grd_fo.values.squeeze()
# print(x)
# print(y)
# print(dat)
plot_grid = ax.contourf(x,
y,
dat,
clevs,
colors=colors_grid,
transform=datacrs) # 填色图
time_str = nmc_verification.nmc_vf_base.tool.time_tools.time_to_str(
grid_fo.gtime[0])
dati_str = time_str[0:4] + "年" + time_str[4:6] + "月" + time_str[
6:8] + "日" + time_str[8:10] + "时"
if type(grid_fo.members[0]) == str:
model_name = grid_fo.members[0]
else:
model_name = str(grid_fo.members[0])
if map_width < 3:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=7)
elif map_width < 4:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=10)
else:
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title, fontsize=11)
colorbar_position_grid = fig.add_axes(rect6) # 位置[左,下,宽,高]
cb = plt.colorbar(plot_grid,
cax=colorbar_position_grid,
orientation='horizontal')
cb.ax.tick_params(labelsize=8) # 设置色标刻度字体大小。
# plt.text(0, 0, "预报(mm)", fontsize=8)
# 绘制填色站点值
sta_ob_in = nmc_verification.nmc_vf_base.function.get_from_sta_data.sta_in_grid_xy(
sta_ob, grid=grid_fo)
colors_sta = [
'#FFFFFF', '#0055FF', '#00FFB4', '#F4FF00', '#FE1B00', '#910000',
'#B800BA'
]
dat = sta_ob_in.values[:, -1]
dat[dat > 1000] = 0
clevs = [0, 0.1, 10, 25, 50, 100, 250, 1000]
cleves_name = [
"0", "0.1-10", "10-25", "25-50", "50-100", "100-250", ">=250"
]
for i in range(len(clevs) - 1):
index0 = np.where((dat >= clevs[i]) & (dat < clevs[i + 1]))
if (len(index0[0]) > 0):
x = np.squeeze(sta_ob_in["lon"].values[index0])
y = np.squeeze(sta_ob_in["lat"].values[index0])
if (len(index0) == 1):
x = np.array([x])
y = np.array([y])
if (i > 0):
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=3,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
transform=ccrs.PlateCarree(),
s=1,
label=cleves_name[i],
linewidths=0.1,
edgecolor="k")
ax.legend(facecolor='whitesmoke',
loc="lower center",
ncol=4,
edgecolor='whitesmoke',
prop={'size': sta_legend_size},
bbox_to_anchor=(0.5 + 0.5 * width_ob_fo_str / map_width, -0.08))
ax7 = plt.axes(rect7)
ax7.axes.set_axis_off()
plt.text(0, 0.00, "观测\n\n预报", fontsize=7)
# 图片显示或保存
if (filename is None):
plt.show()
print()
else:
plt.savefig(filename, dpi=300)
plt.close()
return
