def temper_gg(grd_ob,
grd_fo,
save_path=None,
show=False,
dpi=200,
add_county_line=False):
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 = meteva.base.tool.color_tools.get_clev_and_cmap_by_element_name(
"temp")
clevs, cmap = meteva.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 = meteva.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
# 设置地图背景
ax1 = plt.axes(rect1)
add_china_map_2basemap(ax1,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax1,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax1.set_xlim((grid0.slon, grid0.elon))
ax1.set_ylim((grid0.slat, grid0.elat))
# 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"]
error = grd_fo.values.squeeze() - grd_ob.values.squeeze()
plt.xticks([])
plt.yticks([])
plot_grid = ax1.contourf(x,
y,
grd_ob.values.squeeze(),
levels=clevs,
cmap=cmap) # 填色图
ax1.set_title("实况", fontsize=18, 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 = plt.axes(rect2)
add_china_map_2basemap(ax2,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax2,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax2.set_xlim((grid0.slon, grid0.elon))
ax2.set_ylim((grid0.slat, grid0.elat))
plt.xticks([])
plt.yticks([])
ax2.contourf(x, y, grd_fo.values.squeeze(), levels=clevs, cmap=cmap) # 填色图
ax2.set_title("预报", fontsize=18, 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]
ax3 = plt.axes(rect3)
add_china_map_2basemap(ax3,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax3,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax3.set_xlim((grid0.slon, grid0.elon))
ax3.set_ylim((grid0.slat, grid0.elat))
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plot_grid1 = ax3.contourf(x, y, error, clevs1, cmap="bwr") # 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=18, 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 = meteva.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 (save_path is not None):
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
else:
show = True
if show:
plt.show()
plt.close()
def rain_24h_comprehensive_chinaland_sg(sta_ob,
grd_fo,
save_path=None,
show=False,
dpi=200,
add_county_line=False):
'''
#绘制24小时降水实况与预报综合对比检验图，专为为全国区域设置的画面布局，画面更加紧凑
: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 = meteva.base.get_grid_of_data(grd_fo)
fig = plt.figure(figsize=(10, 7))
# 平面对比图
rect1 = [0.05, 0.43, 0.65, 0.53] # 左下宽高
map_area = 70 * 0.41 * 0.55
ax = plt.axes(rect1)
# 设置地图背景
map_extent = [73, 135, 18, 54]
add_china_map_2basemap(ax,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax.set_xlim((73, 135))
ax.set_ylim((18, 54))
# 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"
]
dat = grd_fo.values.squeeze()
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plot_grid = ax.contourf(x, y, dat, clevs, colors=colors_grid) # 填色图
colorbar_position_grid = fig.add_axes([0.085, 0.93, 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, "预报(mm)", fontsize=9)
# 绘制填色站点值
sta_ob_in = meteva.base.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"
]
pointsize = int(100 * map_area / len(dat))
if (pointsize > 30): pointsize = 30
if (pointsize < 1): pointsize = 1
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],
s=3 * pointsize,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
s=pointsize,
label=cleves_name[i],
linewidths=0.1,
edgecolor="k")
ax.legend(loc="lower left",
facecolor='whitesmoke',
title="观测",
edgecolor='whitesmoke',
fontsize=9)
#设置图片标题
time_str = meteva.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])
title = model_name + " " + dati_str + "起报" + str(
grid_fo.dtimes[0]) + "H时效预报和观测"
ax.set_title(title)
# 散点回归图
rect2 = [0.07, 0.07, 0.21, 0.30] # 左下宽高
ax2 = plt.axes(rect2)
sta_fo = meteva.base.interp_gs_nearest(grd_fo, sta_ob_in)
# print(sta_fo)
data_name = meteva.base.get_stadata_names(sta_ob_in)
ob = sta_ob_in.loc[:, data_name[0]].values
data_name = meteva.base.get_stadata_names(sta_fo)
fo = sta_fo.loc[:, data_name[0]].values
ob_fo = ob + fo
index = np.where(~np.isnan(ob_fo))
ob = ob[index]
fo = fo[index]
ax2.plot(ob, fo, '.', color='k')
maxy = max(np.max(ob), np.max(fo)) + 5
# 绘制比例线
rate = np.sum(fo) / (np.sum(ob) + 1e-30)
ob_line = np.arange(0, (maxy + 1), (maxy + 1) / 30)
fo_rate = ob_line * 1
ax2.plot(ob_line[0:30], fo_rate[0:30], 'b', linestyle='dashed')
# 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='r')
cor = np.corrcoef(ob, fo)
rg_text1 = "R = " + '%.2f' % (cor[0, 1])
rg_text2 = "y = " + '%.2f' % (clf.coef_[0]) + "* x + " + '%.2f' % (
clf.intercept_)
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
if (maxy < 5):
xmi = 0.1
Xmi = 1
elif (maxy < 50):
xmi = 1
if (maxy > 25):
Xmi = 10
else:
Xmi = 5
elif (maxy < 100):
xmi = 2
if (maxy > 50):
Xmi = 20
else:
Xmi = 10
elif (maxy < 250):
xmi = 5
if (maxy > 100):
Xmi = 50
else:
Xmi = 20
elif (maxy < 1000):
xmi = 20
if (maxy > 500):
Xmi = 200
else:
Xmi = 100
else:
xmi = 50
Xmi = 250
xmajorLocator = mpl.ticker.MultipleLocator(Xmi) # 将x主刻度标签设置为次刻度10倍
ymajorLocator = mpl.ticker.MultipleLocator(Xmi) # 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(xmi) # 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 = [0.35, 0.235, 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]
mean_fo = 0
max_fo = 0
mean_ob = 0
max_ob = 0
if len(fo_has) > 1:
mean_fo = np.mean(fo_has)
max_fo = np.max(fo_has)
if len(ob_has) > 1:
mean_ob = np.mean(ob_has)
max_ob = np.max(ob_has)
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) " + "%9d" % len(
ob_has) + " %4d" % len(fo_has) + "\n"
text += "有降水站点数百分比% " + "%11.1f" % (
len(ob_has) / len(ob)) + "%15.1f" % (len(fo_has) / len(fo)) + "\n"
text += "平均降水量(排除无降水) " + "%11.1f" % (mean_ob) + "%15.1f" % (
mean_fo) + "\n"
text += "最大降水量 " + "%11.1f" % (max_ob) + "%15.1f" % (
max_fo) + "\n"
text += "----------------------------------------------------"
plt.text(0, 0, text, fontsize=9)
# 绘制统计检验结果
rect5 = [0.705, 0.08, 0.28, 0.85] # 左下宽高
ax5 = plt.axes(rect5)
ax5.axes.set_axis_off()
mae = meteva.method.continuous.score.mae(ob, fo)
me = meteva.method.continuous.score.me(ob, fo)
mse = meteva.method.continuous.score.mse(ob, fo)
rmse = meteva.method.continuous.score.rmse(ob, fo)
bias_c = meteva.method.continuous.score.bias_m(ob, fo)
cor = meteva.method.continuous.score.corr(ob, fo)
pc_sun_rain = meteva.method.pc_of_sun_rain(ob, fo)
hfmc = meteva.method.yes_or_no.score.hfmc(ob, fo, clevs[1:])
ts = meteva.method.yes_or_no.score.ts(ob, fo, clevs[1:])
ets = meteva.method.yes_or_no.score.ets(ob, fo, clevs[1:])
bias = meteva.method.yes_or_no.score.bias(ob, fo, clevs[1:])
hit_rate = meteva.method.yes_or_no.score.pod(ob, fo, clevs[1:])
mis_rate = meteva.method.yes_or_no.score.mr(ob, fo, clevs[1:])
fal_rate = meteva.method.yes_or_no.score.pofd(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"
text += "晴雨准确率:" + "%6.2f" % pc_sun_rain + "\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" % hfmc[i, 0] + " 空报:" + "%-4d" % hfmc[
i, 1] + " 漏报:" + "%-4d" % hfmc[i, 2] + "\n"
if ts[i] == IV:
ts_str = " NULL"
else:
ts_str = "%5.3f" % ts[i]
if ets[i] == IV:
ets_str = " NULL"
else:
ets_str = "%5.3f" % ets[i]
text += "TS:" + ts_str + " ETS:" + ets_str + "\n"
if hit_rate[i] == IV:
hit_rate_str = " NULL"
else:
hit_rate_str = "%5.3f" % hit_rate[i]
if mis_rate[i] == IV:
mis_rate_str = " NULL"
else:
mis_rate_str = "%5.3f" % mis_rate[i]
text += "Hit rate:" + hit_rate_str + " Miss rate: " + mis_rate_str + "\n"
if fal_rate[i] == IV:
fal_rate_str = " NULL"
else:
fal_rate_str = "%5.3f" % fal_rate[i]
if bias[i] == IV:
bias_str = " NULL"
else:
bias_str = "%5.3f" % bias[i]
text += "False alarm ratio:" + fal_rate_str + " Bias:" + bias_str + "\n\n"
plt.text(0, 0.00, text, fontsize=10)
# 图片显示或保存
if (save_path is not None):
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
else:
show = True
if show:
plt.show()
plt.close()
return
def rain_24h_sg(sta_ob,
grd_fo,
save_path=None,
show=False,
dpi=200,
add_county_line=False):
'''
#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 = meteva.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, 0.00, 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, 0.00, width_ob_fo_str / width,
0.3] # 观测文字
ax = plt.axes(rect1)
# 设置地图背景
add_china_map_2basemap(ax,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "省界"
ax.set_xlim((grid_fo.slon, grid_fo.elon))
ax.set_ylim((grid_fo.slat, grid_fo.elat))
# 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)
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plot_grid = ax.contourf(x, y, dat, clevs, colors=colors_grid) # 填色图
time_str = meteva.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 = meteva.base.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],
s=3,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
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 (save_path is not None):
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
else:
show = True
if show:
plt.show()
plt.close()
return
def rain_24h_comprehensive_sg(sta_ob,
grd_fo,
save_path=None,
show=False,
dpi=200,
add_county_line=False):
'''
#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 = meteva.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
map_area = (hight - title_hight - legend_hight) * map_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
ax = plt.axes(rect1)
# 设置地图背景
add_china_map_2basemap(ax,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "省界"
ax.set_xlim((grid_fo.slon, grid_fo.elon))
ax.set_ylim((grid_fo.slat, grid_fo.elat))
# 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)
plt.xticks([])
plt.yticks([])
plot_grid = ax.contourf(x, y, dat, clevs, colors=colors_grid) # 填色图
time_str = meteva.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 = meteva.base.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"
]
pointsize = int(100 * map_area / len(dat))
if (pointsize > 30): pointsize = 30
if (pointsize < 1): pointsize = 1
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],
s=3 * pointsize,
label=cleves_name[i],
linewidths=0.3,
edgecolor='k')
else:
ax.scatter(x,
y,
c=colors_sta[i],
s=3 * pointsize,
label=cleves_name[i],
linewidths=0.0,
edgecolor="k")
ax.legend(facecolor='gainsboro',
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 = meteva.base.interp_gs_linear(grd_fo, sta_ob_in)
#print(sta_fo)
data_name = meteva.base.get_stadata_names(sta_ob_in)
ob = sta_ob_in[data_name[0]].values
data_name = meteva.base.get_stadata_names(sta_fo)
fo = sta_fo[data_name[0]].values
ob_fo = ob + fo
index = np.where(~np.isnan(ob_fo))
ob = ob[index]
fo = fo[index]
ax2.plot(ob, fo, '.', color='k')
maxy = max(np.max(ob), np.max(fo)) + 5
# 绘制比例线
rate = np.sum(fo) / (np.sum(ob) + 1e-30)
ob_line = np.arange(0, (maxy + 1), (maxy + 1) / 30)
fo_rate = ob_line * 1
ax2.plot(ob_line[0:30], fo_rate[0:30], 'b', linestyle='dashed')
# 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='r')
cor = np.corrcoef(ob, fo)
rg_text1 = "R = " + '%.2f' % (cor[0, 1])
rg_text2 = "y = " + '%.2f' % (clf.coef_[0]) + "* x + " + '%.2f' % (
clf.intercept_)
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
# 设置次刻度间隔
if (maxy < 5):
xmi = 0.1
Xmi = 1
elif (maxy < 50):
xmi = 1
if (maxy > 25):
Xmi = 10
else:
Xmi = 5
elif (maxy < 100):
xmi = 2
if (maxy > 50):
Xmi = 20
else:
Xmi = 10
elif (maxy < 250):
xmi = 5
if (maxy > 100):
Xmi = 50
else:
Xmi = 20
elif (maxy < 1000):
xmi = 20
if (maxy > 500):
Xmi = 200
else:
Xmi = 100
else:
xmi = 50
Xmi = 250
xmajorLocator = mpl.ticker.MultipleLocator(Xmi) # 将x主刻度标签设置为次刻度10倍
ymajorLocator = mpl.ticker.MultipleLocator(Xmi) # 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(xmi) # 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]
mean_fo = 0
max_fo = 0
mean_ob = 0
max_ob = 0
if len(fo_has) > 1:
mean_fo = np.mean(fo_has)
max_fo = np.max(fo_has)
if len(ob_has) > 1:
mean_ob = np.mean(ob_has)
max_ob = np.max(ob_has)
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" % (mean_ob) + "%15.1f" % (mean_fo) + "\n"
text += "最大降水量 " + "%6.1f" % (max_ob) + "%15.1f" % (
max_fo) + "\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 = meteva.method.continuous.score.mae(ob, fo)
me = meteva.method.continuous.score.me(ob, fo)
mse = meteva.method.continuous.score.mse(ob, fo)
rmse = meteva.method.continuous.score.rmse(ob, fo)
bias_c = meteva.method.continuous.score.bias_m(ob, fo)
cor = meteva.method.continuous.score.corr(ob, fo)
pc_sun_rain = meteva.method.pc_of_sun_rain(ob, fo)
hfmc = meteva.method.yes_or_no.score.hfmc(ob, fo, clevs[1:])
ts = meteva.method.yes_or_no.score.ts(ob, fo, clevs[1:])
ets = meteva.method.yes_or_no.score.ets(ob, fo, clevs[1:])
bias = meteva.method.yes_or_no.score.bias(ob, fo, clevs[1:])
hit_rate = meteva.method.yes_or_no.score.pod(ob, fo, clevs[1:])
mis_rate = meteva.method.yes_or_no.score.mr(ob, fo, clevs[1:])
fal_rate = meteva.method.yes_or_no.score.pofd(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"
text += "晴雨准确率:" + "%6.2f" % pc_sun_rain + "\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" % hfmc[i, 0] + " 空报:" + "%-4d" % hfmc[
i, 1] + " 漏报:" + "%-4d" % hfmc[i, 2] + "\n"
if ts[i] == IV:
ts_str = " NULL"
else:
ts_str = "%5.3f" % ts[i]
if ets[i] == IV:
ets_str = " NULL"
else:
ets_str = "%5.3f" % ets[i]
text += "TS:" + ts_str + " ETS:" + ets_str + "\n"
if hit_rate[i] == IV:
hit_rate_str = " NULL"
else:
hit_rate_str = "%5.3f" % hit_rate[i]
if mis_rate[i] == IV:
mis_rate_str = " NULL"
else:
mis_rate_str = "%5.3f" % mis_rate[i]
text += "Hit rate:" + hit_rate_str + " Miss rate: " + mis_rate_str + "\n"
if fal_rate[i] == IV:
fal_rate_str = " NULL"
else:
fal_rate_str = "%5.3f" % fal_rate[i]
if bias[i] == IV:
bias_str = " NULL"
else:
bias_str = "%5.3f" % bias[i]
text += "False alarm ratio:" + fal_rate_str + " Bias:" + bias_str + "\n\n"
plt.text(0, 0.00, text, fontsize=9)
# 图片显示或保存
if (save_path is not None):
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
else:
show = True
if show:
plt.show()
plt.close()
return
def temper_comprehensive_gg(grd_ob,
grd_fo,
save_path=None,
show=False,
dpi=200,
add_county_line=False):
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 = meteva.base.tool.color_tools.get_clev_and_cmap_by_element_name(
"temp")
clevs, cmap = meteva.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 = meteva.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
# 设置地图背景
ax1 = plt.axes(rect1)
add_china_map_2basemap(ax1,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax1,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax1.set_xlim((grid0.slon, grid0.elon))
ax1.set_ylim((grid0.slat, grid0.elat))
# 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"]
plt.xticks([])
plt.yticks([])
plot_grid = ax1.contourf(x,
y,
grd_ob.values.squeeze(),
levels=clevs,
cmap=cmap) # 填色图
ax1.set_title("实况", fontsize=18, 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 = plt.axes(rect2)
plt.xticks([])
plt.yticks([])
add_china_map_2basemap(ax2,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax2,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax2.set_xlim((grid0.slon, grid0.elon))
ax2.set_ylim((grid0.slat, grid0.elat))
ax2.contourf(x, y, grd_fo.values.squeeze(), levels=clevs, cmap=cmap) # 填色图
ax2.set_title("预报", fontsize=18, 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()
ax3 = plt.axes(rect3)
add_china_map_2basemap(ax3,
name='province',
edgecolor='k',
lw=0.3,
encoding='gbk') #"省界"
if add_county_line:
add_china_map_2basemap(ax3,
name="county",
edgecolor='k',
lw=0.2,
encoding='gbk') # "县界"
ax3.set_xlim((grid0.slon, grid0.elon))
ax3.set_ylim((grid0.slat, grid0.elat))
plot_grid1 = ax3.contourf(x, y, error, clevs1, cmap="bwr") # 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=18, loc="left", y=0.0, color="k")
plt.colorbar(plot_grid1,
cax=colorbar_position_grid1,
orientation='vertical')
plt.title("误差(℃)", fontsize=8, verticalalignment='bottom')
time_str = meteva.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()
X = np.zeros((len(fo), 1))
X[:, 0] = fo
clf = LinearRegression().fit(X, ob)
num_max = max(np.max(ob), np.max(fo))
num_min = min(np.min(ob), np.min(fo))
dmm = num_max - num_min
if (num_min != 0):
num_min -= 0.1 * dmm
num_max += dmm * 0.1
dmm = num_max - num_min
ob_line = np.arange(num_min, num_max, dmm / 30)
X = np.zeros((len(ob_line), 1))
X[:, 0] = ob_line
fo_rg = clf.predict(X)
ax2.plot(fo, ob, '.', color='b', markersize=1)
ax2.plot(ob_line, fo_rg, color="r")
ax2.plot(ob_line, ob_line, '--', color="k")
ax2.set_xlim(num_min, num_max)
ax2.set_ylim(num_min, num_max)
ax2.set_xlabel("预报", fontsize=10)
ax2.set_ylabel("观测", fontsize=10)
rg_text2 = "Y = " + '%.2f' % (clf.coef_[0]) + "* X + " + '%.2f' % (
clf.intercept_)
ax2.text(num_min + 0.05 * dmm,
num_min + 0.90 * dmm,
rg_text2,
fontsize=15,
color="r")
# 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 = meteva.method.continuous.score.mae(ob, fo)
mee = meteva.method.continuous.score.me(ob, fo)
msee = meteva.method.continuous.score.mse(ob, fo)
rmsee = meteva.method.continuous.score.rmse(ob, fo)
bias_ce = meteva.method.continuous.score.bias_m(ob, fo)
cor = meteva.method.continuous.score.corr(ob, fo)
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 (save_path is not None):
plt.savefig(save_path, dpi=dpi, bbox_inches='tight')
else:
show = True
if show:
plt.show()
plt.close()
