【meteva】如何优雅使用观测与预报绘制散点回归和RMSE

import meteva.base as meb
import meteva.method as mem
import meteva.product as mpd
import pandas as pd

filename = "/home/mw/input/meteva7050/nmc_met_tutorial-master/nmc_met_tutorial-master/meteva/input/meb/m3.000"  # 替换为你的micaps文件路径
sta = meb.read_stadata_from_micaps3(filename)
meb.set_stadata_names(sta,["ob"])
sta.head()

sta.describe()

sta['ob'][sta['ob'] > 100] = 0

sta.describe()

import numpy as np
ec = sta.copy()
# 生成随机数据
ec['ob']=sta['ob']**2/25
# 设置数据名称
meb.set_stadata_names(ec,["ec"])
# meteva设置时间与高度的语句,因为是直接复制原数据所以注释了
#meb.set_stadata_coords(sta_ob,level = 0,time = datetime.datetime(2018,7,3,9,0),dtime = 0)
ec.head()

sta_merge = meb.combine_on_obTime_id(sta,ec)
sta_merge

ob = sta_merge["ob"].values
fo = sta_merge["ec"].values

help(mem.scatter_regress)

Help on function scatter_regress in module meteva.method.continuous.plot:

scatter_regress(ob, fo, member_list=None, rtype='linear', vmax=None, vmin=None, ncol=None, save_path=None, show=False, dpi=300, title='散点回归图', sup_fontsize=10, width=None, height=None, ylabel='观测')
    绘制观测-预报散点图和线性回归曲线
    :param Ob: 实况数据  任意维numpy数组
    :param Fo: 预测数据 任意维numpy数组,Fo.shape 和Ob.shape一致
    :param save_path:图片保存路径，缺省时不输出图片，而是以默认绘图窗口形式展示
    :return:图片，包含散点图和线性回归图,横坐标为观测值，纵坐标为预报值，横坐标很纵轴标取值范围自动设为一致，在图形中间添加了完美预报的参考线。

mem.scatter_regress(ob,fo,show = True,dpi = 300)

help(mpd.plot)

Help on function plot in module meteva.product.program.plot:

plot(sta_ob_and_fos0, method, s=None, g=None, gll=None, save_dir=None, save_path=None, title=None, **kwargs)

mpd.plot(sta_merge,mem.scatter_regress,show = True,dpi = 600)

help(mpd.rmse_scatter)

Help on function rmse_scatter in module meteva.product.program.error_ana_scatter:

rmse_scatter(sta_ob_and_fos, s=None, g=None, gll=None, group_name_list=None, save_dir=None, save_path=None, show=False, print_max=1, threshold=0, add_county_line=False, map_extend=None, dpi=300, title='均方根误差站点分布', sup_fontsize=10, height=None, width=None)

map_extend=[110,120,20,30]
mpd.rmse_scatter(sta_merge,show = True,map_extend=map_extend)

取值最大的1个站点：
id:51573   lon:89.23  lat:42.95 value:11.72760000000001