【不知道是啥】浅保存哈

# -*- coding: utf-8 -*-
"""
Created on Fri Dec  2 13:46:48 2022@author: Lenovo
"""from sklearn.metrics import make_scorer
import os
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score,mean_squared_error
# from sklearn.preprocessing import StandardScaler
import seaborn as sns  
from scipy.stats import gaussian_kde
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.feature_selection import RFECV
from scipy.interpolate import griddata
from itertools import combinations
from operator import itemgetter
dic = {}
path=r'D:\Fluxnet\try'
outpath=r'D:\Fluxnet\OUTCOME\每种变量组合放在一起之前的仓库'site_list=[]
year_list=[]total_number=[]
post_dropna_number=[]
post_drop_le_abnormal_number=[]
test_number=[]
train_number=[]
N_estimators=[]
Max_depth=[]Rmse_list=[]
R2_list=[]
Bias_list=[]Drivers_column=[]
Filling_rate_list=[]
Feature_list=[]# path1=r'D:\Fluxnet\try'
# path2=r'D:\Fluxnet\try_ndvi'  
# path1=r'D:\Fluxnet\加了土壤水和土壤温度的\MDS_用'
# path2=r'D:\Fluxnet\ndvi777 - SHAOSHAOSHAO'  # for s,j in zip(os.listdir(path1),os.listdir(path2)):
#     print(s)
#     print(os.listdir(path2))
#     sole_s=pd.read_csv(os.path.join(path1,s))
#     sole_j=pd.read_csv(os.path.join(path2,j)) #     sole_s['TIMESTAMP_START']=sole_s['TIMESTAMP_START'].astype('str') 
#     sole_s['TIMESTAMP_START']=pd.to_datetime(sole_s['TIMESTAMP_START'])   #     sole_j=sole_j[['TIMESTAMP_START','NDVI']]
#     sole_j['TIMESTAMP_START'] = pd.to_datetime(sole_j['TIMESTAMP_START'])#     sole_j = sole_j.set_index('TIMESTAMP_START')
#     sole_j = sole_j.resample('1D').interpolate() # 30T 按分钟(T)插值  1D按天插值
#     sole_j = sole_j.reset_index() #     sole=pd.merge(sole_s, sole_j,how='left',on='TIMESTAMP_START') #     sole['NDVI']=sole['NDVI'].interpolate(method='pad') # 1天一个值
#     print(sole)path1 =  r'C:\Users\Lenovo\Desktop\四大类\REALTRY'
for file in os.listdir(path1):sole = pd.read_csv(os.path.join(path1,file))site_list1=[]year_list1=[]test_number1=[]train_number1=[]rmse_list1=[]r2_list1=[]bias_list1=[]sole_raw = solesole_copy = soleprint('原始数据：',sole.shape)sole.dropna(subset=['LE_F_MDS_QC'],axis=0,inplace=True) #删除LE_F_MDS_QC中含有空值的行print('去掉没QC后的原始数据：',sole.shape)trainset=sole[sole['LE_F_MDS_QC']==0]print('观测数据：',trainset.shape)# =============================================================================
#   以LE_F_MDS=20W/m²为界 白天和晚上分别训练
# =============================================================================trainset=trainset[trainset['LE_F_MDS']>=20]print('白天的总量： ',trainset.shape)gap=sole[sole['LE_F_MDS_QC']!=0]print('插补数据：',gap.shape)gap_drople=gap.drop(['LE_F_MDS','LE_F_MDS_QC','TIMESTAMP_START','TIMESTAMP_END']## , 'SW_IN_F_MDS_QC', 'NETRAD',axis=1)# gap_drople=gap_drop.drop(['SW_IN_F_MDS_QC', 'NETRAD'],axis=1)#===============================每行至少有一个/三个不是空值时保留gap_dropna=gap_drople# gap_dropna=gap_drople.dropna(axis=0,thresh=3) print('去空值后的插补数据：',gap_dropna.shape)dff=pd.DataFrame(gap_dropna.isna().sum().sort_values(ascending=False))print('预测集的空值：',dff)#看下训练集的空值，可以看出跟插补集不太一样print('训练集的空值：\n',trainset.drop(['LE_F_MDS','LE_F_MDS_QC','TIMESTAMP_START','TIMESTAMP_END']#,axis=1).isna().sum().sort_values(ascending=False))#==========================获得所有变量组合def combine(list0,o):list1=[]for i in combinations(list0,o):list1.append(i)return list1tianchongliang=[]chabuliang=[]rmseliang=[]site_list=[]ALL_rmse_list=[]rmse_number=[]pinjie_number=[]train_number=[]rmse_list=[]rmse1_list=[]all_rmse1_list=[]r2_list=[]r21_list=[]ALL_r2_list=[]all_r21_list=[]bias_list=[]bias1_list=[]ALL_bias_list=[]all_bias1_list=[]filling_rate_list=[]dic_list = []fig  = plt.figure(figsize=(4,40),dpi=600)fig1 = plt.figure(figsize=(16,36),dpi=600)ALL_x_test = pd.Series()ALL_y_test = pd.Series()qian=0hou=-1for u in reversed(range(3,len(gap_drople.columns)+1)) : fillrate_mid_list=[]col_list=[]list666=[]list666.extend(combine(dff.index,u))#===========================获取不同插补率的组合特征list_score=[]score=[]big_list=[]for i in range(0,len(list666)):sco=f'{gap_drople[list(list666[i])].dropna().shape[0] / gap_drople.shape[0]:.2f}'score+=[f'{gap_drople[list(list666[i])].dropna().shape[0] / gap_drople.shape[0]:.2f}']list_score+=[{'score':sco,'list':list666[i]}]# print(list_score)  print(list_score)#=============================plotkey_list=[a['list'] for a in list_score]len_list = [ len(i) for i in key_list ]score=[np.float64(i) for i in score]plt.rc('font', family='Times New Roman',size=20)plt.figure(figsize=(10,8),dpi=400)plt.scatter(len_list,score)plt.xlabel('Number of drivers', {'family':'Times New Roman','weight':'normal','size':20})plt.ylabel('Filling rate',{'family':'Times New Roman','weight':'normal','size':20})#============================填充率最大对应去的变量列表shunxusorted_list=sorted(list_score, key=lambda list_score: list_score['score'], reverse=True)# print(sorted_list)   # 按降序排列biggest_score=[a['score'] for a in sorted_list][0]      biggest_score_feature_list=[a['list'] for a in sorted_list][0]# print(biggest_score_feature_list)   Feature_list.append(biggest_score_feature_list)filling_rate_list.append(biggest_score)Filling_rate_list.append(biggest_score)#==============================建模准备================================train_copy=trainset.copy()train_copy.drop(['LE_F_MDS_QC','TIMESTAMP_START','TIMESTAMP_END']#,axis=1,inplace=True)#.isna().sum().sort_values(ascending=False)feature=[x for x in biggest_score_feature_list]  train_option=train_copy[feature]train_option['LE_F_MDS']=train_copy['LE_F_MDS']print("Train_option原始数值\n",train_option.shape)#print(train_option.isna().sum().sort_values(ascending=True))#============================去除空值=======================================train_option_dropna=train_option.dropna() #训练数据去空值print('训练集去掉空值后： ',train_option_dropna.shape)c=train_option_dropna    print(c.shape)Drivers=c.drop(['LE_F_MDS'],axis=1)Drivers_column+=[' '.join(Drivers.columns.tolist())]LE=c['LE_F_MDS']x_train,x_test,y_train,y_test=train_test_split(Drivers,LE,test_size=0.20,random_state=(0))                            print(x_train.shape)print(x_test.shape)print(y_train.shape)print(y_test.shape)
# =============================================================================
#      建模
# =============================================================================rf=RandomForestRegressor(n_estimators=1100,max_depth=80,oob_score=True,random_state=(0))   rf.fit(x_train,y_train)    # rf.fit(Drivers,LE)     # pred_oob = rf.oob_prediction_ #袋外预测值# print(len(pred_oob))# print(pred_oob)# rmse=np.sqrt(mean_squared_error(LE, pred_oob)) #袋外均方根误差site_list+=[file.split('_',6)[1]]tianchongliang+=[biggest_score]chabuliang+=[gap.shape[0]]rmseliang+=[len(y_test)]rmse=np.sqrt(mean_squared_error(y_test,rf.predict(x_test)))rmse_list.append(rmse)r2=r2_score(y_test,rf.predict(x_test))  r2_list.append(r2)bias=(rf.predict(x_test)-y_test).mean() # bias=(pred_oob-LE).mean()bias_list.append(bias)# rmse_df=pd.DataFrame({'site':site_list,'rmse':rmse_list#                       ,'rmse量':rmseliang,'插补量':chabuliang#                       ,'插补率':tianchongliang})# rmse_df.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\RMSE', str(file.split('_',6)[1])  +'.csv'),index = False)# =============================================================================
#       单一变量组合线性内插
# =============================================================================s_ori = pd.read_csv(os.path.join(path1,file))  s_ori.loc[:,'LE'] = y_tests_ori.loc[y_test.index,'LE_F_MDS'] = np.nans_ori['LE_F_MDS']= s_ori['LE_F_MDS'].interpolate()rmse1=np.sqrt(mean_squared_error(y_test,s_ori.loc[y_test.index,'LE_F_MDS'] ))rmse1_list.append(rmse1)r21=r2_score(y_test,s_ori.loc[y_test.index,'LE_F_MDS'])  r21_list.append(r21)bias1=(s_ori.loc[y_test.index,'LE_F_MDS']-y_test).mean()bias1_list.append(bias1)rmse_df=pd.DataFrame({'site':site_list,'RF_RMSE':rmse_list,'IP_RMSE':rmse1_list,'rmse量':rmseliang,'插补量':chabuliang,'插补率':tianchongliang,'RF_R2':r2_list,'IP_R2':r21_list,'RF_BIAS':bias_list,'IP_BIAS':bias1_list})print(rmse_df)print(tianchongliang[qian])rmse_df.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\RMSE', str(file.split('_',6)[1])  +'.csv'),index = False)# =============================================================================
#       DYNAMIC RMSE
# =============================================================================print(rmse_list[qian] , rmse_list[hou])print(tianchongliang[qian] , tianchongliang[hou])if qian==0 or rmse_list[qian] < rmse_list[hou] or tianchongliang[qian] > tianchongliang[hou]   :y_test6 = y_test[~y_test.index.isin(ALL_y_test.index)] #在y_test里不在大的合集里x_test6 = pd.Series(rf.predict(x_test),index=y_test.index)x_test6 = x_test6[y_test6.index]ALL_y_test = pd.concat([ALL_y_test, y_test6], axis=0, ignore_index=False)ALL_x_test = pd.concat([ALL_x_test, x_test6], axis=0,ignore_index=False)# print('拼接后\n',ALL_x_test)# print('拼接后\n',ALL_y_test)ALL_rmse=np.sqrt(mean_squared_error(ALL_y_test,ALL_x_test))ALL_rmse_list.append(ALL_rmse)r2=r2_score(y_test,rf.predict(x_test))  ALL_r2_list.append(r2)bias=(rf.predict(x_test)-y_test).mean() # bias=(pred_oob-LE).mean()ALL_bias_list.append(bias)#线性内插综合RMSEs_ori = pd.read_csv(os.path.join(path1,file))  s_ori.loc[:,'LE'] = ALL_y_tests_ori.loc[ALL_y_test.index,'LE_F_MDS'] = np.nans_ori['LE_F_MDS']= s_ori['LE_F_MDS'].interpolate()rmse1=np.sqrt(mean_squared_error(ALL_y_test,s_ori.loc[ALL_y_test.index,'LE_F_MDS'] ))all_rmse1_list.append(rmse1)r21=r2_score(ALL_y_test,s_ori.loc[ALL_y_test.index,'LE_F_MDS'])  all_r21_list.append(r21)bias1=(s_ori.loc[ALL_y_test.index,'LE_F_MDS'] - ALL_y_test).mean()all_bias1_list.append(bias1)pinjie_number.append(len(y_test6))rmse_number.append(len(ALL_y_test))train_number.append(int(trainset.shape[0]*0.2))ALL_rmse_df=pd.DataFrame({'RF_RMSE':ALL_rmse_list,'IP_RMSE':all_rmse1_list,'rmse_number':rmse_number,'pinjie_number':pinjie_number,'train_number':train_number,'RF_R2':ALL_r2_list,'IP_R2':all_r21_list,'RF_BIAS':ALL_bias_list,'IP_BIAS':all_bias1_list}) print(ALL_rmse_df)ALL_rmse_df.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\RMSE_ALL',str(file.split('_',6)[1])  +'.csv'),index = False)qian+=1hou+=1# ===========================高斯核密度散点图===========================# post_gs=pd.DataFrame({'predict':pred_oob,'in_situ':LE,})post_gs=pd.DataFrame({'predict':rf.predict(x_test),'in_situ':y_test,}) post_gs['index']=[i for i in range(post_gs.shape[0])]post_gs=post_gs.set_index('index')x=post_gs['in_situ']y=post_gs['predict']xy = np.vstack([x,y])#计算点密度z = gaussian_kde(xy)(xy)#高斯核密度idx = z.argsort()#根据密度对点进行排序，最密集的点在最后绘制x, y, z = x[idx], y[idx], z[idx]fw = 800ax = fig.add_subplot(len(gap_drople.columns)-1,1,u-2)scatter = ax.scatter(x,y,marker='o',c=z,s=15,label='LST',cmap='jet') # o是实心圆，c=是设置点的颜色，cmap设置色彩范围，'Spectral_r'和'Spectral'色彩映射相反divider = make_axes_locatable(ax) #画色域图# plt.scatter(x, y, c=z, s=7, cmap='jet')# plt.axis([0, fw, 0, fw])  # 设置线的范围# plt.title( file.split('_',6)[1], family = 'Times New Roman',size=21)# plt.text( 10, 700,len(feature), family = 'Times New Roman',size=21)# plt.text(10, 700, 'Driver numbers = %s' % len(feature), family = 'Times New Roman',size=21)# plt.text(10, 600, 'Size = %.f' % len(y), family = 'Times New Roman',size=18) # text的位置需要根据x,y的大小范围进行调整。# plt.text(10, 650, 'RMSE = %.3f W/m²' % rmse, family = 'Times New Roman',size=18)# plt.text(10, 700, 'R² = %.3f' % r2, family = 'Times New Roman',size=18)# plt.text(10, 750, 'BIAS = %.3f W/m²' % bias, family = 'Times New Roman',size=18)ax.set_xlabel('Station LE (W/m²)',family = 'Times New Roman',size=19)ax.set_ylabel('Estimated LE (W/m²)',family = 'Times New Roman',size=19)ax.plot([0,fw], [0,fw], 'gray', lw=2)  # 画的1:1线，线的颜色为black，线宽为0.8ax.set_xlim(0,fw)ax.set_ylim(0,fw)# ax.xaxis.set_tick_params(labelsize=19) # ax.xaxis.set_tick_params(labelsize=19) # plt.xticks(fontproperties='Times New Roman',size=19)# plt.yticks(fontproperties='Times New Roman',size=19)fig.set_tight_layout(True) #================================================================MDSMDS_GAP=s_oriif 'SW_IN' in MDS_GAP.columns.to_list() and 'TA' in  MDS_GAP.columns.to_list() and 'VPD' in  MDS_GAP.columns.to_list():MDS_GAP['Year']=MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])MDS_GAP['Year'] = MDS_GAP['TIMESTAMP_END'].dt.year  #Time stamp is not equidistant (half-)hours in rows: 35040, 35088, 52560, 52608, 70080, 70128, 87600, 87648MDS_GAP['DoY']=MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])doy=[]for k in MDS_GAP['TIMESTAMP_END']:doy += [k.strftime("%j")]MDS_GAP['DoY'] = doy  #Time stamp is not equidistant (half-)hours in rows: 35040, 35088, 52560, 52608, 70080, 70128, 87600, 87648MDS_GAP['Hour'] = MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])hour=[]for l in MDS_GAP['TIMESTAMP_END']:hour += [int(l.strftime('%H'))+float(l.strftime('%M'))/60]MDS_GAP['Hour'] = hour          MDS_GAP.loc[:,'LE'] = y_testprint(MDS_GAP['LE'].dropna().sum())MDS_GAP['LE'].to_csv(os.path.join(r'C:\Users\Lenovo\Desktop\R\用来rmse的原始值666', str(file.split('_',6)[1]) +  str(u)+ '.txt'),sep='	',index = False)MDS_GAP['LE_F_MDS']=s_ori['LE_F_MDS']MDS_GAP.loc[MDS_GAP['LE']>=-9999,['LE']] = -9999MDS_GAP['LE'].fillna(MDS_GAP['LE_F_MDS'],inplace=True)MDS_GAP['Rg']=MDS_GAP['SW_IN']        MDS_GAP['Tair']=MDS_GAP['TA']MDS_GAP['VPD']=MDS_GAP['VPD']# MDS_GAP['NEE']=MDS_GAP['NEE_VUT_REF']MDS_GAP=MDS_GAP[['Year','DoY','Hour','LE','Rg','Tair','VPD']]#,'Tsoil','rH',MDS_GAP.loc[MDS_GAP['Rg'] > 1200 , ['Rg']] = -9999 # Drivers control Rg <= 1200W/m² Ta <= 2.5℃W/m² VPD <= 50hPa# MDS_GAP.loc[MDS_GAP['Tair'] > 2.5 , ['Tair']] ==-9999MDS_GAP.loc[MDS_GAP['VPD'] > 50 , ['VPD']] = -9999#将单位插到第零行的位置上rrow = 0  # 插入的位置value = pd.DataFrame([['-', '-', '-','Wm-2', 'Wm-2', 'degC','hPa']],columns=MDS_GAP.columns)  # 插入的数据  'degC','%',df_tmp1 = MDS_GAP[:row]df_tmp2 = MDS_GAP[row:]# 插入合并数据表MDS_GAP = df_tmp1.append(value).append(df_tmp2)MDS_GAP = MDS_GAP.fillna(-9999) MDS_GAP.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\MDS_TRY666', str(file.split('_',6)[1]) + str(u) + '.txt'),sep='	',index = False)#+ str(gaplong)#==============================================================MDS_ALLs_ori = pd.read_csv(os.path.join(path1,file))MDS_GAP=s_oriif 'SW_IN' in MDS_GAP.columns.to_list() and 'TA' in  MDS_GAP.columns.to_list() and 'VPD' in  MDS_GAP.columns.to_list():MDS_GAP['Year']=MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])MDS_GAP['Year'] = MDS_GAP['TIMESTAMP_END'].dt.year  #老报错 Time stamp is not equidistant (half-)hours in rows: 35040, 35088, 52560, 52608, 70080, 70128, 87600, 87648MDS_GAP['DoY']=MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])doy=[]for k in MDS_GAP['TIMESTAMP_END']:doy += [k.strftime("%j")]MDS_GAP['DoY'] = doy  #老报错 Time stamp is not equidistant (half-)hours in rows: 35040, 35088, 52560, 52608, 70080, 70128, 87600, 87648MDS_GAP['Hour'] = MDS_GAP['TIMESTAMP_END']MDS_GAP['TIMESTAMP_END']=MDS_GAP['TIMESTAMP_END'].astype('str')MDS_GAP['TIMESTAMP_END']=pd.to_datetime(MDS_GAP['TIMESTAMP_END'])hour=[]for l in MDS_GAP['TIMESTAMP_END']:hour += [int(l.strftime('%H'))+float(l.strftime('%M'))/60]MDS_GAP['Hour'] = hour          MDS_GAP.loc[:,'LE'] = ALL_y_testprint(MDS_GAP['LE'].dropna().sum())MDS_GAP['LE'].to_csv(os.path.join(r'C:\Users\Lenovo\Desktop\R\用来ALL_rmse的原始值666', str(file.split('_',6)[1]) + '.txt'),sep='	',index = False)MDS_GAP['LE_F_MDS']=s_ori['LE_F_MDS']MDS_GAP.loc[MDS_GAP['LE']>=-9999,['LE']] = -9999MDS_GAP['LE'].fillna(MDS_GAP['LE_F_MDS'],inplace=True)MDS_GAP['Rg']=MDS_GAP['SW_IN']        MDS_GAP['Tair']=MDS_GAP['TA']MDS_GAP['VPD']=MDS_GAP['VPD']# MDS_GAP['NEE']=MDS_GAP['NEE_VUT_REF']MDS_GAP=MDS_GAP[['Year','DoY','Hour','LE','Rg','Tair','VPD']]#,'Tsoil','rH',MDS_GAP.loc[MDS_GAP['Rg'] > 1200 , ['Rg']] = -9999 # Drivers control Rg <= 1200W/m² Ta <= 2.5℃W/m² VPD <= 50hPa# MDS_GAP.loc[MDS_GAP['Tair'] > 2.5 , ['Tair']] ==-9999MDS_GAP.loc[MDS_GAP['VPD'] > 50 , ['VPD']] = -9999#将单位插到第零行的位置上rrow = 0  # 插入的位置value = pd.DataFrame([['-', '-', '-','Wm-2', 'Wm-2', 'degC','hPa']],columns=MDS_GAP.columns)  # 插入的数据  'degC','%',df_tmp1 = MDS_GAP[:row]df_tmp2 = MDS_GAP[row:]# 插入合并数据表MDS_GAP = df_tmp1.append(value).append(df_tmp2)MDS_GAP = MDS_GAP.fillna(-9999) MDS_GAP.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\ALL_MDS_TRY666', str(file.split('_',6)[1])  + '.txt'),sep='	',index = False)#+ str(gaplong)#==============================复制一下整个的 插补 保存 比较 导出ALL_y_testgap_dropna_copy=gap_dropna.copy()gap_dropna_copy=gap_dropna_copy[feature]gap_dropna_copy=gap_dropna_copy.dropna()gap_dropna_copy.loc[:, 'LE_gap_filled'] = rf.predict(gap_dropna_copy)le=sole.copy()le['LE_F_MDS_QC'].replace([1,2,3], np.nan, inplace=True)le['LE_F_MDS_QC'].replace(0, -9999, inplace=True)le['LE_F_MDS_QC'].fillna(gap_dropna_copy['LE_gap_filled'], inplace=True)le['RMSE']=[rmse]*sole.shape[0]dic0={'TIMESTAMP_START':le['TIMESTAMP_START'].tolist(),'TIMESTAMP_END':le['TIMESTAMP_END'].tolist() ,'LE_Gap_filled'+ str(u): le['LE_F_MDS_QC'].tolist(),'RMSE'+ str(u): le['RMSE'],'Drivers'+ str(u): [' '.join(Drivers.columns.tolist())]*sole.shape[0]}df0 = pd.DataFrame(dic0)dic={'list_name':df0, 'rmse':df0['RMSE'+ str(u)][df0.index[0]]} dic_list += [dic]sorted_dic=sorted(dic_list, key=lambda dic_list: dic_list['rmse'], reverse=False) list_name=[a['list_name'] for a in sorted_dic] # 打印出来的话就是整个dataframe countdf = pd.concat(list_name,axis=1)df = df.loc[:,~df.columns.duplicated()]shunxu = [''.join(list(filter(str.isdigit,x))) for x in df.columns]shunxu0 = list(filter(None,shunxu))shunxu = list(set(shunxu0)) #set的方法会改变顺序 按照原来的index排个序shunxu.sort(key = shunxu0.index)print(shunxu)  #=============================== 变量个数 VS.插补率# fig = plt.subplot(8,5,36+dalei)    # plt.savefig(os.path.join(r'D:\Fluxnet\PIC666\DoubleY',s.split('_',6)[1])#             , bbox_inches='tight', dpi=500)x = [x for x in reversed(range(3,len(gap_drople.columns)+1))] #reversed(range(len(df.index)+1),3)matplotlib does not support generators as inputy1 = rmse_listy2 = filling_rate_listax = fig.add_subplot(len(gap_drople.columns)-1,1,len(gap_drople.columns)-1)fig = plt.figure(figsize=(12,8),dpi=400)ax = fig.add_subplot(1,1,1)line1=ax.plot(x, y1,color='red',linestyle='--',marker='o',linewidth=2.5)ax.set_ylabel('RMSE of 25% tesing set', {'family':'Times New Roman','weight':'normal','size':21},color='red')ax.set_xlabel('Number of drivers',{'family':'Times New Roman','weight':'normal','size':21})ax.tick_params(labelsize=19)# ax1.set_title("")ax2 = ax.twinx()  # this is a important function#ax2.set_ylim([-0.05,1.05]) # 设置y轴取值范围   # ax2.set_yticks([0.0,0.3,0.5,0.7,0.9]) # 设置刻度范围 # ax2.set_yticklabels([0.0,0.3,0.5,0.7,0.9]) # 设置刻度line2=ax2.plot(x, y2,color='blue',marker='o',linewidth=2.5)ax2.tick_params(labelsize=19)ax2.set_ylabel('Filling rate', {'family':'Times New Roman','weight':'normal','size':21},color='blue')# a2.invert_yaxis() #invert_yaxis()翻转纵轴，invert_xaxis()翻转横轴# plt.tick_params(labelsize=19)# plt.xticks(np.arange(5, 13, 1),fontproperties='Times New Roman',size=19)plt.savefig(os.path.join(r'D:\Fluxnet\PIC666\1129',str(file.split('_',6)[1]) +'.png'), bbox_inches='tight', dpi=500)plt.show()# =============================================================================
#      动态插补
# =============================================================================# for latter in shunxu[1:]:#     a = df#     b=a.loc[a['LE_Gap_filled'+ str(shunxu[0])] > -9999, ['LE_Gap_filled'+ str(shunxu[0]), 'Drivers'+ str(shunxu[0]), 'RMSE'+ str(shunxu[0])]] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers'+ str(shunxu[0])]=a.loc[a['LE_Gap_filled'+ str(shunxu[0])] == np.nan, ['Drivers'+ str(shunxu[0])]]#     a['Drivers'+ str(shunxu[0])].fillna( b['Drivers'+ str(shunxu[0])] ,inplace = True ) # 自立门户 新建第一个模型的Drivers#     a['RMSE' + str(shunxu[0])]=a.loc[a['LE_Gap_filled'+str(shunxu[0])] == np.nan, ['RMSE' + str(shunxu[0])]]#     a['RMSE' + str(shunxu[0])].fillna( b['RMSE'+ str(shunxu[0])] ,inplace = True ) # 自立门户 新建第一个模型的RMSE#     b=a.loc[a['LE_Gap_filled'+ str(latter)] > -9999, ['LE_Gap_filled'+ str(latter), 'Drivers'+ str(latter), 'RMSE'+ str(latter)]] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers'+ str(latter)]=a.loc[a['LE_Gap_filled'+ str(latter)] == np.nan, ['Drivers'+ str(latter)]]#     a['Drivers'+ str(latter)].fillna( b['Drivers'+ str(latter)] ,inplace = True ) # 自立门户 新建第一个模型的Drivers#     a['RMSE' + str(latter)]=a.loc[a['LE_Gap_filled'+str(latter)] == np.nan, ['RMSE' + str(latter)]]#     a['RMSE' + str(latter)].fillna( b['RMSE'+ str(latter)] ,inplace = True ) # 自立门户 新建第一个模型的RMSE#     a['LE_Gap_filled'+str(shunxu[0])].fillna(a['LE_Gap_filled'+ str(latter)], inplace=True) # LE Update#     df2=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下#     print(a['LE_Gap_filled'+str(shunxu[0])])#     a['Drivers'+str(shunxu[0])].fillna(a['Drivers'+ str(latter)],inplace=True)  # Drivers Update#     a['RMSE'+str(shunxu[0])].fillna(a['RMSE'+ str(latter)],inplace=True)  # Rmse Update#     # 加一下a的时间#     so=pd.read_csv(os.path.join(path1,file))#     so=so[['TIMESTAMP_START' ,'TIMESTAMP_END','LE_F_MDS']]#     print(a['TIMESTAMP_START'])#     a.to_csv(os.path.join(r'C:\Users\Lenovo\Desktop\R\用来rmse的原始值666', str(file.split('_',6)[1]) + '.csv'),index = False)# # print(a)# a['QC'] = np.nan # a.loc[a['LE_Gap_filled'+ str(shunxu[0])] != -9999, 'QC'] = 1# a.loc[a['LE_Gap_filled'+ str(shunxu[0])] == -9999 , 'QC'] = 0# a['LE_Gap_filled'+ str(shunxu[0])].replace(np.nan,-8888,inplace=True)   # 原本是空值的部分  由于变量缺失过多，压根儿补不了的部分 在原数据集中，QC为3，表示的是ERA的数据# a['LE_Gap_filled'+ str(shunxu[0])].replace(-9999,np.nan,inplace=True)   #        |          空值还有种原因是 因为变量组合的原因，没有补到那一块，所以仍旧空# a['LE_Gap_filled'+ str(shunxu[0])].fillna(so['LE_F_MDS'],inplace=True)#  最后依旧是空值     # a.loc[a['LE_Gap_filled'+ str(shunxu[0])] == -8888 , 'QC'] = -9999# a=a[[ 'TIMESTAMP_END', 'LE_Gap_filled'+ str(shunxu[0]), 'QC',  'Drivers'+ str(shunxu[0]), 'RMSE'+ str(shunxu[0])]]# a= pd.merge(so,a,how='outer',on='TIMESTAMP_END')# a['LE_Gap_filled'+ str(shunxu[0])].fillna(a['LE_F_MDS'],inplace=True)   # a['LE_Gap_filled'+ str(shunxu[0])].replace(-8888,np.nan,inplace=True)    # a=a[['TIMESTAMP_START', 'TIMESTAMP_END', 'LE_Gap_filled'+ str(shunxu[0]), 'QC',  'Drivers' + str(shunxu[0]), 'RMSE'+ str(shunxu[0])]]# bianliangmen = pd.read_csv(os.path.join(path1,file))# bianliangmen = bianliangmen.drop(['TIMESTAMP_START' ,'TIMESTAMP_END','LE_F_MDS'],axis=1).columns# for i in bianliangmen:#     a[str(i)]=np.nan# # print(a.columns)  year   # a['Drivers' + str(shunxu[0])].replace(np.nan,-9999,inplace=True)      # b=a.loc[a['Drivers' + str(shunxu[0])]!=-9999]# for i in b.columns[6:]:#     c=b[b['Drivers' + str(shunxu[0])].str.contains(i)]#     c[i].replace(np.nan,'+',inplace=True)#     a[i]=c[i]# b=a.count(axis=1)-6# b=pd.DataFrame(b)# a['n_drivers']=b# a['n_drivers'].replace([-1,-2,-3],np.nan,inplace=True)# a['Drivers' + str(shunxu[0])].replace(-9999,np.nan,inplace=True)# # print(a)# a.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\FILLED',str(file.split('_',6)[1]) +'.csv'),index = False)# # #     # total_number.append(int(sole.shape[0]))#     # post_dropna_number.append(int(train_option_dropna.shape[0]))#     # post_drop_le_abnormal_number.append(int(c.shape[0]))#     # test_number.append(int(c.shape[0]*0.25))#     # train_number.append(int(c.shape[0]*0.75))#     # # N_estimators.append(n_estimators)#     # # Max_depth.append(max_depth)# # ===========================================================绘制散点图file# s_ori = pd.read_csv(os.path.join(path1,file))# ori = s_ori.loc[s_ori['LE_F_MDS_QC']==0,['TIMESTAMP_START','LE_F_MDS']]# filled = s_ori.loc[s_ori['LE_F_MDS_QC']!=0,['TIMESTAMP_START','LE_F_MDS']]# s_ori['TIMESTAMP_START'] = pd.to_datetime(s_ori['TIMESTAMP_START'])# s_ori['year'] = s_ori['TIMESTAMP_START'].dt.year# gap_filled = a.loc[a['QC'] == 1,['TIMESTAMP_START','LE_Gap_filled'+ str(shunxu[0])]]# fig1 ,ax = plt.subplots(5,1,sharex='col',figsize=(25,9),dpi=300)# ax0 = ax[0]# ax0.plot( 'LE_F_MDS', data=ori, linestyle='none',marker='o')# ax1 = ax[1]# ax1.plot(  'LE_F_MDS', data=filled, color='#ff7f0e',linestyle='none', marker='o')# ax2 = ax[2]# ax2.plot(  'LE_F_MDS', data=ori, alpha=0.6, linestyle='none', marker='o')# ax2.plot( 'LE_F_MDS', data=filled, alpha=0.6, linestyle='none', marker='o')# ax3 = ax[3]# # ax2.plot(  'LE_F_MDS', data=s_ori, alpha=0.6, linestyle='none', marker='o')# ax3.plot('LE_Gap_filled'+ str(shunxu[0]),data=gap_filled, color='#FAA460', linestyle='none', marker='o' )# ax4 = ax[4]# ax4.plot(  'LE_F_MDS', data=ori, alpha=0.6, linestyle='none', marker='o')# ax4.plot('LE_Gap_filled'+ str(shunxu[0]),data=gap_filled,color='#FAA460', alpha=0.6, linestyle='none', marker='o' )# ax0.set_ylabel('in-situ', fontsize=19)# ax1.set_ylabel('MDS', fontsize=19)# ax2.set_ylabel('FLUXNET2015', fontsize=19)# ax3.set_ylabel('RF', fontsize=19)# ax4.set_ylabel('Dynamic', fontsize=19)# nianfen = int(file.split('_',6)[5].split('-',2)[0])# nianfen1 = int(file.split('_',6)[5].split('-',2)[1])# ax2.set_xticks([365*48*x  for x in range(nianfen1+2-nianfen)]) # ax2.set_xticklabels([x  for x in range(nianfen,nianfen1+2)],fontproperties='Times New Roman',size=19)# ax4.set_xlabel('Year', fontsize=19)# plt.savefig(os.path.join(r'D:\Fluxnet\PIC666\1128',str(file.split('_',6)[1]) +'.png')#                   , bbox_inches='tight', dpi=500)# plt.show()#===============================================导出# dic={'SITES':site_list,'YEAR':year_list,'原始数目':total_number#           ,'去掉空值后':post_dropna_number#           ,'去掉LE异常值后':post_drop_le_abnormal_number#           ,'TRAIN_NUMBER':train_number#           ,'TEST_NUMBER':test_number#           # ,'n_estimators':N_estimators,'max_depth':Max_depth#           ,'RMSE':Rmse_list,'R2':R2_list,'BIAS':Bias_list#           ,'Drivers_column':Drivers_column#           ,'Filling_rate' : Filling_rate_list#         }# dic=pd.DataFrame(dic)# # print(dic)# dic.to_csv(r'D:\Fluxnet\OUTCOME\RMSE_ALL\RMSE_All_Day.csv')# dic_sole={#           'RMSE':rmse_list,'R2':r2_list,'BIAS':bias_list#           } # dic_sole=pd.DataFrame(dic_sole)# dic_sole.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\RMSE', str(file.split('_',6)[1])  +'.csv'),index = False)#===============================================Various length of gap# for j,k in zip([0.05,0.075,0.125],[6,24,48]): #一天 七天 一月 一共占总数据的0.25# #48,336,720#   df0=sole.copy()#   print(len(df0))#   df=df0[df0['LE_F_MDS_QC']==0]#   print(df['LE_F_MDS_QC'])#   print(len(df))#   print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')#   #可以开始make gap的位置区间#   start_point=np.arange(df['LE_F_MDS_QC'].index[0],df['LE_F_MDS_QC'].index[-1]-k+1) #k是gap长度 #   #gap的个数#   gap_number=int(len(df)*j/k)#   print(gap_number)#   # 随机选择开始的位置#   # np.random.seed(1) # 每次的随机数都是一样的#   gap_posi=np.random.choice(start_point,gap_number*3) #多一点选择的余地#   posi=sorted(gap_posi) # 排一下顺序}#   print(posi)#   count=0#   gap_qujian=[]#   # 并不是每个随机开始的位置都可以用，不能和以前的gap开始的位置重叠，gap的位置数据量也要充足#   for m,n in enumerate(posi): # m是索引 n是开始的位置（其实也是索引）#       # 单个gap的区间#       # 意思是从第多少位到多少位是gap区间#       gap_danqujian_list =[h for h in np.arange(n,n+k)]#       print(gap_danqujian_list)#       print('==')#       # 整个DataFrame中的gap#       gap_df = df0.iloc[gap_danqujian_list]#       # print(gap_df)#       # gap区间要在限定的范围内#       if np.isin(gap_danqujian_list,start_point).all():#           # 不同长度gap不能重叠#           if m>0 and n-posi[m-1] <= k: #               continue#           # gap区间内要有足够的原始数据#           if len(gap_df.dropna()) / len(gap_df) < 0.5:#               continue#           gap_qujian.extend(gap_danqujian_list)#           print(gap_qujian)#           count += 1#       if count == gap_number: # 每种gap的数目都要达到gap_number,达到规定的比例才会停止#           print('@@@@@@@@@@@@@@@@@@@@@')#           print(count)#           break#   # 要去掉索引对应的le为空的suoyin#   test_df=df0.iloc[gap_qujian] # pd.iloc[[1,2,3]] 查找方括号内数字所在的行#   print(test_df)#   print(len(test_df))#   test=test_df.loc[test_df['LE_F_MDS_QC']==0,].dropna(axis=0) # pd.iloc[[1,2,3]] 查找方括号内数字所在的行#   print(test)#   print(len(test))#   train_index=np.setdiff1d(df0.index,test_df.index) # setdiff1d 前面那个数组有 后边那个没有的值#   print(train_index)#   train_df=df0.iloc[train_index] # # pd.iloc[[1,2,3]] 查找方括号内数字所在的行#   train=train_df.loc[train_df['LE_F_MDS_QC']==0,].dropna(axis=0)#   print(train)#   print(len(train))#   pd.set_option('display.max_columns', None)# # print(test.head(5))#   print(train.shape)#   print(test.shape)#   a=pd.DataFrame(test.isna().sum().sort_values(ascending=False))# # des=test.describe()# # shangxu=des.loc['75%']+1.5*(des.loc['75%']-des.loc['25%'])# # xiaxu=des.loc['25%']-1.5*(des.loc['75%']-des.loc['25%'])# # test=test[(test['LE_F_MDS'] <=shangxu[3])# #           &(test['LE_F_MDS'] >=xiaxu[3])]#  # print(des)#  # des=train.describe()#  # shangxu=des.loc['75%']+1.5*(des.loc['75%']-des.loc['25%'])#  # xiaxu=des.loc['25%']-1.5*(des.loc['75%']-des.loc['25%'])#  # train=train[(train['LE_F_MDS'] <=shangxu[3])#  #             &(train['LE_F_MDS'] >=xiaxu[3])]#  # print(xiaxu)#   train=train.drop(['TIMESTAMP_START','TIMESTAMP_END','LE_F_MDS_QC'],axis=1)#   test=test.drop(['TIMESTAMP_START','TIMESTAMP_END','LE_F_MDS_QC'],axis=1)#   # train_Drivers=train.drop(['LE_F_MDS'],axis=1)#   train_Drivers=train[feature]#   print(train_Drivers.index)#   # test_Drivers=test.drop(['LE_F_MDS'],axis=1) #   test_Drivers=test[feature]#   print(test_Drivers.index)#   train_LE=train['LE_F_MDS']#   print(train_LE.index)#   test_LE=test['LE_F_MDS']#   print(test_LE.index)#   # x_train,x_test,y_train,y_test=train_test_split(Drivers,LE#   #                                                ,test_size=0.25#   #                                                ,random_state=(0))                            #   print(train_Drivers.shape)#   print(test_Drivers.shape)#   print(train_LE.shape)#   print(test_LE.shape)#   # ==============================建模====================================#   rf1=RandomForestRegressor(n_estimators=1100#                             ,max_depth=80#                             ,random_state=(0))   #   rf1.fit(train_Drivers,train_LE)    #   rmse1=np.sqrt(mean_squared_error(test_LE,rf1.predict(test_Drivers)))#   rmse_list1.append(rmse1)#   rmse_df=pd.DataFrame({'rmse':rmse_list1})#   print(rmse_df)#   r2=r2_score(test_LE,rf1.predict(test_Drivers))  #   r2_list1.append(r2)#   r2_df=pd.DataFrame({'r2':r2_list1})#   bias=(rf1.predict(test_Drivers)-test_LE).mean()#   bias_list1.append(bias)#   bias_df=pd.DataFrame({'bias':bias_list1})#   site_list1+=[s.split('_',6)[1]]#   year_list1+=[int(s.split('_',6)[5].split('-',1)[1])#               -int(s.split('_',6)[5].split('-',1)[0])+1]  #   # total_number.append(int(b.shape[0]))#   # post_dropna_number.append(int(a.shape[0]))#   # post_drop_le_abnormal_number.append(int(c.shape[0]))#   test_number1.append(int(test.shape[0]))#   train_number1.append(int(train.shape[0]))#   dic2={'SITES':site_list1,'YEAR':year_list1#         # ,'原始数目':total_number#         # ,'去掉空值后':post_dropna_number#         # ,'去掉LE异常值后':post_drop_le_abnormal_number#         ,'TRAIN_NUMBER':train_number1#         ,'TEST_NUMBER':test_number1#         # ,'n_estimators':N_estimators,'max_depth':Max_depth#         ,'RMSE':rmse_list1,'R2':r2_list1,'BIAS':bias_list1#       }#   dic2=pd.DataFrame(dic2)#   print(dic2)#   dic2.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\GAP_diff', str(s.split('_',6)[1]) + '.csv'),index = False)#========================================读一下八个csv#     dic_list=[]#     for i in range(3,5):#         df=pd.read_csv(os.path.join(outpath,str(s.split('_',6)[1]) + str(i) + '.csv'))#         dic={'list_name':df, 'rmse':df['RMSE'][0]}#         dic_list+=[dic]#         print(dic_list)
#     print('=============================================')#     # df3=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '3' +'.csv'))
#     # df4=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '4' +'.csv'))
#     # df5=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '5' +'.csv'))
#     # df6=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '6' +'.csv'))
#     # df7=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '7' +'.csv'))
#     # df8=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '8' +'.csv'))
#     # df9=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '9' +'.csv'))
#     # df10=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '10' +'.csv'))
#     # df11=pd.read_csv(os.path.join(r'D:\Fluxnet\OUTCOME',str(s.split('_',6)[1]) + '11' +'.csv'))   #     # dic=[{'list_name':df3, 'rmse':df3['RMSE'][0]}
#     #      ,{'list_name':df4, 'rmse':df4['RMSE'][0]}
#     #      ,{'list_name':df5, 'rmse':df5['RMSE'][0]}
#     #      ,{'list_name':df6, 'rmse':df6['RMSE'][0]}
#     #      ,{'list_name':df7, 'rmse':df7['RMSE'][0]}
#     #      ,{'list_name':df8, 'rmse':df8['RMSE'][0]}
#     #      ,{'list_name':df9, 'rmse':df9['RMSE'][0]}
#     #      ,{'list_name':df10, 'rmse':df10['RMSE'][0]}
#     #      ,{'list_name':df11, 'rmse':df11['RMSE'][0]}
#     #     ]#     sorted_dic=sorted(dic_list, key=lambda dic_list: dic_list['rmse'], reverse=False)
#     print(sorted_dic)
#     list_name=[a['list_name'] for a in sorted_dic] # 打印出来的话就是整个dataframe
#     print(list_name)
#     df=pd.concat(list_name,axis=1)#     print(df)
#     df.to_csv(os.path.join(outpath, str(s.split('_',6)[1]) +'6666'+'.csv'))#     a=pd.read_csv(os.path.join(outpath, str(s.split('_',6)[1]) +'6666'+'.csv'))
#     # pd.set_option('display.max_columns', None)
#     df=pd.DataFrame(a.isna().sum().sort_values(ascending=False))
#     print(a)
#     # 直接用fillna来填，可行， 但还要填drivers!!!
#     # 找rmse最低值 对应的来开始填补
#     print(df.columns)# 一# b=a.loc[a['LE_Gap_filled'] > -9999, ['LE_Gap_filled','Drivers','RMSE']]# a['Drivers']=a.loc[a['LE_Gap_filled'] == np.nan, ['Drivers']]# a['Drivers'].fillna( b['Drivers'] ,inplace = True ) # 自立门户 新建第一个模型的Drivers# print(a['Drivers'].describe())# a['RMSE']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE']]# a['RMSE'].fillna( b['RMSE'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE# print(a['RMSE'].describe())# b=a.loc[a['LE_Gap_filled.1'] > -9999, ['LE_Gap_filled.1', 'Drivers.1', 'RMSE.1']] # 只是有LE数值的地方，用来填充上边的空集# a['Drivers.1']=a.loc[a['LE_Gap_filled.1'] == np.nan, ['Drivers.1']]# a['Drivers.1'].fillna( b['Drivers.1'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers# print(a['Drivers.1'].describe())# a['RMSE.1']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.1']]# a['RMSE.1'].fillna( b['RMSE.1'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE# print(a['RMSE.1'].describe())# a['LE_Gap_filled'].fillna(a['LE_Gap_filled.1'], inplace=True) # LE Update# df1=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下# print(df1)# a['Drivers'].fillna(a['Drivers.1'],inplace=True)  # Drivers Update# print(a['Drivers'].describe())# a['RMSE'].fillna(a['RMSE.1'],inplace=True)  # Rmse Update# print(a['RMSE'].describe())#     # 二
#     b=a.loc[a['LE_Gap_filled.2'] > -9999, ['LE_Gap_filled.2', 'Drivers.2', 'RMSE.2']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.2']=a.loc[a['LE_Gap_filled.2'] == np.nan, ['Drivers.2']]
#     a['Drivers.2'].fillna( b['Drivers.2'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.2'].describe())#     a['RMSE.2']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.2']]
#     a['RMSE.2'].fillna( b['RMSE.2'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.2'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.2'], inplace=True) # LE Update
#     df2=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df2)#     a['Drivers'].fillna(a['Drivers.2'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.2'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 三
#     b=a.loc[a['LE_Gap_filled.3'] > -9999, ['LE_Gap_filled.3', 'Drivers.3', 'RMSE.3']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.3']=a.loc[a['LE_Gap_filled.3'] == np.nan, ['Drivers.3']]
#     a['Drivers.3'].fillna( b['Drivers.3'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.3'].describe())#     a['RMSE.3']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.3']]
#     a['RMSE.3'].fillna( b['RMSE.3'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.3'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.3'], inplace=True) # LE Update
#     df3=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df3)#     a['Drivers'].fillna(a['Drivers.3'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.3'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 四
#     b=a.loc[a['LE_Gap_filled.4'] > -9999, ['LE_Gap_filled.4', 'Drivers.4', 'RMSE.4']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.4']=a.loc[a['LE_Gap_filled.4'] == np.nan, ['Drivers.4']]
#     a['Drivers.4'].fillna( b['Drivers.4'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.4'].describe())#     a['RMSE.4']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.4']]
#     a['RMSE.4'].fillna( b['RMSE.4'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.4'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.4'], inplace=True) # LE Update
#     df4=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df4)#     a['Drivers'].fillna(a['Drivers.4'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.4'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 五
#     b=a.loc[a['LE_Gap_filled.5'] > -9999, ['LE_Gap_filled.5', 'Drivers.5', 'RMSE.5']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.5']=a.loc[a['LE_Gap_filled.5'] == np.nan, ['Drivers.5']]
#     a['Drivers.5'].fillna( b['Drivers.5'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.5'].describe())#     a['RMSE.5']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.5']]
#     a['RMSE.5'].fillna( b['RMSE.5'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.5'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.5'], inplace=True) # LE Update
#     df5=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df5)#     a['Drivers'].fillna(a['Drivers.5'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.5'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 六
#     b=a.loc[a['LE_Gap_filled.6'] > -9999, ['LE_Gap_filled.6', 'Drivers.6', 'RMSE.6']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.6']=a.loc[a['LE_Gap_filled.6'] == np.nan, ['Drivers.6']]
#     a['Drivers.6'].fillna( b['Drivers.6'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.6'].describe())#     a['RMSE.6']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.6']]
#     a['RMSE.6'].fillna( b['RMSE.6'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.5'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.6'], inplace=True) # LE Update
#     df6=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df6)#     a['Drivers'].fillna(a['Drivers.6'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.6'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 七
#     b=a.loc[a['LE_Gap_filled.7'] > -9999, ['LE_Gap_filled.7', 'Drivers.7', 'RMSE.7']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.7']=a.loc[a['LE_Gap_filled.7'] == np.nan, ['Drivers.7']]
#     a['Drivers.7'].fillna( b['Drivers.7'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.7'].describe())#     a['RMSE.7']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.7']]
#     a['RMSE.7'].fillna( b['RMSE.7'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.7'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.7'], inplace=True) # LE Update
#     df7=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df7)#     a['Drivers'].fillna(a['Drivers.7'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.7'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 八
#     b=a.loc[a['LE_Gap_filled.8'] > -9999, ['LE_Gap_filled.8', 'Drivers.8', 'RMSE.8']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.8']=a.loc[a['LE_Gap_filled.8'] == np.nan, ['Drivers.8']]
#     a['Drivers.8'].fillna( b['Drivers.8'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.8'].describe())#     a['RMSE.8']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.8']]
#     a['RMSE.8'].fillna( b['RMSE.8'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.8'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.8'], inplace=True) # LE Update
#     df8=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df8)#     a['Drivers'].fillna(a['Drivers.8'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.8'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 九
#     b=a.loc[a['LE_Gap_filled.9'] > -9999, ['LE_Gap_filled.9', 'Drivers.9', 'RMSE.9']] # 只是有LE数值的地方，用来填充上边的空集#     a['Drivers.9']=a.loc[a['LE_Gap_filled.9'] == np.nan, ['Drivers.9']]
#     a['Drivers.9'].fillna( b['Drivers.9'] ,inplace = True ) # 自立门户 新建第二个模型的Drivers
#     print(a['Drivers.9'].describe())#     a['RMSE.9']=a.loc[a['LE_Gap_filled'] == np.nan, ['RMSE.9']]
#     a['RMSE.9'].fillna( b['RMSE.9'] ,inplace = True ) # 自立门户 新建第一个模型的RMSE
#     print(a['RMSE.9'].describe())#     a['LE_Gap_filled'].fillna(a['LE_Gap_filled.9'], inplace=True) # LE Update
#     df8=pd.DataFrame(a.isna().sum().sort_values(ascending=False)) # 统计一下
#     print(df8)#     a['Drivers'].fillna(a['Drivers.9'],inplace=True)  # Drivers Update
#     print(a['Drivers'].describe())#     a['RMSE'].fillna(a['RMSE.9'],inplace=True)  # Rmse Update
#     print(a['RMSE'].describe())#     # 加一下a的时间#     so=pd.read_csv(os.path.join(path1,s))
#     so=so[['TIMESTAMP_START' ,'TIMESTAMP_END','LE_F_MDS']]#     print(a['TIMESTAMP_START'])#     print(a.shape)#     a['QC'] = np.nan
#     a.loc[a['LE_Gap_filled'] > -9999, 'QC'] = 1
#     a.loc[a['LE_Gap_filled'] == -9999 , 'QC'] = 0#     a['LE_Gap_filled'].replace(np.nan,-8888,inplace=True) # 原本是空值的部分  由于变量缺失过多，压根儿补不了的部分 在原数据集中，QC为3，表示的是ERA的数据
#     a['LE_Gap_filled'].replace(-9999,np.nan,inplace=True) #       |          空值还有种原因是 因为变量组合的原因，没有补到那一块，所以仍旧空
#     a['LE_Gap_filled'].fillna(sole['LE_F_MDS'],inplace=True)#  最后依旧是空值     #     a.loc[a['LE_Gap_filled'] == -8888 , 'QC'] = -9999#     print(a.dropna().shape[0]/a.shape[0])#     a=a[[ 'TIMESTAMP_END', 'LE_Gap_filled', 'QC',  'Drivers', 'RMSE']]#     a= pd.merge(so,a,how='outer',on='TIMESTAMP_END')#     a['LE_Gap_filled'].fillna(a['LE_F_MDS'],inplace=True)   
#     a['LE_Gap_filled'].replace(-8888,np.nan,inplace=True)    #     a=a[['TIMESTAMP_START', 'TIMESTAMP_END', 'LE_Gap_filled', 'QC',  'Drivers', 'RMSE']]#     a['SW_IN_F_MDS']=np.nan
#     a['NETRAD']=np.nan
#     a['G_F_MDS']=np.nan
#     a['TA_F_MDS']=np.nan
#     a['RH']=np.nan
#     a['WD']=np.nan 
#     a['WS']=np.nan #     a['PA_F']=np.nan
#     a['VPD_F_MDS']=np.nan
#     a['NDVI']=np.nan
#     a['TS_F_MDS_1']=np.nan
#     a['SWC_F_MDS_1']=np.nan
#     a['TA_F_MDS']=np.nan#     a['Drivers'].replace(np.nan,-9999,inplace=True)#     b=a.loc[a['Drivers']!=-9999]
#     # print(b)#     for i in b.columns[6:]:#         # print(i)#         c=b[b['Drivers'].str.contains(i)]#         c[i].replace(np.nan,'+',inplace=True)#         a[i]=c[i]#     b=a.count(axis=1)-6
#     b=pd.DataFrame(b)#     a['n_drivers']=b#     a['n_drivers'].replace([-1,-2,-3],np.nan,inplace=True)#     a['Drivers'].replace(-9999,np.nan,inplace=True)#     # a.to_csv(os.path.join(path,sole+'.csv'),index = False)#     a.to_csv(os.path.join(r'D:\Fluxnet\OUTCOME\FILLED',str(s.split('_',6)[1]) +'.csv'),index = False)# #  创造空列
# # df["Empty_1"] = ""
# # df["Empty_2"] = np.nan
# # df['Empty_3'] = pd.Series() 

在这里插入代码片

欢迎使用Markdown编辑器

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3. 增加了 图片拖拽 功能，你可以将本地的图片直接拖拽到编辑区域直接展示；
4. 全新的 KaTeX数学公式 语法；
5. 增加了支持甘特图的mermaid语法¹ 功能；
6. 增加了 多屏幕编辑 Markdown文章功能；
7. 增加了 焦点写作模式、预览模式、简洁写作模式、左右区域同步滚轮设置 等功能，功能按钮位于编辑区域与预览区域中间；
8. 增加了 检查列表 功能。

功能快捷键

撤销：Ctrl/Command + Z
重做：Ctrl/Command + Y
加粗：Ctrl/Command + B
斜体：Ctrl/Command + I
标题：Ctrl/Command + Shift + H
无序列表：Ctrl/Command + Shift + U
有序列表：Ctrl/Command + Shift + O
检查列表：Ctrl/Command + Shift + C
插入代码：Ctrl/Command + Shift + K
插入链接：Ctrl/Command + Shift + L
插入图片：Ctrl/Command + Shift + G
查找：Ctrl/Command + F
替换：Ctrl/Command + G

合理的创建标题，有助于目录的生成

直接输入1次#，并按下space后，将生成1级标题。
输入2次#，并按下space后，将生成2级标题。
以此类推，我们支持6级标题。有助于使用TOC语法后生成一个完美的目录。

如何改变文本的样式

强调文本 强调文本

加粗文本 加粗文本

标记文本

删除文本

引用文本

H₂O is是液体。

2¹⁰ 运算结果是 1024.

插入链接与图片

链接: link.

图片:

带尺寸的图片:

居中的图片:

居中并且带尺寸的图片:

当然，我们为了让用户更加便捷，我们增加了图片拖拽功能。

如何插入一段漂亮的代码片

去博客设置页面，选择一款你喜欢的代码片高亮样式，下面展示同样高亮的 代码片.

// An highlighted block
var foo = 'bar';

生成一个适合你的列表

• 项目
  • 项目
    • 项目

1. 项目1
2. 项目2
3. 项目3

• 计划任务
• 完成任务

创建一个表格

一个简单的表格是这么创建的：

项目	Value
电脑	$1600
手机	$12
导管	$1

设定内容居中、居左、居右

使用:---------:居中
使用:----------居左
使用----------:居右

第一列	第二列	第三列
第一列文本居中	第二列文本居右	第三列文本居左

SmartyPants

SmartyPants将ASCII标点字符转换为“智能”印刷标点HTML实体。例如：

TYPE	ASCII	HTML
Single backticks	'Isn't this fun?'	‘Isn’t this fun?’
Quotes	"Isn't this fun?"	“Isn’t this fun?”
Dashes	-- is en-dash, --- is em-dash	– is en-dash, — is em-dash

创建一个自定义列表

Markdown

Text-to-HTML conversion tool

Authors

John

Luke

如何创建一个注脚

一个具有注脚的文本。²

注释也是必不可少的

Markdown将文本转换为 HTML。

KaTeX数学公式

您可以使用渲染LaTeX数学表达式 KaTeX:

Gamma公式展示 $\Gamma (n)=(n-1)!\forall n\in \mathbb{N}$ 是通过欧拉积分

$$\Gamma (z)={\int }_0^{\infty }{t}^{z-1}{e}^{-t}dt.$$

你可以找到更多关于的信息 LaTeX 数学表达式here.

新的甘特图功能，丰富你的文章

• 关于 甘特图 语法，参考 这儿,

UML 图表

可以使用UML图表进行渲染。 Mermaid. 例如下面产生的一个序列图：

这将产生一个流程图。:

• 关于 Mermaid 语法，参考 这儿,

FLowchart流程图

我们依旧会支持flowchart的流程图：

• 关于 Flowchart流程图 语法，参考 这儿.

导出与导入

导出

如果你想尝试使用此编辑器, 你可以在此篇文章任意编辑。当你完成了一篇文章的写作, 在上方工具栏找到 文章导出 ，生成一个.md文件或者.html文件进行本地保存。

导入

如果你想加载一篇你写过的.md文件，在上方工具栏可以选择导入功能进行对应扩展名的文件导入，
继续你的创作。

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1. mermaid语法说明 ↩︎

2. 注脚的解释 ↩︎