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MyPrepaData.py
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#!/usr/bin/env python
# coding: utf-8
import pandas as pd
data = pd.read_csv('2011-2018再分析和流量数据.csv',index_col=0)
def Preparation(data,Qlag,Rlag,reanalysis=[],isreanalysis=False,leadtime=1):
"""
Preparation data for model.
Note: for all reanalysis data, default lag 1.
Parameters
----------
data : pandas DataFrame,
All data.
maxlag: int,range=[1,inf]
max lag.
Qlag: list
all inflow lag.
Rlag: list
all rainfall lag.
reanalysis：list
name of selected reanalysis variables.
Returns
-------
X: array_like
input data.
y:array_like
output data
See Also
--------
rely on pandas
"""
X = pd.DataFrame()
for q in Qlag:
X['Q(t-%d)'%(q+leadtime-1)] = data.Q.shift(q+leadtime-1)
for r in Rlag:
X['R(t-%d)'%(r+leadtime-1)] = data.real_p.shift(r+leadtime-1)
X['y'] = data.Q
if not isreanalysis:
X.dropna(axis=0,how='any',inplace=True)
return X.drop(['y'],axis=1),X.y
else:
redata = data[reanalysis].shift(leadtime)
col = ['%s(t-%d)'%(c,leadtime) for c in reanalysis]
redata.columns = col
X = pd.concat([X,redata],axis=1)
X.dropna(axis=0,how='any',inplace=True)
return X.drop(['y'],axis=1),X.y
def Preparation2(data,Qlag,Rlag,reanalysis={},isreanalysis=False,leadtime=1):
"""
Preparation data for model.
Note: for all reanalysis data, default lag 1.
Parameters
----------
data : pandas DataFrame,
All data.
maxlag: int,range=[1,inf]
max lag.
Qlag: list
all inflow lag.
Rlag: list
all rainfall lag.
reanalysis：dic
name of selected reanalysis variables.
Returns
-------
X: array_like
input data.
y:array_like
output data
See Also
--------
rely on pandas
"""
X = pd.DataFrame()
if leadtime > max(Qlag):
X['Q(t-%d)'%(leadtime)] = data.Q.shift(leadtime)
else:
for q in Qlag:
if leadtime > q:
continue
else:
X['Q(t-%d)'%(q)] = data.Q.shift(q)
if leadtime > max(Rlag):
X['R(t-%d)'%(leadtime)] = data.real_p.shift(leadtime)
else:
for r in Rlag:
if leadtime > r:
continue
else:
X['R(t-%d)'%(r)] = data.real_p.shift(r)
X['y'] = data.Q
if not isreanalysis:
X.dropna(axis=0,how='any',inplace=True)
return X.drop(['y'],axis=1),X.y
else:
for re in reanalysis:
if reanalysis[re] >= leadtime:
X['%s(t-%d)'%(re,reanalysis[re])] = data[re].shift(reanalysis[re])
else:
X['%s(t-%d)'%(re,leadtime)] = data[re].shift(leadtime)
X.dropna(axis=0,how='any',inplace=True)
return X.drop(['y'],axis=1),X.y