import pandas as pd
import numpy as np
from scipy.stats import gaussian_kde
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.compose import TransformedTargetRegressor
from sklearn.model_selection import GridSearchCV,TimeSeriesSplit
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import os
import pdb
def monthly_climatology(daily_input):
if isinstance(daily_input, str):
daily_input = pd.read_csv(daily_input, index_col=0, parse_dates=True)
monthly_mean_columns = [c for c in daily_input.columns if c[0] in ['Q', 'T']]
monthly_mean = daily_input.loc[:, monthly_mean_columns].groupby(by=daily_input.index.month).mean()
#remember to add ['E', 'P']
monthly_sum_columns = [c for c in daily_input.columns if c[0] in ['P','E']]
monthly_sum = daily_input.loc[:, monthly_sum_columns].groupby(by=daily_input.index.month).mean() * 30
#pdb.set_trace()
return pd.concat([monthly_mean, monthly_sum], axis=1)#[monthly_mean_columns,monthly_sum_columns]
def daily_climatology_p_et_ensemble(daily_input,radius=2/3):
# This function takes as input the daily temperature, precipitation and runoff and generates the input-target matrix
runoff = daily_input[['Q']]
temp = daily_input[[c for c in daily_input.columns if c[0] == 'T']]
prec = daily_input[[c for c in daily_input.columns if c[0] == 'P']]
evap = daily_input[[c for c in daily_input.columns if c[0] == 'E']]
# Compute the 30 days average runoff
runoff_30 = runoff.rolling(30, min_periods=30).mean()
# Compute the 30 days average temperature
if not temp.empty:
temp_30 = temp.rolling(30, min_periods=30).mean()
# Compute the 30 days sum evapotranspiration
if not evap.empty:
evap_30 = evap.rolling(30, min_periods=30).sum()
# Compute the 30 days sum precipitation
if not prec.empty:
prec_30 = prec.rolling(30, min_periods=30).sum()
daily_30 = pd.concat([runoff_30 ,prec_30, temp_30, evap_30], axis=1)
daily = daily_30.groupby(by=daily_30.index.day_of_year).mean()
prec_mean=prec_30.groupby(by=prec_30.index.day_of_year).mean()
prec_q75=prec_30.groupby(by=prec_30.index.day_of_year).quantile(q=0.75)
prec_q75=prec_q75.add_suffix('_Q75')
prec_q25=prec_30.groupby(by=prec_30.index.day_of_year).quantile(q=0.25)
prec_q25=prec_q25.add_suffix('_Q25')
temp_q75=pd.DataFrame(data=None, columns=temp_30.columns)
evap_q75=pd.DataFrame(data=None, columns=evap_30.columns)
temp_q25=pd.DataFrame(data=None, columns=temp_30.columns)
evap_q25=pd.DataFrame(data=None, columns=evap_30.columns)
#get the range as 2/3 of the mean difference between the quantile and the mean precipitation over the year.
range75= radius*(prec_q75.mean().mean()-prec_30).mean().mean()
range25= radius*(-prec_q25.mean().mean()+prec_30).mean().mean()
for i in range(1,367):
#get dates where the dayofyear is ==1 for the 3 variables
day_i=(prec_30.index.day_of_year==i)
prec_30_i=prec_30[day_i]
temp_30_i=temp_30[day_i]
evap_30_i=evap_30[day_i]
#compute the mean and interesting quantiles for the precipitation
prec_30_i_mean=prec_30_i.mean(axis=1)
#prec_30_i_mean=prec_30_i.drop(columns=prec_30.columns[-4:]).mean(axis=1)
prec_q75_i=prec_q75[prec_q75.index==i].mean(axis=1)
prec_q25_i=prec_q25[prec_q25.index==i].mean(axis=1)
#select the situations where the precipitation is similar (closer than a range) to the quantile
situations75=(np.abs(np.array(prec_30_i_mean)-np.array(prec_q75_i))<range75)
situations25=(np.abs(np.array(prec_30_i_mean)-np.array(prec_q25_i))<range25)
#average the variables values happened int the selected situations and append it
s=temp_30_i[situations75].mean()
s.name=i
temp_q75=temp_q75.append(s)
s=evap_30_i[situations75].mean()
s.name=i
evap_q75=evap_q75.append(s)
s=temp_30_i[situations25].mean()
s.name=i
temp_q25=temp_q25.append(s)
s=evap_30_i[situations25].mean()
s.name=i
evap_q25=evap_q25.append(s)
temp_q75=temp_q75.add_suffix('_Q75')
evap_q75=evap_q75.add_suffix('_Q75')
temp_q25=temp_q25.add_suffix('_Q25')
evap_q25=evap_q25.add_suffix('_Q25')
#pdb.set_trace()
# Create the input-target matrix
return pd.concat([daily,
temp_q75, prec_q75, evap_q75,
temp_q25, prec_q25, evap_q25,], axis=1).dropna()
def daily_climatology_p_ensemble(daily_input):
runoff = daily_input[['Q']]
temp = daily_input[[c for c in daily_input.columns if c[0] == 'T']]
prec = daily_input[[c for c in daily_input.columns if c[0] == 'P']]
evap = daily_input[[c for c in daily_input.columns if c[0] == 'E']]
# Compute the 30 days average runoff
runoff_30 = runoff.rolling(30, min_periods=30).mean()
# Compute the 30 days average temperature
if not temp.empty:
temp_30 = temp.rolling(30, min_periods=30).mean()
#temp_30 = pd.concat([shift_series_30days(temp_30.loc[:, col], (-t_length + 1, 1)) for col in temp_30], axis=1)
# Compute the 30 days sum evapotranspiration
if not evap.empty:
evap_30 = evap.rolling(30, min_periods=30).sum()
#evap_30 = pd.concat([shift_series_30days(evap_30.loc[:, col], (-t_length + 1, 1)) for col in evap_30], axis=1)
# Compute the 30 days sum precipitation
if not prec.empty:
prec_30 = prec.rolling(30, min_periods=30).sum()
#prec_30 = pd.concat([shift_series_30days(prec_30.loc[:, col], (-t_length + 1, 1)) for col in prec_30], axis=1)
daily_30 = pd.concat([runoff_30, temp_30, evap_30], axis=1)
daily = daily_30.groupby(by=daily_30.index.day_of_year).mean()
prec_mean=prec_30.groupby(by=prec_30.index.day_of_year).mean()
prec_q75 =prec_30.groupby(by=prec_30.index.day_of_year).quantile(q=0.75)
prec_q75=prec_q75.add_suffix('_Q75')
prec_q25 =prec_30.groupby(by=prec_30.index.day_of_year).quantile(q=0.25)
prec_q25=prec_q25.add_suffix('_Q25')
daily = pd.concat([daily,prec_mean,prec_q75,prec_q25], axis=1)
return daily