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Functions_for_TSP.py
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Functions_for_TSP.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jun 29 10:42:09 2016
@author: d_floriello
"""
## Functions for TSP.py
import numpy as np
from numpy import fft
import pandas as pd
import temp
def find_peaks(v,al):
v = v[0:int(v.shape[0]/2)]
vu = np.unique(v)
peaks = []
rm = np.max(vu)
peaks.append(rm)
for i in range(al):
vu_mod = vu[vu < rm]
rm = np.max(vu_mod)
peaks.append(rm)
if len(peaks) >= al:
break
return peaks
#######################################################
def fourierExtrapolation(x, n_predict):
x = np.array(x)
n = x.size
n_harm = 100 # number of harmonics in model
t = np.arange(0, n)
p = np.polyfit(t, x, 1) # find linear trend in x
x_notrend = x - p[0] * t # detrended x
x_freqdom = np.fft.fft(x_notrend) # detrended x in frequency domain
f = np.fft.fftfreq(n) # frequencies
indexes = list(range(n))
# sort indexes by frequency, lower -> higher
indexes.sort(key = lambda i: np.absolute(f[i]))
t = np.arange(0, n + n_predict)
restored_sig = np.zeros(t.size)
for i in indexes[:1 + n_harm * 2]:
ampli = np.absolute(x_freqdom[i]) / n # amplitude
phase = np.angle(x_freqdom[i]) # phase
restored_sig += ampli * np.cos(2 * np.pi * f[i] * t + phase)
return restored_sig + p[0] * t
######################################################
def Signum_Process(v):
sp = []
for i in range(len(v)-1):
sp.append(np.sign(v[i+1] - v[i]))
return np.array(sp)
#####################################################
def RMSE(v):
return np.sqrt(np.mean(v**2))
#####################################################
def Error_Signum_process(v1, v2):
p = v1*v2
return p[p <= 0].size/p.size
#####################################################
def replicate_meteo_variables(meteo, vd):
rtmin = []
rtmax = []
rtmed = []
rrain = []
rvm = []
date_meteo = np.array(meteo[meteo.columns[0]]).tolist()
for i in range(0, np.array(vd).size,24):
ir = date_meteo.index(vd[i])
rtmin.append(np.repeat(meteo["Tmin"].ix[ir], 24, axis = 0))
rtmax.append(np.repeat(meteo["Tmax"].ix[ir], 24, axis = 0))
rtmed.append(np.repeat(meteo["Tmedia"].ix[ir], 24, axis = 0))
rrain.append(np.repeat(meteo["Pioggia"].ix[ir], 24, axis = 0))
rvm.append(np.repeat(meteo["Vento_media"].ix[ir], 24, axis = 0))
meteodict = {"Tmin": np.array(rtmin).flatten(), "Tmax": np.array(rtmax).flatten(),
"Tmedia": np.array(rtmed).flatten(), "Pioggia": np.array(rrain).flatten(),
"Wind": np.array(rvm).flatten()}
return meteodict
#####################################################
def generate_dataset_ARIMA(pun, first_day, meteo, varn):
vector_date = np.array(pun[pun.columns[0]])
vector_ore = np.array(pun[pun.columns[1]])
target = np.array(pun[varn])
global_dates = temp.dates(pd.Series(vector_date))
vac_glob = temp.add_holidays(global_dates)
all_days = temp.generate_days(vector_ore, first_day)
MD = replicate_meteo_variables(meteo, global_dates)
aad = np.array([temp.convert_day_to_angle(v) for v in all_days])
aore = np.sin(vector_ore*np.pi/24)
all_dict = {'holiday' : vac_glob, 'day' : aad, 'ora' : aore}
all_dict.update(MD)
FDF = pd.DataFrame(all_dict)
return FDF, target
####################################################
def finding_missing_dates(date, meteo):
md = []
date = np.unique(np.array(date))
npm = np.array(meteo[meteo.columns[0]])
for d in date:
if d not in npm:
# print(d)
# print(d not in npm)
md.append(d)
return md
###################################################
def update_meteo(meteo1, meteo2):
# meteo2 is supposed to be the most complete one
date1 = np.unique(np.array(meteo1[meteo1.columns[0]]))
date2 = np.unique(np.array(meteo2[meteo2.columns[0]]))
date2list = date2.tolist()
md = []
index = []
for d in date2:
if d not in date1:
md.append(d)
index.append(date2list.index(d))
upmeteo = pd.DataFrame(meteo2.ix[index])
met = {'Data': meteo1[meteo1.columns[0]],
'Tmin': meteo1[meteo1.columns[1]],
'Tmedia': meteo1[meteo1.columns[2]],
'Tmax': meteo1[meteo1.columns[3]],
'Pioggia': meteo1[meteo1.columns[4]],
'Vento_media': meteo1[meteo1.columns[8]]}
nmet = {'Data': upmeteo[upmeteo.columns[0]],
'Tmin': upmeteo[upmeteo.columns[1]],
'Tmedia': upmeteo[upmeteo.columns[2]],
'Tmax': upmeteo[upmeteo.columns[3]],
'Pioggia': upmeteo[upmeteo.columns[4]],
'Vento_media': upmeteo[upmeteo.columns[8]]}
meteodf = pd.DataFrame(met)
nmetdf = pd.DataFrame(nmet)
updatedmeteo = pd.concat([meteodf, nmetdf]).reset_index(drop=True)
return updatedmeteo
##########################################################
def simulate_meteo(meteo1, roma):
index = []
index2 = []
vd = np.array(meteo1[meteo1.columns[0]]).tolist()
vd2 = np.array(roma[roma.columns[0]]).tolist()
for d in roma[roma.columns[0]]:
if d in vd:
index.append(vd.index(d))
index2.append(vd2.index(d))
else:
pass
found = meteo1[meteo1.columns[[1,2,3,4,8]]].ix[index].reset_index(drop=True)
for nc in range(found.shape[1]):
found[found.columns[nc]] = pd.to_numeric(found[found.columns[nc]],errors='coerce')
found.fillna(0)
roma2 = roma[roma.columns[[1,2,3,4,8]]].ix[index2].reset_index(drop=True)
diffdf = found - roma2
return diffdf
##########################################################
def generate_simulated_meteo_dataset(meteo, roma):
diff = simulate_meteo(meteo, roma)
date = np.array(roma[roma.columns[0]]).tolist()
vd = np.array(meteo[meteo.columns[0]]).tolist()
tmin = []
tmed = []
tmax = []
rain = []
vm = []
for d in date:
if d in vd:
tmin.append(meteo['Tmin'].ix[vd.index(d)])
tmed.append(meteo['Tmedia'].ix[vd.index(d)])
tmax.append(meteo['Tmax'].ix[vd.index(d)])
rain.append(meteo['Pioggia'].ix[vd.index(d)])
vm.append(meteo['Vento_media'].ix[vd.index(d)])
else:
tmin.append(roma['Tmin'].ix[date.index(d)] + np.mean(diff['Tmin']))
tmed.append(roma['Tmedia'].ix[date.index(d)] + np.mean(diff['Tmedia']))
tmax.append(roma['Tmax'].ix[date.index(d)] + np.mean(diff['Tmax']))
rain.append(roma['Pioggia'].ix[date.index(d)] + np.mean(diff['Pioggia']))
vm.append(roma['Vento_media'].ix[date.index(d)] + np.mean(diff['Vento_media']))
pdf = {'Data': date, 'Tmin': np.array(tmin), 'Tmedia': np.array(tmed), 'Tmax': np.array(tmax),
'Pioggia': np.array(rain), 'Vento_media': np.array(vm)}
return pd.DataFrame(pdf).ix[:, ['Data', 'Tmin', 'Tmedia', 'Tmax', 'Pioggia', 'Vento_media']]