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FFDE.py
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FFDE.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Apr 26 10:08:15 2017
@author: d_floriello
Functions for Daily Measures Extractor
"""
import zipfile
import os
from os.path import isfile, join
import datetime
import time
from collections import OrderedDict
import pandas as pd
import numpy as np
import shutil
import re
#import unidecode
###################################################################################################
def Unareti_reducer(x):
X = [x[0], x[1], x[2]]
vec = np.repeat(0.0, 24)
for h in range(3,99,4):
vec[list(range(3,99,4)).index(h)] = x[h] + x[h+1] + x[h+2] + x[h+3]
X.extend(vec.tolist())
return X
###############################################################################
def Replacer(s):
return s.replace(",",".")
####################################################################################################
def Elabora_Unareti(df):
df = df.ix[df[df.columns[6]] == "Z001"]
diz = OrderedDict()
lop = list(set(df[df.columns[0]].values.ravel().tolist()))
count = 0
for p in lop:
dfp = df.ix[df[df.columns[0]] == p]
count += 1
dates = list(set(df[df.columns[2]].values.ravel().tolist()))
for d in dates:
dfpd = dfp.ix[dfp[dfp.columns[2]] == d]
mis = list(map(Replacer, dfpd[dfpd.columns[5]].values.ravel().tolist()))
mis2 = list(map(float, mis))
dd = datetime.date(int(str(d)[:4]), int(str(d)[4:6]), int(str(d)[6:]))
ll = [p, 'NORD', dd]
ll.extend(mis2)
diz[count] = ll
count += 1
diz = pd.DataFrame.from_dict(diz, orient = 'index')
diz2 = OrderedDict()
for i in diz.index:
diz2[i] = Unareti_reducer(diz.ix[i].values.ravel())
diz2 = pd.DataFrame.from_dict(diz2, orient = 'index')
diz2.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
return diz2
####################################################################################################
def Aggregate_Unareti():
path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/Settimanali"
lof = os.listdir(path)
if os.path.exists("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/settimanali_elaborati.csv"):
sett = pd.read_csv("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/settimanali_elaborati.csv", sep = ";")
sett_el = sett.values.ravel().tolist()
todo = list(set(lof).difference(set(sett_el)))
Agg = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/Unareti_elaborati.xlsx")
else:
sett_el = []
todo = lof
Agg = pd.DataFrame()
for l in todo:
print(l)
if not 'ORARIE_VALIDATE_12883450152_03728900964_L_201705_201705211100.CSV' in l.upper() or not 'ORARIE_VALIDATE_12883450152_03728900964_L_201705_201705211100(2).csv' in l:
df = pd.read_csv(path + '/' + l, sep = ";", header = None)
DF = Elabora_Unareti(df)
Agg = Agg.append(DF, ignore_index = True)
sett_el.extend(todo)
sett_el = pd.DataFrame.from_dict({'files': sett_el})
sett_el.to_csv("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/settimanali_elaborati.csv", sep = ";")
Agg.to_excel("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/A2A Milano Reti Elettriche S.p.A_12883450152/2017/Unareti_elaborati.xlsx")
return Agg
####################################################################################################
def Aggregator2(df):
v = np.repeat(0.0, 24)
df2 = df[df.columns[2:98]]
df2 = df2.values.ravel().astype(float)
for k in range(1,25):
v[k-1] += np.sum(np.array([x for x in df2[4*(k-1):4*k]], dtype = np.float64))
return v
####################################################################################################
def Converter(s):
points = [m.start() for m in re.finditer('\.', s)]
if len(points) <= 1:
return float(np.where(np.isnan(float(s)), 0, float(s)))
else:
s2 = s[:points[len(points)-1]].replace('.','') + s[points[len(points)-1]:]
return float(np.where(np.isnan(float(s2)), 0, float(s2)))
####################################################################################################
def MeasureExtractor(s):
mis = []
E = [m.start() for m in re.finditer('=', s)]
for e in E:
se = ''
for i in range(2, 50):
if s[e+i] != '"':
se += s[e+i]
else:
break
mis.append(float(se.replace(',','.')))
return mis
####################################################################################################
def FileFilter(ld, directory):
mesi = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
to_be_extracted = []
list_files = [of for of in os.listdir(directory) if isfile(join(directory, of))]
M = datetime.datetime(2017,1,1,0,0,0)
last_file = 0
for f in list_files:
#filedate = datetime.datetime(2017, int(f[2:4]), int(f[5:7]))
fdt = time.ctime(os.path.getmtime(directory + "/" + f))
filedate = datetime.datetime(int(fdt[20:]), mesi.index(fdt[4:7])+1, int(fdt[8:10]), hour = int(fdt[11:13]), minute = int(fdt[14:16]), second = int(fdt[17:19]))
if filedate > ld:
to_be_extracted.append(f)
if filedate > M:
M = filedate
last_file = f
return to_be_extracted, time.ctime(os.path.getmtime(directory + "/" + last_file))
####################################################################################################
def ZIPExtractor():
mesi = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
last_date = open("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/last_date.txt")
ld = last_date.read()
LD = datetime.datetime(int(ld[20:]), mesi.index(ld[4:7])+1, int(ld[8:10]), hour = int(ld[11:13]), minute = int(ld[14:16]), second = int(ld[17:19]))
strm = str(LD.month) if len(str(LD.month)) > 1 else "0" + str(LD.month)
directory = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/" + str(LD.year) + "-" + strm + "/Giornalieri"
tbe2, M = FileFilter(LD, directory)
tbe = [x for x in tbe2 if ".zip" in x and "CSV" not in x]
for t in tbe:
#print(t)
path = directory + "/" + t
zf = zipfile.ZipFile(path)
lzf = [x for x in zf.namelist() if ".zip" in x]
zf.extractall(path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
for z in lzf:
#print(z)
path2 = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP/" + str(z)
with zipfile.ZipFile(path2) as zf2:
right = [str(rf) for rf in zf2.namelist() if 'T1' in str(rf)]
if len(right) > 0:
zf2.extract(right[0], path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TBP")
shutil.rmtree("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
os.makedirs("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
new_last_date = open("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/last_date.txt", 'r+')
new_last_date.write(M)
return 1
####################################################################################################
def D_Monthly_Aggregator2016(month):
cl = ['E', 'F']
for h in range(24):
cl.append(str(h) + '.A')
cl.append(str(h) + '.B')
cl.append(str(h) + '.C')
cl.append(str(h) + '.D')
cl.append('G')
Aggdf = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2016/Aggregatore_orari-2016.xlsx")
Aggdf.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
m = month
diz = OrderedDict()
count = 0
strm = str(m) if len(str(m)) > 1 else "0" + str(m)
crppm = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2016/" + strm + "-2016/_All_CRPP_" + strm + "_2016.xlsx")
pathm = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2016/2016-" + strm + "/Giornalieri/CSV"
csvfiles = os.listdir(pathm)
csvfiles = [cf for cf in csvfiles if 'T1' in cf and '.txt' not in cf]
for cf in csvfiles:
zona = ""
pod = cf[10:24]
date = datetime.datetime(2017, int(cf[2:4]), int(cf[5:7]))
if crppm["ZONA"].ix[crppm["POD"] == pod].values.size > 0:
zona = crppm["ZONA"].ix[crppm["POD"] == pod].values[0]
df = pd.read_csv(pathm + "/" + cf, sep = ";", dtype = object)
df.columns = cl
vec = np.repeat(0.0, 24)
td = []
for h in range(24):
ha = str(h) + '.A'
hb = str(h) + '.B'
hc = str(h) + '.C'
hd = str(h) + '.D'
va = Converter(str(df[ha].ix[0]))
vb = Converter(str(df[hb].ix[0]))
vc = Converter(str(df[hc].ix[0]))
vd = Converter(str(df[hd].ix[0]))
vec[h] = np.sum([va, vb, vc, vd], dtype = np.float64)
td.append(pod)
td.append(zona)
td.append(date)
td.extend(vec.tolist())
diz[count] = td
count += 1
diz = pd.DataFrame.from_dict(diz, orient = 'index')
diz.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
Aggdf = Aggdf.append(diz, ignore_index = True)
return Aggdf
####################################################################################################
def D_Monthly_Aggregator(month):
cl = ['E', 'F']
for h in range(24):
cl.append(str(h) + '.A')
cl.append(str(h) + '.B')
cl.append(str(h) + '.C')
cl.append(str(h) + '.D')
cl.append('G')
Aggdf = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/Aggregatore_orari-2017.xlsx")
Aggdf.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
m = month
diz = OrderedDict()
count = 0
strm = str(m) if len(str(m)) > 1 else "0" + str(m)
if month >= 3:
try:
crppm = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm + "-2017/_All_CRPP_" + strm + "_2017.xlsx", sheetname = 'CRPP')
except:
crppm = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm + "-2017/_All_CRPP_" + strm + "_2017.xlsm", sheetname = 'CRPP')
else:
crppm = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/03-2017/_All_CRPP_03_2017.xlsx", sheetname = 'CRPP')
pathm = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm + "/Giornalieri/CSV"
csvfiles = os.listdir(pathm)
csvfiles = [cf for cf in csvfiles if 'T1' in cf and '.txt' not in cf]
for cf in csvfiles:
zona = ""
pod = cf[10:24]
date = datetime.datetime(2017, int(cf[2:4]), int(cf[5:7]))
if crppm["ZONA"].ix[crppm["POD"] == pod].values.size > 0:
zona = crppm["ZONA"].ix[crppm["POD"] == pod].values[0]
df = pd.read_csv(pathm + "/" + cf, sep = ";", dtype = object)
df.columns = cl
vec = np.repeat(0.0, 24)
td = []
for h in range(24):
ha = str(h) + '.A'
hb = str(h) + '.B'
hc = str(h) + '.C'
hd = str(h) + '.D'
va = Converter(str(df[ha].ix[0]))
vb = Converter(str(df[hb].ix[0]))
vc = Converter(str(df[hc].ix[0]))
vd = Converter(str(df[hd].ix[0]))
vec[h] = np.sum([va, vb, vc, vd], dtype = np.float64)
td.append(pod)
td.append(zona)
td.append(date)
td.extend(vec.tolist())
diz[count] = td
count += 1
diz = pd.DataFrame.from_dict(diz, orient = 'index')
diz.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
Aggdf = Aggdf.append(diz, ignore_index = True)
return Aggdf
####################################################################################################
def Aggregator(today):
cl = ['E', 'F']
for h in range(24):
cl.append(str(h) + '.A')
cl.append(str(h) + '.B')
cl.append(str(h) + '.C')
cl.append(str(h) + '.D')
cl.append('G')
Aggdf = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/Aggregatore_orari-2017.xlsx")
Aggdf.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6',
'7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24']]
m = today.month
diz = OrderedDict()
count = 0
strm1 = str(m) if len(str(m)) > 1 else "0" + str(m)
strm2 = str(m-1) if len(str(m-1)) > 1 else "0" + str(m-1)
# crppm1 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm1 + "-2017/_All_CRPP_" + strm1 + "_2017.xlsx")
# crppm2 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm2 + "-2017/_All_CRPP_" + strm2 + "_2017.xlsx")
if strm1 == "07":
crppm1 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm1 + "-2017/_All_CRPP_" + strm1 + "_2017.xlsm", sheetname = "CRPP")
else:
crppm1 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm1 + "-2017/_All_CRPP_" + strm1 + "_2017.xlsx")
if strm2 == "07":
crppm2 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm2 + "-2017/_All_CRPP_" + strm2 + "_2017.xlsm", sheetname = "CRPP")
else:
crppm2 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/" + strm2 + "-2017/_All_CRPP_" + strm2 + "_2017.xlsx")
pathm = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TBP"
csvfiles = os.listdir(pathm)
csvfiles = [cf for cf in csvfiles if 'T1' in cf and '.txt' not in cf]
for cf in csvfiles:
pod = cf[10:24]
date = datetime.datetime(2017, int(cf[2:4]), int(cf[5:7]))
zona = 0
if date.month == m:
zona = crppm1["ZONA"].ix[crppm1["POD"] == pod].values[0] if crppm1["ZONA"].ix[crppm1["POD"] == pod].values.size > 0 else 0
shutil.copy2(pathm + "/" + cf,"H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm1 + "/Giornalieri/CSV")
else:
zona = crppm2["ZONA"].ix[crppm2["POD"] == pod].values[0] if crppm2["ZONA"].ix[crppm2["POD"] == pod].values.size > 0 else 0
shutil.copy2(pathm + "/" + cf,"H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm2 + "/Giornalieri/CSV")
df = pd.read_csv(pathm + "/" + cf, sep = ";", dtype = object)
df.columns = cl
vec = np.repeat(0.0, 24)
td = []
for h in range(24):
ha = str(h) + '.A'
hb = str(h) + '.B'
hc = str(h) + '.C'
hd = str(h) + '.D'
va = Converter(str(df[ha].ix[0]))
vb = Converter(str(df[hb].ix[0]))
vc = Converter(str(df[hc].ix[0]))
vd = Converter(str(df[hd].ix[0]))
vec[h] = np.sum([va, vb, vc, vd], dtype = np.float64)
td.append(pod)
td.append(zona)
td.append(date)
td.extend(vec.tolist())
diz[count] = td
count += 1
diz = pd.DataFrame.from_dict(diz, orient = 'index')
diz.columns = [['POD', 'Area', 'Giorno', '1', '2', '3', '4', '5', '6','7', '8', '9', '10', '11', '12','13', '14', '15', '16', '17', '18','19', '20', '21', '22', '23', '24']]
Aggdf = Aggdf.append(diz, ignore_index = True)
shutil.rmtree("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TBP")
os.makedirs("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TBP")
Aggdf = Aggdf.drop_duplicates(subset = ["POD", "Giorno"], keep = "first")
return Aggdf
##################################################################################################
def T1_Mover(m):
strm = str(m) if len(str(m)) > 1 else "0" + str(m)
os.makedirs("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm + "/T1")
pathm = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm + "/Giornalieri/CSV/"
csvfiles = os.listdir(pathm)
csvfiles = [cf for cf in csvfiles if 'T1' in cf and '.txt' not in cf]
for cf in csvfiles:
shutil.copy2(pathm + cf, "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/2017-" + strm + "/T1")
return 1
##################################################################################################
####################################################################################################
def T2Extractor():
mesi = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
for m in mesi[3]:
strm = str(mesi.index(m)+1) if len(str(mesi.index(m)+1)) > 1 else "0" + str(mesi.index(m)+1)
directory = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/" + str(2017) + "-" + strm + "/Giornalieri"
os.makedirs(directory + "/T2_" + strm + "-2017")
tbe = os.listdir(directory)
tbe = [x for x in tbe if ".zip" in x]
for t in tbe:
#print(t)
path = directory + "/" + t
zf = zipfile.ZipFile(path)
lzf = [x for x in zf.namelist() if ".zip" in x]
zf.extractall(path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
for z in lzf:
#print(z)
path2 = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP/" + str(z)
with zipfile.ZipFile(path2) as zf2:
right = [str(rf) for rf in zf2.namelist() if 'T2' in str(rf)]
if len(right) > 0:
zf2.extract(right[0], path = directory + "/T2_" + strm + "-2017")
#shutil.rmtree("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
return 1
####################################################################################################
def T1Extractor():
mesi = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
for m in mesi[3]:
strm = str(mesi.index(m)+1) if len(str(mesi.index(m)+1)) > 1 else "0" + str(mesi.index(m)+1)
directory = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/" + str(2017) + "-" + strm + "/Giornalieri"
os.makedirs(directory + "/T1_" + strm + "-2017")
tbe = os.listdir(directory)
tbe = [x for x in tbe if ".zip" in x]
for t in tbe:
#print(t)
path = directory + "/" + t
zf = zipfile.ZipFile(path)
lzf = [x for x in zf.namelist() if ".zip" in x]
zf.extractall(path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
for z in lzf:
#print(z)
path2 = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP/" + str(z)
with zipfile.ZipFile(path2) as zf2:
right = [str(rf) for rf in zf2.namelist() if 'T1' in str(rf)]
if len(right) > 0:
zf2.extract(right[0], path = directory + "/T1_" + strm + "-2017")
#shutil.rmtree("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
return 1
####################################################################################################
def T1_monthly_Extractor(m):
strm = str(m) if len(str(m)) > 1 else "0" + str(m)
directory = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/" + str(2017) + "-" + strm + "/Giornalieri"
os.makedirs(directory + "/T1_" + strm + "-2017")
tbe = os.listdir(directory)
tbe = [x for x in tbe if ".zip" in x]
for t in tbe:
#print(t)
path = directory + "/" + t
zf = zipfile.ZipFile(path)
lzf = [x for x in zf.namelist() if ".zip" in x]
zf.extractall(path = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
for z in lzf:
#print(z)
path2 = "H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP/" + str(z)
with zipfile.ZipFile(path2) as zf2:
right = [str(rf) for rf in zf2.namelist() if 'T1' in str(rf)]
if len(right) > 0:
zf2.extract(right[0], path = directory + "/T1_" + strm + "-2017")
#shutil.rmtree("H:/Energy Management/02. EDM/01. MISURE/3. DISTRIBUTORI/ENEL Distribuzione S.p.A/2017/TEMP")
return 1
####################################################################################################
def AddHolidaysDate(vd):
##### codifica numerica delle vacanze
## 1 Gennaio = 1, Epifania = 2
## Pasqua = 3, Pasquetta = 4
## 25 Aprile = 5, 1 Maggio = 6, 2 Giugno = 7,
## Ferragosto = 8, 1 Novembre = 9
## 8 Dicembre = 10, Natale = 11, S.Stefano = 12, S.Silvestro = 13
holidays = 0
pasquetta = [datetime.datetime(2015,4,6), datetime.datetime(2016,3,28), datetime.datetime(2017,4,17)]
pasqua = [datetime.datetime(2015,4,5), datetime.datetime(2016,3,27), datetime.datetime(2017,4,16)]
if vd.month == 1 and vd.day == 1:
holidays = 1
if vd.month == 1 and vd.day == 6:
holidays = 1
if vd.month == 4 and vd.day == 25:
holidays = 1
if vd.month == 5 and vd.day == 1:
holidays = 1
if vd.month == 6 and vd.day == 2:
holidays = 1
if vd.month == 8 and vd.day == 15:
holidays = 1
if vd.month == 11 and vd.day == 1:
holidays = 1
if vd.month == 12 and vd.day == 8:
holidays = 1
if vd.month == 12 and vd.day == 25:
holidays = 1
if vd.month == 12 and vd.day == 26:
holidays = 1
if vd.month == 12 and vd.day == 31:
holidays = 1
if vd in pasqua:
holidays = 1
if vd in pasquetta:
holidays = 1
return holidays
####################################################################################################
def Bridge(vd):
bridge = 0
if vd.weekday() == 0:
Tues = vd + datetime.timedelta(days = 1)
if AddHolidaysDate(Tues) == 1:
bridge = 1
elif vd.weekday() == 4:
Thur = vd - datetime.timedelta(days = 1)
if AddHolidaysDate(Thur) == 1:
bridge = 1
else:
pass
return bridge
####################################################################################################
def GetMeanCurve(df, var):
mc = OrderedDict()
for y in [2015, 2016]:
dfy = df[var].ix[df.index.year == y]
for m in range(1,13,1):
dfym = dfy.ix[dfy.index.month == m]
Mean = []
for h in range(24):
dfymh = dfym.ix[dfym.index.hour == h].mean()
Mean.append(dfymh)
mc[str(m) + '_' + str(y)] = Mean
mc = pd.DataFrame.from_dict(mc, orient = 'index')
return mc
####################################################################################################
####################################################################################################
def percentageConsumption(db, zona, today):
str_month = str(today.month) if len(str(today.month)) > 1 else "0" + str(today.month)
str_day = str(today.day) if len(str(today.day)) > 1 else "0" + str(today.day)
dr = pd.date_range('2017-01-01', str(today.year) + '-' + str_month + '-' + str_day, freq = 'D')
All317 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/03-2017/_All_CRPP_03_2017.xlsx")
All417 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/04-2017/_All_CRPP_04_2017.xlsx")
All517 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/05-2017/_All_CRPP_05_2017.xlsx")
All617 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/06-2017/_All_CRPP_06_2017.xlsx")
All717 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/07-2017/_All_CRPP_07_2017.xlsx")
All817 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/08-2017/_All_CRPP_08_2017.xlsx")
All917 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/09-2017/_All_CRPP_09_2017.xlsx")
All1017 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/10-2017/_All_CRPP_10_2017.xlsx")
All1117 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/11-2017/_All_CRPP_11_2017.xlsx")
All1217 = pd.read_excel("H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/12-2017/_All_CRPP_12_2017.xlsx")
diz = OrderedDict()
dbz = db.ix[db["Area"] == zona]
for d in dr:
if dr.month <= 3:
All = locals()["All317"]
else:
All = locals()["All" + str(today.month) + str(today.year)]
pods = dbz["POD"].ix[dbz["Giorno"] == d].values.ravel().tolist()
All2 = All.ix[All["Trattamento_01"] == 'O']
totd = np.sum(np.nan_to_num([All2["CONSUMO_TOT"].ix[y] for y in All2.index if All2["POD"].ix[y] in pods]))/1000
#totd = All2["CONSUMO_TOT"].ix[All2["POD"].values.ravel() in pods].sum()
tot = All2["CONSUMO_TOT"].sum()/1000
p = totd/tot
diz[d] = [p]
diz = pd.DataFrame.from_dict(diz, orient = 'index')
return diz
####################################################################################################
def updateCRPP():
path16 = "H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2016/pod-zona per davide/"
path17 = "H:/Energy Management/02. EDM/01. MISURE/4. CRPP/2017/"
mesi = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']
years = ['16', '17']
today = datetime.datetime.now()
str_month = str(today.month) if len(str(today.month)) > 1 else "0" + str(today.month)
DF6 = pd.DataFrame()
DF7 = pd.DataFrame()
for y in years:
count = 0
if y == '16':
for m in mesi:
df = OrderedDict()
crpp = pd.read_excel(path16 + 'CRPP_' + y + m + '.xlsm', sheetname = 'CRPP')
for r in range(crpp.shape[0]):
vec = np.repeat(0,26).tolist()
vec[0] = str(crpp['ZONA'].ix[r]).upper()
vec[1] = crpp['POD'].ix[r]
treatment = str(crpp['Trattamento_' + m].ix[r]).upper()
if treatment == 'O':
vec[mesi.index(m) + 2] = 1
elif treatment == 'M':
vec[mesi.index(m) + 2] = 2
elif treatment == 'F':
vec[mesi.index(m) + 2] = 3
else:
vec[mesi.index(m) + 2] = 0
vec[mesi.index(m) + 14] = crpp['CONSUMO_TOT'].ix[r]
df[count] = vec
count += 1
df = pd.DataFrame.from_dict(df, orient = 'index')
df.columns = [['ZONA', 'POD', 'Trattamento_01', 'Trattamento_02', 'Trattamento_03', 'Trattamento_04', 'Trattamento_05',
'Trattamento_06', 'Trattamento_07', 'Trattamento_08', 'Trattamento_09', 'Trattamento_10',
'Trattamento_11', 'Trattamento_12', 'CONSUMO_TOT_01', 'CONSUMO_TOT_02',
'CONSUMO_TOT_03', 'CONSUMO_TOT_04', 'CONSUMO_TOT_05', 'CONSUMO_TOT_06', 'CONSUMO_TOT_07',
'CONSUMO_TOT_08', 'CONSUMO_TOT_09', 'CONSUMO_TOT_10', 'CONSUMO_TOT_11', 'CONSUMO_TOT_12']]
DF6 = DF6.append(df, ignore_index = True)
DF6 = DF6.groupby(['ZONA', 'POD'])
DF6 = DF6.agg(sum)
DF6.to_excel('C:/Users/d_floriello/Documents/CRPP2016.xlsx')
else:
for m in mesi[2:(mesi.index(str_month) + 2)]:
df = OrderedDict()
crpp = pd.read_excel(path17 + m + '-2017/_All_CRPP_' + m + '_20' + y + '.xlsx')
for r in range(crpp.shape[0]):
vec = np.repeat(0,26).tolist()
vec[0] = str(crpp['ZONA'].ix[r]).upper()
vec[1] = crpp['POD'].ix[r]
treatment = str(crpp['Trattamento_' + m].ix[r]).upper()
if treatment == 'O':
vec[mesi.index(m) + 2] = 1
elif treatment == 'M':
vec[mesi.index(m) + 2] = 2
elif treatment == 'F':
vec[mesi.index(m) + 2] = 3
else:
vec[mesi.index(m) + 2] = 0
vec[mesi.index(m) + 14] = crpp['CONSUMO_TOT'].ix[r]
df[count] = vec
count += 1
df = pd.DataFrame.from_dict(df, orient = 'index')
df.columns = [['ZONA', 'POD', 'Trattamento_01', 'Trattamento_02', 'Trattamento_03', 'Trattamento_04', 'Trattamento_05',
'Trattamento_06', 'Trattamento_07', 'Trattamento_08', 'Trattamento_09', 'Trattamento_10',
'Trattamento_11', 'Trattamento_12', 'CONSUMO_TOT_01', 'CONSUMO_TOT_02',
'CONSUMO_TOT_03', 'CONSUMO_TOT_04', 'CONSUMO_TOT_05', 'CONSUMO_TOT_06', 'CONSUMO_TOT_07',
'CONSUMO_TOT_08', 'CONSUMO_TOT_09', 'CONSUMO_TOT_10', 'CONSUMO_TOT_11', 'CONSUMO_TOT_12']]
DF7 = DF7.append(df, ignore_index = True)
DF7 = DF7.groupby(['ZONA', 'POD'])
DF7 = DF7.agg(sum)
DF7.to_excel('C:/Users/d_floriello/Documents/CRPP2017.xlsx')
return DF6, DF7
####################################################################################################
def MakeExtendedDatasetWithSampleCurve(df, db, meteo, zona, today, days_behind = 2):
#### @PARAM: df is the dataset from Terna, db, All zona those for computing the perc consumption
#### and the sample curve
#### @BRIEF: extended version of the quasi-omonimous function in Sbilanciamento.py
#### every day will have a dummy variable representing it
#wdays = ['Lun', 'Mar', 'Mer', 'Gio', 'Ven', 'Sab', 'Dom']
### Instead of setting or passing days_behind, we could simply use the last day
### present in the DB with the hourly-daily consumption data
psample = percentageConsumption(db, zona, today)
str_month = str(today.month) if len(str(today.month)) > 1 else "0" + str(today.month)
str_day = str(today.day) if len(str(today.day)) > 1 else "0" + str(today.day)
psample = psample.set_index(pd.date_range('2017-01-01', str(today.year) + '-' + str_month + '-' + str_day, freq = 'D'))
dts = OrderedDict()
df = df.ix[df.index.date >= datetime.date(2017,1,3)]
for i in df.index.tolist():
ll = []
hvector = np.repeat(0, 24)
dvector = np.repeat(0, 7)
wd = i.weekday()
bridge = Bridge(i.date())
hol = AddHolidaysDate(i.date())
td = days_behind
if wd == 0:
td += 1
cmym = db[db.columns[10:34]].ix[db["Giorno"] == (i.date()- datetime.timedelta(days = td))].sum(axis = 0).values.ravel()/1000
dvector[wd] = 1
h = i.hour
hvector[h] = 1
dy = i.timetuple().tm_yday
Tmax = meteo['Tmax'].ix[meteo['DATA'] == i.date()].values.ravel()[0]
rain = meteo['PIOGGIA'].ix[meteo['DATA'] == i.date()].values.ravel()[0]
wind = meteo['VENTOMEDIA'].ix[meteo['DATA'] == i.date()].values.ravel()[0]
ps = psample.ix[psample.index.date == i.date()]
ll.extend(dvector.tolist())
ll.extend(hvector.tolist())
ll.extend([dy, Tmax, rain, wind, hol, bridge, ps[0].values[0]])
ll.extend(cmym.tolist())
ll.extend([df['MO [MWh]'].ix[i]])
dts[i] = ll
dts = pd.DataFrame.from_dict(dts, orient = 'index')
dts.columns = [['Lun', 'Mar', 'Mer', 'Gio', 'Ven', 'Sab', 'Dom',
't0','t1','t2','t3','t4','t5','t6','t7','t8','t9','t10','t11','t12','t13','t14','t15','t16','t17','t18','t19','t20','t21','t22','t23',
'pday','tmax','pioggia','vento','holiday','ponte','perc',
'r0','r1','r2','r3','r4','r5','r6','r7','r8','r9','r10','r11','r12','r13','r14','r15','r16','r17','r18','r19','r20','r21','r22','r23','y']]
return dts