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Outages.py
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Outages.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Oct 13 10:29:07 2016
@author: d_floriello
Outages analysis
"""
import pandas as pd
import datetime
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
out = pd.read_excel('C:/Users/d_floriello/Documents/ourages_fran_2016-10-12.xlsx')
today = datetime.datetime(2016, 10, 24)
out = out.ix[out['Fin Indispo'] >= today]
###############################################################################
def update_outages(out, today):
out = out.ix[out['Fin Indispo'] >= today]
ids = np.unique(out['ID Indisponibilité de production'])
ixl = []
for ii in ids:
given = out.ix[out['ID Indisponibilité de production'] == ii]
ixl.append(max(given.index.tolist()))
out = out[['ID Indisponibilité de production', "Type d'indisponibilité", 'Filière',
"Type de l'unité de production", 'Nom du producteur',
'Puissance nominale', 'Puissance disponible restante', 'Cause',
'Statut']]
return out.ix[ixl]
###############################################################################
def get_statistics(out, by, varname):
diz1 = OrderedDict()
diz2 = OrderedDict()
# categorical = ["Type d'indisponibilité", 'Filière',
# "Type de l'unité de production", 'Nom du producteur','Cause',
# 'Statut']
grouped = out.groupby(by = by)
names = list(set(out[by].tolist()))
# if varname in categorical:
for i in names:
il1 = []
il2 = []
for vn in list(set(out[varname].tolist())):
il1.append(np.where(grouped.get_group(i)[varname] == vn)[0].size)
il2 = [i/sum(il1) if sum(il1) > 0 else 0 for i in il1]
diz1[i] = il1
diz2[i] = il2
df1 = pd.DataFrame.from_dict(diz1, orient = 'index')
df2 = pd.DataFrame.from_dict(diz2, orient = 'index')
df1.columns = [list(set(out[varname].tolist()))]
df2.columns = [list(set(out[varname].tolist()))]
return df1, df2
###############################################################################
def get_statistics_all(out):
varnames = ["Type d'indisponibilité", 'Filière',
"Type de l'unité de production", 'Nom du producteur',
'Puissance nominale', 'Puissance disponible restante', 'Cause']
# categorical = ["Type d'indisponibilité", 'Filière',
# "Type de l'unité de production", 'Nom du producteur','Cause',
# 'Statut']
for vn in varnames:
remains = set(varnames).difference(vn)
for rmn in remains:
S, P = get_statistics(out, vn, rmn)
print('Statistics of {} vs {}:'.format(vn, rmn))
print(S)
print(P)
print('##################################')
###############################################################################
def get_simple_count(out, month, var):
today = datetime.datetime(2016, month, 1)
out2 = update_outages(out, today)
print('num outages totali nel mese {}= {}'.format(month, out2.shape[0]))
vn = set(list(out2[var]))
cts = []
for v in vn:
print('percentage of {} outages = {}'.format(v, out2[var].ix[out2[var] == v].count()/out2.shape[0]))
cts.append(out2[var].ix[out2[var] == v].count()/out2.shape[0])
return cts
###############################################################################
def get_remaining_power(out, month):
today = datetime.datetime(2016, month, 1)
out2 = update_outages(out, today)
out2 = out2.reset_index(drop = True)
print('num outages totali nel mese {}= {}'.format(month, out2.shape[0]))
remaining = perc = 0
for r in range(out2.shape[0]):
remaining += out2['Puissance nominale'].ix[r] - out2['Puissance disponible restante'].ix[r]
perc = remaining/out2['Puissance nominale'].sum()
return remaining, perc
###############################################################################
out2 = update_outages(out, today)
varnames = ['ID Indisponibilité de production', "Type d'indisponibilité", 'Filière',
"Type de l'unité de production", 'Nom du producteur',
'Puissance nominale', 'Puissance disponible restante', 'Cause',
'Statut']
S, P = get_statistics(out2, varnames[2], varnames[6])
P.sum(axis = 1)
###############################################################################
##################################### 2015 ####################################
###############################################################################
out = pd.read_excel('C:/Users/d_floriello/Documents/out2015.xlsx')
nmn = ['num outages tot', 'tot indisp', '%tot indisp', 'altro_filiera', 'Hydraulique lacs',
"Hydraulique fil de l'eau / éclusée",
'Nucléaire',
'Charbon',
'Autre',
'Gaz',
'Marin',
'Fioul',
'Hydraulique STEP',
'altro',
'Indisponibilité planifiée', 'Indisponibilité fortuite']#,
# 'altro_prod',
# 'DIRECT ENERGIE',
# 'TOTAL',
# 'GDF-SUEZ',
# 'UNIPER',
# 'EDF',
# 'PSS POWER',
# 'ALPIQ']
diz = OrderedDict()
# il DataFrame risultante verra riempito da diz per righe.
# ogni entrata di diz sarà:
# 0: num outages totali;
# 1: indisponibilita totale in MWh(?)
# 2: indisponibilita inpercentuale
# 3: per ogni tipo di centrale la percentuale fuori uso di quel tipo sul totale outages
# 4: percentuale cause
# 5: percentuale produttori
mon = [1,2,3,4,5,6,7,8,9,10,11,12]
filiera = list(set(out['Filière']))
tipo = list(set(out["Type d'indisponibilité"]))
prod = list(set(out['Nom du producteur']))
cause = list(set(out['Cause']))
filiera = [i for i in filiera if isinstance(i, str)]
tipo = [i for i in tipo if isinstance(i, str)]
prod = [i for i in prod if isinstance(i, str)]
cause = [i for i in cause if isinstance(i, str)]
for m in mon:
vec = []
out2 = out.set_index(out['Fin Indispo'])
atm = out2.ix[out2.index.month > m]
ids = list(set(atm['ID Indisponibilité de production']))
ind = tot = 0
nfil = np.repeat(0, len(filiera)).tolist()
ntip = np.repeat(0, len(tipo)).tolist()
npro = np.repeat(0, len(prod)).tolist()
ncau = np.repeat(0, len(cause)).tolist()
if len(ids) > 0:
vec.append([len(ids)]) ### num outages totali
for i in ids:
# print(ind)
# print(type(ind))
# print(tot)
# print(type(tot))
I = atm.ix[atm['ID Indisponibilité de production'] == i]
M = I.ix[I['Version'] == np.max(I['Version'])]
ind += M['Puissance nominale'].values[0] - M['Puissance disponible restante'].values[0]
tot += M['Puissance nominale'].values[0]
# print(ind)
# print(type(ind))
# print(tot)
# print(type(tot))
for fil in filiera:
if fil == M['Filière'].values[0]:
indx = filiera.index(fil)
nfil[indx] += 1
for tip in tipo:
if tip == M["Type d'indisponibilité"].values[0]:
#print(M["Type d'indisponibilité"].values[0])
indx = tipo.index(tip)
ntip[indx] += 1
#print(ntip)
for pro in prod:
if pro == M['Nom du producteur'].values[0]:
indx = prod.index(pro)
npro[indx] += 1
for cu in cause:
if cu == M['Cause'].values[0]:
indx = cause.index(cu)
ncau[indx] += 1
perc = ind/tot
print(perc)
vec.append([ind])
vec.append([perc])
print((np.array(ntip)/len(ids)).tolist())
vec.append((np.array(nfil)/len(ids)).tolist())
#vec.append((np.array(ntip)/len(ids)).tolist())
vec.append([ntip[0]/len(ids)])
vec.append([ntip[1]/len(ids)])
#vec.append((np.array(npro)/len(ids)).tolist())
#vec.append(ncau/ids.size)
vec= [item for sublist in vec for item in sublist]
#vec = np.array(vec).ravel().tolist()
diz[str(m)] = vec
else:
diz[str(m)] = np.repeat(0,len(nmn)).tolist()
DF = pd.DataFrame.from_dict(diz, orient = 'index')
DF.columns = [nmn]
DF.shape
fs = pd.read_excel('C:/Users/d_floriello/Documents/Prezzi Francia e Svizzera (2015 -2016).xlsx', sheetname = '2015')
fs = fs[fs.columns[[2,3]]].set_index(fs['Data'])
plt.figure()
plt.plot(fs[fs.columns[0]].resample('D').mean())
plt.figure()
DF[DF.columns[3:13]].plot()
from pandas.tools import plotting
plt.figure()
plotting.parallel_coordinates(DF[DF.columns[3:13]])
for i in range(1,13,1):
print(sorted(DF[DF.columns[3:13]].ix[DF.index == str(i)]))
nuc = []
hydro = []
gas = []
coal = []
rim = []
perc = []
for i in range(1,13,1):
nuc.append(get_simple_count(out, i, 'Filière')[3])
hydro.append(get_simple_count(out, i, 'Filière')[2] + get_simple_count(out, i, 'Filière')[5])
gas.append(get_simple_count(out, i, 'Filière')[1])
coal.append(get_simple_count(out, i, 'Filière')[0])
rim.append(get_remaining_power(out, i)[0])
perc.append(get_remaining_power(out, i)[1])
diz2 = OrderedDict()
diz2['nucleare'] = nuc
diz2['hydro'] = hydro
diz2['gas'] = gas
diz2['carbone'] = coal
diz3 = OrderedDict()
diz3['remaining power'] = rim
diz3['%remaining power'] = perc
D2 = pd.DataFrame.from_dict(diz2)
D3 = pd.DataFrame.from_dict(diz3)
plt.figure()
plt.plot(np.array(nuc))
plt.figure()
plt.plot(np.array(fs[fs.columns[0]].resample('M').mean()))
plt.figure()
D3['%remaining power'].plot()
plt.figure()
plt.scatter(np.array(D3['remaining power']), np.array(fs[fs.columns[0]].resample('M').mean()))