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startshell_lightWind5Da.py
52 lines (44 loc) · 2.19 KB
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startshell_lightWind5Da.py
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import matplotlib.pyplot as plt
import numpy as np
import sys
from importlib import reload
home_dir='/home/sabrina/documents/code/'
sys.path.append(home_dir+'tests')
import utilities as ut
import copula_analysis as ca
import copulaModels as mod
import vines
import results
parameter={'offsets':[11, 14,16,18, 20],'date_range':('2010-07-01 18:00:00','2016-07-05 01:00:00'),'first_hour':(0,0)}
#titles=[{'type':'Wind','location':'total','kind':'forecast'},{'type':'Wind','location':'NP','kind':'error'},{'type':'Solar','location':'NP','kind':'error'}]
parameter2 = parameter.copy()
parameter2['offsets'] = [17, 14]
param_list = [parameter, parameter2]
titles=[{'type':'Wind','location':'total','kind':'error'}]
#titles.append({'type':'Wind','location':'SP','kind':'error'})
copula=ca.create_copulaManager(titles,parameter)
gu=vines.cop2d_gumbel
ga=vines.cop2d_gaussian
st=vines.cop2d_student
fr=vines.cop2d_frank
un=vines.cop2d_uniform
cl=vines.cop2d_clayton
list1=[gu,ga,st,fr,un,cl]
list2=[gu,ga,st,fr,un,cl,
lambda unifs: vines.WeightedCopula(unifs,[gu,ga,un], precise=False),
lambda unifs: vines.WeightedCopula(unifs,[gu,st], precise=True),
lambda unifs: vines.WeightedCopula(unifs,[gu,ga,st], precise=True),
lambda unifs: vines.WeightedCopula(unifs,[gu,ga,st], precise=False),
lambda unifs: vines.WeightedCopula(unifs,[gu,fr,st], precise=True),
lambda unifs: vines.WeightedCopula(unifs,[gu,st,un], precise=False),
lambda unifs: vines.WeightedCopula(unifs,[gu,fr,st], precise=False),
lambda unifs: vines.WeightedCopula(unifs,[gu,ga], precise=False),
lambda unifs: vines.WeightedCopula(unifs,[gu,un], precise=False),
lambda unifs: vines.WeightedCopula(unifs, [gu, cl, ga], precise=False)]
cop_all=lambda unifs: vines.WeightedCopula(unifs,list1,precise=True,max_models=10)
list_models=[mod.cop_gaussian,mod.cop_student]
list_models.extend([lambda x, i=i: vines.D_vine(x,list_models=[[i],list1, list1, list1]) for i in list2])
list_models.extend([lambda x, i=i: vines.D_vine(x,list_models=[[i],[un], [un], [un]]) for i in list2])
list_models.append(lambda x: vines.D_vine(x, list_models=[list2, list1, list1, list1]))
res=ca.test_models(copula,list_models=list_models, end_incr=400)
results.compile_results(res, 'horse_races/TEST')