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new_linkcolouralgorithm.py~HEAD
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new_linkcolouralgorithm.py~HEAD
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import csv
#import time
import sys
from pylab import *
from scipy.optimize import brentq
from scipy.optimize import fmin
#import datetime as dt
from localutils import *
from figutils import AtoKh_old
from time import time
from drawnet import draw_powmix_network
from EUgrid import *
import networkx as nx
def get_neighbours(n,matr=np.genfromtxt("./settings/eadmat.txt")):
""" matr should be = np.genfromtxt("./settings/eadmat.txt")"""
neighbours=[]
for j in range(len(matr[n])):
if matr[n][j]>0 and n<>j:
neighbours.append(j)
return neighbours
def get_links(node_id,matr=np.genfromtxt("./settings/eadmat.txt")):
""" matr should be = np.genfromtxt("./settings/eadmat.txt")"""
links=[]
linkno=-1
for j in range(len(matr[node_id])):
for i in range(len(matr[node_id])):
if matr[i][j]>0 and i>j:
linkno+=1
if node_id==i: links.append([linkno,-1])
if node_id==j: links.append([linkno,1])
return links
def get_link_direction(n,N): #getting the first country-node in the link-label as start and the second as end
a,b,c,d,e=au.AtoKh(EU_Nodes())
for k in N:
if str(k.label)==e[n][0][0:2]: start_node=k
for k in N:
if str(k.label)==e[n][0][6:8]: end_node=k
return [start_node,end_node]
def track_link_usage_total(N,F,new=False,lapse=None,alph=None,mode="linear",copper=True):
#tracks the usage of each link for each country,
#alph sets the value of homogenous alphas.
#Mode can be "linear", "square", "random" or "capped".
#set new=True if new model is used.
if alph==None:
alph="hetero"
if lapse== None:
lapse=N[0].nhours
if new:
if copper:
file=open('new_'+str(mode)+'_copper_link_usage_total_alpha='+str(alph)+'.dat','w+')
else:
file = open('new_'+str(mode)+'_constr_link_usage_total_alpha='+str(alph)+'.dat', 'w+')
else:
if copper:
file=open('old_'+str(mode)+'_copper_link_usage_total_alpha='+str(alph)+'.dat','w+')
else:
file=open('old_'+str(mode)+'_constr_link_usage_total_alpha='+str(alph)+'.dat','w+')
matr=np.genfromtxt(N.pathadmat)
a,b,c,d,e=au.AtoKh(EU_Nodes())
f,g,list_F=au.AtoKh_old(N)
boxplot=np.zeros((4,len(e)))
boksplotlabel=[]
links=[]
linksflow=[]
cost=[]
links_ex=[]
links_im=[]
link_mix_export_all=[]
link_mix_import_all=[]
counter=0
link_mix_export_variance_all=[]
link_mix_import_variance_all=[]
maxlex=[]
maxlim=[]
minlex=[]
minlim=[]
meanlex=[]
meanlim=[]
quant99lex=[]
quant99lim=[]
for n in range(len(e)):
counter+=1
link_mix_export_variance=np.zeros(len(N))
link_mix_import_variance=np.zeros(len(N))
total_flow_link=0
total_export_mix=np.zeros((len(N)))
total_import_mix=np.zeros((len(N)))
total_link_mix=np.zeros((len(N)))
link_mix_export=np.zeros((len(N),lapse))
link_mix_import=np.zeros((len(N),lapse))
total_link_mix_percentage=np.zeros((len(N)))
#getting the first country-node in the link-label as start and the second as end
start=get_link_direction(n,N)[0]
end=get_link_direction(n,N)[1]
# determining the links connected to start
start.links=get_links(start.id,matr)
for t in range(lapse):
## Update progress bar.
if mod(t,100)==0 and t>0:
print "\r",round(100.0*(t/float(lapse)),2),"%",
sys.stdout.flush()
for l in start.links:
#if flow indicates export from start_node
if end.label== list_F[l[0]][0] or end.label == list_F[l[0]][1]:
if F[l[0],t]*l[1]>0:
if l[1]==1:
sending_node_label=list_F[l[0]][0]
receiving_node_label=list_F[l[0]][1]
elif l[1]==-1:
sending_node_label=list_F[l[0]][1]
receiving_node_label=list_F[l[0]][0]
else:
print "Warning (234nsd23): Link direction unknown!"
#if flow indicates export from end_node
else:
if l[1]==-1:
sending_node_label=list_F[l[0]][0]
receiving_node_label=list_F[l[0]][1]
elif l[1]==1:
sending_node_label=list_F[l[0]][1]
receiving_node_label=list_F[l[0]][0]
else:
print "Warning (234nsd23): Link direction unknown!"
#finding the nodes corresponding to the sending and receiving nodes
for k in N:
if k.label==sending_node_label:
sending_node=k
elif k.label==receiving_node_label:
receiving_node=k
# if sending_node and receiving_node are at the ends of the link we are looking at, create arrays with the flows being imported and exported from and to each country
if (sending_node==start and receiving_node==end) or (sending_node==end and receiving_node==start):
if sum(receiving_node.power_mix_ex[:,t])==0:
link_mix_import[:,t]=0
elif sum(sending_node.power_mix[:,t])==0:
link_mix_export[:,t]=0
else:
link_mix_import[:,t]=receiving_node.power_mix_ex[:,t]*abs(F[l[0],t])/sum(receiving_node.power_mix_ex[:,t])
link_mix_export[:,t]=sending_node.power_mix[:,t]*abs(F[l[0],t])/sum(sending_node.power_mix[:,t])
#adding the flow over the link for each timestep
total_flow_link+=abs(F[l[0],t])
#adding the arrays with import and export to the array with the total_link_mix
total_link_mix[:] += (link_mix_export[:,t]+link_mix_import[:,t])/2
total_export_mix[:] += link_mix_export[:,t]
total_import_mix[:] += link_mix_import[:,t]
# calculating each countrys usage of the link in percentages of the total usage of the link
total_link_mix_percentage[:]=total_link_mix[:]/total_flow_link*100
#print sum(total_link_mix_percentage[:])
links=np.append(links,total_link_mix[:])
linksflow=np.append(linksflow,total_flow_link)
cost=np.append(cost,total_link_mix_percentage)
links_ex=np.append(links_ex,total_export_mix[:])
links_im=np.append(links_im,total_import_mix[:])
link_mix_export_all.append(link_mix_export)
link_mix_import_all.append(link_mix_import)
for l in range(len(N)):
m=np.mean(link_mix_export[l])
o=np.mean(link_mix_import[l])
link_mix_export_variance[l]=(sum(((link_mix_export[l,t]-m)**2 for t in range(lapse)))/lapse)
link_mix_import_variance[l]=(sum(((link_mix_import[l,t]-o)**2 for t in range(lapse)))/lapse)
maxlex.append(max(link_mix_export_all[n][l]))
maxlim.append(max(link_mix_import_all[n][l]))
minlex.append(min(link_mix_export_all[n][l]))
minlim.append(min(link_mix_import_all[n][l]))
meanlex.append(np.mean(link_mix_export_all[n][l]))
meanlim.append(np.mean(link_mix_import_all[n][l]))
quant99lex.append(np.percentile(link_mix_export_all[n][l],99))
quant99lim.append(np.percentile(link_mix_import_all[n][l],99))
link_mix_export_variance_all.append(link_mix_export_variance)
link_mix_import_variance_all.append(link_mix_import_variance)
print sum(total_link_mix_percentage[:])
print counter
# saving information to human-readable file
for k in N:
x="country", str(k.label), "has a total usage of link", e[n], "of" ,str(total_link_mix_percentage[k.id]), "% after", str(t), "hours"
file.write(str(x) + '\n')
# sorting the total_link_mix_percentage, such that the higher percentages are first
a=sorted(total_link_mix_percentage,reverse=True)
#for the 4 highest values we save the usage for each link and the corresponding countries
print a
for i in range(4):
boxplot[i,n]=a[i]
for h in N:
if a[i]==total_link_mix_percentage[h.id]:
boksplotlabel=np.append(boksplotlabel,str(h.label))
maxlex=np.array(maxlex).reshape((50,30))
maxlim=np.array(maxlim).reshape((50,30))
minlex=np.array(minlex).reshape((50,30))
minlim=np.array(minlim).reshape((50,30))
meanlex=np.array(meanlex).reshape((50,30))
meanlim=np.array(meanlim).reshape((50,30))
quant99lex=np.array(quant99lex).reshape((50,30))
quant99lim=np.array(quant99lim).reshape((50,30))
if new:
if copper:
np.save('linkcolouring/new_'+str(mode)+'_copper_boxplot_alpha='+str(alph),boxplot)
np.save('linkcolouring/new_'+str(mode)+'_copper_boksplotlabel_alpha='+str(alph),boksplotlabel)
np.save('linkcolouring/new_'+str(mode)+'_copper_links_mix_alpha='+str(alph),links)
np.save('linkcolouring/new_'+str(mode)+'_copper_links_flow_alpha='+str(alph),linksflow)
np.save('linkcolouring/new_'+str(mode)+'_copper_cost_alpha='+str(alph),cost)
np.save('linkcolouring/new_'+str(mode)+'_copper_links_ex_alpha='+str(alph),links_ex)
np.save('linkcolouring/new_'+str(mode)+'_copper_links_im_alpha='+str(alph),links_im)
np.save('linkcolouring/new_'+str(mode)+'_copper_link_mix_import_all_alpha='+str(alph),link_mix_import_all)
np.save('linkcolouring/new_'+str(mode)+'_copper_link_mix_export_all_alpha='+str(alph),link_mix_export_all)
np.save('linkcolouring/new_'+str(mode)+'_copper_link_mix_import_variance_all_alpha='+str(alph),link_mix_import_variance_all)
np.save('linkcolouring/new_'+str(mode)+'_copper_link_mix_export_variance_all_alpha='+str(alph),link_mix_export_variance_all)
np.save('linkcolouring/new_'+str(mode)+'_copper_maxlex_alpha='+str(alph),maxlex)
np.save('linkcolouring/new_'+str(mode)+'_copper_maxlim_alpha='+str(alph),maxlim)
np.save('linkcolouring/new_'+str(mode)+'_copper_minlex_alpha='+str(alph),minlex)
np.save('linkcolouring/new_'+str(mode)+'_copper_minlim_alpha='+str(alph),minlim)
np.save('linkcolouring/new_'+str(mode)+'_copper_meanlex_alpha='+str(alph),meanlex)
np.save('linkcolouring/new_'+str(mode)+'_copper_meanlim_alpha='+str(alph),meanlim)
np.save('linkcolouring/new_'+str(mode)+'_copper_quant99lex_alpha='+str(alph),quant99lex)
np.save('linkcolouring/new_'+str(mode)+'_copper_quant99lim_alpha='+str(alph),quant99lim)
else:
np.save('linkcolouring/new_'+str(mode)+'_constr_boxplot_alpha='+str(alph),boxplot)
np.save('linkcolouring/new_'+str(mode)+'_constr_boksplotlabel_alpha='+str(alph),boksplotlabel)
np.save('linkcolouring/new_'+str(mode)+'_constr_links_mix_alpha='+str(alph),links)
np.save('linkcolouring/new_'+str(mode)+'_constr_links_flow_alpha='+str(alph),linksflow)
np.save('linkcolouring/new_'+str(mode)+'_constr_cost_alpha='+str(alph),cost)
np.save('linkcolouring/new_'+str(mode)+'_constr_links_ex_alpha='+str(alph),links_ex)
np.save('linkcolouring/new_'+str(mode)+'_constr_links_im_alpha='+str(alph),links_im)
np.save('linkcolouring/new_'+str(mode)+'_constr_link_mix_import_all_alpha='+str(alph),link_mix_import_all)
np.save('linkcolouring/new_'+str(mode)+'_constr_link_mix_export_all_alpha='+str(alph),link_mix_export_all)
np.save('linkcolouring/new_'+str(mode)+'_constr_link_mix_import_variance_all_alpha='+str(alph),link_mix_import_variance_all)
np.save('linkcolouring/new_'+str(mode)+'_constr_link_mix_export_variance_all_alpha='+str(alph),link_mix_export_variance_all)
np.save('linkcolouring/new_'+str(mode)+'_constr_maxlex_alpha='+str(alph),maxlex)
np.save('linkcolouring/new_'+str(mode)+'_constr_maxlim_alpha='+str(alph),maxlim)
np.save('linkcolouring/new_'+str(mode)+'_constr_minlex_alpha='+str(alph),minlex)
np.save('linkcolouring/new_'+str(mode)+'_constr_minlim_alpha='+str(alph),minlim)
np.save('linkcolouring/new_'+str(mode)+'_constr_meanlex_alpha='+str(alph),meanlex)
np.save('linkcolouring/new_'+str(mode)+'_constr_meanlim_alpha='+str(alph),meanlim)
np.save('linkcolouring/new_'+str(mode)+'_constr_quant99lex_alpha='+str(alph),quant99lex)
np.save('linkcolouring/new_'+str(mode)+'_constr_quant99lim_alpha='+str(alph),quant99lim)
else:
if copper:
np.save('linkcolouring/old_'+str(mode)+'_copper_boxplot_alpha='+str(alph),boxplot)
np.save('linkcolouring/old_'+str(mode)+'_copper_boksplotlabel_alpha='+str(alph),boksplotlabel)
np.save('linkcolouring/old_'+str(mode)+'_copper_links_mix_alpha='+str(alph),links)
np.save('linkcolouring/old_'+str(mode)+'_copper_links_flow_alpha='+str(alph),linksflow)
np.save('linkcolouring/old_'+str(mode)+'_copper_cost_alpha='+str(alph),cost)
np.save('linkcolouring/old_'+str(mode)+'_copper_links_ex_alpha='+str(alph),links_ex)
np.save('linkcolouring/old_'+str(mode)+'_copper_links_im_alpha='+str(alph),links_im)
np.save('linkcolouring/old_'+str(mode)+'_copper_link_mix_import_all_alpha='+str(alph),link_mix_import_all)
np.save('linkcolouring/old_'+str(mode)+'_copper_link_mix_export_all_alpha='+str(alph),link_mix_export_all)
np.save('linkcolouring/old_'+str(mode)+'_copper_link_mix_export_variance_all_alpha='+str(alph),link_mix_export_variance_all)
np.save('linkcolouring/old_'+str(mode)+'_copper_link_mix_import_variance_all_alpha='+str(alph),link_mix_import_variance_all)
np.save('linkcolouring/old_'+str(mode)+'_copper_maxlex_alpha='+str(alph),maxlex)
np.save('linkcolouring/old_'+str(mode)+'_copper_maxlim_alpha='+str(alph),maxlim)
np.save('linkcolouring/old_'+str(mode)+'_copper_minlex_alpha='+str(alph),minlex)
np.save('linkcolouring/old_'+str(mode)+'_copper_minlim_alpha='+str(alph),minlim)
np.save('linkcolouring/old_'+str(mode)+'_copper_meanlex_alpha='+str(alph),meanlex)
np.save('linkcolouring/old_'+str(mode)+'_copper_meanlim_alpha='+str(alph),meanlim)
np.save('linkcolouring/old_'+str(mode)+'_copper_quant99lex_alpha='+str(alph),quant99lex)
np.save('linkcolouring/old_'+str(mode)+'_copper_quant99lim_alpha='+str(alph),quant99lim)
else:
np.save('linkcolouring/old_'+str(mode)+'_constr_boxplot_alpha='+str(alph),boxplot)
np.save('linkcolouring/old_'+str(mode)+'_constr_boksplotlabel_alpha='+str(alph),boksplotlabel)
np.save('linkcolouring/old_'+str(mode)+'_constr_links_mix_alpha='+str(alph),links)
np.save('linkcolouring/old_'+str(mode)+'_constr_links_flow_alpha='+str(alph),linksflow)
np.save('linkcolouring/old_'+str(mode)+'_constr_cost_alpha='+str(alph),cost)
np.save('linkcolouring/old_'+str(mode)+'_constr_links_ex_alpha='+str(alph),links_ex)
np.save('linkcolouring/old_'+str(mode)+'_constr_links_im_alpha='+str(alph),links_im)
np.save('linkcolouring/old_'+str(mode)+'_constr_link_mix_import_all_alpha='+str(alph),link_mix_import_all)
np.save('linkcolouring/old_'+str(mode)+'_constr_link_mix_export_all_alpha='+str(alph),link_mix_export_all)
np.save('linkcolouring/old_'+str(mode)+'_constr_link_mix_export_variance_all_alpha='+str(alph),link_mix_export_variance_all)
np.save('linkcolouring/old_'+str(mode)+'_constr_link_mix_import_variance_all_alpha='+str(alph),link_mix_import_variance_all)
np.save('linkcolouring/old_'+str(mode)+'_constr_maxlex_alpha='+str(alph),maxlex)
np.save('linkcolouring/old_'+str(mode)+'_constr_maxlim_alpha='+str(alph),maxlim)
np.save('linkcolouring/old_'+str(mode)+'_constr_minlex_alpha='+str(alph),minlex)
np.save('linkcolouring/old_'+str(mode)+'_constr_minlim_alpha='+str(alph),minlim)
np.save('linkcolouring/old_'+str(mode)+'_constr_meanlex_alpha='+str(alph),meanlex)
np.save('linkcolouring/old_'+str(mode)+'_constr_meanlim_alpha='+str(alph),meanlim)
np.save('linkcolouring/old_'+str(mode)+'_constr_quant99lex_alpha='+str(alph),quant99lex)
np.save('linkcolouring/old_'+str(mode)+'_constr_quant99lim_alpha='+str(alph),quant99lim)
return boxplot,boksplotlabel
def boksplot(a,b,alph=None,new=False,mode="linear",copper=True): # draws a columndiagram displaying the 4 biggest users and their usage of each link.
#Insert new_boxplot and new_boksplotlabel or variants of these.
#a is an array representing the portion of usage of the different links for the 4 biggest users at each link.
#b is an array holding the corresponding labels of these different users.
#Mode can be "linear", "square" or "random".
if alph==None:
alph="hetero"
c=np.zeros((50))
f,g,h,j,e=au.AtoKh(EU_Nodes())
width=1.0
n=np.arange(len(e))
E = [i[0] for i in e]
fig=plt.figure(figsize=(18,10))
for i in range(50):
c[i]=100
plt.text(i+width/8,c[i]-a[0,i]+0.4,b[i*4],color='w')
plt.text(i+width/8.,c[i]-a[0,i]-a[1,i]+0.4,b[1+i*4],color='w')
plt.text(i+width/8.,c[i]-a[0,i]-a[1,i]-a[2,i]+0.4,b[2+i*4],color='w')
plt.text(i+width/8.,c[i]-a[0,i]-a[1,i]-a[2,i]-a[3,i]+0.4,b[3+i*4],color='w')
plt.ion()
plt.show()
p1=plt.bar(range(50),c,width,color=au.blue)
p2=plt.bar(range(50),c-a[0,:],width,color=au.red)
p3=plt.bar(range(50),c-a[0,:]-a[1,:],width,color=au.gree)
p4=plt.bar(range(50),c-a[0,:]-a[1,:]-a[2,:],width,color=au.oran)
p5=plt.bar(range(50),c-a[0,:]-a[1,:]-a[2,:]-a[3,:],width,color=au.purp)
plt.xticks(n+width/2.,E,rotation=90)
plt.yticks(np.arange(0,101,5))
subplots_adjust(left=None, bottom=0.2, right=None, top=None, wspace=None, hspace=None)
plt.legend( (p1[0], p2[0],p3[0],p4[0],p5[0]), ('Primary user', 'Secondary user','Tertiary user','Quaternary user', 'The rest'), 'upper center',fancybox=True,prop={'size':10})
if new:
if copper:
if mode =="linear":
plt.text(0.03,92, "Linkusg rel to linkflow,new,linear,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_linear_copper_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "square":
plt.text(0.03,92, "Linkusg rel to linkflow,new,square,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_square_copper_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "random":
plt.text(0.03,92, "Linkusg rel to linkflow,new,random,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_random_copper_columndiagram_alpha="+str(alph)+".pdf")
else:
if mode =="linear":
plt.text(0.03,92, "Linkusg rel to linkflow,new,linear,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_linear_constr_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "square":
plt.text(0.03,92, "Linkusg rel to linkflow,new,square,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_square_constr_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "random":
plt.text(0.03,92, "Linkusg rel to linkflow,new,random,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_random_constr_columndiagram_alpha="+str(alph)+".pdf")
else:
if copper:
if mode =="linear":
plt.text(0.03,92, "Linkusg rel to linkflow,old,linear,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_linear_copper_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "square":
plt.text(0.03,92, "Linkusg rel to linkflow,old,square,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_square_copper_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "random":
plt.text(0.03,92, "Linkusg rel to linkflow,old,random,copper,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_random_copper_columndiagram_alpha="+str(alph)+".pdf")
else:
if mode =="linear":
plt.text(0.03,92, "Linkusg rel to linkflow,old,linear,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_linear_constr_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "square":
plt.text(0.03,92, "Linkusg rel to linkflow,old,square,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_square_constr_columndiagram_alpha="+str(alph)+".pdf")
elif mode == "random":
plt.text(0.03,92, "Linkusg rel to linkflow,old,random,constr,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_random_constr_columndiagram_alpha="+str(alph)+".pdf")
def costplot(links,linksflow,N,F,Graf=None,alph=None,new=False,mode="linear",copper=True,ports="ex_and_im",load=True): # prints different columndiagrams and saves values in arrays
#Mode can be "linear", "square", "random" or "capped".
#links is the links_mix or variations of that
#set load=True for a graph that sorts countrys by their average load.
if N==None:
N=EU_Nodes()
if alph==None:
alph="hetero"
d,f,g,r,e=au.AtoKh(EU_Nodes())
if new:
file=open('new_'+str(mode)+'_copper_costplot_alpha='+str(alph)+'.dat','w+')
else:
file=open('old_'+str(mode)+'_copper_costplot_alpha='+str(alph)+'.dat','w+')
capacities_today=[]
capacities_year=[]
width=1.0
gyell="#CCFF00"
E = [n.label for n in N]
D = [n.label for n in N]
C = [n.label for n in N]
fig=plt.figure(figsize=(13,7))
blue_div_yellow_kms=np.zeros(len(N))
blue_div_yellow_cost=np.zeros(len(N))
blue_div_yellow=np.zeros(len(N))
red_div_yellow_kms=np.zeros(len(N))
red_div_yellow_cost=np.zeros(len(N))
red_div_yellow=np.zeros(len(N))
green_div_yellow_kms=np.zeros(len(N))
green_div_yellow_cost=np.zeros(len(N))
green_div_yellow=np.zeros(len(N))
blue=np.zeros(len(N))
Blue_GWKms=np.zeros(len(N))
Green_GWkms=np.zeros(len(N))
Blue_cost=np.zeros(len(N))
Green_cost=np.zeros(len(N))
green=np.zeros(len(N))
c=np.zeros(len(N))
c_kms=np.zeros(len(N))
c_kms_avg=np.zeros(len(N))
c_cost=np.zeros(len(N))
c_cost_final=np.zeros(len(N))
c_kms_avg_final=np.zeros(len(N))
d=np.zeros(len(N))
d_cost=np.zeros(len(N))
cost=np.zeros(len(N))
Graford=np.zeros(len(N))
t=np.zeros(len(N))
t_kms=np.zeros(len(N))
t_cost=np.zeros(len(N))
today_usage=np.zeros(len(N))
usage_year=np.zeros(len(N))
usage_year_kms=np.zeros(len(N))
usage_year_cost=np.zeros(len(N))
today_usage_kms=np.zeros(len(N))
total_usage=np.zeros(len(N))
total_usage_year=np.zeros(len(N))
total_usage_kms_year=np.zeros(len(N))
total_usage_cost_year=np.zeros(len(N))
total_usage_kms=np.zeros(len(N))
total_usage_cost=np.zeros(len(N))
today_usage_cost=np.zeros(len(N))
cost_rev=np.zeros(len(N))
usage_rel_to_share_of_load=np.zeros(len(N))
usage_rel_to_share_of_load_kms=np.zeros(len(N))
usage_rel_to_share_of_load_cost=np.zeros(len(N))
j=np.arange(len(N))
total_mean_load=0
total_capacity=0
total_capacity_kms=0
total_capacities_2050_cost=0
today_cap=0
today_cap_kms=0
today_cap_cost=0
counter=0
loads=[]
tot_today=0
tot_usage=0
tot_usage_rel=0
tot_today_kms=0
link_kms=[686,250,217,523,766,278,397,82,914,175,358,577,289,201,262,342,436,879,1109,1053,418,483,464,520,520,808,534,524,298,328,1052,503,753,691,305,371,117,642,446,281,291,317,163,812,356,591,523,491,277,263]
link_prizes=[400,400,400,400,400,400,1500,1500,1500,400,1500,400,400,400,400,1500,400,400,400,400,400,1500,1500,400,400,1500,400,400,400,400,1500,400,400,400,400,400,400,400,400,400,400,400,400,1500,400,400,1500,400,400,400]
link_kms_average=sum(link_kms)/len(link_kms)
for i in range(len(e)):
capacities_today.append(max(r[i*2:i*2+2]))
tot_today+=max(r[i*2:i*2+2])
tot_today_kms+=max(r[i*2:i*2+2])*link_kms[i]
for l in range(len(F)):
capacities_year.append(max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99))))
total_capacity+=max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))
total_capacity_kms+=max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))*link_kms[l]
if link_prizes[l]==1500: #If DC then a converter is needed at every end of the cable/HV-line
total_capacities_2050_cost+=max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))*150000*2+max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))*link_kms[l]*link_prizes[l]
else:
total_capacities_2050_cost+=max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))*link_kms[l]*link_prizes[l]
Capacities_year=np.array(capacities_year)
for n in N:
total_mean_load+=n.mean
loads.append(n.mean)
loadssort=sorted(loads,reverse=True)
np.save("total_cap",total_capacity)
np.save("total_cap_kms",total_capacity_kms)
np.save("total_cap_today",tot_today)
np.save("total_cap_kms_today",tot_today_kms)
for n in N:
cap=0
attached_links=get_links(n.id)
today_cap=0
today_cap_kms=0
today_cap_cost=0
cap_year=0
cap_year_kms=0
cap_cost_year=0
for l in attached_links:
cap_year+=capacities_year[l[0]]
cap_year_kms+=capacities_year[l[0]]*link_kms[l[0]]
today_cap+=capacities_today[l[0]]
today_cap_kms+=capacities_today[l[0]]*link_kms[l[0]]
if link_prizes[l[0]]==1500: #If link is DC and two converter stations are needed
today_cap_cost+=capacities_today[l[0]]*link_kms[l[0]]*link_prizes[l[0]]+capacities_today[l[0]]*150000*2
cap_cost_year+=capacities_year[l[0]]*link_kms[l[0]]*link_prizes[l[0]]+capacities_year[l[0]]*150000*2
else:
today_cap_cost+=capacities_today[l[0]]*link_kms[l[0]]*link_prizes[l[0]]
cap_cost_year+=capacities_year[l[0]]*link_kms[l[0]]*link_prizes[l[0]]
today_usage[n.id]=today_cap*0.5 #red in "MW"
today_usage_kms[n.id]=today_cap_kms*0.5 #red in "MWKm"
today_usage_cost[n.id]=today_cap_cost*0.5 #red in Euros
usage_year[n.id]=cap_year*0.5 #green in "MW"
usage_year_kms[n.id]=cap_year_kms*0.5 #green in "MWKm"
usage_year_cost[n.id]=cap_cost_year*0.5 #green in Euros
for n in N:
for l in range(len(F)):
c[n.id]+=links[n.id+30*l]/linksflow[l]*max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))
c_kms[n.id]+=links[n.id+30*l]/linksflow[l]*max(abs(np.percentile(F[l],1)),abs(np.percentile(F[l],99)))*link_kms[l]
t[n.id]=n.mean*total_capacity/total_mean_load #yellow in "MW"
t_kms[n.id]=n.mean*total_capacity_kms/total_mean_load #yellow in "MWKm"
t_cost[n.id]=n.mean*total_capacities_2050_cost/total_mean_load #yellow in "MW"
c_cost[n.id]=c_kms[n.id]/total_capacity_kms*total_capacities_2050_cost #blue in Euros
c[n.id]=c[n.id]/1000 #blue in "GW"
c_kms[n.id]=c_kms[n.id]/1000 #blue in "GW"
c_kms_avg[n.id]=c_kms[n.id]/link_kms_average
k=sorted(c,reverse=True)
k_kms=sorted(c_kms,reverse=True)
k_cost=sorted(c_cost,reverse=True)
if load:
for h in N:
for n in N:
if n.mean==loadssort[h.id]:
E[h.id]=n.label
usage_rel_to_share_of_load[h.id]=t[n.id]/1000 #yellow in "GW"
blue[h.id]=c[n.id]
green[h.id]=usage_year[n.id]/1000
total_usage[h.id]=today_usage[n.id]/1000
red_div_yellow[h.id]=total_usage[h.id]/usage_rel_to_share_of_load[h.id]
total_usage_year[h.id]=usage_year[n.id]/1000
tot_usage+=k[h.id]
tot_usage_rel+=t[h.id]
red_div_yellow[25]=0
else:
for h in N:
for n in N:
if n.label==Grafsorted[h.id][0]:
E[h.id]=n.label
d[h.id]=c[n.id]/sum(abs(n.mismatch))*1000000000
usage_rel_to_share_of_load[h.id]=t[n.id]/1000
total_usage[h.id]=today_usage[n.id]/1000
total_usage_year[h.id]=usage_year[n.id]/1000
tot_usage+=k[h.id]
tot_usage_rel+=t[h.id]
plt.ion()
plt.show()
p1=plt.bar(np.arange(30),red_div_yellow,width=0.7,color=au.oran)
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("red_div_yellow_GW-new"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage)+str(total_usage_year)+"red_div_yellow"+str(red_div_yellow) + '\n')
np.savez("./figures/new_GW_costplots/GW-numbers"+str(mode)+str(alph)+str(ports),labels=E, Blue=blue,Green=green,Yellow=usage_rel_to_share_of_load)
savefig("./figures/new_GW_costplots/red_yellow/new_"+str(mode)+"_copper_costdiagram_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/red_yellow/new_"+str(mode)+"_constr_costdiagram_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("red_div_yellow_GW-old"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage)+str(total_usage_year)+"red_div_yellow"+str(red_div_yellow) + '\n')
np.savez("./figures/old_GW_costplots/GW-numbers"+str(mode)+str(alph)+str(ports),labels=E, Blue=blue,Green=green,Yellow=usage_rel_to_share_of_load)
savefig("./figures/old_GW_costplots/red_yellow/old_"+str(mode)+"_copper_costdiagram_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GW_costplots/red_yellow/old_"+str(mode)+"_constr_costdiagram_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
blue_div_green=blue/green
blue_div_green_avg=round(blue_div_green.sum()/(len(N)),2)
plt.ion()
plt.show()
p1=plt.bar(np.arange(30),blue_div_green,width=0.7,color=au.cafe)
plt.axhline(blue_div_green_avg,linestyle="dashed",color="k")
plt.annotate(str(blue_div_green_avg),(-0.5,blue_div_green_avg),(1,blue_div_green_avg+0.5),arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('I-factor')
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("blue_div_green_GW-new"+str(E)+"blue_div_green"+str(blue_div_green) + '\n')
savefig("./figures/new_GW_costplots/blue_div_green/new_"+str(mode)+"_copper_costdiagram_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/blue_div_green/new_"+str(mode)+"_constr_costdiagram_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("red_div_yellow_GW-old"+str(E)+"blue_div_green"+str(blue_div_green) + '\n')
savefig("./figures/old_GW_costplots/blue_div_green/old_"+str(mode)+"_copper_costdiagram_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GW_costplots/blue_div_green/old_"+str(mode)+"_constr_costdiagram_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
usage_rel_to_share_of_load_kms[h.id]=t_kms[n.id]/1000 #yellow in GWKms
Blue_GWKms[h.id]=c_kms[n.id]
Green_GWkms[h.id]=usage_year_kms[n.id]/1000
total_usage_kms[h.id]=today_usage_kms[n.id]/1000
red_div_yellow_kms[h.id]=total_usage_kms[h.id]/usage_rel_to_share_of_load_kms[h.id]
blue_div_green_kms=Blue_GWKms/Green_GWkms
blue_div_green_kms_avg=round(blue_div_green_kms.sum()/(len(N)),2)
p1=plt.bar(np.arange(30),blue_div_green_kms,width=0.7,color=au.oran)
plt.axhline(blue_div_green_kms_avg,linestyle="dashed",color="k")
plt.annotate(str(blue_div_green_kms_avg),(-0.5,blue_div_green_kms_avg),(1,blue_div_green_kms_avg+0.5),arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('I-factor')
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("blue_div_green_GWKm-new"+str(E)+"blue_div_green"+str(blue_div_green_kms) + '\n')
np.savez("./figures/new_GWKMs_costplots/GW-numbers"+str(mode)+str(alph)+str(ports),labels=E, Blue=Blue_GWKms,Green=Green_GWkms,Yellow=usage_rel_to_share_of_load_kms)
savefig("./figures/new_GWKMs_costplots/blue_div_green/new_"+str(mode)+"_copper_costdiagram_kms_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GWKMs_costplots/blue_div_green/new_"+str(mode)+"_constr_costdiagram_kms_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("blue_div_green_GWKm-old"+str(E)+"blue_div_green"+str(blue_div_green) + '\n')
np.savez("./figures/old_GWKMs_costplots/GW-numbers"+str(mode)+str(alph)+str(ports),labels=E, Blue=Blue_GWKms,Green=Green_GWkms,Yellow=usage_rel_to_share_of_load_kms)
savefig("./figures/old_GWKMs_costplots/blue_div_green/old_"+str(mode)+"_copper_costdiagram_kms_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GWKMs_costplots/blue_div_green/old_"+str(mode)+"_constr_costdiagram_kms_blue_div_green"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
p1=plt.bar(np.arange(30),red_div_yellow_kms,width=0.7,color=au.oran)
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("red_div_yellow_GWKm-new"+str(E)+str(usage_rel_to_share_of_load_kms)+str(total_usage_kms)+"red_div_yellow"+str(red_div_yellow_kms) + '\n')
savefig("./figures/new_GWKMs_costplots/red_yellow/new_"+str(mode)+"_copper_costdiagram_kms_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GWKMs_costplots/red_yellow/new_"+str(mode)+"_constr_costdiagram_kms_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("red_div_yellow_GWKm-old"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage)+str(total_usage_year)+"red_div_yellow"+str(red_div_yellow) + '\n')
savefig("./figures/old_GWKMs_costplots/red_yellow/old_"+str(mode)+"_copper_costdiagram_kms_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GWKMs_costplots/red_yellow/old_"+str(mode)+"_constr_costdiagram_kms_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.mean==loadssort[h.id]:
E[h.id]=n.label
usage_rel_to_share_of_load_cost[h.id]=t_cost[n.id] #yellow in Euros
total_usage_cost[h.id]=today_usage_cost[n.id] #red in Euros
Blue_cost[h.id]=c_cost[n.id]
Green_cost[h.id]=usage_year_cost[n.id]
#total_usage_cost_year[h.id]=usage_year_cost[n.id]
red_div_yellow_cost[h.id]=total_usage_cost[h.id]/usage_rel_to_share_of_load_cost[h.id]
p1=plt.bar(np.arange(30),red_div_yellow_cost,width=0.7,color=au.oran)
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("red_div_yellow_Euro-new"+str(E)+str(usage_rel_to_share_of_load_cost)+str(total_usage_cost)+"red_div_yellow"+str(red_div_yellow_cost) + '\n')
savefig("./figures/new_TEuro_costplots/red_yellow/new_"+str(mode)+"copper_costdiagram_cost_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
np.save("./figures/new_TEuro_costplots/New_costs"+str(mode)+str(ports)+"alpha="+str(alph),Blue_cost)
np.save("./figures/new_TEuro_costplots/New_costs_old_order"+str(mode)+str(ports)+"alpha="+str(alph),c_cost)
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_TEuro_costplots/red_yellow/new_"+str(mode)+"_constr_costdiagram_cost_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
np.save("./figures/old_TEuro_costplots/Old_costs"+str(mode)+str(ports)+"alpha="+str(alph),Blue_cost)
np.save("./figures/old_TEuro_costplots/Old_costs_old_order"+str(mode)+str(ports)+"alpha="+str(alph),c_cost)
file.write("red_div_yellow_GWKm-old"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage)+str(total_usage_year)+"red_div_yellow"+str(red_div_yellow) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/red_yellow/old_"+str(mode)+"_copper_costdiagram_cost_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/red_yellow/old_"+str(mode)+"_constr_costdiagram_cost_red_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
usage_rel_to_share_of_load_kms[h.id]=t_kms[n.id]/1000
#total_usage_kms[h.id]=today_usage_kms[n.id]/1000
total_usage_kms_year[h.id]=usage_year_kms[n.id]/1000
green_div_yellow_kms[h.id]=total_usage_kms_year[h.id]/usage_rel_to_share_of_load_kms[h.id]
green_div_yellow_kms_avg=round(green_div_yellow_kms.sum()/(len(N)),2)
p1=plt.bar(np.arange(30),green_div_yellow_kms,width=0.7,color=gyell)
plt.axhline(green_div_yellow_kms_avg,linestyle="dashed",color="k")
plt.annotate(str(green_div_yellow_kms_avg),(-0.5,green_div_yellow_kms_avg),(1,green_div_yellow_kms_avg+0.5),arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('I-factor')
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
file.write("green_div_yellow_GWKm-new"+str(E)+str(usage_rel_to_share_of_load_kms)+str(total_usage_kms_year)+"green_div_yellow"+str(green_div_yellow_kms) + '\n')
savefig("./figures/new_GWKMs_costplots/green_yellow/new_"+str(mode)+"_copper_costdiagram_kms_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GWKMs_costplots/green_yellow/new_"+str(mode)+"_constr_costdiagram_kms_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("green_div_yellow_GWKm-old"+str(E)+str(usage_rel_to_share_of_load_kms)+str(total_usage_kms_year)+"green_div_yellow"+str(green_div_yellow_kms) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_GWKMs_costplots/green_yellow/old_"+str(mode)+"_copper_costdiagram_kms_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GWKMs_costplots/green_yellow/old_"+str(mode)+"_constr_costdiagram_kms_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
usage_rel_to_share_of_load[h.id]=t[n.id]/1000
total_usage_year[h.id]=usage_year[n.id]/1000
green_div_yellow[h.id]=total_usage_year[h.id]/usage_rel_to_share_of_load[h.id]
green_div_yellow_avg=round(green_div_yellow.sum()/(len(N)),2)
p1=plt.bar(np.arange(30),green_div_yellow,width=0.7,color=gyell)
plt.axhline(green_div_yellow_avg,linestyle="dashed",color="k")
plt.annotate(str(green_div_yellow_avg),(-0.5,green_div_yellow_avg),(1,green_div_yellow_avg+0.5),arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('I-factor')
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
file.write("green_div_yellow_GWKm-new"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage_year)+"green_div_yellow"+str(green_div_yellow) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/green_yellow/new_"+str(mode)+"_copper_costdiagram_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/green_yellow/new_"+str(mode)+"_constr_costdiagram_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("green_div_yellow_GWKm-old"+str(E)+str(usage_rel_to_share_of_load)+str(total_usage_year)+"green_div_yellow"+str(green_div_yellow) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_GW_costplots/green_yellow/old_"+str(mode)+"_copper_costdiagram_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GW_costplots/green_yellow/old_"+str(mode)+"_constr_costdiagram_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.mean==loadssort[h.id]:
E[h.id]==n.label
usage_rel_to_share_of_load_cost[h.id]=t_cost[n.id]
#total_usage_cost[h.id]=today_usage_cost[n.id]
total_usage_cost_year[h.id]=usage_year_cost[n.id]
green_div_yellow_cost[h.id]=total_usage_cost_year[h.id]/usage_rel_to_share_of_load_cost[h.id]
p1=plt.bar(np.arange(30),green_div_yellow,width=0.7,color=gyell)
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
file.write("green_div_yellow_cost-new"+str(E)+str(usage_rel_to_share_of_load_cost)+str(total_usage_cost_year)+"green_div_yellow"+str(green_div_yellow_cost) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_TEuro_costplots/green_yellow/new_"+str(mode)+"linear_copper_costdiagram_cost_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_TEuro_costplots/green_yellow/new_"+str(mode)+"_constr_costdiagram_cost_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("green_div_yellow_cost-old"+str(E)+str(usage_rel_to_share_of_load_cost)+str(total_usage_cost_year)+"green_div_yellow"+str(green_div_yellow_cost) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/green_yellow/old_"+str(mode)+"_copper_costdiagram_cost_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/green_yellow/old_"+str(mode)+"_constr_costdiagram_cost_green_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
blue_div_yellow[h.id]=c[n.id]/t[n.id]*1000
blue_div_yellow_avg=round(blue_div_yellow.sum()/(len(N)),2)
p1=plt.bar(np.arange(30),blue_div_yellow,width=0.7,color=au.kelp)
plt.axhline(blue_div_yellow_avg,linestyle="dashed",color="k")
plt.annotate(str(blue_div_yellow_avg),(-0.5,blue_div_yellow_avg),(1,blue_div_yellow_avg+0.5),arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=.2"))
plt.ylabel('I-factor')
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
file.write("blue_div_yellow-new"+str(E)+str(blue_div_yellow) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/blue_div_yellow/new_"+str(mode)+"_copper_costdiagram_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_GW_costplots/blue_div_yellow/new_"+str(mode)+"_constr_costdiagram_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("blue_div_yellow-old"+str(E)+str(blue_div_yellow) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_GW_costplots/blue_div_yellow/old_"+str(mode)+"_copper_costdiagram_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_GW_costplots/blue_div_yellow/old_"+str(mode)+"_constr_costdiagram_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
blue_div_yellow_cost[h.id]=c_cost[n.id]/t_cost[n.id]
p1=plt.bar(np.arange(30),blue_div_yellow_cost,width=0.7,color=au.kelp)
plt.xlim(j[0]-0.5)
plt.xticks(j+0.4,E,rotation=90)
if new:
if copper:
file.write("blue_div_yellow_Euro-new"+str(E)+str(blue_div_yellow_cost) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_TEuro_costplots/blue_div_yellow/new_"+str(mode)+"_copper_costdiagram_cost_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,new,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/new_TEuro_costplots/blue_div_yellow/new_"+str(mode)+"_constr_costdiagram_cost_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
if copper:
file.write("blue_div_yellow_Euro-old"+str(E)+str(blue_div_yellow_cost) + '\n')
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,copper,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
#plt.text(0.03,0.03, "old linear", fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/blue_div_yellow/old_"+str(mode)+"_copper_costdiagram_cost_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
else:
#plt.text(0.03,0.03, "cost rel to meanload,old,linear,constr,"+str(ports)+"ports,alpha="+str(alph), fontsize=15, color='w')
savefig("./figures/old_TEuro_costplots/blue_div_yellow/old_"+str(mode)+"_constr_costdiagram_cost_blue_div_yell"+str(ports)+"ports_alpha="+str(alph)+".pdf")
close()
for h in N:
for n in N:
if n.label==E[h.id]:
blue_div_yellow_kms[h.id]=c_kms[n.id]/t_kms[n.id]*1000