def MC(variables_techno, variables_interv, CF, CF_categories_name, iterations,
UP_list, system_number, path, systems, progress):
from numpy import log, random, sqrt, array, sign, exp, transpose, float32, max, matrix
from numpy.linalg import solve
from scipy.sparse import lil_matrix, identity
from fix_inverse import fix_inverse
from scipy.linalg import inv
from time import time, sleep
import os
import csv
from Tkinter import Tk, IntVar
import ttk
import threading
def matrices(UP_list, CF, variables_techno, variables_interv):
MA = lil_matrix((len(UP_list), len(UP_list)))
MB = lil_matrix((CF.shape[1], len(UP_list)))
if len(variables_techno["lognormal"]) > 0:
MA[array(variables_techno["lognormal"][:, 0], int),
array(variables_techno["lognormal"][:, 1], int)] = sign(
variables_techno["lognormal"][:, 2]) * random.lognormal(
array(variables_techno["lognormal"][:, 3], float32),
array(variables_techno["lognormal"][:, 4], float32))
if len(variables_techno["normal"]) > 0:
MA[array(variables_techno["normal"][:, 0], int),
array(variables_techno["normal"][:, 1], int)] = random.normal(
array(variables_techno["normal"][:, 2], float32),
array(variables_techno["normal"][:, 3], float32))
if len(variables_techno["triangle"]) > 0:
MA[array(variables_techno["triangle"][:, 0], int),
array(variables_techno["triangle"][:, 1], int)] = sign(
variables_techno["triangle"][:, 2]) * random.triangular(
array(variables_techno["triangle"][:, 3], float32),
abs(array(variables_techno["triangle"][:, 2], float32)),
array(variables_techno["triangle"][:, 4], float32))
if len(variables_techno["deterministe"]) > 0:
MA[array(variables_techno["deterministe"][:, 0], int),
array(variables_techno["deterministe"][:, 1], int
)] = variables_techno["deterministe"][:, 2]
if len(variables_interv["lognormal"]) > 0:
MB[array(variables_interv["lognormal"][:, 0], int),
array(variables_interv["lognormal"][:, 1], int)] = sign(
variables_interv["lognormal"][:, 2]) * random.lognormal(
array(variables_interv["lognormal"][:, 3], float32),
array(variables_interv["lognormal"][:, 4], float32))
if len(variables_interv["normal"]) > 0:
MB[array(variables_interv["normal"][:, 0], int),
array(variables_interv["normal"][:, 1], int)] = random.normal(
array(variables_interv["normal"][:, 2], float32),
array(variables_interv["normal"][:, 3], float32))
if len(variables_interv["triangle"]) > 0:
MB[array(variables_interv["triangle"][:, 0], int),
array(variables_interv["triangle"][:, 1], int)] = sign(
variables_interv["triangle"][:, 2]) * random.triangular(
array(variables_interv["triangle"][:, 3], float32),
abs(array(variables_interv["triangle"][:, 2], float32)),
array(variables_interv["triangle"][:, 4], float32))
if len(variables_interv["agregated"]) > 0:
MB[array(variables_interv["agregated"][:, 0], int),
array(
variables_interv["agregated"][:, 1], int
)] = variables_interv["agregated"][:, 2] * random.lognormal(
array(variables_interv["agregated"][:, 3], float32),
array(variables_interv["agregated"][:, 4], float32))
if len(variables_interv["deterministe"]) > 0:
MB[array(variables_interv["deterministe"][:, 0], int),
array(variables_interv["deterministe"][:, 1], int
)] = variables_interv["deterministe"][:, 2]
return MA, MB
if system_number == "all":
f = identity(len(UP_list))
else:
f = lil_matrix((len(UP_list), 1))
f[0, 0] = 1
for k in range(iterations):
progress.set((k + 1) * 100 / iterations)
MA, MB = matrices(UP_list, CF, variables_techno, variables_interv)
if system_number == "all":
MAinv = inv(MA.todense())
Z = (identity(len(MA.todense())) - MA)
MAinv = fix_inverse(Z, MAinv)
impact = CF * MB * MAinv * f
for sys in range(impact.shape[1]):
with open(os.path.join(path,
str(sys) + ".csv"), "ab") as fichier:
csv_results = csv.writer(fichier)
csv_results.writerow([k] + list(impact[:, sys]))
#print '\r Progress of Monte-Carlo simulations : '+str(int(100*(k+1)/iterations))+"% ",
else:
s = solve(MA.todense(), f.todense())
impact = array(CF * MB * s)[:, 0]
with open(os.path.join(path,
str(system_number) + ".csv"),
"ab") as fichier:
csv_results = csv.writer(fichier)
csv_results.writerow([k] + impact)
def calculExecution():
'''
Construction of technology and intervention matrices with uncertainties informations
Construction of Caracterisation facteurs matrix
'''
runButton.config(state="disabled")
global pb_hD
system_filename = os.path.join(path,"programme","..","databases",database) #export from Simapro
infoFrame1=Frame(informationsFrame)
infoFrame1.pack()
Label(infoFrame1,text="Reading the database and constructing the matrices...").pack(side=LEFT)
(system_meta_info, UP_meta_info, UP_list, EF_list,all_flow, technology_matrix,
intervention_matrix, CF_matrices, CF_categories, CF_units, EF_unit,
unit_converter, infrastructure_rescale, uncertainty_info) = SimaPro_reader(system_filename, impact_method)
Label(infoFrame1,text="Done").pack()
CF_matrix = CF_matrices[impact_method]
CF_categories=CF_categories[impact_method]
CF_units=CF_units[impact_method]
CF_categories_name=[ re.sub("\W","_",cat) for cat in CF_categories]
EF_by_number = {}
for (compartment, substance, subcompartment) in EF_list:
EF = [compartment, substance, subcompartment]
EF_number = EF_list.index(EF)
EF = (compartment, substance, subcompartment)
EF_by_number[EF_number] = EF
###Transformation of the technology and intervention matrices for testing the fonctionnality of the algorithme.
infoFrame2=Frame(informationsFrame)
infoFrame2.pack()
Label(infoFrame2,text="transformating matrices for tests ...").pack(side=LEFT)
(technology_matrix,intervention_matrix,uncertainty_info, UP_list, CF_transformed)=transformation_matrices(technology_matrix,intervention_matrix,uncertainty_info, UP_list, CF_matrix)
Label(infoFrame2,text="Done").pack()
print_results(path+project_name, project_name, UP_list, EF_list, database, impact_method, CF_categories, CF_units, iterations, disaggregation_criterion, uncertainty_info, CF_matrix)
###Calculation of the determinists scores
infoFrame3=Frame(informationsFrame)
infoFrame3.pack()
Label(infoFrame3,text="Calculating deterministic scores ...").pack(side=LEFT)
t0=time.time()
#inverse_technology_matrix=spsolve(technology_matrix, identity(technology_matrix.shape[0]))
inverse_technology_matrix = inv(technology_matrix.todense())
tinv=time.time()-t0
#print "temps d'inversion : "+str(tinv)
Z = (identity(len(technology_matrix.todense())) - technology_matrix)
inverse_technology_matrix = fix_inverse(Z, inverse_technology_matrix)
intensity_matrix = matrix(intervention_matrix.dot(inverse_technology_matrix))
all_system_scores, all_unit_scores = calculate_all_scores(identity(len(technology_matrix.todense())),intensity_matrix, intervention_matrix, CF_matrix)
Label(infoFrame3,text="Done").pack()
if correlatedMC.get():
###Monte-Carlo in the correlated case and storage of the matrices (laws'parameters)
Label(informationsFrame,text="Uncertainty analysis under a fully-correlated assumption...").pack()
pb_hD.pack()
essai=0
while 1:
try:
os.mkdir(os.path.join(path,project_name,"correlated_impacts"+str(essai)))
break
except:
essai+=1
(variables_technologique, variables_intervention)=MC_correlated_preparation(technology_matrix, intervention_matrix, uncertainty_info['technology'], uncertainty_info['intervention'])
MC(variables_technologique, variables_intervention, CF_matrix, CF_categories_name, iterations, UP_list, "all", os.path.join(path,project_name,"correlated_impacts"+str(essai)), [], progress)
infoFrame4=Frame(informationsFrame)
infoFrame4.pack()
Label(infoFrame4,text="Printing parameters and covariances ...").pack(side=LEFT)
sigma_correlated, mu_correlated, sign_correlated=calcul_parameters(UP_list, os.path.join(path,project_name,"correlated_impacts"), len(CF_categories))
results_cor = csv.writer(open(os.path.join(path,project_name,"Monte-Carlo_results_correle.csv"), "wb"))
results_cor.writerow(["index", "processus"]+["impact "+category for category in CF_categories]+["sign "+category for category in CF_categories]+["mu "+category for category in CF_categories]+["sigma "+category for category in CF_categories])
for up in range(len(UP_list)-4):
results_cor.writerow([up, UP_list[up]]+[impact[0] for impact in all_system_scores[:,up].tolist()]+[ssign[0] for ssign in sign_correlated[:,up].tolist()]+[mmu[0] for mmu in mu_correlated[:,up].tolist()]+[sigma[0] for sigma in sigma_correlated[:,up].tolist()])
###Calculation of the cariance-covariance matrices
calcul_vcv(UP_list, impact_method, CF_categories_name, os.path.join(path,project_name))
Label(infoFrame4,text="Done").pack()
if nocorrelatedMC.get():
sigma_correlated, mu_correlated, sign_correlated=calcul_parameters(UP_list, os.path.join(path,project_name,"correlated_impacts"), len(CF_categories))
essai=0
while 1:
try:
os.mkdir(os.path.join(path,project_name,"nocorrelated_impacts"+str(essai)))
break
except:
essai+=1
###Monte-Carlo in the correlated case and storage of the matrices (laws'parameters)
Label(informationsFrame,text="Uncertainty analysis under a fully-uncorrelated assumption...").pack()
full_results_UP = {}
full_results_EF = {}
level_reached = {}
system_scores = {}
child_list = {}
score_list_EF = {}
coefficient_list = {}
link_UP_EF_full_result = {}
systems=[]
for proc in UP_list:
systems.append({proc:1})
processRunned=Label(informationsFrame,text="")
processRunned.pack()
for system_number in range(nocorrBegin.get(),max(nocorrEnd.get(),len(UP_list)-4)): #disaggregation for every system
processRunned.config(text="Process "+str(system_number))
pb_hD.pack()
full_results_UP = {}
full_results_EF = {}
level_reached = {}
system_scores = {}
child_list = {}
score_list_EF = {}
coefficient_list = {}
link_UP_EF_full_result = {}
final_demand_vector = build_final_demand_vector(systems[system_number], UP_list)
start_time = time.time()
full_results_UP = {}
level_reached = {}
#Desagregation of the system
full_results_UP, level_reached, system_scores = systematic_disaggregation_UP(disaggregation_criterion,full_results_UP, level_reached, system_scores,UP_meta_info, UP_list, EF_list, technology_matrix, intervention_matrix, CF_matrix, CF_categories, EF_unit, uncertainty_info, intensity_matrix, Z, all_system_scores, all_unit_scores, impact_method, final_demand_vector, system_number,systems)
UP_list_desag=construct_UP_list_desag(full_results_UP,UP_list)
tree(UP_list_desag, UP_meta_info, impact_method, CF_categories, all_system_scores, all_unit_scores, CF_units, os.path.join(path,project_name,"trees"))
(variables_technologique, variables_intervention)=MC_nocorrelated_preparation(technology_matrix, intervention_matrix, uncertainty_info['technology'], uncertainty_info['intervention'], UP_list, UP_list_desag, mu_correlated, sign_correlated, sigma_correlated)
MC(variables_technologique, variables_intervention, CF_transformed, CF_categories_name, iterations, UP_list_desag, system_number, os.path.join(path,project_name,"nocorrelated_impacts"+str(essai)), systems, progress)
infoFrame5=Frame(informationsFrame)
infoFrame5.pack()
Label(infoFrame5,text="Printing parameters ...").pack(side=LEFT)
sigma_nocorrelated, mu_nocorrelated, sign_nocorrelated=calcul_parameters(UP_list[:-4], os.path.join(path,project_name,"correlated_impacts"), len(CF_categories))
results_nocor = csv.writer(open(os.path.join(path,project_name,"Monte-Carlo_results_nocorrele.csv"), "wb"))
results_nocor.writerow(["index", "processus"]+["impact "+category for category in CF_categories]+["sign "+category for category in CF_categories]+["mu "+category for category in CF_categories]+["sigma "+category for category in CF_categories])
for up in range(len(UP_list)-4):
results_nocor.writerow([up, UP_list[up]]+[impact[0] for impact in all_system_scores[:,up].tolist()]+[ssign[0] for ssign in sign_nocorrelated[:,up].tolist()]+[mmu[0] for mmu in mu_nocorrelated[:,up].tolist()]+[sigma[0] for sigma in sigma_nocorrelated[:,up].tolist()])
Label(infoFrame5,text="Done").pack(side=LEFT)
def calculExecution():
'''
Construction of technology and intervention matrices with uncertainties informations
Construction of Caracterisation facteurs matrix
'''
runButton.config(state="disabled")
global pb_hD
system_filename = os.path.join(path, "programme", "..", "databases",
database) #export from Simapro
infoFrame1 = Frame(informationsFrame)
infoFrame1.pack()
Label(infoFrame1,
text="Reading the database and constructing the matrices...").pack(
side=LEFT)
(system_meta_info, UP_meta_info, UP_list, EF_list, all_flow,
technology_matrix, intervention_matrix, CF_matrices, CF_categories,
CF_units, EF_unit, unit_converter, infrastructure_rescale,
uncertainty_info) = SimaPro_reader(system_filename, impact_method)
Label(infoFrame1, text="Done").pack()
CF_matrix = CF_matrices[impact_method]
CF_categories = CF_categories[impact_method]
CF_units = CF_units[impact_method]
CF_categories_name = [re.sub("\W", "_", cat) for cat in CF_categories]
EF_by_number = {}
for (compartment, substance, subcompartment) in EF_list:
EF = [compartment, substance, subcompartment]
EF_number = EF_list.index(EF)
EF = (compartment, substance, subcompartment)
EF_by_number[EF_number] = EF
###Transformation of the technology and intervention matrices for testing the fonctionnality of the algorithme.
infoFrame2 = Frame(informationsFrame)
infoFrame2.pack()
Label(infoFrame2,
text="transformating matrices for tests ...").pack(side=LEFT)
(technology_matrix, intervention_matrix, uncertainty_info, UP_list,
CF_transformed) = transformation_matrices(technology_matrix,
intervention_matrix,
uncertainty_info, UP_list,
CF_matrix)
Label(infoFrame2, text="Done").pack()
print_results(path + project_name, project_name, UP_list, EF_list,
database, impact_method, CF_categories, CF_units, iterations,
disaggregation_criterion, uncertainty_info, CF_matrix)
###Calculation of the determinists scores
infoFrame3 = Frame(informationsFrame)
infoFrame3.pack()
Label(infoFrame3,
text="Calculating deterministic scores ...").pack(side=LEFT)
t0 = time.time()
#inverse_technology_matrix=spsolve(technology_matrix, identity(technology_matrix.shape[0]))
inverse_technology_matrix = inv(technology_matrix.todense())
tinv = time.time() - t0
#print "temps d'inversion : "+str(tinv)
Z = (identity(len(technology_matrix.todense())) - technology_matrix)
inverse_technology_matrix = fix_inverse(Z, inverse_technology_matrix)
intensity_matrix = matrix(
intervention_matrix.dot(inverse_technology_matrix))
all_system_scores, all_unit_scores = calculate_all_scores(
identity(len(technology_matrix.todense())), intensity_matrix,
intervention_matrix, CF_matrix)
Label(infoFrame3, text="Done").pack()
if correlatedMC.get():
###Monte-Carlo in the correlated case and storage of the matrices (laws'parameters)
Label(
informationsFrame,
text="Uncertainty analysis under a fully-correlated assumption..."
).pack()
pb_hD.pack()
essai = 0
while 1:
try:
os.mkdir(
os.path.join(path, project_name,
"correlated_impacts" + str(essai)))
break
except:
essai += 1
(variables_technologique,
variables_intervention) = MC_correlated_preparation(
technology_matrix, intervention_matrix,
uncertainty_info['technology'], uncertainty_info['intervention'])
MC(variables_technologique, variables_intervention, CF_matrix,
CF_categories_name, iterations, UP_list, "all",
os.path.join(path, project_name,
"correlated_impacts" + str(essai)), [], progress)
infoFrame4 = Frame(informationsFrame)
infoFrame4.pack()
Label(infoFrame4,
text="Printing parameters and covariances ...").pack(side=LEFT)
sigma_correlated, mu_correlated, sign_correlated = calcul_parameters(
UP_list, os.path.join(path, project_name, "correlated_impacts"),
len(CF_categories))
results_cor = csv.writer(
open(
os.path.join(path, project_name,
"Monte-Carlo_results_correle.csv"), "wb"))
results_cor.writerow(
["index", "processus"] +
["impact " + category for category in CF_categories] +
["sign " + category for category in CF_categories] +
["mu " + category for category in CF_categories] +
["sigma " + category for category in CF_categories])
for up in range(len(UP_list) - 4):
results_cor.writerow(
[up, UP_list[up]] +
[impact[0] for impact in all_system_scores[:, up].tolist()] +
[ssign[0] for ssign in sign_correlated[:, up].tolist()] +
[mmu[0] for mmu in mu_correlated[:, up].tolist()] +
[sigma[0] for sigma in sigma_correlated[:, up].tolist()])
###Calculation of the cariance-covariance matrices
calcul_vcv(UP_list, impact_method, CF_categories_name,
os.path.join(path, project_name))
Label(infoFrame4, text="Done").pack()
if nocorrelatedMC.get():
sigma_correlated, mu_correlated, sign_correlated = calcul_parameters(
UP_list, os.path.join(path, project_name, "correlated_impacts"),
len(CF_categories))
essai = 0
while 1:
try:
os.mkdir(
os.path.join(path, project_name,
"nocorrelated_impacts" + str(essai)))
break
except:
essai += 1
###Monte-Carlo in the correlated case and storage of the matrices (laws'parameters)
Label(
informationsFrame,
text="Uncertainty analysis under a fully-uncorrelated assumption..."
).pack()
full_results_UP = {}
full_results_EF = {}
level_reached = {}
system_scores = {}
child_list = {}
score_list_EF = {}
coefficient_list = {}
link_UP_EF_full_result = {}
systems = []
for proc in UP_list:
systems.append({proc: 1})
processRunned = Label(informationsFrame, text="")
processRunned.pack()
for system_number in range(nocorrBegin.get(),
max(nocorrEnd.get(),
len(UP_list) -
4)): #disaggregation for every system
processRunned.config(text="Process " + str(system_number))
pb_hD.pack()
full_results_UP = {}
full_results_EF = {}
level_reached = {}
system_scores = {}
child_list = {}
score_list_EF = {}
coefficient_list = {}
link_UP_EF_full_result = {}
final_demand_vector = build_final_demand_vector(
systems[system_number], UP_list)
start_time = time.time()
full_results_UP = {}
level_reached = {}
#Desagregation of the system
full_results_UP, level_reached, system_scores = systematic_disaggregation_UP(
disaggregation_criterion, full_results_UP, level_reached,
system_scores, UP_meta_info, UP_list, EF_list,
technology_matrix, intervention_matrix, CF_matrix,
CF_categories, EF_unit, uncertainty_info, intensity_matrix, Z,
all_system_scores, all_unit_scores, impact_method,
final_demand_vector, system_number, systems)
UP_list_desag = construct_UP_list_desag(full_results_UP, UP_list)
tree(UP_list_desag, UP_meta_info, impact_method, CF_categories,
all_system_scores, all_unit_scores, CF_units,
os.path.join(path, project_name, "trees"))
(variables_technologique,
variables_intervention) = MC_nocorrelated_preparation(
technology_matrix, intervention_matrix,
uncertainty_info['technology'],
uncertainty_info['intervention'], UP_list, UP_list_desag,
mu_correlated, sign_correlated, sigma_correlated)
MC(
variables_technologique, variables_intervention,
CF_transformed, CF_categories_name, iterations, UP_list_desag,
system_number,
os.path.join(path, project_name,
"nocorrelated_impacts" + str(essai)), systems,
progress)
infoFrame5 = Frame(informationsFrame)
infoFrame5.pack()
Label(infoFrame5, text="Printing parameters ...").pack(side=LEFT)
sigma_nocorrelated, mu_nocorrelated, sign_nocorrelated = calcul_parameters(
UP_list[:-4], os.path.join(path,
project_name, "correlated_impacts"),
len(CF_categories))
results_nocor = csv.writer(
open(
os.path.join(path, project_name,
"Monte-Carlo_results_nocorrele.csv"), "wb"))
results_nocor.writerow(
["index", "processus"] +
["impact " + category for category in CF_categories] +
["sign " + category for category in CF_categories] +
["mu " + category for category in CF_categories] +
["sigma " + category for category in CF_categories])
for up in range(len(UP_list) - 4):
results_nocor.writerow(
[up, UP_list[up]] +
[impact[0] for impact in all_system_scores[:, up].tolist()] +
[ssign[0] for ssign in sign_nocorrelated[:, up].tolist()] +
[mmu[0] for mmu in mu_nocorrelated[:, up].tolist()] +
[sigma[0] for sigma in sigma_nocorrelated[:, up].tolist()])
Label(infoFrame5, text="Done").pack(side=LEFT)
def MC(variables_techno, variables_interv, CF, CF_categories_name, iterations, UP_list, system_number, path, systems, progress): from numpy import log, random, sqrt, array, sign, exp, transpose, float32, max, matrix from numpy.linalg import solve from scipy.sparse import lil_matrix, identity from fix_inverse import fix_inverse from scipy.linalg import inv from time import time,sleep import os import csv from Tkinter import Tk, IntVar import ttk import threading def matrices(UP_list,CF, variables_techno, variables_interv): MA=lil_matrix((len(UP_list),len(UP_list))) MB=lil_matrix((CF.shape[1],len(UP_list))) if len(variables_techno["lognormal"])>0: MA[array(variables_techno["lognormal"][:,0],int),array(variables_techno["lognormal"][:,1],int)] =sign(variables_techno["lognormal"][:,2])*random.lognormal(array(variables_techno["lognormal"][:,3],float32),array(variables_techno["lognormal"][:,4],float32)) if len(variables_techno["normal"])>0: MA[array(variables_techno["normal"][:,0],int),array(variables_techno["normal"][:,1],int)] = random.normal(array(variables_techno["normal"][:,2],float32),array(variables_techno["normal"][:,3],float32)) if len(variables_techno["triangle"])>0: MA[array(variables_techno["triangle"][:,0],int),array(variables_techno["triangle"][:,1],int)] = sign(variables_techno["triangle"][:,2])*random.triangular(array(variables_techno["triangle"][:,3],float32),abs(array(variables_techno["triangle"][:,2],float32)),array(variables_techno["triangle"][:,4],float32)) if len(variables_techno["deterministe"])>0: MA[array(variables_techno["deterministe"][:,0],int),array(variables_techno["deterministe"][:,1],int)] = variables_techno["deterministe"][:,2] if len(variables_interv["lognormal"])>0: MB[array(variables_interv["lognormal"][:,0],int),array(variables_interv["lognormal"][:,1],int)] =sign(variables_interv["lognormal"][:,2])*random.lognormal(array(variables_interv["lognormal"][:,3],float32),array(variables_interv["lognormal"][:,4],float32)) if len(variables_interv["normal"])>0: MB[array(variables_interv["normal"][:,0],int),array(variables_interv["normal"][:,1],int)] = random.normal(array(variables_interv["normal"][:,2],float32),array(variables_interv["normal"][:,3],float32)) if len(variables_interv["triangle"])>0: MB[array(variables_interv["triangle"][:,0],int),array(variables_interv["triangle"][:,1],int)] = sign(variables_interv["triangle"][:,2])*random.triangular(array(variables_interv["triangle"][:,3],float32),abs(array(variables_interv["triangle"][:,2],float32)),array(variables_interv["triangle"][:,4],float32)) if len(variables_interv["agregated"])>0: MB[array(variables_interv["agregated"][:,0],int),array(variables_interv["agregated"][:,1],int)] = variables_interv["agregated"][:,2]*random.lognormal(array(variables_interv["agregated"][:,3],float32),array(variables_interv["agregated"][:,4],float32)) if len(variables_interv["deterministe"])>0: MB[array(variables_interv["deterministe"][:,0],int),array(variables_interv["deterministe"][:,1],int)] = variables_interv["deterministe"][:,2] return MA, MB if system_number=="all": f=identity(len(UP_list)) else: f=lil_matrix((len(UP_list),1)) f[0,0]=1 for k in range(iterations): progress.set((k+1)*100/iterations) MA,MB=matrices(UP_list,CF, variables_techno, variables_interv) if system_number=="all": MAinv=inv(MA.todense()) Z = (identity(len(MA.todense())) - MA) MAinv = fix_inverse(Z, MAinv) impact=CF * MB * MAinv * f for sys in range(impact.shape[1]): with open(os.path.join(path,str(sys)+".csv"), "ab") as fichier: csv_results = csv.writer(fichier) csv_results.writerow([k+1]+list(impact[:,sys])) #print '\r Progress of Monte-Carlo simulations : '+str(int(100*(k+1)/iterations))+"% ", else: s=solve(MA.todense(),f.todense()) impact=array(CF*MB*s)[:,0] with open(os.path.join(path,str(system_number)+".csv"), "ab") as fichier: csv_results = csv.writer(fichier) csv_results.writerow([k+1]+list(impact))
