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KS.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 11 20:39:47 2021
@author: shahe
"""
import csv
import numpy as np
import subprocess
import pandas as pd
import os
import matplotlib.pyplot as plt
import time
def generate_KS_dGDP_time_series(param_vector, length_time_series, parameter_names = [], seed_for_KS = None):
'''
inputs:
param_vector - 58 long, names provided here, to prevent changing current calibration code.
length_time_series
output:
time-series of differences in GDP, from KS 0.1 model.
'''
# Pseudocode
# Take param_vector, and write csv with it, newconf.csv
# Needs to account for variable number of params
#print (parameter_names)
parameter_names_copy = parameter_names.copy()
parameter_names_copy.insert(0, 'Par')
param_vector = np.append(param_vector, seed_for_KS)
print (f'param_vector in KS = {param_vector}')
#['Par', 'Crec', 'gG', 'mLim', 'tr', 'Lambda', 'Lambda0', 'r', 'L1rdMax', 'L1shortMax', 'NW10', 'Phi3', 'Phi4', 'alpha1', 'beta1', 'alpha2', 'beta2', 'd1', 'gamma', 'm1', 'mu1', 'nu', 'x1inf', 'x1sup', 'x5', 'xi', 'zeta1', 'zeta2', 'NW20', 'Phi1', 'Phi2', 'chi', 'd2', 'e0', 'e1', 'e2', 'e3', 'e4', 'e5', 'e6', 'e7', 'e8', 'f2min', 'iota', 'kappaMax', 'kappaMin', 'm2', 'mu20', 'omega1', 'omega2', 'u', 'upsilon', 'delta', 'phi', 'psi1', 'psi2', 'psi3', 'w0min']
#parameter_names = ['Par', 'chi']
#chi_value = param_vector[30]
#param_vector = np.array([chi_value])
param_values_list = list(param_vector)
param_values_list.insert(0, 'Cfg1')
rows = zip(parameter_names_copy, param_values_list)
row_list = []
row_list.append(parameter_names_copy)
row_list.append(param_values_list)
#print (f'rows = {rows}')
with open('nextconf.csv', 'w', newline='') as file:
writer = csv.writer(file)
for row in rows:
#print (f'row = {row}')
writer.writerow(row)
print ('nextconf.csv writing complete')
# Change benchmark with newconf.csv:
#os.system("PATH C:\\Users\shahe\\LSD\\gnu\\bin;%PATH% & lsdNW -t -z -f Benchmark_gdp_outputs.lsd")
#os.system("PATH C:\\Users\shahe\\LSD\\gnu\\bin;%PATH% & lsdNW -t -z -f nextconf.lsd")
#os.system("PATH C:\\Users\shahe\\LSD\\gnu\\bin;%PATH% & C:\\Users\\shahe\\LSD\\lwi\\lsd_confgen -f Benchmark_gdp_outputs_16_march_2021.lsd -c nextconf.csv -o nextconf & lsdNW -t -z -f nextconf.lsd")
os.system("PATH C:\\Users\shahe\\LSD\\gnu\\bin;%PATH% & C:\\Users\\shahe\\LSD\\lwi\\lsd_confgen -f Benchmark_gdp_outputs_16_march_2021.lsd -c nextconf.csv -o nextconf & lsdNW -c 1 -t -z -f nextconf.lsd")
print ('os command executed')
# Read in resultant csv file
output_file_path = 'nextconf_' + str(seed_for_KS) + '.csv'
print (f'output_file_path= {output_file_path}')
output_data = pd.read_csv(output_file_path)
print ('csv read complete')
#Isolate dGDP time-series, return as numpy array
dGDP_data = output_data['dGDP'].values
#print (f'dGDP_data = {dGDP_data}')
#GDP_data = output_data['GDP'].values
#print (f'GDP_data = {GDP_data}')
#plt.figure()
#plt.plot(GDP_data)
#timestr = time.strftime("%Y%m%d-%H%M%S")
#plt.savefig(r'C:\Users\shahe\OneDrive\Documents\Utrecht_19_20\Thesis\Numerical_Experiments\Figures\KS\GDP_KS_' + timestr + r'.pdf', format = 'pdf', bbox_inches='tight')
#plt.close()
print ('entire KS function complete')
#print (f'dGDP_data = {dGDP_data}')
return dGDP_data
def return_all_KS_init_arrays(indices_params_to_vary):
n_country_params = 1
# [Name, lower_bound, init, upper_bound]
tr_info = [r'$tr$', 0.0, 0.1, 0.3]
country_parameter_names = [tr_info[0]]
lower_bounds_country_params = np.array([tr_info[1]])
init_values_country_params = np.array([tr_info[2]])
upper_bounds_country_params = np.array([tr_info[3]])
n_financial_market_params = 2
Lambda_info = [r'$Lambda$', 0.01, 2.0, 4.0]
r_info = [r'$r$', 0.005, 0.01, 0.05]
financial_market_parameter_names = [Lambda_info[0], r_info[0]]
lower_bounds_financial_market_params = np.array([Lambda_info[1], r_info[1]])
init_values_financial_market_params = np.array([Lambda_info[2], r_info[2]])
upper_bounds_financial_market_params = np.array([Lambda_info[3], r_info[3]])
n_capital_market_params = 16
NW10_info = [r'$NW10$', 200, 1000, 5000]
Phi3_info = [r'$Phi3$', 0.0, 0.1, 0.50]
Phi4_info = [r'$Phi4$', 0.50, 0.9, 1.0]
alpha1_info = [r'$alpha1$', 1.0, 3.0, 5.0]
beta1_info = [r'$beta1$', 1.0, 3.0, 5.0]
alpha2_info = [r'$alpha2$', 1.0, 2.0, 5.0]
beta2_info = [r'$beta2$', 1.0, 4.0, 5.0]
gamma_info = [r'$gamma$', 0.2, 0.5, 0.8]
mu1_info = [r'$mu1$', 0.01, 0.08, 0.2]
nu_info = [r'$nu$', 0.01, 0.04, 0.2]
x1inf_info = [r'$x1inf$', -0.3, -0.15, -0.1]
x1sup_info = [r'$x1sup$', 0.1, 0.15, 0.30]
x5_info = [r'$x5$', 0.0, 0.3, 1.0]
xi_info = [r'$xi$', 0.2, 0.5, 0.8]
zeta1_info = [r'$zeta1$', 0.1, 0.3, 0.6]
zeta2_info = [r'$zeta2$', 0.1, 0.3, 0.6]
capital_market_parameter_names = [
NW10_info[0],
Phi3_info[0],
Phi4_info[0],
alpha1_info[0],
beta1_info[0],
alpha2_info[0],
beta2_info[0],
gamma_info[0],
mu1_info[0],
nu_info[0],
x1inf_info[0],
x1sup_info[0],
x5_info[0],
xi_info[0],
zeta1_info[0],
zeta2_info[0]
]
lower_bounds_capital_market_params = np.array([
NW10_info[1],
Phi3_info[1],
Phi4_info[1],
alpha1_info[1],
beta1_info[1],
alpha2_info[1],
beta2_info[1],
gamma_info[1],
mu1_info[1],
nu_info[1],
x1inf_info[1],
x1sup_info[1],
x5_info[1],
xi_info[1],
zeta1_info[1],
zeta2_info[1]
])
init_values_capital_market_params = np.array([
NW10_info[2],
Phi3_info[2],
Phi4_info[2],
alpha1_info[2],
beta1_info[2],
alpha2_info[2],
beta2_info[2],
gamma_info[2],
mu1_info[2],
nu_info[2],
x1inf_info[2],
x1sup_info[2],
x5_info[2],
xi_info[2],
zeta1_info[2],
zeta2_info[2]
])
upper_bounds_capital_market_params = np.array([
NW10_info[3],
Phi3_info[3],
Phi4_info[3],
alpha1_info[3],
beta1_info[3],
alpha2_info[3],
beta2_info[3],
gamma_info[3],
mu1_info[3],
nu_info[3],
x1inf_info[3],
x1sup_info[3],
x5_info[3],
xi_info[3],
zeta1_info[3],
zeta2_info[3]
])
n_consumer_market_params = 12
NW20_info = [r'$NW20$', 200, 1000, 5000]
Phi1_info = [r'$Phi1$', 0.0, 0.1, 0.50]
Phi2_info = [r'$Phi2$', 0.50, 0.9, 1.0]
chi_info = [r'$chi$', 0.2, 1.0, 5.0]
f2min_info = [r'$f2min$', 1e-06, 1e-05, 0.001]
iota_info = [r'$iota$', 0.0, 0.1, 0.3]
m2_info = [r'$m2$', 10.0, 40.0, 100.0]
mu20_info = [r'$mu20$', 0.1, 0.3, 0.5]
omega1_info = [r'$omega1$', 0.2, 1.0, 5.0]
omega2_info = [r'$omega2$', 0.2, 1.0, 5.0]
u_info = [r'$u$', 0.5, 0.75, 1.0]
upsilon_info = [r'$upsilon$', 0.01, 0.04, 0.1]
consumer_market_parameter_names = [
NW20_info[0],
Phi1_info[0],
Phi2_info[0],
chi_info[0],
f2min_info[0],
iota_info[0],
m2_info[0],
mu20_info[0],
omega1_info[0],
omega2_info[0],
u_info[0],
upsilon_info[0]
]
lower_bounds_consumer_market_params = np.array([
NW20_info[1],
Phi1_info[1],
Phi2_info[1],
chi_info[1],
f2min_info[1],
iota_info[1],
m2_info[1],
mu20_info[1],
omega1_info[1],
omega2_info[1],
u_info[1],
upsilon_info[1]
])
init_values_consumer_market_params = np.array([
NW20_info[2],
Phi1_info[2],
Phi2_info[2],
chi_info[2],
f2min_info[2],
iota_info[2],
m2_info[2],
mu20_info[2],
omega1_info[2],
omega2_info[2],
u_info[2],
upsilon_info[2]
])
upper_bounds_consumer_market_params = np.array([
NW20_info[3],
Phi1_info[3],
Phi2_info[3],
chi_info[3],
f2min_info[3],
iota_info[3],
m2_info[3],
mu20_info[3],
omega1_info[3],
omega2_info[3],
u_info[3],
upsilon_info[3]
])
n_labor_supply_params = 2
phi_info = [r'$phi$', 0.0, 0.4, 1.0]
psi2_info = [r'$psi2$', 0.95, 1.0, 1.05]
labor_supply_parameter_names = [
phi_info[0],
psi2_info[0]
]
lower_bounds_labor_supply_params = np.array([
phi_info[1],
psi2_info[1]
])
init_values_labor_supply_params = np.array([
phi_info[2],
psi2_info[2]
])
upper_bounds_labor_supply_params = np.array([
phi_info[3],
psi2_info[3]
])
all_parameter_names = np.concatenate((country_parameter_names,
financial_market_parameter_names,
capital_market_parameter_names,
consumer_market_parameter_names,
labor_supply_parameter_names))
all_param_names_stripped_of_dollar = [i.replace('$', '') for i in all_parameter_names]
parameter_lower_bounds_vector = np.concatenate((lower_bounds_country_params,
lower_bounds_financial_market_params,
lower_bounds_capital_market_params,
lower_bounds_consumer_market_params,
lower_bounds_labor_supply_params))
parameter_upper_bounds_vector = np.concatenate((upper_bounds_country_params,
upper_bounds_financial_market_params,
upper_bounds_capital_market_params,
upper_bounds_consumer_market_params,
upper_bounds_labor_supply_params))
init_values_vector = np.concatenate((init_values_country_params,
init_values_financial_market_params,
init_values_capital_market_params,
init_values_consumer_market_params,
init_values_labor_supply_params))
vector_non_randomised_parameter_values = init_values_vector.copy()
vector_non_randomised_parameter_values[indices_params_to_vary] = np.inf
return all_param_names_stripped_of_dollar, parameter_lower_bounds_vector, parameter_upper_bounds_vector, init_values_vector, vector_non_randomised_parameter_values