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simulate_hy_ns_lifecycle_sales.py
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simulate_hy_ns_lifecycle_sales.py
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import numpy as np
import time
import subprocess
from SCEconomy_hy_ns_lifecycle_sales import Economy, split_shock
import pickle
if __name__ == '__main__':
import sys
args = sys.argv
num_core = int(args[1])
from markov import calc_trans, Stationary
#generate shock sequene
path_to_data_i_s = './tmp/data_i_s'
path_to_data_is_o = './tmp/data_is_o'
path_to_data_sales_shock = './tmp/data_sales_shock'
num_pop = 100_000
sim_time = 3_000
#save and split shocks for istate
# prob = np.load('./input_data/transition_matrix.npy')
prob = np.load('./DeBacker/prob_epsz.npy')
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
data_i_s = np.ones((num_pop, sim_time), dtype = int)
data_i_s[:, 0] = 7 #initial state. it does not matter if simulation is long enough.
calc_trans(data_i_s, data_rand, prob)
data_i_s = data_i_s[:, 2000:]
np.save(path_to_data_i_s + '.npy' , data_i_s)
split_shock(path_to_data_i_s, 100_000, num_core)
del data_rand, data_i_s
#save and split shocks for is_old
prob_yo = np.array([[44./45., 1./45.], [3./45., 42./45.]]) #[[y -> y, y -> o], [o -> y, o ->o]]
np.random.seed(2)
data_rand = np.random.rand(num_pop, sim_time+1) #+1 is added since this matters in calculation
data_is_o = np.ones((num_pop, sim_time+1), dtype = int)
data_is_o[:, 0] = 0 #initial state. it does not matter if simulation is long enough.
calc_trans(data_is_o, data_rand, prob_yo)
data_is_o = data_is_o[:, 2000:]
np.save(path_to_data_is_o + '.npy' , data_is_o)
split_shock(path_to_data_is_o, 100_000, num_core)
del data_rand, data_is_o
#prob of being 1 is 0.15
# data_sales_shock = np.random.choice(2, (num_pop, sim_time), p = [0.50, 0.50])
data_sales_shock = np.random.choice(2, (num_pop, sim_time), p = [0.85, 0.15])
data_sales_shock = data_sales_shock[:, 2000:]
np.save(path_to_data_sales_shock + '.npy' , data_sales_shock)
split_shock(path_to_data_sales_shock, 100_000, num_core)
del data_sales_shock
# #save and split shocks for is_old
# prob_sales = np.array([[0.5, 0.5], [0.5, 0.5]]) #[[y -> y, y -> o], [o -> y, o ->o]]
# np.random.seed(3)
# data_rand = np.random.rand(num_pop, sim_time) #+1 is added since this matters in calculation
# data_sales_shock = np.ones((num_pop, sim_time), dtype = int)
# data_sales_shock[:, 0] = 0 #initial state. it does not matter if simulation is long enough.
# calc_trans(data_sales_shock, data_rand, prob_sales)
# data_sales_shock = data_sales_shock[:, 2000:]
# np.save(path_to_data_sales_shock + '.npy' , data_sales_shock)
# split_shock(path_to_data_sales_shock, 100_000, num_core)
# del data_rand, data_sales_shock
# taub = np.array([0.137, 0.185, 0.202, 0.238, 0.266, 0.28]) * 0.50 #large one
# psib = np.array([0.007026139999999993, 0.02013013999999999, 0.03, 0.08398847999999996, 0.19024008000000006, 0.2648964800000001])
taup = 0.20
def curvedspace(begin, end, curve, num=100):
import numpy as np
ans = np.linspace(0, (end - begin)**(1.0/curve), num) ** (curve) + begin
ans[-1] = end #so that the last element is exactly end
return ans
# additional info
agrid2 = curvedspace(0., 200., 2., 40)
kapgrid2 = curvedspace(0., 3.0, 2., 30)
zgrid2 = np.load('./input_data/zgrid.npy') ** 2.0
p_, rc_, pkap_, kapbar_, ome_, varpi_ = 1.6959895478680314, 0.0022859333983255795, 0.012559094599466355, 0.05827055544624726, 0.46388548260346824, 0.6002410397243539
# 1.6120266141599342, 0.004489583936630955, 0.00618750541475882, 0.07182001341947838, 0.46388548260346824, 0.6002410397243539
###end defining additional parameters#
print('Solving the model with the given prices...')
print('Do not simulate more than one models at the same time...')
econ = Economy(agrid = agrid2, kapgrid = kapgrid2, zgrid = zgrid2, rho = 0.01, upsilon = 0.50, prob = prob,
ome = ome_, varpi = varpi_, path_to_data_i_s = path_to_data_i_s, path_to_data_is_o = path_to_data_is_o,
scaling_n = 1.82, scaling_b = 1.82, taup = taup
)
econ.set_prices(p = p_, rc = rc_, pkap = pkap_, kapbar = kapbar_)
with open('econ.pickle', mode='wb') as f: pickle.dump(econ, f)
t0 = time.time()
#don't forget to replace import argument
result = subprocess.run(['mpiexec', '-n', str(num_core), 'python', 'SCEconomy_hy_ns_lifecycle_sales.py'], stdout=subprocess.PIPE)
t1 = time.time()
detailed_output_file = './log/test.txt'
f = open(detailed_output_file, 'ab') #use byte mode
f.write(result.stdout)
f.close()
with open('econ.pickle', mode='rb') as f: econ = pickle.load(f)
p = econ.p
rc = econ.rc
pkap = econ.pkap
kapbar = econ.kapbar
# moms = econ.moms
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0)
# if p != p_ or rc != rc_:
# print('err: input prices and output prices do not coincide.')
# print('p = ', p, ', p_ = ', p_)
# print('rc = ', rc, ', rc_ = ', rc_)
#calc main moments
econ.print_parameters()
###calculate other important variables###
##econ.calc_sweat_eq_value()
econ.calc_age()
econ.simulate_other_vars()
econ.calc_moments()
econ.save_result()