def run_one(params, res_dict=None):
"""
Once you have the parameters, this function completes one loop to get
a dictionary of results.
For the first loop leave res_dict as None.
"""
pi = params['pi'][0]
# lambda_ = params['lambda_'][0]
np.random.seed(42)
w_grid = params['w_grid'][0]
z_grid = params['z_grid'][0]
if res_dict:
print("Reusing res_dict.")
v = res_dict['Tv']
out = res_dict['rigid_out']
else:
v = Interp(w_grid, -w_grid + 27.3, kind='linear')
out = ss_output_flexible(params) # ss output w/ flexible wages
# Get close with linear first. Then do a few cubic to finish up
Tv, ws, rest = iter_bellman(v,
tol=0.005,
strict=False,
log=False,
params=params,
pi=pi,
aggL=out,
kind='linear')
Tvc = Interp(Tv.X, Tv.Y, kind='cubic')
Tv, ws, rest = iter_bellman(Tvc,
tol=0.005,
strict=False,
log=False,
params=params,
pi=pi,
aggL=out)
res_dict = {'Tv': Tv, 'ws': ws, 'rest': rest}
flex_ws = Interp(z_grid, ss_wage_flexible(params, shock=z_grid))
#-------------------------------------------------------------------------
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30)
pth, shocks = pths[28], shks[28] # a period in steady state
g = ecdf(np.sort(pth))
shocks = np.sort(shocks)
#-------------------------------------------------------------------------
rigid_out = get_rigid_output(ws, params, flex_ws, g)
res_dict['gp'] = g
res_dict['rigid_out'] = rigid_out
return res_dict
def wage_dist_ecdf_refactor():
"""
This implements the refactor of gp's to ecdfs.
Also takes care of the output.
"""
with open('results/fixup_notice.txt', 'a') as f:
t = str(datetime.datetime.now())
f.write("FIXED gps AT {}\n".format(t))
params = load_params()
params['results_path/'] = 'results/', 'a'
all_files = ar.get_all_files(params)
gps = ar.read_output(all_files, kind='gp')
wses = ar.read_output(all_files, kind='ws')
z_grid = params['z_grid'][0]
flex_ws = Interp(z_grid, ss_wage_flexible(params, shock=z_grid))
for key in gps.iterkeys():
piname, lambda_ = [str(x).replace('.', '') for x in key]
out_name = 'results/gp_' + piname + '_' + lambda_ + '.pkl'
shutil.copy2(out_name, 'results/replaced_results/')
ws = wses[key]
params['pi'] = key[0], 'you'
params['lambda_'] = key[1], 'idiot'
new_g, shocks = get_new_g(ws, params)
with open(out_name, 'w') as f:
cPickle.dump(new_g, f)
print("Fixed wage distribution for {}.".format(key))
new_rigid_out = get_rigid_output(ws, params, flex_ws, new_g, shocks)
out_name = 'results/rigid_output_' + piname + '_' + lambda_ + '_.txt'
with open(out_name, 'w') as f:
f.write(str(new_rigid_out))
with open('results/fixup_notice.txt', 'a') as f:
f.write("Fixed {}\n".format(key))
def run_one(params, res_dict=None):
"""
Once you have the parameters, this function completes one loop to get
a dictionary of results.
For the first loop leave res_dict as None.
"""
pi = params['pi'][0]
# lambda_ = params['lambda_'][0]
np.random.seed(42)
w_grid = params['w_grid'][0]
z_grid = params['z_grid'][0]
if res_dict:
print("Reusing res_dict.")
v = res_dict['Tv']
out = res_dict['rigid_out']
else:
v = Interp(w_grid, -w_grid + 27.3, kind='linear')
out = ss_output_flexible(params) # ss output w/ flexible wages
# Get close with linear first. Then do a few cubic to finish up
Tv, ws, rest = iter_bellman(v, tol=0.005, strict=False, log=False,
params=params, pi=pi, aggL=out, kind='linear')
Tvc = Interp(Tv.X, Tv.Y, kind='cubic')
Tv, ws, rest = iter_bellman(Tvc, tol=0.005, strict=False, log=False,
params=params, pi=pi, aggL=out)
res_dict = {'Tv': Tv, 'ws': ws, 'rest': rest}
flex_ws = Interp(z_grid, ss_wage_flexible(params, shock=z_grid))
#-------------------------------------------------------------------------
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30)
pth, shocks = pths[28], shks[28] # a period in steady state
g = ecdf(np.sort(pth))
shocks = np.sort(shocks)
#-------------------------------------------------------------------------
rigid_out = get_rigid_output(ws, params, flex_ws, g)
res_dict['gp'] = g
res_dict['rigid_out'] = rigid_out
return res_dict
