def make_and_save(pi,
lambda_,
wses=None,
nperiods=4,
log=True,
figkwargs=None,
axkwargs=None):
"""
Wrapper for all the plotting funcitonality.
Returns an axes and does IO.
"""
if wses is None:
params = load_params()
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
df = get_df(pi, lambda_, wses[pi, lambda_], params)
fig, ax = plot_wage_change_dist(df,
pi,
lambda_,
nperiods=nperiods,
log=log,
figkwargs=figkwargs,
axkwargs=axkwargs)
savefig_(fig, pi, lambda_, nperiods=nperiods, log=log)
return fig, ax
def main():
params = load_params()
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
keys = wses.keys()
pis, lambdas = zip(*keys) # FTW
pis_u, lambdas_u = sorted(set(pis)), sorted(set(lambdas)) # unique
for pi, lambda_ in keys:
make_and_save(pi, lambda_)
print('Saved {}, {}'.format(pi, lambda_))
def main():
params = load_params()
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
keys = wses.keys()
pis, lambdas = zip(*keys) # FTW
pis_u, lambdas_u = sorted(set(pis)), sorted(set(lambdas)) # unique
for pi, lambda_ in keys:
make_and_save(pi, lambda_)
print('Saved {}, {}'.format(pi, lambda_))
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 make_and_save(pi, lambda_, wses=None, nperiods=4, log=True, figkwargs=None,
axkwargs=None):
"""
Wrapper for all the plotting funcitonality.
Returns an axes and does IO.
"""
if wses is None:
params = load_params()
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
df = get_df(pi, lambda_, wses[pi, lambda_], params)
fig, ax = plot_wage_change_dist(df, pi, lambda_, nperiods=nperiods,
log=log, figkwargs=figkwargs,
axkwargs=axkwargs)
savefig_(fig, pi, lambda_, nperiods=nperiods, log=log)
return fig, ax
def get_g(pi, lambda_, period=28):
"""
Helper function to get to a wage distribution.
Warning: Will not touch the params in your global state.
If you go on the to more things make sure to adjust those params.
"""
import analyze_run as ar
params = load_params()
params['pi'] = pi, 'a'
params['lambda_'] = lambda_, 'b'
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
ws = wses[(pi, lambda_)]
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30, seed=42)
pth, shocks = pths[28], shks[28]
shocks = np.sort(shocks)
g = ecdf(np.sort(pth))
return g, shocks
def get_g(pi, lambda_, period=28):
"""
Helper function to get to a wage distribution.
Warning: Will not touch the params in your global state.
If you go on the to more things make sure to adjust those params.
"""
import analyze_run as ar
params = load_params()
params['pi'] = pi, 'a'
params['lambda_'] = lambda_, 'b'
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
ws = wses[(pi, lambda_)]
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30, seed=42)
pth, shocks = pths[28], shks[28]
shocks = np.sort(shocks)
g = ecdf(np.sort(pth))
return g, shocks
