def main():
bootstrap()
display_introduction()
factory = Router()
command = None
while not is_command_exit(command):
clear_screen()
command = get_command()
try:
factory.execute_command(command)
except Exception as e:
print(e)
def plot_bar(variations, q=None, show_title=False):
index = np.arange(len(variations))
bar_width = 0.8
mu = np.empty(len(variations))
se = np.empty(len(variations))
for i, v in enumerate(variations):
scores = v.LOO_scores
if q is None:
f = np.mean
else:
def f(vals):
return np.percentile(vals, q)
mu[i], se[i] = bootstrap(scores, f)
fig = plt.figure()
ax = fig.add_axes([0.12, 0.12, 0.8, 0.8])
ax.bar(
index,
mu,
yerr=se,
width=bar_width,
color='b',
error_kw={
'ecolor': '0.3',
'linewidth': 2
},
)
ttl = ''
if q is None:
ttl = 'Mean $R^2$ dependence on priors'
ylabel = 'mean $R^2$'
else:
ttl = '{} percentile $R^2$ dependence on priors'.format(q)
ylabel = '{} percentile $R^2$'.format(q)
if show_title:
ax.set_title(ttl, fontsize=fontsize)
ax.set_xlabel('which priors', fontsize=fontsize)
ax.set_ylabel(ylabel, fontsize=fontsize)
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels([latex_label(v.theta, v.sigma) for v in variations])
ticks = [0, 0.1, 0.2, 0.3]
ax.set_yticks(ticks)
ax.set_yticklabels(['{:g}'.format(t) for t in ticks])
ax.tick_params(axis='both', labelsize=fontsize)
ax.set_ylim(0, 0.3)
return fig
def analyze_variant(theta,sigma):
theta_priors = priors_name if theta else None
sigma_prior = 'normal' if sigma else None
shape = Sigslope(theta_priors)
fitter = Fitter(shape,sigma_prior)
fits = get_all_fits(data,fitter,allow_new_computation=False)
LOO_scores = [f.LOO_score for f in iterate_fits(fits) if f.LOO_score is not None]
mu,sem = bootstrap(LOO_scores, np.mean)
return Bunch(
theta = theta,
sigma = sigma,
LOO_scores = LOO_scores,
mu = mu,
sem = sem,
)
def analyze_variant(theta, sigma):
theta_priors = priors_name if theta else None
sigma_prior = 'normal' if sigma else None
shape = Sigslope(theta_priors)
fitter = Fitter(shape, sigma_prior)
fits = get_all_fits(data, fitter, allow_new_computation=False)
LOO_scores = [
f.LOO_score for f in iterate_fits(fits) if f.LOO_score is not None
]
mu, sem = bootstrap(LOO_scores, np.mean)
return Bunch(
theta=theta,
sigma=sigma,
LOO_scores=LOO_scores,
mu=mu,
sem=sem,
)
def plot_bar(variations, q=None, show_title=False):
index = np.arange(len(variations))
bar_width = 0.8
mu = np.empty(len(variations))
se = np.empty(len(variations))
for i,v in enumerate(variations):
scores = v.LOO_scores
if q is None:
f = np.mean
else:
def f(vals): return np.percentile(vals,q)
mu[i],se[i] = bootstrap(scores, f)
fig = plt.figure()
ax = fig.add_axes([0.12,0.12,0.8,0.8])
ax.bar(
index,
mu,
yerr=se,
width=bar_width,
color='b',
error_kw = {'ecolor': '0.3', 'linewidth': 2},
)
ttl = ''
if q is None:
ttl = 'Mean $R^2$ dependence on priors'
ylabel = 'mean $R^2$'
else:
ttl = '{} percentile $R^2$ dependence on priors'.format(q)
ylabel = '{} percentile $R^2$'.format(q)
if show_title:
ax.set_title(ttl, fontsize=fontsize)
ax.set_xlabel('which priors', fontsize=fontsize)
ax.set_ylabel(ylabel, fontsize=fontsize)
ax.set_xticks(index + bar_width/2)
ax.set_xticklabels([latex_label(v.theta,v.sigma) for v in variations])
ticks = [0,0.1,0.2,0.3]
ax.set_yticks(ticks)
ax.set_yticklabels(['{:g}'.format(t) for t in ticks])
ax.tick_params(axis='both', labelsize=fontsize)
ax.set_ylim(0,0.3)
return fig
def setUpClass(cls):
bootstrap()
from datetime import datetime, timedelta
import pytest
from app.storage import Storage
from utils.bootstrap import bootstrap
bootstrap()
storage = Storage()
class TestToken:
@pytest.fixture(scope='class')
def new_token(self):
return storage.add_token()
def test_add_token(self, new_token):
token, expires = new_token
assert token
assert not expires
def test_verify_expire_token(self, new_token):
token, expires = new_token
assert storage.verify_token(token)
assert storage.expire_token(token)
def test_expired_token(self, new_token):
token, expires = new_token
assert not storage.verify_token(token)
def test_delete_token(self, new_token):
token, expires = new_token