if fitter is not None:
theta, L, LOO_predictions,_ = fitter.fit(series.ages, series.expression, loo=loo)
print 'L = {}'.format(L)
fig = plot_series(series, fitter.shape, theta, LOO_predictions)
else:
fig = plot_series(series)
if filename is None:
ensure_dir(results_dir())
filename = join(results_dir(), 'fits.png')
print 'Saving figure to {}'.format(filename)
save_figure(fig, filename)
if b_show:
plt.show(block=True)
if __name__ == '__main__':
disable_all_warnings()
cfg.fontsize = 18
cfg.xtick_fontsize = 18
cfg.ytick_fontsize = 18
parser = get_common_parser(include_pathway=False)
parser.add_argument('-g', '--genes', default='HTR1A HTR1E')
parser.add_argument('-r', '--region', default='VFC')
group = parser.add_mutually_exclusive_group()
group.add_argument('--loo', help='Show LOO predictions', action='store_true')
group.add_argument('--nofit', help='Only show the data points', action='store_true')
parser.add_argument('--filename', help='Where to save the figure. Default: results/fit.png')
parser.add_argument('--show', help='Show figure and wait before exiting', action='store_true')
args = parser.parse_args()
data, fitter = process_common_inputs(args)
genes = args.genes.split()
ax.set_ylabel('width by bootstrap', fontsize=cfg.fontsize)
ax.set_title('change distribution of single fit vs. bootstrap',
fontsize=cfg.fontsize)
return fig
pathway = '17full' # 'serotonin'
gene_regions = [
('HTR1E', 'VFC'),
('HTR1A', 'MFC'),
]
n_bins = 50
n_samples = 10
disable_all_warnings()
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load('both').restrict_pathway(pathway).scale_ages(age_scaler)
shape = Sigmoid(priors='sigmoid_wide')
fitter = Fitter(shape, sigma_prior='normal')
fits = get_all_fits(data, fitter, allow_new_computation=False)
dirname = 'bootstrap'
fits = add_change_distributions(data, fitter, fits, n_bins=n_bins)
fig = plot_bootstrap_onset_variance(data, fits)
save_figure(fig,
'{}/onset-variance-{}.png'.format(dirname, pathway),
under_results=True,