def plot_change_width_scatter(data, fitter, fits):
bin_edges = fits.change_distribution_params.bin_edges
bin_centers = fits.change_distribution_params.bin_centers
shape = fitter.shape
def get_width(weights):
return change_distribution_width_cumsum(bin_centers, weights)
width_pairs = []
for fit in iterate_fits(fits):
weights_single = calc_change_distribution(shape, fit.theta, bin_edges)
width_single = get_width(weights_single)
width_bootstrap = get_width(fit.change_distribution_weights)
width_pairs.append( (width_single, width_bootstrap) )
width_single,width_bootstrap = zip(*width_pairs)
fig = plt.figure()
ax = fig.add_axes([0.12,0.12,0.8,0.8])
maxw = int(math.ceil(max(max(width_single), max(width_bootstrap))))
minw = int(math.floor(min(min(width_single), min(width_bootstrap))))
ax.scatter(width_single, width_bootstrap, alpha=0.8)
ax.plot(np.mean(width_single), np.mean(width_bootstrap), 'rx', markersize=8, markeredgewidth=2, label='mean')
ax.plot([minw, maxw], [minw, maxw],'k--')
ax.set_xlim(minw,maxw)
ax.set_ylim(minw,maxw)
ticks = range(minw, maxw+1)
ax.set_yticks(ticks)
ax.set_xticks(ticks)
ax.set_xticklabels([str(t) for t in ticks], fontsize=cfg.fontsize)
ax.set_yticklabels([str(t) for t in ticks], fontsize=cfg.fontsize)
ax.set_xlabel('width of single fit', fontsize=cfg.fontsize)
ax.set_ylabel('width by bootstrap', fontsize=cfg.fontsize)
ax.set_title('change distribution of single fit vs. bootstrap', fontsize=cfg.fontsize)
return fig
def plot_change_width_scatter(data, fitter, fits):
bin_edges = fits.change_distribution_params.bin_edges
bin_centers = fits.change_distribution_params.bin_centers
shape = fitter.shape
def get_width(weights):
return change_distribution_width_cumsum(bin_centers, weights)
width_pairs = []
for fit in iterate_fits(fits):
weights_single = calc_change_distribution(shape, fit.theta, bin_edges)
width_single = get_width(weights_single)
width_bootstrap = get_width(fit.change_distribution_weights)
width_pairs.append((width_single, width_bootstrap))
width_single, width_bootstrap = zip(*width_pairs)
fig = plt.figure()
ax = fig.add_axes([0.12, 0.12, 0.8, 0.8])
maxw = int(math.ceil(max(max(width_single), max(width_bootstrap))))
minw = int(math.floor(min(min(width_single), min(width_bootstrap))))
ax.scatter(width_single, width_bootstrap, alpha=0.8)
ax.plot(np.mean(width_single),
np.mean(width_bootstrap),
'rx',
markersize=8,
markeredgewidth=2,
label='mean')
ax.plot([minw, maxw], [minw, maxw], 'k--')
ax.set_xlim(minw, maxw)
ax.set_ylim(minw, maxw)
ticks = range(minw, maxw + 1)
ax.set_yticks(ticks)
ax.set_xticks(ticks)
ax.set_xticklabels([str(t) for t in ticks], fontsize=cfg.fontsize)
ax.set_yticklabels([str(t) for t in ticks], fontsize=cfg.fontsize)
ax.set_xlabel('width of single fit', fontsize=cfg.fontsize)
ax.set_ylabel('width by bootstrap', fontsize=cfg.fontsize)
ax.set_title('change distribution of single fit vs. bootstrap',
fontsize=cfg.fontsize)
return fig
data = GeneData.load('both').scale_ages(age_scaler)
shape = Sigslope('sigslope80')
fitter = Fitter(shape, sigma_prior='normal')
bin_edges, bin_centers = get_bins(data)
GRs = [
('GLRA2', 'STC', (5, 12)),
('HTR2B', 'MD', (3.5, 6)),
('SLC18A2', 'HIP', (3.5, 5.5)),
]
for g, r, yrange in GRs:
print 'Doing {}@{}...'.format(g, r)
series = data.get_one_series(g, r)
theta, sigma, _, _ = fitter.fit(series.ages, series.single_expression)
weights = calc_change_distribution(shape, theta, bin_edges)
fig = plot_one_series(series, shape, theta, bin_centers, weights, yrange)
save_figure(fig,
'RP/fit-examples-{}-{}.png'.format(g, r),
under_results=True)
# now with bootstrap estimate of change distribution
print 'Doing bootstrap...'
theta_samples = fitter.parametric_bootstrap(series.ages, theta, sigma)
weights = calc_bootstrap_change_distribution(shape, theta_samples,
bin_edges)
fig = plot_one_series(series, shape, theta, bin_centers, weights, yrange)
save_figure(fig,
'RP/fit-examples-{}-{}-bootstrap.png'.format(g, r),
under_results=True)
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load('both').scale_ages(age_scaler)
shape = Sigslope('sigslope80')
fitter = Fitter(shape, sigma_prior='normal')
bin_edges, bin_centers = get_bins(data)
GRs = [
('GLRA2','STC', (5, 12)),
('HTR2B','MD', (3.5, 6)),
('SLC18A2','HIP', (3.5, 5.5)),
]
for g,r,yrange in GRs:
print 'Doing {}@{}...'.format(g,r)
series = data.get_one_series(g,r)
theta,sigma,_,_ = fitter.fit(series.ages, series.single_expression)
weights = calc_change_distribution(shape, theta, bin_edges)
fig = plot_one_series(series, shape, theta, bin_centers, weights, yrange)
save_figure(fig,'RP/fit-examples-{}-{}.png'.format(g,r), under_results=True)
# now with bootstrap estimate of change distribution
print 'Doing bootstrap...'
theta_samples = fitter.parametric_bootstrap(series.ages, theta, sigma)
weights = calc_bootstrap_change_distribution(shape, theta_samples, bin_edges)
fig = plot_one_series(series, shape, theta, bin_centers, weights, yrange)
save_figure(fig,'RP/fit-examples-{}-{}-bootstrap.png'.format(g,r), under_results=True)
