def whole_best(benchmark=None,
data=None,
title_kwargs=None,
normalize_error=False,
**kwargs):
data = data if data is not None else retrieve_scores(benchmark)
models = [model for model in all_models if model in data['model'].values]
fig, ax = pyplot.subplots(figsize=(5, 4))
ax.set_title(benchmark_label_replace[benchmark], **(title_kwargs or {}))
ceiling, ceiling_err = get_ceiling(benchmark,
which='both',
normalize_scale=normalize_error)
_plot_bars(ax,
models=models,
data=data,
text_kwargs=dict(fontdict=dict(fontsize=6)),
**kwargs)
if is_iterable(ceiling_err
) or not np.isnan(ceiling_err): # no performance benchmarks
ceiling_y = 1 # we already normalized so ceiling == 1
xlim = ax.get_xlim()
ax.plot([-50, +50], [ceiling_y, ceiling_y], color='gray')
shaded_errorbar(x=[-50, +50],
y=np.array([ceiling_y, ceiling_y]),
error=([ceiling_err[0], ceiling_err[0]],
[ceiling_err[1], ceiling_err[1]]),
ax=ax,
alpha=0,
shaded_kwargs=dict(color='darkgray', alpha=.5))
ax.set_xlim(xlim)
ax.set_ylim(
[-.05, max(1.05 + ceiling_err[-1],
max(data['score']) + .05)])
ax.set_xticks([])
ax.set_xticklabels([])
ax.yaxis.set_major_locator(MultipleLocator(base=0.2))
ax.yaxis.set_major_formatter(score_formatter)
ax.spines['bottom'].set_position(('data', 0))
ax.spines['bottom'].set_linewidth(0.75)
savefig(fig, Path(__file__).parent / f'bars-{benchmark}')
def shaded_errorbar(x,
y,
error,
ax=None,
shaded_kwargs=None,
vertical=False,
**kwargs):
if (len(np.array(y).shape) == 1 and not is_iterable(error)) \
or len(np.array(error).shape) == 1: # symmetric error (only single vector)
error_low, error_high = error, error
else: # asymmetric error
assert len(error) == 2
error = np.vectorize(lambda e: max(e, 0))(
error) # guard against negative values
error_low, error_high = error
shaded_kwargs = shaded_kwargs or {}
shaded_kwargs = {**dict(linewidth=0.0), **shaded_kwargs}
ax = ax or pyplot.gca()
line = ax.plot(x, y, **kwargs)
if not vertical:
ax.fill_between(x, y - error_low, y + error_high, **shaded_kwargs)
else:
ax.fill_betweenx(y, x - error_low, x + error_high, **shaded_kwargs)
return line
def _determine_visual_degrees(visual_degrees, stimulus_set):
if not visual_degrees:
visual_degrees = stimulus_set['degrees']
if not is_iterable(visual_degrees):
visual_degrees = np.array([visual_degrees] * len(stimulus_set))
return visual_degrees
def make_list(element):
if not is_iterable(element) or isinstance(element, (str, bytes)):
return [element]
return element