def plot_bindist(obs_table, b, base_pdf=None):
def _kde_method(X, coords):
kde = mlab.GaussianKDE(X, None)
return kde.evaluate(coords)
obs = obs_table.Observable.unique()
if len(obs) != 1:
raise ValueError("Must be only one observable")
obs = obs[0]
figure, ax = plt.subplots()
plt.title("%s [Bin %d]" % (obs, b+1))
colors = plotutils.color_names_to_rgb(colorlist)
alpha = 1
if base_pdf:
base = obs_table[obs_table.PDF.get_values()==base_pdf].Result[0]
else:
base = None
results = obs_table.Result.unique(
)
for result in results:
if base is not None:
cv = base.central_value.as_matrix()
data = result._violin_data(rel_to=cv)
else:
data = data = result._violin_data()
if isinstance(data, list):
stats = data[b]
else:
stats = violin_stats(data, _kde_method)[b]
color = next(colors)
alphacolor = color + (alpha,)
plt.plot(stats['coords'], stats['vals'], color=color,label=result.pdf.label)
plt.fill_between(stats['coords'], 0, stats['vals'], color=alphacolor,
)
alpha /= 2
plt.ylabel("Distribution")
if base_pdf:
plt.xlabel('Ratio to %s' % base_pdf.label)
else:
plt.xlabel("Observable value")
ax.yaxis.set_major_locator(MaxNLocator(nbins=10, prune="both"))
ax.xaxis.set_major_locator(MaxNLocator(nbins=8, prune="both"))
plt.legend()
yield (obs, b), figure
def compare_violins(results, base_pdf = None):
if not isinstance(results, dict):
combined = aggregate_results(results)
else:
combined = results
for obs in combined:
figure = plt.figure()
norms = None
handles = []
plt.title(str(obs), y=1.05)
colors = plotutils.color_names_to_rgb(colorlist)
alpha = 1
base = combined[obs].get(base_pdf, None)
results = sorted(combined[obs].values(), key = lambda x: x!=base)
for result in results:
if base is not None:
cv = base.central_value.as_matrix()
data = result._violin_data(rel_to=cv)
else:
data = data = result._violin_data()
color = next(colors) + (alpha,)
alpha /= 2
plot, handle, norms = result.violin_plot(data, color=color,
showextrema=False,
normvalues=norms)
handles.append(handle)
plt.xlabel('bins')
if base_pdf:
plt.ylabel('Ratio to %s' % base_pdf.label)
else:
plt.ylabel("Observable value")
plt.xticks(range(1,len(result.central_value) + 1))
#Small style
dfs = plt.yticks()[0] - 1
l = len(dfs) // 2 + 1 - ((len(dfs) // 2) % 2)
mdiff = np.max(dfs)
plt.yticks(np.linspace(-mdiff, mdiff, l) + 1)
#
plt.legend(handles=handles)
yield (obs,), figure
def compare_cis(results, base_pdf = None):
if not isinstance(results, dict):
combined = aggregate_results(results)
else:
combined = results
for obs in combined:
figure = plt.figure()
colors = plotutils.color_names_to_rgb(colorlist)
plt.title(str(obs), y=1.05)
base = combined[obs].get(base_pdf, None)
results = sorted(combined[obs].values(), key = lambda x: x!=base)
l = len(results)
if l < 5:
delta = iter(np.linspace(-0.05*l, 0.05*l, l))
else:
delta = iter(np.linspace(-0.25, 0.25, l))
#x = iter(np.arange(1, len(results) + 1))
for result in results:
x = np.arange(1, result.nbins+1) + next(delta)
#Without copy the /= operator affects the underlying data.
data_cv = result.central_value.as_matrix().copy()
data_ci = result.errorbar68.as_matrix().copy()
if base is not None:
base_cv = base.central_value.as_matrix()
data_cv /= base_cv
data_ci /= base_cv[:,np.newaxis]
data_ci = np.abs(data_ci)
color = next(colors)
plt.errorbar(x, data_cv, yerr=data_ci.T,
linestyle='none',
color=color,
label=result.pdf.label, elinewidth = 2,
capsize=10)
if isinstance(result, MCResult):
#Without copy the /= operator affects the underlying data.
data_std = result.rel_std_interval().as_matrix().copy()
if base is not None:
data_std /= base_cv[:,np.newaxis]
data_std = np.abs(data_std)
plt.errorbar(x, data_cv, yerr=data_std.T,
linestyle='none',
color=color,
elinewidth = 2,
capsize=15)
plt.xlabel('bins')
if base_pdf:
plt.ylabel('Ratio to %s' % base_pdf.label)
else:
plt.ylabel("Observable value")
plt.xticks(range(1,len(result.central_value) + 1))
plt.legend()
plt.xlim(0.5, results[0].nbins + 0.5)
plt.grid(axis='x')
#Small style
dfs = plt.yticks()[0] - 1
l = len(dfs) // 2 + 1 - ((len(dfs) // 2) % 2)
mdiff = np.max(dfs)
plt.yticks(np.linspace(-mdiff, mdiff, l) + 1)
yield (obs,), figure
