def _mask_X(X, diffs, correlation_threshold=DEFAULT_CORRELATION_THRESHOLD):
cc, threshold = bin_corrs_from_X(diffs, X, correlation_threshold=
correlation_threshold)
mask = np.abs(cc) > threshold
Xm = X[mask]
logging.debug("Masked shape is %s" % (Xm.shape,))
return Xm
def plot_correlations(results):
for result in results:
pdf = result.pdf
obs = result.obs
Qs = iter(obs.meanQ)
xgrid = pdf.make_xgrid()
fl = pdf.make_flavors()
figure, axarr = plt.subplots(len(fl), sharex=True,
sharey=True,
figsize=(8, len(fl)+3))
for b in result.binlabels:
Q = next(Qs)
X = get_X(pdf, Q=Q, xgrid=xgrid, fl=fl, reshape=True)
values, threshold = bin_corrs_from_X(result._all_vals.ix[b], X)
ind = 0
for f, axis in zip(fl, axarr):
step = len(xgrid)
current_vals = values[ind:ind+step]
ind+=step
line, = axis.plot(xgrid, current_vals)
stacked = np.array([xgrid, current_vals]).T
sel_ranges = split_ranges(stacked, abs(current_vals)>threshold,
filter_falses=True)
for arr in sel_ranges:
x,y = arr.T
axis.plot(x,y, linewidth=3, color=line.get_color())
axis.axvspan(np.min(x), np.max(x), color="#eeeeff")
axis.set_ylim([-1,1])
axis.set_xscale('log')
axis.set_ylabel("$%s$"%PDG_PARTONS[f])
axis.axhline(threshold, c='r', ls='--')
axis.axhline(-threshold, c='r', ls='--')
axarr[0].set_title(str(obs) + "\n")
plt.xlabel("$x$")
figure.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in figure.axes[:-1]], visible=False)
yield (obs,pdf), figure
