def h1n(n,
nx,
ny,
names,
h1ds,
bins,
ranges,
xlabels,
ylabels,
titles=None,
legends=None,
figsize=(10, 10)):
fig = plt.figure(figsize=figsize)
stats = {}
for i in range(n):
ax = fig.add_subplot(nx, ny, i + 1)
x = h1ds[i]
r = ranges[i]
x1 = loc_elem_1d(x, find_nearest(x, r[0]))
x2 = loc_elem_1d(x, find_nearest(x, r[1]))
xmin = min(x1, x2)
xmax = max(x1, x2)
x2 = x[xmin:xmax]
o = np.ones(len(x2))
mu, std = weighted_mean_and_std(x2, o)
stats[names[i]] = Measurement(mu, std)
ax.set_xlabel(xlabels[i], fontsize=11)
ax.set_ylabel(ylabels[i], fontsize=11)
ax.hist(x,
bins=bins[i],
range=r,
histtype='step',
edgecolor='black',
linewidth=1.5,
label=r'$\mu={:7.2f},\ \sigma={:7.2f}$'.format(mu, std))
ax.legend(fontsize=10, loc=legends[i])
plt.grid(True)
if titles:
plt.title(titles[i])
plt.tight_layout()
return stats
def h1d(x,
bins=None,
range=None,
weights=None,
xlabel='Variable',
ylabel='Frequency',
title=None,
legend='upper right',
figsize=(6, 6)):
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(1, 1, 1)
x1 = loc_elem_1d(x, find_nearest(x, range[0]))
x2 = loc_elem_1d(x, find_nearest(x, range[1]))
xmin = min(x1, x2)
xmax = max(x1, x2)
mu, std = weighted_mean_and_std(x[xmin:xmax], np.ones(len(x[xmin:xmax])))
ax.set_xlabel(xlabel, fontsize=11)
ax.set_ylabel(ylabel, fontsize=11)
ax.hist(x,
bins=bins,
range=range,
weights=weights,
histtype='step',
edgecolor='black',
linewidth=1.5,
label=r'$\mu={:7.2f},\ \sigma={:7.2f}$'.format(mu, std))
ax.legend(fontsize=10, loc=legend)
plt.grid(True)
if title:
plt.title(title)
return mu, std
def bin_to_last_ratio(array, bins, xbin):
return np.sum(array[loc_elem_1d(bins, xbin):-1]) / np.sum(array)
def bin_ratio(array, bins, xbin):
return array[loc_elem_1d(bins, xbin)] / np.sum(array)