def hist_preach(infile):
""" Builds histograms of P_reach based on MMC output text
"""
Hist = namedtuple('Hist', 'counts edges')
df = pd.read_csv(infile,
delim_whitespace=True,
header=None,
names='ei l ef'.split())
# If the muon doesn't reach, MMC saves ef as -distance traveled
df[df < 0] = 0
preach = []
for (ei, l), efs in df.groupby(['ei', 'l']):
bins = calc_bins(efs['ef'])
histo = Hist(*np.histogram(efs['ef'], bins=bins, density=True))
[
preach.append((ei, l, ef, ew, val)) for ef, ew, val in zip(
centers(histo.edges), np.ediff1d(histo.edges), histo.counts)
]
return np.asarray(preach)
def hist_preach(infile, plotdir=None):
import pandas as pd
napf = 36
df = pd.read_csv(infile,
delim_whitespace=True,
header=None,
names='ei l ef'.split())
# If the muon doesn't reach, MMC saves ef as -distance traveled
df[df < 0] = 0
for idx, (ei, sdf) in enumerate(df.groupby('ei')):
if idx % napf == 0:
if idx > 0:
# plt.legend(fontsize=6)
plt.tight_layout()
if plotdir is not None:
plt.savefig(
os.path.join(os.path.expanduser(plotdir),
'{}.png'.format((idx - 1) / napf)))
fig, axs = plt.subplots(6, 6, figsize=(10, 10))
# fig.text(0.5, 0.04, r'$E_f$', ha='center', va='center')
# fig.text(0.06, 0.5, r'$P(E_f|E_i, l)$', ha='center', va='center', rotation='vertical')
axs = axs.flatten()
ax = axs[idx % napf]
ax.set_prop_cycle('color', plt.cm.Blues(np.linspace(0.3, 1, 100)))
plt.sca(ax)
plt.title(r'${:.2g}$ GeV'.format(ei), fontdict={'fontsize': 8})
for l, efs in sdf.groupby('l'):
bins = calc_bins(efs['ef'])
counts, edges = np.histogram(efs['ef'], bins=bins, density=True)
plt.plot(centers(edges), counts, label='{:.3g} km'.format(l / 1e3))
plt.yscale('log')
# plt.xlim(sdf['ef'].min(), sdf['ef'].max()*1.1)
# plt.legend(fontsize=6)
plt.tight_layout()
if plotdir is not None:
plt.savefig(
os.path.join(os.path.expanduser(plotdir), '{}.png'.format(
(idx - 1) / napf)))