130 def plot(self, data_x, data_y, loc=4, yerr=None):
131 """Plot data_y over data_x with the correct settings for this result. Mostly used internally."""
132 import matplotlib.pyplot
as plt
134 plt.errorbar(data_x, data_y, ls=
"-", marker=
"o",
135 color=self.
color, label=self.
label, yerr=yerr, lw=4)
137 plt.plot(data_x, data_y, ls=
"-", marker=
"o",
140 if self.
label is not None:
141 plt.legend(loc=loc, frameon=
True)
162 """Print mostfully useful information about this result."""
165 primaries = pr_data[self.
pr_data.is_prompt == 1]
166 primaries_mc = mc_data[self.
mc_data.is_prompt == 1]
169 print(
"Fake", 100 * primaries.is_fake.mean(), 100 * pr_data.is_fake.mean())
170 print(
"Clone", 100 * primaries.is_clone.mean(), 100 * pr_data.is_clone.mean())
171 print(
"Ghost", 100 * primaries.is_ghost.mean(), 100 * pr_data.is_ghost.mean())
172 print(
"Fitted", 100 * primaries.is_fitted.mean(), 100 * pr_data.is_fitted.mean())
173 print(
"Found", 100 * primaries_mc.is_matched.mean(), 100 * mc_data.is_matched.mean())
174 print(
"Found2", 100.0 - 100 * primaries_mc.is_missing.mean(), 100.0 - 100 * mc_data.is_missing.mean())
175 print(
"Merged", 100 * primaries_mc.is_merged.mean(), 100 * mc_data.is_merged.mean())
176 print(
"Hit-Eff", 100 * primaries_mc.hit_efficiency.mean(), 100 * mc_data.hit_efficiency.mean())
177 print(
"Wrong Hits", primaries.number_of_wrong_hits.mean(), pr_data.number_of_wrong_hits.mean())