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TrackingValidationResult Class Reference

Public Member Functions

def __init__ (self, filename, label=None, color_index=0, additional_information=None)
 
def get_figure_of_merits (self)
 
def get_figures_of_merit_latex (self)
 
def plot_efficiency_point (self)
 
def grouped_by_pt_data (self, mc_data=None)
 
def plot (self, data_x, data_y, loc=4, yerr=None)
 
def plot_finding_efficiency (self, data=None)
 
def plot_hit_efficiency (self, data=None)
 
def print_useful_information (self)
 
def append_to_dataframe (self, df)
 

Static Public Member Functions

def from_calculations (calculations, key="output_file_name", parameter_part=None)
 

Public Attributes

 filename
 The root filename.
 
 pr_data
 The pr data.
 
 mc_data
 the mc data
 
 mc_prompts
 the mc prompt data
 
 pr_prompts
 the pr prompt data
 
 label
 the label
 
 color
 the color index
 
 finding_efficiency
 the finding efficiency
 
 hit_efficiency
 the hit efficiency
 
 fake_rate
 the fake rate
 
 clone_rate
 the clone rate
 
 additional_information
 the additional information
 

Detailed Description

This class represents a loaded validation root file. It has methods for plotting the typically needed graphs.

Definition at line 15 of file root_handler.py.

Constructor & Destructor Documentation

◆ __init__()

def __init__ (   self,
  filename,
  label = None,
  color_index = 0,
  additional_information = None 
) 
Create a new validation result from the given filename.
   Additional options for plotting (e.g. color or label) can be given.

Definition at line 18 of file root_handler.py.

18 def __init__(self, filename, label=None, color_index=0, additional_information=None):
19 """Create a new validation result from the given filename.
20 Additional options for plotting (e.g. color or label) can be given."""
21
22 self.filename = filename
23
24 self.pr_data = uproot.open(self.filename)["pr_tree/pr_tree"].arrays(library="pd")
25 self.pr_data["is_prompt"] = (
26 np.sqrt(self.pr_data.x_truth ** 2 + self.pr_data.y_truth ** 2) < 0.5) & (self.pr_data.is_primary == 1)
27
28 self.mc_data = uproot.open(self.filename)["mc_tree/mc_tree"].arrays(library="pd")
29 self.mc_data["is_prompt"] = (
30 np.sqrt(self.mc_data.x_truth ** 2 + self.mc_data.y_truth ** 2) < 0.5) & (self.mc_data.is_primary == 1)
31
32
33 self.mc_prompts = self.mc_data[self.mc_data.is_prompt == 1]
34
35 self.pr_prompts = self.pr_data[self.pr_data.is_prompt == 1]
36
37 import seaborn as sb
38
39 colors = sb.color_palette()
40
41
42 self.label = label
43
44 self.color = colors[color_index % len(colors)]
45
46
47 self.finding_efficiency = None
48
49 self.hit_efficiency = None
50
51 self.fake_rate = None
52
53 self.clone_rate = None
54 self.get_figure_of_merits()
55
56
57 self.additional_information = additional_information
58

Member Function Documentation

◆ append_to_dataframe()

def append_to_dataframe (   self,
  df 
) 
Append the main results to a already consisting dataframe.

Definition at line 179 of file root_handler.py.

179 def append_to_dataframe(self, df):
180 """Append the main results to a already consisting dataframe."""
181 result = {"finding_efficiency": self.finding_efficiency,
182 "hit_efficiency": self.hit_efficiency,
183 "clone_rate": self.clone_rate,
184 "fake_rate": self.fake_rate,
185 "file_name": self.filename}
186 if self.additional_information:
187 result.update(self.additional_information)
188 return df.append(result, ignore_index=True)

◆ from_calculations()

def from_calculations (   calculations,
  key = "output_file_name",
  parameter_part = None 
)
static 
Create validation results from an ipython calculation.

Definition at line 60 of file root_handler.py.

60 def from_calculations(calculations, key="output_file_name", parameter_part=None):
61 """Create validation results from an ipython calculation."""
62 if parameter_part:
63 return [
64 TrackingValidationResult(
65 c.get(key),
66 label=c.get_parameters()[parameter_part],
67 color_index=i) for i,
68 c in enumerate(calculations)]
69 else:
70 return [
71 TrackingValidationResult(
72 c.get(key),
73 label=c.get_parameters(),
74 color_index=i) for i,
75 c in enumerate(calculations)]
76

◆ get_figure_of_merits()

def get_figure_of_merits (   self) 
Return the figures of merit from the file. Mosty used for internal seeting of the properties.

Definition at line 77 of file root_handler.py.

77 def get_figure_of_merits(self):
78 """Return the figures of merit from the file. Mosty used for internal seeting of the properties."""
79 if self.finding_efficiency is None:
80 overview = uproot.open(
81 self.filename)["ExpertMCSideTrackingValidationModule_overview_figures_of_merit"].arrays(
82 library="pd")
83 self.finding_efficiency = overview.finding_efficiency[0]
84 self.hit_efficiency = overview.hit_efficiency[0]
85
86 overview = uproot.open(
87 self.filename)["ExpertPRSideTrackingValidationModule_overview_figures_of_merit"].arrays(
88 library="pd")
89 self.clone_rate = overview.clone_rate[0]
90 self.fake_rate = overview.fake_rate[0]
91
92 return dict(finding_efficiency=self.finding_efficiency,
93 hit_efficiency=self.hit_efficiency,
94 clone_rate=self.clone_rate,
95 fake_rate=self.fake_rate)
96

◆ get_figures_of_merit_latex()

def get_figures_of_merit_latex (   self) 
Print out the figures of merit as a LaTeX-ready table.

Definition at line 97 of file root_handler.py.

97 def get_figures_of_merit_latex(self):
98 """Print out the figures of merit as a LaTeX-ready table."""
99 results = self.get_figure_of_merits()
100
101 latex_string = r'\begin{table}' + "\n"
102 latex_string += r' \begin{tabular}{cc} \toprule' + "\n"
103 latex_string += r' & \\ \midrule' + "\n"
104 latex_string += r' Finding Efficiency & ' + f"{100 * results['finding_efficiency']:.2f}" + r' \% \\' + "\n"
105 latex_string += r' Hit Efficiency & ' + f"{100 * results['hit_efficiency']:.2f}" + r' \% \\' + "\n"
106 latex_string += r' Fake Rate & ' + f"{100 * results['fake_rate']:.2f}" + r' \% \\' + "\n"
107 latex_string += r' Clone Rate & ' + f"{100 * results['clone_rate']:.2f}" + r' \% \\ \bottomrule' + "\n"
108 latex_string += r' \end{tabular}' + "\n"
109 latex_string += r'\end{table}'
110
111 return latex_string
112

◆ grouped_by_pt_data()

def grouped_by_pt_data (   self,
  mc_data = None 
) 
Convenience function to return the input data (or the internal mc_data) grouped by pt.

Definition at line 120 of file root_handler.py.

120 def grouped_by_pt_data(self, mc_data=None):
121 """Convenience function to return the input data (or the internal mc_data) grouped by pt."""
122 if mc_data is None:
123 mc_data = self.mc_data
124
125 pt_values = pd.cut(mc_data.pt_truth, np.linspace(mc_data.pt_truth.min(), mc_data.pt_truth.max(), 10))
126 grouped = mc_data.groupby(pt_values)
127
128 return grouped
129

◆ plot()

def plot (   self,
  data_x,
  data_y,
  loc = 4,
  yerr = None 
) 
Plot data_y over data_x with the correct settings for this result. Mostly used internally.

Definition at line 130 of file root_handler.py.

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
133 if yerr is not None:
134 plt.errorbar(data_x, data_y, ls="-", marker="o",
135 color=self.color, label=self.label, yerr=yerr, lw=4)
136 else:
137 plt.plot(data_x, data_y, ls="-", marker="o",
138 color=self.color, label=self.label, lw=4)
139
140 if self.label is not None:
141 plt.legend(loc=loc, frameon=True)
142
plot
Definition: plot.py:1

◆ plot_efficiency_point()

def plot_efficiency_point (   self) 
Plot a oint in the finding-efficiency/hit-efficiency plane.

Definition at line 113 of file root_handler.py.

113 def plot_efficiency_point(self):
114 """Plot a oint in the finding-efficiency/hit-efficiency plane."""
115 import matplotlib.pyplot as plt
116 self.plot(100 * self.finding_efficiency, 100 * self.hit_efficiency, loc=3)
117 plt.xlabel("finding efficiency")
118 plt.ylabel("hit efficiency")
119

◆ plot_finding_efficiency()

def plot_finding_efficiency (   self,
  data = None 
) 
Plot the finding efficiency over pt.

Definition at line 143 of file root_handler.py.

143 def plot_finding_efficiency(self, data=None):
144 """Plot the finding efficiency over pt."""
145 import matplotlib.pyplot as plt
146 grouped = self.grouped_by_pt_data(data)
147
148 self.plot(grouped.median().pt_truth, grouped.mean().is_matched, yerr=1 / np.sqrt(grouped.count().is_matched))
149 plt.xlabel(r"$p_T$ of the MC tracks (in GeV)")
150 plt.ylabel("Finding Efficiency")
151

◆ plot_hit_efficiency()

def plot_hit_efficiency (   self,
  data = None 
) 
Plot the hit efficiency over pt.

Definition at line 152 of file root_handler.py.

152 def plot_hit_efficiency(self, data=None):
153 """Plot the hit efficiency over pt."""
154 import matplotlib.pyplot as plt
155 grouped = self.grouped_by_pt_data(data)
156
157 self.plot(grouped.median().pt_truth, grouped.mean().hit_efficiency, yerr=1 / np.sqrt(grouped.sum().mc_number_of_hits))
158 plt.xlabel(r"$p_T$ of the MC tracks (in GeV)")
159 plt.ylabel("Hit Efficiency")
160

◆ print_useful_information()

def print_useful_information (   self) 
Print mostfully useful information about this result.

Definition at line 161 of file root_handler.py.

161 def print_useful_information(self):
162 """Print mostfully useful information about this result."""
163 pr_data = self.pr_data
164 mc_data = self.mc_data
165 primaries = pr_data[self.pr_data.is_prompt == 1]
166 primaries_mc = mc_data[self.mc_data.is_prompt == 1]
167
168 print(self.label)
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())
178

Member Data Documentation

◆ additional_information

additional_information

the additional information

Definition at line 57 of file root_handler.py.

◆ clone_rate

clone_rate

the clone rate

Definition at line 53 of file root_handler.py.

◆ color

color

the color index

Definition at line 44 of file root_handler.py.

◆ fake_rate

fake_rate

the fake rate

Definition at line 51 of file root_handler.py.

◆ filename

filename

The root filename.

Definition at line 22 of file root_handler.py.

◆ finding_efficiency

finding_efficiency

the finding efficiency

Definition at line 47 of file root_handler.py.

◆ hit_efficiency

hit_efficiency

the hit efficiency

Definition at line 49 of file root_handler.py.

◆ label

label

the label

Definition at line 42 of file root_handler.py.

◆ mc_data

mc_data

the mc data

Definition at line 28 of file root_handler.py.

◆ mc_prompts

mc_prompts

the mc prompt data

Definition at line 33 of file root_handler.py.

◆ pr_data

pr_data

The pr data.

Definition at line 24 of file root_handler.py.

◆ pr_prompts

pr_prompts

the pr prompt data

Definition at line 35 of file root_handler.py.

The documentation for this class was generated from the following file: