FacetRelationFilterTrainingRun Class Reference

Inheritance diagram for FacetRelationFilterTrainingRun:

Collaboration diagram for FacetRelationFilterTrainingRun:

Public Member Functions
def	identifier (self)
def	create_path (self)

Public Attributes
	task
	Post-process events according to the user's desired task (train, eval, explore)
	variables
	Signal some variables to select in the classification analysis.
	groupby
	group output by superlayer_id
	auxiliaries
	auxiliary histograms

Static Public Attributes
int	n_events = 1000
	Suggested number for this analysis.
str	generator_module = "generic"
	Suggested generator module.
str	task = "explore"
	Default task set to explore.
str	truth = "truth_positive"
	Name of the truth variables.

Detailed Description

Run for recording facets encountered at the filter

Definition at line 23 of file trainFacetRelationFilter.py.

Member Function Documentation

create_path()

def create_path

(

self

)

Setup the recording path after the simulation

Reimplemented from ReadOrGenerateEventsRun.

Definition at line 43 of file trainFacetRelationFilter.py.

    def create_path(self):
        """Setup the recording path after the simulation"""
        path = super().create_path()
 
        
        if self.task == "train":
            var_sets = [
                "mva",
                "filter(truth)",
            ]
 
        elif self.task == "eval":
            var_sets = [
                "basic",
                "filter(chi2)",
                "filter(chi2_old)",
                "filter(simple)",
                "filter(truth)",
            ]
 
            self.variables = [
                "chi2_weight",
                "chi2_accept",
                "chi2_old_weight",
                "chi2_old_accept",
                "simple_accept",
            ]
 
            self.groupby = ["", "superlayer_id"]
            self.auxiliaries = [
                "superlayer_id",
            ]
 
        elif self.task == "explore":
            var_sets = [
                "basic",
                "bend",
                "fit",
                "filter(chi2)",
                "filter(simple)",
                "filter(truth)",
            ]
 
            
            self.variables = [
                # "delta_phi",
                # "delta_phi_pull",
                # "delta_phi_pull_per_r",
                # "delta_curv",
                # "delta_curv_pull",
                # "delta_curv_pull_per_r",
 
                # "cos_delta",
                # "from_middle_cos_delta",
                # "to_middle_cos_delta",
 
                "chi2_0",
                # "chi2_0_per_s",
                # "erf_0",
                # "fit_0_phi0",
                # "fit_0_cos_delta",
 
                # "chi2_1",
                # "chi2_1_per_s",
                # "fit_1_phi0",
                # "fit_1_cos_delta",
 
                # "chi2",
                # "chi2_per_s",
                # "fit_phi0",
                # "fit_cos_delta",
 
                # "phi0_from_sigma",
                # "phi0_to_sigma",
 
                # "phi0_ref_pull",
                # "phi0_ref_diff",
                # "phi0_ref_sigma",
 
                # "chi2_comb",
                # "phi0_comb_pull",
                # "phi0_comb_diff",
                # "phi0_comb_sigma",
 
                # "chi2_kari_unit",
                # "abs_curv_unit",
 
                # "chi2_kari_l",
                # "abs_curv_l",
 
                # "chi2_kari_pseudo",
                # "abs_curv_pseudo",
 
                # "chi2_kari_proper",
                # "abs_curv_proper",
            ]
 
            
            self.groupby = ["", "superlayer_id"]
            
            self.auxiliaries = [
                "superlayer_id",
            ]
 
        path.add_module("TFCDC_WireHitPreparer",
                        flightTimeEstimation="outwards",
                        UseNLoops=1.0)
 
        path.add_module("TFCDC_ClusterPreparer")
 
        path.add_module("TFCDC_SegmentFinderFacetAutomaton",
                        FacetRelationFilter="unionrecording",
                        FacetRelationFilterParameters={
                            "rootFileName": self.sample_file_name,
                            "varSets": var_sets,
                        })
 
        return path
 
 

identifier()

def identifier

(

self

)

Database identifier of the filter being trained

Definition at line 39 of file trainFacetRelationFilter.py.

    def identifier(self):
        """Database identifier of the filter being trained"""
        return "trackfindingcdc_FacetRelationFilter.xml"
 

Member Data Documentation

auxiliaries

auxiliaries

auxiliary histograms

Definition at line 72 of file trainFacetRelationFilter.py.

generator_module

str generator_module = "generic"

static

Suggested generator module.

Definition at line 29 of file trainFacetRelationFilter.py.

groupby

groupby

group output by superlayer_id

Definition at line 71 of file trainFacetRelationFilter.py.

n_events

int n_events = 1000

static

Suggested number for this analysis.

Definition at line 26 of file trainFacetRelationFilter.py.

task [1/2]

str task = "explore"

static

Default task set to explore.

Definition at line 33 of file trainFacetRelationFilter.py.

task [2/2]

task

Post-process events according to the user's desired task (train, eval, explore)

Definition at line 48 of file trainFacetRelationFilter.py.

truth

str truth = "truth_positive"

static

Name of the truth variables.

Definition at line 36 of file trainFacetRelationFilter.py.

variables

variables

Signal some variables to select in the classification analysis.

Definition at line 63 of file trainFacetRelationFilter.py.

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

• tracking/trackFindingCDC/train/trainFacetRelationFilter.py