GroupedDEDXEstimationTrainer Class Reference

Inheritance diagram for GroupedDEDXEstimationTrainer:

Public Member Functions
def	create_dedx_bins (self, data)
def	create_p_bins (self, data)
def	use_only_the_highest_values (self, data, number_of_values=None)
def	create_fit_data (self, dedx_bin)
def	fit_p_to_dedx_bin (self, dedx_bin)

Static Public Attributes
int	number_of_bins_in_dedx = 20
	number of dE/dx bins
int	number_of_bins_in_p = 29
	number of track-momentum bins
int	number_of_head_values_used_to_fit = 20
	number of head values in fit

Detailed Description

Train a neural network for dE/dx-based particle identification

Definition at line 42 of file train.py.

Member Function Documentation

create_dedx_bins()

def create_dedx_bins	(		self,
			data
	)

Construct the dE/dx bins and then populate them with the data

Definition at line 52 of file train.py.

    def create_dedx_bins(self, data):
        """Construct the dE/dx bins and then populate them with the data"""
        dedx_bins = np.linspace(
            data[
                self.dedx_column].min(), data[
                self.dedx_column].max(), GroupedDEDXEstimationTrainer.number_of_bins_in_dedx)
        dedx_cuts = pd.cut(data[self.dedx_column], dedx_bins)
        return data.groupby(dedx_cuts), dedx_bins
 

create_fit_data()

def create_fit_data	(		self,
			dedx_bin
	)

Fit track-momentum values

Definition at line 74 of file train.py.

    def create_fit_data(self, dedx_bin):
        """Fit track-momentum values"""
        p_binned_data, p_bins = self.create_p_bins(dedx_bin)
 
        number_of_p_values = pd.Series(p_binned_data.count().p.values, name="number_of_p_values")
        p_bin_centers = pd.Series(0.5 * (p_bins[:-1] + p_bins[1:]), name="p_bin_centers")
 
        all_fit_data = pd.DataFrame([number_of_p_values, p_bin_centers]).T
        fit_data = self.use_only_the_highest_values(all_fit_data, GroupedDEDXEstimationTrainer.number_of_head_values_used_to_fit)
 
        return fit_data
 

create_p_bins()

def create_p_bins	(		self,
			data
	)

Construct the momentum bins and then populate them with the data

Definition at line 61 of file train.py.

    def create_p_bins(self, data):
        """Construct the momentum bins and then populate them with the data"""
        p_bins = np.linspace(data.p.min(), data.p.max(), GroupedDEDXEstimationTrainer.number_of_bins_in_p)
        p_cuts = pd.cut(data.p, p_bins)
        return data.groupby(p_cuts), p_bins
 

fit_p_to_dedx_bin()

def fit_p_to_dedx_bin	(		self,
			dedx_bin
	)

Fit the track-momentum values in the selected dE/dx bin, then train on the fitted values

Definition at line 86 of file train.py.

    def fit_p_to_dedx_bin(self, dedx_bin):
        """Fit the track-momentum values in the selected dE/dx bin, then train on the fitted values"""
        fit_data = self.create_fit_data(dedx_bin)
        return self.train_function(fit_data)
 
 

use_only_the_highest_values()

def use_only_the_highest_values	(		self,
			data,
			number_of_values = None
	)

Sort the data then select only the highest N values

Definition at line 67 of file train.py.

    def use_only_the_highest_values(self, data, number_of_values=None):
        """Sort the data then select only the highest N values"""
        if number_of_values is None:
            return data
        else:
            return data.sort("number_of_p_values", ascending=False).head(number_of_values).sort()
 

Member Data Documentation

number_of_bins_in_dedx

int number_of_bins_in_dedx = 20

static

number of dE/dx bins

Definition at line 46 of file train.py.

number_of_bins_in_p

int number_of_bins_in_p = 29

static

number of track-momentum bins

Definition at line 48 of file train.py.

number_of_head_values_used_to_fit

int number_of_head_values_used_to_fit = 20

static

number of head values in fit

Definition at line 50 of file train.py.

---

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

• tracking/studies/low_momentum_vxd_estimation/train.py