➤ torchic
Tools for pOstpRoCessing in HIgh-energy physiCs
torchic is a Python package designed to support researchers in computational high-energy physics with optimized tools and utilities.
➤ Installation
To install torchic, use pip:
pip install torchic
➤ Features
Provides optimized tools specifically tailored for computational high-energy physics. Includes a variety of modules and functions to streamline research workflows. Accessible examples and manual tests to help validate functionality.
histogram Module
The histogram module in torchic offers utilities for creating and managing histograms, including support for 1D and 2D histograms with customizable axes and loading options.
AxisSpec Class
The AxisSpec class provides a flexible way to define histogram axes, with parameters for binning and axis labels.
- Attributes
bins(int): The number of bins for the axis.xmin(double)xmax(double)name(str): Name of the histogramtitle(str): Title of the histogram
HistLoadInfo Class
The HistLoadInfo class encapsulates metadata and loading instructions for histograms, ensuring consistency in how histograms are managed and used within datasets.
- Attributes
file_path(str): Path to the file containing histogram data.hist_name(str): The name of the histogram within the file.
build_TH1 Function
Generates a 1D histogram (TH1) with the specified data and axis specifications.
- Parameters
data(array-like): The data to be binned in the histogram.axis_spec(AxisSpec): The specification for the histogram's axis, including bin count, range, and label.
build_TH2 Function
Generates a 2D histogram (TH2) with data mapped over two axes, suitable for representing joint distributions or correlations.
- Parameters
data_x(array-like): The data for the x-axis.data_y(array-like): The data for the y-axis.x_axis_spec(AxisSpec): Specification for the x-axis, including bins, range, and label.y_axis_spec(AxisSpec): Specification for the y-axis, including bins, range, and label.
load_hist Function
Loads a histogram based on the specified HistLoadInfo metadata, providing an easy interface for accessing pre-saved histograms.
- Parameters
load_info(HistLoadInfo): An instance ofHistLoadInfothat contains the necessary file path and options for loading the histogram.
The histogram module simplifies the process of creating, configuring, and loading histograms, supporting both 1D and 2D data analysis.
➤ Dataset Class
The Dataset class in torchic provides a structured way to handle, manipulate, and analyze datasets, especially for computational high-energy physics research. Key features include subset management, efficient access to data columns, and histogram-building capabilities.
add_subset(subset_name, condition)
Adds a new subset to the dataset. This is useful for organizing data into manageable parts or categories.
dataset = Dataset()
dataset.add_subset("subset_name", condition)
__getitem__()
Accesses data columns. With __getitem__, you can retrieve either a specific column across all data or a particular column from a subset.
build_hist(column_name, axis_spec, **kwargs) -> TH1F
build_hist(column_name_x, column_name_y, axis_spec_x, axis_spec_y, **kwargs) -> TH2F
Creates a ROOT histogram from a column of the dataset.
Examples:
dataset.add_subset("column_name", dataset._data["A"] < 5)
subset_column = dataset["subset_name:column_name"]
# Getting a Column from a Specific Subset
axis_spec = AxisSpec(nbins, xmin, xmax, hist_name, hist_title)
dataset.build_hist(column_name, axis_spec, subset="subset_name")
# Generates a histogram for the specified column. This is especially helpful for visualizing distributions in your data.
The Dataset class makes it straightforward to organize data, retrieve specific columns, and generate histograms, making it ideal for physics research workflows.
➤ Testing
Manual tests are provided in the tests folder to verify the functionality of each module. Before using torchic in a production environment, it’s recommended to run these tests to ensure everything works as expected.
➤ Examples
Refer to the examples folder for use cases and example scripts that illustrate how to integrate torchic tools into your projects.
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