pyhisto 0.1.2

Python Histogram Library

Python Histogram module

This librabry provides histogram classes to perform data analysis.

Contrary to some data analysis library such as Pandas which generate histogram on demand from the whoel set of data, this library just stores the histogram in bins.

The histograms are exported as ascii files (and can be reloaded from these files).

Getting Started

The following instructions will get you a copy of the project up and running on your local machine.

Installing

There are several ways to install the module:

• :thumbsup: (recommended): use pip. The pyhisto is uploaded to the PyPi repository, so just running pip install pyhisto should install the library on your local environnement.
• Download the pyhist.tar.gz file, unzip and run the setup script withing the directory:$ python3 setup.py install
• Get a local copy of the git directory (not recommenced)

Dependencies

pyhisto is build with no dependencies for the use of histograms. For more advanced features in the pyhist.tools submodule, you will need numpy and matplotlib installed. If they are not, the features using them won't work, but the rest of the library will still load and be useable.

Usage

The pyhisto module can be tested live (in cunjunction with another library) on Binder:

Bin

The basic for 1D histograms is a Bin object. That is an object that keep record of the number of counts between to values (edges).

Main properties are:

• Bin.count
• Bin.lowedge
• Bin.upedge

Main methods are:

• Bin.width()
• Bin.center()

The bin can be typed into a str or float. Using Bin(fromstring=z), the Bin is instanciated using the z string, as formated by str(Bin). Bin objects can be added together, or mutliplied and divied by floats. Comparison operators (<, >, ...) on a Bin are comparing Bin.count.

Histogram

The basis class is Histogram which is Bin container with added function for management.

Histogram is instantiated several ways:

• Histogram(nbins: int, xmin: float = -0.5, xmax: Optional): where nbins is the number of bins, xmin the low edge of the first bin, and xmax the up edge of the last bin. If xmin is not given, it is set at -0.5 (so that it's centered on 0). If xmax is not given it is taken as xmin + nbins.
• Histogram(fromstring: str) where the argument is a string, formatted as str(Histogram) return.
• Histogram(fromfile: :str) where the argument is a file path where to read a string (just like fromstring).
• Histogram(frombins: list) with a list of Bin object.

When used as a container/list (i.e. Histogram[i], for in loops, ...) Histogram behaves like a list of the bins. +, -, *, \ operators either operates between one histogram and a scalar, or between two histograms of the same number of bins (no checking of bins limits is done).

-empty

index, find, fastindex, slice fill, fast fill

-copye, empty copy

xy

autocrop, eval

Lazy histograms

Lazy histograms are simpler interfaces to the histograms, their are designed to be litgher in memory and faster in usage, but with limited capabilities. They are recommended when filling the histograms from a data sources when speed is an imporant factor.

Ghost histograms

Ghost histograms have the same interface as lazy histograms, but do nothing. They are intended as a way to avoid time-expensive if checks when filling several histograms by filling the non interesting data into a ghosthistogram directly.

Output files

Development status

Analytics

• All badges

Caveats

Some part of the code have a non-pythonic taste for several reasons:

• Adaptation of non python code from other physics data analysis tools
• Compatibility with early version of the code
• Practicality beats purity

The goal is to develop the module under the hood and improve it's performance, portabilty, simplicity.

Any comments and suggestions are helpful.

Authors

• Greg Henning - ghenning​.at.​iphc․cnrs․fr

License

This project is licensed under the CeCILL FREE SOFTWARE LICENSE AGREEMENT.

See LICENSE for more.