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Simple histogram classes, designed for data manipulation

Project Description

Overview

Matplotlib histograms are geared around drawing, not data manipulation. Numpy direct support for histograms is extremely limited, and not very different from matpotlib. This is intended to turn into a set of very lightweight classes for shuffling data around. This is very much a work-in-progress.

The only required depenency is numpy, and the package is designed to work for python >= 2.6

Usage

A summary of usage, taken from the hists.py docstring follows:

Importing:
>>> from simplehist import Hist
Initialise with bin indices:
>>> a = Hist([0, 1, 2, 3])
>>> a.bincount
3
>>> a.bins
(0, 1, 2, 3)
>>> a.data
array([ 0.,  0.,  0.])
Optionally include data:
>>> a = Hist([0, 1, 2, 3], data=[1, 0.2, 3])
>>> a.data
array([ 1. ,  0.2,  3. ])
Or just specify the blank data type:
>>> a = Hist([0, 1, 2, 3], dtype=int)
>>> a.data
array([0, 0, 0])
You can do arithmetic operations in place or seperately:
>>> a = Hist([0, 1, 2, 3], data=[1, 0.2, 3])
>>> b = a + a
>>> b -= a
>>> a.data == b.data
array([ True,  True,  True], dtype=bool)
And you can fill bins from values:
>>> a = Hist([0,1,2,3])
>>> a.fill(1.4, weight=3)
>>> a.data
array([ 0.,  3.,  0.])
Even out of range:
>>> a = Hist([0,1])
>>> a.fill(-10)
>>> a.underflow
1.0
If you use pyROOT, you can convert from 1D histograms:
>>> type(source)
<class 'ROOT.TH1D'>
>>> convert = fromTH1(source)
>>> type(convert)
<class 'simplehist.hists.Hist'>

And you can draw histograms, using any of the options that can be passed to matplotlib.pyplot.hist:

>>> hist_object.draw_hist(lw=2)
Release History

Release History

This version
History Node

0.1

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
SimpleHist-0.1.tar.gz (9.8 kB) Copy SHA256 Checksum SHA256 Source Jan 16, 2011

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