Metadata-Version: 2.1
Name: bpf4
Version: 0.8.2
Summary: Peace-wise interpolation and lazy evaluation in cython
Home-page: https://github.com/gesellkammer/bpf4
Author: eduardo moguillansky
Author-email: eduardo.moguillansky@gmail.com
Maintainer: 
Maintainer-email: 
License: UNKNOWN
Download-URL: https://github.com/gesellkammer/bpf4
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.8)
Requires-Dist: matplotlib
Requires-Dist: scipy

BPF4
====

[![Build Status](https://travis-ci.org/gesellkammer/bpf4.svg?branch=master)](https://travis-ci.org/gesellkammer/bpf4)

About
-----

bpf4 is a library for curve fitting and break point functions in python. It is mainly programmed in Cython for efficiency. It has been used in itself for functional and numerical analysis.

Example
-------

Find the intersection between two curves

    >>> from bpf4 import bpf  # this imports the api
    >>> import pylab
    >>> a = bpf.spline((0, 0), (1, 5), (2, 3), (5, 10))  # each point (x, y)
    >>> b = bpf.expon((0, -10), (2,15), (5, 3), exp=3)
    >>> a.plot() # uses matplotlib
    >>> b.plot() 
    >>> zeros = (a - b).zeros()
    >>> pylab.plot(zeros, a.map(zeros), 'o')


![1](https://github.com/gesellkammer/bpf4/raw/master/pics/zeros.png)

Features
--------

Many interpolation types besides linear:

* spline
* half-cosine
* exponential
* fibonacci
* exponantial half-cosine
* pchip
* logarithmic
* etc. 

Interpolation types can be mixed, so that each segment has a different interpolation (with the exception of spline interpolation)  
Curves can be combined non-destructively. Following from the example above.  

    >>> c = (a + b).sin().abs()
    >>> c[1.5:4].plot()  # plot only the range (1.5, 4)

![2](https://github.com/gesellkammer/bpf4/raw/master/pics/sinabs.png)

Syntax support for shifting, scaling and slicing a bpf

    >>> a >> 2        # a shifted to the right
    >>> (a * 5) ^ 2   # scale the x coord by 2, scale the y coord by 5
    >>> a[2:2.5]      # slice only a portion of the bpf
    >>> a[::0.01]     # sample the bpf with an interval of 0.01

Derivation and Integration: `c.derivative().plot()` or `c.integrated().integrated().plot()`  
Numerical integration: `c.integrate_between(2, 4)`  

Dependencies
------------

* cython >= 0.19 
* numpy

Installation
------------

To install the latest version (recommended)

    $> git clone https://github.com/gesellkammer/bpf4.git
    $> cd bpf4
    $> python setup.py install

Otherwise, via pip or easy_install

    $> pip install bpf4


