Pice-wise interpolation
Project description
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')
.. figure:: https://github.com/gesellkammer/bpf4/raw/master/pics/zeros.png
:alt: 1
1
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)
.. figure:: https://github.com/gesellkammer/bpf4/raw/master/pics/sinabs.png
:alt: 2
2
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
====
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')
.. figure:: https://github.com/gesellkammer/bpf4/raw/master/pics/zeros.png
:alt: 1
1
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)
.. figure:: https://github.com/gesellkammer/bpf4/raw/master/pics/sinabs.png
:alt: 2
2
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
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