EcoPy: Ecological Data Analysis in Python
Project description
EcoPy: Python for Ecological Data Analyses
******************************************
**EcoPy** provides tools for ecological data analyses. In general, it focuses on multivariate data analysis, which can be useful in any field, but with particular attention to those methods widely used in ecology. `The homepage, with full documentation and examples, can be found here <http://ecopy.readthedocs.org>`_
Install via 'pip install ecopy'
What's New
=======
0.0.81
-------
- Updated PCA to use SVD instead of eigen decomposition
License
=====
**EcoPy** is distributed under the MIT license
Version
=====
0.0.8
Examples
======
Transforming a site x species matrix, dividing by site totals::
import ecopy as ep
varespec = ep.load_data('varespec')
newMat = ep.transform(varespec, method='total', axis=1)
Calculating Bray-Curtis dissimilarities on the new matrix::
brayMat = ep.distance(newMat, method='bray')
PCA on US Arrests data::
USArrests = ep.load_data('USArrests')
prcomp = ep.pca(USArrests, scaled = True)
prcomp.summary()
prcomp.biplot(scale = 0)
prcomp.biplot(scale = 1, obsNames = True)
Full online documentation is a work in progress
TO-DO
====
- MINIMUM SPANNING TREE
- PROCRUSTES ROTATION
- LINEAR/SURFACE ENVIRONMENTAL FITTING
- MAXENT WRAPPER
- MANY MANY OTHER THINGS
******************************************
**EcoPy** provides tools for ecological data analyses. In general, it focuses on multivariate data analysis, which can be useful in any field, but with particular attention to those methods widely used in ecology. `The homepage, with full documentation and examples, can be found here <http://ecopy.readthedocs.org>`_
Install via 'pip install ecopy'
What's New
=======
0.0.81
-------
- Updated PCA to use SVD instead of eigen decomposition
License
=====
**EcoPy** is distributed under the MIT license
Version
=====
0.0.8
Examples
======
Transforming a site x species matrix, dividing by site totals::
import ecopy as ep
varespec = ep.load_data('varespec')
newMat = ep.transform(varespec, method='total', axis=1)
Calculating Bray-Curtis dissimilarities on the new matrix::
brayMat = ep.distance(newMat, method='bray')
PCA on US Arrests data::
USArrests = ep.load_data('USArrests')
prcomp = ep.pca(USArrests, scaled = True)
prcomp.summary()
prcomp.biplot(scale = 0)
prcomp.biplot(scale = 1, obsNames = True)
Full online documentation is a work in progress
TO-DO
====
- MINIMUM SPANNING TREE
- PROCRUSTES ROTATION
- LINEAR/SURFACE ENVIRONMENTAL FITTING
- MAXENT WRAPPER
- MANY MANY OTHER THINGS
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