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.. badges
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.. logo
.. image:: docs/_static/topopy.png :align: center :alt: topopy
.. end_logo
.. introduction
A library for computing topological data structures stemming from Morse Theory. Given a set of arbitrarily arranged points in any dimension, this library is able to construct approximate topological structures using a neighborhood graph to simulate manifold structures.
.. end_introduction
.. installation
Installation
Currently, you can use pip_ to install this package
and all of its prerequisite libraries::
pip install topopy
.. _pip: https://pip.pypa.io/en/stable/
Or to install from source, install all of the prerequiste libraries:
* scipy_
* numpy_
* scikit-learn_
* networkx_
* nglpy_
.. _scipy: https://www.scipy.org/
.. _numpy: http://www.numpy.org/
.. _scikit-learn: http://scikit-learn.org/)
.. _networkx: https://networkx.github.io/
.. _nglpy: https://github.com/maljovec/nglpy
And then clone and build the source repository::
git clone https://github.com/maljovec/topopy.git
cd topopy
make
python setup.py [develop|install]
.. end_installation
.. usage
Example Usage
::
import nglpy as ngl
import numpy as np
import topopy
def hill(_x):
_x = np.atleast_2d(_x)
x = _x[:, 0]
y = _x[:, 1]
return np.exp(- ((x - .55)**2 + (y-.75)**2)/.125) + 0.01*(x+y)
X = np.random.rand(100,2)
Y = hill(X)
graph = ngl.EmptyRegionGraph(beta=1.0, relaxed=False, p=2.0)
msc = topopy.MorseSmaleComplex(graph=graph,
gradient='steepest',
normalization='feature')
msc.build(X, Y)
msc.get_partitions()
.. end_usage
FAQs
A library for computing topological data structures
We found that topopy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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