semvec/pypi-packages huggingface dataset:
mincepy-sci 0.2.0
Plugins to enable common scientific and machine learning type to be saved by mincePy
Requires Python: >=3.9
Keywords: concurrent, database, nosql, object-store, optimistic-locking, orm, schemaless

.. _mincePy: https://github.com/muhrin/mincepy
.. _mincepy documentation: https://mincepy.readthedocs.org/
.. _issue: https://github.com/muhrin/mincepy_sci/issues

.. _ase: https://wiki.fysik.dtu.dk/ase/
.. _e3nn: https://docs.e3nn.org/en/stable/
.. _jax: https://jax.readthedocs.io/
.. _numpy: https://numpy.org/
.. _pandas: https://pandas.pydata.org/
.. _plams: https://www.scm.com/doc/plams/index.html
.. _pyilt2: http://wgserve.de/pyilt2/
.. _pymatgen: https://pymatgen.org/
.. _pytorch: https://pytorch.org/
.. _rdkit: https://www.rdkit.org/


mincePy Sci
===========

.. image:: https://codecov.io/gh/muhrin/mincepy_sci/branch/develop/graph/badge.svg
    :target: https://codecov.io/gh/muhrin/mincepy_sci
    :alt: Coverage

.. image:: https://github.com/muhrin/mincepy_sci/actions/workflows/ci.yml/badge.svg
    :target: https://github.com/muhrin/mincepy_sci/actions/workflows/ci.yml
    :alt: Github actions

.. image:: https://img.shields.io/pypi/v/mincepy-sci.svg
    :target: https://pypi.python.org/pypi/mincepy_sci/
    :alt: Latest Version

.. image:: https://img.shields.io/pypi/pyversions/mincepy-sci.svg
    :target: https://pypi.python.org/pypi/mincepy_sci/

.. image:: https://img.shields.io/pypi/l/mincepy-sci.svg
    :target: https://pypi.python.org/pypi/mincepy_sci/

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black

mincePy: move the database to one side and let your objects take centre stage.

MincePy Sci is a set of plugins for `mincePy`_ that enable common scientific data formats to be saved.

See `mincepy documentation`_ for more information.

This plugin provide support for:

`e3nn`_:
    * ``o3``:
        * ``Irrep`` and ``Irreps``
        * ``ReducedTensorProduct``
        * ``TensorProduct``
        * ``TensorSquare``
    * ``nn``:
        * ``Gate``
        * ``Shortcut``
        * ``Extract``
        * ``Activation``
    * ``math``:
        * ``normalize2mom``


`ase`_:
    * ``Atoms``
    * ``Cell``

`jax`_:
    * ``Array``

`numpy`_:
    * ``ndarray``


`pandas`_:
    * ``DataFrame``


`plams`_:
    * ``Settings``
    * ``Molecule``
    * ``Results``


`pyilt2`_:
    * ``dataset``

(unfortunately the pypi version is out of date and so you should use an updated version from `here <https://github.com/muhrin/pyilt2>`_).

`pymatgen`_:
    * ``Structure``
    * ``Molecule``
    * ``BandStructure``
    * ``CompleteDos``
    * ``PeriodicSite``


`pytorch`_:
    * ``Tensor``
    * ``Conv2d``
    * ``MaxPool2d``
    * ``Linear``
    * ``ModuleList``
    * ``ModuleDict``


`rdkit`_:
    * ``Mol``

Contributing
------------

We'd love to get more data types supported by `mincePy`_!
For now, the easiest way is to follow the examples set by the existing types and contribute a PR.
If you'd like more (i.e. any) documentation on how to do this or would like to request that someone with more expertise makes a plugin just create an `issue`_ and we'll see what we can do.
si-clad 0.0.2
This package provides a Statistical Inference framework for testing the anomaly results obtained by DBSCAN algorithm.

# Statistical Inference for Cluster-based Anomaly Detection

This package provides a Statistical Inference framework for Cluster-based Anomaly Detection, with controllable FPR. In particular, we propose a valid p-value for testing the anomaly results obtained by DBSCAN algorithm. Basically, the problem is decomposed into multiple tractable sub-problems to enable an efficient test with the highest TPR while controlling the FPR through the divide-and-conquer approach. 

This package has the following requirements:

    mpmath
    numpy>=1.23.0
    scipy>=1.4.1

## Installization

You can install this package from PyPI using:

`pip install si-clad`

### Example
```python 
from si_clad import SI_CLAD, generate, DBSCAN_AD

n = 50
d = 10
delta =  0 #no true outliers
X, Sigma, _ = generate(n, d, delta)

minpts =  10
eps = 3
O, _ = DBSCAN_AD(eps, minpts).fit(X)
p_value = SI_CLAD(X, Sigma, minpts, eps, O, j = None) #randomly choose an outlier j for testing

print(p_value)

```