semvec/pypi-packages huggingface dataset:
climtrends 0.0.2
A utility for Bayesian trend regression with a variety of statistical models
Requires Python: >=3.6

# ClimTrends

`ClimTrends` is a python package aimed at making it easy to calculate linear
trends in a variety of statistical models.  The current implementation
includes:

  * Normal Distribution - trend in mean
  * Poisson Distribution - trend in mean
  * Exponential Distribution - trend in mean
  * Gamma Distribution - trend in mean and standard deviation
  * GEV Distribution - trend in mean

The module utilizes `datetime`-like objects as the input time value, which
makes it easy to interoperate with data from
[netCDF4](http://unidata.github.io/netcdf4-python/) and
[xarray](http://xarray.pydata.org/en/stable/). All models use a Bayesian
framework (using the [emcee](http://dfm.io/emcee/current/) package) and assume
a uniform prior on all model parameters. This module is object-oriented and
designed to be easily extendable for regressions of other distributions.  To do
so, one needs to sub-class the `ClimTrendModel` class and implement a few
required routines; see `TrendNormalModel.py` or other `Trend*Model.py` files
for examples.

# Getting started

```python
# dates - a set of input dates
# data - corresponding data from those dates

import climtrends
import numpy as np

# initialize the MCMC model
linear_model = climtrends.TrendNormalModel(dates, data)

# run the sampler
linear_model.run_mcmc_sampler(num_samples = 1000)

# get samples of the slopes
slopes = linear_model.get_mean_trend_samples()

# get the 5th and 95th percentile slopes
slopes_5 = np.percentile(slopes, 5)
slopes_95 = np.percentile(slopes, 95)
```

# Known Issues

* The model assumes that input data are vectors; things will likely break if not.
* Probably other issues exist.  This code is tested and is verified to work in some base cases,
  but it is still in alpha stage and has not been tested across a range of settings.
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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virtual-warehouse 0.2.1
Warehouse visualisation app
Requires Python: >=3.6,<3.9
Keywords: visualization,warehouse

# Virtual Warehouse
[![Documentation Status](https://readthedocs.org/projects/virtual-warehouse/badge/?version=latest)](https://virtual-warehouse.readthedocs.io/en/latest/?badge=latest) 
[![DeepSource](https://deepsource.io/gh/Breta01/virtual-warehouse.svg/?label=active+issues&show_trend=true)](https://deepsource.io/gh/Breta01/virtual-warehouse/?ref=repository-badge)

![Virtual Warehouse](docs/source/_static/banner.png)

**Documentation:** <https://virtual-warehouse.readthedocs.io>
asyncio37 0.1.3
Backport of asyncio in Python 3.7

asyncio37
---------

This package replaces default implementation of ``asyncio`` in Python 3.6 with
implementation in Python 3.7. A ``contextvars`` library 
`from MagicStack <https://github.com/MagicStack/contextvars>`_ is also
included to make sure the package functions well.

Notes
=====
After installation, this package will replace the builtin implementation of
``asyncio`` in Python 3.6. To use the builtin implementation, you have to
uninstall this package or add an environment variable ``USE_BUILTIN_ASYNCIO=1``
to avoid loading the replacements.

As ``PyThreadState`` in Python 3.6 does not include an unique identifier,
we cannot cache it when calling ``get_running_loop``. Hence the performance
of the library may be degraded comparing to the original module in Python 3.7.

License
=======
The whole package is released under Apache License 2.0. Codes under ``asyncio``
module is copied from Python 3.7 source which is released under PSF License
Agreement with minimal modifications. Codes in ``contextvars.py`` is copied from
MagicStack codes released under Apache License 2.0.
Concentration-Free-Outlier-Factor 0.3.1
Calculate the Concentration Free Outlier Factor score, based on Angiulli's work
Requires Python: >=3.6

[![PyPI](https://github.com/luk-f/pyCFOF/actions/workflows/python-publish.yml/badge.svg)](https://github.com/luk-f/pyCFOF/actions/workflows/python-publish.yml)

# pyCFOF

## Pour commencer

### Installation

Lancer `pip install -r requirements.txt` ou `python3 -m pip install -r requirements.txt`.

Ou à partir du dépôt `pip install Concentration-Free-Outlier-Factor`.

### Utilisation

    >>> from pyCFOF import ConcentrationFreeOutlierFactor as CFOF
    >>> X = [[-1.1], [0.2], [101.1], [0.3]]
    >>> cfof = CFOF(n_neighbors=len(X), rho=[0.1])
    >>> cfof.fit_predict(X)
    array([[ 1],
           [ 1],
           [-1],
           [ 1]])
    >>> cfof.outlier_factor_
    array([[0.75],
           [0.5 ],
           [1.  ],
           [0.5 ]])

## Remerciements

Développements des travaux de :
 - Fabrizio Angiulli, [CFOF: A Concentration Free Measure for Anomaly Detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1):Article 4, 2020](https://dl.acm.org/doi/abs/10.1145/3362158)


Utilisation de :
 - [Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011](https://scikit-learn.org/stable/index.html)