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arviz-0.9.0


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مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

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مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

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توضیحات

Exploratory analysis of Bayesian models
ویژگی مقدار
سیستم عامل -
نام فایل arviz-0.9.0
نام arviz
نسخه کتابخانه 0.9.0
نگهدارنده []
ایمیل نگهدارنده []
نویسنده ArviZ Developers
ایمیل نویسنده -
آدرس صفحه اصلی http://github.com/arviz-devs/arviz
آدرس اینترنتی https://pypi.org/project/arviz/
مجوز Apache-2.0
<img src="https://raw.githubusercontent.com/arviz-devs/arviz-project/main/arviz_logos/ArviZ.png#gh-light-mode-only" width=200></img> <img src="https://raw.githubusercontent.com/arviz-devs/arviz-project/main/arviz_logos/ArviZ_white.png#gh-dark-mode-only" width=200></img> [![PyPI version](https://badge.fury.io/py/arviz.svg)](https://badge.fury.io/py/arviz) [![Azure Build Status](https://dev.azure.com/ArviZ/ArviZ/_apis/build/status/arviz-devs.arviz?branchName=main)](https://dev.azure.com/ArviZ/ArviZ/_build/latest?definitionId=1&branchName=main) [![codecov](https://codecov.io/gh/arviz-devs/arviz/branch/main/graph/badge.svg)](https://codecov.io/gh/arviz-devs/arviz) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/arviz-devs/community) [![DOI](http://joss.theoj.org/papers/10.21105/joss.01143/status.svg)](https://doi.org/10.21105/joss.01143) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2540945.svg)](https://doi.org/10.5281/zenodo.2540945) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) ArviZ (pronounced "AR-_vees_") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, data storage, model checking, comparison and diagnostics. ### ArviZ in other languages ArviZ also has a Julia wrapper available [ArviZ.jl](https://julia.arviz.org/). ## Documentation The ArviZ documentation can be found in the [official docs](https://python.arviz.org/en/latest/index.html). First time users may find the [quickstart](https://python.arviz.org/en/latest/getting_started/Introduction.html) to be helpful. Additional guidance can be found in the [user guide](https://python.arviz.org/en/latest/user_guide/index.html). ## Installation ### Stable ArviZ is available for installation from [PyPI](https://pypi.org/project/arviz/). The latest stable version can be installed using pip: ``` pip install arviz ``` ArviZ is also available through [conda-forge](https://anaconda.org/conda-forge/arviz). ``` conda install -c conda-forge arviz ``` ### Development The latest development version can be installed from the main branch using pip: ``` pip install git+git://github.com/arviz-devs/arviz.git ``` Another option is to clone the repository and install using git and setuptools: ``` git clone https://github.com/arviz-devs/arviz.git cd arviz python setup.py install ``` ------------------------------------------------------------------------------- ## [Gallery](https://python.arviz.org/en/latest/examples/index.html) <p> <table> <tr> <td> <a href="https://python.arviz.org/en/latest/examples/plot_forest_ridge.html"> <img alt="Ridge plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_forest_ridge.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_forest.html"> <img alt="Forest Plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_forest.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_violin.html"> <img alt="Violin Plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_violin.png" /> </a> </td> </tr> <tr> <td> <a href="https://python.arviz.org/en/latest/examples/plot_ppc.html"> <img alt="Posterior predictive plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_ppc.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_dot.html"> <img alt="Joint plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_dot.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_posterior.html"> <img alt="Posterior plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_posterior.png" /> </a> </td> </tr> <tr> <td> <a href="https://python.arviz.org/en/latest/examples/plot_density.html"> <img alt="Density plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_density.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_pair.html"> <img alt="Pair plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_pair.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_pair_hex.html"> <img alt="Hexbin Pair plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_pair_hex.png" /> </a> </td> </tr> <tr> <td> <a href="https://python.arviz.org/en/latest/examples/plot_trace.html"> <img alt="Trace plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_trace.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_energy.html"> <img alt="Energy Plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_energy.png" /> </a> </td> <td> <a href="https://python.arviz.org/en/latest/examples/plot_rank.html"> <img alt="Rank Plot" src="https://raw.githubusercontent.com/arviz-devs/arviz/gh-pages/_images/mpl_plot_rank.png" /> </a> </td> </tr> </table> ## Dependencies ArviZ is tested on Python 3.7, 3.8 and 3.9, and depends on NumPy, SciPy, xarray, and Matplotlib. ## Citation If you use ArviZ and want to cite it please use [![DOI](http://joss.theoj.org/papers/10.21105/joss.01143/status.svg)](https://doi.org/10.21105/joss.01143) Here is the citation in BibTeX format ``` @article{arviz_2019, doi = {10.21105/joss.01143}, url = {https://doi.org/10.21105/joss.01143}, year = {2019}, publisher = {The Open Journal}, volume = {4}, number = {33}, pages = {1143}, author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin}, title = {ArviZ a unified library for exploratory analysis of Bayesian models in Python}, journal = {Journal of Open Source Software} } ``` ## Contributions ArviZ is a community project and welcomes contributions. Additional information can be found in the [Contributing Readme](https://github.com/arviz-devs/arviz/blob/main/CONTRIBUTING.md) ## Code of Conduct ArviZ wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/arviz-devs/arviz/blob/main/CODE_OF_CONDUCT.md) ## Donations ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate [here](https://numfocus.org/donate-to-arviz). ## Sponsors [![NumFOCUS](https://www.numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png)](https://numfocus.org)


نیازمندی

مقدار نام
>=60.0.0 setuptools
>=3.2 matplotlib
>=1.20.0 numpy
>=1.8.0 scipy
- packaging
>=1.3.0 pandas
>=0.21.0 xarray
>=1.0.2 h5netcdf
>=4.1.0 typing-extensions
>=0.3 xarray-einstats
- numba
- netcdf4
<3.0,>=1.4.0 bokeh
- contourpy
- ujson
- dask[distributed]
>=2.5.0 zarr


زبان مورد نیاز

مقدار نام
>=3.8 Python


نحوه نصب


نصب پکیج whl arviz-0.9.0:

    pip install arviz-0.9.0.whl


نصب پکیج tar.gz arviz-0.9.0:

    pip install arviz-0.9.0.tar.gz