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[WIP] Add ArviZ installation guide [docs] #1551

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1 change: 1 addition & 0 deletions CHANGELOG.md
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### Documentation
* Added "Label guide" page and API section for `arviz.labels` module ([1201](https://github.com/arviz-devs/arviz/pull/1201))
* Add "Installation guide" page to the documentation ([1551](https://github.com/arviz-devs/arviz/pull/1551))

## v0.11.2 (2021 Feb 21)
### New features
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92 changes: 92 additions & 0 deletions doc/source/getting_started/Installation.rst
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##################
Installation guide
##################

This section provides detailed information about installing ArviZ. Most ArviZ
functionality is available with the basic requirements, but ArviZ also has optional
dependencies to further enhance the library. This guide will cover both basic and fully-fledged ArviZ installs and several installation methods.


******
Stable
******

ArviZ can be installed either using pip or conda-forge.

Using pip
=========

.. code:: bash

pip install arviz

Use the below pip command to install ArviZ with all of it's :ref:`Optional-dependencies`.

.. code:: bash

pip install arviz[all]

Using conda-forge
=================

.. code:: bash

conda install -c conda-forge arviz

.. _dev-version:

***********
Development
***********

If you want to install the latest development version of ArviZ, then you
may use

.. code:: bash

pip install git+https://github.com/arviz-devs/arviz


************
Dependencies
************

Required dependencies
=====================

The required dependencies for installing ArviZ are:

.. literalinclude:: ../../../requirements.txt

.. _Optional-dependencies:

Optional dependencies
=====================

The list of optional dependencies to further enhance ArviZ are.

.. literalinclude:: ../../../requirements-optional.txt


- Numba

Necessary to speed up the code computation. The installation details can be found
`here <https://numba.pydata.org/numba-doc/latest/user/installing.html>`_. Further details on enhanced functionality provided in ArviZ by Numba can be
`found here <https://arviz-devs.github.io/arviz/user_guide/Numba.html>`_.

- Bokeh

Necessary for creating advanced interactive visualisations. The Bokeh installation guide can be found `over here <http://docs.bokeh.org/en/dev/docs/first_steps/installation.html>`_.

- UltraJSON

If available, ArviZ makes use of faster ujson when :func:`arviz.from_json` is
invoked. UltraJSON can be either installed via `pip <https://pypi.org/project/ujson/>`_ or `conda <https://anaconda.org/anaconda/ujson>`_.

- Dask

Necessary to scale the packages and the surrounding ecosystem. The installation details can be found `at this link <https://docs.dask.org/en/latest/install.html>`_.




1 change: 1 addition & 0 deletions doc/source/getting_started/index.md
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# Getting Started

```{toctree}
Installation
Introduction
XarrayforArviZ
CreatingInferenceData
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6 changes: 1 addition & 5 deletions doc/source/index.rst
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Expand Up @@ -43,11 +43,7 @@ Alternatively you can use conda-forge

conda install -c conda-forge arviz

For the latest (unstable) version

.. code:: bash

pip install git+https://github.com/arviz-devs/arviz
To install the latest development version of ArviZ, please check the :ref:`Installation guide <dev-version>` for details.

**Contributions** and **issue reports** are very welcome at `the github repository <https://github.com/arviz-devs/arviz>`_. We have a `contributing guide <https://github.com/arviz-devs/arviz/blob/main/CONTRIBUTING.md>`_ to help you through the process. If you have any doubts, please do not hesitate to contact us on `gitter <https://gitter.im/arviz-devs/community>`_.

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