Skip to content

Commit

Permalink
Add schema specification to docs
Browse files Browse the repository at this point in the history
  • Loading branch information
OriolAbril committed Nov 17, 2019
1 parent 51e41af commit a314e5e
Show file tree
Hide file tree
Showing 13 changed files with 10,201 additions and 1,255 deletions.
5 changes: 3 additions & 2 deletions doc/conf.py
Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
"matplotlib.sphinxext.plot_directive",
"numpydoc",
"nbsphinx",
"m2r",
"IPython.sphinxext.ipython_directive",
"IPython.sphinxext.ipython_console_highlighting",
"gallery_generator",
Expand All @@ -79,8 +80,8 @@
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = ".rst"
source_suffix = ['.rst', '.md']
# source_suffix = ".rst"

# The master toctree document.
master_doc = "index"
Expand Down
6 changes: 5 additions & 1 deletion doc/index.rst
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,11 @@ ArviZ: Exploratory analysis of Bayesian models

ArviZ is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, sample diagnostics, model checking, and comparison.

The goal is to provide backend-agnostic tools for diagnostics and visualizations of Bayesian inference in Python, by first converting inference data into `xarray <https://xarray.pydata.org/en/stable/>`_ objects. See :doc:`here <notebooks/XarrayforArviZ>` for more on xarray and ArviZ.
The goal is to provide backend-agnostic tools for diagnostics and visualizations of Bayesian inference in Python,
by first converting inference data into `xarray <https://xarray.pydata.org/en/stable/>`_ objects.
See :doc:`here <notebooks/XarrayforArviZ>` for more on xarray and ArviZ usage
and :doc:`here <schema/schema>` for more on ``InferenceData`` structure
and specification.



Expand Down
2 changes: 1 addition & 1 deletion doc/notebooks/InferenceDataCookbook.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
"metadata": {},
"source": [
"# Inference Data Cookbook\n",
"`InferenceData` is the central data format for ArviZ. `InferenceData` itself is just a container that maintains references to one or more `xarray.Dataset`. Below are various ways to generate an `InferenceData` object. See [here](XarrayforArviZ.ipynb) for more on xarray."
"`InferenceData` is the central data format for ArviZ. `InferenceData` itself is just a container that maintains references to one or more `xarray.Dataset`. See the `InferenceData` structure specification [here](../schema/schema.html). Below are various ways to generate an `InferenceData` object. See [here](XarrayforArviZ.ipynb) for more on xarray."
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions doc/notebooks/Introduction.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,7 @@
"\n",
"For much more powerful querying, analysis and plotting, we can use built-in ArviZ utilities to convert PyMC3 objects to xarray datasets. Note we are also giving some information about labelling.\n",
"\n",
"ArviZ is built to work with `InferenceData`, and the more *groups* it has access to, the more powerful analyses it can perform. Here is a plot of the trace, which is common in PyMC3 workflows. Note the intelligent labels."
"ArviZ is built to work with `InferenceData`, and the more *groups* it has access to, the more powerful analyses it can perform. See the `InferenceData` structure specification [here](../schema/schema.html). Here is a plot of the trace, which is common in PyMC3 workflows. Note the intelligent labels."
]
},
{
Expand Down Expand Up @@ -470,7 +470,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.8"
}
},
"nbformat": 4,
Expand Down
4 changes: 3 additions & 1 deletion doc/notebooks/XarrayforArviZ.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,8 @@
"\n",
"</ul> \n",
"\n",
"See the `InferenceData` structure specification [here](../schema/schema.html).\n",
"\n",
"\n",
"## Why not Pandas Dataframes or Numpy Arrays?\n",
"Data from probabilistic programming is naturally high dimensional. To add to the complexity ArviZ must handle the data generated from multiple Bayesian Modeling libraries, such as PyMC3 and PyStan. This is an application that the *xarray* package handles quite well. The xarray package lets users manage high dimensional data with human readable dimensions and coordinates quite easily.\n",
Expand Down Expand Up @@ -259,7 +261,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.8"
}
},
"nbformat": 4,
Expand Down

0 comments on commit a314e5e

Please sign in to comment.