Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Added figure to InferenceData tutorial #510

Merged
merged 3 commits into from
Jan 11, 2019
Merged

Added figure to InferenceData tutorial #510

Show file tree
Hide file tree
Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
2d073ef
Added figure to InferenceData tutorial
canyon289 Jan 7, 2019
8b9528a
Remove html for proper github rendering
canyon289 Jan 7, 2019
8c67740
Update per feedback
canyon289 Jan 8, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file added doc/notebooks/InferenceDataStructure.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
11 changes: 7 additions & 4 deletions doc/notebooks/XarrayforArviZ.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to xarray, InferenceData, and netCDF for ArviZ"
"# Introduction to xarray, InferenceData, and NetCDF for ArviZ"
]
},
{
Expand Down Expand Up @@ -44,7 +44,10 @@
"## 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",
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

nitpick:

  • PyMC3 and PyStan

  • and a few lines below where it says "the inspiration between InferenceData" I guess it should be "the inspiration for InferenceData"

"\n",
"Although seemingly more complex at a glance the Arviz devs believe that the usage of *xarray*, *InferenceData*, and *NetCDF* will simplify the handling, referencing, and serialization of data generated by MCMC runs."
"![InferenceData Structure](InferenceDataStructure.png) \n",
"\n",
"Above is a visual representation of the data structures and their relationships. Although seemingly more complex at a glance the ArviZ devs believe that the usage of *xarray*, *InferenceData*, and *NetCDF* will simplify the handling, referencing, and serialization of data generated during Bayesian analysis. \n",
"\n"
]
},
{
Expand Down Expand Up @@ -171,7 +174,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"It should be noted that the observed dataset contains only 8 data variables and doesn't have a chain and draw dimension or coordinates unlike posterior. This difference in sizes is the motivating reason behind *InferenceData*. Rather than force multiple different sized arrays into one array, or force users to manage multiple objects corresponding to different datasets, it is easier to hold references to each *xarray.Dataset* in an *InferenceData* object."
"It should be noted that the observed dataset contains only 8 data variables and doesn't have a chain and draw dimension or coordinates unlike posterior. This difference in sizes is the motivating reason behind *InferenceData*. Rather than force multiple different sized arrays into one array, or have users to manage multiple objects corresponding to different datasets, it is easier to hold references to each *xarray.Dataset* in an *InferenceData* object."
]
},
{
Expand Down Expand Up @@ -256,7 +259,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
"version": "3.5.6"
}
},
"nbformat": 4,
Expand Down