# crunch-cube

Open Source Python implementation of the API for working with CrunchCubes

## Introduction

This package contains the implementation of the CrunchCube API. It is used to
extract useful information from CrunchCube responses (we'll refer to them as
_cubes_ in the subsequent text). _Cubes_ are obtained from the *Crunch.io*
platform, as JSON responses to the specific _queries_ created by the user.
These queries specify which data the user wants to extract from the Crunch.io
system. The most common usage is to obtain the following:

 - Cross correlation between different variable
 - Margins of the cross tab _cube_
 - Proportions of the cross tab _cube_ (e.g. proportions of each single element to the entire sample size)
 - Percentages

When the data is obtained from the Crunch.io platform, it needs to be
interpreted to the form that's convenient for a user. The actual shape of the
_cube_ JSON contains many internal details, which are not of essence to the
end-user (but are still necessary for proper _cube_ functionality).

The job of this library is to provide a convenient API that handles those
intricacies, and enables the user to quickly and easily obtain (extract) the
relevant data from the _cube_. Such data is best represented in a table-like
format. For this reason, the most of the API functions return some form of the
`ndarray` type, from the `numpy` package. Each function is explained in greater
detail, uner its own section, under the API subsection of this document.

## Installation

The `cr.cube` package can be installed by using the `pip install`:

    pip install cr.cube


### For developers

For development mode, `cr.cube` needs to be installed from the local checkout
of the `crunch-cube` repository. It is strongly advised to use `virtualenv`.
Assuming you've created and activated a virtual environment `venv`, navigate
to the top-level folder of the repo, on the local file system, and run:

    pip install -e .

or

    python setup.py develop

### Running tests

To setup and run tests, you will need to install `cr.cube` as well as testing
dependencies. To do this, from the root directory, simply run:

    pip install -e .[testing]

And then tests can be run using `py.test` in the root directory:

    pytest

## Usage

After the `cr.cube` package has been successfully installed, the usage is as
simple as:


    from cr.cube.crunch_cube import CrunchCube

    ### Obtain the crunch cube JSON from the Crunch.io
    ### And store it in the 'cube_JSON_response' variable

    cube = CrunchCube(cube_JSON_response)
    cube.as_array()

    ### Outputs:
    #
    # np.array([
    #     [5, 2],
    #     [5, 3]
    # ])

## API

### `as_array`

Tabular, or matrix, representation of the _cube_. The detailed description can
be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `margin`

Calculates margins of the _cube_. The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `proportions`

Calculates proportions of single variable elements to the whole sample size.
The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `percentages`

Calculates percentages of single variable elements to the whole sample size.
The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

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## Changes

### 2.3.1
- Consolidate weighted counts
- Fix bug with weighted counts for numeric arrays

### 2.3.0
- Consolidation of weighted counts such that bases are no longer calculated by
  adding across subvariables.
- Removed the `_Slice.table_margin_unpruned` property, instead use
  `_Slice.table_margin_range` to get the unpruned range of table margins.

### 2.2.3
- More sort-by-value support including a fallback to payload order


For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).
