# 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.cube import Cube
>>> ### Obtain the crunch cube JSON payload using app.crunch.io, pycrunch, rcrunch or scrunch
>>> ### And store it in the 'cube_JSON_response' variable
>>> cube = Cube(cube_JSON_response)
>>> print(cube)
Cube(name='MyCube', dimension_types='CAT x CAT')
>>> cube.counts
np.array([[1169, 547],
[1473, 1261]])
## 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).
---
[](https://travis-ci.org/Crunch-io/crunch-cube)
[](https://codecov.io/gh/Crunch-io/crunch-cube)
[](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)
[](https://www.codefactor.io/repository/github/crunch-io/crunch-cube)
---
## Changes
### 3.0.42
- Inflate cubes that are single column filters
### 3.0.41
- Remove `deepcopy` from dimension module due to a performance issue
### 3.0.40
- Fix bug with weighted vs unweighted in pairwise effect calculation
### 3.0.39
- Remove cube response deepcopy due to a performance issue
### 3.0.38
- Improve calculation of DoF for pairwise comparison
- Use effective counts as column bases for DoF
### 3.0.37
- Add squared counts as a cube measure
- Enable calculating pairwise stats with effective denominator
### 3.0.36
- Fix bug in pairwise sig values for means.
### 3.0.35
- Fix bug where categorical dimension would sometimes be interpreted as `MR_CATS`.
For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).
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"description": "# crunch-cube\n\nOpen Source Python implementation of the API for working with CrunchCubes\n\n## Introduction\n\nThis package contains the implementation of the CrunchCube API. It is used to\nextract useful information from CrunchCube responses (we'll refer to them as\n_cubes_ in the subsequent text). _Cubes_ are obtained from the *Crunch.io*\nplatform, as JSON responses to the specific _queries_ created by the user.\nThese queries specify which data the user wants to extract from the Crunch.io\nsystem. The most common usage is to obtain the following:\n\n - Cross correlation between different variable\n - Margins of the cross tab _cube_\n - Proportions of the cross tab _cube_ (e.g. proportions of each single element to the entire sample size)\n - Percentages\n\nWhen the data is obtained from the Crunch.io platform, it needs to be\ninterpreted to the form that's convenient for a user. The actual shape of the\n_cube_ JSON contains many internal details, which are not of essence to the\nend-user (but are still necessary for proper _cube_ functionality).\n\nThe job of this library is to provide a convenient API that handles those\nintricacies, and enables the user to quickly and easily obtain (extract) the\nrelevant data from the _cube_. Such data is best represented in a table-like\nformat. For this reason, the most of the API functions return some form of the\n`ndarray` type, from the `numpy` package. Each function is explained in greater\ndetail, uner its own section, under the API subsection of this document.\n\n## Installation\n\nThe `cr.cube` package can be installed by using the `pip install`:\n\n pip install cr.cube\n\n\n### For developers\n\nFor development mode, `cr.cube` needs to be installed from the local checkout\nof the `crunch-cube` repository. It is strongly advised to use `virtualenv`.\nAssuming you've created and activated a virtual environment `venv`, navigate\nto the top-level folder of the repo, on the local file system, and run:\n\n pip install -e .\n\nor\n\n python setup.py develop\n\n### Running tests\n\nTo setup and run tests, you will need to install `cr.cube` as well as testing\ndependencies. To do this, from the root directory, simply run:\n\n pip install -e .[testing]\n\nAnd then tests can be run using `py.test` in the root directory:\n\n pytest\n\n## Usage\n\nAfter the `cr.cube` package has been successfully installed, the usage is as\nsimple as:\n\n\n >>> from cr.cube.cube import Cube\n\n >>> ### Obtain the crunch cube JSON payload using app.crunch.io, pycrunch, rcrunch or scrunch\n >>> ### And store it in the 'cube_JSON_response' variable\n\n >>> cube = Cube(cube_JSON_response)\n >>> print(cube)\n Cube(name='MyCube', dimension_types='CAT x CAT')\n >>> cube.counts\n np.array([[1169, 547],\n [1473, 1261]])\n\n## API\n\n### `as_array`\n\nTabular, or matrix, representation of the _cube_. The detailed description can\nbe found\n[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).\n\n### `margin`\n\nCalculates margins of the _cube_. The detailed description can be found\n[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).\n\n### `proportions`\n\nCalculates proportions of single variable elements to the whole sample size.\nThe detailed description can be found\n[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).\n\n### `percentages`\n\nCalculates percentages of single variable elements to the whole sample size.\nThe detailed description can be found\n[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).\n\n---\n[](https://travis-ci.org/Crunch-io/crunch-cube)\n[](https://codecov.io/gh/Crunch-io/crunch-cube)\n[](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)\n[](https://www.codefactor.io/repository/github/crunch-io/crunch-cube)\n---\n\n## Changes\n\n### 3.0.42\n- Inflate cubes that are single column filters\n\n### 3.0.41\n- Remove `deepcopy` from dimension module due to a performance issue\n\n### 3.0.40\n- Fix bug with weighted vs unweighted in pairwise effect calculation\n\n### 3.0.39\n- Remove cube response deepcopy due to a performance issue\n\n### 3.0.38\n- Improve calculation of DoF for pairwise comparison\n- Use effective counts as column bases for DoF\n\n### 3.0.37\n- Add squared counts as a cube measure\n- Enable calculating pairwise stats with effective denominator\n\n### 3.0.36\n- Fix bug in pairwise sig values for means.\n\n### 3.0.35\n- Fix bug where categorical dimension would sometimes be interpreted as `MR_CATS`.\n\n\nFor a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).\n\n\n",
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