Metadata-Version: 2.1
Name: cr.cube
Version: 2.1.32
Summary: Crunch.io Cube library
Home-page: https://github.com/Crunch-io/crunch-cube/
Author: Crunch.io
Author-email: dev@crunch.io
License: MIT License
Description: # 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).
        
        ---
        [![Build Status](https://travis-ci.org/Crunch-io/crunch-cube.png?branch=master)](https://travis-ci.org/Crunch-io/crunch-cube)
        [![Coverage Status](https://codecov.io/gh/Crunch-io/crunch-cube/branch/master/graph/badge.svg?token=C6auKOj8tZ)](https://codecov.io/gh/Crunch-io/crunch-cube)
        [![Documentation Status](https://readthedocs.org/projects/crunch-cube/badge/?version=latest)](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)
        ---
        
        ## Changes
        
        ### 2.1.32
        - Fix scale_std_dev and scale_std_err for stripes when total counts is 0.
        
        ### 2.1.31
        - Implement sort-by-value for all measures that have been consolidated so far.
        - Zscores measure consolidation
        
        ### 2.1.30
        - Fix population counts for categorical array
        
        ### 2.1.29
        - Omit rows/columns margin on subtotal difference.
        
        ### 2.1.28
        - fix: pairwise mean indices in case of empty numpy array
        - population fraction for Categorical Dates
        - Omit scale median on the row of a row subtotal difference or the column of a column subtotal difference.
        
        ### 2.1.27
        - fix: population counts for cat dates
        - fix: filtered population fraction for a univariate cat date filter
        
        ### 2.1.26
        - fix: overlaps for MR x MR
        
        ### 2.1.25
        - fix: sort-by-value keyword to "percent"
        
        ### 2.1.24
        - Wire up `_Strand` sort-by-value for "univariate-measure" keyword case.
        - This should fix the existing alpha-Sentry error on sort-by-value for
          FREQUENCY analyses (aka. 1D card, `_Strand`).
        
        
        For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: testing
