Metadata-Version: 2.1
Name: cr.cube
Version: 1.6.10
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 Crunch Cubes
        
        ## Introduction
        
        This package contains the implementation of the Crunch Cube API. It is used to
        extract useful information from Crunch Cube 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 Crunch Cube package can be installed by using the `pip install`:
        
            pip install cr.cube
        
        
        ### For developers
        
        For development mode, Crunch Cube needs to be installed from the local checkout
        of the `crunch-cube` repository. Navigate to the top-level folder of the repo,
        on the local file system, and run:
        
            python setup.py develop
        
        ## 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://coveralls.io/repos/github/Crunch-io/crunch-cube/badge.svg?branch=master)](https://coveralls.io/github/Crunch-io/crunch-cube?branch=master)
        [![Documentation Status](https://readthedocs.org/projects/crunch-cube/badge/?version=latest)](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)
        
        
        ## Changes
        
        ### 1.0 Initial release
        
        ### 1.1 Fix stray ipdb.
        
        ### 1.2 Support exporter
        
        ### 1.3 Implement Headers & Subtotals
        
        ### 1.4 Update based on tabbook tests from `cr.lib`
        
        #### 1.4.1 Update based on deck tests from `cr.server`
        
        #### 1.4.2 Fix bugs discovered by first `cr.exporter` deploy to alpha
        
        #### 1.4.3 Fix bug (exporting 2D crtab with H&S on row only)
        
        #### 1.4.4 Implement obtaining labels with category ids (useful for H&S in exporter)
        
        #### 1.4.5 Fix MR x MR proportions calculation
        
        ### 1.5.0 Start implementing index table functionality
        
        #### 1.5.1 Implement index for MR x MR
        
        #### 1.5.2 Fix bugs with `anchor: 0` for H&S
        
        #### 1.5.3 Fix bugs with invalid input data for H&S
        
        ### 1.6.0 Z-Score and bug fixes.
        
        #### 1.6.1 `standardized_residuals` are now included.
        
        #### 1.6.2 support "Before" and "After" in variable transformations since they exist in zz9 data.
        
        #### 1.6.4 Fixes for 3d Pruning.
        
        #### 1.6.5 Fixes for Pruning and Headers and subtotals.
        - Population size support.
        - Fx various calculations in 3d cubes.
        
        #### 1.6.6 Added support for CubeSlice, which always represents a
        - 2D cube (even if they're the slices of a 3D cube).
        - Various fixes for support of wide-export
        
        #### 1.6.7 Population fraction
        - Various bugfixes and optimizations.
        - Add property `population_fraction`. This is needed for the exporter to be able to calculate the correct population counts, based on weighted/unweighted and filtered/unfiltered states of the cube.
        - Apply newly added `population_fraction` to the calculation of `population_counts`.
        - Modify API for `scale_means`. It now accepts additional parameters `hs_dims` (defaults to `None`) and `prune` (defaults to `False`). Also, the format of the return value is slightly different in nature. It is a list of lists of numpy arrrays. It functions like this:
        
            - The outermost list corresponds to cube slices. If cube.ndim < 3, then it's a single-element list
            - Inner lists have either 1 or 2 elements (if they're a 1D cube slice, or a 2D cube slice, respectively).
            - If there are scale means defined on the corresponding dimension of the cube slice, then the inner list element is a numpy array with scale means. If it doesn't have scale means defined (numeric values), then the element is `None`.
        
        - Add property `ca_dim_ind` to `CubeSlice`.
        - Add property `is_double_mr` to `CubeSlice` (which is needed since it differs from the interpretation of the cube. E.g. MR x CA x MR will render slices which are *not* double MRs).
        - Add `shape`, `ndim`, and `scale_means` to `CubeSlice`, for accessibility.
        - `index` now also operates on slices (no api change).
        
        #### 1.6.8 Scale Means Marginal
        - Add capability to calculate the scale means marginal. This is used when analysing a 2D cube, and obtaining a sort of a "scale mean _total_" for each of the variables constituting a cube.
        
        #### 1.6.9 Bugfix
        - When Categorical Array variable is selected in multitable export, and Scale Means is selected, the cube fails, because it tries to access the non-existing slice (the CA is only _interpreted_ as multiple slices in tabbooks). This fix makes sure that the export cube doesn't fail in such case.
        
        #### 1.6.10 Fix README on pypi
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: testing
