Merge 340ffbd into f4d0ca7

.gitignore

@@ -88,6 +88,8 @@ celerybeat-schedule
 venv/
 venv3/
 ENV/
+Pipenv
+Pipenv.lock
 
 # Spyder project settings
 .spyderproject

README.md

@@ -53,7 +53,7 @@ or
 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]
+    pip install -e ".[testing]"
 
 And then tests can be run using `py.test` in the root directory:
 
@@ -65,45 +65,109 @@ After the `cr.cube` package has been successfully installed, the usage is as
 simple as:
 
 
-    from cr.cube.crunch_cube import CrunchCube
+    from cr.cube.cube import Cube
 
     ### 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()
+    cube = Cube(cube_JSON_response)
+    slice_ = cube.partitions[0]
+    print(slice_.column_proportions)
 
-    ### Outputs:
+    ### Output (2D Cube):
     #
-    # np.array([
-    #     [5, 2],
-    #     [5, 3]
+    # np.ndarray([
+    # [0.38227848 0.33855186 0.39252336 0.44117647]
+    # [0.09620253 0.10665362 0.18691589 0.08823529]
+    # [0.47848101 0.44520548 0.57943925 0.52941176]
+    # [0.         0.         0.         0.        ]
+    # [0.09113924 0.09099804 0.14018692 0.17647059]
+    # [0.25316456 0.23091977 0.08411215 0.05882353]
+    # [0.09113924 0.13111546 0.13084112 0.14705882]
+    # [0.07594937 0.07632094 0.05607477 0.08823529]
+    # [0.01012658 0.02544031 0.00934579 0.        ]
     # ])
-
+
+    print(slice_.row_proportions)
+
+    ### Output (2D Cube:
+    #
+    # np.ndarray([
+    # [0.27256318 0.62454874 0.07581227 0.02707581]
+    # [0.22352941 0.64117647 0.11764706 0.01764706]
+    # [0.26104972 0.62845304 0.08563536 0.02486188]
+    # [       nan        nan        nan        nan]
+    # [0.24       0.62       0.1        0.04      ]
+    # [0.28818444 0.68011527 0.0259366  0.00576369]
+    # [0.19047619 0.70899471 0.07407407 0.02645503]
+    # [0.25641026 0.66666667 0.05128205 0.02564103]
+    # [0.12903226 0.83870968 0.03225806 0.        ]
+    # ])
+
+    # How to get the data values (%) from the cube
+
+    print(slice_.column_percentages)
+
+    ### Output:
+    #
+    # np.ndarray([
+    # [38.2278481 , 33.85518591, 39.25233645, 44.11764706],
+    # [ 9.62025316, 10.66536204, 18.69158879,  8.82352941],
+    # [47.84810127, 44.52054795, 57.94392523, 52.94117647],
+    # [ 0.        ,  0.        ,  0.        ,  0.        ],
+    # [ 9.11392405,  9.09980431, 14.01869159, 17.64705882],
+    # [25.3164557 , 23.09197652,  8.41121495,  5.88235294],
+    # [ 9.11392405, 13.11154599, 13.08411215, 14.70588235],
+    # [ 7.59493671,  7.63209393,  5.60747664,  8.82352941],
+    # [ 1.01265823,  2.54403131,  0.93457944,  0.        ]
+    # ]) 
+
+    print(cube.base_counts)
+
+    ### Output:
+    #
+    # np.ndarray([
+    # [151 346  42  15]
+    # [ 38 109  20   3]
+    # [  0   0   0   0]
+    # [ 36  93  15   6]
+    # [100 236   9   2]
+    # [ 36 134  14   5]
+    # [ 30  78   6   3]
+    # [  4  26   1   0]
+    # ])
+
 ## API
 
-### `as_array`
+### `base_counts` or `counts`
 
-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).
+`cube.counts` `cube.base_counts`
 
-### `margin`
+Tabular, or matrix, representation of the _cube_. 
 
-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).
+[^Comment]: [here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).
 
-### `proportions`
+### `rows_margin`
+`slice_.rows_margin`
+
+Calculates rows margin of the _slice_.
+
+[^Comment]: [here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).
+
+### `row_proportions` and `column_proportions`
+`slice_.row_proportions` `slice_.column_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`
+[^Comment]: [here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).
+
+### `row_percentages` and `column_percentages`
+
+`slice_.row_percentages` `slice_.column_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).
+
+[^Comment]: [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)

src/cr/cube/crunch_cube.py

@@ -123,6 +123,10 @@ def as_array(
         if prune:
             array = self._prune_body(array, transforms=include_transforms_for_dims)
 
+        warnings.warn(
+            "Deprecated. Use `cr.cube.cube.base_counts or cr.cube.cube.counts` instead.",
+            DeprecationWarning,
+        )
         return self._drop_mr_cat_dims(array)
 
     @lazyproperty