Cube Objects

Cube

class cr.cube.cube.Cube(response: Union[str, Dict[KT, VT]], cube_idx: Optional[int] = None, transforms: Optional[Dict[KT, VT]] = None, population: Optional[int] = None, mask_size: int = 0)

Provides access to individual slices on a cube-result.

It also provides some attributes of the overall cube-result.

cube_idx must be None (or omitted) for a single-cube CubeSet. This indicates the CubeSet contains only a single cube and influences behaviors like CA-as-0th.

available_measures

frozenset of available CUBE_MEASURE members in the cube response.

counts_with_missings

ndarray of weighted, unweighted or valid counts including missing values.

The difference from .counts is that this property includes value for missing categories.

covariance

Optional float64 ndarray of the cube_covariance if the measure exists.

cube_index

Offset of this cube within its CubeSet.

description

Return the description of the cube.

dimension_types

Tuple of DIMENSION_TYPE member for each dimension of cube.

dimensions

List of visible dimensions.

A cube involving a multiple-response (MR) variable has two dimensions for that variable (subvariables and categories dimensions), but is “collapsed” into a single effective dimension for cube-user purposes (its categories dimension is supressed). This collection will contain a single dimension for each MR variable and therefore may have fewer dimensions than appear in the cube response.

has_weighted_counts

True if cube response has weighted count data.

inflate() → cr.cube.cube.Cube

Return new Cube object with rows-dimension added.

A multi-cube (tabbook) response formed from a function (e.g. mean()) on a numeric variable arrives without a rows-dimension.

means

Optional float64 ndarray of the cube_means if the measure exists.

missing

Get missing count of a cube.

n_responses

Total (int) number of responses considered.

name

Return the name of the cube.

If the cube has 2 diensions, return the name of the second one. In case of a different number of dimensions, default to returning the name of the last one. In case of no dimensions, return the empty string.

ndim

int count of dimensions for this cube.

overlaps

Optional float64 ndarray of cube_overlaps if the measure exists.

The array has as many dimensions as there are defined in the cube query, plus the extra subvariables dimension as the last dimension.

partitions

Sequence of _Slice, _Strand, or _Nub objects from this cube-result.

population_fraction

The filtered/unfiltered ratio for cube response.

This value is required for properly calculating population on a cube where a filter has been applied. Returns 1.0 for an unfiltered cube. Returns np.nan if the unfiltered count is zero, which would otherwise result in a divide-by-zero error.

stddev

Optional float64 ndarray of the cube_stddev if the measure exists.

sums

Optional float64 ndarray of the cube_sum if the measure exists.

title

str alternate-name given to cube-result.

This value is suitable for naming a Strand when displayed as a column. In this use-case it is a stand-in for the columns-dimension name since a strand has no columns dimension.

unweighted_counts

ndarray of unweighted counts, valid elements only.

Unweighted counts are drawn from the result.counts field of the cube result. These counts are always present, even when the measure is numeric and there are no count measures. These counts are always unweighted, regardless of whether the cube is “weighted”.

In case of presence of valid counts in the cube response the counts are replaced with the valid counts measure.

unweighted_valid_counts

Optional float64 ndarray of unweighted_valid_counts if the measure exists.

valid_counts_summary_range

Optional (min, max) tuple of summary valid counts

valid_overlaps

Optional float64 ndarray of cube_valid_overlaps if the measure exists.

The array has as many dimensions as there are defined in the cube query, plus the extra subvariables dimension as the last dimension.

weighted_counts

ndarray of weighted counts, valid elements only.

In case of presence of valid counts in the cube response the weighted counts are replaced with the valid counts measure.

weighted_valid_counts

Optional float64 ndarray of weighted_valid_counts if the measure exists.

CubeSet

class cr.cube.cube.CubeSet(cube_responses: List[Dict[KT, VT]], transforms: Dict[KT, VT], population: int, min_base: int)

Represents a multi-cube cube-response.

Also works just fine for a single cube-response passed inside a sequence, allowing uniform handling of single and multi-cube responses.

cube_responses is a sequence of cube-response dicts received from Crunch. The sequence can contain a single item, such as a cube-response for a slide, but it must be contained in a sequence. A tabbook cube-response sequence can be passed as it was received.

transforms is a sequence of transforms dicts corresponding in order to the cube-responses. population is the estimated target population and is used when a population-projection measure is requested. min_base is an integer representing the minimum sample-size used for indicating values that are unreliable by reason of insufficient sample (base).

available_measures

frozenset of available measures of the first cube in this set.

can_show_pairwise

True if all 2D cubes in a multi-cube set can provide pairwise comparison.

description

str description of first cube in this set.

has_weighted_counts

True if cube-responses include a weighted-count measure.

is_ca_as_0th

True for multi-cube when first cube represents a categorical-array.

A “CA-as-0th” tabbook tab is “3D” in the sense it is “sliced” into one table (partition-set) for each of the CA subvariables.

missing_count

The number of missing values from first cube in this set.

n_responses

Total number of responses considered from first cube in this set.

name

str name of first cube in this set.

partition_sets

Sequence of cube-partition collections across all cubes of this cube-set.

This value might look like the following for a ca-as-0th tabbook. For example:

(
    (_Strand, _Slice, _Slice),
    (_Strand, _Slice, _Slice),
    (_Strand, _Slice, _Slice),
)

and might often look like this for a typical slide:

((_Slice,))

Each partition set represents the partitions for a single “stacked” table. A 2D slide has a single partition-set of a single _Slice object, as in the second example above. A 3D slide would have multiple partition sets, each of a single _Slice. A tabook will have multiple partitions in each set, the first being a _Strand and the rest being _Slice objects. Multiple partition sets only arise for a tabbook in the CA-as-0th case.

population_fraction

The filtered/unfiltered ratio for this cube-set.

This value is required for properly calculating population on a cube where a filter has been applied. Returns 1.0 for an unfiltered cube. Returns np.nan if the unfiltered count is zero, which would otherwise result in a divide-by-zero error.

valid_counts_summary_range

The valid count summary values from first cube in this set.