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Two level pairwise t #204
Two level pairwise t #204
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@lazyproperty | ||
def _df(self): | ||
# if the cube to which the slice belongs is a CATxMRxITSELF |
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If it's possible i would add a docstring for degree of freedom just summarising the existing comment here
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I didn't write this class, I just reordered its methods while I was cleaning up the module, so I don't know what this method is supposed to do (and don't really want to right at the moment :).
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some minor comments
k, fixed the two docstrings, explanations on the rest :) |
Basic manipulation of transforms is general to all cube-partition sub-types. Move ownership of `._transforms_arg` instance variable to CubePartition in preparation for moving other shared transforms responsibilities.
Generalize `._transforms_dict` property to CubePartition.
Replace redundant local implementations with property on base class.
Also generally clean up while in the neighborhood. * Old-style classes have unexpected behavior differences in corner cases, in particular with mocks in unit tests. Make these new-style classes by adding (object) superclass. * Normalize method ordering. * Improve docstrings and formatting.
Make PairwiseSignificance.pairwise_indices() a classmethod call rather than requiring caller to instantiate a PairwiseSignificance object and then make the call.
* Add Python-literal expection facility * Condense same-form _Slice.pairwise_indices integration tests into a single parametrized test. * Add or rename a fixture or two as required.
Also add a couple omitted cases while we're in there and it's so easy.
Avoid using numpy types as interface (return) types. Use a tuple for the container of the column-idx tuples instead of an np.ndarray. Using a Python-native type makes using the method easier, requiring no special methods the user may not be as familiar with. Using an immutable type in a lazyproperty is wise because it prevents a caller from mutating a value that may be returned to a different caller in another call.
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Changes required for two-level pairwise-t display, primarily adding
_Slice.pairwise_indices_alt
and_Slice.scale_mean_pairwise_indices_alt
along with processing the transforms-dict construction parameter for multiple threshold values.