Welcome to kwarray’s documentation!¶
The kwarray
module implements a small set of pure-python extensions to
numpy and torch along with a few select algorithms. Each module contains
module level docstring that gives a rough idea of the utilities in each module,
and each function or class itself contains a docstring with more details and
examples.
KWarray is part of Kitware’s computer vision Python suite:
Function Usefulness¶
Function name |
Usefulness |
---|---|
475 |
|
202 |
|
98 |
|
77 |
|
72 |
|
60 |
|
59 |
|
53 |
|
48 |
|
33 |
|
31 |
|
30 |
|
29 |
|
27 |
|
25 |
|
20 |
|
19 |
|
15 |
|
14 |
|
14 |
|
10 |
|
10 |
|
10 |
|
10 |
|
9 |
|
9 |
|
7 |
|
6 |
|
6 |
|
5 |
|
5 |
|
3 |
|
3 |
|
1 |
|
1 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
|
0 |
- kwarray package
- Submodules
- kwarray.algo_assignment module
- kwarray.algo_setcover module
- kwarray.arrayapi module
- kwarray.dataframe_light module
- kwarray.distributions module
- kwarray.fast_rand module
- kwarray.util_averages module
- kwarray.util_groups module
- kwarray.util_misc module
- kwarray.util_numpy module
- kwarray.util_random module
- kwarray.util_robust module
- kwarray.util_slices module
- kwarray.util_slider module
- kwarray.util_torch module
- Module contents
ArrayAPI
DataFrameArray
DataFrameLight
FlatIndexer
LocLight
NoSupportError
RunningStats
SlidingWindow
Stitcher
apply_embedded_slice()
apply_grouping()
arglexmax()
argmaxima()
argminima()
atleast_nd()
boolmask()
dtype_info()
embed_slice()
ensure_rng()
equal_with_nan()
find_robust_normalizers()
generalized_logistic()
group_consecutive()
group_consecutive_indices()
group_indices()
group_items()
isect_flags()
iter_reduce_ufunc()
maxvalue_assignment()
mincost_assignment()
mindist_assignment()
normalize()
one_hot_embedding()
one_hot_lookup()
padded_slice()
random_combinations()
random_product()
robust_normalize()
seed_global()
setcover()
shuffle()
standard_normal()
standard_normal32()
standard_normal64()
stats_dict()
uniform()
uniform32()
unique_rows()
- Submodules