ChangelogΒΆ
0.4.1 2018-09-12
- [Feature] #117: (via #118) Reductions now support a negative axis.
- [Feature] #127: Improve element-wise performance
- [Feature] #128: Improve indexing performance
- [Feature] #80: (via #146) Allow faux in-place operations
- [Feature] #145: (via #148) Support
COO.nonzero
andnp.argwhere
- [Feature] #153: (via #154) Add support for saving and loading
COO
files from disk - [Feature] #159: Numba code now releases the GIL. This leads to better multi-threaded performance in Dask.
- [Feature] #160: Added a
sparse.roll
function. - [Feature] #165: The fill-value can now be something other than zero or
False
. - [Feature] #172: Indexing for
COO
now accepts a single one-dimensional array index. - [Feature] #175: Added
COO.any
andCOO.all
methods. - [Feature] #179: (via #180) Allow specifying a fill-value when converting from NumPy arrays.
- [Feature] #124: (via #182) Allow mixed
ndarray
-COO
operations if the result is sparse. - [Support] #141:
COO
is now always canonical
0.3.0 2018-02-22
0.2.0 2018-01-25
- [Feature] #35: Support broadcasting in element-wise operations
- [Feature] #38: Add support for bitwise bindary operations like
&
and|
- [Feature] #37: Add support for Ellipsis (
...
) andNone
when indexing - [Feature] #40: Add support for
triu
andtril
- [Feature] #46: Support more operators and remove all special cases
- [Feature] #49: NumPy universal functions and reductions now work on
COO
arrays - [Feature] #55:
COO(COO)
now copies the original object - [Feature] #41: Add
random
function for generating random sparse arrays - [Feature] #57: Extend indexing support
- [Feature] #67:
scalar op COO
now works for all operators - [Feature] #68:
len(COO)
now works - [Feature] #69: Support
.size
and.density
- [Feature] #85: Add
DOK
type - [Feature] #87: Support faster
np.array(COO)
- [Bug] #47: (via #48) Fix
nnz
for scalars - [Bug] #32: (via #51) Fix concatenate and stack for large arrays
- [Bug] #61: Validate axes for
.transpose()
- [Bug] #82: (via #83) Fix sum for large arrays
- [Support] #70: Minimum required SciPy version is now 0.19
- [Support] #43: Documentation added for the package