Phiml

Version 1.12 sees major performance improvements for eager execution.

**Other highlights**

* `sort` now supports sorting by key. Added `*sort` aliases.
* The linear solver `scipy-lsqr` is now available, `scipy-direct` now performs a convergence check by default.
* Rank-deficient linear systems are now solved by discarding `n` rows/cols from the sparse matrix.
* New functions `unravel_index`
* New submodule `phiml.math.perm`
* New aliases `p...` for reducing all primal dims
* `where()` now supports dataclasses
* Add `dataclasses.copy()`

This release adds official support for custom dataclasses in the `phiml.dataclasses` module.

**Highlights**

* Dataclass decorators `sliceable`, `data_eq`
* Replacing some fields of a dataclass instance keeps all unaffected caches from `cached_property`
* Dataclasses no longer need to handle `None` values in comparisons when used in JIT or other tracing.
* Shorthand function `*max`, `*min`, `*prod`, `*pack` for specific dim types
* New syntax for item name renaming while slicing, e.g. `tensor['y,x->a,b]` selects the slices x and y and renames them.
* Add `squeeze` to remove singleton dims
* Improve formatting of sparse matrices
* `clip()` now accepts `Shape` for the upper limit
* Dim packing functions can now also be used for unpacking when passing one input and multiple output dims.

Bug fixes plus

* Performance optimization for `stack()`
* experimental save/load
* `at_min, at_max` now support sparse matrices

Various smaller bug fixes, `map` now exposes `expand_results`.

**Highlights**

* `convolve` now behaves like matrix multiplication, reducing dual dims of the kernel
* `Tensor Tensor` can now be used to reduce channel dims in the absence of dual dims
* Improved support for shape spec strings, `concat` now supports packing using the syntax `t->name:t`
* Multi-dimensional `cumulative_sum`
* Improved support for non-uniform and sparse tensors
* New functions `d2s`, `contains`, `count_occurrences`, `Tensor.map()`, `ravel_index` and aliases `rotate`, `cross`
* `Shape` concatenation via `Shape + Shape`