Fixes to support Python >= 3.8

.github/workflows/pypi-test.yml

@@ -1,7 +1,4 @@
-# This workflow will install Python dependencies, run tests and lint with a single version of Python
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-
-name: Test the library
+name: Unit tests 
 
 on:
  push:
@@ -13,17 +10,22 @@ jobs:
  build:
 
  runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: [ '3.8', '3.9', '3.10', '3.11', '3.12' ]
 
+ name: Python ${{matrix.python-version }}
  steps:
  - uses: actions/checkout@v2
- - name: Set up Python 3.9
+ - name: Setup Python
  uses: actions/setup-python@v2
  with:
- python-version: 3.9
+ python-version: ${{matrix.python-version }}
+ cache: 'pip'
  - name: Install dependencies
  run: |
  python -m pip install --upgrade pip
- pip install flake8 pytest tox scipy
+ pip install flake8 pytest tox
  # - name: Lint with flake8
  # run: |
  # # stop the build if there are Python syntax errors or undefined names

setup.cfg

@@ -41,7 +41,7 @@ package_dir =
  =src
 
 # Require a min/specific Python version (comma-separated conditions)
-# python_requires = >=3.8
+python_requires = >=3.8
 
 # Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0.
 # Version specifiers like >=2.2,<3.0 avoid problems due to API changes in
@@ -50,6 +50,7 @@ package_dir =
 install_requires =
  importlib-metadata; python_version<"3.8"
  numpy
+ biocutils
 
 
 [options.packages.find]

src/delayedarray/SparseNdarray.py

@@ -1463,7 +1463,7 @@ def _recursive_concatenate_SparseNdarray(contents: list, final_shape: Tuple[int,
  return new_contents
 
 
-def _concatenate_SparseNdarray(xs: list[SparseNdarray], along: int):
+def _concatenate_SparseNdarray(xs: List[SparseNdarray], along: int):
  all_contents = []
  all_shapes = []
  for x in xs:

src/delayedarray/chunk_shape.py

@@ -1,6 +1,7 @@
 from functools import singledispatch
 from typing import Any, Tuple
 from numpy import ndarray
+from biocutils.package_utils import is_package_installed
 
 from .SparseNdarray import SparseNdarray
 
@@ -51,28 +52,23 @@ def chunk_shape_SparseNdarray(x: SparseNdarray):
 
 
 # If scipy is installed, we add all the methods for the various scipy.sparse matrices.
-has_sparse = False
-try:
- import scipy.sparse
- has_sparse = True
-except:
- pass
 
+if is_package_installed("scipy"):
+ import scipy.sparse as sp
 
-if has_sparse:
  @chunk_shape.register
- def chunk_shape_csc_matrix(x: scipy.sparse.csc_matrix):
+ def chunk_shape_csc_matrix(x: sp.csc_matrix):
  """See :py:meth:`~delayedarray.chunk_shape.chunk_shape`."""
  return (x.shape[0], 1)
 
 
  @chunk_shape.register
- def chunk_shape_csr_matrix(x: scipy.sparse.csr_matrix):
+ def chunk_shape_csr_matrix(x: sp.csr_matrix):
  """See :py:meth:`~delayedarray.chunk_shape.chunk_shape`."""
  return (1, x.shape[1])
 
 
  @chunk_shape.register
- def chunk_shape_coo_matrix(x: scipy.sparse.coo_matrix):
+ def chunk_shape_coo_matrix(x: sp.coo_matrix):
  """See :py:meth:`~delayedarray.chunk_shape.chunk_shape`."""
  return x.shape # ???? well, let's just do our best.

src/delayedarray/extract_dense_array.py

@@ -1,6 +1,7 @@
 from functools import singledispatch
 from numpy import array, ndarray, ix_, asfortranarray
 from typing import Any, Optional, Tuple, Sequence
+from biocutils.package_utils import is_package_installed
 
 from ._subset import _spawn_indices, _is_subset_noop
 from .SparseNdarray import SparseNdarray, _extract_dense_array_from_SparseNdarray
@@ -58,17 +59,9 @@ def _sanitize_to_fortran(x):
  else: 
  return asfortranarray(x)
 
+if is_package_installed("scipy"):
+ import scipy.sparse as sp
 
-# If scipy is installed, we add all the methods for the various scipy.sparse matrices.
-has_sparse = False
-try:
- import scipy.sparse
- has_sparse = True
-except:
- pass
-
-
-if has_sparse:
  def _extract_dense_array_sparse(x, subset: Optional[Tuple[Sequence[int], ...]] = None):
  if _is_subset_noop(x.shape, subset):
  tmp = x
@@ -77,21 +70,26 @@ def _extract_dense_array_sparse(x, subset: Optional[Tuple[Sequence[int], ...]] =
  return tmp.toarray(order="F")
 
  @extract_dense_array.register
- def extract_dense_array_csc_matrix(x: scipy.sparse.csc_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_dense_array_csc_matrix(x: sp.csc_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_dense_array.extract_dense_array`."""
  return _extract_dense_array_sparse(x, subset)
 
  @extract_dense_array.register
- def extract_dense_array_csr_matrix(x: scipy.sparse.csr_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_dense_array_csr_matrix(x: sp.csr_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_dense_array.extract_dense_array`."""
  return _extract_dense_array_sparse(x, subset)
 
  @extract_dense_array.register
- def extract_dense_array_coo_matrix(x: scipy.sparse.coo_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_dense_array_coo_matrix(x: sp.coo_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_dense_array.extract_dense_array`."""
  return _extract_dense_array_sparse(x, subset)
 
- @extract_dense_array.register
- def extract_dense_array_sparse_array(x: scipy.sparse.sparray, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_dense_array_sparse_array(x, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_dense_array.extract_dense_array`."""
  return _extract_dense_array_sparse(x, subset)
+
+ try:
+ extract_dense_array.register(sp.sparray, extract_dense_array_sparse_array)
+ except Exception:
+ pass
+

src/delayedarray/extract_sparse_array.py

@@ -2,6 +2,7 @@
 from numpy import array, ndarray, ix_
 from bisect import bisect_left
 from typing import Any, Optional, Tuple, Sequence
+from biocutils.package_utils import is_package_installed
 
 from ._subset import _spawn_indices, _is_subset_noop, _is_subset_consecutive
 from .SparseNdarray import SparseNdarray, _extract_sparse_array_from_SparseNdarray
@@ -44,25 +45,17 @@ def extract_sparse_array_SparseNdarray(x: SparseNdarray, subset: Optional[Tuple[
  else:
  return _extract_sparse_array_from_SparseNdarray(x, subset)
 
+if is_package_installed("scipy"):
+ import scipy.sparse as sp
 
-# If scipy is installed, we add all the methods for the various scipy.sparse matrices.
-has_sparse = False
-try:
- import scipy.sparse
- has_sparse = True
-except:
- pass
-
-
-if has_sparse:
  def _set_empty_contents(contents):
  for x in contents:
  if x is not None:
  return contents
  return contents
 
  @extract_sparse_array.register
- def extract_sparse_array_csc_matrix(x: scipy.sparse.csc_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_sparse_array_csc_matrix(x: sp.csc_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_sparse_array.extract_sparse_array`."""
  if subset is None:
  subset = _spawn_indices(x.shape)
@@ -120,7 +113,7 @@ def extract_sparse_array_csc_matrix(x: scipy.sparse.csc_matrix, subset: Optional
  return SparseNdarray((*final_shape,), new_contents, dtype=x.dtype, index_dtype=x.indices.dtype, check=False)
 
  @extract_sparse_array.register
- def extract_sparse_array_csr_matrix(x: scipy.sparse.csr_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_sparse_array_csr_matrix(x: sp.csr_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_sparse_array.extract_sparse_array`."""
  if subset is None:
  subset = _spawn_indices(x.shape)
@@ -180,7 +173,7 @@ def extract_sparse_array_csr_matrix(x: scipy.sparse.csr_matrix, subset: Optional
  return SparseNdarray((*final_shape,), new_contents, dtype=x.dtype, index_dtype=x.indices.dtype, check=False)
 
  @extract_sparse_array.register
- def extract_sparse_array_coo_matrix(x: scipy.sparse.coo_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
+ def extract_sparse_array_coo_matrix(x: sp.coo_matrix, subset: Optional[Tuple[Sequence[int], ...]] = None):
  """See :py:meth:`~delayedarray.extract_sparse_array.extract_sparse_array`."""
  if subset is None:
  subset = _spawn_indices(x.shape)

src/delayedarray/is_sparse.py

@@ -1,5 +1,6 @@
 from functools import singledispatch
 from typing import Any
+from biocutils.package_utils import is_package_installed
 
 from .SparseNdarray import SparseNdarray
 
@@ -29,26 +30,21 @@ def is_sparse_SparseNdarray(x: SparseNdarray):
 
 
 # If scipy is installed, we add all the methods for the various scipy.sparse matrices.
-has_sparse = False
-try:
- import scipy.sparse
- has_sparse = True
-except:
- pass
 
+if is_package_installed("scipy"):
+ import scipy.sparse as sp
 
-if has_sparse:
  @is_sparse.register
- def is_sparse_csc_matrix(x: scipy.sparse.csc_matrix):
+ def is_sparse_csc_matrix(x: sp.csc_matrix):
  """See :py:meth:`~delayedarray.is_sparse.is_sparse`."""
  return True
 
  @is_sparse.register
- def is_sparse_csr_matrix(x: scipy.sparse.csr_matrix):
+ def is_sparse_csr_matrix(x: sp.csr_matrix):
  """See :py:meth:`~delayedarray.is_sparse.is_sparse`."""
  return True
 
  @is_sparse.register
- def is_sparse_coo_matrix(x: scipy.sparse.coo_matrix):
+ def is_sparse_coo_matrix(x: sp.coo_matrix):
  """See :py:meth:`~delayedarray.is_sparse.is_sparse`."""
  return True