Adapt package for Python 3.8 (#10)

- In addition updates github action to test the package from python 3.8 to 3.12. 
- Most changes deal with registering dispatchers for scipy's sparray.

.github/workflows/pypi-test.yml

@@ -13,13 +13,18 @@ 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

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
@@ -50,7 +50,7 @@ package_dir =
 install_requires =
     importlib-metadata; python_version<"3.8"
     numpy
-    biocutils>=0.0.2
+    biocutils>=0.0.4
 
 [options.packages.find]
 where = src

src/biocgenerics/colnames.py

@@ -1,7 +1,7 @@
 from functools import singledispatch
 from typing import List
 
-from .utils import _is_package_installed
+from biocutils.package_utils import is_package_installed
 
 __author__ = "jkanche"
 __copyright__ = "jkanche"
@@ -27,7 +27,7 @@ def colnames(x) -> list:
     raise NotImplementedError(f"`colnames` is not supported for class: '{type(x)}'.")
 
 
-if _is_package_installed("pandas") is True:
+if is_package_installed("pandas") is True:
     from pandas import DataFrame
 
     @colnames.register(DataFrame)
@@ -54,7 +54,7 @@ def set_colnames(x, names: List[str]):
     )
 
 
-if _is_package_installed("pandas") is True:
+if is_package_installed("pandas") is True:
     from pandas import DataFrame
 
     @set_colnames.register(DataFrame)

src/biocgenerics/combine.py

@@ -1,6 +1,7 @@
 from functools import singledispatch
 from typing import Any
 
+from biocutils.package_utils import is_package_installed
 from numpy import ndarray
 
 from .combine_rows import combine_rows
@@ -9,7 +10,6 @@
     _is_1d_dense_arrays,
     _is_1d_sparse_arrays,
     _is_any_element_list,
-    _is_package_installed,
 )
 
 __author__ = "jkanche"
@@ -48,7 +48,7 @@ def _combine_dense_arrays(*x: ndarray):
     return combine_rows(*x)
 
 
-if _is_package_installed("scipy") is True:
+if is_package_installed("scipy") is True:
     import scipy.sparse as sp
 
     def _combine_sparse(*x):
@@ -60,11 +60,18 @@ def _combine_sparse(*x):
 
         return combine_rows(*x)
 
-    combine.register(sp.sparray, _combine_sparse)
-    combine.register(sp.spmatrix, _combine_sparse)
+    try:
+        combine.register(sp.sparray, _combine_sparse)
+    except Exception:
+        pass
 
+    try:
+        combine.register(sp.spmatrix, _combine_sparse)
+    except Exception:
+        pass
 
-if _is_package_installed("pandas") is True:
+
+if is_package_installed("pandas") is True:
     from pandas import DataFrame, Series
 
     @combine.register(Series)

src/biocgenerics/combine_cols.py

@@ -3,13 +3,10 @@
 from warnings import warn
 
 from biocutils import is_list_of_type
+from biocutils.package_utils import is_package_installed
 from numpy import hstack, ndarray
 
-from .utils import (
-    _convert_sparse_to_dense,
-    _do_arrays_match,
-    _is_package_installed,
-)
+from .utils import _convert_sparse_to_dense, _do_arrays_match
 
 __author__ = "jkanche"
 __copyright__ = "jkanche"
@@ -76,44 +73,84 @@ def _combine_cols_dense_arrays(*x: ndarray):
     raise ValueError("All elements must be 2-dimensional matrices.")
 
 
-if _is_package_installed("scipy") is True:
+if is_package_installed("scipy") is True:
     import scipy.sparse as sp
 
-    def _combine_cols_sparse_arrays(*x):
-        if is_list_of_type(x, (sp.sparray, sp.spmatrix)):
+    def _combine_cols_sparse_matrices(*x):
+        if is_list_of_type(x, sp.spmatrix):
             sp_conc = sp.hstack(x)
 
             if _do_arrays_match(x, 0) is not True:
                 raise ValueError("1st dimension does not match across all elements.")
 
             first = x[0]
-            if isinstance(first, (sp.csr_matrix, sp.csr_array)):
+            if isinstance(first, sp.csr_matrix):
                 return sp_conc.tocsr()
-            elif isinstance(first, (sp.csc_matrix, sp.csc_array)):
+            elif isinstance(first, sp.csc_matrix):
                 return sp_conc.tocsc()
-            elif isinstance(first, (sp.bsr_matrix, sp.bsr_array)):
+            elif isinstance(first, sp.bsr_matrix):
                 return sp_conc.tobsr()
-            elif isinstance(first, (sp.coo_matrix, sp.coo_array)):
+            elif isinstance(first, sp.coo_matrix):
                 return sp_conc.tocoo()
-            elif isinstance(first, (sp.dia_matrix, sp.dia_array)):
+            elif isinstance(first, sp.dia_matrix):
                 return sp_conc.todia()
-            elif isinstance(first, (sp.lil_matrix, sp.lil_array)):
+            elif isinstance(first, sp.lil_matrix):
                 return sp_conc.tolil()
             else:
                 return sp_conc
 
-        warn("Not all elements are scipy sparse arrays.")
+        warn("Not all elements are scipy sparse matrices.")
 
-        if is_list_of_type(x, (ndarray, sp.sparray, sp.spmatrix)):
+        if is_list_of_type(x, (ndarray, sp.spmatrix)):
             return _generic_combine_cols_dense_sparse(x)
 
         raise ValueError("All elements must be 2-dimensional matrices.")
 
-    combine_cols.register(sp.sparray, _combine_cols_sparse_arrays)
-    combine_cols.register(sp.spmatrix, _combine_cols_sparse_arrays)
+    try:
 
+        def _combine_cols_sparse_arrays(*x):
+            if is_list_of_type(x, sp.sparray):
+                sp_conc = sp.hstack(x)
 
-if _is_package_installed("pandas") is True:
+                if _do_arrays_match(x, 0) is not True:
+                    raise ValueError(
+                        "1st dimension does not match across all elements."
+                    )
+
+                first = x[0]
+                if isinstance(first, sp.csr_array):
+                    return sp_conc.tocsr()
+                elif isinstance(first, sp.csc_array):
+                    return sp_conc.tocsc()
+                elif isinstance(first, sp.bsr_array):
+                    return sp_conc.tobsr()
+                elif isinstance(first, sp.coo_array):
+                    return sp_conc.tocoo()
+                elif isinstance(first, sp.dia_array):
+                    return sp_conc.todia()
+                elif isinstance(first, sp.lil_array):
+                    return sp_conc.tolil()
+                else:
+                    return sp_conc
+
+            warn("Not all elements are scipy sparse arrays.")
+
+            if is_list_of_type(x, (ndarray, sp.sparray, sp.spmatrix)):
+                return _generic_combine_cols_dense_sparse(x)
+
+            raise ValueError("All elements must be 2-dimensional arrays.")
+
+        combine_cols.register(sp.sparray, _combine_cols_sparse_arrays)
+    except Exception:
+        pass
+
+    try:
+        combine_cols.register(sp.spmatrix, _combine_cols_sparse_matrices)
+    except Exception:
+        pass
+
+
+if is_package_installed("pandas") is True:
     from pandas import DataFrame, concat
 
     @combine_cols.register(DataFrame)

src/biocgenerics/combine_rows.py

@@ -3,12 +3,12 @@
 from warnings import warn
 
 from biocutils import is_list_of_type
+from biocutils.package_utils import is_package_installed
 from numpy import concatenate, ndarray
 
 from .utils import (
     _convert_sparse_to_dense,
     _do_arrays_match,
-    _is_package_installed,
 )
 
 __author__ = "jkanche"
@@ -76,44 +76,84 @@ def _combine_rows_dense_arrays(*x: ndarray):
     raise ValueError("All elements must be 2-dimensional matrices.")
 
 
-if _is_package_installed("scipy") is True:
+if is_package_installed("scipy") is True:
     import scipy.sparse as sp
 
-    def _combine_rows_sparse_arrays(*x):
-        if is_list_of_type(x, (sp.sparray, sp.spmatrix)):
+    def _combine_rows_sparse_matrices(*x):
+        if is_list_of_type(x, sp.spmatrix):
             sp_conc = sp.vstack(x)
 
             if _do_arrays_match(x, 1) is not True:
                 raise ValueError("2nd dimension does not match across all elements.")
 
             first = x[0]
-            if isinstance(first, (sp.csr_matrix, sp.csr_array)):
+            if isinstance(first, sp.csr_matrix):
                 return sp_conc.tocsr()
-            elif isinstance(first, (sp.csc_matrix, sp.csc_array)):
+            elif isinstance(first, sp.csc_matrix):
                 return sp_conc.tocsc()
-            elif isinstance(first, (sp.bsr_matrix, sp.bsr_array)):
+            elif isinstance(first, sp.bsr_matrix):
                 return sp_conc.tobsr()
-            elif isinstance(first, (sp.coo_matrix, sp.coo_array)):
+            elif isinstance(first, sp.coo_matrix):
                 return sp_conc.tocoo()
-            elif isinstance(first, (sp.dia_matrix, sp.dia_array)):
+            elif isinstance(first, sp.dia_matrix):
                 return sp_conc.todia()
-            elif isinstance(first, (sp.lil_matrix, sp.lil_array)):
+            elif isinstance(first, sp.lil_matrix):
                 return sp_conc.tolil()
             else:
                 return sp_conc
 
-        warn("Not all elements are scipy sparse arrays.")
+        warn("Not all elements are scipy sparse matrices.")
 
-        if is_list_of_type(x, (ndarray, sp.sparray, sp.spmatrix)):
+        if is_list_of_type(x, (ndarray, sp.spmatrix)):
             return _generic_combine_rows_dense_sparse(x)
 
         raise ValueError("All elements must be 2-dimensional matrices.")
 
-    combine_rows.register(sp.sparray, _combine_rows_sparse_arrays)
-    combine_rows.register(sp.spmatrix, _combine_rows_sparse_arrays)
+    try:
 
+        def _combine_rows_sparse_arrays(*x):
+            if is_list_of_type(x, sp.sparray):
+                sp_conc = sp.vstack(x)
 
-if _is_package_installed("pandas") is True:
+                if _do_arrays_match(x, 1) is not True:
+                    raise ValueError(
+                        "2nd dimension does not match across all elements."
+                    )
+
+                first = x[0]
+                if isinstance(first, sp.csr_array):
+                    return sp_conc.tocsr()
+                elif isinstance(first, sp.csc_array):
+                    return sp_conc.tocsc()
+                elif isinstance(first, sp.bsr_array):
+                    return sp_conc.tobsr()
+                elif isinstance(first, sp.coo_array):
+                    return sp_conc.tocoo()
+                elif isinstance(first, sp.dia_array):
+                    return sp_conc.todia()
+                elif isinstance(first, sp.lil_array):
+                    return sp_conc.tolil()
+                else:
+                    return sp_conc
+
+            warn("Not all elements are scipy sparse arrays.")
+
+            if is_list_of_type(x, (ndarray, sp.sparray, sp.spmatrix)):
+                return _generic_combine_rows_dense_sparse(x)
+
+            raise ValueError("All elements must be 2-dimensional arrays.")
+
+        combine_rows.register(sp.sparray, _combine_rows_sparse_arrays)
+    except Exception:
+        pass
+
+    try:
+        combine_rows.register(sp.spmatrix, _combine_rows_sparse_matrices)
+    except Exception:
+        pass
+
+
+if is_package_installed("pandas") is True:
     from pandas import DataFrame, concat
 
     @combine_rows.register(DataFrame)