Compatibility with NumPy 2.0 (#14)

Update changelog as well

CHANGELOG.md

@@ -1,7 +1,10 @@
 # Changelog
 
-## Version 0.1 (development)
+## Version 0.2.0
 
-- Feature A added
-- FIX: nasty bug #1729 fixed
-- add your changes here!
+- Changes to support NumPy's 2.0 release.
+
+## Version 0.1.0 - 0.1.3
+
+First release of the package to access, download and save
+datasets.

tests/conftest.py

@@ -7,3 +7,7 @@
 """
 
 # import pytest
+
+# only if certificates are not available for local testing
+# from gypsum_client.config import REQUESTS_MOD
+# REQUESTS_MOD["verify"] = False

tests/test_polish_dataset.py

@@ -120,31 +120,31 @@ def test_polish_dataset_attempts_integer_conversions():
     sce = SingleCellExperiment(assays={"counts": mat})
 
     y = polish_dataset(sce)
-    assert y.assays["counts"].dtype == np.int_
+    assert np.issubdtype(y.assays["counts"].dtype, np.integer)
 
     mat = np.random.poisson(0.1, (100, 10)).astype(float)
     sce = SingleCellExperiment(assays={"counts": sparse.csr_matrix(mat)})
 
     y = polish_dataset(sce)
     assert sparse.issparse(y.assays["counts"])
-    assert y.assays["counts"].dtype == np.int_
+    assert np.issubdtype(y.assays["counts"].dtype, np.integer)
 
     mat = np.random.poisson(3, (100, 10)) * 1.5
     sce = SingleCellExperiment(assays={"counts": mat})
 
     y = polish_dataset(sce)
-    assert y.assays["counts"].dtype == np.float_
+    assert np.issubdtype(y.assays["counts"].dtype, np.floating)
 
 
 def test_polish_dataset_works_with_na_values():
     mat = np.random.poisson(0.1, (100, 10))
-    mat = mat.astype(np.float_)
+    mat = mat.astype(np.float64)
     mat.ravel()[np.random.choice(mat.size, 10, replace=False)] = np.nan
     sce = SingleCellExperiment(assays={"counts": sparse.csr_matrix(mat)})
 
     y = polish_dataset(sce)
     assert sparse.issparse(y.assays["counts"])
-    assert y.assays["counts"].dtype == np.float_
+    assert np.issubdtype(y.assays["counts"].dtype, np.floating)
 
 
 def test_polish_dataset_forbids_highly_nested_altexps():