Fixes to get it to work for NumPy 2. (#27)

setup.cfg

@@ -16,7 +16,7 @@ url = https://github.com/ArtifactDB/dolomite-base
 # Add here related links, for example:
 project_urls =
     Documentation = https://github.com/ArtifactDB/dolomite-base
-#    Source = https://github.com/pyscaffold/pyscaffold/
+    Source = https://github.com/ArtifactDB/dolomite-base
 #    Changelog = https://pyscaffold.org/en/latest/changelog.html
 #    Tracker = https://github.com/pyscaffold/pyscaffold/issues
 #    Conda-Forge = https://anaconda.org/conda-forge/pyscaffold
@@ -50,13 +50,12 @@ python_requires = >=3.8
 install_requires =
     importlib-metadata; python_version<"3.8"
     biocutils>=0.1.5
-    biocframe>=0.5.10
+    biocframe>=0.5.11
     numpy
     jsonschema
     dolomite-schemas>=0.0.8
     h5py
 
-
 [options.packages.find]
 where = src
 exclude =

src/dolomite_base/choose_missing_placeholder.py

@@ -77,7 +77,7 @@ def choose_missing_float_placeholder(x: Sequence[float], dtype: type = numpy.flo
             can_nan = False
             break
     if can_nan:
-        return dtype(numpy.NaN)
+        return dtype(numpy.nan)
 
     if not numpy.inf in x:
         return dtype(numpy.inf)

src/dolomite_base/save_simple_list.py

@@ -337,7 +337,7 @@ def _save_simple_list_recursive_MaskedConstant(x: np.ma.core.MaskedConstant, ext
     if handle is None:
         return { "type": "number", "values": None}
     else:
-        _save_scalar_hdf5(handle, x=np.NaN, dtype=float, missing_placeholder=np.NaN)
+        _save_scalar_hdf5(handle, x=np.nan, dtype=float, missing_placeholder=np.nan)
         return
 
 
@@ -431,9 +431,9 @@ def _save_simple_list_recursive_none(x: None, externals: list, handle):
 def _sanitize_float_json(x):
     if np.isnan(x):
         return "NaN"
-    elif x == np.Inf:
+    elif x == np.inf:
         return "Inf"
-    elif x == -np.Inf:
+    elif x == -np.inf:
         return "-Inf"
     return float(x)
 

src/dolomite_base/write_vector_to_hdf5.py

@@ -133,7 +133,7 @@ def write_integer_vector_to_hdf5(
         if not allow_float_promotion:
             raise ValueError("cannot save out-of-range integers without type promotion")
         if missed:
-            placeholder = numpy.NaN
+            placeholder = numpy.nan
             x = _fill_with_placeholder(x, numpy.float64, placeholder)
     else:
         if missed:
@@ -142,7 +142,7 @@ def write_integer_vector_to_hdf5(
                 exceeds = True
                 if not allow_float_promotion:
                     raise ValueError("cannot find a suitable missing value placeholder without type promotion")
-                placeholder = numpy.NaN
+                placeholder = numpy.nan
                 x = _fill_with_placeholder(x, numpy.float64, placeholder)
             else:
                 x = _fill_with_placeholder(x, placeholder.dtype.type, placeholder)

tests/test_data_frame.py

@@ -239,15 +239,15 @@ def test_data_frame_large_integers():
 
 def test_data_frame_special_floats():
     df = BiocFrame({
-        "sumire": [ np.NaN, np.Inf, -np.Inf ],
-        "kanon": np.array([ np.NaN, np.Inf, -np.Inf ]),
-        "chisato": np.ma.array([ np.NaN, 4, 5 ], mask=[0, 1, 1]) # distinguish NaN from missing.
+        "sumire": [ np.nan, np.inf, -np.inf ],
+        "kanon": np.array([ np.nan, np.inf, -np.inf ]),
+        "chisato": np.ma.array([ np.nan, 4, 5 ], mask=[0, 1, 1]) # distinguish NaN from missing.
     })
 
     def as_expected(x):
         assert np.isnan(x[0])
-        assert x[1] == np.Inf
-        assert x[2] == -np.Inf
+        assert x[1] == np.inf
+        assert x[2] == -np.inf
 
     def as_expected_masked(x):
         assert np.isnan(x[0])

tests/test_load_vector_from_hdf5.py

@@ -75,7 +75,7 @@ def test_load_vector_from_hdf5_floats():
         ghandle = handle.create_group("yourmom")
         dl.write_float_vector_to_hdf5(ghandle, "FOO1", [1.1, 2.2, 3.3, 4.4])
         dl.write_float_vector_to_hdf5(ghandle, "FOO2", [1.1, 2.2, 4.4, None])
-        dl.write_float_vector_to_hdf5(ghandle, "FOO3", [1.1, 2.2, 4.4, None, numpy.NaN]) # check for correct behavior with non-NaN placeholders
+        dl.write_float_vector_to_hdf5(ghandle, "FOO3", [1.1, 2.2, 4.4, None, numpy.nan]) # check for correct behavior with non-NaN placeholders
         dl.write_integer_vector_to_hdf5(ghandle, "FOO4", [1, 2, 3, 2**32], h5type="u8") # check for correct promotion of integer storage types.
 
     with h5py.File(path, "r") as handle:

tests/test_simple_list.py

@@ -192,8 +192,8 @@ def test_simple_list_large_integers():
 
 def test_simple_list_special_float():
     everything = {
-        "a": np.NaN,
-        "b": FloatList([np.Inf, -np.Inf, np.NaN])
+        "a": np.nan,
+        "b": FloatList([np.inf, -np.inf, np.nan])
     }
 
     # Stage as JSON.
@@ -202,8 +202,8 @@ def test_simple_list_special_float():
 
     roundtrip = dl.read_object(dir)
     assert np.isnan(roundtrip["a"])
-    assert roundtrip["b"][0] == np.Inf
-    assert roundtrip["b"][1] == -np.Inf
+    assert roundtrip["b"][0] == np.inf
+    assert roundtrip["b"][1] == -np.inf
     assert np.isnan(roundtrip["b"][2])
 
     # Stage as HDF5.
@@ -212,8 +212,8 @@ def test_simple_list_special_float():
 
     roundtrip = dl.read_object(dir)
     assert np.isnan(roundtrip["a"])
-    assert roundtrip["b"][0] == np.Inf
-    assert roundtrip["b"][1] == -np.Inf
+    assert roundtrip["b"][0] == np.inf
+    assert roundtrip["b"][1] == -np.inf
     assert np.isnan(roundtrip["b"][2])