Account for differing histogram* signatures across numpy versions.

unyt/_array_functions.py

@@ -158,20 +158,25 @@ def _sanitize_range(_range, units):
     return new_range.squeeze()
 
 
-@implements(np.histogram)
-def histogram(
-    a,
-    bins=10,
-    range=None,
-    density=None,
-    weights=None,
-    *args,
-    **kwargs,
-):
+def _histogram(a, bins=10, range=None, density=None, weights=None, normed=None):
     range = _sanitize_range(range, units=[a.units])
-    counts, bins = np.histogram._implementation(
-        np.asarray(a), bins, range, *args, **kwargs
-    )
+    if NUMPY_VERSION > Version("1.23"):
+        counts, bins = np.histogram._implementation(
+            np.asarray(a),
+            bins=bins,
+            range=range,
+            density=density,
+            weights=np.asarray(weights) if weights is not None else None,
+        )
+    else:
+        counts, bins = np.histogram._implementation(
+            np.asarray(a),
+            bins=bins,
+            range=range,
+            normed=normed,
+            weights=np.asarray(weights) if weights is not None else None,
+            density=density,
+        )
     # a and/or weights could have units, only apply if present
     # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want
     # a unyt_array if weights are not a unyt_array and not density
@@ -182,12 +187,42 @@ def histogram(
     return counts, bins * a.units
 
 
-@implements(np.histogram2d)
-def histogram2d(x, y, bins=10, range=None, density=None, weights=None, *args, **kwargs):
+if NUMPY_VERSION > Version("1.23"):
+
+    @implements(np.histogram)
+    def histogram(a, bins=10, range=None, density=None, weights=None):
+        return _histogram(a, bins=bins, range=range, density=density, weights=weights)
+
+else:
+
+    @implements(np.histogram)
+    def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):
+        return _histogram(
+            a, bins=bins, range=range, normed=normed, weights=weights, density=density
+        )
+
+
+def _histogram2d(x, y, bins=10, range=None, density=None, weights=None, normed=None):
     range = _sanitize_range(range, units=[x.units, y.units])
-    counts, xbins, ybins = np.histogram2d._implementation(
-        np.asarray(x), np.asarray(y), bins, range, *args, **kwargs
-    )
+    if NUMPY_VERSION > Version("1.23"):
+        counts, xbins, ybins = np.histogram2d._implementation(
+            np.asarray(x),
+            np.asarray(y),
+            bins=bins,
+            range=range,
+            density=density,
+            weights=np.asarray(weights) if weights is not None else None,
+        )
+    else:
+        counts, xbins, ybins = np.histogram2d._implementation(
+            np.asarray(x),
+            np.asarray(y),
+            bins=bins,
+            range=range,
+            normed=normed,
+            weights=np.asarray(weights) if weights is not None else None,
+            density=density,
+        )
     # x, y and/or weights could have units, only apply if present
     # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want
     # a unyt_array if weights are not a unyt_array and not density
@@ -201,15 +236,49 @@ def histogram2d(x, y, bins=10, range=None, density=None, weights=None, *args, **
     return counts, xbins * x.units, ybins * y.units
 
 
-@implements(np.histogramdd)
-def histogramdd(
-    sample, bins=10, range=None, density=None, weights=None, *args, **kwargs
-):
-    units = [_.units for _ in sample]
+if NUMPY_VERSION > Version("1.23"):
+
+    @implements(np.histogram2d)
+    def histogram2d(x, y, bins=10, range=None, density=None, weights=None):
+        return _histogram2d(
+            x, y, bins=bins, range=range, density=density, weights=weights
+        )
+
+else:
+
+    @implements(np.histogram2d)
+    def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None):
+        return _histogram2d(
+            x,
+            y,
+            bins=bins,
+            range=range,
+            normed=normed,
+            weights=weights,
+            density=density,
+        )
+
+
+def _histogramdd(sample, bins=10, range=None, density=None, weights=None, normed=None):
+    units = [getattr(_, "units", NULL_UNIT) for _ in sample]
     range = _sanitize_range(range, units=units)
-    counts, bins = np.histogramdd._implementation(
-        [np.asarray(_) for _ in sample], bins, range, *args, **kwargs
-    )
+    if NUMPY_VERSION > Version("1.23"):
+        counts, bins = np.histogramdd._implementation(
+            [np.asarray(_) for _ in sample],
+            bins=bins,
+            range=range,
+            density=density,
+            weights=np.asarray(weights) if weights is not None else None,
+        )
+    else:
+        counts, bins = np.histogramdd._implementation(
+            [np.asarray(_) for _ in sample],
+            bins=bins,
+            range=range,
+            normed=normed,
+            weights=np.asarray(weights) if weights is not None else None,
+            density=density,
+        )
     # sample(s) and/or weights could have units, only apply if present
     # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want
     # a unyt_array if weights are not a unyt_array and not density
@@ -222,6 +291,30 @@ def histogramdd(
     return counts, tuple(_bin * u for _bin, u in zip(bins, units))
 
 
+if NUMPY_VERSION > Version("1.23"):
+
+    @implements(np.histogramdd)
+    def histogramdd(sample, bins=10, range=None, density=None, weights=None):
+        return _histogramdd(
+            sample, bins=bins, range=range, density=density, weights=weights
+        )
+
+else:
+
+    @implements(np.histogramdd)
+    def histogramdd(
+        sample, bins=10, range=None, normed=None, weights=None, density=None
+    ):
+        return _histogramdd(
+            sample,
+            bins=bins,
+            range=range,
+            normed=normed,
+            weights=weights,
+            density=density,
+        )
+
+
 @implements(np.histogram_bin_edges)
 def histogram_bin_edges(a, *args, **kwargs):
     return (
@@ -600,9 +693,8 @@ def nanquantile(a, *args, **kwargs):
 
 @implements(np.linalg.det)
 def linalg_det(a, *args, **kwargs):
-    return (
-        np.linalg.det._implementation(np.asarray(a), *args, **kwargs)
-        * a.units ** (a.shape[0])
+    return np.linalg.det._implementation(np.asarray(a), *args, **kwargs) * a.units ** (
+        a.shape[0]
     )