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Fix numpy comparisons; use == for dtypes and isinstance for arrays #2235

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Merged
merged 2 commits into from
Aug 21, 2025
Merged

Fix numpy comparisons; use == for dtypes and isinstance for arrays #2235

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ddb9454
Fix numpy dtype comparisons to use `==` instead of `is` operator
kounelisagis Aug 19, 2025
89bc936
Refactor type checks to use `isinstance` for numpy array validation
kounelisagis Aug 19, 2025
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6 changes: 3 additions & 3 deletions tiledb/dense_array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -441,7 +441,7 @@ def _setitem_impl(self, selection, val, nullmaps: dict):
labels = {
name: (
data
if not type(data) is np.ndarray or data.dtype is np.dtype("O")
if not type(data) is np.ndarray or data.dtype == np.dtype("O")
else np.ascontiguousarray(
data, dtype=self.schema.dim_label(name).dtype
)
Expand All @@ -458,7 +458,7 @@ def _setitem_impl(self, selection, val, nullmaps: dict):

attributes.append(attr._internal_name)
# object arrays are var-len and handled later
if type(attr_val) is np.ndarray and attr_val.dtype is not np.dtype("O"):
if type(attr_val) is np.ndarray and attr_val.dtype != np.dtype("O"):
if attr.isnullable and name not in nullmaps:
try:
nullmaps[name] = ~np.ma.masked_invalid(attr_val).mask
Expand Down Expand Up @@ -527,7 +527,7 @@ def _setitem_impl(self, selection, val, nullmaps: dict):
name = attr.name
attributes.append(attr._internal_name)
# object arrays are var-len and handled later
if type(val) is np.ndarray and val.dtype is not np.dtype("O"):
if type(val) is np.ndarray and val.dtype != np.dtype("O"):
val = np.ascontiguousarray(val, dtype=attr.dtype)
try:
if attr.isvar:
Expand Down
2 changes: 1 addition & 1 deletion tiledb/sparse_array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ def _setitem_impl_sparse(self, selection, val, nullmaps: dict):
labels = {
name: (
data
if not type(data) is np.ndarray or data.dtype is np.dtype("O")
if not type(data) is np.ndarray or data.dtype == np.dtype("O")
else np.ascontiguousarray(data, dtype=self.schema.dim_label(name).dtype)
)
for name, data in val.items()
Expand Down
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