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Move _check_mask into core/mask.py and drop dead numpy 1.21/1.22 guards #607

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Move _check_mask into core/mask.py and drop dead numpy 1.21/1.22 guards #607

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28 changes: 1 addition & 27 deletions graphblas/core/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,11 @@

from .. import backend, config
from .. import replace as replace_singleton
from ..dtypes import BOOL
from ..exceptions import check_status
from . import NULL
from .descriptor import lookup as descriptor_lookup
from .expr import AmbiguousAssignOrExtract, Updater
from .mask import Mask
from .mask import Mask, _check_mask
from .operator import UNKNOWN_OPCLASS, binary_from_string, find_opclass, get_typed_op
from .utils import _Pointer, libget, output_type

Expand Down Expand Up @@ -171,31 +170,6 @@ def _expect_op(self, op, values, *, within, **kwargs):
AmbiguousAssignOrExtract._expect_type = _expect_type


def _check_mask(mask, output=None, strict_kind=False):
if not isinstance(mask, Mask):
# Convert bool objects to value masks
if output_type(mask).__name__ in {"Vector", "Matrix"}:
if mask.dtype != BOOL:
raise TypeError(
f"Mask must be boolean objects (got {mask.dtype}) "
"or indicate values (M.V) or structure (M.S)"
)
mask = mask.V # auto-compute (will raise if disabled)
else:
raise TypeError(f"Invalid mask: {type(mask)}")
if output is not None:
if output.ndim == 1 and mask.parent.ndim != 1:
raise TypeError(f"Mask object must be type Vector; got {type(mask.parent)}")
# A full-tensor op (ewise, mxm, apply, extract, ...) into a Matrix needs
# a Matrix mask. Assignment is exempt (`strict_kind` stays False for it):
# a Vector mask on a Matrix row/column assign is valid and is validated
# separately in Matrix.__setitem__. Without this, a Vector mask on a
# full-Matrix op leaked a raw cffi "struct GB_Matrix_opaque" error.
if strict_kind and output.ndim == 2 and mask.parent.ndim != 2:
raise TypeError(f"Mask object must be type Matrix; got {type(mask.parent)}")
return mask


# Curated hints for common attribute-access mistakes on Vector/Matrix/Scalar.
# Only consulted from __getattr__, which fires solely on a genuine attribute
# miss, so the normal (slotted) attribute hot path is untouched.
Expand Down
2 changes: 1 addition & 1 deletion graphblas/core/expr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -336,7 +336,7 @@ def new(self, dtype=None, *, mask=None, input_mask=None, name=None, **opts):
if input_mask is not None:
if mask is not None:
raise TypeError("mask and input_mask arguments cannot both be given")
from .base import _check_mask
from .mask import _check_mask

input_mask = _check_mask(input_mask, self.parent)
mask = self._input_mask_to_mask(input_mask, **opts)
Expand Down
31 changes: 25 additions & 6 deletions graphblas/core/mask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,8 +75,6 @@ def new(self, dtype=None, *, complement=False, mask=None, name=None, **opts):
val(self, **opts) << True
return val

from .base import _check_mask

mask = _check_mask(mask)
d = _COMPLEMENT_MASKS if complement else _COMBINE_MASKS
func = d[type(self), type(mask)]
Expand All @@ -95,8 +93,6 @@ def __and__(self, other, **opts):
This uses faster recipes than the above for all combinations of input mask types,
and aims to be memory efficient when operating on complemented masks.
"""
from .base import _check_mask

other = _check_mask(other)
complement = self.complement or other.complement
d = _COMPLEMENT_MASKS if complement else _COMBINE_MASKS
Expand All @@ -121,8 +117,6 @@ def __or__(self, other, **opts):
This uses faster recipes than the above for all combinations of input mask types,
and aims to be memory efficient when operating on complemented masks.
"""
from .base import _check_mask

other = _check_mask(other)
func = _MASK_OR[type(self), type(other)]
return func(self, other, opts)
Expand Down Expand Up @@ -202,6 +196,31 @@ def _name_html(self):
return f"~{self.parent._name_html}.V"


def _check_mask(mask, output=None, strict_kind=False):
if not isinstance(mask, Mask):
# Convert bool objects to value masks
if utils.output_type(mask).__name__ in {"Vector", "Matrix"}:
if mask.dtype != BOOL:
raise TypeError(
f"Mask must be boolean objects (got {mask.dtype}) "
"or indicate values (M.V) or structure (M.S)"
)
mask = mask.V # auto-compute (will raise if disabled)
else:
raise TypeError(f"Invalid mask: {type(mask)}")
if output is not None:
if output.ndim == 1 and mask.parent.ndim != 1:
raise TypeError(f"Mask object must be type Vector; got {type(mask.parent)}")
# A full-tensor op (ewise, mxm, apply, extract, ...) into a Matrix needs
# a Matrix mask. Assignment is exempt (`strict_kind` stays False for it):
# a Vector mask on a Matrix row/column assign is valid and is validated
# separately in Matrix.__setitem__. Without this, a Vector mask on a
# full-Matrix op leaked a raw cffi "struct GB_Matrix_opaque" error.
if strict_kind and output.ndim == 2 and mask.parent.ndim != 2:
raise TypeError(f"Mask object must be type Matrix; got {type(mask.parent)}")
return mask


# Recipes to combine two masks.
# Legend:
# A: any
Expand Down
9 changes: 3 additions & 6 deletions graphblas/core/matrix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,11 @@
check_status_carg,
)
from . import _supports_udfs, automethods, ffi, lib, utils
from .base import BaseExpression, BaseType, _check_mask, _is_recording, call
from .base import BaseExpression, BaseType, _is_recording, call
from .descriptor import lookup as descriptor_lookup
from .dtypes import _raise_dtype_or_arraylike
from .expr import _ALL_INDICES, AmbiguousAssignOrExtract, IndexerResolver, InfixExprBase, Updater
from .mask import Mask, StructuralMask, ValueMask
from .mask import Mask, StructuralMask, ValueMask, _check_mask
from .operator import (
UNKNOWN_OPCLASS,
_get_typed_op_from_exprs,
Expand Down Expand Up @@ -1727,10 +1727,7 @@ def from_dicts(
else:
# If we know the dtype, then using `np.fromiter` is much faster
dtype = lookup_dtype(dtype)
if dtype.np_type.subdtype is not None and np.__version__[:5] in {"1.21.", "1.22."}:
values, dtype = values_to_numpy_buffer(list(iter_values), dtype) # FLAKY COVERAGE
else:
values = np.fromiter(iter_values, dtype.np_type)
values = np.fromiter(iter_values, dtype.np_type)
return getattr(cls, methodname)(
*args, indptr, col_indices, values, dtype, nrows=nrows, ncols=ncols, name=name
)
Expand Down
9 changes: 3 additions & 6 deletions graphblas/core/vector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,11 +6,11 @@
from ..dtypes import _INDEX, FP64, INT64, lookup_dtype, unify
from ..exceptions import DimensionMismatch, GrB_NO_VALUE, NoValue, check_status, check_status_carg
from . import _supports_udfs, automethods, ffi, lib, utils
from .base import BaseExpression, BaseType, _check_mask, _is_recording, call
from .base import BaseExpression, BaseType, _is_recording, call
from .descriptor import lookup as descriptor_lookup
from .dtypes import _raise_dtype_or_arraylike
from .expr import _ALL_INDICES, AmbiguousAssignOrExtract, IndexerResolver, InfixExprBase, Updater
from .mask import Mask, StructuralMask, ValueMask
from .mask import Mask, StructuralMask, ValueMask, _check_mask
from .operator import (
UNKNOWN_OPCLASS,
_get_typed_op_from_exprs,
Expand Down Expand Up @@ -2177,10 +2177,7 @@ def from_dict(cls, d, dtype=None, *, size=None, name=None):
else:
# If we know the dtype, then using `np.fromiter` is much faster
dtype = lookup_dtype(dtype)
if dtype.np_type.subdtype is not None and np.__version__[:5] in {"1.21.", "1.22."}:
values, dtype = values_to_numpy_buffer(list(d.values()), dtype) # FLAKY COVERAGE
else:
values = np.fromiter(d.values(), dtype.np_type)
values = np.fromiter(d.values(), dtype.np_type)
if size is None and indices.size == 0:
size = 0
return cls.from_coo(indices, values, dtype, size=size, name=name)
Expand Down
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