diff --git a/graphblas/core/matrix.py b/graphblas/core/matrix.py
index 8b9b4b678..e922ead87 100644
--- a/graphblas/core/matrix.py
+++ b/graphblas/core/matrix.py
@@ -1,6 +1,6 @@
import itertools
import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
import numpy as np
@@ -12,7 +12,14 @@
from .descriptor import lookup as descriptor_lookup
from .expr import _ALL_INDICES, AmbiguousAssignOrExtract, IndexerResolver, Updater
from .mask import Mask, StructuralMask, ValueMask
-from .operator import UNKNOWN_OPCLASS, find_opclass, get_semiring, get_typed_op, op_from_string
+from .operator import (
+ UNKNOWN_OPCLASS,
+ _dict_to_func,
+ find_opclass,
+ get_semiring,
+ get_typed_op,
+ op_from_string,
+)
from .scalar import (
_COMPLETE,
_MATERIALIZE,
@@ -2279,7 +2286,11 @@ def apply(self, op, right=None, *, left=None):
right = False # most basic form of 0 when unifying dtypes
if left is not None:
raise TypeError("Do not pass `left` when applying IndexUnaryOp")
-
+ elif opclass == UNKNOWN_OPCLASS and isinstance(op, Mapping):
+ if left is not None:
+ raise TypeError("Do not pass `left` when applying a Mapping")
+ op = _dict_to_func(op, right)
+ right = None
if left is None and right is None:
op = get_typed_op(op, self.dtype, kind="unary")
self._expect_op(
diff --git a/graphblas/core/operator.py b/graphblas/core/operator.py
index eca7c9d75..78bf37cc9 100644
--- a/graphblas/core/operator.py
+++ b/graphblas/core/operator.py
@@ -3597,3 +3597,25 @@ def aggregator_from_string(string):
from .. import agg # noqa: E402 isort:skip
agg.from_string = aggregator_from_string
+
+
+def _dict_to_func(d, default):
+ # This probably doesn't work on UDTs, and we could probably be smarter with dtypes
+ if default is None:
+ default = False
+ keys, vals = zip(*d.items())
+ keys = np.array(keys)
+ lookup_dtype(keys.dtype)
+ vals = np.array(vals)
+ lookup_dtype(vals.dtype)
+ p = np.argsort(keys)
+ keys = keys[p]
+ vals = vals[p]
+
+ def func(x):
+ i = np.searchsorted(keys, x)
+ if i < keys.size and keys[i] == x:
+ return vals[i]
+ return default
+
+ return func
diff --git a/graphblas/core/vector.py b/graphblas/core/vector.py
index dd183d856..0ba8cec11 100644
--- a/graphblas/core/vector.py
+++ b/graphblas/core/vector.py
@@ -1,5 +1,6 @@
import itertools
import warnings
+from collections.abc import Mapping
import numpy as np
@@ -11,7 +12,14 @@
from .descriptor import lookup as descriptor_lookup
from .expr import _ALL_INDICES, AmbiguousAssignOrExtract, IndexerResolver, Updater
from .mask import Mask, StructuralMask, ValueMask
-from .operator import UNKNOWN_OPCLASS, find_opclass, get_semiring, get_typed_op, op_from_string
+from .operator import (
+ UNKNOWN_OPCLASS,
+ _dict_to_func,
+ find_opclass,
+ get_semiring,
+ get_typed_op,
+ op_from_string,
+)
from .scalar import (
_COMPLETE,
_MATERIALIZE,
@@ -1315,7 +1323,11 @@ def apply(self, op, right=None, *, left=None):
right = False # most basic form of 0 when unifying dtypes
if left is not None:
raise TypeError("Do not pass `left` when applying IndexUnaryOp")
-
+ elif opclass == UNKNOWN_OPCLASS and isinstance(op, Mapping):
+ if left is not None:
+ raise TypeError("Do not pass `left` when applying a Mapping")
+ op = _dict_to_func(op, right)
+ right = None
if left is None and right is None:
op = get_typed_op(op, self.dtype, kind="unary")
self._expect_op(
diff --git a/graphblas/tests/test_matrix.py b/graphblas/tests/test_matrix.py
index 40676f71a..160217235 100644
--- a/graphblas/tests/test_matrix.py
+++ b/graphblas/tests/test_matrix.py
@@ -1232,6 +1232,23 @@ def test_apply_indexunary(A):
A.apply(select.valueeq, left=s3)
+def test_apply_dict():
+ rows = [0, 0, 0, 0]
+ cols = [1, 3, 4, 6]
+ vals = [1, 1, 2, 0]
+ V = Matrix.from_coo(rows, cols, vals)
+ # Use right as default
+ W1 = V.apply({1: 10, 2: 20}, 100).new()
+ expected = Matrix.from_coo(rows, cols, [10, 10, 20, 100])
+ assert W1.isequal(expected)
+ # Default is 0 if unspecified
+ W2 = V.apply({0: 10, 2: 20}).new()
+ expected = Matrix.from_coo(rows, cols, [0, 0, 20, 10])
+ assert W2.isequal(expected)
+ with pytest.raises(TypeError, match="left"):
+ V.apply({0: 10, 2: 20}, left=999)
+
+
def test_select(A):
A3 = Matrix.from_coo([0, 3, 3, 6], [3, 0, 2, 4], [3, 3, 3, 3], nrows=7, ncols=7)
w1 = A.select(select.valueeq, 3).new()
diff --git a/graphblas/tests/test_vector.py b/graphblas/tests/test_vector.py
index 8505313e4..7da626b3b 100644
--- a/graphblas/tests/test_vector.py
+++ b/graphblas/tests/test_vector.py
@@ -724,6 +724,29 @@ def test_apply_indexunary(v):
v.apply(indexunary.valueeq, left=s2)
+def test_apply_dict(v):
+ # Use right as default
+ w1 = v.apply({1: 10, 2: 20}, 100).new()
+ expected = Vector.from_coo([1, 3, 4, 6], [10, 10, 20, 100])
+ assert w1.isequal(expected)
+ # Default is 0 if unspecified
+ w2 = v.apply({0: 10, 2: 20}).new()
+ expected = Vector.from_coo([1, 3, 4, 6], [0, 0, 20, 10])
+ assert w2.isequal(expected)
+ # Scalar default can up-cast dtype
+ w3 = v.apply({1: 10, 2: 20}, 0.5).new()
+ expected = Vector.from_coo([1, 3, 4, 6], [10, 10, 20, 0.5])
+ assert w3.isequal(expected)
+ with pytest.raises(TypeError, match="left"):
+ v.apply({0: 10, 2: 20}, left=999)
+ with pytest.raises(ValueError, match="Unknown dtype"):
+ v.apply({0: 10, 2: object()})
+ import numba
+
+ with pytest.raises(numba.TypingError): # TODO: this error and message should be better
+ v.apply({0: 10, 2: 20}, object())
+
+
def test_select(v):
result = Vector.from_coo([1, 3], [1, 1], size=7)
w1 = v.select(select.valueeq, 1).new()
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