feat: update to latest awkward==2.5.0

pyproject.toml

@@ -36,7 +36,7 @@ classifiers = [
   "Topic :: Software Development",
 ]
 dependencies = [
-  "awkward >=2.4.5",
+  "awkward >=2.5.0",
   "dask >=2023.04.0",
   "typing_extensions >=4.8.0",
 ]

src/dask_awkward/lib/core.py

@@ -7,7 +7,7 @@
 import operator
 import sys
 import warnings
-from collections.abc import Callable, Hashable, Sequence
+from collections.abc import Callable, Hashable, Mapping, Sequence
 from enum import IntEnum
 from functools import cached_property, partial, wraps
 from numbers import Number
@@ -17,13 +17,14 @@
 import dask.config
 import numpy as np
 from awkward._do import remove_structure as ak_do_remove_structure
-from awkward._nplikes.typetracer import (
+from awkward.highlevel import NDArrayOperatorsMixin, _dir_pattern
+from awkward.typetracer import (
     MaybeNone,
     OneOf,
     TypeTracerArray,
+    create_unknown_scalar,
     is_unknown_scalar,
 )
-from awkward.highlevel import NDArrayOperatorsMixin, _dir_pattern
 from dask.base import (
     DaskMethodsMixin,
     dont_optimize,
@@ -140,7 +141,9 @@ def key(self) -> Key:
         return (self._name, 0)
 
     def _check_meta(self, m: Any) -> Any | None:
-        if isinstance(m, (MaybeNone, OneOf)) or is_unknown_scalar(m):
+        if m is None:
+            return m
+        elif isinstance(m, (MaybeNone, OneOf)) or is_unknown_scalar(m):
             return m
         elif isinstance(m, ak.Array) and len(m) == 1:
             return m
@@ -348,12 +351,9 @@ def new_scalar_object(dsk: HighLevelGraph, name: str, *, meta: Any) -> Scalar:
         Resulting collection.
 
     """
-    if meta is None:
-        meta = ak.Array(TypeTracerArray._new(dtype=np.dtype(None), shape=()))
-
     if isinstance(meta, MaybeNone):
         meta = ak.Array(meta.content)
-    else:
+    elif meta is not None:
         try:
             if ak.backend(meta) != "typetracer":
                 raise TypeError(
@@ -411,9 +411,7 @@ def new_known_scalar(
         dtype = np.dtype(dtype)
     llg = AwkwardMaterializedLayer({(name, 0): s}, previous_layer_names=[])
     hlg = HighLevelGraph.from_collections(name, llg, dependencies=())
-    return Scalar(
-        hlg, name, meta=TypeTracerArray._new(dtype=dtype, shape=()), known_value=s
-    )
+    return Scalar(hlg, name, meta=create_unknown_scalar(dtype), known_value=s)
 
 
 class Record(Scalar):
@@ -819,7 +817,14 @@ def layout(self) -> Content:
         raise ValueError("This collection's meta is None; unknown layout.")
 
     @property
-    def behavior(self) -> dict:
+    def attrs(self) -> dict:
+        """awkward Array attrs dictionary."""
+        if self._meta is not None:
+            return self._meta.attrs
+        raise ValueError("This collection's meta is None; no attrs property available.")
+
+    @property
+    def behavior(self) -> Mapping:
         """awkward Array behavior dictionary."""
         if self._meta is not None:
             return self._meta.behavior
@@ -1486,7 +1491,8 @@ def new_array_object(
     name: str,
     *,
     meta: ak.Array | None = None,
-    behavior: dict | None = None,
+    behavior: Mapping | None = None,
+    attrs: Mapping[str, Any] | None = None,
     npartitions: int | None = None,
     divisions: tuple[int, ...] | tuple[None, ...] | None = None,
 ) -> Array:
@@ -1505,6 +1511,10 @@ def new_array_object(
         typetracer for the new Array. If the configuration option
         ``awkward.compute-unknown-meta`` is set to ``False``,
         undefined `meta` will be assigned an empty typetracer.
+    behavior : dict, optional
+        Custom ak.behavior for the output array.
+    attrs : dict, optional
+        Custom attributes for the output array.
     npartitions : int, optional
         Total number of partitions; if used `divisions` will be a
         tuple of length `npartitions` + 1 with all elements``None``.
@@ -1548,6 +1558,8 @@ def new_array_object(
 
     if behavior is not None:
         actual_meta.behavior = behavior
+    if attrs is not None:
+        actual_meta.attrs = attrs
 
     out = Array(dsk, name, actual_meta, divs)
     if actual_meta.__doc__ != actual_meta.__class__.__doc__:
@@ -1877,6 +1889,8 @@ def non_trivial_reduction(
     keepdims: bool,
     mask_identity: bool,
     reducer: Callable,
+    behavior: Mapping | None = None,
+    attrs: Mapping[str, Any] | None = None,
     combiner: Callable | None = None,
     token: str | None = None,
     dtype: Any | None = None,
@@ -2225,7 +2239,11 @@ def typetracer_array(a: ak.Array | Array) -> ak.Array:
     if isinstance(a, Array):
         return a._meta
     elif isinstance(a, ak.Array):
-        return ak.Array(a.layout.to_typetracer(forget_length=True))
+        return ak.Array(
+            a.layout.to_typetracer(forget_length=True),
+            behavior=a._behavior,
+            attrs=a._attrs,
+        )
     else:
         msg = (
             "`a` should be an awkward array or a Dask awkward collection.\n"

src/dask_awkward/lib/io/io.py

@@ -3,7 +3,7 @@
 import functools
 import logging
 import math
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, cast
 
@@ -65,8 +65,9 @@ def __call__(self, start: int, stop: int, **kwargs: Any) -> ak.Array:
 def from_awkward(
     source: ak.Array,
     npartitions: int,
-    behavior: dict | None = None,
+    behavior: Mapping | None = None,
     label: str | None = None,
+    attrs: Mapping[str, Any] | None = None,
 ) -> Array:
     """Create an Array collection from a concrete :class:`awkward.Array` object.
 
@@ -76,8 +77,12 @@ def from_awkward(
         The concrete awkward array.
     npartitions : int
         The total number of partitions for the collection.
+    behavior : dict, optional
+        Custom ak.behavior for the output array.
     label : str, optional
         Label for the task.
+    attrs : mapping, optional
+        Custom attributes for the output array.
 
     Returns
     -------
@@ -112,25 +117,35 @@ def from_awkward(
         divisions=locs,
         meta=meta,
         behavior=behavior,
+        attrs=attrs,
     )
 
 
 class _FromListsFn:
-    def __init__(self, behavior: dict | None = None):
+    def __init__(self, behavior: Mapping | None, attrs: Mapping[str, Any] | None):
         self.behavior = behavior
+        self.attrs = attrs
 
     def __call__(self, x: list) -> ak.Array:
-        return ak.Array(x, behavior=self.behavior)
+        return ak.Array(x, behavior=self.behavior, attrs=self.attrs)
 
 
-def from_lists(source: list) -> Array:
+def from_lists(
+    source: list,
+    behavior: Mapping | None = None,
+    attrs: Mapping[str, Any] | None = None,
+) -> Array:
     """Create an Array collection from a list of lists.
 
     Parameters
     ----------
     source : list[list[Any]]
         List of lists, each outer list will become a partition in the
         collection.
+    behavior : dict, optional
+        Custom ak.behavior for the output array.
+    attrs : mapping, optional
+        Custom attributes for the output array.
 
     Returns
     -------
@@ -152,9 +167,9 @@ def from_lists(source: list) -> Array:
     lists = list(source)
     divs = (0, *np.cumsum(list(map(len, lists))))
     return from_map(
-        _FromListsFn(),
+        _FromListsFn(behavior=behavior, attrs=attrs),
         lists,
-        meta=typetracer_array(ak.Array(lists[0])),
+        meta=typetracer_array(ak.Array(lists[0], attrs=attrs, behavior=behavior)),
         divisions=divs,
         label="from-lists",
     )
@@ -163,9 +178,10 @@ def from_lists(source: list) -> Array:
 def from_delayed(
     source: list[Delayed] | Delayed,
     meta: ak.Array | None = None,
-    behavior: dict | None = None,
+    behavior: Mapping | None = None,
     divisions: tuple[int, ...] | tuple[None, ...] | None = None,
     prefix: str = "from-delayed",
+    attrs: Mapping[str, Any] | None = None,
 ) -> Array:
     """Create an Array collection from a set of :class:`~dask.delayed.Delayed` objects.
 
@@ -179,10 +195,14 @@ def from_delayed(
         Metadata (typetracer array) if known, if ``None`` the first
         partition (first element of the list of ``Delayed`` objects)
         will be computed to determine the metadata.
+    behavior : dict, optional
+        Custom ak.behavior for the output array.
     divisions : tuple[int | None, ...], optional
         Partition boundaries (if known).
     prefix : str
         Prefix for the keys in the task graph.
+    attrs : mapping, optional
+        Custom attributes for the output array.
 
     Returns
     -------
@@ -206,11 +226,7 @@ def from_delayed(
             raise ValueError("divisions must be a tuple of length len(source) + 1")
     hlg = HighLevelGraph.from_collections(name, dsk, dependencies=parts)
     return new_array_object(
-        hlg,
-        name=name,
-        meta=meta,
-        behavior=behavior,
-        divisions=divs,
+        hlg, name=name, meta=meta, behavior=behavior, divisions=divs, attrs=attrs
     )
 
 
@@ -346,13 +362,21 @@ def to_dask_array(
         return new_da_object(graph, name, meta=None, chunks=chunks, dtype=dtype)
 
 
-def from_dask_array(array: DaskArray, behavior: dict | None = None) -> Array:
+def from_dask_array(
+    array: DaskArray,
+    behavior: Mapping | None = None,
+    attrs: Mapping[str, Any] | None = None,
+) -> Array:
     """Convert a Dask Array collection to a Dask Awkard Array collection.
 
     Parameters
     ----------
     array : dask.array.Array
         Array to convert.
+    behavior : dict, optional
+        Custom ak.behavior for the output array.
+    attrs : mapping, optional
+        Custom attributes for the output array.
 
     Returns
     -------
@@ -389,11 +413,18 @@ def from_dask_array(array: DaskArray, behavior: dict | None = None) -> Array:
     hlg = HighLevelGraph.from_collections(name, layer, dependencies=[array])
     if np.any(np.isnan(array.chunks)):
         return new_array_object(
-            hlg, name, npartitions=array.npartitions, meta=meta, behavior=behavior
+            hlg,
+            name,
+            npartitions=array.npartitions,
+            meta=meta,
+            behavior=behavior,
+            attrs=attrs,
         )
     else:
         divs = (0, *np.cumsum(array.chunks))
-        return new_array_object(hlg, name, divisions=divs, meta=meta, behavior=behavior)
+        return new_array_object(
+            hlg, name, divisions=divs, meta=meta, behavior=behavior, attrs=attrs
+        )
 
 
 def to_dataframe(
@@ -522,6 +553,8 @@ def from_map(
         number of partitions in the output collection (only one
         element of each iterable will be passed to `func` for each
         partition).
+    args : tuple
+        Tuple of positional arguments to append after mapped arguments.
     label : str, optional
         String to use as the function-name label in the output
         collection-key names.

src/dask_awkward/lib/io/json.py

@@ -3,7 +3,7 @@
 import abc
 import logging
 import math
-from collections.abc import Callable
+from collections.abc import Callable, Mapping
 from typing import TYPE_CHECKING, Any, Literal, overload
 
 import awkward as ak
@@ -43,7 +43,7 @@ def __init__(
         form: Form,
         compression: str | None = None,
         schema: str | dict | list | None = None,
-        behavior: dict | None = None,
+        behavior: Mapping | None = None,
         **kwargs: Any,
     ) -> None:
         self.compression = compression
@@ -91,7 +91,7 @@ def __init__(
         form: Form,
         compression: str | None = None,
         schema: str | dict | list | None = None,
-        behavior: dict | None = None,
+        behavior: Mapping | None = None,
         **kwargs: Any,
     ) -> None:
         super().__init__(
@@ -125,7 +125,7 @@ def __init__(
         form: Form,
         compression: str | None = None,
         schema: str | dict | list | None = None,
-        behavior: dict | None = None,
+        behavior: Mapping | None = None,
         **kwargs: Any,
     ) -> None:
         super().__init__(
@@ -163,7 +163,7 @@ def __init__(
         form: Form,
         compression: str | None = None,
         schema: str | dict | list | None = None,
-        behavior: dict | None = None,
+        behavior: Mapping | None = None,
         **kwargs: Any,
     ) -> None:
         super().__init__(
@@ -432,7 +432,8 @@ def from_json(
     initial: int = 1024,
     resize: float = 8,
     highlevel: bool = True,
-    behavior: dict | None = None,
+    behavior: Mapping | None = None,
+    attrs: Mapping[str, Any] | None = None,
     blocksize: int | str | None = None,
     delimiter: bytes | None = None,
     compression: str | None = "infer",