Merge branch 'main' into arrayapi

Author: lshaw8317

.github/workflows/wasm.yml

@@ -9,8 +9,8 @@ on:
 
 env:
   CIBW_BUILD_VERBOSITY: 1
-  # cibuildwheel cannot choose for a specified version of pyodide yet
-  # PYODIDE_VERSION: 0.27.2
+  # In case you want to specify a version of pyodide
+  # PYODIDE_VERSION: 0.28.2
 
 jobs:
   build_wheels_wasm:
@@ -25,8 +25,8 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        cibw_build: ["cp3{11,12,13}-*"]
-        p_ver: ["3.11-3.13"]
+        cibw_build: ["cp3{12,13,14}-*"]
+        p_ver: ["3.12-3.14"]
 
     steps:
       - name: Checkout repo

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.11
+    rev: v0.13.0
     hooks:
       - id: ruff-check
         args: ["--fix", "--show-fixes"]

README.rst

@@ -115,8 +115,8 @@ In addition, other people have participated to the project in different
 aspects:
 
 - Jan Sellner, contributed the mmap support for NDArray/SChunk objects.
-- Dimitri Papadopoulos, contributed a large bunch of improvements to the
-  in many aspects of the project.  His attention to detail is remarkable.
+- Dimitri Papadopoulos, contributed a large bunch of improvements to
+  many aspects of the project.  His attention to detail is remarkable.
 - And many others that have contributed with bug reports, suggestions and
   improvements.
 

ROADMAP-TO-4.0.md

@@ -5,7 +5,7 @@ List of desired features for a 4.0 release
 
 * Have a completely specified format for the `TreeStore` and `DictStore`.  The format should allow to have containers either in memory or on disk.  Also, it should allow a sparse or contiguous storage.  The user will be able to specify these properties by following the same conventions than for NDArray objects (alas, `urlpath` and `contiguous` params).
 
-    * New `.save()` and `.to_cframe()` methods should be implemented to convert from in-memory representations to on disk and viceversa.
+    * New `.save()` and `.to_cframe()` methods should be implemented to convert from in-memory representations to on disk and vice-versa.
     * The format for `TreeStore` and `DictStore` will initially be defined at Python level, and documented only in the Python-Blosc2 repository.  An implementation in the C library is desirable, but not mandatory at this time.
 
 * A new `Table` object should be implemented based on the `TreeStore` class (a subclass?), with a label ('table'?) in metalayers indicating that the contents of the tree can be interpreted as regular table.  As `TreeStore` is hierarchical, a subtree can also be interpreted as a `Table` if there a label in the metalayer of the subtree (or group in HDF5 parlance); that can lead to tables than can have different subtables embedded.  It is not clear yet if should impose the same number of rows for all the columns.

bench/ndarray/compute_dists.py

@@ -36,9 +36,10 @@
 blosc2.storage_dflts["contiguous"] = storage.contiguous
 blosc2.storage_dflts["mode"] = storage.mode
 
-urlpath = dict((aname, None) for aname in ("a", "b", "c"))
 if persistent:
-    urlpath = dict((aname, f"{aname}.b2nd") for aname in ("a", "b", "c"))
+    urlpath = {aname: f"{aname}.b2nd" for aname in ("a", "b", "c")}
+else:
+    urlpath = {aname: None for aname in ("a", "b", "c")}
 
 btimes = []
 bspeeds = []

doc/conf.py

@@ -81,7 +81,6 @@ def linkcode_resolve(domain, info):
 
     import importlib
     import inspect
-    import os
 
     # Modify this to point to your package
     module_name = info["module"]

doc/getting_started/tutorials/08.schunk-slicing_and_beyond.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Slicing, extending and serializing with SChunks\n",
     "\n",
-    "The usual way to store generic binary data in python-blosc2 is through a `SChunk` (super-chunk) object, where the data is split into chunks of the same size, which we studied in the last tutorial. We saw how to retrieve, update or append data in the form of chunks. In fact, one can work with the individual multi-byte items composing the data (and not the bytes directly), using native SChunk methods - such operations will be the subject of this tutorial. We will use NumPy arrays as data sources, but everything we're going to do woul work equally well with any Python object supporting the [Buffer Protocol](https://docs.python.org/3/c-api/buffer.html).\n",
+    "The usual way to store generic binary data in python-blosc2 is through a `SChunk` (super-chunk) object, where the data is split into chunks of the same size, which we studied in the last tutorial. We saw how to retrieve, update or append data in the form of chunks. In fact, one can work with the individual multi-byte items composing the data (and not the bytes directly), using native SChunk methods - such operations will be the subject of this tutorial. We will use NumPy arrays as data sources, but everything we're going to do would work equally well with any Python object supporting the [Buffer Protocol](https://docs.python.org/3/c-api/buffer.html).\n",
     "\n",
     "First, we create our own `SChunk` instance; this time, let's fill it with data upon creation."
    ]
@@ -93,7 +93,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "As you can see, the data is returned as a bytes object. If we want to get a more meaningful container instead, we can use `get_slice`. This method requires an initailised buffer into which to load the bytes, and one may pass any Python object (supporting the Buffer Protocol) as the `out` param to fill it with the data.  In this case we will use a NumPy array container."
+   "source": "As you can see, the data is returned as a bytes object. If we want to get a more meaningful container instead, we can use `get_slice`. This method requires an initialised buffer into which to load the bytes, and one may pass any Python object (supporting the Buffer Protocol) as the `out` param to fill it with the data.  In this case we will use a NumPy array container."
   },
   {
    "cell_type": "code",

doc/python-blosc2.rst

@@ -72,7 +72,7 @@
                 <h3 style="margin-top: 10px; margin-bottom: 15px;">Hierarchical Structures</h3>
             </div>
 
-        Efficiently store data hierachically with the `TreeStore class <https://www.blosc.org/python-blosc2/reference/tree_store.html#blosc2.TreeStore>`_ for convenience and optimized `performance <https://www.blosc.org/posts/new-treestore-blosc2/>`_.
+        Efficiently store data hierarchically with the `TreeStore class <https://www.blosc.org/python-blosc2/reference/tree_store.html#blosc2.TreeStore>`_ for convenience and optimized `performance <https://www.blosc.org/posts/new-treestore-blosc2/>`_.
 
     .. grid-item-card::
         :class-card: intro-card text-center no-border

pyproject.toml

@@ -124,8 +124,8 @@ ignore = [
     "RET508",
     "RUF005",
     "RUF015",
+    "RUF059",
     "SIM108",
-    "UP038",  # https://github.com/astral-sh/ruff/issues/7871
 ]
 
 [tool.ruff.lint.extend-per-file-ignores]

src/blosc2/dict_store.py

@@ -392,7 +392,7 @@ def to_b2z(self, overwrite=False, filename=None) -> os.PathLike[Any] | str:
                     filepaths.append(filepath)
 
         # Sort filepaths by file size from largest to smallest
-        filepaths.sort(key=lambda f: os.path.getsize(f), reverse=True)
+        filepaths.sort(key=os.path.getsize, reverse=True)
 
         with zipfile.ZipFile(self.b2z_path, "w", zipfile.ZIP_STORED) as zf:
             # Write all files (except estore_path) first (sorted by size)