Merge branch 'main' into some-utils-typing

.github/workflows/minimal.yml

@@ -15,7 +15,7 @@ jobs:
     steps:
     - uses: actions/checkout@v4
     - name: Setup Miniconda
-      uses: conda-incubator/setup-miniconda@v3.0.2
+      uses: conda-incubator/setup-miniconda@v3.0.3
       with:
           channels: conda-forge
           environment-file: environment.yml

.github/workflows/python-package.yml

@@ -42,7 +42,7 @@ jobs:
       with:
         fetch-depth: 0
     - name: Setup Miniconda
-      uses: conda-incubator/setup-miniconda@v3.0.2
+      uses: conda-incubator/setup-miniconda@v3.0.3
       with:
           channels: conda-forge
           python-version: ${{ matrix.python-version }}

.github/workflows/releases.yml

@@ -64,7 +64,7 @@ jobs:
         with:
           name: releases
           path: dist
-      - uses: pypa/gh-action-pypi-publish@v1.8.12
+      - uses: pypa/gh-action-pypi-publish@v1.8.14
         with:
           user: __token__
           password: ${{ secrets.pypi_password }}

.github/workflows/windows-testing.yml

@@ -21,7 +21,7 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: conda-incubator/setup-miniconda@v3.0.2
+      - uses: conda-incubator/setup-miniconda@v3.0.3
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

docs/installation.rst

@@ -6,6 +6,11 @@ Zarr depends on NumPy. It is generally best to `install NumPy
 appropriate for your operating system and Python distribution. Other dependencies should be
 installed automatically if using one of the installation methods below.
 
+Note: Zarr has endorsed `Scientific-Python SPEC 0 <https://scientific-python.org/specs/spec-0000/>`_ and now follows the version support window as outlined below: 
+
+- Python: 36 months after initial release
+- Core package dependencies (e.g. NumPy): 24 months after initial release
+
 Install Zarr from PyPI::
 
     $ pip install zarr

docs/tutorial.rst

@@ -868,7 +868,7 @@ implementations of the ``MutableMapping`` interface for Amazon S3 (`S3Map
 Distributed File System (`HDFSMap
 <https://hdfs3.readthedocs.io/en/latest/api.html#hdfs3.mapping.HDFSMap>`_) and
 Google Cloud Storage (`GCSMap
-<http://gcsfs.readthedocs.io/en/latest/api.html#gcsfs.mapping.GCSMap>`_), which
+<https://gcsfs.readthedocs.io/en/latest/api.html#gcsfs.core.GCSFileSystem.get_mapper>`_), which
 can be used with Zarr.
 
 Here is an example using S3Map to read an array created previously::

requirements_dev_optional.txt

@@ -8,14 +8,14 @@ ipywidgets==8.1.2
 # don't let pyup change pinning for azure-storage-blob, need to pin to older
 # version to get compatibility with azure storage emulator on appveyor (FIXME)
 azure-storage-blob==12.16.0 # pyup: ignore
-redis==5.0.2
+redis==5.0.3
 types-redis
 types-setuptools
 pymongo==4.6.2
 # optional test requirements
 coverage
 pytest-cov==4.1.0
-pytest-doctestplus==1.2.0
+pytest-doctestplus==1.2.1
 pytest-timeout==2.3.1
 h5py==3.10.0
 fsspec==2023.12.2