diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 29281f5be9..9503f3df8a 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -1,4 +1,4 @@ Contributing ============ -Please see the [project documentation](https://zarr.readthedocs.io/en/stable/contributing.html) for information about contributing to Zarr. +Please see the [project documentation](https://zarr.readthedocs.io/en/stable/developers/contributing.html) for information about contributing to Zarr. diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 705cd31cb5..84bb89d82a 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -57,7 +57,22 @@ body: id: reproduce attributes: label: Steps to reproduce - description: Minimal, reproducible code sample, a copy-pastable example if possible. + description: Minimal, reproducible code sample. Must list dependencies in [inline script metadata](https://packaging.python.org/en/latest/specifications/inline-script-metadata/#example). When put in a file named `issue.py` calling `uv run issue.py` should show the issue. + value: | + ```python + # /// script + # requires-python = ">=3.11" + # dependencies = [ + # "zarr@git+https://github.com/zarr-developers/zarr-python.git@main", + # ] + # /// + # + # This script automatically imports the development branch of zarr to check for issues + + import zarr + # your reproducer code + # zarr.print_debug_info() + ``` validations: required: true - type: textarea diff --git a/.github/workflows/gpu_test.yml b/.github/workflows/gpu_test.yml index c7056a2c4b..752440719b 100644 --- a/.github/workflows/gpu_test.yml +++ b/.github/workflows/gpu_test.yml @@ -25,7 +25,7 @@ jobs: strategy: matrix: python-version: ['3.11'] - numpy-version: ['2.1'] + numpy-version: ['2.2'] dependency-set: ["minimal"] steps: diff --git a/.github/workflows/hypothesis.yaml b/.github/workflows/hypothesis.yaml index 0a320de00b..776f859d6e 100644 --- a/.github/workflows/hypothesis.yaml +++ b/.github/workflows/hypothesis.yaml @@ -25,12 +25,19 @@ jobs: strategy: matrix: - python-version: ['3.11'] - numpy-version: ['2.1'] + python-version: ['3.12'] + numpy-version: ['2.2'] dependency-set: ["optional"] steps: - uses: actions/checkout@v4 + - name: Set HYPOTHESIS_PROFILE based on trigger + run: | + if [[ "${{ github.event_name }}" == "schedule" || "${{ github.event_name }}" == "workflow_dispatch" ]]; then + echo "HYPOTHESIS_PROFILE=nightly" >> $GITHUB_ENV + else + echo "HYPOTHESIS_PROFILE=ci" >> $GITHUB_ENV + fi - name: Set up Python uses: actions/setup-python@v5 with: @@ -58,6 +65,7 @@ jobs: if: success() id: status run: | + echo "Using Hypothesis profile: $HYPOTHESIS_PROFILE" hatch env run --env test.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} run-hypothesis # explicitly save the cache so it gets updated, also do this even if it fails. diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 4160fa3506..7cfce41312 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -21,7 +21,7 @@ jobs: strategy: matrix: python-version: ['3.11', '3.12', '3.13'] - numpy-version: ['1.25', '2.1'] + numpy-version: ['1.25', '2.2'] dependency-set: ["minimal", "optional"] os: ["ubuntu-latest"] include: @@ -30,7 +30,7 @@ jobs: dependency-set: 'optional' os: 'macos-latest' - python-version: '3.13' - numpy-version: '2.1' + numpy-version: '2.2' dependency-set: 'optional' os: 'macos-latest' - python-version: '3.11' @@ -38,7 +38,7 @@ jobs: dependency-set: 'optional' os: 'windows-latest' - python-version: '3.13' - numpy-version: '2.1' + numpy-version: '2.2' dependency-set: 'optional' os: 'windows-latest' runs-on: ${{ matrix.os }} @@ -61,6 +61,8 @@ jobs: hatch env create test.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} hatch env run -e test.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} list-env - name: Run Tests + env: + HYPOTHESIS_PROFILE: ci run: | hatch env run --env test.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} run-coverage - name: Upload coverage @@ -102,7 +104,7 @@ jobs: hatch env run -e ${{ matrix.dependency-set }} list-env - name: Run Tests run: | - hatch env run --env ${{ matrix.dependency-set }} run + hatch env run --env ${{ matrix.dependency-set }} run-coverage - name: Upload coverage uses: codecov/codecov-action@v5 with: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 75ef0face8..fd50366a1c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,7 @@ ci: default_stages: [pre-commit, pre-push] repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.9 + rev: v0.11.9 hooks: - id: ruff args: ["--fix", "--show-fixes"] @@ -37,8 +37,10 @@ repos: - obstore>=0.5.1 # Tests - pytest + - hypothesis + - s3fs - repo: https://github.com/scientific-python/cookie - rev: 2025.01.22 + rev: 2025.05.02 hooks: - id: sp-repo-review - repo: https://github.com/pre-commit/pygrep-hooks diff --git a/changes/1661.feature.rst b/changes/1661.feature.rst deleted file mode 100644 index 38d60b23c1..0000000000 --- a/changes/1661.feature.rst +++ /dev/null @@ -1 +0,0 @@ -Add experimental ObjectStore storage class based on obstore. \ No newline at end of file diff --git a/changes/2622.feature.rst b/changes/2622.feature.rst deleted file mode 100644 index f5c7cbe192..0000000000 --- a/changes/2622.feature.rst +++ /dev/null @@ -1 +0,0 @@ -Add ``zarr.from_array`` using concurrent streaming of source data \ No newline at end of file diff --git a/changes/2714.misc.rst b/changes/2714.misc.rst deleted file mode 100644 index 9ab55089d2..0000000000 --- a/changes/2714.misc.rst +++ /dev/null @@ -1 +0,0 @@ -Make warning filters in the tests more specific, so warnings emitted by tests added in the future are more likely to be caught instead of ignored. diff --git a/changes/2718.bugfix.rst b/changes/2718.bugfix.rst deleted file mode 100644 index 48ddf8b5a8..0000000000 --- a/changes/2718.bugfix.rst +++ /dev/null @@ -1,3 +0,0 @@ -0-dimensional arrays are now returning a scalar. Therefore, the return type of ``__getitem__`` changed -to NDArrayLikeOrScalar. This change is to make the behavior of 0-dimensional arrays consistent with -``numpy`` scalars. \ No newline at end of file diff --git a/changes/2774.feature.rst b/changes/2774.feature.rst new file mode 100644 index 0000000000..4df83f54ec --- /dev/null +++ b/changes/2774.feature.rst @@ -0,0 +1 @@ +Add `zarr.storage.FsspecStore.from_mapper()` so that `zarr.open()` supports stores of type `fsspec.mapping.FSMap`. \ No newline at end of file diff --git a/changes/2802.fix.rst b/changes/2802.fix.rst deleted file mode 100644 index 471ddf66f4..0000000000 --- a/changes/2802.fix.rst +++ /dev/null @@ -1 +0,0 @@ -Fix `fill_value` serialization for `NaN` in `ArrayV2Metadata` and add property-based testing of round-trip serialization \ No newline at end of file diff --git a/changes/2871.feature.rst b/changes/2871.feature.rst new file mode 100644 index 0000000000..a39f30c558 --- /dev/null +++ b/changes/2871.feature.rst @@ -0,0 +1,8 @@ +Added public API for Buffer ABCs and implementations. + +Use :mod:`zarr.buffer` to access buffer implementations, and +:mod:`zarr.abc.buffer` for the interface to implement new buffer types. + +Users previously importing buffer from ``zarr.core.buffer`` should update their +imports to use :mod:`zarr.buffer`. As a reminder, all of ``zarr.core`` is +considered a private API that's not covered by zarr-python's versioning policy. \ No newline at end of file diff --git a/changes/2874.feature.rst b/changes/2874.feature.rst new file mode 100644 index 0000000000..4c50532ae0 --- /dev/null +++ b/changes/2874.feature.rst @@ -0,0 +1,9 @@ +Adds zarr-specific data type classes. This replaces the internal use of numpy data types for zarr +v2 and a fixed set of string enums for zarr v3. This change is largely internal, but it does +change the type of the ``dtype`` and ``data_type`` fields on the ``ArrayV2Metadata`` and +``ArrayV3Metadata`` classes. It also changes the JSON metadata representation of the +variable-length string data type, but the old metadata representation can still be +used when reading arrays. The logic for automatically choosing the chunk encoding for a given data +type has also changed, and this necessitated changes to the ``config`` API. + +For more on this new feature, see the `documentation `_ \ No newline at end of file diff --git a/changes/2921.bugfix.rst b/changes/2921.bugfix.rst new file mode 100644 index 0000000000..65db48654f --- /dev/null +++ b/changes/2921.bugfix.rst @@ -0,0 +1 @@ +Ignore stale child metadata when reconsolidating metadata. diff --git a/changes/2924.chore.rst b/changes/2924.chore.rst deleted file mode 100644 index 7bfbb2e1c7..0000000000 --- a/changes/2924.chore.rst +++ /dev/null @@ -1,2 +0,0 @@ -Define a new versioning policy based on Effective Effort Versioning. This replaces the old -Semantic Versioning-based policy. \ No newline at end of file diff --git a/changes/2944.misc.rst b/changes/2944.misc.rst deleted file mode 100644 index 48356a1fef..0000000000 --- a/changes/2944.misc.rst +++ /dev/null @@ -1 +0,0 @@ -Avoid an unnecessary memory copy when writing Zarr to a local file diff --git a/changes/2991.doc.rst b/changes/2991.doc.rst deleted file mode 100644 index 828cfcdb2f..0000000000 --- a/changes/2991.doc.rst +++ /dev/null @@ -1 +0,0 @@ -Updated the 3.0 migration guide to include the removal of "." syntax for getting group members. diff --git a/changes/2996.bugfix.rst b/changes/2996.bugfix.rst deleted file mode 100644 index 977dc79d0b..0000000000 --- a/changes/2996.bugfix.rst +++ /dev/null @@ -1,4 +0,0 @@ -Fixes `ConsolidatedMetadata` serialization of `nan`, `inf`, and `-inf` to be -consistent with the behavior of `ArrayMetadata`. - - diff --git a/changes/3021.feature.rst b/changes/3021.feature.rst new file mode 100644 index 0000000000..8805797ce3 --- /dev/null +++ b/changes/3021.feature.rst @@ -0,0 +1 @@ +Implemented ``move`` for ``LocalStore`` and ``ZipStore``. This allows users to move the store to a different root path. \ No newline at end of file diff --git a/changes/3066.feature.rst b/changes/3066.feature.rst new file mode 100644 index 0000000000..89d5ddb1c6 --- /dev/null +++ b/changes/3066.feature.rst @@ -0,0 +1 @@ +Added `~zarr.errors.GroupNotFoundError`, which is raised when attempting to open a group that does not exist. diff --git a/changes/3068.bugfix.rst b/changes/3068.bugfix.rst new file mode 100644 index 0000000000..9ada322c13 --- /dev/null +++ b/changes/3068.bugfix.rst @@ -0,0 +1 @@ +Trying to open an array with ``mode='r'`` when the store is not read-only now raises an error. diff --git a/changes/3081.feature.rst b/changes/3081.feature.rst new file mode 100644 index 0000000000..8cf83ea7c2 --- /dev/null +++ b/changes/3081.feature.rst @@ -0,0 +1 @@ +Adds ``fill_value`` to the list of attributes displayed in the output of the ``AsyncArray.info()`` method. \ No newline at end of file diff --git a/changes/3082.feature.rst b/changes/3082.feature.rst new file mode 100644 index 0000000000..e990d1f3a0 --- /dev/null +++ b/changes/3082.feature.rst @@ -0,0 +1 @@ +Use :py:func:`numpy.zeros` instead of :py:func:`np.full` for a performance speedup when creating a `zarr.core.buffer.NDBuffer` with `fill_value=0`. \ No newline at end of file diff --git a/changes/3100.bugfix.rst b/changes/3100.bugfix.rst new file mode 100644 index 0000000000..11f06628c0 --- /dev/null +++ b/changes/3100.bugfix.rst @@ -0,0 +1,3 @@ +For Zarr format 2, allow fixed-length string arrays to be created without automatically inserting a +``Vlen-UT8`` codec in the array of filters. Fixed-length string arrays do not need this codec. This +change fixes a regression where fixed-length string arrays created with Zarr Python 3 could not be read with Zarr Python 2.18. \ No newline at end of file diff --git a/changes/3103.bugfix.rst b/changes/3103.bugfix.rst new file mode 100644 index 0000000000..93aecce908 --- /dev/null +++ b/changes/3103.bugfix.rst @@ -0,0 +1,7 @@ +When creating arrays without explicitly specifying a chunk size using `zarr.create` and other +array creation routines, the chunk size will now set automatically instead of defaulting to the data shape. +For large arrays this will result in smaller default chunk sizes. +To retain previous behaviour, explicitly set the chunk shape to the data shape. + +This fix matches the existing chunking behaviour of +`zarr.save_array` and `zarr.api.asynchronous.AsyncArray.create`. diff --git a/changes/3127.bugfix.rst b/changes/3127.bugfix.rst new file mode 100644 index 0000000000..35d7f5d329 --- /dev/null +++ b/changes/3127.bugfix.rst @@ -0,0 +1,2 @@ +When `zarr.save` has an argument `path=some/path/` and multiple arrays in `args`, the path resulted in `some/path/some/path` due to using the `path` +argument twice while building the array path. This is now fixed. \ No newline at end of file diff --git a/changes/3128.bugfix.rst b/changes/3128.bugfix.rst new file mode 100644 index 0000000000..b93416070e --- /dev/null +++ b/changes/3128.bugfix.rst @@ -0,0 +1 @@ +Fix `zarr.open` default for argument `mode` when `store` is `read_only` \ No newline at end of file diff --git a/changes/3130.feature.rst b/changes/3130.feature.rst new file mode 100644 index 0000000000..7a64582f06 --- /dev/null +++ b/changes/3130.feature.rst @@ -0,0 +1 @@ +Port more stateful testing actions from `Icechunk `_. diff --git a/changes/3138.feature.rst b/changes/3138.feature.rst new file mode 100644 index 0000000000..ecd339bf9c --- /dev/null +++ b/changes/3138.feature.rst @@ -0,0 +1 @@ +Adds a `with_read_only` convenience method to the `Store` abstract base class (raises `NotImplementedError`) and implementations to the `MemoryStore`, `ObjectStore`, `LocalStore`, and `FsspecStore` classes. \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 9bb1c48901..68bf003ad5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -105,7 +105,7 @@ def skip_submodules( "roadmap": "developers/roadmap.html", "installation": "user-guide/installation.html", "api": "api/zarr/index", - "release": "release-notes" + "release": "release-notes.html", } # The language for content autogenerated by Sphinx. Refer to documentation diff --git a/docs/developers/contributing.rst b/docs/developers/contributing.rst index fa65f71d48..03388e1544 100644 --- a/docs/developers/contributing.rst +++ b/docs/developers/contributing.rst @@ -251,14 +251,11 @@ See the `towncrier`_ docs for more. .. _towncrier: https://towncrier.readthedocs.io/en/stable/tutorial.html -Development best practices, policies and procedures ---------------------------------------------------- - The following information is mainly for core developers, but may also be of interest to contributors. Merging pull requests -~~~~~~~~~~~~~~~~~~~~~ +--------------------- Pull requests submitted by an external contributor should be reviewed and approved by at least one core developer before being merged. Ideally, pull requests submitted by a core developer @@ -268,10 +265,10 @@ Pull requests should not be merged until all CI checks have passed (GitHub Actio Codecov) against code that has had the latest main merged in. Compatibility and versioning policies -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------------- Versioning -"""""""""" +~~~~~~~~~~ Versions of this library are identified by a triplet of integers with the form ``..``, for example ``3.0.4``. A release of ``zarr-python`` is associated with a new version identifier. That new identifier is generated by incrementing exactly one of the components of @@ -323,7 +320,7 @@ backwards-compatible changes wherever possible. When a backwards-incompatible ch users should be notified well in advance, e.g. via informative deprecation warnings. Data format compatibility -^^^^^^^^^^^^^^^^^^^^^^^^^ +""""""""""""""""""""""""" The Zarr library is an implementation of a file format standard defined externally -- see the `Zarr specifications website `_ for the list of @@ -340,36 +337,28 @@ breaking changes may be more frequent than usual. Release procedure -~~~~~~~~~~~~~~~~~ - -.. note:: - - Most of the release process is now handled by GitHub workflow which should - automatically push a release to PyPI if a tag is pushed. +----------------- Pre-release -""""""""""" +~~~~~~~~~~~ 1. Make sure that all pull requests which will be included in the release - have been properly documented as changelog files in :file:`changes`. -2. Run ``towncrier build --version x.y.z`` to create the changelog. + have been properly documented as changelog files in the :file:`changes/` directory. +2. Run ``towncrier build --version x.y.z`` to create the changelog, and commit the result + to the main branch. Releasing -""""""""" -To make a new release, go to -https://github.com/zarr-developers/zarr-python/releases and -click "Draft a new release". Choose a version number prefixed -with a `v` (e.g. `v0.0.0`). For pre-releases, include the -appropriate suffix (e.g. `v0.0.0a1` or `v0.0.0rc2`). - - -Set the description of the release to:: +~~~~~~~~~ +1. Go to https://github.com/zarr-developers/zarr-python/releases +2. Click "Draft a new release". +3. Choose a version number prefixed with a `v` (e.g. `v0.0.0`). + For pre-releases, include the appropriate suffix (e.g. `v0.0.0a1` or `v0.0.0rc2`). +4. Set the description of the release to:: See release notes https://zarr.readthedocs.io/en/stable/release-notes.html#release-0-0-0 -replacing the correct version numbers. For pre-release versions, -the URL should omit the pre-release suffix, e.g. "a1" or "rc1". - -Click on "Generate release notes" to auto-file the description. + replacing the correct version numbers. For pre-release versions, + the URL should omit the pre-release suffix, e.g. "a1" or "rc1". +5. Click on "Generate release notes" to auto-fill the description. After creating the release, the documentation will be built on https://readthedocs.io. Full releases will be available under @@ -378,9 +367,8 @@ pre-releases will be available under `/latest `_. Post-release -"""""""""""" +~~~~~~~~~~~~ - Review and merge the pull request on the `conda-forge feedstock `_ that will be automatically generated. -- Create a new "Unreleased" section in the release notes diff --git a/docs/release-notes.rst b/docs/release-notes.rst index c585e4f0d3..a89046dd6d 100644 --- a/docs/release-notes.rst +++ b/docs/release-notes.rst @@ -3,6 +3,80 @@ Release notes .. towncrier release notes start +3.0.8 (2025-05-19) +------------------ + +.. warning:: + + In versions 3.0.0 to 3.0.7 opening arrays or groups with ``mode='a'`` (the default for many builtin functions) + would cause any existing paths in the store to be deleted. This is fixed in 3.0.8, and + we recommend all users upgrade to avoid this bug that could cause unintentional data loss. + +Features +~~~~~~~~ + +- Added a `print_debug_info` function for bug reports. (:issue:`2913`) + + +Bugfixes +~~~~~~~~ + +- Fix a bug that prevented the number of initialized chunks being counted properly. (:issue:`2862`) +- Fixed sharding with GPU buffers. (:issue:`2978`) +- Fix structured `dtype` fill value serialization for consolidated metadata (:issue:`2998`) +- It is now possible to specify no compressor when creating a zarr format 2 array. + This can be done by passing ``compressor=None`` to the various array creation routines. + + The default behaviour of automatically choosing a suitable default compressor remains if the compressor argument is not given. + To reproduce the behaviour in previous zarr-python versions when ``compressor=None`` was passed, pass ``compressor='auto'`` instead. (:issue:`3039`) +- Fixed the typing of ``dimension_names`` arguments throughout so that it now accepts iterables that contain `None` alongside `str`. (:issue:`3045`) +- Using various functions to open data with ``mode='a'`` no longer deletes existing data in the store. (:issue:`3062`) +- Internally use `typesize` constructor parameter for :class:`numcodecs.blosc.Blosc` to improve compression ratios back to the v2-package levels. (:issue:`2962`) +- Specifying the memory order of Zarr format 2 arrays using the ``order`` keyword argument has been fixed. (:issue:`2950`) + + +Misc +~~~~ + +- :issue:`2972`, :issue:`3027`, :issue:`3049` + + +3.0.7 (2025-04-22) +------------------ + +Features +~~~~~~~~ + +- Add experimental ObjectStore storage class based on obstore. (:issue:`1661`) +- Add ``zarr.from_array`` using concurrent streaming of source data (:issue:`2622`) + + +Bugfixes +~~~~~~~~ + +- 0-dimensional arrays are now returning a scalar. Therefore, the return type of ``__getitem__`` changed + to NDArrayLikeOrScalar. This change is to make the behavior of 0-dimensional arrays consistent with + ``numpy`` scalars. (:issue:`2718`) +- Fix `fill_value` serialization for `NaN` in `ArrayV2Metadata` and add property-based testing of round-trip serialization (:issue:`2802`) +- Fixes `ConsolidatedMetadata` serialization of `nan`, `inf`, and `-inf` to be + consistent with the behavior of `ArrayMetadata`. (:issue:`2996`) + + +Improved Documentation +~~~~~~~~~~~~~~~~~~~~~~ + +- Updated the 3.0 migration guide to include the removal of "." syntax for getting group members. (:issue:`2991`, :issue:`2997`) + + +Misc +~~~~ +- Define a new versioning policy based on Effective Effort Versioning. This replaces the old Semantic + Versioning-based policy. (:issue:`2924`, :issue:`2910`) +- Make warning filters in the tests more specific, so warnings emitted by tests added in the future + are more likely to be caught instead of ignored. (:issue:`2714`) +- Avoid an unnecessary memory copy when writing Zarr to a local file (:issue:`2944`) + + 3.0.6 (2025-03-20) ------------------ diff --git a/docs/user-guide/arrays.rst b/docs/user-guide/arrays.rst index a62b2ea0fa..c27f1296b9 100644 --- a/docs/user-guide/arrays.rst +++ b/docs/user-guide/arrays.rst @@ -182,7 +182,8 @@ which can be used to print useful diagnostics, e.g.:: >>> z.info Type : Array Zarr format : 3 - Data type : DataType.int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (10000, 10000) Chunk shape : (1000, 1000) Order : C @@ -199,7 +200,8 @@ prints additional diagnostics, e.g.:: >>> z.info_complete() Type : Array Zarr format : 3 - Data type : DataType.int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (10000, 10000) Chunk shape : (1000, 1000) Order : C @@ -209,8 +211,8 @@ prints additional diagnostics, e.g.:: Serializer : BytesCodec(endian=) Compressors : (BloscCodec(typesize=4, cname=, clevel=3, shuffle=, blocksize=0),) No. bytes : 400000000 (381.5M) - No. bytes stored : 9696520 - Storage ratio : 41.3 + No. bytes stored : 3558573 + Storage ratio : 112.4 Chunks Initialized : 100 .. note:: @@ -246,7 +248,7 @@ built-in delta filter:: The default compressor can be changed by setting the value of the using Zarr's :ref:`user-guide-config`, e.g.:: - >>> with zarr.config.set({'array.v2_default_compressor.numeric': {'id': 'blosc'}}): + >>> with zarr.config.set({'array.v2_default_compressor.default': {'id': 'blosc'}}): ... z = zarr.create_array(store={}, shape=(100000000,), chunks=(1000000,), dtype='int32', zarr_format=2) >>> z.filters () @@ -286,7 +288,8 @@ Here is an example using a delta filter with the Blosc compressor:: >>> z.info Type : Array Zarr format : 3 - Data type : DataType.int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (10000, 10000) Chunk shape : (1000, 1000) Order : C @@ -600,7 +603,8 @@ Sharded arrays can be created by providing the ``shards`` parameter to :func:`za >>> a.info_complete() Type : Array Zarr format : 3 - Data type : DataType.uint8 + Data type : UInt8() + Fill value : 0 Shape : (10000, 10000) Shard shape : (1000, 1000) Chunk shape : (100, 100) @@ -608,10 +612,10 @@ Sharded arrays can be created by providing the ``shards`` parameter to :func:`za Read-only : False Store type : LocalStore Filters : () - Serializer : BytesCodec(endian=) + Serializer : BytesCodec(endian=None) Compressors : (ZstdCodec(level=0, checksum=False),) No. bytes : 100000000 (95.4M) - No. bytes stored : 3981552 + No. bytes stored : 3981473 Storage ratio : 25.1 Shards Initialized : 100 diff --git a/docs/user-guide/config.rst b/docs/user-guide/config.rst index 91ffe50b91..5a9d26f2b9 100644 --- a/docs/user-guide/config.rst +++ b/docs/user-guide/config.rst @@ -43,39 +43,30 @@ This is the current default configuration:: >>> zarr.config.pprint() {'array': {'order': 'C', - 'v2_default_compressor': {'bytes': {'checksum': False, - 'id': 'zstd', - 'level': 0}, - 'numeric': {'checksum': False, - 'id': 'zstd', - 'level': 0}, - 'string': {'checksum': False, + 'v2_default_compressor': {'default': {'checksum': False, 'id': 'zstd', - 'level': 0}}, - 'v2_default_filters': {'bytes': [{'id': 'vlen-bytes'}], - 'numeric': None, - 'raw': None, - 'string': [{'id': 'vlen-utf8'}]}, - 'v3_default_compressors': {'bytes': [{'configuration': {'checksum': False, - 'level': 0}, - 'name': 'zstd'}], - 'numeric': [{'configuration': {'checksum': False, + 'level': 0}, + 'variable-length-string': {'checksum': False, + 'id': 'zstd', + 'level': 0}}, + 'v2_default_filters': {'default': None, + 'variable-length-string': [{'id': 'vlen-utf8'}]}, + 'v3_default_compressors': {'default': [{'configuration': {'checksum': False, 'level': 0}, 'name': 'zstd'}], - 'string': [{'configuration': {'checksum': False, - 'level': 0}, - 'name': 'zstd'}]}, - 'v3_default_filters': {'bytes': [], 'numeric': [], 'string': []}, - 'v3_default_serializer': {'bytes': {'name': 'vlen-bytes'}, - 'numeric': {'configuration': {'endian': 'little'}, - 'name': 'bytes'}, - 'string': {'name': 'vlen-utf8'}}, - 'write_empty_chunks': False}, - 'async': {'concurrency': 10, 'timeout': None}, - 'buffer': 'zarr.core.buffer.cpu.Buffer', - 'codec_pipeline': {'batch_size': 1, - 'path': 'zarr.core.codec_pipeline.BatchedCodecPipeline'}, - 'codecs': {'blosc': 'zarr.codecs.blosc.BloscCodec', + 'variable-length-string': [{'configuration': {'checksum': False, + 'level': 0}, + 'name': 'zstd'}]}, + 'v3_default_filters': {'default': [], 'variable-length-string': []}, + 'v3_default_serializer': {'default': {'configuration': {'endian': 'little'}, + 'name': 'bytes'}, + 'variable-length-string': {'name': 'vlen-utf8'}}, + 'write_empty_chunks': False}, + 'async': {'concurrency': 10, 'timeout': None}, + 'buffer': 'zarr.buffer.cpu.Buffer', + 'codec_pipeline': {'batch_size': 1, + 'path': 'zarr.core.codec_pipeline.BatchedCodecPipeline'}, + 'codecs': {'blosc': 'zarr.codecs.blosc.BloscCodec', 'bytes': 'zarr.codecs.bytes.BytesCodec', 'crc32c': 'zarr.codecs.crc32c_.Crc32cCodec', 'endian': 'zarr.codecs.bytes.BytesCodec', @@ -85,7 +76,7 @@ This is the current default configuration:: 'vlen-bytes': 'zarr.codecs.vlen_utf8.VLenBytesCodec', 'vlen-utf8': 'zarr.codecs.vlen_utf8.VLenUTF8Codec', 'zstd': 'zarr.codecs.zstd.ZstdCodec'}, - 'default_zarr_format': 3, - 'json_indent': 2, - 'ndbuffer': 'zarr.core.buffer.cpu.NDBuffer', - 'threading': {'max_workers': None}} + 'default_zarr_format': 3, + 'json_indent': 2, + 'ndbuffer': 'zarr.buffer.cpu.NDBuffer', + 'threading': {'max_workers': None}} diff --git a/docs/user-guide/consolidated_metadata.rst b/docs/user-guide/consolidated_metadata.rst index 3c015dcfca..4cd72dbc74 100644 --- a/docs/user-guide/consolidated_metadata.rst +++ b/docs/user-guide/consolidated_metadata.rst @@ -47,7 +47,7 @@ that can be used.: >>> from pprint import pprint >>> pprint(dict(sorted(consolidated_metadata.items()))) {'a': ArrayV3Metadata(shape=(1,), - data_type=, + data_type=Float64(endianness='little'), chunk_grid=RegularChunkGrid(chunk_shape=(1,)), chunk_key_encoding=DefaultChunkKeyEncoding(name='default', separator='/'), @@ -60,7 +60,7 @@ that can be used.: node_type='array', storage_transformers=()), 'b': ArrayV3Metadata(shape=(2, 2), - data_type=, + data_type=Float64(endianness='little'), chunk_grid=RegularChunkGrid(chunk_shape=(2, 2)), chunk_key_encoding=DefaultChunkKeyEncoding(name='default', separator='/'), @@ -73,7 +73,7 @@ that can be used.: node_type='array', storage_transformers=()), 'c': ArrayV3Metadata(shape=(3, 3, 3), - data_type=, + data_type=Float64(endianness='little'), chunk_grid=RegularChunkGrid(chunk_shape=(3, 3, 3)), chunk_key_encoding=DefaultChunkKeyEncoding(name='default', separator='/'), @@ -114,3 +114,23 @@ removed, or modified, consolidated metadata may not be desirable. metadata. .. _Consolidated Metadata: https://github.com/zarr-developers/zarr-specs/pull/309 + +Stores Without Support for Consolidated Metadata +------------------------------------------------ + +Some stores may want to opt out of the consolidated metadata mechanism. This +may be for several reasons like: + +* They want to maintain read-write consistency, which is challenging with + consolidated metadata. +* They have their own consolidated metadata mechanism. +* They offer good enough performance without need for consolidation. + +This type of store can declare it doesn't want consolidation by implementing +`Store.supports_consolidated_metadata` and returning `False`. For stores that don't support +consolidation, Zarr will: + +* Raise an error on `consolidate_metadata` calls, maintaining the store in + its unconsolidated state. +* Raise an error in `AsyncGroup.open(..., use_consolidated=True)` +* Not use consolidated metadata in `AsyncGroup.open(..., use_consolidated=None)` diff --git a/docs/user-guide/data_types.rst b/docs/user-guide/data_types.rst new file mode 100644 index 0000000000..87c8efc1f5 --- /dev/null +++ b/docs/user-guide/data_types.rst @@ -0,0 +1,172 @@ +Data types +========== + +Zarr's data type model +---------------------- + +Every Zarr array has a "data type", which defines the meaning and physical layout of the +array's elements. As Zarr Python is tightly integrated with `NumPy `_, +it's easy to create arrays with NumPy data types: + +.. code-block:: python + + >>> import zarr + >>> import numpy as np + >>> z = zarr.create_array(store={}, shape=(10,), dtype=np.dtype('uint8')) + >>> z + + +Unlike NumPy arrays, Zarr arrays are designed to accessed by Zarr +implementations in different programming languages. This means Zarr data types must be interpreted +correctly when clients read an array. Each Zarr data type defines procedures for +encoding and decoding both the data type itself, and scalars from that data type to and from Zarr array metadata. And these serialization procedures +depend on the Zarr format. + +Data types in Zarr version 2 +----------------------------- + +Version 2 of the Zarr format defined its data types relative to +`NumPy's data types `_, +and added a few non-NumPy data types as well. Thus the JSON identifier for a NumPy-compatible data +type is just the NumPy ``str`` attribute of that data type: + +.. code-block:: python + + >>> import zarr + >>> import numpy as np + >>> import json + >>> + >>> store = {} + >>> np_dtype = np.dtype('int64') + >>> z = zarr.create_array(store=store, shape=(1,), dtype=np_dtype, zarr_format=2) + >>> dtype_meta = json.loads(store['.zarray'].to_bytes())["dtype"] + >>> dtype_meta + '>> assert dtype_meta == np_dtype.str + +.. note:: + The ``<`` character in the data type metadata encodes the + `endianness `_, + or "byte order", of the data type. Following NumPy's example, + in Zarr version 2 each data type has an endianness where applicable. + However, Zarr version 3 data types do not store endianness information. + +In addition to defining a representation of the data type itself (which in the example above was +just a simple string ``"M[10s]"`` in + Zarr V2. This is more compact, but can be harder to parse. + +For more about data types in Zarr V3, see the +`V3 specification `_. + +Data types in Zarr Python +------------------------- + +The two Zarr formats that Zarr Python supports specify data types in two different ways: +data types in Zarr version 2 are encoded as NumPy-compatible strings, while data types in Zarr version +3 are encoded as either strings or ``JSON`` objects, +and the Zarr V3 data types don't have any associated endianness information, unlike Zarr V2 data types. + +To abstract over these syntactical and semantic differences, Zarr Python uses a class called +`ZDType <../api/zarr/dtype/index.html#zarr.dtype.ZDType>`_ provide Zarr V2 and Zarr V3 compatibility +routines for ""native" data types. In this context, a "native" data type is a Python class, +typically defined in another library, that models an array's data type. For example, ``np.uint8`` is a native +data type defined in NumPy, which Zarr Python wraps with a ``ZDType`` instance called +`UInt8 <../api/zarr/dtype/index.html#zarr.dtype.ZDType>`_. + +Each data type supported by Zarr Python is modeled by ``ZDType`` subclass, which provides an +API for the following operations: + +- Wrapping / unwrapping a native data type +- Encoding / decoding a data type to / from Zarr V2 and Zarr V3 array metadata. +- Encoding / decoding a scalar value to / from Zarr V2 and Zarr V3 array metadata. + + +Example Usage +~~~~~~~~~~~~~ + +Create a ``ZDType`` from a native data type: + +.. code-block:: python + + >>> from zarr.core.dtype import Int8 + >>> import numpy as np + >>> int8 = Int8.from_native_dtype(np.dtype('int8')) + +Convert back to native data type: + +.. code-block:: python + + >>> native_dtype = int8.to_native_dtype() + >>> assert native_dtype == np.dtype('int8') + +Get the default scalar value for the data type: + +.. code-block:: python + + >>> default_value = int8.default_scalar() + >>> assert default_value == np.int8(0) + + +Serialize to JSON for Zarr V2 and V3 + +.. code-block:: python + + >>> json_v2 = int8.to_json(zarr_format=2) + >>> json_v2 + {'name': '|i1', 'object_codec_id': None} + >>> json_v3 = int8.to_json(zarr_format=3) + >>> json_v3 + 'int8' + +Serialize a scalar value to JSON: + +.. code-block:: python + + >>> json_value = int8.to_json_scalar(42, zarr_format=3) + >>> json_value + 42 + +Deserialize a scalar value from JSON: + +.. code-block:: python + + >>> scalar_value = int8.from_json_scalar(42, zarr_format=3) + >>> assert scalar_value == np.int8(42) diff --git a/docs/user-guide/extending.rst b/docs/user-guide/extending.rst index 7647703fbb..4487e07ddf 100644 --- a/docs/user-guide/extending.rst +++ b/docs/user-guide/extending.rst @@ -83,7 +83,10 @@ Coming soon. Custom array buffers -------------------- -Coming soon. +Zarr-python provides control over where and how arrays stored in memory through +:mod:`zarr.buffer`. Currently both CPU (the default) and GPU implementations are +provided (see :ref:`user-guide-gpu` for more). You can implement your own buffer +classes by implementing the interface defined in :mod:`zarr.abc.buffer`. Other extensions ---------------- diff --git a/docs/user-guide/groups.rst b/docs/user-guide/groups.rst index 4268004f70..4237a9df50 100644 --- a/docs/user-guide/groups.rst +++ b/docs/user-guide/groups.rst @@ -128,7 +128,8 @@ property. E.g.:: >>> bar.info_complete() Type : Array Zarr format : 3 - Data type : DataType.int64 + Data type : Int64(endianness='little') + Fill value : 0 Shape : (1000000,) Chunk shape : (100000,) Order : C @@ -140,11 +141,12 @@ property. E.g.:: No. bytes : 8000000 (7.6M) No. bytes stored : 1614 Storage ratio : 4956.6 - Chunks Initialized : 0 + Chunks Initialized : 10 >>> baz.info Type : Array Zarr format : 3 - Data type : DataType.float32 + Data type : Float32(endianness='little') + Fill value : 0.0 Shape : (1000, 1000) Chunk shape : (100, 100) Order : C diff --git a/docs/user-guide/index.rst b/docs/user-guide/index.rst index c50713332b..ea34ac2561 100644 --- a/docs/user-guide/index.rst +++ b/docs/user-guide/index.rst @@ -8,6 +8,7 @@ User guide installation arrays + data_types groups attributes storage diff --git a/docs/user-guide/performance.rst b/docs/user-guide/performance.rst index 42d830780f..7d24c87373 100644 --- a/docs/user-guide/performance.rst +++ b/docs/user-guide/performance.rst @@ -91,7 +91,8 @@ To use sharding, you need to specify the ``shards`` parameter when creating the >>> z6.info Type : Array Zarr format : 3 - Data type : DataType.uint8 + Data type : UInt8() + Fill value : 0 Shape : (10000, 10000, 1000) Shard shape : (1000, 1000, 1000) Chunk shape : (100, 100, 100) @@ -99,7 +100,7 @@ To use sharding, you need to specify the ``shards`` parameter when creating the Read-only : False Store type : MemoryStore Filters : () - Serializer : BytesCodec(endian=) + Serializer : BytesCodec(endian=None) Compressors : (ZstdCodec(level=0, checksum=False),) No. bytes : 100000000000 (93.1G) @@ -121,7 +122,8 @@ ratios, depending on the correlation structure within the data. E.g.:: >>> c.info_complete() Type : Array Zarr format : 3 - Data type : DataType.int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (10000, 10000) Chunk shape : (1000, 1000) Order : C @@ -140,7 +142,8 @@ ratios, depending on the correlation structure within the data. E.g.:: >>> f.info_complete() Type : Array Zarr format : 3 - Data type : DataType.int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (10000, 10000) Chunk shape : (1000, 1000) Order : F diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb deleted file mode 100644 index eba6b5880b..0000000000 --- a/notebooks/advanced_indexing.ipynb +++ /dev/null @@ -1,2798 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced indexing" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.1.5.dev144'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "import numpy as np\n", - "np.random.seed(42)\n", - "import cProfile\n", - "zarr.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Functionality and API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a Boolean (mask) array\n", - "\n", - "Supported via ``get/set_mask_selection()`` and ``.vindex[]``. Also supported via ``get/set_orthogonal_selection()`` and ``.oindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = [False, True, False, True, False, True, False, True, False, True]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.oindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 100, 2, 300, 4, 500, 6, 700, 8, 900])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[ix] = a[ix] * 100\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# if using .oindex, indexing array can be any array-like, e.g., Zarr array\n", - "zix = zarr.array(ix, chunks=2)\n", - "za = zarr.array(a, chunks=2)\n", - "za.oindex[zix] # will not load all zix into memory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a 1D integer (coordinate) array\n", - "\n", - "Supported via ``get/set_coordinate_selection()`` and ``.vindex[]``. Also supported via ``get/set_orthogonal_selection()`` and ``.oindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = [1, 3, 5, 7, 9]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.oindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 100, 2, 300, 4, 500, 6, 700, 8, 900])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[ix] = a[ix] * 100\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a multi-dimensional integer (coordinate) array\n", - "\n", - "Supported via ``get/set_coordinate_selection()`` and ``.vindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = np.array([[1, 3, 5], [2, 4, 6]])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 3, 5],\n", - " [2, 4, 6]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 20, 30, 40, 50, 60, 7, 8, 9])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slicing a 1D array with step > 1\n", - "\n", - "Slices with step > 1 are supported via ``get/set_basic_selection()``, ``get/set_orthogonal_selection()``, ``__getitem__`` and ``.oindex[]``. Negative steps are not supported." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za[1::2]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[1::2] = a[1::2] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Orthogonal (outer) indexing of multi-dimensional arrays\n", - "\n", - "Orthogonal (a.k.a. outer) indexing is supported with either Boolean or integer arrays, in combination with integers and slices. This functionality is provided via the ``get/set_orthogonal_selection()`` methods. For convenience, this functionality is also available via the ``.oindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# orthogonal indexing with Boolean arrays\n", - "ix0 = [False, True, False, True, False]\n", - "ix1 = [True, False, True]\n", - "za.get_orthogonal_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# orthogonal indexing with integer arrays\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "za.get_orthogonal_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 4, 5],\n", - " [ 9, 10, 11]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# combine with slice\n", - "za.oindex[[1, 3], :]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 2],\n", - " [ 3, 5],\n", - " [ 6, 8],\n", - " [ 9, 11],\n", - " [12, 14]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# combine with slice\n", - "za.oindex[:, [0, 2]]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 42],\n", - " [ 6, 7, 8],\n", - " [42, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items via Boolean selection\n", - "ix0 = [False, True, False, True, False]\n", - "ix1 = [True, False, True]\n", - "selection = ix0, ix1\n", - "value = 42\n", - "za.set_orthogonal_selection(selection, value)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 44],\n", - " [ 6, 7, 8],\n", - " [44, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [46, 4, 46],\n", - " [ 6, 7, 8],\n", - " [46, 10, 46],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items via integer selection\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "selection = ix0, ix1\n", - "value = 46\n", - "za.set_orthogonal_selection(selection, value)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [48, 4, 48],\n", - " [ 6, 7, 8],\n", - " [48, 10, 48],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1] = 48\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Coordinate indexing of multi-dimensional arrays\n", - "\n", - "Selecting arbitrary points from a multi-dimensional array by indexing with integer (coordinate) arrays is supported. This functionality is provided via the ``get/set_coordinate_selection()`` methods. For convenience, this functionality is also available via the ``.vindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "za.get_coordinate_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.vindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.set_coordinate_selection((ix0, ix1), 42)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.vindex[ix0, ix1] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mask indexing of multi-dimensional arrays\n", - "\n", - "Selecting arbitrary points from a multi-dimensional array by a Boolean array is supported. This functionality is provided via the ``get/set_mask_selection()`` methods. For convenience, this functionality is also available via the ``.vindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix = np.zeros_like(a, dtype=bool)\n", - "ix[1, 0] = True\n", - "ix[3, 2] = True\n", - "za.get_mask_selection(ix)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.set_mask_selection(ix, 42)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.vindex[ix] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Selecting fields from arrays with a structured dtype\n", - "\n", - "All ``get/set_selection_...()`` methods support a ``fields`` argument which allows retrieving/replacing data for a specific field or fields. Also h5py-like API is supported where fields can be provided within ``__getitem__``, ``.oindex[]`` and ``.vindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(b'aaa', 1, 4.2), (b'bbb', 2, 8.4), (b'ccc', 3, 12.6)],\n", - " dtype=[('foo', 'S3'), ('bar', '\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'baz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices" - ] - } - ], - "source": [ - "a['foo', 'baz']" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(b'aaa', 4.2), (b'bbb', 8.4), (b'ccc', 12.6)],\n", - " dtype=[('foo', 'S3'), ('baz', '", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mza\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'baz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEllipsis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mget_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 661\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 662\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_basic_selection_nd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 665\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;31m# setup indexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/indexing.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, selection, array)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 276\u001b[0m raise IndexError('unsupported selection item for basic indexing; expected integer '\n\u001b[0;32m--> 277\u001b[0;31m 'or slice, got {!r}'.format(type(dim_sel)))\n\u001b[0m\u001b[1;32m 278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mdim_indexers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim_indexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: unsupported selection item for basic indexing; expected integer or slice, got " - ] - } - ], - "source": [ - "za[['foo', 'baz']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1D Benchmarking" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "800000000" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c = np.arange(100000000)\n", - "c.nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 480 ms, sys: 16 ms, total: 496 ms\n", - "Wall time: 141 ms\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (100000000,)\n", - "Chunk shape : (97657,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 800000000 (762.9M)\n", - "No. bytes stored : 11854081 (11.3M)\n", - "Storage ratio : 67.5\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time zc = zarr.array(c)\n", - "zc.info" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "121 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "254 ms ± 942 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool dense selection" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9997476" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# relatively dense selection - 10%\n", - "ix_dense_bool = np.random.binomial(1, 0.1, size=c.shape[0]).astype(bool)\n", - "np.count_nonzero(ix_dense_bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "243 ms ± 5.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "433 ms ± 6.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "548 ms ± 5.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "import tempfile\n", - "import cProfile\n", - "import pstats\n", - "\n", - "def profile(statement, sort='time', restrictions=(7,)):\n", - " with tempfile.NamedTemporaryFile() as f:\n", - " cProfile.run(statement, filename=f.name)\n", - " pstats.Stats(f.name).sort_stats(sort).print_stats(*restrictions)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:17:48 2017 /tmp/tmpruua2rs_\n", - "\n", - " 98386 function calls in 0.483 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 83 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1025 0.197 0.000 0.197 0.000 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.149 0.000 0.159 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.044 0.000 0.231 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1024 0.009 0.000 0.009 0.000 {built-in method numpy.core.multiarray.count_nonzero}\n", - " 1025 0.007 0.000 0.238 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.006 0.000 0.207 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/core.py:337()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Method ``nonzero`` is being called internally within numpy to convert bool to int selections, no way to avoid." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:18:06 2017 /tmp/tmp7_bautep\n", - "\n", - " 52382 function calls in 0.592 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 2 0.219 0.110 0.219 0.110 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.096 0.000 0.101 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.094 0.047 0.094 0.047 ../zarr/indexing.py:630()\n", - " 1024 0.044 0.000 0.167 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.029 0.029 0.029 0.029 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 1 0.021 0.021 0.181 0.181 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "``.vindex[]`` is a bit slower, possibly because internally it converts to a coordinate array first." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int dense selection" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000000" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix_dense_int = np.random.choice(c.shape[0], size=c.shape[0]//10, replace=True)\n", - "ix_dense_int_sorted = ix_dense_int.copy()\n", - "ix_dense_int_sorted.sort()\n", - "len(ix_dense_int)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "62.2 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "355 ms ± 3.53 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "351 ms ± 3.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "128 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.71 s ± 5.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.68 s ± 3.87 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:09 2017 /tmp/tmpgmu5btr_\n", - "\n", - " 95338 function calls in 0.424 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 89 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.141 0.141 0.184 0.184 ../zarr/indexing.py:369(__init__)\n", - " 1024 0.099 0.000 0.106 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.046 0.000 0.175 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.027 0.000 0.027 0.000 ../zarr/indexing.py:424(__iter__)\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 1 0.010 0.010 0.010 0.010 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/function_base.py:1848(diff)\n", - " 1025 0.006 0.000 0.059 0.000 ../zarr/indexing.py:541(__iter__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_int_sorted]')" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:13 2017 /tmp/tmpay1gvnx8\n", - "\n", - " 52362 function calls in 0.398 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 85 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 2 0.107 0.054 0.107 0.054 ../zarr/indexing.py:630()\n", - " 1024 0.091 0.000 0.096 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.041 0.000 0.160 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.040 0.040 0.213 0.213 ../zarr/indexing.py:603(__init__)\n", - " 1 0.029 0.029 0.029 0.029 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 2048 0.011 0.000 0.011 0.000 ../zarr/indexing.py:695()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_int_sorted]')" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:20 2017 /tmp/tmpngsf6zpp\n", - "\n", - " 120946 function calls in 1.793 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 92 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.128 1.128 1.128 1.128 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 1024 0.139 0.000 0.285 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.132 0.132 1.422 1.422 ../zarr/indexing.py:369(__init__)\n", - " 1 0.120 0.120 0.120 0.120 {method 'take' of 'numpy.ndarray' objects}\n", - " 1024 0.116 0.000 0.123 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.034 0.000 0.034 0.000 ../zarr/indexing.py:424(__iter__)\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_int]')" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:22 2017 /tmp/tmpbskhj8de\n", - "\n", - " 50320 function calls in 1.730 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 86 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.116 1.116 1.116 1.116 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 1024 0.133 0.000 0.275 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2 0.121 0.060 0.121 0.060 ../zarr/indexing.py:654()\n", - " 1024 0.113 0.000 0.119 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.100 0.050 0.100 0.050 ../zarr/indexing.py:630()\n", - " 1 0.030 0.030 0.030 0.030 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.024 0.024 1.427 1.427 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_int]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When indices are not sorted, zarr needs to partially sort them so the occur in chunk order, so we only have to visit each chunk once. This sorting dominates the processing time and is unavoidable AFAIK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool sparse selection" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9932" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# relatively sparse selection\n", - "ix_sparse_bool = np.random.binomial(1, 0.0001, size=c.shape[0]).astype(bool)\n", - "np.count_nonzero(ix_sparse_bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15.7 ms ± 38.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "156 ms ± 2.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "133 ms ± 2.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:20:09 2017 /tmp/tmpb7nqc9ax\n", - "\n", - " 98386 function calls in 0.191 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 83 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.093 0.000 0.098 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.017 0.000 0.017 0.000 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.007 0.000 0.007 0.000 {built-in method numpy.core.multiarray.count_nonzero}\n", - " 1024 0.007 0.000 0.129 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.005 0.000 0.052 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.005 0.000 0.025 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_sparse_bool]')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:20:09 2017 /tmp/tmphsko8nvh\n", - "\n", - " 52382 function calls in 0.160 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.093 0.000 0.098 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.017 0.008 0.017 0.008 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1025 0.008 0.000 0.014 0.000 ../zarr/indexing.py:674(__iter__)\n", - " 1024 0.006 0.000 0.127 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/indexing.py:695()\n", - " 2054 0.003 0.000 0.003 0.000 ../zarr/core.py:337()\n", - " 1024 0.002 0.000 0.005 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/core/arrayprint.py:381(wrapper)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_sparse_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int sparse selection" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix_sparse_int = np.random.choice(c.shape[0], size=c.shape[0]//10000, replace=True)\n", - "ix_sparse_int_sorted = ix_sparse_int.copy()\n", - "ix_sparse_int_sorted.sort()\n", - "len(ix_sparse_int)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "18.9 µs ± 392 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20.3 µs ± 155 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "125 ms ± 296 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "109 ms ± 428 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "132 ms ± 489 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "108 ms ± 579 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:21:12 2017 /tmp/tmp0b0o2quo\n", - "\n", - " 120946 function calls in 0.196 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 92 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.105 0.000 0.111 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2048 0.006 0.000 0.013 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 1025 0.006 0.000 0.051 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.006 0.000 0.141 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/core.py:337()\n", - " 15373 0.004 0.000 0.010 0.000 {built-in method builtins.isinstance}\n", - " 1025 0.004 0.000 0.005 0.000 ../zarr/indexing.py:424(__iter__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_sparse_int]')" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:21:19 2017 /tmp/tmpdwju98kn\n", - "\n", - " 50320 function calls in 0.167 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 86 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.105 0.000 0.111 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.009 0.000 0.017 0.000 ../zarr/indexing.py:674(__iter__)\n", - " 1024 0.006 0.000 0.142 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/indexing.py:695()\n", - " 2054 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - " 1 0.003 0.003 0.162 0.162 ../zarr/core.py:591(_get_selection)\n", - " 1027 0.003 0.000 0.003 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_sparse_int]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For sparse selections, processing time is dominated by decompression, so we can't do any better." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### sparse bool selection as zarr array" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Typezarr.core.Array
Data typebool
Shape(100000000,)
Chunk shape(390625,)
OrderC
Read-onlyFalse
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes100000000 (95.4M)
No. bytes stored507131 (495.2K)
Storage ratio197.2
Chunks initialized256/256
" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : bool\n", - "Shape : (100000000,)\n", - "Chunk shape : (390625,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 100000000 (95.4M)\n", - "No. bytes stored : 507131 (495.2K)\n", - "Storage ratio : 197.2\n", - "Chunks initialized : 256/256" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zix_sparse_bool = zarr.array(ix_sparse_bool)\n", - "zix_sparse_bool.info" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "387 ms ± 5.47 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[zix_sparse_bool]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### slice with step" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "80.3 ms ± 377 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit np.array(c[::2])" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "168 ms ± 837 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "136 ms ± 1.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::10]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "104 ms ± 1.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::100]" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100 ms ± 1.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:22:44 2017 /tmp/tmpg9dxqcpg\n", - "\n", - " 49193 function calls in 0.211 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 55 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.104 0.000 0.110 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.067 0.000 0.195 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.005 0.000 0.013 0.000 ../zarr/indexing.py:278(__iter__)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - " 2050 0.003 0.000 0.003 0.000 ../zarr/indexing.py:90(ceildiv)\n", - " 1025 0.003 0.000 0.006 0.000 ../zarr/indexing.py:109(__iter__)\n", - " 1024 0.003 0.000 0.003 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc[::2]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2D Benchmarking" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000000,)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000, 1000)" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = c.reshape(-1, 1000)\n", - "d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Typezarr.core.Array
Data typeint64
Shape(100000, 1000)
Chunk shape(3125, 32)
OrderC
Read-onlyFalse
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes800000000 (762.9M)
No. bytes stored39228864 (37.4M)
Storage ratio20.4
Chunks initialized1024/1024
" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (100000, 1000)\n", - "Chunk shape : (3125, 32)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 800000000 (762.9M)\n", - "No. bytes stored : 39228864 (37.4M)\n", - "Storage ratio : 20.4\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zd = zarr.array(d)\n", - "zd.info" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool orthogonal selection" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "ix0 = np.random.binomial(1, 0.5, size=d.shape[0]).astype(bool)\n", - "ix1 = np.random.binomial(1, 0.5, size=d.shape[1]).astype(bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "101 ms ± 577 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit d[np.ix_(ix0, ix1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "373 ms ± 5.45 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int orthogonal selection" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "ix0 = np.random.choice(d.shape[0], size=int(d.shape[0] * .5), replace=True)\n", - "ix1 = np.random.choice(d.shape[1], size=int(d.shape[1] * .5), replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "174 ms ± 4.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit d[np.ix_(ix0, ix1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "566 ms ± 12.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### coordinate (point) selection" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000000" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n = int(d.size * .1)\n", - "ix0 = np.random.choice(d.shape[0], size=n, replace=True)\n", - "ix1 = np.random.choice(d.shape[1], size=n, replace=True)\n", - "n" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "243 ms ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit d[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.03 s ± 17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.vindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:24:31 2017 /tmp/tmp7c68z70p\n", - "\n", - " 62673 function calls in 2.065 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.112 1.112 1.112 1.112 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 3 0.244 0.081 0.244 0.081 ../zarr/indexing.py:654()\n", - " 3 0.193 0.064 0.193 0.064 ../zarr/indexing.py:630()\n", - " 1024 0.170 0.000 0.350 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1024 0.142 0.000 0.151 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1 0.044 0.044 0.044 0.044 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.043 0.043 1.676 1.676 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zd.vindex[ix0, ix1]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Points need to be partially sorted so all points in the same chunk are grouped and processed together. This requires ``argsort`` which dominates time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## h5py comparison\n", - "\n", - "N.B., not really fair because using slower compressor, but for interest..." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py\n", - "import tempfile" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "h5f = h5py.File(tempfile.mktemp(), driver='core', backing_store=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hc = h5f.create_dataset('c', data=c, compression='gzip', compression_opts=1, chunks=zc.chunks, shuffle=True)\n", - "hc" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.16 s, sys: 172 ms, total: 1.33 s\n", - "Wall time: 1.32 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, ..., 99999997, 99999998, 99999999])" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time hc[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.11 s, sys: 0 ns, total: 1.11 s\n", - "Wall time: 1.11 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1063, 28396, 37229, ..., 99955875, 99979354, 99995791])" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time hc[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# # this is pathological, takes minutes \n", - "# %time hc[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 38.3 s, sys: 136 ms, total: 38.4 s\n", - "Wall time: 38.1 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 0, 1000, 2000, ..., 99997000, 99998000, 99999000])" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is pretty slow\n", - "%time hc[::1000]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/blosc_microbench.ipynb b/notebooks/blosc_microbench.ipynb deleted file mode 100644 index 9361d8e95b..0000000000 --- a/notebooks/blosc_microbench.ipynb +++ /dev/null @@ -1,200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import zarr\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 110 ms per loop\n", - "1 loop, best of 3: 235 ms per loop\n", - "Array((100000000,), int64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 11.2M; ratio: 67.8; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='i8')\n", - "data = np.arange(100000000, dtype='i8')\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 3: 331 ms per loop\n", - "1 loop, best of 3: 246 ms per loop\n", - "Array((100000000,), float64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 724.8M; ratio: 1.1; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='f8')\n", - "data = np.random.normal(size=100000000)\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.2.dev0+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import sys\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 92.7 ms per loop\n", - "1 loop, best of 3: 230 ms per loop\n", - "Array((100000000,), int64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 11.2M; ratio: 67.8; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='i8')\n", - "data = np.arange(100000000, dtype='i8')\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 3: 338 ms per loop\n", - "1 loop, best of 3: 253 ms per loop\n", - "Array((100000000,), float64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 724.8M; ratio: 1.1; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='f8')\n", - "data = np.random.normal(size=100000000)\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/dask_2d_subset.ipynb b/notebooks/dask_2d_subset.ipynb deleted file mode 100644 index 6e88b510d5..0000000000 --- a/notebooks/dask_2d_subset.ipynb +++ /dev/null @@ -1,869 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook has some profiling of Dask used to make a selection along both first and second axes of a large-ish multidimensional array. The use case is making selections of genotype data, e.g., as required for making a web-browser for genotype data as in www.malariagen.net/apps/ag1000g." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 2.1.1\n", - "dask 0.11.0\n" - ] - } - ], - "source": [ - "import zarr; print('zarr', zarr.__version__)\n", - "import dask; print('dask', dask.__version__)\n", - "import dask.array as da\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Real data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Group(/, 8)\n", - " arrays: 1; samples\n", - " groups: 7; 2L, 2R, 3L, 3R, UNKN, X, Y_unplaced\n", - " store: DirectoryStore" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# here's the real data\n", - "callset = zarr.open_group('/kwiat/2/coluzzi/ag1000g/data/phase1/release/AR3.1/variation/main/zarr2/zstd/ag1000g.phase1.ar3',\n", - " mode='r')\n", - "callset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3R/calldata/genotype, (22632425, 765, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 32.2G; nbytes_stored: 1.0G; ratio: 31.8; initialized: 34540/34540\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: DirectoryStore" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# here's the array we're going to work with\n", - "g = callset['3R/calldata/genotype']\n", - "g" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n", - "Wall time: 5.13 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# wrap as dask array with very simple chunking of first dim only\n", - "%time gd = da.from_array(g, chunks=(g.chunks[0], None, None))\n", - "gd" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((22632425,), dtype('bool'), 13167162)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load condition used to make selection on first axis\n", - "dim0_condition = callset['3R/variants/FILTER_PASS'][:]\n", - "dim0_condition.shape, dim0_condition.dtype, np.count_nonzero(dim0_condition)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# invent a random selection for second axis\n", - "dim1_indices = sorted(np.random.choice(765, size=100, replace=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.3 s, sys: 256 ms, total: 15.5 s\n", - "Wall time: 15.5 s\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# setup the 2D selection - this is the slow bit\n", - "%time gd_sel = gd[dim0_condition][:, dim1_indices]\n", - "gd_sel" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.21 s, sys: 152 ms, total: 1.36 s\n", - "Wall time: 316 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " ..., \n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 1],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]]], dtype=int8)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now load a slice from this new selection - quick!\n", - "%time gd_sel[1000000:1100000].compute(optimize_graph=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 105406881 function calls (79072145 primitive calls) in 26.182 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "13167268/6 6.807 0.000 9.038 1.506 slicing.py:623(check_index)\n", - " 2 4.713 2.356 5.831 2.916 slicing.py:398(partition_by_size)\n", - "13167270/2 4.470 0.000 8.763 4.382 slicing.py:540(posify_index)\n", - " 52669338 4.118 0.000 4.119 0.000 {built-in method builtins.isinstance}\n", - " 2 2.406 1.203 8.763 4.382 slicing.py:563()\n", - " 1 0.875 0.875 0.875 0.875 slicing.py:44()\n", - " 13182474 0.600 0.000 0.600 0.000 {built-in method builtins.len}\n", - " 2 0.527 0.264 0.527 0.264 slicing.py:420(issorted)\n", - " 13189168 0.520 0.000 0.520 0.000 {method 'append' of 'list' objects}\n", - " 2 0.271 0.136 0.271 0.136 slicing.py:479()\n", - " 2 0.220 0.110 0.220 0.110 {built-in method builtins.sorted}\n", - " 1 0.162 0.162 0.162 0.162 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.113 0.056 26.071 13.035 core.py:1024(__getitem__)\n", - " 2 0.112 0.056 6.435 3.217 slicing.py:441(take_sorted)\n", - " 1 0.111 0.111 26.182 26.182 :1()\n", - " 2 0.060 0.030 24.843 12.422 slicing.py:142(slice_with_newaxes)\n", - " 106/3 0.039 0.000 1.077 0.359 slicing.py:15(sanitize_index)\n", - " 3 0.037 0.012 0.037 0.012 {built-in method _hashlib.openssl_md5}\n", - " 6726 0.012 0.000 0.017 0.000 slicing.py:567(insert_many)\n", - " 3364 0.004 0.000 0.021 0.000 slicing.py:156()\n", - " 20178 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 8 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 2 0.000 0.000 25.920 12.960 slicing.py:60(slice_array)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 106/4 0.000 0.000 0.037 0.009 utils.py:502(__call__)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 2 0.000 0.000 0.037 0.019 base.py:343(tokenize)\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 2 0.000 0.000 24.763 12.381 slicing.py:170(slice_wrap_lists)\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 6.962 3.481 slicing.py:487(take)\n", - " 1 0.000 0.000 26.182 26.182 {built-in method builtins.exec}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 1 0.000 0.000 0.037 0.037 base.py:314(normalize_array)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 2 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 4 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# what's taking so long?\n", - "import cProfile\n", - "cProfile.run('gd[dim0_condition][:, dim1_indices]', sort='time')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 105406881 function calls (79072145 primitive calls) in 25.630 seconds\n", - "\n", - " Ordered by: cumulative time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 25.630 25.630 {built-in method builtins.exec}\n", - " 1 0.107 0.107 25.630 25.630 :1()\n", - " 2 0.102 0.051 25.523 12.761 core.py:1024(__getitem__)\n", - " 2 0.001 0.000 25.381 12.691 slicing.py:60(slice_array)\n", - " 2 0.049 0.024 24.214 12.107 slicing.py:142(slice_with_newaxes)\n", - " 2 0.000 0.000 24.147 12.073 slicing.py:170(slice_wrap_lists)\n", - "13167268/6 6.664 0.000 8.855 1.476 slicing.py:623(check_index)\n", - "13167270/2 4.354 0.000 8.466 4.233 slicing.py:540(posify_index)\n", - " 2 2.277 1.139 8.465 4.233 slicing.py:563()\n", - " 2 0.000 0.000 6.826 3.413 slicing.py:487(take)\n", - " 2 0.111 0.056 6.331 3.165 slicing.py:441(take_sorted)\n", - " 2 4.628 2.314 5.704 2.852 slicing.py:398(partition_by_size)\n", - " 52669338 4.026 0.000 4.026 0.000 {built-in method builtins.isinstance}\n", - " 106/3 0.071 0.001 1.167 0.389 slicing.py:15(sanitize_index)\n", - " 1 0.943 0.943 0.943 0.943 slicing.py:44()\n", - " 13182474 0.581 0.000 0.581 0.000 {built-in method builtins.len}\n", - " 13189168 0.497 0.000 0.497 0.000 {method 'append' of 'list' objects}\n", - " 2 0.495 0.248 0.495 0.248 slicing.py:420(issorted)\n", - " 2 0.281 0.141 0.281 0.141 slicing.py:479()\n", - " 2 0.234 0.117 0.234 0.117 {built-in method builtins.sorted}\n", - " 1 0.152 0.152 0.152 0.152 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.039 0.020 base.py:343(tokenize)\n", - " 106/4 0.000 0.000 0.039 0.010 utils.py:502(__call__)\n", - " 1 0.000 0.000 0.039 0.039 base.py:314(normalize_array)\n", - " 3 0.039 0.013 0.039 0.013 {built-in method _hashlib.openssl_md5}\n", - " 3364 0.003 0.000 0.019 0.000 slicing.py:156()\n", - " 6726 0.012 0.000 0.016 0.000 slicing.py:567(insert_many)\n", - " 20178 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 8 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 4 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 2 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "cProfile.run('gd[dim0_condition][:, dim1_indices]', sort='cumtime')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array((20000000, 200, 2), int8, chunks=(10000, 100, 2), order=C)\n", - " nbytes: 7.5G; nbytes_stored: 2.7G; ratio: 2.8; initialized: 4000/4000\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: dict" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create a synthetic dataset for profiling\n", - "a = zarr.array(np.random.randint(-1, 4, size=(20000000, 200, 2), dtype='i1'),\n", - " chunks=(10000, 100, 2), compressor=zarr.Blosc(cname='zstd', clevel=1, shuffle=2))\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# create a synthetic selection for first axis\n", - "c = np.random.randint(0, 2, size=a.shape[0], dtype=bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# create a synthetic selection for second axis\n", - "s = sorted(np.random.choice(a.shape[1], size=100, replace=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 208 ms, sys: 0 ns, total: 208 ms\n", - "Wall time: 206 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d = da.from_array(a, chunks=(a.chunks[0], None, None))\n", - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 12 s, sys: 200 ms, total: 12.2 s\n", - "Wall time: 12.2 s\n" - ] - } - ], - "source": [ - "%time ds = d[c][:, s]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 80095589 function calls (60091843 primitive calls) in 19.467 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "10001773/6 4.872 0.000 6.456 1.076 slicing.py:623(check_index)\n", - " 2 3.517 1.758 4.357 2.179 slicing.py:398(partition_by_size)\n", - "10001775/2 3.354 0.000 6.484 3.242 slicing.py:540(posify_index)\n", - " 40007358 2.965 0.000 2.965 0.000 {built-in method builtins.isinstance}\n", - " 2 1.749 0.875 6.484 3.242 slicing.py:563()\n", - " 1 0.878 0.878 0.878 0.878 slicing.py:44()\n", - " 10019804 0.451 0.000 0.451 0.000 {built-in method builtins.len}\n", - " 10027774 0.392 0.000 0.392 0.000 {method 'append' of 'list' objects}\n", - " 2 0.363 0.181 0.363 0.181 slicing.py:420(issorted)\n", - " 2 0.270 0.135 4.786 2.393 slicing.py:441(take_sorted)\n", - " 1 0.207 0.207 0.207 0.207 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.158 0.079 0.158 0.079 {built-in method builtins.sorted}\n", - " 1 0.094 0.094 19.467 19.467 :1()\n", - " 2 0.079 0.040 19.373 9.686 core.py:1024(__getitem__)\n", - " 2 0.035 0.017 18.147 9.074 slicing.py:142(slice_with_newaxes)\n", - " 3 0.033 0.011 0.033 0.011 {built-in method _hashlib.openssl_md5}\n", - " 106/3 0.028 0.000 1.112 0.371 slicing.py:15(sanitize_index)\n", - " 8002 0.015 0.000 0.020 0.000 slicing.py:567(insert_many)\n", - " 4002 0.004 0.000 0.023 0.000 slicing.py:156()\n", - " 24006 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 8 0.001 0.000 0.001 0.000 {method 'update' of 'dict' objects}\n", - " 2 0.001 0.000 0.001 0.000 slicing.py:479()\n", - " 2 0.000 0.000 19.259 9.630 slicing.py:60(slice_array)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 106/4 0.000 0.000 0.034 0.008 utils.py:502(__call__)\n", - " 2 0.000 0.000 18.089 9.044 slicing.py:170(slice_wrap_lists)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 5.149 2.574 slicing.py:487(take)\n", - " 2 0.000 0.000 0.034 0.017 base.py:343(tokenize)\n", - " 1 0.000 0.000 0.033 0.033 base.py:314(normalize_array)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 1 0.000 0.000 19.467 19.467 {built-in method builtins.exec}\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 4 0.000 0.000 0.001 0.000 dicttoolz.py:19(merge)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 2 0.000 0.000 0.001 0.000 exceptions.py:15(merge)\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 4 0.000 0.000 0.001 0.000 functoolz.py:217(__call__)\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "cProfile.run('d[c][:, s]', sort='time')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 452 ms, sys: 8 ms, total: 460 ms\n", - "Wall time: 148 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[[ 2, -1],\n", - " [ 2, 3],\n", - " [ 3, 0],\n", - " ..., \n", - " [ 1, 3],\n", - " [-1, -1],\n", - " [ 1, 1]],\n", - "\n", - " [[ 1, -1],\n", - " [ 2, 2],\n", - " [-1, 2],\n", - " ..., \n", - " [ 2, -1],\n", - " [ 1, 3],\n", - " [-1, -1]],\n", - "\n", - " [[ 1, -1],\n", - " [ 2, 0],\n", - " [ 0, 3],\n", - " ..., \n", - " [ 2, 2],\n", - " [ 3, 2],\n", - " [ 0, 2]],\n", - "\n", - " ..., \n", - " [[ 1, 2],\n", - " [ 3, -1],\n", - " [ 2, 1],\n", - " ..., \n", - " [ 1, 2],\n", - " [ 1, 0],\n", - " [ 2, 0]],\n", - "\n", - " [[ 1, 2],\n", - " [ 1, 0],\n", - " [ 2, 3],\n", - " ..., \n", - " [-1, 2],\n", - " [ 3, 3],\n", - " [ 1, -1]],\n", - "\n", - " [[-1, 3],\n", - " [ 2, 2],\n", - " [ 1, 1],\n", - " ..., \n", - " [ 3, 3],\n", - " [ 0, 0],\n", - " [ 0, 2]]], dtype=int8)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time ds[1000000:1100000].compute(optimize_graph=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 80055494 function calls (60052157 primitive calls) in 19.425 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "10001670/3 5.032 0.000 6.671 2.224 slicing.py:623(check_index)\n", - " 1 3.459 3.459 4.272 4.272 slicing.py:398(partition_by_size)\n", - "10001671/1 3.287 0.000 6.378 6.378 slicing.py:540(posify_index)\n", - " 40006704 2.999 0.000 2.999 0.000 {built-in method builtins.isinstance}\n", - " 1 1.731 1.731 6.378 6.378 slicing.py:563()\n", - " 1 0.849 0.849 0.849 0.849 slicing.py:44()\n", - " 10011685 0.433 0.000 0.433 0.000 {built-in method builtins.len}\n", - " 10015670 0.381 0.000 0.381 0.000 {method 'append' of 'list' objects}\n", - " 1 0.355 0.355 0.355 0.355 slicing.py:420(issorted)\n", - " 1 0.196 0.196 0.196 0.196 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 1 0.193 0.193 0.193 0.193 slicing.py:479()\n", - " 1 0.157 0.157 0.157 0.157 {built-in method builtins.sorted}\n", - " 1 0.085 0.085 4.707 4.707 slicing.py:441(take_sorted)\n", - " 1 0.085 0.085 19.425 19.425 :1()\n", - " 1 0.079 0.079 19.341 19.341 core.py:1024(__getitem__)\n", - " 1 0.034 0.034 18.157 18.157 slicing.py:142(slice_with_newaxes)\n", - " 2 0.033 0.017 0.033 0.017 {built-in method _hashlib.openssl_md5}\n", - " 1 0.026 0.026 1.071 1.071 slicing.py:15(sanitize_index)\n", - " 4001 0.007 0.000 0.009 0.000 slicing.py:567(insert_many)\n", - " 2001 0.002 0.000 0.011 0.000 slicing.py:156()\n", - " 12003 0.001 0.000 0.001 0.000 {method 'pop' of 'list' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 1 0.000 0.000 19.228 19.228 slicing.py:60(slice_array)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 1 0.000 0.000 0.033 0.033 base.py:314(normalize_array)\n", - " 1 0.000 0.000 18.111 18.111 slicing.py:170(slice_wrap_lists)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 1 0.000 0.000 5.062 5.062 slicing.py:487(take)\n", - " 1 0.000 0.000 0.033 0.033 base.py:343(tokenize)\n", - " 1 0.000 0.000 19.425 19.425 {built-in method builtins.exec}\n", - " 2 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 3 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 2 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 1 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 2 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 4 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 1 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 1 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 4 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 2 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 2 0.000 0.000 0.033 0.017 utils.py:502(__call__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 1 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 1 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 2 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 1 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# problem is in fact just the dim0 selection\n", - "cProfile.run('d[c]', sort='time')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb deleted file mode 100644 index ba4391737a..0000000000 --- a/notebooks/dask_copy.ipynb +++ /dev/null @@ -1,1518 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Profile array copy via dask threaded scheduler" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook profiles a very simple array copy operation, using synthetic data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.0.1.dev18+dirty\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#d4821cb3-378c-411d-a941-d0708c0c532b\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", - "import numpy as np\n", - "import h5py\n", - "import bcolz\n", - "# don't let bcolz use multiple threads internally, we want to \n", - "# see whether dask can make good use of multiple CPUs\n", - "bcolz.set_nthreads(1)\n", - "import multiprocessing\n", - "import dask\n", - "import dask.array as da\n", - "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n", - "from dask.diagnostics.profile_visualize import visualize\n", - "from cachey import nbytes\n", - "import bokeh\n", - "from bokeh.io import output_notebook\n", - "output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tempfile\n", - "import operator\n", - "from functools import reduce\n", - "from zarr.util import human_readable_size\n", - "\n", - "\n", - "def h5fmem(**kwargs):\n", - " \"\"\"Convenience function to create an in-memory HDF5 file.\"\"\"\n", - "\n", - " # need a file name even tho nothing is ever written\n", - " fn = tempfile.mktemp()\n", - "\n", - " # file creation args\n", - " kwargs['mode'] = 'w'\n", - " kwargs['driver'] = 'core'\n", - " kwargs['backing_store'] = False\n", - "\n", - " # open HDF5 file\n", - " h5f = h5py.File(fn, **kwargs)\n", - "\n", - " return h5f\n", - "\n", - "\n", - "def h5d_diagnostics(d):\n", - " \"\"\"Print some diagnostics on an HDF5 dataset.\"\"\"\n", - " \n", - " print(d)\n", - " nbytes = reduce(operator.mul, d.shape) * d.dtype.itemsize\n", - " cbytes = d._id.get_storage_size()\n", - " if cbytes > 0:\n", - " ratio = nbytes / cbytes\n", - " else:\n", - " ratio = np.inf\n", - " r = ' compression: %s' % d.compression\n", - " r += '; compression_opts: %s' % d.compression_opts\n", - " r += '; shuffle: %s' % d.shuffle\n", - " r += '\\n nbytes: %s' % human_readable_size(nbytes)\n", - " r += '; nbytes_stored: %s' % human_readable_size(cbytes)\n", - " r += '; ratio: %.1f' % ratio\n", - " r += '; chunks: %s' % str(d.chunks)\n", - " print(r)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def profile_dask_copy(src, dst, chunks, num_workers=multiprocessing.cpu_count(), dt=0.1, lock=True):\n", - " dsrc = da.from_array(src, chunks=chunks)\n", - " with Profiler() as prof, ResourceProfiler(dt=dt) as rprof:\n", - " da.store(dsrc, dst, num_workers=num_workers, lock=lock)\n", - " visualize([prof, rprof], min_border_top=60, min_border_bottom=60)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NumPy arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1314, 2727, 2905, ..., 1958, 1325, 1971], dtype=uint16)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# a1 = np.arange(400000000, dtype='i4')\n", - "a1 = np.random.normal(2000, 1000, size=200000000).astype('u2')\n", - "a1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'381.5M'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "human_readable_size(a1.nbytes)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a2 = np.empty_like(a1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "chunks = 2**20, # 4M" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 56 ms, sys: 36 ms, total: 92 ms\n", - "Wall time: 91.7 ms\n" - ] - } - ], - "source": [ - "%time a2[:] = a1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(a1, a2, chunks, lock=True, dt=.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(a1, a2, chunks, lock=False, dt=.01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Zarr arrays (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((200000000,), uint16, chunks=(1048576,), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 381.5M; nbytes_stored: 318.2M; ratio: 1.2; initialized: 191/191\n", - " store: builtins.dict" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z1 = zarr.array(a1, chunks=chunks, compression='blosc', \n", - " compression_opts=dict(cname='lz4', clevel=1, shuffle=2))\n", - "z1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((200000000,), uint16, chunks=(1048576,), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 381.5M; nbytes_stored: 294; ratio: 1360544.2; initialized: 0/191\n", - " store: builtins.dict" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z2 = zarr.empty_like(z1)\n", - "z2" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(z1, z2, chunks, lock=True, dt=.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(z1, z2, chunks, lock=False, dt=0.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 251 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison, using blosc internal threads\n", - "%timeit -n3 -r5 z2[:] = z1" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - } - ], - "source": [ - "%prun z2[:] = z1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Without the dask lock, we get better CPU utilisation. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## HDF5 datasets (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h5f = h5fmem()\n", - "h5f" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " compression: lzf; compression_opts: None; shuffle: True\n", - " nbytes: 381.5M; nbytes_stored: 357.4M; ratio: 1.1; chunks: (1048576,)\n" - ] - } - ], - "source": [ - "h1 = h5f.create_dataset('h1', data=a1, chunks=chunks, compression='lzf', shuffle=True)\n", - "h5d_diagnostics(h1)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " compression: lzf; compression_opts: None; shuffle: True\n", - " nbytes: 762.9M; nbytes_stored: 0; ratio: inf; chunks: (1048576,)\n" - ] - } - ], - "source": [ - "h2 = h5f.create_dataset('h2', shape=h1.shape, chunks=h1.chunks, \n", - " compression=h1.compression, compression_opts=h1.compression_opts, \n", - " shuffle=h1.shuffle)\n", - "h5d_diagnostics(h2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(h1, h2, chunks, lock=True, dt=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(h1, h2, chunks, lock=False, dt=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bcolz carrays (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "carray((200000000,), uint16)\n", - " nbytes := 381.47 MB; cbytes := 318.98 MB; ratio: 1.20\n", - " cparams := cparams(clevel=1, shuffle=2, cname='lz4', quantize=0)\n", - " chunklen := 1048576; chunksize: 2097152; blocksize: 16384\n", - "[1314 2727 2905 ..., 1958 1325 1971]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = bcolz.carray(a1, chunklen=chunks[0],\n", - " cparams=bcolz.cparams(cname='lz4', clevel=1, shuffle=2))\n", - "c1" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "carray((200000000,), uint16)\n", - " nbytes := 381.47 MB; cbytes := 2.00 MB; ratio: 190.73\n", - " cparams := cparams(clevel=1, shuffle=2, cname='lz4', quantize=0)\n", - " chunklen := 1048576; chunksize: 2097152; blocksize: 4096\n", - "[0 0 0 ..., 0 0 0]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c2 = bcolz.zeros(a1.shape, chunklen=chunks[0], dtype=a1.dtype, \n", - " cparams=bcolz.cparams(cname='lz4', clevel=1, shuffle=2))\n", - "c2" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(c1, c2, chunks, lock=True, dt=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# not sure it's safe to use bcolz without a lock, but what the heck...\n", - "profile_dask_copy(c1, c2, chunks, lock=False, dt=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 649 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison\n", - "%timeit -n3 -r5 c2[:] = c1" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 557 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison\n", - "%timeit -n3 -r5 c1.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/dask_count_alleles.ipynb b/notebooks/dask_count_alleles.ipynb deleted file mode 100644 index 8b9b7cec6e..0000000000 --- a/notebooks/dask_count_alleles.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Profile allele count from genotype data via dask.array" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.0.1.dev18+dirty\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#b153ad5f-436a-4afb-945c-87790add89c8\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", - "import numpy as np\n", - "import h5py\n", - "import multiprocessing\n", - "import dask\n", - "import dask.array as da\n", - "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n", - "from dask.diagnostics.profile_visualize import visualize\n", - "from cachey import nbytes\n", - "import bokeh\n", - "from bokeh.io import output_notebook\n", - "output_notebook()\n", - "from functools import reduce\n", - "import operator\n", - "import allel" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "callset = h5py.File('/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/ag1000g.phase1.ar3.pass.h5',\n", - " mode='r')\n", - "genotype = callset['3R/calldata/genotype']\n", - "genotype" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((13167162, 765, 2), int8, chunks=(6553, 200, 2), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 18.8G; nbytes_stored: 683.2M; ratio: 28.1; initialized: 8040/8040\n", - " store: builtins.dict" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# copy into a zarr array\n", - "# N.B., chunks in HDF5 are too small really, use something bigger\n", - "chunks = (genotype.chunks[0], genotype.chunks[1] * 20, genotype.chunks[2])\n", - "genotype_zarr = zarr.array(genotype, chunks=chunks, compression='blosc',\n", - " compression_opts=dict(cname='lz4', clevel=1, shuffle=2))\n", - "genotype_zarr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to perform an allele count. Compare serial and parallel implementations, and compare working direct from HDF5 versus from Zarr." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 50s, sys: 512 ms, total: 1min 51s\n", - "Wall time: 1min 50s\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))
nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;
compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);
data: bcolz.carray_ext.carray
0123
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...

" - ], - "text/plain": [ - "AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))\n", - " nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;\n", - " compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);\n", - " data: bcolz.carray_ext.carray" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# linear implementation from HDF5 on disk\n", - "allel.GenotypeChunkedArray(genotype).count_alleles()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2min 27s, sys: 2.14 s, total: 2min 29s\n", - "Wall time: 1min 23s\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))
nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;
compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);
data: bcolz.carray_ext.carray
0123
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" - ], - "text/plain": [ - "AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))\n", - " nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;\n", - " compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);\n", - " data: bcolz.carray_ext.carray" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# linear implementation from zarr in memory\n", - "# (although blosc can use multiple threads internally)\n", - "allel.GenotypeChunkedArray(genotype_zarr).count_alleles()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# multi-threaded implementation from HDF5 on disk\n", - "gd = allel.model.dask.GenotypeDaskArray.from_array(genotype, chunks=chunks)\n", - "ac = gd.count_alleles(max_allele=3)\n", - "with Profiler() as prof, ResourceProfiler(dt=1) as rprof:\n", - " ac.compute(num_workers=8)\n", - "visualize([prof, rprof], min_border_bottom=60, min_border_top=60);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# multi-threaded implementation from zarr in memory\n", - "gdz = allel.model.dask.GenotypeDaskArray.from_array(genotype_zarr, chunks=chunks)\n", - "acz = gdz.count_alleles(max_allele=3)\n", - "with Profiler() as prof, ResourceProfiler(dt=1) as rprof:\n", - " acz.compute(num_workers=8)\n", - "visualize([prof, rprof], min_border_bottom=60, min_border_top=60);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb deleted file mode 100644 index b262e63fa0..0000000000 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ /dev/null @@ -1,548 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.1.1.dev7+dirty\n", - "blosc ('1.10.0.dev', '$Date:: 2016-07-20 #$')\n" - ] - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "import functools\n", - "import timeit\n", - "import zarr\n", - "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", - "print('blosc', blosc.version())\n", - "import numpy as np\n", - "import h5py\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "callset = h5py.File('/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/ag1000g.phase1.ar3.pass.h5',\n", - " mode='r')\n", - "genotype = callset['3R/calldata/genotype']\n", - "genotype" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "n_variants = 500000" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(500000, 765, 2)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genotype_sample = genotype[1000000:1000000+n_variants, ...]\n", - "genotype_sample.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "765000000" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbytes = genotype_sample.nbytes\n", - "nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(685, 765, 2)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1M chunks of first dimension\n", - "chunks = (int(2**20 / (genotype_sample.shape[1] * genotype_sample.shape[2])), \n", - " genotype_sample.shape[1], \n", - " genotype_sample.shape[2])\n", - "chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "blosc.get_nthreads()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((500000, 765, 2), int8, chunks=(685, 765, 2), order=C)\n", - " compression: blosc; compression_opts: {'cname': 'lz4', 'clevel': 1, 'shuffle': 2}\n", - " nbytes: 729.6M; nbytes_stored: 23.0M; ratio: 31.7; initialized: 730/730\n", - " store: builtins.dict" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zarr.array(genotype_sample, chunks=chunks, compression_opts=dict(cname='lz4', clevel=1, shuffle=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((500000, 765, 2), int8, chunks=(685, 765, 2), order=C)\n", - " compression: blosc; compression_opts: {'cname': 'zstd', 'clevel': 1, 'shuffle': 2}\n", - " nbytes: 729.6M; nbytes_stored: 12.0M; ratio: 60.7; initialized: 730/730\n", - " store: builtins.dict" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zarr.array(genotype_sample, chunks=chunks, compression_opts=dict(cname='zstd', clevel=1, shuffle=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "compression_configs = (\n", - " (None, None),\n", - " ('zlib', 1),\n", - " ('bz2', 1),\n", - " ('lzma', dict(preset=1)),\n", - " ('blosc', dict(cname='snappy', clevel=0, shuffle=0)),\n", - " ('blosc', dict(cname='snappy', clevel=0, shuffle=2)),\n", - " ('blosc', dict(cname='snappy', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='snappy', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='lz4hc', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='lz4hc', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='lz4hc', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='lz4hc', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=5, shuffle=2)),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def log(*msg):\n", - " print(*msg, file=sys.stdout)\n", - " sys.stdout.flush()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "@functools.lru_cache(maxsize=None)\n", - "def compression_ratios():\n", - " x = list()\n", - " for compression, compression_opts in compression_configs:\n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", - " compression_opts=compression_opts)\n", - " ratio = z.nbytes / z.nbytes_stored\n", - " x.append(ratio)\n", - " log(compression, compression_opts, ratio)\n", - " return x\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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diDzlq1H0cI+BWTYNkfRaiOuXRMMQ00NEewD5erJyXXtZrwEze4GokbUk1PVK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F2V6vG9BvuXretxkh6VtCpfB0lHSXpE0hnNxG1mZmZm5bVyDlZf/BPmYcC8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bKunNlUMC31SxtqUT2L5UtyIilqTth4G70vaDwM5p+8MUHZSz0uNNKN7AL6+cNCLmA59qcmnz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\nJMnHk9yU5GOt11yV5P1Jbmj237w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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ratios = compression_ratios() \n", - "labels = ['%s - %s' % (c, o)\n", - " for c, o in compression_configs]\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Compression ratio', fontsize=14, y=1.01)\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = [ratios[i] for i in y]\n", - "ax.barh(bottom=np.array(y)+.2, width=np.array(x), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = [ratios[i] for i in y]\n", - "ax.barh(bottom=np.array(y)+.2, width=np.array(x), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "ax.set_xlim(0, max(ratios)+3)\n", - "ax.set_ylim(0, len(ratios))\n", - "ax.set_xlabel('compression ratio')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "@functools.lru_cache(maxsize=None)\n", - "def compression_decompression_times(repeat=3, number=1):\n", - " c = list()\n", - " d = list()\n", - " for compression, compression_opts in compression_configs:\n", - " \n", - " def compress():\n", - " zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", - " compression_opts=compression_opts)\n", - " \n", - " t = timeit.Timer(stmt=compress, globals=locals())\n", - " compress_times = t.repeat(repeat=repeat, number=number)\n", - " c.append(compress_times)\n", - " \n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", - " compression_opts=compression_opts)\n", - " \n", - " def decompress():\n", - " z[:]\n", - " \n", - " t = timeit.Timer(stmt=decompress, globals=locals())\n", - " decompress_times = t.repeat(repeat=repeat, number=number)\n", - " d.append(decompress_times)\n", - " \n", - " log(compression, compression_opts, compress_times, decompress_times)\n", - " \n", - " return c, d\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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fchPRg0AWK3oIxc/TM0g6IqxNyuVKomllLyp6iEJFnnrnEk13W0g0zW9+Wp37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Cl9cZ1WSRotabGkublta9qRSy2S9pM0o4m4Nem/NWtdFb+1pMclfTt/DknDW8itYa3S6zu6zv4T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\nSnVAnbYeCewDfDL3Ok+sihmUb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CVwmdL6pHZg+0zdcjNb4sdPAgWn4XFgBz8+BDhS0jl+\n3gcYAiwrDGpmC4BOzlWVefYF7gC+YWYvSDoNmGZmLyUT0NX9uXcCXjKzha7bGz5eVqbcnO4gRc+a\ngc8Bv6wiXxIzuwfo1vVeThtwi6S78GvtTDOz94G/SHoZGFhnv6K0vQ8CdtYa4/WTtKmZ/b0gUG2u\n/oLif4Avlqh+TdKwEo5Xhuaqygf10kI4qnnRQtg2D1oIu+ZBC2HXfGhpaYloSw6EXdfQ1NTUwRbj\nxo0rKddIB6vTW/4SMp0ePs1spaTdgUOBrwGjSalXFR0DSacCJ/s4h5vZikxdL+CPwDvAtDrmUOCd\nEjqPBbYB9jSzdqVNJ/qWkG/PnLez5pqIFOV6ugv6VOI64JeeMgewD7Cf22dzUurmKjM7vwt9V3PO\nys5J0p8l7UqKZJ2Sqeok71GutckRJOf+KOACSZ/w8uLr+CHgfVIktECn1NcaELC3mb3XhbZI2hy4\nF/iOmc0vIXI1sEjS6WZ2U1fGCIIgCIIgCErTyDVYQyUV0ti+ADxUVD8PGCVpK0m9gTHAbE+3621m\ndwLfBYZ7tOR5SUcDSOojaZNsZ2Z2rZntaWbDs86V17Wb2Q7AY6QH/FI8CIyWtJWPMaCMXMHJ6A+8\n4s7VAcDQEjKVuA84Y3UDaY8a2lTEo1X9zOyHhTIzO8HMdjCzjwL/CfxPKedK0kT5+rAyLAMGedoi\nkvr5dctSaU63AecCW5jZEzXId4VOESNJ3y/cNyUbpCjSEDObDZwHbAH0qzDGy8CHJQ2QtDHwmS7o\nOQNYvS7LXyjUhKdq3gVM9H8jpTgf+OdwrtY2TY1WoAfT1GgFeihNjVagh9LUaAV6LBFlyYewa/00\n0sF6CjhN0hLS5hLXe7kBuBN0HimW3grM97SowUCLpFbgZpcBOBE4Q1Ib8DD1p2sB/B7YqlSFp/Rd\nSnLyWoErs/pmRf3vZGCk63MCsLSETKn2BS4mRZMW+6YJ3ysWkDRC0g0V5lPM2cCuSptcLJRUT3rh\nbsBL5So92nI8cI2kRSQnYeMisUpz+pW3vy1TdkkF+U5IOlJSc5m633rfB0p6TlJhvdmuwIpSbZze\nwCS/jguAq83sbyXkCvft+67nfJKDuLSEbIc2JTgT2Etpc5Qn6BjRK8yn3Fw/B+wHfClznXcrkunj\nm10EQRAEQRAE3Yzil7vX4Gt9tjaz86oKb0B4ytmNZlYuurfeIml60bb8PRpfB9hmZttVkLF189cJ\n1ndaiDfXedFC2DYPWgi75kEL655dRU94Hoy1QvkQdi2PJMysU2ZaI9dgrYtMAW7a0B66q2Fmqyif\nOrlesyFdZ0ljSDsi/qAG6bzVCYIgCNYRBg4cWl0oCIKaiQhWEAQdkGTxvRAEQRAEQVCZchGsRq7B\nCoIgCIIgCIIg6FGEgxUEQbAWqOWHCYOuEbbNh7BrPoRd8yNsmw9h1/oJBysIgiAIgiAIgqCbiDVY\nQRB0INZgBUEQBEEQVCfWYAVBEARBEARBEORMOFhBEARrgchhz4+wbT6EXfMh7JofYdt8CLvWT/wO\nVhAEnZDid7CCIAiCoLsZOHggK15Y0Wg1gpyJNVhBEHRAktHcaC2CIAiCoAfSDPHs3XOINVhBEARB\nEARBEAQ50xAHS9JQSY+XqZslafja1snHHiJpoaTpmbLljdClHJL2lzShBrnl/resrYvkN5f0vKT/\nzpTNkjSkSrsJkkZV0feeauPXQ7ZPSSdJuqiGNtMlvS5pao1jXCTpxCr1Z9WuNUjaSVKrpAWSdpR0\nhqQlkm72efykSvta53qSpN9LWpadg6Qxkp6S9K169A66iXXqm6SHEbbNh7BrPoRd8yNsmwuxBqt+\nGhnBWhfjo8cAM8zssEzZuqhnLTpZmeNyXAzM7po6demSR5+19P8D4IQc9KiHY4A7zGyEmS0Hvg4c\nZGZf9Pp6r2snJA0ALgRGAnsDF0nqD2BmtwL7A+FgBUEQBEEQ5EQjHayNJE3yN/i3S+pbLOBv3Bf7\n53Iv6+VRk8WS2iSd6eXDJN0vaZGkxyTt2AWdtgReKSp7NaPPiT5mq6SJXjZB0tWSHpb0jKRjvXwz\nSTNdlzZJR3n5UElLvd0ySZMlHeztl0nay+U2lTRe0lyPeBzparwL/LWGubxaXCDp5657q6RXJP2X\nl48AtgVmFDX5C/BBlXFWuk5IGunzWOR6b1Y0fsk5SXpE0s4ZuVmShlewQZa3gDeq6IiZzapFLsMq\n7xuPND3p87olI/Nx1/UZSae7bIeIoaSzPdp1GPBN4OuSHpB0HfBRYHrhHs602UbSLyU96p996pjr\noaSXBH81s5Wka/rpjB1eBvrXYYegu+jKN1JQG2HbfAi75kPYNT/CtrnQ1NTUaBXWOxq5i+BOwJfN\nbK6k8cCpwFWFSknbAZcDe5Ie4u93J+UFYLCZ7eZyW3iTycD3zWyqpD50zXnsDbRnC8xsbx9nF+B8\nYB8ze13SlhmxQWa2rzsJU4EpwNvAMWb2hqStgbleBzAM+KyZLZH0GPB5b3+Uj3EscAHwgJl9xSMQ\n8yTNNLNHgEdcpxHAKWb21eKJFPQuKjvZ2w0BpgMTJAn4ETAWOLhI/rhqBjOzb3mfGwG/AEab2UJJ\n/XAHJUPJOXm744FmSYPcngslXVpGPjv+7YVjd8BGmFlzNb1rmNdVmdNvAzuY2XuZ+w3SPdxEcliW\nSbq20LxzdzZd0vXAqkLfkg4Fmvx+OikjfzVwlZnNkfRPwH3ALjXOdTDwfOb8RS/LUv3fxqzM8Q7E\nf1pBEARBEGzwtLS01JQy2UgH6zkzm+vHk4DTyThYpBSnWWb2GoCkycAo4BJgR0lXA78GZvjD/PZm\nNhXAzN6tVxl3NHZ3XUpxICm963UfY2Wm7i4vWypp20KXwGVK65Page0zdcvNbIkfPwkUnIbHSY+z\nAIcAR0o6x8/7AEOAZYVBzWwB0Mm5qjLPvsAdwDfM7AVJpwHTzOylZAK6uj/3TsBLZrbQdXvDx8vK\nlJvTHaRISzPwOeCXVeRLYmb3AN263stpA26RdBd+rZ1pZvY+8BdJLwMD6+xXlLb3QcDOWmO8fpI2\nNbO/FwT+wbm+JmmYmf2hrMQBXew5KM9ywlHNi7BtPoRd8yHsmh9h21xoaWmJKJbT1NTUwRbjxo0r\nKddIB6vTW/4SMp0ePs1spaTdSalQXwNGk1KvKjoGkk4FTvZxDjezFZm6XsAfgXeAaXXMocA7JXQe\nC2wD7Glm7UqbTvQtId+eOW9nzTURKcr1dBf0qcR1wC89ZQ5gH2A/t8/mpNTNVWZ2fhf6ruaclZ2T\npD9L2pUUyTolU9VJ3qNca5MjSM79UcAFkj7h5cXX8UPA+6RIaIFOqa81IGBvM3uvC21fJEXVCnyE\njvEoSBGyRZJON7ObujBGEARBEARBUIZGrsEaKqmQxvYF4KGi+nnAKElbSeoNjAFme7pdbzO7E/gu\nMNyjJc9LOhpAUh9Jm2Q7M7NrzWxPMxueda68rt3MdgAeIz3gl+JBYLSkrXyMAWXkCk5Gf+AVd64O\nAIaWkKnEfcAZqxtIe9TQpiIerepnZj8slJnZCWa2g5l9FPhP4H9KOVeSJsrXh5VhGTDI0xaR1M+v\nW5ZKc7oNOBfYwsyeqEG+K3SKGEn6fuG+KdkgRZGGmNls4DxgC6BfhTFeBj4saYCkjYHPdEHPGcDq\ndVn+QqFW7gMOltTf79GDvSzL+cA/h3O1lom3qvkRts2HsGs+hF3zI2ybCxG9qp9GOlhPAadJWkLa\nXOJ6LzcAd4LOA1qAVmC+p0UNBloktQI3uwzAicAZktqAh6k/XQvg98BWpSo8pe9SkpPXClyZ1Tcr\n6n8nAyNdnxOApSVkSrUvcDEpmrTYN034XrGApBGSbqgwn2LOBnZV2uRioaR60gt3A14qV+nRluOB\nayQtIjkJGxeJVZrTr7z9bZmySyrId0LSkZKay9T91vs+UNJzkgrrzXYFKv2kem9gkl/HBcDVZva3\nEnKF+/Z913M+ybFZWkK2Q5sSnAnspbQ5yhN0jOgV5lNyrp7CejHpZcGjwLiidFaAPr7ZRRAEQRAE\nQdDNKH5Neg2+1mdrMzuvqvAGhKTNgRvNrFx0b71F0vSibfl7NL4OsM3MtqsgYzSvPZ02GGJtQH6E\nbfMh7JoPYdf8WB9s2wzr27N3rMEqjyTMrFNmWiPXYK2LTAFu2tAeuqthZqsonzq5XrMhXWdJY0g7\nIv6gqnBz3toEQRAEwYbHwMFdSbAK1jcighUEQQckWXwvBEEQBEEQVKZcBKuRa7CCIAiCIAiCIAh6\nFOFgBUEQrAVq+WHCoGuEbfMh7JoPYdf8CNvmQ9i1fsLBCoIgCIIgCIIg6CZiDVYQBB2INVhBEARB\nEATViTVYQRAEQRAEQRAEORMOVhAEwVogctjzI2ybD2HXfAi75kfYNh/CrvUTv4MVBEEnpE7R7qCb\nGDhwKCtWPNtoNYIgCIIgyIlYgxUEQQckGcT3Qn6I+N4NgiAIgvWfWIMVBEEQBEEQBEGQMzU5WJKG\nSnq8TN0sScO7V63akDRE0kJJ0zNlyxuhSzkk7S9pQg1yyzPy95STkbRVN+pVcpxMfUW9/b6YVUWm\n2++PbJ+1XG9Ju0maI6lN0t2S+tXQpmK/klbVrvHqNj+U9LikKyRtI2mupAWS9qvl2tY41x9IWipp\nkaRfSdoiU/dbSfMkbVuv7kF30NJoBXossT4gH8Ku+RB2zY+wbT6EXeunngjWupjTcgwww8wOy5St\ni3rWopOVOa63n3qo1l+9ejeCWsa/ETjXzHYH7gTO7YZ+uzLvk4HdzOzbwEHAYjMbYWa/q7G/WmRm\nAB83sz2Ap4HvrG5sNgpYABxRt+ZBEARBEARBTdTjYG0kaZKkJZJul9S3WEDSGEmL/XO5l/WSNMHL\n2iSd6eXDJN3vb9ofk7RjF/TfEnilqOzVjD4n+pitkiZ62QRJV0t6WNIzko718s0kzXRd2iQd5eVD\nPSIwQdIySZMlHeztl0nay+U2lTQ+E5U40tV4F/hrDXN5NXPcX9K9kp6SdG2mfHWOp6SzPBqyOGPT\nTb1dq5eP9vKRru8i12+z7MCSpnkksFXSSklfrFHvD4DXvI9emQjNIkmnFQu73ea4jW9zfQ+VdHtG\nZnVkTdIhxfJV7FaOj7kTAzAT+GwNbV51HQZJmu32WSxp3zWq6hKf6xxJH/bCCYV7ys9X+d+7gX7A\nAknnAlcAx3i/fel4bcdKetTrrpNW7zhRda5mNtPM2v10LvCRIpEVpH83wVqnqdEK9FiampoarUKP\nJOyaD2HX/Ajb5kPYtX7q2UVwJ+DLZjZX0njgVOCqQqWk7YDLgT2BlcD97qS8AAw2s91crpCyNBn4\nvplNldSHrq0H6w20ZwvMbG8fZxfgfGAfM3tdUvahcpCZ7StpZ2AqMAV4GzjGzN6QtDXp4XSqyw8D\nPmtmSyQ9Bnze2x/lYxwLXAA8YGZfkdQfmCdpppk9AjziOo0ATjGzrxZPpKC3MxLYGXgOuE/SsWY2\npVCplB53ksv1Bh6V1OJ6vmhmn3G5zSVtBPwCGG1mC5XS494qGvuITL//D7jLzFYV9C6Hmb0AHOen\nXwWGkiI0VmRv3KbfBT5lZm+5k3EWcBnwM0mbmNlbwPHALS5/QQn5S8rZTdI04CtmtqJI1SclHWVm\nU4HP0dnpKDW3Qr9fAH5jZpe5o1Nw8jYD5pjZdyVdQYpOfb9UV97f0ZL+ZmaF1MaXgRFmdoafF+bw\nL26DfzWzDyT9FBgLTKpxrln+g3Tts7ST7pkqNGeOmwjnIAiCIAiCDZ2WlpaaUibrcWqeM7O5fjwJ\n2K+ofiQwy8xe8zfok4FRwB+BHT1qdCiwyh/yt/cHXszsXTN7uw5d8Ifd3UkOXCkOBO4ws9d9jJWZ\nuru8bClQWI8i4DJJbaQox/Zas1ZluZkt8eMnvR7gcWAHPz4EOE9SK2mxRR9gSFYhM1tQyrkqwTwz\n+5OlrcZupbOt9wPuNLO3zexNkoP4b67PwZIuk7SfO0k7AS+Z2ULX4Y1MhGM1krYBbgbGeLt6OQj4\nmetcbG+ATwK7AA+7jU4EhpjZB8BvgCMl9Salr00tJ19JATM7oozD8R/AaZLmkxyjd+uY13zgy5Iu\nJDmPb3r5O2b2az9ewJr7oJha9zsvpP99ChgOzPd5Hwh8tJNw+bmmQaULgPfM7JaiqheB3aqr05z5\nNFUXD2qgpdEK9FhifUA+hF3zIeyaH2HbfAi7rqGpqYnm5ubVn3LUE8EqXv9Raj1Ip4dJM1spaXfg\nUOBrwGjgm6VkO3QknUqKChhwePZhUlIvkuP2DjCtjjkUeKeEzmOBbYA9zaxdaUOBviXk2zPn7ayx\noUhRrqe7oE8xtdi6cyOzpz0KdThwsaQHSM5kNVv3Ijlyze505oFI6+XGlqi7DfgG8Dow38zedAe6\nnHxdmNnvSfcfkj5GHWuQzOwhSaO8zU2SrjSzScB7GbEPWHMfvI+/uPA5bFSnugImmtkFdbZb04H0\nJdI9cGCJ6inAhZKWmNkuXR0jCIIgCIIgKE09EayhkrJpUw8V1c8DRknayiMRY4DZnurV28zuJKWI\nDTezN4DnJR0NIKmPpE2ynZnZtWa2p5kNL35Tb2btZrYD8BgpnaoUDwKj5TuzSRpQRq7gfPQHXnHn\n6gBSuluxTCXuA85Y3UDao4Y25dhbae1XL9L8im39EGn9Tl+l9VT/DjzkaZpvedTiR6RIyDJgkKcn\nIqmfX58sVwBtZnZHKWWU1nBNrKLz/cAphb5L2HsusK+kYV6/qTs7ALNd15NZk9JWSb4uMuujepHu\nwev9fHtJM6u0HUK6L8aTNsso7IhY7p54FtjLj4+mo4NV6T4q1D0AHJfReYDrUBOSPg2cAxxlZu+U\nEDkRmB7OVSNoarQCPZZYH5APYdd8CLvmR9g2H8Ku9VOPg/UUKc1qCWmR/PVeXkgJWwGcR8qDaSVF\nIu4BBgMtnu50s8tAetA7w1PyHgYGdkH/3wMlt7b2lL5LSU5eK3BlVt+sqP+dDIx0fU4AlpaQKdW+\nwMWkjUAWK21p/71iAUkjJN1QYT4F5gHXkNIR/2Bmd2XHNrNW4CZS+tojwA1m1gbsSlr71QpcCFxi\nZu+RnLRrJC0i7TK3cdF4ZwOHKG1ysVDSZ4rqhwB/r6LzjcDzwGIff0yRzn8GvgTc6jaeQ0pfxFMW\n7wU+7X8rylPmGiht1jGoRNUYScuAJaQ1ajd5+XZ0jESVoglok7SQtH7rx5V0AH4O7O82+CTwZqau\nUiSyYKelJCdwhs97BtBpThXm+hPSZhr3+7W8tqh+AGl3wSAIgiAIgiAH5Etm1ksknQNsbWbnVRUO\nuoxv4nCzmT3RaF26E6WdDv9kZvc2Wpe1hW+asdjMflZBxhq/+35PpIXkr4v1+Xt3XaSlpSXesOZA\n2DUfwq75EbbNh7BreSRhZp0ylOpZg7UuMoW0LmZ60W9hBd2I/25Tj8PMftpoHdYmkmaT1g2W2u0w\nCIIgCIIg6AbW6whWEATdT4pgBXkxcOBQVqx4ttFqBEEQBEHwD9JTI1hBEORAvHgJgiAIgiDoGl35\ncd8gCIKgTuJ3RPIjbJsPYdd8CLvmR9g2H8Ku9RMOVhAEQRAEQRAEQTcRa7CCIOiAJIvvhSAIgiAI\ngsqUW4MVEawgCIIgCIIgCIJuIhysIAiCtUDksOdH2DYfwq75EHbNj7BtPoRd6yccrCAIgiAIgiAI\ngm4i1mAFQdCB+B2s9ZOBgwey4oUVjVYjCIIgCDYYyq3BCgcrCIIOSDKaG61FUDfN8ftlQRAEQbA2\niU0ugiAIGsnyRivQc4n1AfkQds2HsGt+hG3zIexaPzU5WJKGSnq8TN0sScO7V63akDRE0kJJ0zNl\n69RjjKT9JU2oQW55Rv6ecjKStupGvUqOk6mvqLffF7OqyHT7/ZHts5brLWk3SXMktUm6W1K/GtpU\n7FfSqto1Xt3mh5Iel3SFpG0kzZW0QNJ+tVzbGuc6QNIMScsk3Sepf6but5LmSdq2Xt2DIAiCIAiC\n2qgngrUu5p4cA8wws8MyZeuinrXoZGWO6+2nHqr1V6/ejaCW8W8EzjWz3YE7gXO7od+uzPtkYDcz\n+zZwELDYzEaY2e9q7K8WmfOAmWa2E/Ag8J3Vjc1GAQuAI+rWPPjH2bHRCvRcmpqaGq1CjyTsmg9h\n1/wI2+ZD2LV+6nGwNpI0SdISSbdL6lssIGmMpMX+udzLekma4GVtks708mGS7pe0SNJjkrry+LEl\n8EpR2asZfU70MVslTfSyCZKulvSwpGckHevlm0ma6bq0STrKy4dKWurtlkmaLOlgb79M0l4ut6mk\n8ZmoxJGuxrvAX2uYy6uZ4/6S7pX0lKRrM+WrczwlneXRkMUZm27q7Vq9fLSXj3R9F7l+m2UHljTN\nI4GtklZK+mKNen8AvOZ99MpEaBZJOq1Y2O02x218m+t7qKTbMzIqbMp2AAAgAElEQVSrI2uSDimW\nr2K3cnzMnRiAmcBna2jzquswSNJst89iSfuuUVWX+FznSPqwF04o3FN+vsr/3g30AxZIOhe4AjjG\n++1Lx2s7VtKjXnedpEJdLXM9GpjoxxNJLyGyrCD9uwmCIAiCIAhyoB4HayfgGjPbBVgFnJqtlLQd\ncDnQBOwBjHQnZQ9gsJnt5hGEQtrZZOAnZrYH8K/A/3ZB/95Ae7bAzPZ2fXYBzgeazGxP4MyM2CAz\n2xc4kvSgC/A2cIyZ7QUcCFyZkR8G/NCjAjsBn/f25/gYABcAD5jZJ739jyRtYmaPmNm3XKcRkm4o\nNZGC3s5I4DRgZ+Cfsw/s3s9w4CSX2wc4WdLuwKeBF81sTzPbDfiNpI2AXwCnu60PAt4qGvsIMxsO\nfAV4Frgrq3c5zOwFMzvOT78KDCVFaPYgXd+szlsD3wU+5TZeAJxFcnj+j6RNXPR44BaXv6CEfFm7\nuaM4qISqTxYcZuBzwEcqzauo3y8Av3H77A4s8vLNgDk+14dI0amSXXl/RwN/N7PhZvYD4ELgF37+\ndmYO/+I2+Fcfsx0YW8dctzWzl11+BVCcDthO+ndTmVmZzzqVdLseE3bMjVgfkA9h13wIu+ZH2DYf\nwq5raGlpobm5efWnHB+qo8/nzGyuH08CTgeuytSPBGaZWSGiMRkYBVwC7CjpauDXwAylNTDbm9lU\nADN7tw498P5FeuCdVEbkQOAOM3vdx1iZqbvLy5ZqzXoUAZdJGkV6CN0+U7fczJb48ZMkpwDgcWAH\nPz4EOFLSOX7eBxgCLCsMamYLSI5INeaZ2Z98nrcC+wFTMvX7AXcWHswlTQH+DbiP5NhdBkwzs99J\n+gTwkpktdB3e8DYdBpS0DXAzcJyZ1b2+iOS4XWe+jVmRvQE+CewCPOzXbiOSg/KBpN+QbPcrUvra\nOSRHvZN8JQXMrFzq238AP5H0X8BUUnSuVuYD491RvdvM2rz8HTP7tR8vIM2/FJ12lilDIf3vU8Bw\nYL7Puy/wcifh8nMt12+BF0m2rcwBNfYeBEEQBEGwgdDU1NQhZXLcuHEl5epxsIof1EqtB+n0MGlm\nKz26cijwNWA08M1Ssh06kk4lRQUMONzfxhfqegF/BN4BptUxhwLvlNB5LLANsKeZtSttKNC3hHx7\n5rydNTYU8Fkze7oL+hRTi607NzJ72qNbhwMXS3qA5ExWs3Uv4Fag2cyWdkHfWhBpvdzYEnW3Ad8A\nXgfmm9mb7lyUk68LM/s96f5D0seoYw2SmT3kTvcRwE2SrjSzScB7GbEPWHMfvI9HhjOOYT0ImGhm\nF9TZrsDLkgaa2cse4SpOoZ0CXChpiUejg7VFrMHKjVgfkA9h13wIu+ZH2DYfwq71U0+K4FBJ2bSp\nh4rq5wGjJG0lqTcwBpjtqV69zexOUorYcI+iPC/paABJfTIpYgCY2bWe6jY861x5XbuZ7QA8Rkqn\nKsWDwGj5zmySBpSRKzgf/YFX3Lk6gJTuVixTifuAM1Y3kPaooU059lZa+9WLNL9iWz9EWr/T19dT\n/TvwkKdpvmVmtwA/IkVClgGDJI1wvfr59clyBdBmZneUUkZpDdfEUnUZ7gdOKfRdwt5zgX0lDfP6\nTd3ZAZjtup5MSmesJl8XmfVRvUj34PV+vr2kmVXaDiHdF+NJm2UUdkQsd088C+zlx0fT0cGqdB8V\n6h4AjsvoPMB1qJWpwJf8+CTg7qL6E4Hp4VwFQRAEQRDkQz0O1lPAaZKWkBbJX+/lhZSwFaQdzFqA\nVlIk4h5gMNAiqZWUgnaetzsROENSG/AwMLAL+v8eKLm1taf0XUpy8lpZs6aqXHRoMmndWBtwArC0\nhEyp9gUuJm0EslhpS/vvFQtUWoNVxDzgGlI64h/M7K7s2GbWCtxESl97BLjBU9d2Beb5fC8ELjGz\n90hO2jWSFgEzgI2LxjsbOERpk4uFkj5TVD8E+HsVnW8EngcW+/hjinT+M+nB/1a38RzSejbMrB24\nl7SG7N5q8pS5BhXWJY2RtAxYQlqjdpOXb0fHSFQpmoA2SQtJ67d+XEkH4OfA/m6DTwJvZuoqRSIL\ndlpKcgJn+LxnAJ3mVGGuVwAH+3w/RVoXmWUA0B1R1qBeYg1WbsT6gHwIu+ZD2DU/wrb5EHatH/mS\nmfUSX++0tZmdV1U46DKSrgBuNrMnGq1Ld6K00+GfzOzeRuuytpD0U9L28D+rIGM0rz2dNhiWk2+a\nYDOsz9/n/wgtLS2RwpIDYdd8CLvmR9g2H8Ku5ZGEmXXKUFrfHaxhpEjOG0W/hRUEQRGSZpPWDZ5g\nZi9WkFt/vxQ2YAYOHsiKF1ZUFwyCIAiCoFvokQ5WEATdjySL74UgCIIgCILKlHOw6lmDFQRBEHSR\nyGHPj7BtPoRd8yHsmh9h23wIu9ZPOFhBEARBEARBEATdRKQIBkHQgUgRDIIgCIIgqE6kCAZBEARB\nEARBEORMOFhBEARrgchhz4+wbT6EXfMh7JofYdt8CLvWTzhYQRAEQRAEQRAE3USswQqCoAPxO1hB\nEAQwcOBQVqx4ttFqBEGwDhO/gxUEQU0kByu+F4Ig2NAR8YwUBEElYpOLIAiChtLSaAV6MC2NVqCH\n0tJoBXoksZ4lP8K2+RB2rZ+aHCxJQyU9XqZulqTh3atWbUgaImmhpOmZsuWN0KUckvaXNKEGueUZ\n+XvKyUjaqhv1KjlOpr6i3n5fzKoi0+33R7bPWq63pIskveD3ykJJn66hTcV+Ja2qXePVbX4o6XFJ\nV0jaRtJcSQsk7VfLta1xrj+QtFTSIkm/krRFpu63kuZJ2rZe3YMgCIIgCILaqCeCtS7GyY8BZpjZ\nYZmydVHPWnSyMsf19lMP1fqrV+9GUOv4V5nZcP/8phv67cq8TwZ2M7NvAwcBi81shJn9rsb+apGZ\nAXzczPYAnga+s7qx2ShgAXBE3ZoH3UBToxXowTQ1WoEeSlOjFeiRNDU1NVqFHkvYNh/CrvVTj4O1\nkaRJkpZIul1S32IBSWMkLfbP5V7WS9IEL2uTdKaXD5N0v79pf0zSjl3Qf0vglaKyVzP6nOhjtkqa\n6GUTJF0t6WFJz0g61ss3kzTTdWmTdJSXD/WIwARJyyRNlnSwt18maS+X21TS+ExU4khX413grzXM\n5dXMcX9J90p6StK1mfLVOZ6SzvJoyOKMTTf1dq1ePtrLR7q+i1y/zbIDS5rmkZ1WSSslfbFGvT8A\nXvM+emUiNIsknVYs7Hab4za+zfU9VNLtGZnVkTVJhxTLV7FbJTrlx1bhVddhkKTZbp/FkvZdo6ou\n8bnOkfRhL5xQuKf8fJX/vRvoByyQdC5wBXCM99uXjtd2rKRHve46SYW6qnM1s5lm1u6nc4GPFIms\nIP27CYIgCIIgCPLAzKp+gKFAO/BJPx8PnOXHs4DhwHbAn4CtSI7bA8BRXjcj09cW/ncucJQf9wH6\n1qJLkV7jgG+WqdsFeAoY4Odb+t8JwG1+vDPwtB/3Bvr58daZ8qEkZ2MXP38MGO/HRwFT/PhS4At+\n3B9YBmxSpNMI4IYqc9of+LuPK1JE4livW+72HQ60AX2BzYAngN2BY4GfZfraHNgI+AMw3Mv6+fXZ\nH5haNPZwYBGweReuxdeA21mzcUrB3oX7Y2tgdsEmwLnAd93uz2bKrwXGlJPP9llCh2nAoBLlF7nt\nFgE3Av3rmNdZwHf8WMBmftwOHO7HVwDnZ+6vYzPt/1bm+CTgvzPnhWv7L8BUoLeX/xQ4oda5FslM\nLdyTmbL/Av6zSjuDizKfWQYWn3/4E3YM265vnw3drlgezJo1K5d+g7BtXoRd1zBr1iy76KKLVn/8\ne4Liz4eonefMbK4fTwJOB67K1I8EZplZIaIxGRgFXALsKOlq4NfADEn9gO3NbCpJs3fr0APvXySn\nYlIZkQOBO8zsdR9jZabuLi9bmlmPIuAySaNID8/bZ+qWm9kSP34SmOnHjwM7+PEhwJGSzvHzPsAQ\nkqOFj7cA+GoN05tnZn/yed4K7AdMydTvB9xpZm+7zBTg34D7gB9JugyYZma/k/QJ4CUzW+g6vOFt\nOgwoaRvgZuA4M6t7fREp5e06MzMfZ2VR/SdJTu/Dfu02AuaY2QeSfkOy3a9I6WvnkHJTOslXUsDM\nyqW+XQt8z8xM0iWk+/YrNc5rPjBe0kbA3WbW5uXvmNmv/XgBaf6lqDVyZv73UySHdL7Puy/wcifh\n8nNNg0oXAO+Z2S1FVS9SU95Pc3WRIAiCIAiCDYimpqYOKZPjxo0rKVePg2VVzqHEw6SZrZS0O3Ao\nKcoxGvhmKdkOHUmnktasGClSsCJT1wv4I/AO6U1+vbxTQuexwDbAnmbWrrShQN8S8u2Z83bW2FDA\nZ83s6S7oU0wttu7cyOxppc0fDgculvQAyZmsZutewK1As5kt7YK+tSBSJHNsibrbgG8ArwPzzexN\ndy7KydeFmWVT634OlN3co0Tbh9zpPgK4SdKVZjYJeC8j9gFr7oP38dTbjGNYDwImmtkFdbZb04H0\nJdI9cGCJ6inAhZKWmNkuXR0j6ApNjVagB9PUaAV6KE2NVqBHEutZ8iNsmw9h1/qpZw3WUEl7+/EX\ngIeK6ucBoyRtJak3Kc1rtqStSelOd5JSwoZ7FOV5SUcDSOojaZNsZ2Z2rZntaWlTghVFde1mtgMp\nXe/4Mvo+CIyW78wmaUAZuYLz0R94xZ2rA0gpesUylbgPOGN1A2mPGtqUY2+ltV+9SPMrtvVDpPU7\nfX091b8DD0naDnjLoxY/IkVClgGDJI1wvfr59clyBdBmZneUUsbXcE2sovP9wCmFvkvYey6wr6Rh\nXr+ppI953WzX9WTgFzXI14WkQZnTY0kplUjaXtLM0q1Wtx1Cui/Gk9ILCzsilrsnngX28uOj6ehg\nVbqPCnUPAMdl1nQNcB1qQmmHxHNI6bfvlBA5EZgezlUQBEEQBEE+1ONgPQWcJmkJaZH89V5eSAlb\nAZxH+uGMVlIk4h5gMNAiqZWUgnaetzsROENSG/AwMLAL+v+etG6lE57SdynJyWsFrszqmxX1v5OB\nka7PCcDSEjKl2he4mLQRyGKlLe2/VywgaYSkGyrMp8A84BpSOuIfzOyu7Nhm1grcREpfe4S0rqsN\n2BWY5/O9ELjEzN4jOWnXSFpEWtO1cdF4ZwOHKG1ysVDSZ4rqh5DWhVXiRuB5YLGPP6ZI5z8DXwJu\ndRvPAXbyunbgXuDT/reiPGWugdJmHYNKVP3Ar8si0tqzb3n5dnSMRJWiCWiTtBD4HPDjSjqQImT7\nuw0+CbyZqasUiSzYaSnpRcQMn/cMoNOcKsz1J6R1dvf7tby2qH4AaXfBYK3T0mgFejAtjVagh9LS\naAV6JPGbQvkRts2HsGv9FDYkWC/x9U5bm9l5VYWDLiPpCuBmM3ui0bp0J0o7Hf7JzO5ttC5rC0k/\nJW0P/7MKMlZjVmpQFy1EylVetBC2zYMWNmy7ijyekVpaWiLlKifCtvkQdi2PJMysU4bS+u5gDSNF\nct6wjr+FFQRBEZJmk9YNnmBmL1aQW3+/FIIgCLqJgQOHsmLFs41WIwiCdZge6WAFQdD9SLL4XgiC\nIAiCIKhMOQernjVYQRAEQReJHPb8CNvmQ9g1H8Ku+RG2zYewa/2EgxUEQRAEQRAEQdBNRIpgEAQd\niBTBIAiCIAiC6kSKYBAEQRAEQRAEQc6EgxUEQbAWiBz2/Ajb5kPYNR/CrvkRts2HsGv9hIMVBEEQ\nBEEQBEHQTcQarCAIOhC/gxUEQbDuMXDwQFa8sKLRagRBkCF+BysIgpqQZDQ3WosgCIKgA80Qz2xB\nsG4Rm1wEQRA0kuWNVqAHE7bNh7BrPoRdcyPWCuVD2LV+anKwJA2V9HiZulmShnevWrUhaYikhZKm\nZ8rWqa8uSftLmlCD3PKM/D3lZCRt1Y16lRwnU19Rb78vZlWR6fb7I9tnLddb0kWSXvB7ZaGkT9fQ\npmK/klbVrvHqNj+U9LikKyRtI2mupAWS9qvl2tY41wGSZkhaJuk+Sf0zdb+VNE/StvXqHgRBEARB\nENRGPRGsdTEufQwww8wOy5Sti3rWopOVOa63n3qo1l+9ejeCWse/ysyG++c33dBvV+Z9MrCbmX0b\nOAhYbGYjzOx3NfZXi8x5wEwz2wl4EPjO6sZmo4AFwBF1ax784+zYaAV6MGHbfAi75kPYNTeampoa\nrUKPJOxaP/U4WBtJmiRpiaTbJfUtFpA0RtJi/1zuZb0kTfCyNklnevkwSfdLWiTpMUld+crZEnil\nqOzVjD4n+pitkiZ62QRJV0t6WNIzko718s0kzXRd2iQd5eVDJS31dsskTZZ0sLdfJmkvl9tU0vhM\nVOJIV+Nd4K81zOXVzHF/SfdKekrStZny1Tmeks7yaMjijE039XatXj7ay0e6votcv82yA0ua5pGd\nVkkrJX2xRr0/AF7zPnplIjSLJJ1WLOx2m+M2vs31PVTS7RmZ1ZE1SYcUy1exWyU65cdW4VXXYZCk\n2W6fxZL2XaOqLvG5zpH0YS+cULin/HyV/70b6AcskHQucAVwjPfbl47XdqykR73uOkmFulrmejQw\n0Y8nkl5CZFlB+ncTBEEQBEEQ5MCH6pDdCfiymc2VNB44FbiqUClpO+ByYE9gJXC/OykvAIPNbDeX\n28KbTAa+b2ZTJfWha+vBegPt2QIz29vH2QU4H9jHzF6XlH2oHGRm+0raGZgKTAHeBo4xszckbQ3M\n9TqAYcBnzWyJpMeAz3v7o3yMY4ELgAfM7CueljVP0kwzewR4xHUaAZxiZl8tnkhBb2cksDPwHHCf\npGPNbEqhUik97iSX6w08KqnF9XzRzD7jcptL2gj4BTDazBZK6ge8VTT2EZl+/x9wl5mtKuhdDjN7\nATjOT78KDCVFaKzI3rhNvwt8yszecifjLOAy4GeSNjGzt4DjgVtc/oIS8peUs5ukacBXzKzUNkvf\ncMfxMeBsM6voPGb6/QLwGzO7zB2dgpO3GTDHzL4r6QpSdOr7pbry/o6W9DczK6Q2vgyMMLMz/Lww\nh39xG/yrmX0g6afAWGBSjXPd1sxe9jFXqHM6YDvpnqlMNvFzB+KNa3ewnLBjXoRt8yHsmg9h19xo\naWmJaEsOhF3X0NLSUtOatHocrOfMbK4fTwJOJ+NgkR72Z5lZIaIxGRhFeiDeUdLVwK+BGf6Qv72Z\nTQUws3fr0APvX8DurkspDgTuMLPXfYyVmbq7vGxp5gFUwGWSRpEeQrfP1C03syV+/CQw048fJz1+\nAhwCHCnpHD/vAwwBlhUGNbMFJEekGvPM7E8+z1uB/UhOYIH9gDvN7G2XmQL8G3Af8CNJlwHTzOx3\nkj4BvGRmC12HN7xNhwElbQPcDBznzlW9HARcZ77FUZG9AT4J7AI87NduI5KD8oGk35Bs9ytS+to5\nQFMp+UoKFBzFElwLfM8dv0tI9+1XapzXfGC8O6p3m1mbl79jZr/24wWk+Zei1shZIf3vU8BwYL7P\nuy/wcifh8nMt12+BF0m2rcwBNfYeBEEQBEGwgdDU1NTB2Rw3blxJuXocrOIHtVLrQTo9TJrZSkm7\nA4cCXwNGA98sJduhI+lUUlTAgMOzb+ol9QL+CLwDTKtjDgXeKaHzWGAbYE8za1faUKBvCfn2zHk7\na2woUpTr6S7oU0wttu7cyOxpj0IdDlws6QGSM1nN1r2AW4FmM1vaBX1rQaT1cmNL1N0GfAN4HZhv\nZm+6c1FOvi7MLJta93Og7OYeJdo+5E73EcBNkq40s0nAexmxD1hzH7yPR2MzjmE9CJhoZhfU2a7A\ny5IGmtnLkgbROYV2CnChpCVmtksXxwi6Qryxzo+wbT6EXfMh7JobEWXJh7Br/dSTljdUUjZt6qGi\n+nnAKElbSeoNjAFme6pXbzO7k5QiNtyjKM9LOhpAUh9Jm2Q7M7NrzWxP35RgRVFdu5ntQEr3Or6M\nvg8Co+U7s0kaUEau4Hz0B15x5+oAUrpbsUwl7gPOWN1A2qOGNuXYW2ntVy/S/Ipt/RBp/U5fpfVU\n/w485Gmab5nZLcCPSJGQZcAgT09EUj+/PlmuANrM7I5Syiit4ZpYqi7D/cAphb5L2HsusK+kYV6/\nqaSPed1s1/VkUjpjNfm6cEejwLHAE16+vaSZpVutbjuEdF+MB250PaH8PfEssJcfH01HB6vSfVSo\newA4TmvWdA1wHWplKvAlPz4JuLuo/kRgejhXQRAEQRAE+VCPg/UUcJqkJaRF8td7eSElbAVpB7MW\noJUUibgHGAy0SGolpaCd5+1OBM6Q1AY8DAzsgv6/B0pube0pfZeSnLxW4MqsvllR/zsZGOn6nAAs\nLSFTqn2Bi0kbgSxW2tL+e8UCkkZIuqHCfArMA64hpSP+wczuyo5tZq3ATaT0tUeAGzx1bVfS2q9W\n4ELgEjN7j+SkXSNpETAD2LhovLOBQ5Q2uVgo6TNF9UOAv1fR+UbgeWCxjz+mSOc/kx78b3UbzyGt\n68PM2oF7gU/734rylLkGSpt1DCpR9QO/LouA/YFvefl2dIxElaIJaJO0EPgc8ONKOpAiZPu7DT4J\nvJmpqxSJLNhpKelFxAyf9wyg05wqzPUK4GBJy0jphpcX1Q8AuiPKGtTLOvUDEj2MsG0+hF3zIeya\nG/F7TfkQdq0frc+/Cu7rnbY2s/OqCgddxjdxuNnMnmi0Lt2J0k6HfzKzexuty9rCN81YbGY/qyBj\nNK89nTYYYmF7foRt8yHsmg9dtWszrM/PbGuD2IwhH8Ku5ZGEmXXKUFrfHaxhpEjOG0W/hRUEQRGS\nZpPWDZ5gZi9WkFt/vxSCIAh6KAMHD2TFC6U2yQ2CoFH0SAcrCILuR5LF90IQBEEQBEFlyjlYXfnt\nqSAIgqBOIoc9P8K2+RB2zYewa36EbfMh7Fo/4WAFQRAEQRAEQRB0E5EiGARBByJFMAiCIAiCoDqR\nIhgE/5+9O4+zo6rzPv75JoBsQ4CoCSAJTEZ5RIUQQFEwacSdVQw7A8OAy0tHVFyGEZUEQUAFjSAq\nYyagiQjMRHYkLOkMBENCCAmQEEHC6hPgUeIERxHTv+ePc25SffuuTVe603zfr9d9dd1Tp6p+9atK\n554+59Q1MzMzMyuZG1hmZuuBx7CXx7kth/NaDue1PM5tOZzX9rmBZWZmZmZm1kc8B8vMuvH3YJmZ\nlW/EiNGsXPl4f4dhZq+AvwfLzFqSGlj+vWBmVi7hz2BmGzY/5MLMrF919ncAg1hnfwcwSHX2dwCD\nVGd/BzBoea5QOZzX9rXUwJI0WtIDddbNljSub8NqjaRRku6TdHOhbEV/xFKPpAmSprVQb0Wh/vX1\n6kjatg/jqnmcwvqGcef7YnaTOn1+fxT32cr1ljRR0oOS1rQaS7P9SlrdWrTdtvm2pAcknS/ptZLm\nSVooab9Wrm2L5/otScsk3S/pvyRtVVj335LmS3p9u7GbmZmZWWva6cEaiP3YhwGzIuJDhbKBGGcr\nMUWd5Xb3045m+2s37v7QyvEfAD4CzOnD/fbmvD8G7BYR/wq8F1gSEXtGxF0t7q+VOrOAt0TEWOAR\n4N/WbhwxHlgIHNh25NYHOvo7gEGso78DGKQ6+juAQaqjvwMYtDo6Ovo7hEHJeW1fOw2sjSVNl7RU\n0lWSNq2uIOkYSUvy67xcNkTStFy2WNJnc/kYSbfmv7TfK2nnXsS/NfBcVdnzhXhOyMdcJOnyXDZN\n0hRJcyU9KunwXL6FpNtyLIslHZLLR+cegWmSlkuaIel9efvlkvbK9TaXNLXQK3FwDuOvwB9bOJfn\nC8vDJN0g6WFJlxTK147xlHRa7g1ZUsjp5nm7Rbn8iFy+d473/hzfFsUDS7ox9wQukrRK0j+2GPca\n4A95H0MKPTT3S/p0deWct7tzjq/M8X5A0lWFOmt71iS9v7p+k7zVFBHLI+KRYv5a8HyOYaSkOTk/\nSyTtuy5UnZ3P9W5Jr8uF0yr3VH6/Ov+8FtgSWCjpy8D5wGF5v5vS/doeJ+mevO6HkirrWjnX2yKi\nK7+dB7yhqspK0r8bMzMzMyvBRm3U3QU4KSLmSZoKfAq4sLJS0nbAecAewCrg1txIeRrYISJ2y/Uq\nQ5ZmAN+MiOskbULv5oMNBbqKBRHxjnycXYGvAO+MiBckFT9UjoyIfSW9GbgOmAn8BTgsIl6UNJz0\n4fS6XH8M8NGIWCrpXuDovP0h+RiHA2cAt0fEyZKGAfMl3RYRvwZ+nWPaE/hERHy8+kQqcWd7A28G\nngRukXR4RMysrFQa5nZirjcUuEdSZ47zmYg4KNf7O0kbA78AjoiI+yRtCfy56tgHFvb7H8A1EbG6\nEnc9EfE0MDG//TgwmtRDE1X5Juf0q8ABEfHn3Mg4DTgX+LGkzSLiz8BRwM9z/TNq1D+7Xt4k3Qic\nHBErG8XdisJ+jwV+FRHn5oZOpZG3BXB3RHxV0vmk3qlv1tpV3t+hkv4nIipDG58F9oyIU/P7yjn8\nn5yDd0XEGkk/AI4DpvfiXP+ZdO2Lukj3TBOTCssd+C+ufaET57EsnTi3ZejEeS1DJ85rOTo7O93b\nUgLndZ3Ozs6W5qS108B6MiLm5eXpwGcoNLBIH/ZnR0SlR2MGMJ70gXhnSVOAm4BZ+UP+9hFxHUBE\n/LWNOMj7F7B7jqWW9wBXR8QL+RirCuuuyWXLtG4+ioBzJY0nfQjdvrBuRUQszcsPAbfl5QeAnfLy\n+4GDJX0pv98EGAUsrxw0IhaSGiLNzI+IJ/J5XgHsR2oEVuwH/DIi/pLrzATeDdwCfEfSucCNEXGX\npLcCv4uI+3IML+Ztuh1Q0muBnwETc+OqXe8Ffhj5kUhV+QbYB9gVmJuv3cakBsoaSb8i5e6/SMPX\nvkT636dH/UYBVBqKfWwBMDU3VK+NiMW5/KWIuCkvLySdfy2t9ppVhv8dAIwDFuTz3hR4tkflJucq\n6Qzg5Yj4edWqZ2jpf/ZJzauYmZmZvYp0dHR0a2xOnjy5ZuJYhU4AACAASURBVL12GljV8z9qzQfp\n8WEyIlZJ2h34APBJ4Ajgc7XqdtuR9ClSr0AAHy7+pV7SEOAx4CXgxjbOoeKlGjEfB7wW2CMiupQe\nKLBpjfpdhfddrMuhSL1cj/Qinmqt5LrnRhGP5F6oDwPfkHQ7qTHZLNdDgCuASRGxrBfxtkKk+XLH\n1Vh3JfAvwAvAgoj4U25c1Ku/3kTEnbnRfSBwmaQLImI68HKh2hrW3Qd/I/fGFhqG7RBweUSc0duY\nJf0T6R54T43VM4GvS1oaEbv29hjWGx39HcAg1tHfAQxSHf0dwCDV0d8BDFruZSmH89q+dobljZZU\nHDZ1Z9X6+cB4SdtKGgocA8zJQ72GRsQvSUPExuVelKckHQogaRNJmxV3FhGXRMQeETGuehhURHRF\nxE7AvaThVLXcARyh/GQ2SdvUqVdpfAwDnsuNq/1Jw92q6zRyC3Dq2g2ksS1sU887lOZ+DSGdX3Wu\n7yTN39lUaT7VR4A78zDNP+dei++QekKWAyPz8EQkbZmvT9H5wOKIuLpWMEpzuC5vEvOtwCcq+66R\n73nAvpLG5PWbS3pjXjcnx/ox1g1pa1T/lSjOddpe0m0NK0ujSPfFVOAnOc5u+6nyOLBXXj6U7g2s\nRvdRZd3twMTCnK5tcgwtkfRBUg/gIRHxUo0qJwA3u3FlZmZmVo52GlgPA5+WtJQ0Sf5HubwyJGwl\ncDppcPEiUk/E9cAOQKekRaQhaKfn7U4ATpW0GJgLjOhF/L8Baj7aOg/pO4fUyFsEXFCMt1g1/5wB\n7J3jOR5YVqNOre0rvkF6EMgSpUfan1VdQdKeki5tcD4V84GLScMRfxsR1xSPHRGLgMtIw9d+DVya\nh669jTT3axHwdeDsiHiZ1Ei7WNL9pKfMvabqeF8A3q/0kIv7JB1UtX4U8L9NYv4J8BSwJB//mKqY\n/x/wT8AVOcd3k+b1kR/KcAPwwfyzYX3qXAOlh3WMrFF+mKSnSMMUb9C6x/pvR/eeqFo6gMWS7gOO\nBL7XKAbg34EJOQf7AH8qrGvUE1nJ0zLSHyJm5fOeBdQ6p5rnClxEepjGrflaXlK1fhvS0wVtvevs\n7wAGsc7+DmCQ6uzvAAapzv4OYNDy9zWVw3ltnzbkbxHP852GR8TpTStbr+WHOPwsIh7s71j6ktKT\nDp+IiBv6O5b1JT80Y0lE/LhBnej/p+8PRp14aFBZOnFuy9CJ81qGTlJexYb8GWwg8sMYyuG81ieJ\niOgxQmlDb2CNIfXkvFj1XVhmVkXSHNK8weMj4pkG9dzAMjMrnRtYZhu6QdnAMrO+lxpYZmZWphEj\nRrNy5eP9HYaZvQL1Gli9+e4pMxvkIsKvPn7Nnj2732MYrC/n1nndkF6VvLpx1fc8V6gczmv73MAy\nMzMzMzPrIx4iaGbdSAr/XjAzMzNrzEMEzczMzMzMSuYGlpnZeuAx7OVxbsvhvJbDeS2Pc1sO57V9\nbmCZmZmZmZn1Ec/BMrNuPAfLzMzMrLl6c7A26o9gzGxgk3r8rjAzs0FixA4jWPn0yv4Ow2zQcg+W\nmXUjKZjU31EMQiuAnfs7iEHKuS2H81qOgZDXSen7Dgebzs5OOjo6+juMQcd5rc9PETQzMzMzMytZ\nSw0sSaMlPVBn3WxJ4/o2rNZIGiXpPkk3F8pW9Ecs9UiaIGlaC/VWFOpfX6+OpG37MK6axymsbxh3\nvi9mN6nT5/dHcZ+tXG9JEyU9KGlNq7E026+k1a1F222bb0t6QNL5kl4raZ6khZL2a+Xatniu20ia\nJWm5pFskDSus+29J8yW9vt3YrQ/091+sBzPnthzOazmc19K4l6Uczmv72unBGoh9yYcBsyLiQ4Wy\ngRhnKzFFneV299OOZvtrN+7+0MrxHwA+Aszpw/325rw/BuwWEf8KvBdYEhF7RsRdLe6vlTqnA7dF\nxC7AHcC/rd04YjywEDiw7cjNzMzMrCXtNLA2ljRd0lJJV0natLqCpGMkLcmv83LZEEnTctliSZ/N\n5WMk3Srpfkn3SurN33S2Bp6rKnu+EM8J+ZiLJF2ey6ZJmiJprqRHJR2ey7eQdFuOZbGkQ3L5aEnL\n8nbLJc2Q9L68/XJJe+V6m0uaWuiVODiH8Vfgjy2cy/OF5WGSbpD0sKRLCuVrx3hKOi33hiwp5HTz\nvN2iXH5ELt87x3t/jm+L4oEl3Zh7AhdJWiXpH1uMew3wh7yPIYUemvslfbq6cs7b3TnHV+Z4PyDp\nqkKdtT1rkt5fXb9J3mqKiOUR8Ugxfy14PscwUtKcnJ8lkvZdF6rOzud6t6TX5cJplXsqv1+df14L\nbAkslPRl4HzgsLzfTel+bY+TdE9e90Np7RMnmp4rcChweV6+nPRHiKKVpH83tr4NqL71Qca5LYfz\nWg7ntTT+vqZyOK/ta+cpgrsAJ0XEPElTgU8BF1ZWStoOOA/YA1gF3JobKU8DO0TEbrneVnmTGcA3\nI+I6SZvQu/lgQ4GuYkFEvCMfZ1fgK8A7I+IFScUPlSMjYl9JbwauA2YCfwEOi4gXJQ0H5uV1AGOA\nj0bEUkn3Akfn7Q/JxzgcOAO4PSJOzsOy5ku6LSJ+Dfw6x7Qn8ImI+Hj1iVTizvYG3gw8Cdwi6fCI\nmFlZqTTM7cRcbyhwj6TOHOczEXFQrvd3kjYGfgEcERH3SdoS+HPVsQ8s7Pc/gGsiYnUl7noi4mlg\nYn77cWA0qYcmqvJNzulXgQMi4s+5kXEacC7wY0mbRcSfgaOAn+f6Z9Sof3a9vEm6ETg5Il7xo5EK\n+z0W+FVEnJsbOpVG3hbA3RHxVUnnk3qnvllrV3l/h0r6n4ioDG18FtgzIk7N7yvn8H9yDt4VEWsk\n/QA4Dpje4rm+PiKezcdcqZ7DAbtI90xjxYGfO+EhLWZmZvaq19nZ2VKDs50G1pMRMS8vTwc+Q6GB\nRfqwPzsiKj0aM4DxpA/EO0uaAtwEzMof8rePiOsAIuKvbcRB3r+A3XMstbwHuDoiXsjHWFVYd00u\nW1b4ACrgXEnjSR9Cty+sWxERS/PyQ8BtefkB0sdPgPcDB0v6Un6/CTAKWF45aEQsJDVEmpkfEU/k\n87wC2I/UCKzYD/hlRPwl15kJvBu4BfiOpHOBGyPiLklvBX4XEfflGF7M23Q7oKTXAj8DJubGVbve\nC/yw8gVKVfkG2AfYFZibr93GpAbKGkm/IuXuv0jD174EdNSq3yiASkOxjy0ApuaG6rURsTiXvxQR\nN+XlhaTzr6XVXrPK8L8DgHHAgnzemwLP9qjc+rlWDyt8hpTbxvZvce/WOjdSy+PclsN5LYfzWhrP\nFSqH87pOR0dHt3xMnjy5Zr12GljVH9RqzQfp8WEyIlZJ2h34APBJ4Ajgc7XqdtuR9ClSr0AAHy7+\npV7SEOAx4CXgxjbOoeKlGjEfB7wW2CMiupQeKLBpjfpdhfddrMuhSL1cj/Qinmqt5LrnRhGP5F6o\nDwPfkHQ7qTHZLNdDgCuASRGxrBfxtkKk+XLH1Vh3JfAvwAvAgoj4U25c1Ku/3kTEnbnRfSBwmaQL\nImI68HKh2hrW3Qd/I/fGFhqG7RBweUSc0cuQn5U0IiKelTSSnkNoZwJfl7Q0Inbt5THMzMzMrI52\nhuWNllQcNnVn1fr5wHhJ20oaChwDzMlDvYZGxC9JQ8TG5V6UpyQdCiBpE0mbFXcWEZdExB4RMa56\nGFREdEXETsC9pOFUtdwBHKH8ZDZJ29SpV2l8DAOey42r/UnD3arrNHILcOraDaSxLWxTzzuU5n4N\nIZ1fda7vJM3f2VRpPtVHgDvzMM0/R8TPge+QekKWAyPz8EQkbZmvT9H5wOKIuLpWMEpzuC6vta7g\nVuATlX3XyPc8YF9JY/L6zSW9Ma+bk2P9GGk4Y7P6r0RxrtP2km5rWFkaRbovpgI/yXF220+Vx4G9\n8vKhdG9gNbqPKutuByYW5nRtk2No1XXAP+XlE4Frq9afANzsxlU/8LyL8ji35XBey+G8lsZzhcrh\nvLavnQbWw8CnJS0lTZL/US6vDAlbSXqCWSewiNQTcT2wA9ApaRFpCNrpebsTgFMlLQbmAiN6Ef9v\ngJqPts5D+s4hNfIWARcU4y1WzT9nAHvneI4HltWoU2v7im+QHgSyROmR9mdVV5C0p6RLG5xPxXzg\nYtJwxN9GxDXFY0fEIuAy0vC1XwOX5qFrbyPN/VoEfB04OyJeJjXSLpZ0PzALeE3V8b4AvF/pIRf3\nSTqoav0o4H+bxPwT4ClgST7+MVUx/z/SB/8rco7vJs3rIyK6gBuAD+afDetT5xooPaxjZI3ywyQ9\nRRqmeIPWPdZ/O7r3RNXSASyWdB9wJPC9RjEA/w5MyDnYB/hTYV2jnshKnpaR/hAxK5/3LKDWOdU8\nV1Jj+X2SlpOGG55XtX4boC96Wc3MzMysBm3I3+Sd5zsNj4jTm1a2XssPcfhZRDzY37H0JaUnHT4R\nETf0dyzrS35oxpKI+HGDOsGk9ReTmZmtZ5NgQ/78ZzZQSCIieoxQ2tAbWGNIPTkvVn0XlplVkTSH\nNG/w+Ih4pkG9DfeXgpmZNTVihxGsfPoVP3DX7FVvUDawzKzvSQr/Xuh7nZ2dfhJTSZzbcjiv5XBe\ny+PclsN5ra9eA6s33z1lZmZmZmZmNbgHy8y6cQ+WmZmZWXPuwTIzMzMzMyuZG1hmZuuBv0ekPM5t\nOZzXcjiv5XFuy+G8ts8NLDMzMzMzsz7iOVhm1o3nYJmZmZk1V28O1kb9EYyZDWxSj98V9gqNGDGa\nlSsf7+8wzMzMrGQeImhmNYRfffx69tkn2rsE1jLPDyiH81oO57U8zm05nNf2NWxgSRot6YE662ZL\nGldOWI1JGiXpPkk3F8pW9Ecs9UiaIGlaC/UGVNxFrcTWrI6kMyWd1ndRdd+npGmSxjepv7WkmZIW\nS5onadcWjjFb0qgm69u6/yVNlLRU0u35/RWS7pf02XwehzfZvpVzPTaf52JJd0narbDuAkkPSZrQ\nTtxmZmZm1rpWerAG4mSMw4BZEfGhQtlAjLOVmAZi3BUbevwVXwEWRcTuwInA9/spjpOBUyLiAEkj\ngb0iYmxETOnDYzwGjM/nejZwaWVFRHwBOAv45z48nlm/6+jo6O8QBiXntRzOa3mc23I4r+1rpYG1\nsaTp+S/vV0natLqCpGMkLcmv83LZkPwX9yX5r+mfzeVjJN2a/3J/r6SdexH31sBzVWXPF+I5IR9z\nkaTLc9k0SVMkzZX0aKW3QNIWkm7LsSyWdEguHy1pWd5uuaQZkt6Xt18uaa9cb3NJU3PPyEJJB+cw\n/gr8sYVzeT7vZ6SkOblnbomkfXP5akln53zdLel1ufygwjFnFcrPlPTTXHe5pFNy+YS8/xskPSzp\nEiUnSfpuIXenSLqgOqfN4q+X9yJJfy/pZkkLcixvkrSVpMcLdTaX9KSkobXq1zj+KlKuG9kVuAMg\nIpYDO1Xy1cDvgTX17uPsSEn35HxWrteJki4qnM/1ksZL+hqwHzBV0reAW4Ad8vXerypP4yR15vO+\nWdKIVs81IuZFROW+mwfsUFVlJenfj5mZmZmVISLqvoDRQBewT34/FTgtL88GxgHbAU8A25IabLcD\nh+R1swr72ir/nAcckpc3ATZtFEOduCYDn6uzblfgYWCb/H7r/HMacGVefjPwSF4eCmyZl4cXykeT\nPszumt/fC0zNy4cAM/PyOcCxeXkYsBzYrCqmPYFLm5zTacC/5WUBW+TlLuDDefl84CuVYxW2PRn4\ndl4+E1iUczsceBIYCUwA/jefl4BZwOHAFsCjwNC8/VzgLb24JvXyfmbhnrkNGJOX3w7cnpd/CUzI\ny0dWctWg/tp91rgvDqpRfg5wQWE/fwX2aPG86t3Hsws5/xBwa14+Efh+of71pB6lyjZ7FO6vJYV6\n0/L12Chfg+GFfExt9Vyr6nyx+r4D3g3c0GS7gPCrz1+ElWP27Nn9HcKg5LyWw3ktj3NbDue1vvx/\ne4/PUq08RfDJiJiXl6cDnwEuLKzfG5gdEX8AkDQDGE8anrSzpCnATcAsSVsC20fEdaSImvU89CBJ\nwO45llreA1wdES/kY6wqrLsmly2T9PrKLoFzlea2dAHbF9atiIilefkh0gd+gAeAnfLy+4GDJX0p\nv98EGEVqaJGPtxD4eJNTW0Dq3dgYuDYiFufylyLipry8EHhvXt5R0lWkBu7GwIrCvq7Nuf29pDtI\njYo/AvMj4glI83+A/SJiptKcoIMkPQxsFBEPNYm1lkZ5R9IWwLuAq/M1JMcNcBVwFDAHOBr4QZP6\nNUXEmXVWnQdMkXQf6dotAta0eF6PUXUfF9bNzD8XkhpMrWj2eL5dgLcCt+bzHgL8rrpSg3NNB5H2\nB04i9ZoVPQO8SdJrIuKl+nuYVFjuyC8zMzOzV6/Ozs6WHvrRSgMrmryHGh8aI2KVpN2BDwCfBI4A\nPlerbrcdSZ8CPpaP8+GIWFlYN4T0gfcl4MYWYq9W/EBZieM44LWknoUupYc2bFqjflfhfRfrcifg\noxHxSC/iWSsi7syNvAOByyRdEBHTgZcL1dYUjnsR8J2IuFHpoQXFD9zFayRqX7NivamkeUoPk3pS\nyjAEeCEiaj0Y4jrgHEnbkHqM7gC2bFC/LRGxmsK8o3yNH2tx21r38Sl5deV+KF6Xv9F96G2PIbVN\nCHgwIvZtc7t1O0gPtrgU+GClwVsREY9JWgY8IemA+o3pSb09vNl65/kB5XBey+G8lse5LYfzuk5H\nR0e3fEyePLlmvVbmYI2W9I68fCxwZ9X6+cB4SdtKGgocA8yRNJw07OyXwFeBcRHxIvCUpEMBJG0i\nabPiziLikojYIyLGFRtXeV1XROxEGq53VJ147wCOkLRtPsY2depVGljDgOdy42p/uvdEtPJlQLcA\np67dQBrbwjY9g0lPrHsuIqYCPyE1NBrFsBXrejZOrFp3aM7tcNLQwAW5fG+luWVDSPm7CyAi5gM7\nkq7dFXXiW9bkFBrmPTdyVkiaWNjnbnndn0jXdApp+Fo0qt8uScNyzyCSPgbMyfciSvPvtmuwbY/7\nuF7V/PNxYGye37Yjqfew7u5rlC0HXidpn3z8jdTCUw8L8Y4C/gv4x4j4bY31uwE7k3qSe9NTaWZm\nZmYNtNLAehj4tKSlpMnxP8rlabJGagSdDnSShl4tiIjrSZPrOyUtAn6W6wCcAJwqaTFprkllAn87\nfkOa89VDHtJ3DqmRtwioPLChXk/cDFLDYzFwPLCsRp1a21d8g/QgkCVKj7Q/q7qCpD0lXdpz0246\ngMV5GNuRwPeaHHcy8J+SFtDzYRRLSNfjbuCsQkP1XuBi0nDH3+ZGQ8VVwNxY94CEYvzDm8TeKO9F\nxwMnKz2w40HSXLaKK0m9ib8olB3XoH4PkiZLOqjGqjcDD+ZG4geAygNXBIwB/tBgt/Xu45r3U0TM\nJTWyHiJdw4XVdeq8r2z/MjAROF/S/aR/U+9s41y/Rvq3cYnSw0bmV63fBng8IrpqbGu2QfJ3tJTD\neS2H81oe57Yczmv7lOZnbVjyfKfhEXF608qvMpLOBFZHxIVV5ROAL0REzUaKpOuBCyNido11BwI7\nR8TFZcTcXyS9BTgpIr7Y37GsL5KOBD4SEcc0qBP12/XWe2JD/H27Iejs7PQQlhI4r+VwXsvj3JbD\nea1PEhHRY0TShtrAGgNcBrwY3b8L61Wv3QaWpGGkYZ6LIuLo9ReprW9Kj99/N+lplbc3qOcGVinc\nwDIzMxtMBlUDy8zKkxpY1tdGjBjNypWP93cYZmZm1kfqNbBamYNlZq8ytb7Twa9X9vrFLy7r78s6\naHl+QDmc13I4r+VxbsvhvLbPDSwzMzMzM7M+4iGCZtaNpPDvBTMzM7PGPETQzMzMzMysZG5gmZmt\nBx7DXh7nthzOazmc1/I4t+VwXtvnBpaZmZmZmVkf8RwsM+vGc7DMzMzMmqs3B2uj/gjGzAY2qcfv\nChugRuwwgpVPr+zvMMzMzCxzD5aZdSMpmNTfUQxCK4CdS9jvpPS9Za9mnZ2ddHR09HcYg47zWg7n\ntTzObTmc1/p69RRBSaMlPVBn3WxJ4/oqwHZIGiXpPkk3F8pW9Ecs9UiaIGlaC/UGVNxFrcTWrI6k\nMyWd1ndRdd+npGmSxjepv7WkmZIWS5onadcWjjFb0qgm69u6/yVNlLRU0u35/RWS7pf02XwehzfZ\nvum55nrfl/RI3vfYQvkFkh6SNKGduM3MzMysda085GIg/mn0MGBWRHyoUDYQ42wlpoEYd8WGHn/F\nV4BFEbE7cCLw/X6K42TglIg4QNJIYK+IGBsRU/rqAJI+BIyJiDcCnwB+VFkXEV8AzgL+ua+OZ20o\no/fKAPyX1ZI4r+VwXsvj3JbDeW1fKw2sjSVNz395v0rSptUVJB0jaUl+nZfLhuS/uC/JPQefzeVj\nJN2a/7p+r6TefOzYGniuquz5Qjwn5GMuknR5LpsmaYqkuZIerfQWSNpC0m05lsWSDsnloyUty9st\nlzRD0vvy9ssl7ZXrbS5pau4ZWSjp4BzGX4E/tnAuz+f9jJQ0J/fMLZG0by5fLensnK+7Jb0ulx9U\nOOasQvmZkn6a6y6XdEoun5D3f4OkhyVdouQkSd8t5O4USRdU57RZ/PXyXiTp7yXdLGlBjuVNkraS\n9HihzuaSnpQ0tFb9GsdfRcp1I7sCdwBExHJgp0q+Gvg9sKbefZwdKemenM/K9TpR0kWF87le0nhJ\nXwP2A6ZK+hZwC7BDvt77VeVpnKTOfN43SxrRxrkeCvw0n+s9wLDC9gArSf9+zMzMzKwErTSwdgEu\njohdgdXAp4orJW0HnAd0AGOBvXMjZSywQ0TslnsOKsPlZgAXRcRY4F3A/+1F3EOBrmJBRLwjx7Mr\nqceiIyL2AIofiEdGxL7AwcD5uewvwGERsRfwHuCCQv0xwLcjYpech6Pz9l/KxwA4A7g9IvbJ239H\n0mYR8euI+HyOaU9Jl9Y6kUrcwLHAryJiHLA7cH8u3wK4O+frTuBjufzOiNgnIvYErgS+XNjt20jX\n413A15V6SwD2Bj4NvBn4B+AjwFXAwZKG5jonAf9RFVtdLea94lLgXyJib1IOfxgR/wMs0rphawfl\nPKypVb/G8T8fEfNyDJMlHVTjuIuBSoP67cAo4A1NzmtiRDxD/fsYYGg+/89Dt1lLPXr1IuIbwL3A\nsRHxZeAQ4NGIGBcRd1XqSdoIuAj4aD7vacA32zjXHYCnCu+fyWUVXaR/P7a+DdjBwBs+f0dLOZzX\ncjiv5XFuy+G8tq+Vpwg+WflQB0wHPgNcWFi/NzA7Iv4AIGkGMB44G9hZ0hTgJmCWpC2B7SPiOoCI\naPbX+B4kidQAmV6nynuAqyPihXyMVYV11+SyZZJeX9klcK7S3JYuYPvCuhURsTQvPwTclpcfAHbK\ny+8nNVC+lN9vQvoAv7xy0IhYCHy8yaktIPVubAxcGxGLc/lLEXFTXl4IvDcv7yjpKmA7YGO6f3y7\nNuf295LuAN5O6k2bHxFPQJr/A+wXETOV5gQdJOlhYKOIeKhJrLU0yjuStiA1+K7O15AcN6RG3lHA\nHOBo4AdN6tcUEWfWWXUeMEXSfaRrtwhY0+J5PUbVfVxYNzP/XAiMbnF/zR7PtwvwVuDWfN5DgN9V\nV2pwrs08A7xJ0msi4qW6tWYXlnfCw9vMzMzsVa+zs7OlBmcrDazqv8bXmnPT40NjRKyStDvwAeCT\nwBHA52rV7bYj6VOkXpoAPhwRKwvrhpA+8L4E3NhC7NWKHygrcRwHvBbYIyK6lB7asGmN+l2F912s\ny51IvQ2P9CKetSLiztzIOxC4TNIFETEdeLlQbU3huBcB34mIG3PvT/EDd/EaifrzpCrlU0m9Tw/T\nvYemLw0BXsg9dNWuA86RtA0wjjScb8sG9dsSEaspzDvK1/ixFretdR+fkldX7ofidfkb3XuGewyp\nbULAg7mntDeeAXYsvH9DLgMgIh6TtAx4QtIBdRvT+/fy6FafG6ml8fyAcjiv5XBey+PclsN5Xaej\no6NbPiZPnlyzXitDBEdLKg5ju7Nq/XxgvKRt8zCzY4A5koaThlD9EvgqMC4iXgSeknQogKRNJG1W\n3FlEXBIRe+ShUyur1nVFxE6koVZH1Yn3DuAISdvmY2xTp16lgTUMeC43rvane09EK18GdAtw6toN\nCk9ta4fSE+uei4ipwE9IDY1GMWzFup6NE6vWHZpzOxyYQOodgzR8c3RuqB4F3AUQEfNJH8qPAa6o\nE9+yJqfQMO+5kbNC0sTCPnfL6/5EuqZTgBsiqVu/XZKG5Z5BJH0MmJPvRZTm323XYNse93G9qvnn\n48BYJTuSeg/r7r5G2XLgdZL2ycffSC089bDgOuCEvO0+wKqIeLZwPruRPupv38ueSjMzMzNroJUG\n1sPApyUtJU2OrzyVLAByI+h0oJM09GpBRFxPmvfRKWkR8LNcB9KHv1MlLQbmAsUJ+K36DbBtrRV5\nSN85pEbeItbNqarXEzeD1PBYDBwPLKtRp9b2Fd8gPQhkidIj7c+qrtBoDlZBB7A4D2M7Evhek+NO\nBv5T0gJ6PoxiCel63A2cVWio3gtcTBru+NvcaKi4CpgbET0ezJEbGQ01yHvR8cDJSg/seJA0D6ni\nSlJv4i8KZcc1qN9Dg3lJbwYezI3ED5Dnh+UheGOAPzTYbb37uOb9FBFzSY2sh0jXcGF1nTrvK9u/\nDEwEzpd0P+nf1DtbPdc8nHSFpEeBH1M1ZxLYBng8Irqqt7WSeQ5WaTw/oBzOazmc1/I4t+VwXtu3\nQX7RcJ7vNDwiTm9a+VVG0pnA6oi4sKp8AvCFiKjZSJF0PXBhRMyuse5AYOeIuLiMmPuLpLcAJ0XE\nF/s7lvVF0pHARyLimAZ1/EXDZfAXDZfGX4JZDue1HM5reZzbcjiv9anOFw1vqA2sMcBlwItV34X1\nqtduA0vSMNIwz0URcfT6i9TWN6XH778b+LeIuL1BvGGqlgAAIABJREFUPTewNiST3MAyMzPrD4Oq\ngWVm5ZHkXwobkBE7jGDl0yubVzQzM7M+Va+B1cocLDN7lYkIv/r4NXv27FL268aV5weUxXkth/Na\nHue2HM5r+9zAMjMzMzMz6yMeImhm3UgK/14wMzMza8xDBM3MzMzMzErmBpaZ2XrgMezlcW7L4byW\nw3ktj3NbDue1fW5gmZmZmZmZ9RHPwTKzbjwHy8zMzKy5enOwNuqPYMxsYJN6/K4wszpGjBjNypWP\n93cYZmY2QHiIoJnVEH71+Wv2AIhhsL76N7fPPvsEg5HnXZTDeS2Pc1sO57V9DRtYkkZLeqDOutmS\nxpUTVmOSRkm6T9LNhbIV/RFLPZImSJrWQr0BFXdRK7E1qyPpTEmn9V1U3fcpaZqk8S1s831Jj0i6\nX9LYFurPljSqyfq27n9JEyUtlXR7fn9Fjuez+TwOb7J903OVdKykxfl1l6TdCusukPSQpAntxG1m\nZmZmrWtliGCUHkX7DgNmRcTphbKBGGcrMQ3EuCs29PgBkPQhYExEvFHSO4AfAfv0QygnA6dExN2S\nRgJ7RcQbc4xNG+MtegwYHxF/lPRB4FLyuUbEFyTNB/4ZmNNHx7OWdfR3AINYR38HMCh1dHT0dwiD\nkvNaHue2HM5r+1oZIrixpOn5L+9XSdq0uoKkYyQtya/zctmQ/Bf3Jfmv6Z/N5WMk3Zr/cn+vpJ17\nEffWwHNVZc8X4jkhH3ORpMtz2TRJUyTNlfRopbdA0haSbsuxLJZ0SC4fLWlZ3m65pBmS3pe3Xy5p\nr1xvc0lTJc2TtFDSwTmMvwJ/bOFcns/7GSlpTu6ZWyJp31y+WtLZOV93S3pdLj+ocMxZhfIzJf00\n110u6ZRcPiHv/wZJD0u6RMlJkr5byN0pki6ozmmz+OvlvUjS30u6WdKCHMubJG0l6fFCnc0lPSlp\naK36NY6/ipTrRg4FfgoQEfcAwySNaLLN74E19e7j7EhJ9+R8Vq7XiZIuKpzP9ZLGS/oasB8wVdK3\ngFuAHfL13q8qT+MkdebzvrkQa9NzjYh5EVG57+YBO1RVWUn692NmZmZmZYiIui9gNNAF7JPfTwVO\ny8uzgXHAdsATwLakBtvtwCF53azCvrbKP+cBh+TlTYBNG8VQJ67JwOfqrNsVeBjYJr/fOv+cBlyZ\nl98MPJKXhwJb5uXhhfLRpA+zu+b39wJT8/IhwMy8fA5wbF4eBiwHNquKaU/g0ibndBrwb3lZwBZ5\nuQv4cF4+H/hK5ViFbU8Gvp2XzwQW5dwOB54ERgITgP/N5yVgFnA4sAXwKDA0bz8XeEsvrkm9vJ9Z\nuGduI/UkAbwduD0v/xKYkJePrOSqQf21+6xxXxxUo/x64F2F97cB41o8r3r38exCzj8E3JqXTwS+\nX3Xs8YVt9ijcX0sK9abl67FRvgbDC/mY2uq5VtX5YvV9B7wbuKHJdgHhV5+/Zg+AGAbrq79zSwxG\ns2fP7u8QBiXntTzObTmc1/ry73+qX60MEXwyIubl5enAZ4ALC+v3BmZHxB8AJM0AxgNnAztLmgLc\nBMyStCWwfURcR4qoWc9DD5IE7J5jqeU9wNUR8UI+xqrCumty2TJJr6/sEjhXaW5LF7B9Yd2KiFia\nlx8ifTAHeADYKS+/HzhY0pfy+02AUaSGFvl4C4GPNzm1BaTejY2BayNicS5/KSJuyssLgffm5R0l\nXUVq4G4MrCjs69qc299LuoPUOPkjMD8inoA0/wfYLyJmKs0JOkjSw8BGEfFQk1hraZR3JG0BvAu4\nOl9DctwAVwFHkYatHQ38oEn9miLizF7E3cxjVN3HhXUz88+FpAZTK5o9nm8X4K3Arfm8hwC/q67U\n7Fwl7Q+cROo1K3oGeJOk10TES/X3MKmw3IGHYJmZmdmrXWdnZ0sP/ejNHKzq91DjQ2NErJK0O/AB\n4JPAEcDnatXttiPpU8DH8nE+HBErC+uGkD7wvgTc2ELs1YofKCtxHAe8ltSz0KX00IZNa9TvKrzv\nYl3uBHw0Ih7pRTxrRcSduZF3IHCZpAsiYjrwcqHamsJxLwK+ExE3Kj20oPiBu3iNRO1rVqw3FfgK\nqQdq2is5jwaGAC9ERK0HQ1wHnCNpG1KP0R3Alg3qt+sZYMfC+zfksqbq3Men5NWV+6F4Xf5G96G3\nPYbUNiHgwYjYt83t1u0gPdjiUuCDlQZvRUQ8JmkZ8ISkA+o3pif19vBWV0d/BzCIdfR3AIOS512U\nw3ktj3NbDud1nY6Ojm75mDx5cs16rczBGq30YACAY4E7q9bPB8ZL2lbSUOAYYI6k4aRhZ78Evkoa\nkvUi8JSkQwEkbSJps+LOIuKSiNgjIsYVG1d5XVdE7EQarndUnXjvAI6QtG0+xjZ16lUaWMOA53Lj\nan+690S08mVAtwCnrt2ghSfU1QwmPbHuuYiYCvyE1NBoFMNWrOvZOLFq3aE5t8NJQwMX5PK9leaW\nDSHl7y6AiJhPaoAcA1xRJ75lTU6hYd4jYjWwQtLEwj53y+v+RLqmU0jD16JR/V64Djgh72MfYFVE\nPJvf3yZpu3ob1rqP61XNPx8Hxub5bTuSeg/r7r5G2XLgdTlOJG0kadcG+6iOdxTwX8A/RsRva6zf\nDdiZ1JPcm55KMzMzM2uglQbWw8CnJS0lTY7/US4PgNwIOh3oJM39WRAR15Mm13dKWgT8LNeB9EH3\nVEmLSXNNmj1soJbfkOZ89ZCH9J1DauQtAioPbKjXEzeD1PBYDBwPLKtRp9b2Fd8gPQhkidIj7c+q\nriBpT0mXNjgfSH+CXSzpPtK8m+81Oe5k4D8lLaDnwyiWkK7H3cBZhYbqvcDFpOGOv82NhoqrgLmx\n7gEJxfiHN4m9Ud6LjgdOVnpgx4OkuWwVV5J6E39RKDuuQf0eJE2WdFCN2G4iNdYeBX4MfCrXFzAG\n+EOD3da7j2veTxExl9TIeoh0DRdW16nzvrL9y8BE4HxJ95P+Tb2z1XMFvkb6t3GJ0sNG5let3wZ4\nPCK6amxrpers7wAGsc7+DmBQ8nfflMN5LY9zWw7ntX1K87M2LHm+0/Do/ph2Iz1FEFgdERdWlU8A\nvhARNRspkq4HLoyI2TXWHQjsHBEXlxFzf5H0FuCkiPhif8eyvkg6EvhIRBzToE7Ub9db73XioWxl\n6aR/cys2xP9Lm+ns7PTQoBI4r+VxbsvhvNYniYjoMSJpQ21gjQEuA16MiA/1czgDSrsNLEnDSMM8\nF0XE0esvUlvflB6//27S0ypvb1DPDSyztgzOBpaZmTU2qBpYZlYeN7DM2uUGlpnZq1G9BlYrc7DM\n7FVHfvnlV4uvESNa/ZaGDYvnXZTDeS2Pc1sO57V9rTym3cxeZfzX+L7nMezlcW7NzGwg8RBBM+tG\nUvj3gpmZmVljHiJoZmZmZmZWMjewzMzWA49hL49zWw7ntRzOa3mc23I4r+1zA8vMzMzMzKyPeA6W\nmXXjOVhmZmZmzXkOlpmZmZmZWcn8mHYz60Hq8ccYMzMzsw3SiB1GsPLplevteB4iaGbdSAom9XcU\ng9AKYOf+DmKQcm7L4byWw3ktj3NbjsGQ10nlfMdnr4YIShot6YE662ZLGtdXAbZD0ihJ90m6uVC2\noj9iqUfSBEnTWqg3oOIuaiW2ZnUknSnptL6Lqvs+JU2TNL6Fbb4v6RFJ90sa20L92ZJGNVnf1v0v\naaKkpZJuz++vyPF8Np/H4U22f0XnKukCSQ9JmtBO3NZHNvT/nAYy57Yczms5nNfyOLflcF7b1soc\nrIHYxXUYMCsiPlQoG4hxthLTQIy7YkOPHwBJHwLGRMQbgU8AP+qnUE4GTomIAySNBPaKiLERMaWv\nDtDoXCPiC8BZwD/31fHMzMzMrLtWGlgbS5qe//J+laRNqytIOkbSkvw6L5cNyX9xXyJpsaTP5vIx\nkm7Nf12/V1Jv2sVbA89VlT1fiOeEfMxFki7PZdMkTZE0V9Kjld4CSVtIui3HsljSIbl8tKRlebvl\nkmZIel/efrmkvXK9zSVNlTRP0kJJB+cw/gr8sYVzeT7vZ6SkOblnbomkfXP5akln53zdLel1ufyg\nwjFnFcrPlPTTXHe5pFNy+YS8/xskPSzpEiUnSfpuIXenSLqgOqfN4q+X9yJJfy/pZkkLcixvkrSV\npMcLdTaX9KSkobXq1zj+KlKuGzkU+ClARNwDDJM0osk2vwfW1LuPsyMl3ZPzWbleJ0q6qHA+10sa\nL+lrwH7AVEnfAm4BdsjXe7+qPI2T1JnP++ZCrH1xritJ/35sfRuwfdWDgHNbDue1HM5reZzbcjiv\nbWulgbULcHFE7AqsBj5VXClpO+A8oAMYC+ydGyljgR0iYreI2B2oDJebAVwUEWOBdwH/txdxDwW6\nigUR8Y4cz67AV4COiNgDKH4gHhkR+wIHA+fnsr8Ah0XEXsB7gAsK9ccA346IXXIejs7bfykfA+AM\n4PaI2Cdv/x1Jm0XEryPi8zmmPSVdWutEKnEDxwK/iohxwO7A/bl8C+DunK87gY/l8jsjYp+I2BO4\nEvhyYbdvI12PdwFfV+otAdgb+DTwZuAfgI8AVwEHSxqa65wE/EdVbHW1mPeKS4F/iYi9STn8YUT8\nD7BI64atHZTzsKZW/RrH/3xEzMsxTJZ0UI3j7gA8VXj/TC5rdF4TI+IZ6t/HAEPz+X8eus1a6tGr\nFxHfAO4Fjo2ILwOHAI9GxLiIuKtST9JGwEXAR/N5TwO+2Yfn2kX692NmZmZmJWjlKYJPVj7UAdOB\nzwAXFtbvDcyOiD8ASJoBjAfOBnaWNAW4CZglaUtg+4i4DiAimv01vgdJIjVAptep8h7g6oh4IR9j\nVWHdNblsmaTXV3YJnKs0t6UL2L6wbkVELM3LDwG35eUHgJ3y8vtJDZQv5febAKOA5ZWDRsRC4ONN\nTm0BqXdjY+DaiFicy1+KiJvy8kLgvXl5R0lXAdsBG9P97wvX5tz+XtIdwNtJvWnzI+IJSPN/gP0i\nYqbSnKCDJD0MbBQRDzWJtZZGeUfSFqQG39X5GpLjhtTIOwqYAxwN/KBJ/Zoi4sxexN3MY1Tdx4V1\nM/PPhcDoFvfX7PF8uwBvBW7N5z0E+F11pVdwrs8Ab5L0moh4qW6t2YXlnfD4677gHJbHuS2H81oO\n57U8zm05nNe1Ojs76ezsbFqvlQZW9V/ja8256fGhMSJWSdod+ADwSeAI4HO16nbbkfQpUi9NAB+O\niJWFdUNIH3hfAm5sIfZqxQ+UlTiOA14L7BERXUoPbdi0Rv2uwvsu1uVOpN6GR3oRz1oRcWdu5B0I\nXCbpgoiYDrxcqLamcNyLgO9ExI2596f4gbt4jUT9eVKV8qmk3qeH6d5D05eGAC/kHrpq1wHnSNoG\nGAfcAWzZoH67ngF2LLx/Qy5rqs59fEpeXbkfitflb3TvGe4xpLYJAQ/mntLeaHiuEfGYpGXAE5IO\nqNuY3r+XRzczMzMbpDo6Oujo6Fj7fvLkyTXrtTJEcLSk4jC2O6vWzwfGS9o2DzM7BpgjaThpCNUv\nga8C4yLiReApSYcCSNpE0mbFnUXEJRGxRx46tbJqXVdE7EQaanVUnXjvAI6QtG0+xjZ16lUaWMOA\n53Ljan+690S08mVAtwCnrt2ghSfU1QwmPbHuuYiYCvyE1NBoFMNWrOvZOLFq3aE5t8OBCaTeMUjD\nN0fnhupRwF0AETGf9KH8GOCKOvEta3IKDfMeEauBFZImFva5W173J9I1nQLcEEnd+r1wHXBC3sc+\nwKqIeDa/vy0Pc62p1n1cr2r++TgwVsmOpN7DuruvUbYceF2OE0kb5eGXrap7rrlsN9LforbvZU+l\n9ZbHsJfHuS2H81oO57U8zm05nNe2tdLAehj4tKSlpMnxlaeSBUBuBJ0OdAKLgAURcT1p3kenpEXA\nz3IdSB/+TpW0GJgLNHvYQC2/AbattSIP6TuH1MhbxLo5VfV64maQGh6LgeOBZTXq1Nq+4hukB4Es\nUXqk/VnVFRrNwSroABZLug84Evhek+NOBv5T0gJ6PoxiCel63A2cVWio3gtcTBru+NvcaKi4Cpgb\nET0ezJEbGQ01yHvR8cDJSg/seJA0D6niSlJv4i8KZcc1qN9DvXlJeYjlCkmPAj8mzyPMQ/DGAH9o\nsNt693HN+yki5pIaWQ+RruHC6jp13le2fxmYCJwv6X7Sv6l3vtJzLdgGeDwiuqq3NTMzM7NXboP8\nouE832l4RJzetPKrjKQzgdURcWFV+QTgCxFRs5Ei6XrgwoiYXWPdgcDOEXFxGTH3F0lvAU6KiC/2\ndyzri6QjgY9ExDEN6viLhs3MzGzwmDSAvmh4AJsJ7KvCFw1b70gaJmk58KdajSuAiLhxsDWuACLi\noVdZ4+oC4IukIahmZmZmVoINsgfLzMojyb8UzMzMbNAYscMIVj69snnFNtXrwWrlKYJm9irjP7z0\nvc7Ozm5PHrK+49yWw3kth/NaHue2HM5r+9yDZWbdSAr/XjAzMzNrbLDNwTIzMzMzMxtw3MAyM1sP\nWvnmd+sd57Yczms5nNfyOLflcF7b5waWmZmZmZlZH/EcLDPrxnOwzMzMzJrzHCwzMzMzM7OSuYFl\nZj1I6vYa+YaR/R3SBs9j2Mvj3JbDeS2H81oe57Yczmv7/D1YZtbTpO5vn530bL+EYWZmZrah8Rws\n63eSVkfE3/V3HACSxgGXA/Mj4uRctiIidu6HWHYHto+Im/P7E4GdImJyk+1uBvYB7oyIQwrlxwBn\nAj+OiO822D6qG1hM8pcPm5mZmRV5DpYNZAPpk/vxwA8qjausrfgk9dW/q7HAh6vKWonlW6Tz6L5h\nxBXABODzrzw0MzMzM6vFDSwbMCRNlrRI0n2SnpY0VdJoScskTZO0XNIMSe+TNDe/3ytvu7ekuyUt\nlHSXpDf2Moytgeeqyp7Px5ggaY6kGyQ9LOmSQuyrJX1H0iJgH0njJHVKWiDpZkkjcr1TJT0k6X5J\nP89lm+dznZfjP1jSxsBZwJE5H0cA/wu82OwEImJ2vXoR8SwwrO2s2CvmMezlcW7L4byWw3ktj3Nb\nDue1fZ6DZQNGRJwJnClpGPDfwEV51RjgoxGxVNK9wNERsa+kQ4AzgI8Ay4D9IqJL0gHAucDEXoQx\nFOiqiusdhbd7A28GngRukXR4RMwEtgB+HRFflLQRMAc4JCJ+L+lI4JvAycC/kob5vSxpq7zPM4Db\nI+LkfO7zgduArwN7RsSp1UFKOjivm9SLc/QfVszMzMxK4gaWDUTTgQsi4n5Jo4EVEbE0r3uI1PgA\neAAYnZe3Bn6ae66CXtzbuWH0FtY17GqZHxFP5PpXAPsBM4E1+SfALsBbgVslidSg+V1etxj4uaRr\ngGty2fuBgyV9Kb/fBBjVKNaIuB64vvWz6+YPksZExG/r1phdWN6pl0exbjo6Ovo7hEHLuS2H81oO\n57U8zm05nNd1Ojs7W+rRcwPLBhRJk4AnI+KnheKXCstdhfddrLuHvwHcERGH50ZZsYlQ2ffZwIFA\nRMS4qnVvIPUcPRoR9zYIsXoOVOX9nwvfzivgwYjYt8b2BwLjgUOAMyS9Ldf/aEQ8UhXTPg3ieCWm\nAPdL+kxEXFazxv4lHdnMzMxsA9XR0dGtwTl5cu3njnmokA0EgrXD3t4LfLbW+iaGAc/k5ZNqVYiI\nr0bEHtWNq7zuaWCHFIY6Ghzn7Xle2BDgKODOGjEuB15XaSBJ2kjSrnndqIiYA5wObEUaWngLsHYY\noKSxeXF1rtMbon7evgL8Q93GlZXCY9jL49yWw3kth/NaHue2HM5r+9zAsoGg0vPzeWB7YEF+sMOk\nqvXVy0XfAs6TtJBe3te5B+pRYNsG1e4FLiYNVfxtRFSG+a2NKyJeJs3/Ol/S/cAi4J15COJ0SYuB\nhcCUiPgfUu/bxpKWSHqA9HALSL1wuxYecrFWfhDGpFoBSvpv4ErgPZKelPS+qiqb5IddmJmZmVkf\n8/dgmRVI+gHwQET8qMa6CcAXit8ttaGR9HpgcURs16COvwfLzMzMrAl/D5ZZa34KnCRpan8H0tfy\nFw3PIvX2mZmZmVkJ3MAyK4iIeyLiHVVfNFxZN2dD7r2KiCsiYmxEfLdp5UndXyN2GFFmaK8KHsNe\nHue2HM5rOZzX8ji35XBe2+enCJpZDx4OaGZmZtY7noNlZt1ICv9eMDMzM2vMc7DMzMzMzMxK5gaW\nmdl64DHs5XFuy+G8lsN5LY9zWw7ntX1uYJmZmZmZmfURz8Eys248B8vMzMysOc/BMjMzMzMzK5kb\nWGbWg6Rur5FvGNnfIW3wPIa9PM5tOZzXcjiv5XFuy+G8ts/fg2VmPU3q/vbZSc/2SxhmZmZmGxrP\nwTLrJ5JGAzdExNtarP8t4GDgJeD/t3fnQZaV9RnHvw8gIgjExDiYGVmMIooiM+CgAWJjAi4ERIyC\nQRHUbEyEimK5JTBdRaKE0kgBEhcyIehIobggZRREhgQVWYZNFiWyCMYZF1DAKBH45Y97Gk/3dPfM\n7XvP9CzfT1VXn/Oec+5971M9XfPr933P+R5wdFXd38f7PQtYAiwA3lNVH5zivJpYYLHYhw9LkiS1\nuQZLWjf1U7VcBOxaVbsDtwHv7vO9fgq8FTilz+skSZK0hiywpNn1uCSfSHJzkvOSbJFkjyTXJlme\n5IYkjwBU1Ver6tHmuiuAef28UVX9pKquAR4e8mfQGnAOe3fMthvm2g1z7Y7ZdsNc+2eBJc2uZwGn\nV9VzgAeAY6rqmqqaX1ULgC8z+YjTm4D/WIv9lCRJ0hpwDZY0S5o1WJdV1Y7N/n7AW6vq0Gb/MOAt\nwAHtB1MleS+woKpePcP3PRF4YNo1WC9uNewInO0aLEmStHFbtmzZuBG90dHRSddgeRdBaXZNrFoK\nIMlzgROAfScUV0cBrwBeMtmLJTkJOBCoZgRsZvab8ZWSJEkbpJGREUZGRh7bHx0dnfQ8pwhKs2uH\nJHs1238GXJ5kW2ApcGRV3Tt2YpKXAe8ADq6qhyZ7sar6u9b0wums8tcWdcs57N0x226YazfMtTtm\n2w1z7Z8jWNLsuhVYlGQJ8G3gTOC1wPbAx5KE34xGnQZsDlzca+aKqjpmTd8oyRzgamBr4NEkxwHP\nqaoHh/mBJEmSNmauwZI0js/BkiRJWj2fgyVJkiRJHbPAkrSqxeO/5sydM4ud2TA4h707ZtsNc+2G\nuXbHbLthrv1zDZakVTgdUJIkaWZcgyVpnCTl7wVJkqTpuQZLkiRJkjpmgSVJa4Fz2Ltjtt0w126Y\na3fMthvm2j8LLEmSJEkaEtdgSRrHNViSJEmr5xosSZIkSeqYBZakVSQZ6Gu7edvN9kdY5ziHvTtm\n2w1z7Ya5dsdsu2Gu/fM5WJJWtXiwy1cuXjmUbkiSJK1vXIMlrWOS3AHsUVX3Jrm8qvZJ8mLg+Ko6\naIDXPQv4E2BlVe02zXk1aIHFYh9WLEmSNmyuwZLWH49VJlW1z2TtM7QEeOmAryFJkqRpWGBJsyTJ\nXya5NsnyJLcnuWTsUOucB1qXbJvkwiS3Jvlwv+9XVZcD9w3Ybc2Qc9i7Y7bdMNdumGt3zLYb5to/\nCyxpllTVR6pqPrAQuBv4wGSntbZfACwCng08I8mh3fdSkiRJ/XANljTLmtGolVU12uy312DdX1Xb\nNGuwRqtqpDnnaOB5VfW2Pt9rB+CLq12D9eJWw47ATn19JNdgSZKkDc6yZcvGjeiNjo5OugbLuwhK\nsyjJUcDTquqYNTh9YsUybj/JQuAjTfsJVXXhjDu234yvlCRJ2iCNjIwwMjLy2P7o6Oik5zlFUJol\nSfYA3g68frrTWtt7JdkhySbAYcDl7ROr6sqqml9VC6YprjLhNbWWOIe9O2bbDXPthrl2x2y7Ya79\ns8CSZs8i4EnApc2NLj7atLdHptrbVwKnAzcB36uqz/XzZkmWAt8Adk7y/WaaoSRJkobINViSxvE5\nWJIkSavnc7AkSZIkqWMWWJK0FjiHvTtm2w1z7Ya5dsdsu2Gu/fMugpJWtXiwy+fMnTOUbkiSJK1v\nXIMlaZwk5e8FSZKk6bkGS5IkSZI6ZoElSWuBc9i7Y7bdMNdumGt3zLYb5to/CyxJkiRJGhLXYEka\nxzVYkiRJq+caLEmSJEnqmAWWJK0FzmHvjtl2w1y7Ya7dMdtumGv/fA6WpFUkq4x2S5IkbTTmzJ3D\nintWzOha12BJGidJDfqgYUmSpPXaYlhdneQaLGkNJHk0ySmt/bcnOWGW+rJD059FrbbTkhw5G/2R\nJEnS6llgSeM9BBya5LdnuyONHwHHJXE67/rujtnuwAbMbLthrt0w1+6YbTfMtW8WWNJ4DwMfBd42\n8UAzonRJkuuSXJxkXtO+JMmpSb6e5L+THNq65vgkVzbXnDiD/vwYuAQ4apL+7J7km81rn59k26b9\n0iTvT/KtJLcm2btp3yTJPzXt1yX58xn0R5IkSdOwwJLGK+AM4IgkW084dhqwpKp2B5Y2+2O2q6q9\ngYOAkwGS7A88s6oWAvOBPZPsM4P+nAwcn1XvPHE28I6mP98G2gXcplW1F/C38NiKqjcDP2vaFwJ/\nkWSHPvujmdpptjuwATPbbphrN8y1O2bbDXPtm9OOpAmq6sEkZwPHAb9sHXoR8Kpm+xyaQqrx+eba\nW5I8pWk7ANg/yXIgwFbAM4HL++zPnUmuAI4Ya0uyDbBtVY291tnAea3LPtt8vwYYK6IOAJ6X5DXN\n/jZNf+5a5U0vbW3viL9cJUnSRm/ZsmVrdNt6CyxpcqcCy4ElrbbpbiXzUGs7re/vq6qPTXVRkkPo\njTwV8JaqWj7Fqe8DPgMsm+R9puvPI/zm33mAt1bVxdNc17Pfas9Qv+7AQrUrZtsNc+2GuXbHbLth\nro8ZGRlhZGTksf3R0dFJz3OKoDReAKrqPnojQm9uHfsG8Lpm+/XAf033GsBXgDcl2Qogye8l+d32\niVX1+aqaX1ULpiiuxvrzHeBm4OBm/37g3rF0qtlUAAAIw0lEQVT1VcAbgMvWoD/HjN0wI8kzkzxh\nimskSZI0A45gSeO1R6k+ACxqtR0LLElyPL2bTxw9yTWP7VfVxUl2Ab7ZLJ96gF5h9uMZ9ucf6I2q\njTkK+JemSLp9df0BPk5vwt/yZj3Xj4BD+uiLBuFf/7pjtt0w126Ya3fMthvm2jcfNCxpHB80LEmS\nNnqLfdCwJK3bfI5Id8y2G+baDXPtjtl2w1z75giWpHGS+EtBkiRt1ObMncOKe1ZMe44jWJLWWFX5\nNeSvE088cdb7sKF+ma25rk9f5mq269vXxprr6oqr6VhgSZIkSdKQWGBJ0lpw5513znYXNlhm2w1z\n7Ya5dsdsu2Gu/XMNlqRxXIMlSZK0ZmqSNVgWWJIkSZI0JE4RlCRJkqQhscCSJEmSpCGxwJIkSZKk\nIbHAkgRAkpcluTXJd5O8c7b7s65LclaSlUluaLU9KclFSb6T5CtJtm0de3eS25LckuSAVvuCJDc0\nuX9obX+OdVGSeUm+luSmJDcmObZpN98BJHl8km8lubbJ9h+bdnMdgiSbJFme5IJm31yHIMmdSa5v\nfm6vbNrMdkBJtk3y6Sanm5LsZa7DY4EliSSbAKcDLwV2BV6XZJfZ7dU6bwm9vNreBXy1qp4FfA14\nN0CS5wCvBZ4NvBz4cJKxuw6dCby5qnYGdk4y8TU3Rg8Db6uqXYEXAYuan0fzHUBVPQTsV1Xzgd2A\nlyTZG3MdluOAm1v75jocjwIjVTW/qhY2bWY7uFOBL1XVs4HnA7dirkNjgSUJYCFwW1XdVVW/Bs4F\nXjnLfVqnVdXlwH0Tml8JnN1snw0c0mwfDJxbVQ9X1Z3AbcDCJNsBW1fVVc15/966ZqNVVSuq6rpm\n+0HgFmAe5juwqvrfZvPx9P4PcB/mOrAk84BXAB9vNZvrcIRV/79qtgNIsg2wb1UtAWjy+jnmOjQW\nWJIA5gJ3t/bvadrUn6dU1UroFQnAU5r2ifn+oGmbSy/rMeY+QZIdgd2BK4A55juYZhrbtcAKYFlV\n3Yy5DsM/A+8A2s++MdfhKODiJFcleUvTZraD2Qn4SZIlzbTWjybZEnMdGgssSeqODxocQJInAp8B\njmtGsibmab59qqpHmymC84B9k4xgrgNJciCwshl1XeWBoy3mOjN7V9UCeiOEi5Lsiz+zg9oMWACc\n0WT7C3rTA811SCywJEHvr1Hbt/bnNW3qz8okcwCaqRM/atp/ADytdd5YvlO1b/SSbEavuDqnqr7Q\nNJvvkFTV/cCXgD0x10HtDRyc5HbgU/TWtp0DrDDXwVXVD5vvPwY+T29Kuz+zg7kHuLuqrm72z6dX\ncJnrkFhgSQK4CnhGkh2SbA4cDlwwy31aH4Txf7G+ADiq2X4j8IVW++FJNk+yE/AM4MpmCsbPkyxs\nFgwf2bpmY/evwM1VdWqrzXwHkOTJY3cFS/IEYH/gWsx1IFX1nqravqqeTu9359eq6g3AFzHXgSTZ\nshnJJslWwAHAjfgzO5BmGuDdSXZumv4IuAlzHZrNZrsDkmZfVT2S5G+Ai+j94eWsqrpllru1Tkuy\nFBgBfifJ94ETgfcDn07yJuAuenddoqpuTnIevTuM/Ro4pqrGpl4sAv4N2ILeHZ2+vDY/x7qoubPd\nEcCNzXqhAt4DnAycZ74z9lTg7OY/QpvQGx28pMnYXIfv/ZjroOYAn0tS9P7P+smquijJ1ZjtoI4F\nPpnkccDtwNHAppjrUOQ3+UiSJEmSBuEUQUmSJEkaEgssSZIkSRoSCyxJkiRJGhILLEmSJEkaEgss\nSZIkSRoSCyxJkiRJGhILLEmStE5JcmmSBVMcOzfJ05vtO5NcNuH4dUluaLbfmOS0ad7nzCQvmuLY\nwUn+fuafQtLGygJLkiStF5L8PrBVVd3eNBWwdZK5zfFdmra26R74uRdwxRTHvgi8OslmA3RZ0kbI\nAkuSJE0ryZZJLkxybZIbkrymab8jyclN2xWtkaUnJ/lMkm81X3/Qep2zmnOvSXJw075Fkk8luSnJ\nZ4EtpujK4fQKn7bzmnaA1wFLJxzfvhkR+06SE1qfaRfgu1VVSY5t3vu6JEsBqqqAbwAHzDA2SRsp\nCyxJkrQ6LwN+UFXzq2o34MutY/c1bWcApzZtpwIfrKq9gD8FPt60vxe4pKpeCLwEOCXJE4C/Bn5R\nVbsCJwJ7TtGPfYCrW/sFnA+8qtk/iFULsBc0x58PvKY19fDlrc/xTmD3qtod+KvWtVcBfzhFXyRp\nUhZYkiRpdW4E9k/yviT7VNUDrWPnNt8/Bbyw2f5j4PQk1wIXAE9MsiW90aB3Ne3LgM2B7ekVMZ8A\nqKobgeun6McOwA8ntP0UuC/JYcDNwC8nHL+4qn5WVb8CPkuvSAN4Kb8psK4HliY5Anikde3/ADtO\n0RdJmpTziiVJ0rSq6rZm5OcVwElJvlpVJ40dbp/afN8E2Kuqft1+nSQAr66q2yZpH9c0VVemOHYe\nvRG0I6e4Ztx+M2q2bVWtaNoOpFfkHQy8N8lzq+rR5r2mW8MlSatwBEuSJE0ryVOBX1bVUuAUoH2H\nv8Oa74cD32y2vwIc17r++a32Y1v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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "co_t, de_t = compression_decompression_times()\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Compression speed', fontsize=14, y=1.01)\n", - "\n", - "\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = (nbytes / 1000000) / np.array([co_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = (nbytes / 1000000) / np.array([co_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "xlim = (0, np.max((nbytes / 1000000) / np.array(co_t)) + 100)\n", - "ax.set_xlim(*xlim)\n", - "ax.set_ylim(0, len(co_t))\n", - "ax.set_xlabel('speed (Mb/s)')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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5nDL1h5gHwo3kZXFK3gvkmUKWxT+BdpkS4pS+WwmdvPnA+GS8yazx76PAETGe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\nrieTpTPWk2+K3P6oAWT34LXp87aSZtZpO5Lsvrie7LCM0omI1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AHmSTobuAw4KvU7mJ62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\nN8xHONpM4+Pjkybeqh/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "co_t, de_t = compression_decompression_times()\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Decompression speed', fontsize=14, y=1.01)\n", - "\n", - "\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = (nbytes / 1000000) / np.array([de_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = (nbytes / 1000000) / np.array([de_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "xlim = (0, np.max((nbytes / 1000000) / np.array(de_t)) + 100)\n", - "ax.set_xlim(*xlim)\n", - "ax.set_ylim(0, len(de_t))\n", - "ax.set_xlabel('speed (Mb/s)')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import cpuinfo" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vendor ID: GenuineIntel\n", - "Hardware Raw: \n", - "Brand: Intel(R) Xeon(R) CPU E3-1505M v5 @ 2.80GHz\n", - "Hz Advertised: 2.8000 GHz\n", - "Hz Actual: 1.1000 GHz\n", - "Hz Advertised Raw: (2800000000, 0)\n", - "Hz Actual Raw: (1100000000, 0)\n", - "Arch: X86_64\n", - "Bits: 64\n", - "Count: 8\n", - "Raw Arch String: x86_64\n", - "L2 Cache Size: 8192 KB\n", - "L2 Cache Line Size: 0\n", - "L2 Cache Associativity: 0\n", - "Stepping: 3\n", - "Model: 94\n", - "Family: 6\n", - "Processor Type: 0\n", - "Extended Model: 0\n", - "Extended Family: 0\n", - "Flags: 3dnowprefetch, abm, acpi, adx, aes, aperfmperf, apic, arat, arch_perfmon, avx, avx2, bmi1, bmi2, bts, clflush, clflushopt, cmov, constant_tsc, cx16, cx8, de, ds_cpl, dtes64, dtherm, dts, eagerfpu, epb, ept, erms, est, f16c, flexpriority, fma, fpu, fsgsbase, fxsr, hle, ht, hwp, hwp_act_window, hwp_epp, hwp_noitfy, ida, invpcid, lahf_lm, lm, mca, mce, mmx, monitor, movbe, mpx, msr, mtrr, nonstop_tsc, nopl, nx, pae, pat, pbe, pcid, pclmulqdq, pdcm, pdpe1gb, pebs, pge, pln, pni, popcnt, pse, pse36, pts, rdrand, rdseed, rdtscp, rep_good, rtm, sep, smap, smep, smx, ss, sse, sse2, sse4_1, sse4_2, ssse3, syscall, tm, tm2, tpr_shadow, tsc, tsc_adjust, tsc_deadline_timer, vme, vmx, vnmi, vpid, x2apic, xgetbv1, xsave, xsavec, xsaveopt, xtopology, xtpr\n" - ] - } - ], - "source": [ - "cpuinfo.main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/object_arrays.ipynb b/notebooks/object_arrays.ipynb deleted file mode 100644 index 714d024907..0000000000 --- a/notebooks/object_arrays.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Object arrays\n", - "\n", - "See [#212](https://github.com/alimanfoo/zarr/pull/212) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.2.0a2.dev82+dirty'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import zarr\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.5.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numcodecs\n", - "numcodecs.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## API changes in Zarr version 2.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creation of an object array requires providing new ``object_codec`` argument:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To maintain backwards compatibility with previously-created data, the object codec is treated as a filter and inserted as the first filter in the chain:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Typezarr.core.Array
Data typeobject
Shape(10,)
Chunk shape(5,)
OrderC
Read-onlyFalse
Filter [0]MsgPack(encoding='utf-8')
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes80
No. bytes stored396
Storage ratio0.2
Chunks initialized0/2
" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : object\n", - "Shape : (10,)\n", - "Chunk shape : (5,)\n", - "Order : C\n", - "Read-only : False\n", - "Filter [0] : MsgPack(encoding='utf-8')\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 80\n", - "No. bytes stored : 396\n", - "Storage ratio : 0.2\n", - "Chunks initialized : 0/2" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z.info" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['foo', 'bar', 1, list([2, 4, 6, 'baz']), {'a': 'b', 'c': 'd'}, None,\n", - " None, None, None, None], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z[0] = 'foo'\n", - "z[1] = b'bar' # msgpack doesn't support bytes objects correctly\n", - "z[2] = 1\n", - "z[3] = [2, 4, 6, 'baz']\n", - "z[4] = {'a': 'b', 'c': 'd'}\n", - "a = z[:]\n", - "a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If no ``object_codec`` is provided, a ``ValueError`` is raised:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "missing object_codec for object array", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/creation.py\u001b[0m in \u001b[0;36mempty\u001b[0;34m(shape, **kwargs)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \"\"\"\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/creation.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(shape, chunks, dtype, compressor, fill_value, order, store, synchronizer, overwrite, path, chunk_store, filters, cache_metadata, read_only, object_codec, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m init_array(store, shape=shape, chunks=chunks, dtype=dtype, compressor=compressor,\n\u001b[1;32m 113\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m chunk_store=chunk_store, filters=filters, object_codec=object_codec)\n\u001b[0m\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;31m# instantiate array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/storage.py\u001b[0m in \u001b[0;36minit_array\u001b[0;34m(store, shape, chunks, dtype, compressor, fill_value, order, overwrite, path, chunk_store, filters, object_codec)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[0mchunk_store\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunk_store\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m object_codec=object_codec)\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/storage.py\u001b[0m in \u001b[0;36m_init_array_metadata\u001b[0;34m(store, shape, chunks, dtype, compressor, fill_value, order, overwrite, path, chunk_store, filters, object_codec)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfilters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;31m# there are no filters so we can be sure there is no object codec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'missing object_codec for object array'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 349\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;31m# one of the filters may be an object codec, issue a warning rather\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: missing object_codec for object array" - ] - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For API backward-compatibility, if object codec is provided via filters, issue a warning but don't raise an error." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/aliman/src/github/alimanfoo/zarr/zarr/storage.py:353: FutureWarning: missing object_codec for object array; this will raise a ValueError in version 3.0\n", - " 'ValueError in version 3.0', FutureWarning)\n" - ] - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, filters=[numcodecs.MsgPack()])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If a user tries to subvert the system and create an object array with no object codec, a runtime check is added to ensure no object arrays are passed down to the compressor (which could lead to nasty errors and/or segfaults):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z._filters = None # try to live dangerously, manually wipe filters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "cannot write object array without object codec", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, selection, value)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1096\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mset_basic_selection\u001b[0;34m(self, selection, value, fields)\u001b[0m\n\u001b[1;32m 1189\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1190\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_basic_selection_nd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_orthogonal_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_set_basic_selection_nd\u001b[0;34m(self, selection, value, fields)\u001b[0m\n\u001b[1;32m 1480\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1482\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_set_selection\u001b[0;34m(self, indexer, value, fields)\u001b[0m\n\u001b[1;32m 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;31m# put data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1530\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_chunk_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_selection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1532\u001b[0m def _chunk_getitem(self, chunk_coords, chunk_selection, out, out_selection,\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_setitem\u001b[0;34m(self, chunk_coords, chunk_selection, value, fields)\u001b[0m\n\u001b[1;32m 1633\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1634\u001b[0m self._chunk_setitem_nosync(chunk_coords, chunk_selection, value,\n\u001b[0;32m-> 1635\u001b[0;31m fields=fields)\n\u001b[0m\u001b[1;32m 1636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_chunk_setitem_nosync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_selection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_setitem_nosync\u001b[0;34m(self, chunk_coords, chunk_selection, value, fields)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1708\u001b[0m \u001b[0;31m# encode chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1709\u001b[0;31m \u001b[0mcdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encode_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1711\u001b[0m \u001b[0;31m# store\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_encode_chunk\u001b[0;34m(self, chunk)\u001b[0m\n\u001b[1;32m 1753\u001b[0m \u001b[0;31m# check object encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1754\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1755\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cannot write object array without object codec'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[0;31m# compress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: cannot write object array without object codec" - ] - } - ], - "source": [ - "z[0] = 'foo'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is another way to subvert the system, wiping filters **after** storing some data. To cover this case a runtime check is added to ensure no object arrays are handled inappropriately during decoding (which could lead to nasty errors and/or segfaults)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['¡Hola mundo!', 'Hej Världen!', 'Servus Woid!', 'Hei maailma!',\n", - " 'Xin chào thế giới', 'Njatjeta Botë!', 'Γεια σου κόσμε!', 'こんにちは世界',\n", - " '世界，你好！', 'Helló, világ!', 'Zdravo svete!', 'เฮลโลเวิลด์'], dtype=object)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numcodecs.tests.common import greetings\n", - "z = zarr.array(greetings, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "cannot read object array without object codec", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# try to live dangerously, manually wipe filters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEllipsis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mget_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 678\u001b[0m return self._get_basic_selection_nd(selection=selection, out=out,\n\u001b[0;32m--> 679\u001b[0;31m fields=fields)\n\u001b[0m\u001b[1;32m 680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 681\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_orthogonal_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_selection\u001b[0;34m(self, indexer, out, fields)\u001b[0m\n\u001b[1;32m 1007\u001b[0m \u001b[0;31m# load chunk selection into output array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m self._chunk_getitem(chunk_coords, chunk_selection, out, out_selection,\n\u001b[0;32m-> 1009\u001b[0;31m drop_axes=indexer.drop_axes, fields=fields)\n\u001b[0m\u001b[1;32m 1010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1011\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_getitem\u001b[0;34m(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)\u001b[0m\n\u001b[1;32m 1597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1598\u001b[0m \u001b[0;31m# decode chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1601\u001b[0m \u001b[0;31m# select data from chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_decode_chunk\u001b[0;34m(self, cdata)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cannot read object array without object codec'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1736\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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Name/foo/bar
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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are lies, damn lies and benchmarks..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.2.0a2.dev22+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import zarr\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'6.2.5'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bsddb3\n", - "bsddb3.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.93'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import lmdb\n", - "lmdb.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import dbm.gnu\n", - "import dbm.ndbm" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "bench_dir = '../data/bench'\n", - "\n", - "\n", - "def clean():\n", - " if os.path.isdir(bench_dir):\n", - " shutil.rmtree(bench_dir)\n", - " os.makedirs(bench_dir)\n", - "\n", - " \n", - "def setup(a, name='foo/bar'):\n", - " global fdict_z, hdict_z, lmdb_z, gdbm_z, ndbm_z, bdbm_btree_z, bdbm_hash_z, zip_z, dir_z\n", - " \n", - " clean()\n", - " fdict_root = zarr.group(store=dict())\n", - " hdict_root = zarr.group(store=zarr.DictStore())\n", - " lmdb_root = zarr.group(store=zarr.LMDBStore(os.path.join(bench_dir, 'lmdb')))\n", - " gdbm_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'gdbm'), open=dbm.gnu.open))\n", - " ndbm_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'ndbm'), open=dbm.ndbm.open))\n", - " bdbm_btree_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'bdbm_btree'), open=bsddb3.btopen))\n", - " bdbm_hash_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'bdbm_hash'), open=bsddb3.hashopen))\n", - " zip_root = zarr.group(store=zarr.ZipStore(os.path.join(bench_dir, 'zip'), mode='w'))\n", - " dir_root = zarr.group(store=zarr.DirectoryStore(os.path.join(bench_dir, 'dir')))\n", - "\n", - " fdict_z = fdict_root.empty_like(name, a)\n", - " hdict_z = hdict_root.empty_like(name, a)\n", - " lmdb_z = lmdb_root.empty_like(name, a)\n", - " gdbm_z = gdbm_root.empty_like(name, a)\n", - " ndbm_z = ndbm_root.empty_like(name, a)\n", - " bdbm_btree_z = bdbm_btree_root.empty_like(name, a)\n", - " bdbm_hash_z = bdbm_hash_root.empty_like(name, a)\n", - " zip_z = zip_root.empty_like(name, a)\n", - " dir_z = dir_root.empty_like(name, a)\n", - "\n", - " # check compression ratio\n", - " fdict_z[:] = a\n", - " return fdict_z.info\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Main benchmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def save(a, z):\n", - " if isinstance(z.store, zarr.ZipStore):\n", - " # needed for zip benchmarks to avoid duplicate entries\n", - " z.store.clear()\n", - " z[:] = a\n", - " if hasattr(z.store, 'flush'):\n", - " z.store.flush()\n", - " \n", - " \n", - "def load(z, a):\n", - " z.get_basic_selection(out=a)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## arange" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Name/foo/bar
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OrderC
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CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
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Chunks initialized1024/1024
" - ], - "text/plain": [ - "Name : /foo/bar\n", - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (500000000,)\n", - "Chunk shape : (488282,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 4000000000 (3.7G)\n", - "No. bytes stored : 59269657 (56.5M)\n", - "Storage ratio : 67.5\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(500000000)\n", - "setup(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "324 ms ± 60.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "302 ms ± 11.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "316 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "938 ms ± 111 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "406 ms ± 8.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.43 s ± 156 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.24 s ± 260 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "519 ms ± 59.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "609 ms ± 48.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, dir_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "454 ms ± 56.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(fdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "428 ms ± 13.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(hdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "429 ms ± 19.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(lmdb_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "459 ms ± 10 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(gdbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "473 ms ± 5.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(ndbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "504 ms ± 8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(bdbm_btree_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "519 ms ± 9.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(bdbm_hash_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "575 ms ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(zip_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "494 ms ± 10.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(dir_z, a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## randint" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Name/foo/bar
Typezarr.core.Array
Data typeint64
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Chunk shape(488282,)
OrderC
Read-onlyFalse
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes4000000000 (3.7G)
No. bytes stored2020785466 (1.9G)
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" - ], - "text/plain": [ - "Name : /foo/bar\n", - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (500000000,)\n", - "Chunk shape : (488282,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 4000000000 (3.7G)\n", - "No. bytes stored : 2020785466 (1.9G)\n", - "Storage ratio : 2.0\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(42)\n", - "a = np.random.randint(0, 2**30, size=500000000)\n", - "setup(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "670 ms ± 78.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "611 ms ± 6.11 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "846 ms ± 24 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.35 s ± 785 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.62 s ± 1.09 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7.84 s ± 1.66 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.49 s ± 808 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.68 s ± 441 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.55 s ± 1.24 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, dir_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "566 ms ± 72.8 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(fdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "521 ms ± 16.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(hdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "532 ms ± 16.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(lmdb_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.2 s ± 10.9 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(gdbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.18 s ± 13.2 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(ndbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.59 s ± 16.7 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(bdbm_btree_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.61 s ± 7.31 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(bdbm_hash_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.33 s ± 19.8 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(zip_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "790 ms ± 56 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(dir_z, a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### dask" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "import dask.array as da" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "def dask_op(source, sink, chunks=None):\n", - " if isinstance(sink.store, zarr.ZipStore):\n", - " sink.store.clear()\n", - " if chunks is None:\n", - " try:\n", - " chunks = sink.chunks\n", - " except AttributeError:\n", - " chunks = source.chunks\n", - " d = da.from_array(source, chunks=chunks, asarray=False, fancy=False, lock=False)\n", - " result = (d // 2) * 2\n", - " da.store(result, sink, lock=False)\n", - " if hasattr(sink.store, 'flush'):\n", - " sink.store.flush()\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compare sources" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.6 s, sys: 1.8 s, total: 17.4 s\n", - "Wall time: 3.07 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.5 s, sys: 104 ms, total: 16.6 s\n", - "Wall time: 2.59 s\n" - ] - } - ], - "source": [ - "%time dask_op(hdict_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.1 s, sys: 524 ms, total: 15.6 s\n", - "Wall time: 3.02 s\n" - ] - } - ], - "source": [ - "%time dask_op(lmdb_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.5 s, sys: 712 ms, total: 17.2 s\n", - "Wall time: 3.13 s\n" - ] - } - ], - "source": [ - "%time dask_op(gdbm_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.3 s, sys: 604 ms, total: 16.9 s\n", - "Wall time: 3.22 s\n" - ] - } - ], - "source": [ - "%time dask_op(ndbm_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19.6 s, sys: 1.24 s, total: 20.9 s\n", - "Wall time: 3.27 s\n" - ] - } - ], - "source": [ - "%time dask_op(bdbm_btree_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 20.3 s, sys: 1.08 s, total: 21.4 s\n", - "Wall time: 3.53 s\n" - ] - } - ], - "source": [ - "%time dask_op(bdbm_hash_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.7 s, sys: 700 ms, total: 16.4 s\n", - "Wall time: 3.12 s\n" - ] - } - ], - "source": [ - "%time dask_op(zip_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.4 s, sys: 1.08 s, total: 18.5 s\n", - "Wall time: 2.91 s\n" - ] - } - ], - "source": [ - "%time dask_op(dir_z, fdict_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compare sinks" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.8 s, sys: 1.4 s, total: 17.2 s\n", - "Wall time: 3.04 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.2 s, sys: 1.6 s, total: 17.8 s\n", - "Wall time: 2.71 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.8 s, sys: 3.05 s, total: 19.8 s\n", - "Wall time: 8.01 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.9 s, sys: 3.01 s, total: 20.9 s\n", - "Wall time: 5.46 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.8 s, sys: 3.39 s, total: 17.2 s\n", - "Wall time: 7.87 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.9 s, sys: 3.27 s, total: 17.2 s\n", - "Wall time: 6.73 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.9 s, sys: 2.5 s, total: 16.4 s\n", - "Wall time: 3.8 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.7 s, sys: 3.72 s, total: 19.4 s\n", - "Wall time: 3.1 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, dir_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "lmdb_z.store.close()\n", - "gdbm_z.store.close()\n", - "ndbm_z.store.close()\n", - "bdbm_btree_z.store.close()\n", - "bdbm_hash_z.store.close()\n", - "zip_z.store.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/zip_benchmark.ipynb b/notebooks/zip_benchmark.ipynb deleted file mode 100644 index 6805552422..0000000000 --- a/notebooks/zip_benchmark.ipynb +++ /dev/null @@ -1,343 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.2.dev0+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3L/calldata/genotype, (7449486, 773, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 10.7G; nbytes_stored: 193.5M; ratio: 56.7; initialized: 11380/11380\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: ZipStore" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "store = zarr.ZipStore('/data/coluzzi/ag1000g/data/phase1/release/AR3.1/haplotypes/main/zarr2/zstd/ag1000g.phase1.ar3.1.haplotypes.zip',\n", - " mode='r')\n", - "grp = zarr.Group(store)\n", - "z = grp['3L/calldata/genotype']\n", - "z" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1832 function calls in 0.024 seconds\n", - "\n", - " Ordered by: cumulative time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.024 0.024 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.024 0.024 :1()\n", - " 1 0.000 0.000 0.024 0.024 core.py:292(__getitem__)\n", - " 20 0.000 0.000 0.023 0.001 core.py:539(_chunk_getitem)\n", - " 20 0.000 0.000 0.020 0.001 core.py:679(_decode_chunk)\n", - " 20 0.000 0.000 0.020 0.001 codecs.py:355(decode)\n", - " 20 0.020 0.001 0.020 0.001 {zarr.blosc.decompress}\n", - " 20 0.000 0.000 0.002 0.000 storage.py:766(__getitem__)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:1235(open)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:821(read)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:901(_read1)\n", - " 80 0.000 0.000 0.001 0.000 zipfile.py:660(read)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:854(_update_crc)\n", - " 40 0.000 0.000 0.000 0.000 {built-in method zlib.crc32}\n", - " 80 0.000 0.000 0.000 0.000 {method 'read' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:937(_read2)\n", - " 80 0.000 0.000 0.000 0.000 core.py:390()\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:953(close)\n", - " 20 0.000 0.000 0.000 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - " 20 0.000 0.000 0.000 0.000 util.py:106(is_total_slice)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:708(__init__)\n", - " 20 0.000 0.000 0.000 0.000 {method 'decode' of 'bytes' objects}\n", - " 20 0.000 0.000 0.000 0.000 core.py:676(_chunk_key)\n", - " 80 0.000 0.000 0.000 0.000 {method 'seek' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.frombuffer}\n", - " 80 0.000 0.000 0.000 0.000 core.py:398()\n", - " 20 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects}\n", - " 20 0.000 0.000 0.000 0.000 core.py:386()\n", - " 20 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 40 0.000 0.000 0.000 0.000 util.py:121()\n", - " 231 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", - " 20 0.000 0.000 0.000 0.000 cp437.py:14(decode)\n", - " 80 0.000 0.000 0.000 0.000 {method 'tell' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:667(close)\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _struct.unpack}\n", - " 140 0.000 0.000 0.000 0.000 {built-in method builtins.max}\n", - " 20 0.000 0.000 0.000 0.000 {function ZipExtFile.close at 0x7f8cd5ca2048}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:1194(getinfo)\n", - " 140 0.000 0.000 0.000 0.000 {built-in method builtins.min}\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1224(current_thread)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:654(__init__)\n", - " 1 0.000 0.000 0.000 0.000 util.py:195(get_chunk_range)\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _codecs.charmap_decode}\n", - " 1 0.000 0.000 0.000 0.000 util.py:166(normalize_array_selection)\n", - " 1 0.000 0.000 0.000 0.000 util.py:198()\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:1715(_fpclose)\n", - " 20 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}\n", - " 63 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", - " 1 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.empty}\n", - " 2 0.000 0.000 0.000 0.000 util.py:182()\n", - " 20 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}\n", - " 1 0.000 0.000 0.000 0.000 util.py:130(normalize_axis_selection)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:636(_get_decompressor)\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1298(main_thread)\n", - " 4 0.000 0.000 0.000 0.000 core.py:373()\n", - " 3 0.000 0.000 0.000 0.000 util.py:187()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "import cProfile\n", - "cProfile.run('z[:10]', sort='cumtime')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.11.0'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import dask\n", - "import dask.array as da\n", - "dask.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = da.from_array(z, chunks=z.chunks)\n", - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3min 35s, sys: 4.36 s, total: 3min 40s\n", - "Wall time: 29.5 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[3, 0],\n", - " [1, 0],\n", - " [2, 0],\n", - " ..., \n", - " [2, 8],\n", - " [8, 8],\n", - " [0, 1]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d.sum(axis=1).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3L/calldata/genotype, (7449486, 773, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 10.7G; nbytes_stored: 193.5M; ratio: 56.7; initialized: 11380/11380\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: DirectoryStore" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# compare with same data via directory store\n", - "store_dir = zarr.DirectoryStore('/data/coluzzi/ag1000g/data/phase1/release/AR3.1/haplotypes/main/zarr2/zstd/ag1000g.phase1.ar3.1.haplotypes')\n", - "grp_dir = zarr.Group(store_dir)\n", - "z_dir = grp_dir['3L/calldata/genotype']\n", - "z_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d_dir = da.from_array(z_dir, chunks=z_dir.chunks)\n", - "d_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3min 39s, sys: 4.91 s, total: 3min 44s\n", - "Wall time: 31.1 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[3, 0],\n", - " [1, 0],\n", - " [2, 0],\n", - " ..., \n", - " [2, 8],\n", - " [8, 8],\n", - " [0, 1]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d_dir.sum(axis=1).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/pyproject.toml b/pyproject.toml index 0b351c3b27..6c18563a1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,6 @@ exclude = [ "/.github", "/bench", "/docs", - "/notebooks" ] [project] @@ -72,18 +71,20 @@ gpu = [ # Development extras test = [ "coverage", - "pytest", + # Pin possibly due to https://github.com/pytest-dev/pytest-cov/issues/693 + "pytest<8.4", "pytest-asyncio", "pytest-cov", "pytest-accept", "rich", "mypy", "hypothesis", + "pytest-xdist", ] remote_tests = [ 'zarr[remote]', "botocore", - "s3fs", + "s3fs>=2023.10.0", "moto[s3,server]", "requests", ] @@ -104,14 +105,14 @@ docs = [ # Optional dependencies to run examples 'numcodecs[msgpack]', 'rich', - 's3fs', + 's3fs>=2023.10.0', 'astroid<4' ] [project.urls] "Bug Tracker" = "https://github.com/zarr-developers/zarr-python/issues" -Changelog = "https://zarr.readthedocs.io/en/stable/release.html" +Changelog = "https://zarr.readthedocs.io/en/stable/release-notes.html" Discussions = "https://github.com/zarr-developers/zarr-python/discussions" Documentation = "https://zarr.readthedocs.io/" Homepage = "https://github.com/zarr-developers/zarr-python" @@ -149,7 +150,7 @@ features = ["test"] [[tool.hatch.envs.test.matrix]] python = ["3.11", "3.12", "3.13"] -numpy = ["1.25", "2.1"] +numpy = ["1.25", "2.2"] deps = ["minimal", "optional"] [tool.hatch.envs.test.overrides] @@ -164,7 +165,7 @@ run = "run-coverage --no-cov" run-pytest = "run" run-verbose = "run-coverage --verbose" run-mypy = "mypy src" -run-hypothesis = "run-coverage --hypothesis-profile ci --run-slow-hypothesis tests/test_properties.py tests/test_store/test_stateful*" +run-hypothesis = "run-coverage -nauto --run-slow-hypothesis tests/test_properties.py tests/test_store/test_stateful*" list-env = "pip list" [tool.hatch.envs.doctest] @@ -185,7 +186,7 @@ features = ["test", "gpu"] [[tool.hatch.envs.gputest.matrix]] python = ["3.11", "3.12", "3.13"] -numpy = ["1.25", "2.1"] +numpy = ["1.25", "2.2"] version = ["minimal"] [tool.hatch.envs.gputest.scripts] @@ -193,7 +194,7 @@ run-coverage = "pytest -m gpu --cov-config=pyproject.toml --cov=pkg --cov-report run = "run-coverage --no-cov" run-verbose = "run-coverage --verbose" run-mypy = "mypy src" -run-hypothesis = "pytest --hypothesis-profile ci tests/test_properties.py tests/test_store/test_stateful*" +run-hypothesis = "run-coverage --hypothesis-profile ci --run-slow-hypothesis tests/test_properties.py tests/test_store/test_stateful*" list-env = "pip list" [tool.hatch.envs.docs] @@ -209,7 +210,6 @@ dependencies = [ 'packaging @ git+https://github.com/pypa/packaging', 'numpy', # from scientific-python-nightly-wheels 'numcodecs @ git+https://github.com/zarr-developers/numcodecs', - 'fsspec @ git+https://github.com/fsspec/filesystem_spec', 's3fs @ git+https://github.com/fsspec/s3fs', 'universal_pathlib @ git+https://github.com/fsspec/universal_pathlib', 'typing_extensions @ git+https://github.com/python/typing_extensions', @@ -252,6 +252,7 @@ dependencies = [ 'obstore==0.5.*', # test deps 'zarr[test]', + 'zarr[remote_tests]', ] [tool.hatch.envs.min_deps.scripts] @@ -280,9 +281,9 @@ extend-exclude = [ "buck-out", "build", "dist", - "notebooks", # temporary, until we achieve compatibility with ruff ≥ 0.6 "venv", "docs", + "tests/test_regression/scripts/", # these are scripts that use a different version of python "src/zarr/v2/", "tests/v2/", ] @@ -291,8 +292,8 @@ extend-exclude = [ extend-select = [ "ANN", # flake8-annotations "B", # flake8-bugbear - "EXE", # flake8-executable "C4", # flake8-comprehensions + "EXE", # flake8-executable "FA", # flake8-future-annotations "FLY", # flynt "FURB", # refurb @@ -310,7 +311,7 @@ extend-select = [ "RUF", "SIM", # flake8-simplify "SLOT", # flake8-slots - "TCH", # flake8-type-checking + "TC", # flake8-type-checking "TRY", # tryceratops "UP", # pyupgrade "W", # pycodestyle warnings @@ -338,6 +339,7 @@ ignore = [ "Q003", "COM812", "COM819", + "TC006", ] [tool.ruff.lint.extend-per-file-ignores] @@ -348,32 +350,39 @@ python_version = "3.11" ignore_missing_imports = true namespace_packages = false - strict = true warn_unreachable = true - enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] [[tool.mypy.overrides]] module = [ - "zarr.v2.*", + "tests.package_with_entrypoint.*", + "zarr.testing.stateful", + "tests.test_codecs.test_transpose", + "tests.test_config", + "tests.test_store.test_zip", + "tests.test_store.test_local", + "tests.test_store.test_fsspec", + "tests.test_store.test_memory", + "tests.test_codecs.test_codecs", ] -ignore_errors = true +strict = false +# TODO: Move the next modules up to the strict = false section +# and fix the errors [[tool.mypy.overrides]] module = [ - "zarr.testing.stateful", # lots of hypothesis decorator errors - "tests.package_with_entrypoint.*", - "tests.test_codecs.test_codecs", - "tests.test_codecs.test_transpose", "tests.test_metadata.*", - "tests.test_store.*", - "tests.test_config", + "tests.test_store.test_core", + "tests.test_store.test_logging", + "tests.test_store.test_object", + "tests.test_store.test_stateful", + "tests.test_store.test_wrapper", "tests.test_group", "tests.test_indexing", "tests.test_properties", "tests.test_sync", - "tests.test_v2", + "tests.test_regression.scripts.*" ] ignore_errors = true diff --git a/src/zarr/__init__.py b/src/zarr/__init__.py index 31796601b3..0d58ecf8e8 100644 --- a/src/zarr/__init__.py +++ b/src/zarr/__init__.py @@ -37,6 +37,54 @@ # in case setuptools scm screw up and find version to be 0.0.0 assert not __version__.startswith("0.0.0") + +def print_debug_info() -> None: + """ + Print version info for use in bug reports. + """ + import platform + from importlib.metadata import version + + def print_packages(packages: list[str]) -> None: + not_installed = [] + for package in packages: + try: + print(f"{package}: {version(package)}") + except ModuleNotFoundError: + not_installed.append(package) + if not_installed: + print("\n**Not Installed:**") + for package in not_installed: + print(package) + + required = [ + "packaging", + "numpy", + "numcodecs", + "typing_extensions", + "donfig", + ] + optional = [ + "botocore", + "cupy-cuda12x", + "fsspec", + "numcodecs", + "s3fs", + "gcsfs", + "universal-pathlib", + "rich", + "obstore", + ] + + print(f"platform: {platform.platform()}") + print(f"python: {platform.python_version()}") + print(f"zarr: {__version__}\n") + print("**Required dependencies:**") + print_packages(required) + print("\n**Optional dependencies:**") + print_packages(optional) + + __all__ = [ "Array", "AsyncArray", @@ -67,6 +115,7 @@ "open_consolidated", "open_group", "open_like", + "print_debug_info", "save", "save_array", "save_group", diff --git a/src/zarr/abc/buffer.py b/src/zarr/abc/buffer.py new file mode 100644 index 0000000000..3d5ac07157 --- /dev/null +++ b/src/zarr/abc/buffer.py @@ -0,0 +1,9 @@ +from zarr.core.buffer.core import ArrayLike, Buffer, BufferPrototype, NDArrayLike, NDBuffer + +__all__ = [ + "ArrayLike", + "Buffer", + "BufferPrototype", + "NDArrayLike", + "NDBuffer", +] diff --git a/src/zarr/abc/codec.py b/src/zarr/abc/codec.py index 16400f5f4b..d9e3520d42 100644 --- a/src/zarr/abc/codec.py +++ b/src/zarr/abc/codec.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import abstractmethod -from typing import TYPE_CHECKING, Any, Generic, TypeVar +from typing import TYPE_CHECKING, Generic, TypeVar from zarr.abc.metadata import Metadata from zarr.core.buffer import Buffer, NDBuffer @@ -12,11 +12,10 @@ from collections.abc import Awaitable, Callable, Iterable from typing import Self - import numpy as np - from zarr.abc.store import ByteGetter, ByteSetter from zarr.core.array_spec import ArraySpec from zarr.core.chunk_grids import ChunkGrid + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType from zarr.core.indexing import SelectorTuple __all__ = [ @@ -93,7 +92,13 @@ def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self: """ return self - def validate(self, *, shape: ChunkCoords, dtype: np.dtype[Any], chunk_grid: ChunkGrid) -> None: + def validate( + self, + *, + shape: ChunkCoords, + dtype: ZDType[TBaseDType, TBaseScalar], + chunk_grid: ChunkGrid, + ) -> None: """Validates that the codec configuration is compatible with the array metadata. Raises errors when the codec configuration is not compatible. @@ -285,7 +290,9 @@ def supports_partial_decode(self) -> bool: ... def supports_partial_encode(self) -> bool: ... @abstractmethod - def validate(self, *, shape: ChunkCoords, dtype: np.dtype[Any], chunk_grid: ChunkGrid) -> None: + def validate( + self, *, shape: ChunkCoords, dtype: ZDType[TBaseDType, TBaseScalar], chunk_grid: ChunkGrid + ) -> None: """Validates that all codec configurations are compatible with the array metadata. Raises errors when a codec configuration is not compatible. diff --git a/src/zarr/abc/store.py b/src/zarr/abc/store.py index 96165f8ba0..1fbdb3146c 100644 --- a/src/zarr/abc/store.py +++ b/src/zarr/abc/store.py @@ -83,6 +83,27 @@ async def open(cls, *args: Any, **kwargs: Any) -> Self: await store._open() return store + def with_read_only(self, read_only: bool = False) -> Store: + """ + Return a new store with a new read_only setting. + + The new store points to the same location with the specified new read_only state. + The returned Store is not automatically opened, and this store is + not automatically closed. + + Parameters + ---------- + read_only + If True, the store will be created in read-only mode. Defaults to False. + + Returns + ------- + A new store of the same type with the new read only attribute. + """ + raise NotImplementedError( + f"with_read_only is not implemented for the {type(self)} store type." + ) + def __enter__(self) -> Self: """Enter a context manager that will close the store upon exiting.""" return self @@ -264,6 +285,18 @@ async def _set_many(self, values: Iterable[tuple[str, Buffer]]) -> None: """ await gather(*starmap(self.set, values)) + @property + def supports_consolidated_metadata(self) -> bool: + """ + Does the store support consolidated metadata?. + + If it doesn't an error will be raised on requests to consolidate the metadata. + Returning `False` can be useful for stores which implement their own + consolidation mechanism outside of the zarr-python implementation. + """ + + return True + @property @abstractmethod def supports_deletes(self) -> bool: diff --git a/src/zarr/api/asynchronous.py b/src/zarr/api/asynchronous.py index 285d777258..3b53095636 100644 --- a/src/zarr/api/asynchronous.py +++ b/src/zarr/api/asynchronous.py @@ -9,20 +9,30 @@ import numpy.typing as npt from typing_extensions import deprecated -from zarr.core.array import Array, AsyncArray, create_array, from_array, get_array_metadata +from zarr.abc.store import Store +from zarr.core.array import ( + Array, + AsyncArray, + CompressorLike, + _get_default_chunk_encoding_v2, + create_array, + from_array, + get_array_metadata, +) from zarr.core.array_spec import ArrayConfig, ArrayConfigLike, ArrayConfigParams from zarr.core.buffer import NDArrayLike from zarr.core.common import ( JSON, AccessModeLiteral, ChunkCoords, + DimensionNames, MemoryOrder, ZarrFormat, _default_zarr_format, _warn_order_kwarg, _warn_write_empty_chunks_kwarg, - parse_dtype, ) +from zarr.core.dtype import ZDTypeLike, get_data_type_from_native_dtype, parse_data_type from zarr.core.group import ( AsyncGroup, ConsolidatedMetadata, @@ -30,8 +40,8 @@ create_hierarchy, ) from zarr.core.metadata import ArrayMetadataDict, ArrayV2Metadata, ArrayV3Metadata -from zarr.core.metadata.v2 import _default_compressor, _default_filters -from zarr.errors import NodeTypeValidationError +from zarr.errors import GroupNotFoundError, NodeTypeValidationError +from zarr.storage import StorePath from zarr.storage._common import make_store_path if TYPE_CHECKING: @@ -80,7 +90,7 @@ _READ_MODES: tuple[AccessModeLiteral, ...] = ("r", "r+", "a") _CREATE_MODES: tuple[AccessModeLiteral, ...] = ("a", "w", "w-") -_OVERWRITE_MODES: tuple[AccessModeLiteral, ...] = ("a", "r+", "w") +_OVERWRITE_MODES: tuple[AccessModeLiteral, ...] = ("w",) def _infer_overwrite(mode: AccessModeLiteral) -> bool: @@ -166,7 +176,8 @@ async def consolidate_metadata( Consolidate the metadata of all nodes in a hierarchy. Upon completion, the metadata of the root node in the Zarr hierarchy will be - updated to include all the metadata of child nodes. + updated to include all the metadata of child nodes. For Stores that do + not support consolidated metadata, this operation raises a ``TypeError``. Parameters ---------- @@ -186,14 +197,25 @@ async def consolidate_metadata( ------- group: AsyncGroup The group, with the ``consolidated_metadata`` field set to include - the metadata of each child node. + the metadata of each child node. If the Store doesn't support + consolidated metadata, this function raises a `TypeError`. + See ``Store.supports_consolidated_metadata``. """ store_path = await make_store_path(store, path=path) + if not store_path.store.supports_consolidated_metadata: + store_name = type(store_path.store).__name__ + raise TypeError( + f"The Zarr Store in use ({store_name}) doesn't support consolidated metadata", + ) + group = await AsyncGroup.open(store_path, zarr_format=zarr_format, use_consolidated=False) group.store_path.store._check_writable() - members_metadata = {k: v.metadata async for k, v in group.members(max_depth=None)} + members_metadata = { + k: v.metadata + async for k, v in group.members(max_depth=None, use_consolidated_for_children=False) + } # While consolidating, we want to be explicit about when child groups # are empty by inserting an empty dict for consolidated_metadata.metadata for k, v in members_metadata.items(): @@ -217,7 +239,6 @@ async def consolidate_metadata( group, metadata=metadata, ) - await group._save_metadata() return group @@ -278,7 +299,7 @@ async def load( async def open( *, store: StoreLike | None = None, - mode: AccessModeLiteral = "a", + mode: AccessModeLiteral | None = None, zarr_version: ZarrFormat | None = None, # deprecated zarr_format: ZarrFormat | None = None, path: str | None = None, @@ -296,6 +317,7 @@ async def open( read/write (must exist); 'a' means read/write (create if doesn't exist); 'w' means create (overwrite if exists); 'w-' means create (fail if exists). + If the store is read-only, the default is 'r'; otherwise, it is 'a'. zarr_format : {2, 3, None}, optional The zarr format to use when saving. path : str or None, optional @@ -313,7 +335,11 @@ async def open( Return type depends on what exists in the given store. """ zarr_format = _handle_zarr_version_or_format(zarr_version=zarr_version, zarr_format=zarr_format) - + if mode is None: + if isinstance(store, (Store, StorePath)) and store.read_only: + mode = "r" + else: + mode = "a" store_path = await make_store_path(store, mode=mode, path=path, storage_options=storage_options) # TODO: the mode check below seems wrong! @@ -321,7 +347,7 @@ async def open( try: metadata_dict = await get_array_metadata(store_path, zarr_format=zarr_format) # TODO: remove this cast when we fix typing for array metadata dicts - _metadata_dict = cast(ArrayMetadataDict, metadata_dict) + _metadata_dict = cast("ArrayMetadataDict", metadata_dict) # for v2, the above would already have raised an exception if not an array zarr_format = _metadata_dict["zarr_format"] is_v3_array = zarr_format == 3 and _metadata_dict.get("node_type") == "array" @@ -430,11 +456,12 @@ async def save_array( shape = arr.shape chunks = getattr(arr, "chunks", None) # for array-likes with chunks attribute overwrite = kwargs.pop("overwrite", None) or _infer_overwrite(mode) + zarr_dtype = get_data_type_from_native_dtype(arr.dtype) new = await AsyncArray._create( store_path, zarr_format=zarr_format, shape=shape, - dtype=arr.dtype, + dtype=zarr_dtype, chunks=chunks, overwrite=overwrite, **kwargs, @@ -494,13 +521,12 @@ async def save_group( raise ValueError("at least one array must be provided") aws = [] for i, arr in enumerate(args): - _path = f"{path}/arr_{i}" if path is not None else f"arr_{i}" aws.append( save_array( store_path, arr, zarr_format=zarr_format, - path=_path, + path=f"arr_{i}", storage_options=storage_options, ) ) @@ -809,7 +835,6 @@ async def open_group( warnings.warn("chunk_store is not yet implemented", RuntimeWarning, stacklevel=2) store_path = await make_store_path(store, mode=mode, storage_options=storage_options, path=path) - if attributes is None: attributes = {} @@ -829,15 +854,15 @@ async def open_group( overwrite=overwrite, attributes=attributes, ) - raise FileNotFoundError(f"Unable to find group: {store_path}") + raise GroupNotFoundError(store, store_path.path) async def create( shape: ChunkCoords | int, *, # Note: this is a change from v2 chunks: ChunkCoords | int | None = None, # TODO: v2 allowed chunks=True - dtype: npt.DTypeLike | None = None, - compressor: dict[str, JSON] | None = None, # TODO: default and type change + dtype: ZDTypeLike | None = None, + compressor: CompressorLike = "auto", fill_value: Any | None = 0, # TODO: need type order: MemoryOrder | None = None, store: str | StoreLike | None = None, @@ -865,7 +890,7 @@ async def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, config: ArrayConfigLike | None = None, **kwargs: Any, @@ -983,21 +1008,13 @@ async def create( _handle_zarr_version_or_format(zarr_version=zarr_version, zarr_format=zarr_format) or _default_zarr_format() ) - + zdtype = parse_data_type(dtype, zarr_format=zarr_format) if zarr_format == 2: - if chunks is None: - chunks = shape - dtype = parse_dtype(dtype, zarr_format) + default_filters, default_compressor = _get_default_chunk_encoding_v2(zdtype) if not filters: - filters = _default_filters(dtype) - if not compressor: - compressor = _default_compressor(dtype) - elif zarr_format == 3 and chunk_shape is None: # type: ignore[redundant-expr] - if chunks is not None: - chunk_shape = chunks - chunks = None - else: - chunk_shape = shape + filters = default_filters # type: ignore[assignment] + if compressor == "auto": + compressor = default_compressor if synchronizer is not None: warnings.warn("synchronizer is not yet implemented", RuntimeWarning, stacklevel=2) @@ -1011,11 +1028,6 @@ async def create( warnings.warn("object_codec is not yet implemented", RuntimeWarning, stacklevel=2) if read_only is not None: warnings.warn("read_only is not yet implemented", RuntimeWarning, stacklevel=2) - if dimension_separator is not None and zarr_format == 3: - raise ValueError( - "dimension_separator is not supported for zarr format 3, use chunk_key_encoding instead" - ) - if order is not None: _warn_order_kwarg() if write_empty_chunks is not None: @@ -1040,15 +1052,13 @@ async def create( ) warnings.warn(UserWarning(msg), stacklevel=1) config_dict["write_empty_chunks"] = write_empty_chunks - if order is not None: - if config is not None: - msg = ( - "Both order and config keyword arguments are set. " - "This is redundant. When both are set, order will be ignored and " - "config will be used." - ) - warnings.warn(UserWarning(msg), stacklevel=1) - config_dict["order"] = order + if order is not None and config is not None: + msg = ( + "Both order and config keyword arguments are set. " + "This is redundant. When both are set, order will be ignored and " + "config will be used." + ) + warnings.warn(UserWarning(msg), stacklevel=1) config_parsed = ArrayConfig.from_dict(config_dict) @@ -1056,12 +1066,13 @@ async def create( store_path, shape=shape, chunks=chunks, - dtype=dtype, + dtype=zdtype, compressor=compressor, fill_value=fill_value, overwrite=overwrite, filters=filters, dimension_separator=dimension_separator, + order=order, zarr_format=zarr_format, chunk_shape=chunk_shape, chunk_key_encoding=chunk_key_encoding, diff --git a/src/zarr/api/synchronous.py b/src/zarr/api/synchronous.py index 4c577936cd..f2dc8757d6 100644 --- a/src/zarr/api/synchronous.py +++ b/src/zarr/api/synchronous.py @@ -7,7 +7,7 @@ import zarr.api.asynchronous as async_api import zarr.core.array from zarr._compat import _deprecate_positional_args -from zarr.core.array import Array, AsyncArray +from zarr.core.array import Array, AsyncArray, CompressorLike from zarr.core.group import Group from zarr.core.sync import sync from zarr.core.sync_group import create_hierarchy @@ -33,10 +33,12 @@ JSON, AccessModeLiteral, ChunkCoords, + DimensionNames, MemoryOrder, ShapeLike, ZarrFormat, ) + from zarr.core.dtype import ZDTypeLike from zarr.storage import StoreLike __all__ = [ @@ -80,7 +82,8 @@ def consolidate_metadata( Consolidate the metadata of all nodes in a hierarchy. Upon completion, the metadata of the root node in the Zarr hierarchy will be - updated to include all the metadata of child nodes. + updated to include all the metadata of child nodes. For Stores that do + not use consolidated metadata, this operation raises a `TypeError`. Parameters ---------- @@ -100,7 +103,10 @@ def consolidate_metadata( ------- group: Group The group, with the ``consolidated_metadata`` field set to include - the metadata of each child node. + the metadata of each child node. If the Store doesn't support + consolidated metadata, this function raises a `TypeError`. + See ``Store.supports_consolidated_metadata``. + """ return Group(sync(async_api.consolidate_metadata(store, path=path, zarr_format=zarr_format))) @@ -157,7 +163,7 @@ def load( def open( store: StoreLike | None = None, *, - mode: AccessModeLiteral = "a", + mode: AccessModeLiteral | None = None, zarr_version: ZarrFormat | None = None, # deprecated zarr_format: ZarrFormat | None = None, path: str | None = None, @@ -175,6 +181,7 @@ def open( read/write (must exist); 'a' means read/write (create if doesn't exist); 'w' means create (overwrite if exists); 'w-' means create (fail if exists). + If the store is read-only, the default is 'r'; otherwise, it is 'a'. zarr_format : {2, 3, None}, optional The zarr format to use when saving. path : str or None, optional @@ -597,9 +604,9 @@ def create( shape: ChunkCoords | int, *, # Note: this is a change from v2 chunks: ChunkCoords | int | bool | None = None, - dtype: npt.DTypeLike | None = None, - compressor: dict[str, JSON] | None = None, # TODO: default and type change - fill_value: Any | None = 0, # TODO: need type + dtype: ZDTypeLike | None = None, + compressor: CompressorLike = "auto", + fill_value: Any | None = None, # TODO: need type order: MemoryOrder | None = None, store: str | StoreLike | None = None, synchronizer: Any | None = None, @@ -626,7 +633,7 @@ def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, config: ArrayConfigLike | None = None, **kwargs: Any, @@ -749,7 +756,7 @@ def create_array( *, name: str | None = None, shape: ShapeLike | None = None, - dtype: npt.DTypeLike | None = None, + dtype: ZDTypeLike | None = None, data: np.ndarray[Any, np.dtype[Any]] | None = None, chunks: ChunkCoords | Literal["auto"] = "auto", shards: ShardsLike | None = None, @@ -761,7 +768,7 @@ def create_array( zarr_format: ZarrFormat | None = 3, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfigLike | None = None, @@ -780,7 +787,7 @@ def create_array( at the root of the store. shape : ChunkCoords, optional Shape of the array. Can be ``None`` if ``data`` is provided. - dtype : npt.DTypeLike, optional + dtype : ZDTypeLike, optional Data type of the array. Can be ``None`` if ``data`` is provided. data : np.ndarray, optional Array-like data to use for initializing the array. If this parameter is provided, the @@ -857,6 +864,7 @@ def create_array( Ignored otherwise. overwrite : bool, default False Whether to overwrite an array with the same name in the store, if one exists. + If `True`, all existing paths in the store will be deleted. config : ArrayConfigLike, optional Runtime configuration for the array. write_data : bool @@ -926,7 +934,7 @@ def from_array( zarr_format: ZarrFormat | None = None, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfigLike | None = None, diff --git a/src/zarr/buffer/__init__.py b/src/zarr/buffer/__init__.py new file mode 100644 index 0000000000..db393f66c7 --- /dev/null +++ b/src/zarr/buffer/__init__.py @@ -0,0 +1,12 @@ +""" +Implementations of the Zarr Buffer interface. + +See Also +======== +zarr.abc.buffer: Abstract base class for the Zarr Buffer interface. +""" + +from zarr.buffer import cpu, gpu +from zarr.core.buffer import default_buffer_prototype + +__all__ = ["cpu", "default_buffer_prototype", "gpu"] diff --git a/src/zarr/buffer/cpu.py b/src/zarr/buffer/cpu.py new file mode 100644 index 0000000000..5307927c06 --- /dev/null +++ b/src/zarr/buffer/cpu.py @@ -0,0 +1,15 @@ +from zarr.core.buffer.cpu import ( + Buffer, + NDBuffer, + as_numpy_array_wrapper, + buffer_prototype, + numpy_buffer_prototype, +) + +__all__ = [ + "Buffer", + "NDBuffer", + "as_numpy_array_wrapper", + "buffer_prototype", + "numpy_buffer_prototype", +] diff --git a/src/zarr/buffer/gpu.py b/src/zarr/buffer/gpu.py new file mode 100644 index 0000000000..dbdc1b1357 --- /dev/null +++ b/src/zarr/buffer/gpu.py @@ -0,0 +1,7 @@ +from zarr.core.buffer.gpu import Buffer, NDBuffer, buffer_prototype + +__all__ = [ + "Buffer", + "NDBuffer", + "buffer_prototype", +] diff --git a/src/zarr/codecs/_v2.py b/src/zarr/codecs/_v2.py index 53edc1f4a1..08853f27f1 100644 --- a/src/zarr/codecs/_v2.py +++ b/src/zarr/codecs/_v2.py @@ -46,9 +46,9 @@ async def _decode_single( chunk = ensure_ndarray_like(chunk) # special case object dtype, because incorrect handling can lead to # segfaults and other bad things happening - if chunk_spec.dtype != object: + if chunk_spec.dtype.dtype_cls is not np.dtypes.ObjectDType: try: - chunk = chunk.view(chunk_spec.dtype) + chunk = chunk.view(chunk_spec.dtype.to_native_dtype()) except TypeError: # this will happen if the dtype of the chunk # does not match the dtype of the array spec i.g. if @@ -56,7 +56,7 @@ async def _decode_single( # is an object array. In this case, we need to convert the object # array to the correct dtype. - chunk = np.array(chunk).astype(chunk_spec.dtype) + chunk = np.array(chunk).astype(chunk_spec.dtype.to_native_dtype()) elif chunk.dtype != object: # If we end up here, someone must have hacked around with the filters. @@ -80,7 +80,7 @@ async def _encode_single( chunk = chunk_array.as_ndarray_like() # ensure contiguous and correct order - chunk = chunk.astype(chunk_spec.dtype, order=chunk_spec.order, copy=False) + chunk = chunk.astype(chunk_spec.dtype.to_native_dtype(), order=chunk_spec.order, copy=False) # apply filters if self.filters: diff --git a/src/zarr/codecs/blosc.py b/src/zarr/codecs/blosc.py index 2fcc041a6b..1c5e52e9a4 100644 --- a/src/zarr/codecs/blosc.py +++ b/src/zarr/codecs/blosc.py @@ -8,10 +8,12 @@ import numcodecs from numcodecs.blosc import Blosc +from packaging.version import Version from zarr.abc.codec import BytesBytesCodec from zarr.core.buffer.cpu import as_numpy_array_wrapper from zarr.core.common import JSON, parse_enum, parse_named_configuration +from zarr.core.dtype.common import HasItemSize from zarr.registry import register_codec if TYPE_CHECKING: @@ -136,14 +138,16 @@ def to_dict(self) -> dict[str, JSON]: } def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self: - dtype = array_spec.dtype + item_size = 1 + if isinstance(array_spec.dtype, HasItemSize): + item_size = array_spec.dtype.item_size new_codec = self if new_codec.typesize is None: - new_codec = replace(new_codec, typesize=dtype.itemsize) + new_codec = replace(new_codec, typesize=item_size) if new_codec.shuffle is None: new_codec = replace( new_codec, - shuffle=(BloscShuffle.bitshuffle if dtype.itemsize == 1 else BloscShuffle.shuffle), + shuffle=(BloscShuffle.bitshuffle if item_size == 1 else BloscShuffle.shuffle), ) return new_codec @@ -163,6 +167,9 @@ def _blosc_codec(self) -> Blosc: "shuffle": map_shuffle_str_to_int[self.shuffle], "blocksize": self.blocksize, } + # See https://github.com/zarr-developers/numcodecs/pull/713 + if Version(numcodecs.__version__) >= Version("0.16.0"): + config_dict["typesize"] = self.typesize return Blosc.from_config(config_dict) async def _decode_single( diff --git a/src/zarr/codecs/bytes.py b/src/zarr/codecs/bytes.py index 750707d36a..d663a3b2cc 100644 --- a/src/zarr/codecs/bytes.py +++ b/src/zarr/codecs/bytes.py @@ -10,6 +10,7 @@ from zarr.abc.codec import ArrayBytesCodec from zarr.core.buffer import Buffer, NDArrayLike, NDBuffer from zarr.core.common import JSON, parse_enum, parse_named_configuration +from zarr.core.dtype.common import HasEndianness from zarr.registry import register_codec if TYPE_CHECKING: @@ -56,7 +57,7 @@ def to_dict(self) -> dict[str, JSON]: return {"name": "bytes", "configuration": {"endian": self.endian.value}} def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self: - if array_spec.dtype.itemsize == 0: + if not isinstance(array_spec.dtype, HasEndianness): if self.endian is not None: return replace(self, endian=None) elif self.endian is None: @@ -71,15 +72,12 @@ async def _decode_single( chunk_spec: ArraySpec, ) -> NDBuffer: assert isinstance(chunk_bytes, Buffer) - if chunk_spec.dtype.itemsize > 0: - if self.endian == Endian.little: - prefix = "<" - else: - prefix = ">" - dtype = np.dtype(f"{prefix}{chunk_spec.dtype.str[1:]}") + # TODO: remove endianness enum in favor of literal union + endian_str = self.endian.value if self.endian is not None else None + if isinstance(chunk_spec.dtype, HasEndianness): + dtype = replace(chunk_spec.dtype, endianness=endian_str).to_native_dtype() # type: ignore[call-arg] else: - dtype = np.dtype(f"|{chunk_spec.dtype.str[1:]}") - + dtype = chunk_spec.dtype.to_native_dtype() as_array_like = chunk_bytes.as_array_like() if isinstance(as_array_like, NDArrayLike): as_nd_array_like = as_array_like diff --git a/src/zarr/codecs/crc32c_.py b/src/zarr/codecs/crc32c_.py index ab8a57eba7..6da673ceac 100644 --- a/src/zarr/codecs/crc32c_.py +++ b/src/zarr/codecs/crc32c_.py @@ -40,7 +40,9 @@ async def _decode_single( inner_bytes = data[:-4] # Need to do a manual cast until https://github.com/numpy/numpy/issues/26783 is resolved - computed_checksum = np.uint32(crc32c(cast(typing_extensions.Buffer, inner_bytes))).tobytes() + computed_checksum = np.uint32( + crc32c(cast("typing_extensions.Buffer", inner_bytes)) + ).tobytes() stored_checksum = bytes(crc32_bytes) if computed_checksum != stored_checksum: raise ValueError( @@ -55,7 +57,7 @@ async def _encode_single( ) -> Buffer | None: data = chunk_bytes.as_numpy_array() # Calculate the checksum and "cast" it to a numpy array - checksum = np.array([crc32c(cast(typing_extensions.Buffer, data))], dtype=np.uint32) + checksum = np.array([crc32c(cast("typing_extensions.Buffer", data))], dtype=np.uint32) # Append the checksum (as bytes) to the data return chunk_spec.prototype.buffer.from_array_like(np.append(data, checksum.view("B"))) diff --git a/src/zarr/codecs/sharding.py b/src/zarr/codecs/sharding.py index 42b1313fac..cd8676b4d1 100644 --- a/src/zarr/codecs/sharding.py +++ b/src/zarr/codecs/sharding.py @@ -43,6 +43,7 @@ parse_shapelike, product, ) +from zarr.core.dtype.npy.int import UInt64 from zarr.core.indexing import ( BasicIndexer, SelectorTuple, @@ -58,6 +59,7 @@ from typing import Self from zarr.core.common import JSON + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType MAX_UINT_64 = 2**64 - 1 ShardMapping = Mapping[ChunkCoords, Buffer] @@ -115,7 +117,7 @@ class _ShardIndex(NamedTuple): def chunks_per_shard(self) -> ChunkCoords: result = tuple(self.offsets_and_lengths.shape[0:-1]) # The cast is required until https://github.com/numpy/numpy/pull/27211 is merged - return cast(ChunkCoords, result) + return cast("ChunkCoords", result) def _localize_chunk(self, chunk_coords: ChunkCoords) -> ChunkCoords: return tuple( @@ -355,7 +357,11 @@ def __init__( object.__setattr__(self, "index_location", index_location_parsed) # Use instance-local lru_cache to avoid memory leaks - object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec)) + + # numpy void scalars are not hashable, which means an array spec with a fill value that is + # a numpy void scalar will break the lru_cache. This is commented for now but should be + # fixed. See https://github.com/zarr-developers/zarr-python/issues/3054 + # object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec)) object.__setattr__(self, "_get_index_chunk_spec", lru_cache()(self._get_index_chunk_spec)) object.__setattr__(self, "_get_chunks_per_shard", lru_cache()(self._get_chunks_per_shard)) @@ -371,7 +377,7 @@ def __setstate__(self, state: dict[str, Any]) -> None: object.__setattr__(self, "index_location", parse_index_location(config["index_location"])) # Use instance-local lru_cache to avoid memory leaks - object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec)) + # object.__setattr__(self, "_get_chunk_spec", lru_cache()(self._get_chunk_spec)) object.__setattr__(self, "_get_index_chunk_spec", lru_cache()(self._get_index_chunk_spec)) object.__setattr__(self, "_get_chunks_per_shard", lru_cache()(self._get_chunks_per_shard)) @@ -402,7 +408,13 @@ def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self: return replace(self, codecs=evolved_codecs) return self - def validate(self, *, shape: ChunkCoords, dtype: np.dtype[Any], chunk_grid: ChunkGrid) -> None: + def validate( + self, + *, + shape: ChunkCoords, + dtype: ZDType[TBaseDType, TBaseScalar], + chunk_grid: ChunkGrid, + ) -> None: if len(self.chunk_shape) != len(shape): raise ValueError( "The shard's `chunk_shape` and array's `shape` need to have the same number of dimensions." @@ -439,7 +451,10 @@ async def _decode_single( # setup output array out = chunk_spec.prototype.nd_buffer.create( - shape=shard_shape, dtype=shard_spec.dtype, order=shard_spec.order, fill_value=0 + shape=shard_shape, + dtype=shard_spec.dtype.to_native_dtype(), + order=shard_spec.order, + fill_value=0, ) shard_dict = await _ShardReader.from_bytes(shard_bytes, self, chunks_per_shard) @@ -483,7 +498,10 @@ async def _decode_partial_single( # setup output array out = shard_spec.prototype.nd_buffer.create( - shape=indexer.shape, dtype=shard_spec.dtype, order=shard_spec.order, fill_value=0 + shape=indexer.shape, + dtype=shard_spec.dtype.to_native_dtype(), + order=shard_spec.order, + fill_value=0, ) indexed_chunks = list(indexer) @@ -678,12 +696,12 @@ def _shard_index_size(self, chunks_per_shard: ChunkCoords) -> int: def _get_index_chunk_spec(self, chunks_per_shard: ChunkCoords) -> ArraySpec: return ArraySpec( shape=chunks_per_shard + (2,), - dtype=np.dtype(" ArraySpec: diff --git a/src/zarr/codecs/transpose.py b/src/zarr/codecs/transpose.py index 1aa1eb40e2..be89690441 100644 --- a/src/zarr/codecs/transpose.py +++ b/src/zarr/codecs/transpose.py @@ -12,10 +12,11 @@ from zarr.registry import register_codec if TYPE_CHECKING: - from typing import Any, Self + from typing import Self from zarr.core.buffer import NDBuffer from zarr.core.chunk_grids import ChunkGrid + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType def parse_transpose_order(data: JSON | Iterable[int]) -> tuple[int, ...]: @@ -23,7 +24,7 @@ def parse_transpose_order(data: JSON | Iterable[int]) -> tuple[int, ...]: raise TypeError(f"Expected an iterable. Got {data} instead.") if not all(isinstance(a, int) for a in data): raise TypeError(f"Expected an iterable of integers. Got {data} instead.") - return tuple(cast(Iterable[int], data)) + return tuple(cast("Iterable[int]", data)) @dataclass(frozen=True) @@ -45,7 +46,12 @@ def from_dict(cls, data: dict[str, JSON]) -> Self: def to_dict(self) -> dict[str, JSON]: return {"name": "transpose", "configuration": {"order": tuple(self.order)}} - def validate(self, shape: tuple[int, ...], dtype: np.dtype[Any], chunk_grid: ChunkGrid) -> None: + def validate( + self, + shape: tuple[int, ...], + dtype: ZDType[TBaseDType, TBaseScalar], + chunk_grid: ChunkGrid, + ) -> None: if len(self.order) != len(shape): raise ValueError( f"The `order` tuple needs have as many entries as there are dimensions in the array. Got {self.order}." diff --git a/src/zarr/codecs/vlen_utf8.py b/src/zarr/codecs/vlen_utf8.py index 0ef423793d..b7c0418b2e 100644 --- a/src/zarr/codecs/vlen_utf8.py +++ b/src/zarr/codecs/vlen_utf8.py @@ -10,7 +10,6 @@ from zarr.abc.codec import ArrayBytesCodec from zarr.core.buffer import Buffer, NDBuffer from zarr.core.common import JSON, parse_named_configuration -from zarr.core.strings import cast_to_string_dtype from zarr.registry import register_codec if TYPE_CHECKING: @@ -49,6 +48,7 @@ def to_dict(self) -> dict[str, JSON]: def evolve_from_array_spec(self, array_spec: ArraySpec) -> Self: return self + # TODO: expand the tests for this function async def _decode_single( self, chunk_bytes: Buffer, @@ -60,8 +60,7 @@ async def _decode_single( decoded = _vlen_utf8_codec.decode(raw_bytes) assert decoded.dtype == np.object_ decoded.shape = chunk_spec.shape - # coming out of the code, we know this is safe, so don't issue a warning - as_string_dtype = cast_to_string_dtype(decoded, safe=True) + as_string_dtype = decoded.astype(chunk_spec.dtype.to_native_dtype(), copy=False) return chunk_spec.prototype.nd_buffer.from_numpy_array(as_string_dtype) async def _encode_single( diff --git a/src/zarr/core/_info.py b/src/zarr/core/_info.py index 845552c8be..d57d17f934 100644 --- a/src/zarr/core/_info.py +++ b/src/zarr/core/_info.py @@ -1,13 +1,15 @@ +from __future__ import annotations + import dataclasses import textwrap -from typing import Any, Literal +from typing import TYPE_CHECKING, Literal -import numcodecs.abc -import numpy as np +if TYPE_CHECKING: + import numcodecs.abc -from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec, BytesBytesCodec -from zarr.core.common import ZarrFormat -from zarr.core.metadata.v3 import DataType + from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec, BytesBytesCodec + from zarr.core.common import ZarrFormat + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType @dataclasses.dataclass(kw_only=True) @@ -67,7 +69,7 @@ def byte_info(size: int) -> str: return f"{size} ({human_readable_size(size)})" -@dataclasses.dataclass(kw_only=True) +@dataclasses.dataclass(kw_only=True, frozen=True, slots=True) class ArrayInfo: """ Visual summary for an Array. @@ -78,7 +80,8 @@ class ArrayInfo: _type: Literal["Array"] = "Array" _zarr_format: ZarrFormat - _data_type: np.dtype[Any] | DataType + _data_type: ZDType[TBaseDType, TBaseScalar] + _fill_value: object _shape: tuple[int, ...] _shard_shape: tuple[int, ...] | None = None _chunk_shape: tuple[int, ...] | None = None @@ -97,6 +100,7 @@ def __repr__(self) -> str: Type : {_type} Zarr format : {_zarr_format} Data type : {_data_type} + Fill value : {_fill_value} Shape : {_shape}""") if self._shard_shape is not None: diff --git a/src/zarr/core/array.py b/src/zarr/core/array.py index f2c88c508b..cd6b33a28c 100644 --- a/src/zarr/core/array.py +++ b/src/zarr/core/array.py @@ -22,7 +22,6 @@ import numcodecs import numcodecs.abc import numpy as np -import numpy.typing as npt from typing_extensions import deprecated import zarr @@ -30,6 +29,7 @@ from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec, BytesBytesCodec, Codec from zarr.abc.store import Store, set_or_delete from zarr.codecs._v2 import V2Codec +from zarr.codecs.bytes import BytesCodec from zarr.core._info import ArrayInfo from zarr.core.array_spec import ArrayConfig, ArrayConfigLike, parse_array_config from zarr.core.attributes import Attributes @@ -54,18 +54,25 @@ ZARRAY_JSON, ZATTRS_JSON, ChunkCoords, + DimensionNames, MemoryOrder, ShapeLike, ZarrFormat, _default_zarr_format, _warn_order_kwarg, concurrent_map, - parse_dtype, parse_order, parse_shapelike, product, ) +from zarr.core.config import categorize_data_type from zarr.core.config import config as zarr_config +from zarr.core.dtype import ( + ZDType, + ZDTypeLike, + parse_data_type, +) +from zarr.core.dtype.common import HasEndianness, HasItemSize from zarr.core.indexing import ( BasicIndexer, BasicSelection, @@ -101,12 +108,11 @@ T_ArrayMetadata, ) from zarr.core.metadata.v2 import ( - _default_compressor, - _default_filters, + CompressorLikev2, parse_compressor, parse_filters, ) -from zarr.core.metadata.v3 import DataType, parse_node_type_array +from zarr.core.metadata.v3 import parse_node_type_array from zarr.core.sync import sync from zarr.errors import MetadataValidationError from zarr.registry import ( @@ -116,13 +122,17 @@ get_pipeline_class, ) from zarr.storage._common import StorePath, ensure_no_existing_node, make_store_path +from zarr.storage._utils import _relativize_path if TYPE_CHECKING: from collections.abc import Iterator, Sequence from typing import Self + import numpy.typing as npt + from zarr.abc.codec import CodecPipeline from zarr.codecs.sharding import ShardingCodecIndexLocation + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar from zarr.core.group import AsyncGroup from zarr.storage import StoreLike @@ -137,7 +147,8 @@ def parse_array_metadata(data: Any) -> ArrayMetadata: if isinstance(data, ArrayMetadata): return data elif isinstance(data, dict): - if data["zarr_format"] == 3: + zarr_format = data.get("zarr_format") + if zarr_format == 3: meta_out = ArrayV3Metadata.from_dict(data) if len(meta_out.storage_transformers) > 0: msg = ( @@ -146,9 +157,11 @@ def parse_array_metadata(data: Any) -> ArrayMetadata: ) raise ValueError(msg) return meta_out - elif data["zarr_format"] == 2: + elif zarr_format == 2: return ArrayV2Metadata.from_dict(data) - raise TypeError + else: + raise ValueError(f"Invalid zarr_format: {zarr_format}. Expected 2 or 3") + raise TypeError # pragma: no cover def create_codec_pipeline(metadata: ArrayMetadata) -> CodecPipeline: @@ -157,8 +170,7 @@ def create_codec_pipeline(metadata: ArrayMetadata) -> CodecPipeline: elif isinstance(metadata, ArrayV2Metadata): v2_codec = V2Codec(filters=metadata.filters, compressor=metadata.compressor) return get_pipeline_class().from_codecs([v2_codec]) - else: - raise TypeError + raise TypeError # pragma: no cover async def get_array_metadata( @@ -265,17 +277,6 @@ def __init__( store_path: StorePath, config: ArrayConfigLike | None = None, ) -> None: - if isinstance(metadata, dict): - zarr_format = metadata["zarr_format"] - # TODO: remove this when we extensively type the dict representation of metadata - _metadata = cast(dict[str, JSON], metadata) - if zarr_format == 2: - metadata = ArrayV2Metadata.from_dict(_metadata) - elif zarr_format == 3: - metadata = ArrayV3Metadata.from_dict(_metadata) - else: - raise ValueError(f"Invalid zarr_format: {zarr_format}. Expected 2 or 3") - metadata_parsed = parse_array_metadata(metadata) config_parsed = parse_array_config(config) @@ -293,7 +294,7 @@ async def create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: Literal[2], fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -301,7 +302,7 @@ async def create( dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLikev2 | Literal["auto"] = "auto", # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -317,7 +318,7 @@ async def create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: Literal[3], fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -330,7 +331,7 @@ async def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -345,7 +346,7 @@ async def create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: Literal[3] = 3, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -358,7 +359,7 @@ async def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -373,7 +374,7 @@ async def create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: ZarrFormat, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -386,13 +387,13 @@ async def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # v2 only chunks: ShapeLike | None = None, dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLike = "auto", # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -408,7 +409,7 @@ async def create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: ZarrFormat = 3, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -421,13 +422,13 @@ async def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # v2 only chunks: ShapeLike | None = None, dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLike = "auto", # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -444,7 +445,7 @@ async def create( The store where the array will be created. shape : ShapeLike The shape of the array. - dtype : npt.DTypeLike + dtype : ZDTypeLike The data type of the array. zarr_format : ZarrFormat, optional The Zarr format version (default is 3). @@ -473,7 +474,7 @@ async def create( These defaults can be changed by modifying the value of ``array.v3_default_filters``, ``array.v3_default_serializer`` and ``array.v3_default_compressors`` in :mod:`zarr.core.config`. - dimension_names : Iterable[str], optional + dimension_names : Iterable[str | None], optional The names of the dimensions (default is None). Zarr format 3 only. Zarr format 2 arrays should not use this parameter. chunks : ShapeLike, optional @@ -549,7 +550,7 @@ async def _create( *, # v2 and v3 shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike | ZDType[TBaseDType, TBaseScalar], zarr_format: ZarrFormat = 3, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -562,13 +563,13 @@ async def _create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # v2 only chunks: ShapeLike | None = None, dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLike = "auto", # runtime overwrite: bool = False, data: npt.ArrayLike | None = None, @@ -578,18 +579,21 @@ async def _create( See :func:`AsyncArray.create` for more details. Deprecated in favor of :func:`zarr.api.asynchronous.create_array`. """ + + dtype_parsed = parse_data_type(dtype, zarr_format=zarr_format) store_path = await make_store_path(store) - dtype_parsed = parse_dtype(dtype, zarr_format) shape = parse_shapelike(shape) if chunks is not None and chunk_shape is not None: raise ValueError("Only one of chunk_shape or chunks can be provided.") - + item_size = 1 + if isinstance(dtype_parsed, HasItemSize): + item_size = dtype_parsed.item_size if chunks: - _chunks = normalize_chunks(chunks, shape, dtype_parsed.itemsize) + _chunks = normalize_chunks(chunks, shape, item_size) else: - _chunks = normalize_chunks(chunk_shape, shape, dtype_parsed.itemsize) + _chunks = normalize_chunks(chunk_shape, shape, item_size) config_parsed = parse_array_config(config) result: AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata] @@ -602,13 +606,14 @@ async def _create( raise ValueError( "filters cannot be used for arrays with zarr_format 3. Use array-to-array codecs instead." ) - if compressor is not None: + if compressor != "auto": raise ValueError( "compressor cannot be used for arrays with zarr_format 3. Use bytes-to-bytes codecs instead." ) if order is not None: _warn_order_kwarg() + config_parsed = replace(config_parsed, order=order) result = await cls._create_v3( store_path, @@ -666,41 +671,47 @@ async def _create( @staticmethod def _create_metadata_v3( shape: ShapeLike, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], chunk_shape: ChunkCoords, fill_value: Any | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, attributes: dict[str, JSON] | None = None, ) -> ArrayV3Metadata: """ Create an instance of ArrayV3Metadata. """ + filters: tuple[ArrayArrayCodec, ...] + compressors: tuple[BytesBytesCodec, ...] shape = parse_shapelike(shape) - codecs = list(codecs) if codecs is not None else _get_default_codecs(np.dtype(dtype)) + if codecs is None: + filters, serializer, compressors = _get_default_chunk_encoding_v3(dtype) + codecs_parsed = (*filters, serializer, *compressors) + else: + codecs_parsed = tuple(codecs) + chunk_key_encoding_parsed: ChunkKeyEncodingLike if chunk_key_encoding is None: chunk_key_encoding_parsed = {"name": "default", "separator": "/"} else: chunk_key_encoding_parsed = chunk_key_encoding - if dtype.kind in "UTS": - warn( - f"The dtype `{dtype}` is currently not part in the Zarr format 3 specification. It " - "may not be supported by other zarr implementations and may change in the future.", - category=UserWarning, - stacklevel=2, - ) + if fill_value is None: + # v3 spec will not allow a null fill value + fill_value_parsed = dtype.default_scalar() + else: + fill_value_parsed = fill_value + chunk_grid_parsed = RegularChunkGrid(chunk_shape=chunk_shape) return ArrayV3Metadata( shape=shape, data_type=dtype, chunk_grid=chunk_grid_parsed, chunk_key_encoding=chunk_key_encoding_parsed, - fill_value=fill_value, - codecs=codecs, + fill_value=fill_value_parsed, + codecs=codecs_parsed, # type: ignore[arg-type] dimension_names=tuple(dimension_names) if dimension_names else None, attributes=attributes or {}, ) @@ -711,7 +722,7 @@ async def _create_v3( store_path: StorePath, *, shape: ShapeLike, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], chunk_shape: ChunkCoords, config: ArrayConfig, fill_value: Any | None = None, @@ -722,7 +733,7 @@ async def _create_v3( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, attributes: dict[str, JSON] | None = None, overwrite: bool = False, ) -> AsyncArray[ArrayV3Metadata]: @@ -759,31 +770,22 @@ async def _create_v3( @staticmethod def _create_metadata_v2( shape: ChunkCoords, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], chunks: ChunkCoords, order: MemoryOrder, dimension_separator: Literal[".", "/"] | None = None, fill_value: float | None = None, filters: Iterable[dict[str, JSON] | numcodecs.abc.Codec] | None = None, - compressor: dict[str, JSON] | numcodecs.abc.Codec | None = None, + compressor: CompressorLikev2 = None, attributes: dict[str, JSON] | None = None, ) -> ArrayV2Metadata: if dimension_separator is None: dimension_separator = "." - - dtype = parse_dtype(dtype, zarr_format=2) - - # inject VLenUTF8 for str dtype if not already present - if np.issubdtype(dtype, np.str_): - filters = filters or [] - from numcodecs.vlen import VLenUTF8 - - if not any(isinstance(x, VLenUTF8) or x["id"] == "vlen-utf8" for x in filters): - filters = list(filters) + [VLenUTF8()] - + if fill_value is None: + fill_value = dtype.default_scalar() # type: ignore[assignment] return ArrayV2Metadata( shape=shape, - dtype=np.dtype(dtype), + dtype=dtype, chunks=chunks, order=order, dimension_separator=dimension_separator, @@ -799,14 +801,14 @@ async def _create_v2( store_path: StorePath, *, shape: ChunkCoords, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], chunks: ChunkCoords, order: MemoryOrder, config: ArrayConfig, dimension_separator: Literal[".", "/"] | None = None, fill_value: float | None = None, filters: Iterable[dict[str, JSON] | numcodecs.abc.Codec] | None = None, - compressor: dict[str, JSON] | numcodecs.abc.Codec | None = None, + compressor: CompressorLike = "auto", attributes: dict[str, JSON] | None = None, overwrite: bool = False, ) -> AsyncArray[ArrayV2Metadata]: @@ -818,6 +820,17 @@ async def _create_v2( else: await ensure_no_existing_node(store_path, zarr_format=2) + compressor_parsed: CompressorLikev2 + if compressor == "auto": + _, compressor_parsed = _get_default_chunk_encoding_v2(dtype) + elif isinstance(compressor, BytesBytesCodec): + raise ValueError( + "Cannot use a BytesBytesCodec as a compressor for zarr v2 arrays. " + "Use a numcodecs codec directly instead." + ) + else: + compressor_parsed = compressor + metadata = cls._create_metadata_v2( shape=shape, dtype=dtype, @@ -826,7 +839,7 @@ async def _create_v2( dimension_separator=dimension_separator, fill_value=fill_value, filters=filters, - compressor=compressor, + compressor=compressor_parsed, attributes=attributes, ) @@ -897,7 +910,7 @@ async def open( store_path = await make_store_path(store) metadata_dict = await get_array_metadata(store_path, zarr_format=zarr_format) # TODO: remove this cast when we have better type hints - _metadata_dict = cast(ArrayV3MetadataDict, metadata_dict) + _metadata_dict = cast("ArrayV3MetadataDict", metadata_dict) return cls(store_path=store_path, metadata=_metadata_dict) @property @@ -1025,7 +1038,17 @@ def compressors(self) -> tuple[numcodecs.abc.Codec, ...] | tuple[BytesBytesCodec ) @property - def dtype(self) -> np.dtype[Any]: + def _zdtype(self) -> ZDType[TBaseDType, TBaseScalar]: + """ + The zarr-specific representation of the array data type + """ + if self.metadata.zarr_format == 2: + return self.metadata.dtype + else: + return self.metadata.data_type + + @property + def dtype(self) -> TBaseDType: """Returns the data type of the array. Returns @@ -1033,7 +1056,7 @@ def dtype(self) -> np.dtype[Any]: np.dtype Data type of the array """ - return self.metadata.dtype + return self._zdtype.to_native_dtype() @property def order(self) -> MemoryOrder: @@ -1044,7 +1067,10 @@ def order(self) -> MemoryOrder: bool Memory order of the array """ - return self._config.order + if self.metadata.zarr_format == 2: + return self.metadata.order + else: + return self._config.order @property def attrs(self) -> dict[str, JSON]: @@ -1276,14 +1302,14 @@ async def _get_selection( out_buffer = prototype.nd_buffer.create( shape=indexer.shape, dtype=out_dtype, - order=self._config.order, + order=self.order, fill_value=self.metadata.fill_value, ) if product(indexer.shape) > 0: # need to use the order from the metadata for v2 _config = self._config if self.metadata.zarr_format == 2: - _config = replace(_config, order=self.metadata.order) + _config = replace(_config, order=self.order) # reading chunks and decoding them await self.codec_pipeline.read( @@ -1390,21 +1416,22 @@ async def _set_selection( if isinstance(array_like, np._typing._SupportsArrayFunc): # TODO: need to handle array types that don't support __array_function__ # like PyTorch and JAX - array_like_ = cast(np._typing._SupportsArrayFunc, array_like) - value = np.asanyarray(value, dtype=self.metadata.dtype, like=array_like_) + array_like_ = cast("np._typing._SupportsArrayFunc", array_like) + value = np.asanyarray(value, dtype=self.dtype, like=array_like_) else: if not hasattr(value, "shape"): - value = np.asarray(value, self.metadata.dtype) + value = np.asarray(value, self.dtype) # assert ( # value.shape == indexer.shape # ), f"shape of value doesn't match indexer shape. Expected {indexer.shape}, got {value.shape}" - if not hasattr(value, "dtype") or value.dtype.name != self.metadata.dtype.name: + if not hasattr(value, "dtype") or value.dtype.name != self.dtype.name: if hasattr(value, "astype"): # Handle things that are already NDArrayLike more efficiently - value = value.astype(dtype=self.metadata.dtype, order="A") + value = value.astype(dtype=self.dtype, order="A") else: - value = np.array(value, dtype=self.metadata.dtype, order="A") - value = cast(NDArrayLike, value) + value = np.array(value, dtype=self.dtype, order="A") + value = cast("NDArrayLike", value) + # We accept any ndarray like object from the user and convert it # to a NDBuffer (or subclass). From this point onwards, we only pass # Buffer and NDBuffer between components. @@ -1684,15 +1711,10 @@ async def info_complete(self) -> Any: def _info( self, count_chunks_initialized: int | None = None, count_bytes_stored: int | None = None ) -> Any: - _data_type: np.dtype[Any] | DataType - if isinstance(self.metadata, ArrayV2Metadata): - _data_type = self.metadata.dtype - else: - _data_type = self.metadata.data_type - return ArrayInfo( _zarr_format=self.metadata.zarr_format, - _data_type=_data_type, + _data_type=self._zdtype, + _fill_value=self.metadata.fill_value, _shape=self.shape, _order=self.order, _shard_shape=self.shards, @@ -1711,7 +1733,9 @@ def _info( # TODO: Array can be a frozen data class again once property setters (e.g. shape) are removed @dataclass(frozen=False) class Array: - """Instantiate an array from an initialized store.""" + """ + A Zarr array. + """ _async_array: AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata] @@ -1724,7 +1748,7 @@ def create( *, # v2 and v3 shape: ChunkCoords, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: ZarrFormat = 3, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -1737,13 +1761,13 @@ def create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # v2 only chunks: ChunkCoords | None = None, dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLike = "auto", # runtime overwrite: bool = False, config: ArrayConfigLike | None = None, @@ -1759,7 +1783,7 @@ def create( The array store that has already been initialized. shape : ChunkCoords The shape of the array. - dtype : npt.DTypeLike + dtype : ZDTypeLike The data type of the array. chunk_shape : ChunkCoords, optional The shape of the Array's chunks. @@ -1782,7 +1806,7 @@ def create( These defaults can be changed by modifying the value of ``array.v3_default_filters``, ``array.v3_default_serializer`` and ``array.v3_default_compressors`` in :mod:`zarr.core.config`. - dimension_names : Iterable[str], optional + dimension_names : Iterable[str | None], optional The names of the dimensions (default is None). Zarr format 3 only. Zarr format 2 arrays should not use this parameter. chunks : ChunkCoords, optional @@ -1853,7 +1877,7 @@ def _create( *, # v2 and v3 shape: ChunkCoords, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, zarr_format: ZarrFormat = 3, fill_value: Any | None = None, attributes: dict[str, JSON] | None = None, @@ -1866,13 +1890,13 @@ def _create( | None ) = None, codecs: Iterable[Codec | dict[str, JSON]] | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, # v2 only chunks: ChunkCoords | None = None, dimension_separator: Literal[".", "/"] | None = None, order: MemoryOrder | None = None, filters: list[dict[str, JSON]] | None = None, - compressor: dict[str, JSON] | None = None, + compressor: CompressorLike = "auto", # runtime overwrite: bool = False, config: ArrayConfigLike | None = None, @@ -2425,11 +2449,11 @@ def __getitem__(self, selection: Selection) -> NDArrayLikeOrScalar: """ fields, pure_selection = pop_fields(selection) if is_pure_fancy_indexing(pure_selection, self.ndim): - return self.vindex[cast(CoordinateSelection | MaskSelection, selection)] + return self.vindex[cast("CoordinateSelection | MaskSelection", selection)] elif is_pure_orthogonal_indexing(pure_selection, self.ndim): return self.get_orthogonal_selection(pure_selection, fields=fields) else: - return self.get_basic_selection(cast(BasicSelection, pure_selection), fields=fields) + return self.get_basic_selection(cast("BasicSelection", pure_selection), fields=fields) def __setitem__(self, selection: Selection, value: npt.ArrayLike) -> None: """Modify data for an item or region of the array. @@ -2524,11 +2548,11 @@ def __setitem__(self, selection: Selection, value: npt.ArrayLike) -> None: """ fields, pure_selection = pop_fields(selection) if is_pure_fancy_indexing(pure_selection, self.ndim): - self.vindex[cast(CoordinateSelection | MaskSelection, selection)] = value + self.vindex[cast("CoordinateSelection | MaskSelection", selection)] = value elif is_pure_orthogonal_indexing(pure_selection, self.ndim): self.set_orthogonal_selection(pure_selection, value, fields=fields) else: - self.set_basic_selection(cast(BasicSelection, pure_selection), value, fields=fields) + self.set_basic_selection(cast("BasicSelection", pure_selection), value, fields=fields) @_deprecate_positional_args def get_basic_selection( @@ -3646,7 +3670,7 @@ def update_attributes(self, new_attributes: dict[str, JSON]) -> Array: # TODO: remove this cast when type inference improves new_array = sync(self._async_array.update_attributes(new_attributes)) # TODO: remove this cast when type inference improves - _new_array = cast(AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata], new_array) + _new_array = cast("AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata]", new_array) return type(self)(_new_array) def __repr__(self) -> str: @@ -3730,7 +3754,12 @@ async def chunks_initialized( store_contents = [ x async for x in array.store_path.store.list_prefix(prefix=array.store_path.path) ] - return tuple(chunk_key for chunk_key in array._iter_chunk_keys() if chunk_key in store_contents) + store_contents_relative = [ + _relativize_path(path=key, prefix=array.store_path.path) for key in store_contents + ] + return tuple( + chunk_key for chunk_key in array._iter_chunk_keys() if chunk_key in store_contents_relative + ) def _build_parents( @@ -3764,13 +3793,6 @@ def _build_parents( return parents -def _get_default_codecs( - np_dtype: np.dtype[Any], -) -> tuple[Codec, ...]: - filters, serializer, compressors = _get_default_chunk_encoding_v3(np_dtype) - return filters + (serializer,) + compressors - - FiltersLike: TypeAlias = ( Iterable[dict[str, JSON] | ArrayArrayCodec | numcodecs.abc.Codec] | ArrayArrayCodec @@ -3779,7 +3801,11 @@ def _get_default_codecs( | Literal["auto"] | None ) -CompressorLike: TypeAlias = dict[str, JSON] | BytesBytesCodec | numcodecs.abc.Codec | None +# Union of acceptable types for users to pass in for both v2 and v3 compressors +CompressorLike: TypeAlias = ( + dict[str, JSON] | BytesBytesCodec | numcodecs.abc.Codec | Literal["auto"] | None +) + CompressorsLike: TypeAlias = ( Iterable[dict[str, JSON] | BytesBytesCodec | numcodecs.abc.Codec] | dict[str, JSON] @@ -3815,7 +3841,7 @@ async def from_array( zarr_format: ZarrFormat | None = None, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfig | ArrayConfigLike | None = None, @@ -3923,7 +3949,7 @@ async def from_array( For Zarr format 2, the default is ``{"name": "v2", "separator": "."}}``. If not specified and the data array has the same zarr format as the target array, the chunk key encoding of the data array is used. - dimension_names : Iterable[str], optional + dimension_names : Iterable[str | None], optional The names of the dimensions (default is None). Zarr format 3 only. Zarr format 2 arrays should not use this parameter. If not specified, defaults to the dimension names of the data array. @@ -4066,7 +4092,7 @@ async def init_array( *, store_path: StorePath, shape: ShapeLike, - dtype: npt.DTypeLike, + dtype: ZDTypeLike, chunks: ChunkCoords | Literal["auto"] = "auto", shards: ShardsLike | None = None, filters: FiltersLike = "auto", @@ -4077,7 +4103,7 @@ async def init_array( zarr_format: ZarrFormat | None = 3, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, overwrite: bool = False, config: ArrayConfigLike | None, ) -> AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata]: @@ -4089,7 +4115,7 @@ async def init_array( StorePath instance. The path attribute is the name of the array to initialize. shape : ChunkCoords Shape of the array. - dtype : npt.DTypeLike + dtype : ZDTypeLike Data type of the array. chunks : ChunkCoords, optional Chunk shape of the array. @@ -4173,7 +4199,7 @@ async def init_array( from zarr.codecs.sharding import ShardingCodec, ShardingCodecIndexLocation - dtype_parsed = parse_dtype(dtype, zarr_format=zarr_format) + zdtype = parse_data_type(dtype, zarr_format=zarr_format) shape_parsed = parse_shapelike(shape) chunk_key_encoding_parsed = _parse_chunk_key_encoding( chunk_key_encoding, zarr_format=zarr_format @@ -4187,8 +4213,15 @@ async def init_array( else: await ensure_no_existing_node(store_path, zarr_format=zarr_format) + item_size = 1 + if isinstance(zdtype, HasItemSize): + item_size = zdtype.item_size + shard_shape_parsed, chunk_shape_parsed = _auto_partition( - array_shape=shape_parsed, shard_shape=shards, chunk_shape=chunks, dtype=dtype_parsed + array_shape=shape_parsed, + shard_shape=shards, + chunk_shape=chunks, + item_size=item_size, ) chunks_out: tuple[int, ...] meta: ArrayV2Metadata | ArrayV3Metadata @@ -4204,9 +4237,8 @@ async def init_array( raise ValueError("Zarr format 2 arrays do not support `serializer`.") filters_parsed, compressor_parsed = _parse_chunk_encoding_v2( - compressor=compressors, filters=filters, dtype=np.dtype(dtype) + compressor=compressors, filters=filters, dtype=zdtype ) - if dimension_names is not None: raise ValueError("Zarr format 2 arrays do not support dimension names.") if order is None: @@ -4216,7 +4248,7 @@ async def init_array( meta = AsyncArray._create_metadata_v2( shape=shape_parsed, - dtype=dtype_parsed, + dtype=zdtype, chunks=chunk_shape_parsed, dimension_separator=chunk_key_encoding_parsed.separator, fill_value=fill_value, @@ -4230,9 +4262,9 @@ async def init_array( compressors=compressors, filters=filters, serializer=serializer, - dtype=dtype_parsed, + dtype=zdtype, ) - sub_codecs = cast(tuple[Codec, ...], (*array_array, array_bytes, *bytes_bytes)) + sub_codecs = cast("tuple[Codec, ...]", (*array_array, array_bytes, *bytes_bytes)) codecs_out: tuple[Codec, ...] if shard_shape_parsed is not None: index_location = None @@ -4245,7 +4277,7 @@ async def init_array( ) sharding_codec.validate( shape=chunk_shape_parsed, - dtype=dtype_parsed, + dtype=zdtype, chunk_grid=RegularChunkGrid(chunk_shape=shard_shape_parsed), ) codecs_out = (sharding_codec,) @@ -4254,9 +4286,14 @@ async def init_array( chunks_out = chunk_shape_parsed codecs_out = sub_codecs + if config is None: + config = {} + if order is not None and isinstance(config, dict): + config["order"] = config.get("order", order) + meta = AsyncArray._create_metadata_v3( shape=shape_parsed, - dtype=dtype_parsed, + dtype=zdtype, fill_value=fill_value, chunk_shape=chunks_out, chunk_key_encoding=chunk_key_encoding_parsed, @@ -4275,7 +4312,7 @@ async def create_array( *, name: str | None = None, shape: ShapeLike | None = None, - dtype: npt.DTypeLike | None = None, + dtype: ZDTypeLike | None = None, data: np.ndarray[Any, np.dtype[Any]] | None = None, chunks: ChunkCoords | Literal["auto"] = "auto", shards: ShardsLike | None = None, @@ -4287,7 +4324,7 @@ async def create_array( zarr_format: ZarrFormat | None = 3, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfigLike | None = None, @@ -4304,7 +4341,7 @@ async def create_array( at the root of the store. shape : ChunkCoords, optional Shape of the array. Can be ``None`` if ``data`` is provided. - dtype : npt.DTypeLike | None + dtype : ZDTypeLike | None Data type of the array. Can be ``None`` if ``data`` is provided. data : Array-like data to use for initializing the array. If this parameter is provided, the ``shape`` and ``dtype`` parameters must be identical to ``data.shape`` and ``data.dtype``, @@ -4466,7 +4503,7 @@ def _parse_keep_array_attr( order: MemoryOrder | None, zarr_format: ZarrFormat | None, chunk_key_encoding: ChunkKeyEncodingLike | None, - dimension_names: Iterable[str] | None, + dimension_names: DimensionNames, ) -> tuple[ ChunkCoords | Literal["auto"], ShardsLike | None, @@ -4477,7 +4514,7 @@ def _parse_keep_array_attr( MemoryOrder | None, ZarrFormat, ChunkKeyEncodingLike | None, - Iterable[str] | None, + DimensionNames, ]: if isinstance(data, Array): if chunks == "keep": @@ -4498,7 +4535,7 @@ def _parse_keep_array_attr( compressors = "auto" if serializer == "keep": if zarr_format == 3 and data.metadata.zarr_format == 3: - serializer = cast(SerializerLike, data.serializer) + serializer = cast("SerializerLike", data.serializer) else: serializer = "auto" if fill_value is None: @@ -4564,62 +4601,50 @@ def _parse_chunk_key_encoding( def _get_default_chunk_encoding_v3( - np_dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], ) -> tuple[tuple[ArrayArrayCodec, ...], ArrayBytesCodec, tuple[BytesBytesCodec, ...]]: """ Get the default ArrayArrayCodecs, ArrayBytesCodec, and BytesBytesCodec for a given dtype. """ - dtype = DataType.from_numpy(np_dtype) - if dtype == DataType.string: - dtype_key = "string" - elif dtype == DataType.bytes: - dtype_key = "bytes" - else: - dtype_key = "numeric" - default_filters = zarr_config.get("array.v3_default_filters").get(dtype_key) - default_serializer = zarr_config.get("array.v3_default_serializer").get(dtype_key) - default_compressors = zarr_config.get("array.v3_default_compressors").get(dtype_key) + dtype_category = categorize_data_type(dtype) - filters = tuple(_parse_array_array_codec(codec_dict) for codec_dict in default_filters) - serializer = _parse_array_bytes_codec(default_serializer) - compressors = tuple(_parse_bytes_bytes_codec(codec_dict) for codec_dict in default_compressors) + filters = zarr_config.get("array.v3_default_filters").get(dtype_category) + compressors = zarr_config.get("array.v3_default_compressors").get(dtype_category) + serializer = zarr_config.get("array.v3_default_serializer").get(dtype_category) - return filters, serializer, compressors + return ( + tuple(_parse_array_array_codec(f) for f in filters), + _parse_array_bytes_codec(serializer), + tuple(_parse_bytes_bytes_codec(c) for c in compressors), + ) def _get_default_chunk_encoding_v2( - np_dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], ) -> tuple[tuple[numcodecs.abc.Codec, ...] | None, numcodecs.abc.Codec | None]: """ Get the default chunk encoding for Zarr format 2 arrays, given a dtype """ + dtype_category = categorize_data_type(dtype) + filters = zarr_config.get("array.v2_default_filters").get(dtype_category) + compressor = zarr_config.get("array.v2_default_compressor").get(dtype_category) + if filters is not None: + filters = tuple(numcodecs.get_codec(f) for f in filters) - compressor_dict = _default_compressor(np_dtype) - filter_dicts = _default_filters(np_dtype) - - compressor = None - if compressor_dict is not None: - compressor = numcodecs.get_codec(compressor_dict) - - filters = None - if filter_dicts is not None: - filters = tuple(numcodecs.get_codec(f) for f in filter_dicts) - - return filters, compressor + return filters, numcodecs.get_codec(compressor) def _parse_chunk_encoding_v2( *, compressor: CompressorsLike, filters: FiltersLike, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], ) -> tuple[tuple[numcodecs.abc.Codec, ...] | None, numcodecs.abc.Codec | None]: """ Generate chunk encoding classes for Zarr format 2 arrays with optional defaults. """ default_filters, default_compressor = _get_default_chunk_encoding_v2(dtype) - _filters: tuple[numcodecs.abc.Codec, ...] | None _compressor: numcodecs.abc.Codec | None @@ -4658,7 +4683,7 @@ def _parse_chunk_encoding_v3( compressors: CompressorsLike, filters: FiltersLike, serializer: SerializerLike, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], ) -> tuple[tuple[ArrayArrayCodec, ...], ArrayBytesCodec, tuple[BytesBytesCodec, ...]]: """ Generate chunk encoding classes for v3 arrays with optional defaults. @@ -4676,12 +4701,15 @@ def _parse_chunk_encoding_v3( if isinstance(filters, dict | Codec): maybe_array_array = (filters,) else: - maybe_array_array = cast(Iterable[Codec | dict[str, JSON]], filters) + maybe_array_array = cast("Iterable[Codec | dict[str, JSON]]", filters) out_array_array = tuple(_parse_array_array_codec(c) for c in maybe_array_array) if serializer == "auto": out_array_bytes = default_array_bytes else: + # TODO: ensure that the serializer is compatible with the ndarray produced by the + # array-array codecs. For example, if a sequence of array-array codecs produces an + # array with a single-byte data type, then the serializer should not specify endiannesss. out_array_bytes = _parse_array_bytes_codec(serializer) if compressors is None: @@ -4693,10 +4721,21 @@ def _parse_chunk_encoding_v3( if isinstance(compressors, dict | Codec): maybe_bytes_bytes = (compressors,) else: - maybe_bytes_bytes = cast(Iterable[Codec | dict[str, JSON]], compressors) + maybe_bytes_bytes = cast("Iterable[Codec | dict[str, JSON]]", compressors) out_bytes_bytes = tuple(_parse_bytes_bytes_codec(c) for c in maybe_bytes_bytes) + # specialize codecs as needed given the dtype + + # TODO: refactor so that the config only contains the name of the codec, and we use the dtype + # to create the codec instance, instead of storing a dict representation of a full codec. + + # TODO: ensure that the serializer is compatible with the ndarray produced by the + # array-array codecs. For example, if a sequence of array-array codecs produces an + # array with a single-byte data type, then the serializer should not specify endiannesss. + if isinstance(out_array_bytes, BytesCodec) and not isinstance(dtype, HasEndianness): + # The default endianness in the bytescodec might not be None, so we need to replace it + out_array_bytes = replace(out_array_bytes, endian=None) return out_array_array, out_array_bytes, out_bytes_bytes @@ -4726,8 +4765,8 @@ def _parse_data_params( *, data: np.ndarray[Any, np.dtype[Any]] | None, shape: ShapeLike | None, - dtype: npt.DTypeLike | None, -) -> tuple[np.ndarray[Any, np.dtype[Any]] | None, ShapeLike, npt.DTypeLike]: + dtype: ZDTypeLike | None, +) -> tuple[np.ndarray[Any, np.dtype[Any]] | None, ShapeLike, ZDTypeLike]: """ Ensure an array-like ``data`` parameter is consistent with the ``dtype`` and ``shape`` parameters. diff --git a/src/zarr/core/array_spec.py b/src/zarr/core/array_spec.py index 59d3cc6b40..279bf6edf0 100644 --- a/src/zarr/core/array_spec.py +++ b/src/zarr/core/array_spec.py @@ -3,8 +3,6 @@ from dataclasses import dataclass, fields from typing import TYPE_CHECKING, Any, Literal, Self, TypedDict, cast -import numpy as np - from zarr.core.common import ( MemoryOrder, parse_bool, @@ -19,6 +17,7 @@ from zarr.core.buffer import BufferPrototype from zarr.core.common import ChunkCoords + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType class ArrayConfigParams(TypedDict): @@ -64,7 +63,7 @@ def from_dict(cls, data: ArrayConfigParams) -> Self: """ kwargs_out: ArrayConfigParams = {} for f in fields(ArrayConfig): - field_name = cast(Literal["order", "write_empty_chunks"], f.name) + field_name = cast("Literal['order', 'write_empty_chunks']", f.name) if field_name not in data: kwargs_out[field_name] = zarr_config.get(f"array.{field_name}") else: @@ -90,7 +89,7 @@ def parse_array_config(data: ArrayConfigLike | None) -> ArrayConfig: @dataclass(frozen=True) class ArraySpec: shape: ChunkCoords - dtype: np.dtype[Any] + dtype: ZDType[TBaseDType, TBaseScalar] fill_value: Any config: ArrayConfig prototype: BufferPrototype @@ -98,17 +97,16 @@ class ArraySpec: def __init__( self, shape: ChunkCoords, - dtype: np.dtype[Any], + dtype: ZDType[TBaseDType, TBaseScalar], fill_value: Any, config: ArrayConfig, prototype: BufferPrototype, ) -> None: shape_parsed = parse_shapelike(shape) - dtype_parsed = np.dtype(dtype) fill_value_parsed = parse_fill_value(fill_value) object.__setattr__(self, "shape", shape_parsed) - object.__setattr__(self, "dtype", dtype_parsed) + object.__setattr__(self, "dtype", dtype) object.__setattr__(self, "fill_value", fill_value_parsed) object.__setattr__(self, "config", config) object.__setattr__(self, "prototype", prototype) diff --git a/src/zarr/core/buffer/core.py b/src/zarr/core/buffer/core.py index 1318f868a0..0e24c5b326 100644 --- a/src/zarr/core/buffer/core.py +++ b/src/zarr/core/buffer/core.py @@ -159,7 +159,7 @@ def create_zero_length(cls) -> Self: if cls is Buffer: raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'") return cls( - cast(ArrayLike, None) + cast("ArrayLike", None) ) # This line will never be reached, but it satisfies the type checker @classmethod @@ -207,7 +207,7 @@ def from_buffer(cls, buffer: Buffer) -> Self: if cls is Buffer: raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'") return cls( - cast(ArrayLike, None) + cast("ArrayLike", None) ) # This line will never be reached, but it satisfies the type checker @classmethod @@ -227,7 +227,7 @@ def from_bytes(cls, bytes_like: BytesLike) -> Self: if cls is Buffer: raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'") return cls( - cast(ArrayLike, None) + cast("ArrayLike", None) ) # This line will never be reached, but it satisfies the type checker def as_array_like(self) -> ArrayLike: @@ -255,6 +255,19 @@ def as_numpy_array(self) -> npt.NDArray[Any]: """ ... + def as_buffer_like(self) -> BytesLike: + """Returns the buffer as an object that implements the Python buffer protocol. + + Notes + ----- + Might have to copy data, since the implementation uses `.as_numpy_array()`. + + Returns + ------- + An object that implements the Python buffer protocol + """ + return memoryview(self.as_numpy_array()) # type: ignore[arg-type] + def to_bytes(self) -> bytes: """Returns the buffer as `bytes` (host memory). @@ -358,7 +371,7 @@ def create( "Cannot call abstract method on the abstract class 'NDBuffer'" ) return cls( - cast(NDArrayLike, None) + cast("NDArrayLike", None) ) # This line will never be reached, but it satisfies the type checker @classmethod @@ -395,7 +408,7 @@ def from_numpy_array(cls, array_like: npt.ArrayLike) -> Self: "Cannot call abstract method on the abstract class 'NDBuffer'" ) return cls( - cast(NDArrayLike, None) + cast("NDArrayLike", None) ) # This line will never be reached, but it satisfies the type checker def as_ndarray_like(self) -> NDArrayLike: @@ -427,16 +440,7 @@ def as_scalar(self) -> ScalarType: """Returns the buffer as a scalar value""" if self._data.size != 1: raise ValueError("Buffer does not contain a single scalar value") - item = self.as_numpy_array().item() - scalar: ScalarType - - if np.issubdtype(self.dtype, np.datetime64): - unit: str = np.datetime_data(self.dtype)[0] # Extract the unit (e.g., 'Y', 'D', etc.) - scalar = np.datetime64(item, unit) - else: - scalar = self.dtype.type(item) # Regular conversion for non-datetime types - - return scalar + return cast("ScalarType", self.as_numpy_array()[()]) @property def dtype(self) -> np.dtype[Any]: @@ -491,7 +495,9 @@ def all_equal(self, other: Any, equal_nan: bool = True) -> bool: return np.array_equal( self._data, other, - equal_nan=equal_nan if self._data.dtype.kind not in "USTOV" else False, + equal_nan=equal_nan + if self._data.dtype.kind not in ("U", "S", "T", "O", "V") + else False, ) def fill(self, value: Any) -> None: diff --git a/src/zarr/core/buffer/cpu.py b/src/zarr/core/buffer/cpu.py index 8464518818..3140d75111 100644 --- a/src/zarr/core/buffer/cpu.py +++ b/src/zarr/core/buffer/cpu.py @@ -154,7 +154,8 @@ def create( order: Literal["C", "F"] = "C", fill_value: Any | None = None, ) -> Self: - if fill_value is None: + # np.zeros is much faster than np.full, and therefore using it when possible is better. + if fill_value is None or (isinstance(fill_value, int) and fill_value == 0): return cls(np.zeros(shape=tuple(shape), dtype=dtype, order=order)) else: return cls(np.full(shape=tuple(shape), fill_value=fill_value, dtype=dtype, order=order)) @@ -223,5 +224,10 @@ def numpy_buffer_prototype() -> core.BufferPrototype: return core.BufferPrototype(buffer=Buffer, nd_buffer=NDBuffer) -register_buffer(Buffer) -register_ndbuffer(NDBuffer) +register_buffer(Buffer, qualname="zarr.buffer.cpu.Buffer") +register_ndbuffer(NDBuffer, qualname="zarr.buffer.cpu.NDBuffer") + + +# backwards compatibility +register_buffer(Buffer, qualname="zarr.core.buffer.cpu.Buffer") +register_ndbuffer(NDBuffer, qualname="zarr.core.buffer.cpu.NDBuffer") diff --git a/src/zarr/core/buffer/gpu.py b/src/zarr/core/buffer/gpu.py index 77d2731c71..7ea6d53fe3 100644 --- a/src/zarr/core/buffer/gpu.py +++ b/src/zarr/core/buffer/gpu.py @@ -103,7 +103,7 @@ def from_bytes(cls, bytes_like: BytesLike) -> Self: return cls.from_array_like(cp.frombuffer(bytes_like, dtype="B")) def as_numpy_array(self) -> npt.NDArray[Any]: - return cast(npt.NDArray[Any], cp.asnumpy(self._data)) + return cast("npt.NDArray[Any]", cp.asnumpy(self._data)) def __add__(self, other: core.Buffer) -> Self: other_array = other.as_array_like() @@ -204,7 +204,7 @@ def as_numpy_array(self) -> npt.NDArray[Any]: ------- NumPy array of this buffer (might be a data copy) """ - return cast(npt.NDArray[Any], cp.asnumpy(self._data)) + return cast("npt.NDArray[Any]", cp.asnumpy(self._data)) def __getitem__(self, key: Any) -> Self: return self.__class__(self._data.__getitem__(key)) @@ -220,5 +220,9 @@ def __setitem__(self, key: Any, value: Any) -> None: buffer_prototype = BufferPrototype(buffer=Buffer, nd_buffer=NDBuffer) -register_buffer(Buffer) -register_ndbuffer(NDBuffer) +register_buffer(Buffer, qualname="zarr.buffer.gpu.Buffer") +register_ndbuffer(NDBuffer, qualname="zarr.buffer.gpu.NDBuffer") + +# backwards compatibility +register_buffer(Buffer, qualname="zarr.core.buffer.gpu.Buffer") +register_ndbuffer(NDBuffer, qualname="zarr.core.buffer.gpu.NDBuffer") diff --git a/src/zarr/core/chunk_grids.py b/src/zarr/core/chunk_grids.py index d3e40c26ed..4bf03c89de 100644 --- a/src/zarr/core/chunk_grids.py +++ b/src/zarr/core/chunk_grids.py @@ -64,6 +64,9 @@ def _guess_chunks( if isinstance(shape, int): shape = (shape,) + if typesize == 0: + return shape + ndims = len(shape) # require chunks to have non-zero length for all dimensions chunks = np.maximum(np.array(shape, dtype="=f8"), 1) @@ -204,7 +207,7 @@ def _auto_partition( array_shape: tuple[int, ...], chunk_shape: tuple[int, ...] | Literal["auto"], shard_shape: ShardsLike | None, - dtype: np.dtype[Any], + item_size: int, ) -> tuple[tuple[int, ...] | None, tuple[int, ...]]: """ Automatically determine the shard shape and chunk shape for an array, given the shape and dtype of the array. @@ -214,7 +217,6 @@ def _auto_partition( of the array; if the `chunk_shape` is also "auto", then the chunks will be set heuristically as well, given the dtype and shard shape. Otherwise, the chunks will be returned as-is. """ - item_size = dtype.itemsize if shard_shape is None: _shards_out: None | tuple[int, ...] = None if chunk_shape == "auto": diff --git a/src/zarr/core/chunk_key_encodings.py b/src/zarr/core/chunk_key_encodings.py index 103472c3b4..91dfc90365 100644 --- a/src/zarr/core/chunk_key_encodings.py +++ b/src/zarr/core/chunk_key_encodings.py @@ -20,7 +20,7 @@ def parse_separator(data: JSON) -> SeparatorLiteral: if data not in (".", "/"): raise ValueError(f"Expected an '.' or '/' separator. Got {data} instead.") - return cast(SeparatorLiteral, data) + return cast("SeparatorLiteral", data) class ChunkKeyEncodingParams(TypedDict): @@ -48,7 +48,7 @@ def from_dict(cls, data: dict[str, JSON] | ChunkKeyEncodingLike) -> ChunkKeyEnco data = {"name": data["name"], "configuration": {"separator": data["separator"]}} # TODO: remove this cast when we are statically typing the JSON metadata completely. - data = cast(dict[str, JSON], data) + data = cast("dict[str, JSON]", data) # configuration is optional for chunk key encodings name_parsed, config_parsed = parse_named_configuration(data, require_configuration=False) diff --git a/src/zarr/core/codec_pipeline.py b/src/zarr/core/codec_pipeline.py index 628a7e0487..23c27e40c6 100644 --- a/src/zarr/core/codec_pipeline.py +++ b/src/zarr/core/codec_pipeline.py @@ -17,19 +17,17 @@ from zarr.core.common import ChunkCoords, concurrent_map from zarr.core.config import config from zarr.core.indexing import SelectorTuple, is_scalar -from zarr.core.metadata.v2 import _default_fill_value from zarr.registry import register_pipeline if TYPE_CHECKING: from collections.abc import Iterable, Iterator from typing import Self - import numpy as np - from zarr.abc.store import ByteGetter, ByteSetter from zarr.core.array_spec import ArraySpec from zarr.core.buffer import Buffer, BufferPrototype, NDBuffer from zarr.core.chunk_grids import ChunkGrid + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType T = TypeVar("T") U = TypeVar("U") @@ -64,7 +62,7 @@ def fill_value_or_default(chunk_spec: ArraySpec) -> Any: # validated when decoding the metadata, but we support reading # Zarr V2 data and need to support the case where fill_value # is None. - return _default_fill_value(dtype=chunk_spec.dtype) + return chunk_spec.dtype.default_scalar() else: return fill_value @@ -134,7 +132,9 @@ def __iter__(self) -> Iterator[Codec]: yield self.array_bytes_codec yield from self.bytes_bytes_codecs - def validate(self, *, shape: ChunkCoords, dtype: np.dtype[Any], chunk_grid: ChunkGrid) -> None: + def validate( + self, *, shape: ChunkCoords, dtype: ZDType[TBaseDType, TBaseScalar], chunk_grid: ChunkGrid + ) -> None: for codec in self: codec.validate(shape=shape, dtype=dtype, chunk_grid=chunk_grid) @@ -296,7 +296,9 @@ def _merge_chunk_array( is_complete_chunk: bool, drop_axes: tuple[int, ...], ) -> NDBuffer: - if chunk_selection == () or is_scalar(value.as_ndarray_like(), chunk_spec.dtype): + if chunk_selection == () or is_scalar( + value.as_ndarray_like(), chunk_spec.dtype.to_native_dtype() + ): chunk_value = value else: chunk_value = value[out_selection] @@ -317,7 +319,7 @@ def _merge_chunk_array( if existing_chunk_array is None: chunk_array = chunk_spec.prototype.nd_buffer.create( shape=chunk_spec.shape, - dtype=chunk_spec.dtype, + dtype=chunk_spec.dtype.to_native_dtype(), order=chunk_spec.order, fill_value=fill_value_or_default(chunk_spec), ) diff --git a/src/zarr/core/common.py b/src/zarr/core/common.py index 3308ca3247..2ba5914ea5 100644 --- a/src/zarr/core/common.py +++ b/src/zarr/core/common.py @@ -10,16 +10,15 @@ from typing import ( TYPE_CHECKING, Any, + Generic, Literal, + TypedDict, TypeVar, cast, overload, ) -import numpy as np - from zarr.core.config import config as zarr_config -from zarr.core.strings import _STRING_DTYPE if TYPE_CHECKING: from collections.abc import Awaitable, Callable, Iterator @@ -40,6 +39,15 @@ JSON = str | int | float | Mapping[str, "JSON"] | Sequence["JSON"] | None MemoryOrder = Literal["C", "F"] AccessModeLiteral = Literal["r", "r+", "a", "w", "w-"] +DimensionNames = Iterable[str | None] | None + +TName = TypeVar("TName", bound=str) +TConfig = TypeVar("TConfig", bound=Mapping[str, object]) + + +class NamedConfig(TypedDict, Generic[TName, TConfig]): + name: TName + configuration: TConfig def product(tup: ChunkCoords) -> int: @@ -157,7 +165,7 @@ def parse_fill_value(data: Any) -> Any: def parse_order(data: Any) -> Literal["C", "F"]: if data in ("C", "F"): - return cast(Literal["C", "F"], data) + return cast("Literal['C', 'F']", data) raise ValueError(f"Expected one of ('C', 'F'), got {data} instead.") @@ -167,16 +175,6 @@ def parse_bool(data: Any) -> bool: raise ValueError(f"Expected bool, got {data} instead.") -def parse_dtype(dtype: Any, zarr_format: ZarrFormat) -> np.dtype[Any]: - if dtype is str or dtype == "str": - if zarr_format == 2: - # special case as object - return np.dtype("object") - else: - return _STRING_DTYPE - return np.dtype(dtype) - - def _warn_write_empty_chunks_kwarg() -> None: # TODO: link to docs page on array configuration in this message msg = ( @@ -201,4 +199,4 @@ def _warn_order_kwarg() -> None: def _default_zarr_format() -> ZarrFormat: """Return the default zarr_version""" - return cast(ZarrFormat, int(zarr_config.get("default_zarr_format", 3))) + return cast("ZarrFormat", int(zarr_config.get("default_zarr_format", 3))) diff --git a/src/zarr/core/config.py b/src/zarr/core/config.py index c565cb0708..05d048ef74 100644 --- a/src/zarr/core/config.py +++ b/src/zarr/core/config.py @@ -36,11 +36,21 @@ if TYPE_CHECKING: from donfig.config_obj import ConfigSet + from zarr.core.dtype.wrapper import ZDType + class BadConfigError(ValueError): _msg = "bad Config: %r" +# These values are used for rough categorization of data types +# we use this for choosing a default encoding scheme based on the data type. Specifically, +# these categories are keys in a configuration dictionary. +# it is not a part of the ZDType class because these categories are more of an implementation detail +# of our config system rather than a useful attribute of any particular data type. +DTypeCategory = Literal["variable-length-string", "default"] + + class Config(DConfig): # type: ignore[misc] """The Config will collect configuration from config files and environment variables @@ -64,7 +74,7 @@ def enable_gpu(self) -> ConfigSet: Configure Zarr to use GPUs where possible. """ return self.set( - {"buffer": "zarr.core.buffer.gpu.Buffer", "ndbuffer": "zarr.core.buffer.gpu.NDBuffer"} + {"buffer": "zarr.buffer.gpu.Buffer", "ndbuffer": "zarr.buffer.gpu.NDBuffer"} ) @@ -78,31 +88,24 @@ def enable_gpu(self) -> ConfigSet: "order": "C", "write_empty_chunks": False, "v2_default_compressor": { - "numeric": {"id": "zstd", "level": 0, "checksum": False}, - "string": {"id": "zstd", "level": 0, "checksum": False}, - "bytes": {"id": "zstd", "level": 0, "checksum": False}, + "default": {"id": "zstd", "level": 0, "checksum": False}, + "variable-length-string": {"id": "zstd", "level": 0, "checksum": False}, }, "v2_default_filters": { - "numeric": None, - "string": [{"id": "vlen-utf8"}], - "bytes": [{"id": "vlen-bytes"}], - "raw": None, + "default": None, + "variable-length-string": [{"id": "vlen-utf8"}], }, - "v3_default_filters": {"numeric": [], "string": [], "bytes": []}, + "v3_default_filters": {"default": [], "variable-length-string": []}, "v3_default_serializer": { - "numeric": {"name": "bytes", "configuration": {"endian": "little"}}, - "string": {"name": "vlen-utf8"}, - "bytes": {"name": "vlen-bytes"}, + "default": {"name": "bytes", "configuration": {"endian": "little"}}, + "variable-length-string": {"name": "vlen-utf8"}, }, "v3_default_compressors": { - "numeric": [ + "default": [ {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, ], - "string": [ - {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, - ], - "bytes": [ - {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, + "variable-length-string": [ + {"name": "zstd", "configuration": {"level": 0, "checksum": False}} ], }, }, @@ -125,8 +128,8 @@ def enable_gpu(self) -> ConfigSet: "vlen-utf8": "zarr.codecs.vlen_utf8.VLenUTF8Codec", "vlen-bytes": "zarr.codecs.vlen_utf8.VLenBytesCodec", }, - "buffer": "zarr.core.buffer.cpu.Buffer", - "ndbuffer": "zarr.core.buffer.cpu.NDBuffer", + "buffer": "zarr.buffer.cpu.Buffer", + "ndbuffer": "zarr.buffer.cpu.NDBuffer", } ], ) @@ -134,6 +137,20 @@ def enable_gpu(self) -> ConfigSet: def parse_indexing_order(data: Any) -> Literal["C", "F"]: if data in ("C", "F"): - return cast(Literal["C", "F"], data) + return cast("Literal['C', 'F']", data) msg = f"Expected one of ('C', 'F'), got {data} instead." raise ValueError(msg) + + +def categorize_data_type(dtype: ZDType[Any, Any]) -> DTypeCategory: + """ + Classify a ZDType. The return value is a string which belongs to the type ``DTypeCategory``. + + This is used by the config system to determine how to encode arrays with the associated data type + when the user has not specified a particular serialization scheme. + """ + from zarr.core.dtype import VariableLengthUTF8 + + if isinstance(dtype, VariableLengthUTF8): + return "variable-length-string" + return "default" diff --git a/src/zarr/core/dtype/__init__.py b/src/zarr/core/dtype/__init__.py new file mode 100644 index 0000000000..735690d4bc --- /dev/null +++ b/src/zarr/core/dtype/__init__.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Final, TypeAlias + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeJSON, +) +from zarr.core.dtype.npy.bool import Bool +from zarr.core.dtype.npy.bytes import NullTerminatedBytes, RawBytes, VariableLengthBytes +from zarr.core.dtype.npy.complex import Complex64, Complex128 +from zarr.core.dtype.npy.float import Float16, Float32, Float64 +from zarr.core.dtype.npy.int import Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64 +from zarr.core.dtype.npy.structured import ( + Structured, +) +from zarr.core.dtype.npy.time import DateTime64, TimeDelta64 + +if TYPE_CHECKING: + from zarr.core.common import ZarrFormat + +from collections.abc import Mapping + +import numpy as np +import numpy.typing as npt + +from zarr.core.common import JSON +from zarr.core.dtype.npy.string import ( + FixedLengthUTF32, + VariableLengthUTF8, +) +from zarr.core.dtype.registry import DataTypeRegistry +from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType + +__all__ = [ + "Bool", + "Complex64", + "Complex128", + "DataTypeRegistry", + "DataTypeValidationError", + "DateTime64", + "FixedLengthUTF32", + "Float16", + "Float32", + "Float64", + "Int8", + "Int16", + "Int32", + "Int64", + "NullTerminatedBytes", + "RawBytes", + "Structured", + "TBaseDType", + "TBaseScalar", + "TimeDelta64", + "TimeDelta64", + "UInt8", + "UInt16", + "UInt32", + "UInt64", + "VariableLengthUTF8", + "ZDType", + "data_type_registry", + "parse_data_type", +] + +data_type_registry = DataTypeRegistry() + +IntegerDType = Int8 | Int16 | Int32 | Int64 | UInt8 | UInt16 | UInt32 | UInt64 +INTEGER_DTYPE: Final = Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64 + +FloatDType = Float16 | Float32 | Float64 +FLOAT_DTYPE: Final = Float16, Float32, Float64 + +ComplexFloatDType = Complex64 | Complex128 +COMPLEX_FLOAT_DTYPE: Final = Complex64, Complex128 + +StringDType = FixedLengthUTF32 | VariableLengthUTF8 +STRING_DTYPE: Final = FixedLengthUTF32, VariableLengthUTF8 + +TimeDType = DateTime64 | TimeDelta64 +TIME_DTYPE: Final = DateTime64, TimeDelta64 + +BytesDType = RawBytes | NullTerminatedBytes | VariableLengthBytes +BYTES_DTYPE: Final = RawBytes, NullTerminatedBytes, VariableLengthBytes + +AnyDType = ( + Bool + | IntegerDType + | FloatDType + | ComplexFloatDType + | StringDType + | BytesDType + | Structured + | TimeDType + | VariableLengthBytes +) +# mypy has trouble inferring the type of variablelengthstring dtype, because its class definition +# depends on the installed numpy version. That's why the type: ignore statement is needed here. +ANY_DTYPE: Final = ( + Bool, + *INTEGER_DTYPE, + *FLOAT_DTYPE, + *COMPLEX_FLOAT_DTYPE, + *STRING_DTYPE, + *BYTES_DTYPE, + Structured, + *TIME_DTYPE, + VariableLengthBytes, +) + +# This type models inputs that can be coerced to a ZDType +ZDTypeLike: TypeAlias = npt.DTypeLike | ZDType[TBaseDType, TBaseScalar] | Mapping[str, JSON] | str + +for dtype in ANY_DTYPE: + # mypy does not know that all the elements of ANY_DTYPE are subclasses of ZDType + data_type_registry.register(dtype._zarr_v3_name, dtype) # type: ignore[arg-type] + + +# TODO: find a better name for this function +def get_data_type_from_native_dtype(dtype: npt.DTypeLike) -> ZDType[TBaseDType, TBaseScalar]: + """ + Get a data type wrapper (an instance of ``ZDType``) from a native data type, e.g. a numpy dtype. + """ + if not isinstance(dtype, np.dtype): + na_dtype: np.dtype[np.generic] + if isinstance(dtype, list): + # this is a valid _VoidDTypeLike check + na_dtype = np.dtype([tuple(d) for d in dtype]) + else: + na_dtype = np.dtype(dtype) + else: + na_dtype = dtype + return data_type_registry.match_dtype(dtype=na_dtype) + + +def get_data_type_from_json( + dtype_spec: DTypeJSON, *, zarr_format: ZarrFormat +) -> ZDType[TBaseDType, TBaseScalar]: + """ + Given a JSON representation of a data type and a Zarr format version, + attempt to create a ZDType instance from the registered ZDType classes. + """ + return data_type_registry.match_json(dtype_spec, zarr_format=zarr_format) + + +def parse_data_type( + dtype_spec: ZDTypeLike, + *, + zarr_format: ZarrFormat, +) -> ZDType[TBaseDType, TBaseScalar]: + """ + Interpret the input as a ZDType instance. + """ + if isinstance(dtype_spec, ZDType): + return dtype_spec + # dict and zarr_format 3 means that we have a JSON object representation of the dtype + if zarr_format == 3 and isinstance(dtype_spec, Mapping): + return get_data_type_from_json(dtype_spec, zarr_format=3) + # otherwise, we have either a numpy dtype string, or a zarr v3 dtype string, and in either case + # we can create a numpy dtype from it, and do the dtype inference from that + return get_data_type_from_native_dtype(dtype_spec) # type: ignore[arg-type] diff --git a/src/zarr/core/dtype/common.py b/src/zarr/core/dtype/common.py new file mode 100644 index 0000000000..6f61b6775e --- /dev/null +++ b/src/zarr/core/dtype/common.py @@ -0,0 +1,224 @@ +from __future__ import annotations + +import warnings +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from typing import ( + ClassVar, + Final, + Generic, + Literal, + TypedDict, + TypeGuard, + TypeVar, +) + +from zarr.core.common import NamedConfig + +EndiannessStr = Literal["little", "big"] +ENDIANNESS_STR: Final = "little", "big" + +SpecialFloatStrings = Literal["NaN", "Infinity", "-Infinity"] +SPECIAL_FLOAT_STRINGS: Final = ("NaN", "Infinity", "-Infinity") + +JSONFloatV2 = float | SpecialFloatStrings +JSONFloatV3 = float | SpecialFloatStrings | str + +ObjectCodecID = Literal["vlen-utf8", "vlen-bytes", "vlen-array", "pickle", "json2", "msgpack2"] +# These are the ids of the known object codecs for zarr v2. +OBJECT_CODEC_IDS: Final = ("vlen-utf8", "vlen-bytes", "vlen-array", "pickle", "json2", "msgpack2") + +# This is a wider type than our standard JSON type because we need +# to work with typeddict objects which are assignable to Mapping[str, object] +DTypeJSON = str | int | float | Sequence["DTypeJSON"] | None | Mapping[str, object] + +# The DTypeJSON_V2 type exists because ZDType.from_json takes a single argument, which must contain +# all the information necessary to decode the data type. Zarr v2 supports multiple distinct +# data types that all used the "|O" data type identifier. These data types can only be +# discriminated on the basis of their "object codec", i.e. a special data type specific +# compressor or filter. So to figure out what data type a zarr v2 array has, we need the +# data type identifier from metadata, as well as an object codec id if the data type identifier +# is "|O". +# So we will pack the name of the dtype alongside the name of the object codec id, if applicable, +# in a single dict, and pass that to the data type inference logic. +# These type variables have a very wide bound because the individual zdtype +# classes can perform a very specific type check. + +# This is the JSON representation of a structured dtype in zarr v2 +StructuredName_V2 = Sequence["str | StructuredName_V2"] + +# This models the type of the name a dtype might have in zarr v2 array metadata +DTypeName_V2 = StructuredName_V2 | str + +TDTypeNameV2_co = TypeVar("TDTypeNameV2_co", bound=DTypeName_V2, covariant=True) +TObjectCodecID_co = TypeVar("TObjectCodecID_co", bound=None | str, covariant=True) + + +class DTypeConfig_V2(TypedDict, Generic[TDTypeNameV2_co, TObjectCodecID_co]): + name: TDTypeNameV2_co + object_codec_id: TObjectCodecID_co + + +DTypeSpec_V2 = DTypeConfig_V2[DTypeName_V2, None | str] + + +def check_structured_dtype_v2_inner(data: object) -> TypeGuard[StructuredName_V2]: + """ + A type guard for the inner elements of a structured dtype. This is a recursive check because + the type is itself recursive. + + This check ensures that all the elements are 2-element sequences beginning with a string + and ending with either another string or another 2-element sequence beginning with a string and + ending with another instance of that type. + """ + if isinstance(data, (str, Mapping)): + return False + if not isinstance(data, Sequence): + return False + if len(data) != 2: + return False + if not (isinstance(data[0], str)): + return False + if isinstance(data[-1], str): + return True + elif isinstance(data[-1], Sequence): + return check_structured_dtype_v2_inner(data[-1]) + return False + + +def check_structured_dtype_name_v2(data: Sequence[object]) -> TypeGuard[StructuredName_V2]: + return all(check_structured_dtype_v2_inner(d) for d in data) + + +def check_dtype_name_v2(data: object) -> TypeGuard[DTypeName_V2]: + """ + Type guard for narrowing the type of a python object to an valid zarr v2 dtype name. + """ + if isinstance(data, str): + return True + elif isinstance(data, Sequence): + return check_structured_dtype_name_v2(data) + return False + + +def check_dtype_spec_v2(data: object) -> TypeGuard[DTypeSpec_V2]: + """ + Type guard for narrowing a python object to an instance of DTypeSpec_V2 + """ + if not isinstance(data, Mapping): + return False + if set(data.keys()) != {"name", "object_codec_id"}: + return False + if not check_dtype_name_v2(data["name"]): + return False + return isinstance(data["object_codec_id"], str | None) + + +# By comparison, The JSON representation of a dtype in zarr v3 is much simpler. +# It's either a string, or a structured dict +DTypeSpec_V3 = str | NamedConfig[str, Mapping[str, object]] + + +def check_dtype_spec_v3(data: object) -> TypeGuard[DTypeSpec_V3]: + """ + Type guard for narrowing the type of a python object to an instance of + DTypeSpec_V3, i.e either a string or a dict with a "name" field that's a string and a + "configuration" field that's a mapping with string keys. + """ + if isinstance(data, str) or ( # noqa: SIM103 + isinstance(data, Mapping) + and set(data.keys()) == {"name", "configuration"} + and isinstance(data["configuration"], Mapping) + and all(isinstance(k, str) for k in data["configuration"]) + ): + return True + return False + + +def unpack_dtype_json(data: DTypeSpec_V2 | DTypeSpec_V3) -> DTypeJSON: + """ + Return the array metadata form of the dtype JSON representation. For the Zarr V3 form of dtype + metadata, this is a no-op. For the Zarr V2 form of dtype metadata, this unpacks the dtype name. + """ + if isinstance(data, Mapping) and set(data.keys()) == {"name", "object_codec_id"}: + return data["name"] + return data + + +class DataTypeValidationError(ValueError): ... + + +class ScalarTypeValidationError(ValueError): ... + + +@dataclass(frozen=True) +class HasLength: + """ + A mix-in class for data types with a length attribute, such as fixed-size collections + of unicode strings, or bytes. + """ + + length: int + + +@dataclass(frozen=True) +class HasEndianness: + """ + A mix-in class for data types with an endianness attribute + """ + + endianness: EndiannessStr = "little" + + +@dataclass(frozen=True) +class HasItemSize: + """ + A mix-in class for data types with an item size attribute. + This mix-in bears a property ``item_size``, which denotes the size of each element of the data + type, in bytes. + """ + + @property + def item_size(self) -> int: + raise NotImplementedError + + +@dataclass(frozen=True) +class HasObjectCodec: + """ + A mix-in class for data types that require an object codec id. + This class bears the property ``object_codec_id``, which is the string name of an object + codec that is required to encode and decode the data type. + + In zarr-python 2.x certain data types like variable-length strings or variable-length arrays + used the catch-all numpy "object" data type for their in-memory representation. But these data + types cannot be stored as numpy object data types, because the object data type does not define + a fixed memory layout. So these data types required a special codec, called an "object codec", + that effectively defined a compact representation for the data type, which was used to encode + and decode the data type. + + Zarr-python 2.x would not allow the creation of arrays with the "object" data type if an object + codec was not specified, and thus the name of the object codec is effectively part of the data + type model. + """ + + object_codec_id: ClassVar[str] + + +class UnstableSpecificationWarning(FutureWarning): ... + + +def v3_unstable_dtype_warning(dtype: object) -> None: + """ + Emit this warning when a data type does not have a stable zarr v3 spec + """ + msg = ( + f"The data type ({dtype}) does not have a Zarr V3 specification. " + "That means that the representation of array saved with this data type may change without " + "warning in a future version of Zarr Python. " + "Arrays stored with this data type may be unreadable by other Zarr libraries. " + "Use this data type at your own risk! " + "Check https://github.com/zarr-developers/zarr-extensions/tree/main/data-types for the " + "status of data type specifications for Zarr V3." + ) + warnings.warn(msg, category=UnstableSpecificationWarning, stacklevel=2) diff --git a/src/zarr/core/dtype/npy/__init__.py b/src/zarr/core/dtype/npy/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/zarr/core/dtype/npy/bool.py b/src/zarr/core/dtype/npy/bool.py new file mode 100644 index 0000000000..d8d52468bf --- /dev/null +++ b/src/zarr/core/dtype/npy/bool.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, ClassVar, Literal, Self, TypeGuard, overload + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasItemSize, + check_dtype_spec_v2, +) +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + + +@dataclass(frozen=True, kw_only=True, slots=True) +class Bool(ZDType[np.dtypes.BoolDType, np.bool_], HasItemSize): + """ + Wrapper for numpy boolean dtype. + + Attributes + ---------- + name : str + The name of the dtype. + dtype_cls : ClassVar[type[np.dtypes.BoolDType]] + The numpy dtype class. + """ + + _zarr_v3_name: ClassVar[Literal["bool"]] = "bool" + _zarr_v2_name: ClassVar[Literal["|b1"]] = "|b1" + dtype_cls = np.dtypes.BoolDType + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + """ + Create a Bool from a np.dtype('bool') instance. + """ + if cls._check_native_dtype(dtype): + return cls() + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self: Self) -> np.dtypes.BoolDType: + """ + Create a NumPy boolean dtype instance from this ZDType + """ + return self.dtype_cls() + + @classmethod + def _check_json_v2( + cls, + data: DTypeJSON, + ) -> TypeGuard[DTypeConfig_V2[Literal["|b1"], None]]: + """ + Check that the input is a valid JSON representation of a Bool. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] == cls._zarr_v2_name + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[Literal["bool"]]: + return data == cls._zarr_v3_name + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v2_name!r}" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls: type[Self], data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal["|b1"], None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> Literal["bool"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal["|b1"], None] | Literal["bool"]: + if zarr_format == 2: + return {"name": self._zarr_v2_name, "object_codec_id": None} + elif zarr_format == 3: + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> bool: + # Anything can become a bool + return True + + def cast_scalar(self, data: object) -> np.bool_: + if self._check_scalar(data): + return np.bool_(data) + msg = f"Cannot convert object with type {type(data)} to a numpy boolean." + raise TypeError(msg) + + def default_scalar(self) -> np.bool_: + """ + Get the default value for the boolean dtype. + + Returns + ------- + np.bool_ + The default value. + """ + return np.False_ + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> bool: + """ + Convert a scalar to a python bool. + + Parameters + ---------- + data : object + The value to convert. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + bool + The JSON-serializable format. + """ + return bool(data) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.bool_: + """ + Read a JSON-serializable value as a numpy boolean scalar. + + Parameters + ---------- + data : JSON + The JSON-serializable value. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + np.bool_ + The numpy boolean scalar. + """ + if self._check_scalar(data): + return np.bool_(data) + raise TypeError(f"Invalid type: {data}. Expected a boolean.") # pragma: no cover + + @property + def item_size(self) -> int: + return 1 diff --git a/src/zarr/core/dtype/npy/bytes.py b/src/zarr/core/dtype/npy/bytes.py new file mode 100644 index 0000000000..e363c75053 --- /dev/null +++ b/src/zarr/core/dtype/npy/bytes.py @@ -0,0 +1,369 @@ +from __future__ import annotations + +import base64 +import re +from dataclasses import dataclass +from typing import Any, ClassVar, Literal, Self, TypedDict, TypeGuard, cast, overload + +import numpy as np + +from zarr.core.common import JSON, NamedConfig, ZarrFormat +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasItemSize, + HasLength, + HasObjectCodec, + check_dtype_spec_v2, + v3_unstable_dtype_warning, +) +from zarr.core.dtype.npy.common import check_json_str +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +BytesLike = np.bytes_ | str | bytes | int + + +class FixedLengthBytesConfig(TypedDict): + length_bytes: int + + +NullTerminatedBytesJSONV3 = NamedConfig[Literal["null_terminated_bytes"], FixedLengthBytesConfig] +RawBytesJSONV3 = NamedConfig[Literal["raw_bytes"], FixedLengthBytesConfig] + + +@dataclass(frozen=True, kw_only=True) +class NullTerminatedBytes(ZDType[np.dtypes.BytesDType[int], np.bytes_], HasLength, HasItemSize): + dtype_cls = np.dtypes.BytesDType + _zarr_v3_name: ClassVar[Literal["null_terminated_bytes"]] = "null_terminated_bytes" + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls(length=dtype.itemsize) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.BytesDType[int]: + return self.dtype_cls(self.length) + + @classmethod + def _check_json_v2(cls, data: DTypeJSON) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid representation of a numpy S dtype. We expect + something like ``{"name": "|S10", "object_codec_id": None}`` + """ + return ( + check_dtype_spec_v2(data) + and isinstance(data["name"], str) + and re.match(r"^\|S\d+$", data["name"]) is not None + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[NullTerminatedBytesJSONV3]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and isinstance(data["configuration"], dict) + and "length_bytes" in data["configuration"] + ) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + name = data["name"] + return cls(length=int(name[2:])) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a string like '|S1', '|S2', etc" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls(length=data["configuration"]["length_bytes"]) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> NullTerminatedBytesJSONV3: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[str, None] | NullTerminatedBytesJSONV3: + if zarr_format == 2: + return {"name": self.to_native_dtype().str, "object_codec_id": None} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + return { + "name": self._zarr_v3_name, + "configuration": {"length_bytes": self.length}, + } + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[BytesLike]: + # this is generous for backwards compatibility + return isinstance(data, BytesLike) + + def _cast_scalar_unchecked(self, data: BytesLike) -> np.bytes_: + # We explicitly truncate the result because of the following numpy behavior: + # >>> x = np.dtype('S3').type('hello world') + # >>> x + # np.bytes_(b'hello world') + # >>> x.dtype + # dtype('S11') + + if isinstance(data, int): + return self.to_native_dtype().type(str(data)[: self.length]) + else: + return self.to_native_dtype().type(data[: self.length]) + + def cast_scalar(self, data: object) -> np.bytes_: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy bytes scalar." + raise TypeError(msg) + + def default_scalar(self) -> np.bytes_: + return np.bytes_(b"") + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + as_bytes = self.cast_scalar(data) + return base64.standard_b64encode(as_bytes).decode("ascii") + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.bytes_: + if check_json_str(data): + return self.to_native_dtype().type(base64.standard_b64decode(data.encode("ascii"))) + raise TypeError( + f"Invalid type: {data}. Expected a base64-encoded string." + ) # pragma: no cover + + @property + def item_size(self) -> int: + return self.length + + +@dataclass(frozen=True, kw_only=True) +class RawBytes(ZDType[np.dtypes.VoidDType[int], np.void], HasLength, HasItemSize): + # np.dtypes.VoidDType is specified in an odd way in numpy + # it cannot be used to create instances of the dtype + # so we have to tell mypy to ignore this here + dtype_cls = np.dtypes.VoidDType # type: ignore[assignment] + _zarr_v3_name: ClassVar[Literal["raw_bytes"]] = "raw_bytes" + + @classmethod + def _check_native_dtype( + cls: type[Self], dtype: TBaseDType + ) -> TypeGuard[np.dtypes.VoidDType[Any]]: + """ + Numpy void dtype comes in two forms: + * If the ``fields`` attribute is ``None``, then the dtype represents N raw bytes. + * If the ``fields`` attribute is not ``None``, then the dtype represents a structured dtype, + + In this check we ensure that ``fields`` is ``None``. + + Parameters + ---------- + dtype : TDType + The dtype to check. + + Returns + ------- + Bool + True if the dtype matches, False otherwise. + """ + return cls.dtype_cls is type(dtype) and dtype.fields is None # type: ignore[has-type] + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls(length=dtype.itemsize) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" # type: ignore[has-type] + ) + + def to_native_dtype(self) -> np.dtypes.VoidDType[int]: + # Numpy does not allow creating a void type + # by invoking np.dtypes.VoidDType directly + return cast("np.dtypes.VoidDType[int]", np.dtype(f"V{self.length}")) + + @classmethod + def _check_json_v2(cls, data: DTypeJSON) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid representation of a numpy S dtype. We expect + something like ``{"name": "|V10", "object_codec_id": None}`` + """ + return ( + check_dtype_spec_v2(data) + and isinstance(data["name"], str) + and re.match(r"^\|V\d+$", data["name"]) is not None + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[RawBytesJSONV3]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and isinstance(data["configuration"], dict) + and set(data["configuration"].keys()) == {"length_bytes"} + ) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + name = data["name"] + return cls(length=int(name[2:])) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a string like '|V1', '|V2', etc" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls(length=data["configuration"]["length_bytes"]) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> RawBytesJSONV3: ... + + def to_json(self, zarr_format: ZarrFormat) -> DTypeConfig_V2[str, None] | RawBytesJSONV3: + if zarr_format == 2: + return {"name": self.to_native_dtype().str, "object_codec_id": None} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + return {"name": self._zarr_v3_name, "configuration": {"length_bytes": self.length}} + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> bool: + return isinstance(data, np.bytes_ | str | bytes | np.void) + + def _cast_scalar_unchecked(self, data: object) -> np.void: + native_dtype = self.to_native_dtype() + # Without the second argument, numpy will return a void scalar for dtype V1. + # The second argument ensures that, if native_dtype is something like V10, + # the result will actually be a V10 scalar. + return native_dtype.type(data, native_dtype) + + def cast_scalar(self, data: object) -> np.void: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy void scalar." + raise TypeError(msg) + + def default_scalar(self) -> np.void: + return self.to_native_dtype().type(("\x00" * self.length).encode("ascii")) + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + return base64.standard_b64encode(self.cast_scalar(data).tobytes()).decode("ascii") + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.void: + if check_json_str(data): + return self.to_native_dtype().type(base64.standard_b64decode(data)) + raise TypeError(f"Invalid type: {data}. Expected a string.") # pragma: no cover + + @property + def item_size(self) -> int: + return self.length + + +@dataclass(frozen=True, kw_only=True) +class VariableLengthBytes(ZDType[np.dtypes.ObjectDType, bytes], HasObjectCodec): + dtype_cls = np.dtypes.ObjectDType + _zarr_v3_name: ClassVar[Literal["variable_length_bytes"]] = "variable_length_bytes" + object_codec_id: ClassVar[Literal["vlen-bytes"]] = "vlen-bytes" + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls() + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.ObjectDType: + return self.dtype_cls() + + @classmethod + def _check_json_v2( + cls, + data: DTypeJSON, + ) -> TypeGuard[DTypeConfig_V2[Literal["|O"], Literal["vlen-bytes"]]]: + """ + Check that the input is a valid JSON representation of a numpy O dtype, and that the + object codec id is appropriate for variable-length UTF-8 strings. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] == "|O" + and data["object_codec_id"] == cls.object_codec_id + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[Literal["variable_length_bytes"]]: + return data == cls._zarr_v3_name + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string '|O' and an object_codec_id of {cls.object_codec_id}" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json( + self, zarr_format: Literal[2] + ) -> DTypeConfig_V2[Literal["|O"], Literal["vlen-bytes"]]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> Literal["variable_length_bytes"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal["|O"], Literal["vlen-bytes"]] | Literal["variable_length_bytes"]: + if zarr_format == 2: + return {"name": "|O", "object_codec_id": self.object_codec_id} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def default_scalar(self) -> bytes: + return b"" + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + return base64.standard_b64encode(data).decode("ascii") # type: ignore[arg-type] + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> bytes: + if check_json_str(data): + return base64.standard_b64decode(data.encode("ascii")) + raise TypeError(f"Invalid type: {data}. Expected a string.") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[BytesLike]: + return isinstance(data, BytesLike) + + def _cast_scalar_unchecked(self, data: BytesLike) -> bytes: + if isinstance(data, str): + return bytes(data, encoding="utf-8") + return bytes(data) + + def cast_scalar(self, data: object) -> bytes: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to bytes." + raise TypeError(msg) diff --git a/src/zarr/core/dtype/npy/common.py b/src/zarr/core/dtype/npy/common.py new file mode 100644 index 0000000000..264561f25c --- /dev/null +++ b/src/zarr/core/dtype/npy/common.py @@ -0,0 +1,503 @@ +from __future__ import annotations + +import base64 +import struct +import sys +from collections.abc import Sequence +from typing import ( + TYPE_CHECKING, + Any, + Final, + Literal, + SupportsComplex, + SupportsFloat, + SupportsIndex, + SupportsInt, + TypeGuard, + TypeVar, +) + +import numpy as np + +from zarr.core.dtype.common import ( + ENDIANNESS_STR, + SPECIAL_FLOAT_STRINGS, + EndiannessStr, + JSONFloatV2, + JSONFloatV3, +) + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + +IntLike = SupportsInt | SupportsIndex | bytes | str +FloatLike = SupportsIndex | SupportsFloat | bytes | str +ComplexLike = SupportsFloat | SupportsIndex | SupportsComplex | bytes | str | None +DateTimeUnit = Literal[ + "Y", "M", "W", "D", "h", "m", "s", "ms", "us", "μs", "ns", "ps", "fs", "as", "generic" +] +DATETIME_UNIT: Final = ( + "Y", + "M", + "W", + "D", + "h", + "m", + "s", + "ms", + "us", + "μs", + "ns", + "ps", + "fs", + "as", + "generic", +) + +NumpyEndiannessStr = Literal[">", "<", "="] +NUMPY_ENDIANNESS_STR: Final = ">", "<", "=" + +TFloatDType_co = TypeVar( + "TFloatDType_co", + bound=np.dtypes.Float16DType | np.dtypes.Float32DType | np.dtypes.Float64DType, + covariant=True, +) +TFloatScalar_co = TypeVar( + "TFloatScalar_co", bound=np.float16 | np.float32 | np.float64, covariant=True +) + +TComplexDType_co = TypeVar( + "TComplexDType_co", bound=np.dtypes.Complex64DType | np.dtypes.Complex128DType, covariant=True +) +TComplexScalar_co = TypeVar("TComplexScalar_co", bound=np.complex64 | np.complex128, covariant=True) + + +def endianness_from_numpy_str(endianness: NumpyEndiannessStr) -> EndiannessStr: + """ + Convert a numpy endianness string literal to a human-readable literal value. + + Parameters + ---------- + endianness : Literal[">", "<", "="] + The numpy string representation of the endianness. + + Returns + ------- + Endianness + The human-readable representation of the endianness. + + Raises + ------ + ValueError + If the endianness is invalid. + """ + match endianness: + case "=": + # Use the local system endianness + return sys.byteorder + case "<": + return "little" + case ">": + return "big" + raise ValueError(f"Invalid endianness: {endianness!r}. Expected one of {NUMPY_ENDIANNESS_STR}") + + +def endianness_to_numpy_str(endianness: EndiannessStr) -> NumpyEndiannessStr: + """ + Convert an endianness literal to its numpy string representation. + + Parameters + ---------- + endianness : Endianness + The endianness to convert. + + Returns + ------- + Literal[">", "<"] + The numpy string representation of the endianness. + + Raises + ------ + ValueError + If the endianness is invalid. + """ + match endianness: + case "little": + return "<" + case "big": + return ">" + raise ValueError( + f"Invalid endianness: {endianness!r}. Expected one of {ENDIANNESS_STR} or None" + ) + + +def get_endianness_from_numpy_dtype(dtype: np.dtype[np.generic]) -> EndiannessStr: + """ + Gets the endianness from a numpy dtype that has an endianness. This function will + raise a ValueError if the numpy data type does not have a concrete endianness. + """ + endianness = dtype.byteorder + if dtype.byteorder in NUMPY_ENDIANNESS_STR: + return endianness_from_numpy_str(endianness) # type: ignore [arg-type] + raise ValueError(f"The dtype {dtype} has an unsupported endianness: {endianness}") + + +def float_from_json_v2(data: JSONFloatV2) -> float: + """ + Convert a JSON float to a float (Zarr v2). + + Parameters + ---------- + data : JSONFloat + The JSON float to convert. + + Returns + ------- + float + The float value. + """ + match data: + case "NaN": + return float("nan") + case "Infinity": + return float("inf") + case "-Infinity": + return float("-inf") + case _: + return float(data) + + +def float_from_json_v3(data: JSONFloatV3) -> float: + """ + Convert a JSON float to a float (v3). + + Parameters + ---------- + data : JSONFloat + The JSON float to convert. + + Returns + ------- + float + The float value. + + Notes + ----- + Zarr V3 allows floats to be stored as hex strings. To quote the spec: + "...for float32, "NaN" is equivalent to "0x7fc00000". + This representation is the only way to specify a NaN value other than the specific NaN value + denoted by "NaN"." + """ + + if isinstance(data, str): + if data in SPECIAL_FLOAT_STRINGS: + return float_from_json_v2(data) # type: ignore[arg-type] + if not data.startswith("0x"): + msg = ( + f"Invalid float value: {data!r}. Expected a string starting with the hex prefix" + " '0x', or one of 'NaN', 'Infinity', or '-Infinity'." + ) + raise ValueError(msg) + if len(data[2:]) == 4: + dtype_code = ">e" + elif len(data[2:]) == 8: + dtype_code = ">f" + elif len(data[2:]) == 16: + dtype_code = ">d" + else: + msg = ( + f"Invalid hexadecimal float value: {data!r}. " + "Expected the '0x' prefix to be followed by 4, 8, or 16 numeral characters" + ) + raise ValueError(msg) + return float(struct.unpack(dtype_code, bytes.fromhex(data[2:]))[0]) + return float_from_json_v2(data) + + +def bytes_from_json(data: str, *, zarr_format: ZarrFormat) -> bytes: + """ + Convert a JSON string to bytes + + Parameters + ---------- + data : str + The JSON string to convert. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + bytes + The bytes. + """ + if zarr_format == 2: + return base64.b64decode(data.encode("ascii")) + # TODO: differentiate these as needed. This is a spec question. + if zarr_format == 3: + return base64.b64decode(data.encode("ascii")) + raise ValueError(f"Invalid zarr format: {zarr_format}. Expected 2 or 3.") # pragma: no cover + + +def bytes_to_json(data: bytes, zarr_format: ZarrFormat) -> str: + """ + Convert bytes to JSON. + + Parameters + ---------- + data : bytes + The bytes to store. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + str + The bytes encoded as ascii using the base64 alphabet. + """ + # TODO: decide if we are going to make this implementation zarr format-specific + return base64.b64encode(data).decode("ascii") + + +def float_to_json_v2(data: float | np.floating[Any]) -> JSONFloatV2: + """ + Convert a float to JSON (v2). + + Parameters + ---------- + data : float or np.floating + The float value to convert. + + Returns + ------- + JSONFloat + The JSON representation of the float. + """ + if np.isnan(data): + return "NaN" + elif np.isinf(data): + return "Infinity" if data > 0 else "-Infinity" + return float(data) + + +def float_to_json_v3(data: float | np.floating[Any]) -> JSONFloatV3: + """ + Convert a float to JSON (v3). + + Parameters + ---------- + data : float or np.floating + The float value to convert. + + Returns + ------- + JSONFloat + The JSON representation of the float. + """ + # v3 can in principle handle distinct NaN values, but numpy does not represent these explicitly + # so we just reuse the v2 routine here + return float_to_json_v2(data) + + +def complex_float_to_json_v3( + data: complex | np.complexfloating[Any, Any], +) -> tuple[JSONFloatV3, JSONFloatV3]: + """ + Convert a complex number to JSON as defined by the Zarr V3 spec. + + Parameters + ---------- + data : complex or np.complexfloating + The complex value to convert. + + Returns + ------- + tuple[JSONFloat, JSONFloat] + The JSON representation of the complex number. + """ + return float_to_json_v3(data.real), float_to_json_v3(data.imag) + + +def complex_float_to_json_v2( + data: complex | np.complexfloating[Any, Any], +) -> tuple[JSONFloatV2, JSONFloatV2]: + """ + Convert a complex number to JSON as defined by the Zarr V2 spec. + + Parameters + ---------- + data : complex | np.complexfloating + The complex value to convert. + + Returns + ------- + tuple[JSONFloat, JSONFloat] + The JSON representation of the complex number. + """ + return float_to_json_v2(data.real), float_to_json_v2(data.imag) + + +def complex_float_from_json_v2(data: tuple[JSONFloatV2, JSONFloatV2]) -> complex: + """ + Convert a JSON complex float to a complex number (v2). + + Parameters + ---------- + data : tuple[JSONFloat, JSONFloat] + The JSON complex float to convert. + + Returns + ------- + np.complexfloating + The complex number. + """ + return complex(float_from_json_v2(data[0]), float_from_json_v2(data[1])) + + +def complex_float_from_json_v3(data: tuple[JSONFloatV3, JSONFloatV3]) -> complex: + """ + Convert a JSON complex float to a complex number (v3). + + Parameters + ---------- + data : tuple[JSONFloat, JSONFloat] + The JSON complex float to convert. + + Returns + ------- + np.complexfloating + The complex number. + """ + return complex(float_from_json_v3(data[0]), float_from_json_v3(data[1])) + + +def check_json_float_v2(data: JSON) -> TypeGuard[JSONFloatV2]: + """ + Check if a JSON value represents a float (v2). + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a float, False otherwise. + """ + if data == "NaN" or data == "Infinity" or data == "-Infinity": + return True + return isinstance(data, float | int) + + +def check_json_float_v3(data: JSON) -> TypeGuard[JSONFloatV3]: + """ + Check if a JSON value represents a float (v3). + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a float, False otherwise. + """ + return check_json_float_v2(data) or (isinstance(data, str) and data.startswith("0x")) + + +def check_json_complex_float_v2(data: JSON) -> TypeGuard[tuple[JSONFloatV2, JSONFloatV2]]: + """ + Check if a JSON value represents a complex float, as per the behavior of zarr-python 2.x + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a complex float, False otherwise. + """ + return ( + not isinstance(data, str) + and isinstance(data, Sequence) + and len(data) == 2 + and check_json_float_v2(data[0]) + and check_json_float_v2(data[1]) + ) + + +def check_json_complex_float_v3(data: JSON) -> TypeGuard[tuple[JSONFloatV3, JSONFloatV3]]: + """ + Check if a JSON value represents a complex float, as per the zarr v3 spec + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a complex float, False otherwise. + """ + return ( + not isinstance(data, str) + and isinstance(data, Sequence) + and len(data) == 2 + and check_json_float_v3(data[0]) + and check_json_float_v3(data[1]) + ) + + +def check_json_int(data: JSON) -> TypeGuard[int]: + """ + Check if a JSON value is an integer. + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is an integer, False otherwise. + """ + return bool(isinstance(data, int)) + + +def check_json_str(data: JSON) -> TypeGuard[str]: + """ + Check if a JSON value is a string. + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a string, False otherwise. + """ + return bool(isinstance(data, str)) + + +def check_json_bool(data: JSON) -> TypeGuard[bool]: + """ + Check if a JSON value is a boolean. + + Parameters + ---------- + data : JSON + The JSON value to check. + + Returns + ------- + Bool + True if the data is a boolean, False otherwise. + """ + return isinstance(data, bool) diff --git a/src/zarr/core/dtype/npy/complex.py b/src/zarr/core/dtype/npy/complex.py new file mode 100644 index 0000000000..38e506f1bc --- /dev/null +++ b/src/zarr/core/dtype/npy/complex.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import ( + TYPE_CHECKING, + ClassVar, + Literal, + Self, + TypeGuard, + overload, +) + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasEndianness, + HasItemSize, + check_dtype_spec_v2, +) +from zarr.core.dtype.npy.common import ( + ComplexLike, + TComplexDType_co, + TComplexScalar_co, + check_json_complex_float_v2, + check_json_complex_float_v3, + complex_float_from_json_v2, + complex_float_from_json_v3, + complex_float_to_json_v2, + complex_float_to_json_v3, + endianness_to_numpy_str, + get_endianness_from_numpy_dtype, +) +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + + +@dataclass(frozen=True) +class BaseComplex(ZDType[TComplexDType_co, TComplexScalar_co], HasEndianness, HasItemSize): + # This attribute holds the possible zarr v2 JSON names for the data type + _zarr_v2_names: ClassVar[tuple[str, ...]] + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> TComplexDType_co: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) # type: ignore[return-value] + + @classmethod + def _check_json_v2(cls, data: DTypeJSON) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid JSON representation of this data type. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] in cls._zarr_v2_names + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[str]: + return data == cls._zarr_v3_name + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected {cls._zarr_v3_name}." + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> str: ... + + def to_json(self, zarr_format: ZarrFormat) -> DTypeConfig_V2[str, None] | str: + """ + Convert the wrapped data type to a JSON-serializable form. + + Parameters + ---------- + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + str + The JSON-serializable representation of the wrapped data type + """ + if zarr_format == 2: + return {"name": self.to_native_dtype().str, "object_codec_id": None} + elif zarr_format == 3: + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[ComplexLike]: + return isinstance(data, ComplexLike) + + def _cast_scalar_unchecked(self, data: ComplexLike) -> TComplexScalar_co: + return self.to_native_dtype().type(data) # type: ignore[return-value] + + def cast_scalar(self, data: object) -> TComplexScalar_co: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy float scalar." + raise TypeError(msg) + + def default_scalar(self) -> TComplexScalar_co: + """ + Get the default value, which is 0 cast to this dtype + + Returns + ------- + Int scalar + The default value. + """ + return self._cast_scalar_unchecked(0) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> TComplexScalar_co: + """ + Read a JSON-serializable value as a numpy float. + + Parameters + ---------- + data : JSON + The JSON-serializable value. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + TScalar_co + The numpy float. + """ + if zarr_format == 2: + if check_json_complex_float_v2(data): + return self._cast_scalar_unchecked(complex_float_from_json_v2(data)) + raise TypeError( + f"Invalid type: {data}. Expected a float or a special string encoding of a float." + ) + elif zarr_format == 3: + if check_json_complex_float_v3(data): + return self._cast_scalar_unchecked(complex_float_from_json_v3(data)) + raise TypeError( + f"Invalid type: {data}. Expected a float or a special string encoding of a float." + ) + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> JSON: + """ + Convert an object to a JSON-serializable float. + + Parameters + ---------- + data : _BaseScalar + The value to convert. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + JSON + The JSON-serializable form of the complex number, which is a list of two floats, + each of which is encoding according to a zarr-format-specific encoding. + """ + if zarr_format == 2: + return complex_float_to_json_v2(self.cast_scalar(data)) + elif zarr_format == 3: + return complex_float_to_json_v3(self.cast_scalar(data)) + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + +@dataclass(frozen=True, kw_only=True) +class Complex64(BaseComplex[np.dtypes.Complex64DType, np.complex64]): + dtype_cls = np.dtypes.Complex64DType + _zarr_v3_name: ClassVar[Literal["complex64"]] = "complex64" + _zarr_v2_names: ClassVar[tuple[str, ...]] = (">c8", " int: + return 8 + + +@dataclass(frozen=True, kw_only=True) +class Complex128(BaseComplex[np.dtypes.Complex128DType, np.complex128], HasEndianness): + dtype_cls = np.dtypes.Complex128DType + _zarr_v3_name: ClassVar[Literal["complex128"]] = "complex128" + _zarr_v2_names: ClassVar[tuple[str, ...]] = (">c16", " int: + return 16 diff --git a/src/zarr/core/dtype/npy/float.py b/src/zarr/core/dtype/npy/float.py new file mode 100644 index 0000000000..7b7243993f --- /dev/null +++ b/src/zarr/core/dtype/npy/float.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, ClassVar, Literal, Self, TypeGuard, overload + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasEndianness, + HasItemSize, + ScalarTypeValidationError, + check_dtype_spec_v2, +) +from zarr.core.dtype.npy.common import ( + FloatLike, + TFloatDType_co, + TFloatScalar_co, + check_json_float_v2, + check_json_float_v3, + endianness_to_numpy_str, + float_from_json_v2, + float_from_json_v3, + float_to_json_v2, + float_to_json_v3, + get_endianness_from_numpy_dtype, +) +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + + +@dataclass(frozen=True) +class BaseFloat(ZDType[TFloatDType_co, TFloatScalar_co], HasEndianness, HasItemSize): + # This attribute holds the possible zarr v2 JSON names for the data type + _zarr_v2_names: ClassVar[tuple[str, ...]] + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> TFloatDType_co: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) # type: ignore[return-value] + + @classmethod + def _check_json_v2(cls, data: DTypeJSON) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid JSON representation of this data type. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] in cls._zarr_v2_names + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[str]: + return data == cls._zarr_v3_name + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected {cls._zarr_v3_name}." + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> str: ... + + def to_json(self, zarr_format: ZarrFormat) -> DTypeConfig_V2[str, None] | str: + """ + Convert the wrapped data type to a JSON-serializable form. + + Parameters + ---------- + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + str + The JSON-serializable representation of the wrapped data type + """ + if zarr_format == 2: + return {"name": self.to_native_dtype().str, "object_codec_id": None} + elif zarr_format == 3: + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[FloatLike]: + return isinstance(data, FloatLike) + + def _cast_scalar_unchecked(self, data: FloatLike) -> TFloatScalar_co: + return self.to_native_dtype().type(data) # type: ignore[return-value] + + def cast_scalar(self, data: object) -> TFloatScalar_co: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy float scalar." + raise ScalarTypeValidationError(msg) + + def default_scalar(self) -> TFloatScalar_co: + """ + Get the default value, which is 0 cast to this dtype + + Returns + ------- + Int scalar + The default value. + """ + return self._cast_scalar_unchecked(0) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> TFloatScalar_co: + """ + Read a JSON-serializable value as a numpy float. + + Parameters + ---------- + data : JSON + The JSON-serializable value. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + TScalar_co + The numpy float. + """ + if zarr_format == 2: + if check_json_float_v2(data): + return self._cast_scalar_unchecked(float_from_json_v2(data)) + else: + raise TypeError( + f"Invalid type: {data}. Expected a float or a special string encoding of a float." + ) + elif zarr_format == 3: + if check_json_float_v3(data): + return self._cast_scalar_unchecked(float_from_json_v3(data)) + else: + raise TypeError( + f"Invalid type: {data}. Expected a float or a special string encoding of a float." + ) + else: + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> float | str: + """ + Convert an object to a JSON-serializable float. + + Parameters + ---------- + data : _BaseScalar + The value to convert. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + JSON + The JSON-serializable form of the float, which is potentially a number or a string. + See the zarr specifications for details on the JSON encoding for floats. + """ + if zarr_format == 2: + return float_to_json_v2(self.cast_scalar(data)) + elif zarr_format == 3: + return float_to_json_v3(self.cast_scalar(data)) + else: + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + +@dataclass(frozen=True, kw_only=True) +class Float16(BaseFloat[np.dtypes.Float16DType, np.float16]): + dtype_cls = np.dtypes.Float16DType + _zarr_v3_name = "float16" + _zarr_v2_names: ClassVar[tuple[Literal[">f2"], Literal["f2", " int: + return 2 + + +@dataclass(frozen=True, kw_only=True) +class Float32(BaseFloat[np.dtypes.Float32DType, np.float32]): + dtype_cls = np.dtypes.Float32DType + _zarr_v3_name = "float32" + _zarr_v2_names: ClassVar[tuple[Literal[">f4"], Literal["f4", " int: + return 4 + + +@dataclass(frozen=True, kw_only=True) +class Float64(BaseFloat[np.dtypes.Float64DType, np.float64]): + dtype_cls = np.dtypes.Float64DType + _zarr_v3_name = "float64" + _zarr_v2_names: ClassVar[tuple[Literal[">f8"], Literal["f8", " int: + return 8 diff --git a/src/zarr/core/dtype/npy/int.py b/src/zarr/core/dtype/npy/int.py new file mode 100644 index 0000000000..79d3ce2d47 --- /dev/null +++ b/src/zarr/core/dtype/npy/int.py @@ -0,0 +1,686 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import ( + TYPE_CHECKING, + ClassVar, + Literal, + Self, + SupportsIndex, + SupportsInt, + TypeGuard, + TypeVar, + overload, +) + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasEndianness, + HasItemSize, + check_dtype_spec_v2, +) +from zarr.core.dtype.npy.common import ( + check_json_int, + endianness_to_numpy_str, + get_endianness_from_numpy_dtype, +) +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + +_NumpyIntDType = ( + np.dtypes.Int8DType + | np.dtypes.Int16DType + | np.dtypes.Int32DType + | np.dtypes.Int64DType + | np.dtypes.UInt8DType + | np.dtypes.UInt16DType + | np.dtypes.UInt32DType + | np.dtypes.UInt64DType +) +_NumpyIntScalar = ( + np.int8 | np.int16 | np.int32 | np.int64 | np.uint8 | np.uint16 | np.uint32 | np.uint64 +) +TIntDType_co = TypeVar("TIntDType_co", bound=_NumpyIntDType, covariant=True) +TIntScalar_co = TypeVar("TIntScalar_co", bound=_NumpyIntScalar, covariant=True) +IntLike = SupportsInt | SupportsIndex | bytes | str + + +@dataclass(frozen=True) +class BaseInt(ZDType[TIntDType_co, TIntScalar_co], HasItemSize): + # This attribute holds the possible zarr V2 JSON names for the data type + _zarr_v2_names: ClassVar[tuple[str, ...]] + + @classmethod + def _check_json_v2(cls, data: object) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid JSON representation of this data type. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] in cls._zarr_v2_names + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: object) -> TypeGuard[str]: + """ + Check that a JSON value is consistent with the zarr v3 spec for this data type. + """ + return data == cls._zarr_v3_name + + def _check_scalar(self, data: object) -> TypeGuard[IntLike]: + """ + Check that a python object is IntLike + """ + return isinstance(data, IntLike) + + def _cast_scalar_unchecked(self, data: IntLike) -> TIntScalar_co: + """ + Create an integer without any type checking of the input. + """ + return self.to_native_dtype().type(data) # type: ignore[return-value] + + def cast_scalar(self, data: object) -> TIntScalar_co: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy integer." + raise TypeError(msg) + + def default_scalar(self) -> TIntScalar_co: + """ + Get the default value, which is 0 cast to this dtype + + Returns + ------- + Int scalar + The default value. + """ + return self._cast_scalar_unchecked(0) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> TIntScalar_co: + """ + Read a JSON-serializable value as a numpy int scalar. + + Parameters + ---------- + data : JSON + The JSON-serializable value. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + TScalar_co + The numpy scalar. + """ + if check_json_int(data): + return self._cast_scalar_unchecked(data) + raise TypeError(f"Invalid type: {data}. Expected an integer.") + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> int: + """ + Convert an object to JSON-serializable scalar. + + Parameters + ---------- + data : _BaseScalar + The value to convert. + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + int + The JSON-serializable form of the scalar. + """ + return int(self.cast_scalar(data)) + + +@dataclass(frozen=True, kw_only=True) +class Int8(BaseInt[np.dtypes.Int8DType, np.int8]): + dtype_cls = np.dtypes.Int8DType + _zarr_v3_name: ClassVar[Literal["int8"]] = "int8" + _zarr_v2_names: ClassVar[tuple[Literal["|i1"]]] = ("|i1",) + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + """ + Create a Int8 from a np.dtype('int8') instance. + """ + if cls._check_native_dtype(dtype): + return cls() + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self: Self) -> np.dtypes.Int8DType: + return self.dtype_cls() + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v2_names[0]!r}" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal["|i1"], None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> Literal["int8"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal["|i1"], None] | Literal["int8"]: + """ + Convert the wrapped data type to a JSON-serializable form. + + Parameters + ---------- + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + str + The JSON-serializable representation of the wrapped data type + """ + if zarr_format == 2: + return {"name": self._zarr_v2_names[0], "object_codec_id": None} + elif zarr_format == 3: + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + @property + def item_size(self) -> int: + return 1 + + +@dataclass(frozen=True, kw_only=True) +class UInt8(BaseInt[np.dtypes.UInt8DType, np.uint8]): + dtype_cls = np.dtypes.UInt8DType + _zarr_v3_name: ClassVar[Literal["uint8"]] = "uint8" + _zarr_v2_names: ClassVar[tuple[Literal["|u1"]]] = ("|u1",) + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + """ + Create a Bool from a np.dtype('uint8') instance. + """ + if cls._check_native_dtype(dtype): + return cls() + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self: Self) -> np.dtypes.UInt8DType: + return self.dtype_cls() + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v2_names[0]!r}" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal["|u1"], None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> Literal["uint8"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal["|u1"], None] | Literal["uint8"]: + """ + Convert the wrapped data type to a JSON-serializable form. + + Parameters + ---------- + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + str + The JSON-serializable representation of the wrapped data type + """ + if zarr_format == 2: + return {"name": self._zarr_v2_names[0], "object_codec_id": None} + elif zarr_format == 3: + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + @property + def item_size(self) -> int: + return 1 + + +@dataclass(frozen=True, kw_only=True) +class Int16(BaseInt[np.dtypes.Int16DType, np.int16], HasEndianness): + dtype_cls = np.dtypes.Int16DType + _zarr_v3_name: ClassVar[Literal["int16"]] = "int16" + _zarr_v2_names: ClassVar[tuple[Literal[">i2"], Literal["i2", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.Int16DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names!r}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">i2", " Literal["int16"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">i2", " int: + return 2 + + +@dataclass(frozen=True, kw_only=True) +class UInt16(BaseInt[np.dtypes.UInt16DType, np.uint16], HasEndianness): + dtype_cls = np.dtypes.UInt16DType + _zarr_v3_name: ClassVar[Literal["uint16"]] = "uint16" + _zarr_v2_names: ClassVar[tuple[Literal[">u2"], Literal["u2", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.UInt16DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of UInt16. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of UInt16. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">u2", " Literal["uint16"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">u2", " int: + return 2 + + +@dataclass(frozen=True, kw_only=True) +class Int32(BaseInt[np.dtypes.Int32DType, np.int32], HasEndianness): + dtype_cls = np.dtypes.Int32DType + _zarr_v3_name: ClassVar[Literal["int32"]] = "int32" + _zarr_v2_names: ClassVar[tuple[Literal[">i4"], Literal["i4", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.Int32DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">i4", " Literal["int32"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">i4", " int: + return 4 + + +@dataclass(frozen=True, kw_only=True) +class UInt32(BaseInt[np.dtypes.UInt32DType, np.uint32], HasEndianness): + dtype_cls = np.dtypes.UInt32DType + _zarr_v3_name: ClassVar[Literal["uint32"]] = "uint32" + _zarr_v2_names: ClassVar[tuple[Literal[">u4"], Literal["u4", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.UInt32DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">u4", " Literal["uint32"]: ... + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">u4", " int: + return 4 + + +@dataclass(frozen=True, kw_only=True) +class Int64(BaseInt[np.dtypes.Int64DType, np.int64], HasEndianness): + dtype_cls = np.dtypes.Int64DType + _zarr_v3_name: ClassVar[Literal["int64"]] = "int64" + _zarr_v2_names: ClassVar[tuple[Literal[">i8"], Literal["i8", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.Int64DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">i8", " Literal["int64"]: ... + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">i8", " int: + return 8 + + +@dataclass(frozen=True, kw_only=True) +class UInt64(BaseInt[np.dtypes.UInt64DType, np.uint64], HasEndianness): + dtype_cls = np.dtypes.UInt64DType + _zarr_v3_name: ClassVar[Literal["uint64"]] = "uint64" + _zarr_v2_names: ClassVar[tuple[Literal[">u8"], Literal["u8", " np.dtypes.UInt64DType: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls().newbyteorder(byte_order) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Going via numpy ensures that we get the endianness correct without + # annoying string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected one of the strings {cls._zarr_v2_names}." + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[Literal[">u8", " Literal["uint64"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal[">u8", " Self: + if cls._check_native_dtype(dtype): + return cls(endianness=get_endianness_from_numpy_dtype(dtype)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + @property + def item_size(self) -> int: + return 8 diff --git a/src/zarr/core/dtype/npy/string.py b/src/zarr/core/dtype/npy/string.py new file mode 100644 index 0000000000..4a1114617a --- /dev/null +++ b/src/zarr/core/dtype/npy/string.py @@ -0,0 +1,302 @@ +from __future__ import annotations + +import re +from dataclasses import dataclass +from typing import ( + TYPE_CHECKING, + ClassVar, + Literal, + Protocol, + Self, + TypedDict, + TypeGuard, + overload, + runtime_checkable, +) + +import numpy as np + +from zarr.core.common import NamedConfig +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasEndianness, + HasItemSize, + HasLength, + HasObjectCodec, + check_dtype_spec_v2, + v3_unstable_dtype_warning, +) +from zarr.core.dtype.npy.common import ( + check_json_str, + endianness_to_numpy_str, + get_endianness_from_numpy_dtype, +) +from zarr.core.dtype.wrapper import TDType_co, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + from zarr.core.dtype.wrapper import TBaseDType + +_NUMPY_SUPPORTS_VLEN_STRING = hasattr(np.dtypes, "StringDType") + + +@runtime_checkable +class SupportsStr(Protocol): + def __str__(self) -> str: ... + + +class LengthBytesConfig(TypedDict): + length_bytes: int + + +# TODO: Fix this terrible name +FixedLengthUTF32JSONV3 = NamedConfig[Literal["fixed_length_utf32"], LengthBytesConfig] + + +@dataclass(frozen=True, kw_only=True) +class FixedLengthUTF32( + ZDType[np.dtypes.StrDType[int], np.str_], HasEndianness, HasLength, HasItemSize +): + dtype_cls = np.dtypes.StrDType + _zarr_v3_name: ClassVar[Literal["fixed_length_utf32"]] = "fixed_length_utf32" + code_point_bytes: ClassVar[int] = 4 # utf32 is 4 bytes per code point + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + endianness = get_endianness_from_numpy_dtype(dtype) + return cls( + length=dtype.itemsize // (cls.code_point_bytes), + endianness=endianness, + ) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> np.dtypes.StrDType[int]: + byte_order = endianness_to_numpy_str(self.endianness) + return self.dtype_cls(self.length).newbyteorder(byte_order) + + @classmethod + def _check_json_v2(cls, data: DTypeJSON) -> TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that the input is a valid JSON representation of a numpy U dtype. + """ + return ( + check_dtype_spec_v2(data) + and isinstance(data["name"], str) + and re.match(r"^[><]U\d+$", data["name"]) is not None + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[FixedLengthUTF32JSONV3]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and "configuration" in data + and isinstance(data["configuration"], dict) + and set(data["configuration"].keys()) == {"length_bytes"} + and isinstance(data["configuration"]["length_bytes"], int) + ) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> FixedLengthUTF32JSONV3: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[str, None] | FixedLengthUTF32JSONV3: + if zarr_format == 2: + return {"name": self.to_native_dtype().str, "object_codec_id": None} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + return { + "name": self._zarr_v3_name, + "configuration": {"length_bytes": self.length * self.code_point_bytes}, + } + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + # Construct the numpy dtype instead of string parsing. + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + raise DataTypeValidationError( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a string representation of a numpy U dtype." + ) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls(length=data["configuration"]["length_bytes"] // cls.code_point_bytes) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected {cls._zarr_v3_name}." + raise DataTypeValidationError(msg) + + def default_scalar(self) -> np.str_: + return np.str_("") + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + return str(data) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.str_: + if check_json_str(data): + return self.to_native_dtype().type(data) + raise TypeError(f"Invalid type: {data}. Expected a string.") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[str | np.str_ | bytes | int]: + # this is generous for backwards compatibility + return isinstance(data, str | np.str_ | bytes | int) + + def cast_scalar(self, data: object) -> np.str_: + if self._check_scalar(data): + # We explicitly truncate before casting because of the following numpy behavior: + # >>> x = np.dtype('U3').type('hello world') + # >>> x + # np.str_('hello world') + # >>> x.dtype + # dtype('U11') + + if isinstance(data, int): + return self.to_native_dtype().type(str(data)[: self.length]) + else: + return self.to_native_dtype().type(data[: self.length]) + raise TypeError( + f"Cannot convert object with type {type(data)} to a numpy unicode string scalar." + ) + + @property + def item_size(self) -> int: + return self.length * self.code_point_bytes + + +def check_vlen_string_json_scalar(data: object) -> TypeGuard[int | str | float]: + """ + This function checks the type of JSON-encoded variable length strings. It is generous for + backwards compatibility, as zarr-python v2 would use ints for variable length strings + fill values + """ + return isinstance(data, int | str | float) + + +# VariableLengthUTF8 is defined in two places, conditioned on the version of numpy. +# If numpy 2 is installed, then VariableLengthUTF8 is defined with the numpy variable length +# string dtype as the native dtype. Otherwise, VariableLengthUTF8 is defined with the numpy object +# dtype as the native dtype. +class UTF8Base(ZDType[TDType_co, str], HasObjectCodec): + """ + A base class for the variable length UTF-8 string data type. This class should not be used + as data type, but as a base class for other variable length string data types. + """ + + _zarr_v3_name: ClassVar[Literal["variable_length_utf8"]] = "variable_length_utf8" + object_codec_id: ClassVar[Literal["vlen-utf8"]] = "vlen-utf8" + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + return cls() + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + @classmethod + def _check_json_v2( + cls, + data: DTypeJSON, + ) -> TypeGuard[DTypeConfig_V2[Literal["|O"], Literal["vlen-utf8"]]]: + """ + Check that the input is a valid JSON representation of a numpy O dtype, and that the + object codec id is appropriate for variable-length UTF-8 strings. + """ + return ( + check_dtype_spec_v2(data) + and data["name"] == "|O" + and data["object_codec_id"] == cls.object_codec_id + ) + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[Literal["variable_length_utf8"]]: + return data == cls._zarr_v3_name + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + return cls() + msg = ( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected the string '|O'" + ) + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + return cls() + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected {cls._zarr_v3_name}." + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json( + self, zarr_format: Literal[2] + ) -> DTypeConfig_V2[Literal["|O"], Literal["vlen-utf8"]]: ... + @overload + def to_json(self, zarr_format: Literal[3]) -> Literal["variable_length_utf8"]: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[Literal["|O"], Literal["vlen-utf8"]] | Literal["variable_length_utf8"]: + if zarr_format == 2: + return {"name": "|O", "object_codec_id": self.object_codec_id} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + return self._zarr_v3_name + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def default_scalar(self) -> str: + return "" + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + raise TypeError(f"Invalid type: {data}. Expected a string.") + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> str: + if not check_vlen_string_json_scalar(data): + raise TypeError(f"Invalid type: {data}. Expected a string or number.") + return str(data) + + def _check_scalar(self, data: object) -> TypeGuard[SupportsStr]: + return isinstance(data, SupportsStr) + + def _cast_scalar_unchecked(self, data: SupportsStr) -> str: + return str(data) + + def cast_scalar(self, data: object) -> str: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + raise TypeError(f"Cannot convert object with type {type(data)} to a python string.") + + +if _NUMPY_SUPPORTS_VLEN_STRING: + + @dataclass(frozen=True, kw_only=True) + class VariableLengthUTF8(UTF8Base[np.dtypes.StringDType]): # type: ignore[type-var] + dtype_cls = np.dtypes.StringDType + + def to_native_dtype(self) -> np.dtypes.StringDType: + return self.dtype_cls() + +else: + # Numpy pre-2 does not have a variable length string dtype, so we use the Object dtype instead. + @dataclass(frozen=True, kw_only=True) + class VariableLengthUTF8(UTF8Base[np.dtypes.ObjectDType]): # type: ignore[no-redef] + dtype_cls = np.dtypes.ObjectDType + + def to_native_dtype(self) -> np.dtypes.ObjectDType: + return self.dtype_cls() diff --git a/src/zarr/core/dtype/npy/structured.py b/src/zarr/core/dtype/npy/structured.py new file mode 100644 index 0000000000..07e3000826 --- /dev/null +++ b/src/zarr/core/dtype/npy/structured.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal, Self, TypeGuard, cast, overload + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + DTypeSpec_V3, + HasItemSize, + StructuredName_V2, + check_dtype_spec_v2, + check_structured_dtype_name_v2, + v3_unstable_dtype_warning, +) +from zarr.core.dtype.npy.common import ( + bytes_from_json, + bytes_to_json, + check_json_str, +) +from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType + +if TYPE_CHECKING: + from collections.abc import Sequence + + from zarr.core.common import JSON, NamedConfig, ZarrFormat + +StructuredScalarLike = list[object] | tuple[object, ...] | bytes | int + + +@dataclass(frozen=True, kw_only=True) +class Structured(ZDType[np.dtypes.VoidDType[int], np.void], HasItemSize): + dtype_cls = np.dtypes.VoidDType # type: ignore[assignment] + _zarr_v3_name = "structured" + fields: tuple[tuple[str, ZDType[TBaseDType, TBaseScalar]], ...] + + @classmethod + def _check_native_dtype(cls, dtype: TBaseDType) -> TypeGuard[np.dtypes.VoidDType[int]]: + """ + Check that this dtype is a numpy structured dtype + + Parameters + ---------- + dtype : np.dtypes.DTypeLike + The dtype to check. + + Returns + ------- + TypeGuard[np.dtypes.VoidDType] + True if the dtype matches, False otherwise. + """ + return isinstance(dtype, cls.dtype_cls) and dtype.fields is not None # type: ignore[has-type] + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + from zarr.core.dtype import get_data_type_from_native_dtype + + fields: list[tuple[str, ZDType[TBaseDType, TBaseScalar]]] = [] + if cls._check_native_dtype(dtype): + # fields of a structured numpy dtype are either 2-tuples or 3-tuples. we only + # care about the first element in either case. + for key, (dtype_instance, *_) in dtype.fields.items(): # type: ignore[union-attr] + dtype_wrapped = get_data_type_from_native_dtype(dtype_instance) + fields.append((key, dtype_wrapped)) + + return cls(fields=tuple(fields)) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" # type: ignore[has-type] + ) + + def to_native_dtype(self) -> np.dtypes.VoidDType[int]: + return cast( + "np.dtypes.VoidDType[int]", + np.dtype([(key, dtype.to_native_dtype()) for (key, dtype) in self.fields]), + ) + + @classmethod + def _check_json_v2( + cls, + data: DTypeJSON, + ) -> TypeGuard[DTypeConfig_V2[StructuredName_V2, None]]: + return ( + check_dtype_spec_v2(data) + and not isinstance(data["name"], str) + and check_structured_dtype_name_v2(data["name"]) + and data["object_codec_id"] is None + ) + + @classmethod + def _check_json_v3( + cls, data: DTypeJSON + ) -> TypeGuard[NamedConfig[Literal["structured"], dict[str, Sequence[tuple[str, DTypeJSON]]]]]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and isinstance(data["configuration"], dict) + and set(data["configuration"].keys()) == {"fields"} + ) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + # avoid circular import + from zarr.core.dtype import get_data_type_from_json + + if cls._check_json_v2(data): + # structured dtypes are constructed directly from a list of lists + # note that we do not handle the object codec here! this will prevent structured + # dtypes from containing object dtypes. + return cls( + fields=tuple( # type: ignore[misc] + ( # type: ignore[misc] + f_name, + get_data_type_from_json( + {"name": f_dtype, "object_codec_id": None}, zarr_format=2 + ), + ) + for f_name, f_dtype in data["name"] + ) + ) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a JSON array of arrays" + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + # avoid circular import + from zarr.core.dtype import get_data_type_from_json + + if cls._check_json_v3(data): + config = data["configuration"] + meta_fields = config["fields"] + return cls( + fields=tuple( + (f_name, get_data_type_from_json(f_dtype, zarr_format=3)) + for f_name, f_dtype in meta_fields + ) + ) + msg = f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a JSON object with the key {cls._zarr_v3_name!r}" + raise DataTypeValidationError(msg) + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[StructuredName_V2, None]: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> DTypeSpec_V3: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[StructuredName_V2, None] | DTypeSpec_V3: + if zarr_format == 2: + fields = [ + [f_name, f_dtype.to_json(zarr_format=zarr_format)["name"]] + for f_name, f_dtype in self.fields + ] + return {"name": fields, "object_codec_id": None} + elif zarr_format == 3: + v3_unstable_dtype_warning(self) + fields = [ + [f_name, f_dtype.to_json(zarr_format=zarr_format)] # type: ignore[list-item] + for f_name, f_dtype in self.fields + ] + base_dict = { + "name": self._zarr_v3_name, + "configuration": {"fields": fields}, + } + return cast("DTypeSpec_V3", base_dict) + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def _check_scalar(self, data: object) -> TypeGuard[StructuredScalarLike]: + # TODO: implement something more precise here! + return isinstance(data, (bytes, list, tuple, int, np.void)) + + def _cast_scalar_unchecked(self, data: StructuredScalarLike) -> np.void: + na_dtype = self.to_native_dtype() + if isinstance(data, bytes): + res = np.frombuffer(data, dtype=na_dtype)[0] + elif isinstance(data, list | tuple): + res = np.array([tuple(data)], dtype=na_dtype)[0] + else: + res = np.array([data], dtype=na_dtype)[0] + return cast("np.void", res) + + def cast_scalar(self, data: object) -> np.void: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy structured scalar." + raise TypeError(msg) + + def default_scalar(self) -> np.void: + return self._cast_scalar_unchecked(0) + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.void: + if check_json_str(data): + as_bytes = bytes_from_json(data, zarr_format=zarr_format) + dtype = self.to_native_dtype() + return cast("np.void", np.array([as_bytes]).view(dtype)[0]) + raise TypeError(f"Invalid type: {data}. Expected a string.") + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> str: + return bytes_to_json(self.cast_scalar(data).tobytes(), zarr_format) + + @property + def item_size(self) -> int: + # Lets have numpy do the arithmetic here + return self.to_native_dtype().itemsize diff --git a/src/zarr/core/dtype/npy/time.py b/src/zarr/core/dtype/npy/time.py new file mode 100644 index 0000000000..1f9080475c --- /dev/null +++ b/src/zarr/core/dtype/npy/time.py @@ -0,0 +1,359 @@ +from __future__ import annotations + +from dataclasses import dataclass +from datetime import datetime, timedelta +from typing import ( + TYPE_CHECKING, + ClassVar, + Literal, + Self, + TypedDict, + TypeGuard, + TypeVar, + cast, + get_args, + overload, +) + +import numpy as np + +from zarr.core.common import NamedConfig +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeConfig_V2, + DTypeJSON, + HasEndianness, + HasItemSize, + check_dtype_spec_v2, +) +from zarr.core.dtype.npy.common import ( + DATETIME_UNIT, + DateTimeUnit, + check_json_int, + endianness_to_numpy_str, + get_endianness_from_numpy_dtype, +) +from zarr.core.dtype.wrapper import TBaseDType, ZDType + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + +_DTypeName = Literal["datetime64", "timedelta64"] +TimeDeltaLike = str | int | bytes | np.timedelta64 | timedelta | None +DateTimeLike = str | int | bytes | np.datetime64 | datetime | None + + +def datetime_from_int(data: int, *, unit: DateTimeUnit, scale_factor: int) -> np.datetime64: + """ + Convert an integer to a datetime64. + + Parameters + ---------- + data : int + The integer to convert. + unit : DateTimeUnit + The unit of the datetime64. + scale_factor : int + The scale factor of the datetime64. + + Returns + ------- + np.datetime64 + The datetime64 value. + """ + dtype_name = f"datetime64[{scale_factor}{unit}]" + return cast("np.datetime64", np.int64(data).view(dtype_name)) + + +def datetimelike_to_int(data: np.datetime64 | np.timedelta64) -> int: + """ + Convert a datetime64 or a timedelta64 to an integer. + + Parameters + ---------- + data : np.datetime64 | np.timedelta64 + The value to convert. + + Returns + ------- + int + An integer representation of the scalar. + """ + return data.view(np.int64).item() + + +def check_json_time(data: JSON) -> TypeGuard[Literal["NaT"] | int]: + """ + Type guard to check if the input JSON data is the literal string "NaT" + or an integer. + """ + return check_json_int(data) or data == "NaT" + + +BaseTimeDType_co = TypeVar( + "BaseTimeDType_co", + bound=np.dtypes.TimeDelta64DType | np.dtypes.DateTime64DType, + covariant=True, +) +BaseTimeScalar_co = TypeVar( + "BaseTimeScalar_co", bound=np.timedelta64 | np.datetime64, covariant=True +) + + +class TimeConfig(TypedDict): + unit: DateTimeUnit + scale_factor: int + + +DateTime64JSONV3 = NamedConfig[Literal["numpy.datetime64"], TimeConfig] +TimeDelta64JSONV3 = NamedConfig[Literal["numpy.timedelta64"], TimeConfig] + + +@dataclass(frozen=True, kw_only=True, slots=True) +class TimeDTypeBase(ZDType[BaseTimeDType_co, BaseTimeScalar_co], HasEndianness, HasItemSize): + _zarr_v2_names: ClassVar[tuple[str, ...]] + # this attribute exists so that we can programmatically create a numpy dtype instance + # because the particular numpy dtype we are wrapping does not allow direct construction via + # cls.dtype_cls() + _numpy_name: ClassVar[_DTypeName] + scale_factor: int + unit: DateTimeUnit + + def __post_init__(self) -> None: + if self.scale_factor < 1: + raise ValueError(f"scale_factor must be > 0, got {self.scale_factor}.") + if self.scale_factor >= 2**31: + raise ValueError(f"scale_factor must be < 2147483648, got {self.scale_factor}.") + if self.unit not in get_args(DateTimeUnit): + raise ValueError(f"unit must be one of {get_args(DateTimeUnit)}, got {self.unit!r}.") + + @classmethod + def from_native_dtype(cls, dtype: TBaseDType) -> Self: + if cls._check_native_dtype(dtype): + unit, scale_factor = np.datetime_data(dtype.name) + unit = cast("DateTimeUnit", unit) + return cls( + unit=unit, + scale_factor=scale_factor, + endianness=get_endianness_from_numpy_dtype(dtype), + ) + raise DataTypeValidationError( + f"Invalid data type: {dtype}. Expected an instance of {cls.dtype_cls}" + ) + + def to_native_dtype(self) -> BaseTimeDType_co: + # Numpy does not allow creating datetime64 or timedelta64 via + # np.dtypes.{dtype_name}() + # so we use np.dtype with a formatted string. + dtype_string = f"{self._numpy_name}[{self.scale_factor}{self.unit}]" + return np.dtype(dtype_string).newbyteorder(endianness_to_numpy_str(self.endianness)) # type: ignore[return-value] + + @overload # type: ignore[override] + def to_json(self, zarr_format: Literal[2]) -> DTypeConfig_V2[str, None]: ... + @overload + def to_json(self, zarr_format: Literal[3]) -> DateTime64JSONV3 | TimeDelta64JSONV3: ... + + def to_json( + self, zarr_format: ZarrFormat + ) -> DTypeConfig_V2[str, None] | DateTime64JSONV3 | TimeDelta64JSONV3: + if zarr_format == 2: + name = self.to_native_dtype().str + return {"name": name, "object_codec_id": None} + elif zarr_format == 3: + return cast( + "DateTime64JSONV3 | TimeDelta64JSONV3", + { + "name": self._zarr_v3_name, + "configuration": {"unit": self.unit, "scale_factor": self.scale_factor}, + }, + ) + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> int: + return datetimelike_to_int(data) # type: ignore[arg-type] + + @property + def item_size(self) -> int: + return 8 + + +@dataclass(frozen=True, kw_only=True, slots=True) +class TimeDelta64(TimeDTypeBase[np.dtypes.TimeDelta64DType, np.timedelta64], HasEndianness): + """ + A wrapper for the ``TimeDelta64`` data type defined in numpy. + Scalars of this type can be created by performing arithmetic with ``DateTime64`` scalars. + Like ``DateTime64``, ``TimeDelta64`` is parametrized by a unit, but unlike ``DateTime64``, the + unit for ``TimeDelta64`` is optional. + """ + + # mypy infers the type of np.dtypes.TimeDelta64DType to be + # "Callable[[Literal['Y', 'M', 'W', 'D'] | Literal['h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as']], Never]" + dtype_cls = np.dtypes.TimeDelta64DType # type: ignore[assignment] + _zarr_v3_name: ClassVar[Literal["numpy.timedelta64"]] = "numpy.timedelta64" + _zarr_v2_names = (">m8", " TypeGuard[DTypeConfig_V2[str, None]]: + if not check_dtype_spec_v2(data): + return False + name = data["name"] + # match m[M], etc + # consider making this a standalone function + if not isinstance(name, str): + return False + if not name.startswith(cls._zarr_v2_names): + return False + if len(name) == 3: + # no unit, and + # we already checked that this string is either m8 + return True + else: + return name[4:-1].endswith(DATETIME_UNIT) and name[-1] == "]" + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[DateTime64JSONV3]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and isinstance(data["configuration"], dict) + and set(data["configuration"].keys()) == {"unit", "scale_factor"} + ) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = ( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a string " + f"representation of an instance of {cls.dtype_cls}" # type: ignore[has-type] + ) + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + unit = data["configuration"]["unit"] + scale_factor = data["configuration"]["scale_factor"] + return cls(unit=unit, scale_factor=scale_factor) + msg = ( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a dict " + f"with a 'name' key with the value 'numpy.timedelta64', " + "and a 'configuration' key with a value of a dict with a 'unit' key and a " + "'scale_factor' key" + ) + raise DataTypeValidationError(msg) + + def _check_scalar(self, data: object) -> TypeGuard[TimeDeltaLike]: + if data is None: + return True + return isinstance(data, str | int | bytes | np.timedelta64 | timedelta) + + def _cast_scalar_unchecked(self, data: TimeDeltaLike) -> np.timedelta64: + return self.to_native_dtype().type(data, f"{self.scale_factor}{self.unit}") + + def cast_scalar(self, data: object) -> np.timedelta64: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy timedelta64 scalar." + raise TypeError(msg) + + def default_scalar(self) -> np.timedelta64: + return np.timedelta64("NaT") + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.timedelta64: + if check_json_time(data): + return self.to_native_dtype().type(data, f"{self.scale_factor}{self.unit}") + raise TypeError(f"Invalid type: {data}. Expected an integer.") # pragma: no cover + + +@dataclass(frozen=True, kw_only=True, slots=True) +class DateTime64(TimeDTypeBase[np.dtypes.DateTime64DType, np.datetime64], HasEndianness): + dtype_cls = np.dtypes.DateTime64DType # type: ignore[assignment] + _zarr_v3_name: ClassVar[Literal["numpy.datetime64"]] = "numpy.datetime64" + _zarr_v2_names = (">M8", " TypeGuard[DTypeConfig_V2[str, None]]: + """ + Check that JSON input is a string representation of a NumPy datetime64 data type, like "M8[10s]". This function can be used as a type guard to narrow the type of unknown JSON + input. + """ + if not check_dtype_spec_v2(data): + return False + name = data["name"] + if not isinstance(name, str): + return False + if not name.startswith(cls._zarr_v2_names): + return False + if len(name) == 3: + # no unit, and + # we already checked that this string is either M8 + return True + else: + return name[4:-1].endswith(DATETIME_UNIT) and name[-1] == "]" + + @classmethod + def _check_json_v3(cls, data: DTypeJSON) -> TypeGuard[DateTime64JSONV3]: + return ( + isinstance(data, dict) + and set(data.keys()) == {"name", "configuration"} + and data["name"] == cls._zarr_v3_name + and isinstance(data["configuration"], dict) + and set(data["configuration"].keys()) == {"unit", "scale_factor"} + ) + + @classmethod + def _from_json_v2(cls, data: DTypeJSON) -> Self: + if cls._check_json_v2(data): + name = data["name"] + return cls.from_native_dtype(np.dtype(name)) + msg = ( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a string " + f"representation of an instance of {cls.dtype_cls}" # type: ignore[has-type] + ) + raise DataTypeValidationError(msg) + + @classmethod + def _from_json_v3(cls, data: DTypeJSON) -> Self: + if cls._check_json_v3(data): + unit = data["configuration"]["unit"] + scale_factor = data["configuration"]["scale_factor"] + return cls(unit=unit, scale_factor=scale_factor) + msg = ( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}, expected a dict " + f"with a 'name' key with the value 'numpy.datetime64', " + "and a 'configuration' key with a value of a dict with a 'unit' key and a " + "'scale_factor' key" + ) + raise DataTypeValidationError(msg) + + def _check_scalar(self, data: object) -> TypeGuard[DateTimeLike]: + if data is None: + return True + return isinstance(data, str | int | bytes | np.datetime64 | datetime) + + def _cast_scalar_unchecked(self, data: DateTimeLike) -> np.datetime64: + return self.to_native_dtype().type(data, f"{self.scale_factor}{self.unit}") + + def cast_scalar(self, data: object) -> np.datetime64: + if self._check_scalar(data): + return self._cast_scalar_unchecked(data) + msg = f"Cannot convert object with type {type(data)} to a numpy datetime scalar." + raise TypeError(msg) + + def default_scalar(self) -> np.datetime64: + return np.datetime64("NaT") + + def from_json_scalar(self, data: JSON, *, zarr_format: ZarrFormat) -> np.datetime64: + if check_json_time(data): + return self._cast_scalar_unchecked(data) + raise TypeError(f"Invalid type: {data}. Expected an integer.") # pragma: no cover diff --git a/src/zarr/core/dtype/registry.py b/src/zarr/core/dtype/registry.py new file mode 100644 index 0000000000..1d2a97a90a --- /dev/null +++ b/src/zarr/core/dtype/registry.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Self + +import numpy as np + +from zarr.core.dtype.common import ( + DataTypeValidationError, + DTypeJSON, +) + +if TYPE_CHECKING: + from importlib.metadata import EntryPoint + + from zarr.core.common import ZarrFormat + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType + + +# This class is different from the other registry classes, which inherit from +# dict. IMO it's simpler to just do a dataclass. But long-term we should +# have just 1 registry class in use. +@dataclass(frozen=True, kw_only=True) +class DataTypeRegistry: + contents: dict[str, type[ZDType[TBaseDType, TBaseScalar]]] = field( + default_factory=dict, init=False + ) + + lazy_load_list: list[EntryPoint] = field(default_factory=list, init=False) + + def lazy_load(self) -> None: + for e in self.lazy_load_list: + self.register(e.load()._zarr_v3_name, e.load()) + + self.lazy_load_list.clear() + + def register(self: Self, key: str, cls: type[ZDType[TBaseDType, TBaseScalar]]) -> None: + # don't register the same dtype twice + if key not in self.contents or self.contents[key] != cls: + self.contents[key] = cls + + def unregister(self, key: str) -> None: + """Unregister a data type by its key.""" + if key in self.contents: + del self.contents[key] + else: + raise KeyError(f"Data type '{key}' not found in registry.") + + def get(self, key: str) -> type[ZDType[TBaseDType, TBaseScalar]]: + return self.contents[key] + + def match_dtype(self, dtype: TBaseDType) -> ZDType[TBaseDType, TBaseScalar]: + if dtype == np.dtype("O"): + msg = ( + f"Zarr data type resolution from {dtype} failed. " + 'Attempted to resolve a zarr data type from a numpy "Object" data type, which is ' + 'ambiguous, as multiple zarr data types can be represented by the numpy "Object" ' + "data type. " + "In this case you should construct your array by providing a specific Zarr data " + 'type. For a list of Zarr data types that are compatible with the numpy "Object"' + "data type, see https://github.com/zarr-developers/zarr-python/issues/3117" + ) + raise ValueError(msg) + matched: list[ZDType[TBaseDType, TBaseScalar]] = [] + for val in self.contents.values(): + with contextlib.suppress(DataTypeValidationError): + matched.append(val.from_native_dtype(dtype)) + if len(matched) == 1: + return matched[0] + elif len(matched) > 1: + msg = ( + f"Zarr data type resolution from {dtype} failed. " + f"Multiple data type wrappers found that match dtype '{dtype}': {matched}. " + "You should unregister one of these data types, or avoid Zarr data type inference " + "entirely by providing a specific Zarr data type when creating your array." + "For more information, see https://github.com/zarr-developers/zarr-python/issues/3117" + ) + raise ValueError(msg) + raise ValueError(f"No Zarr data type found that matches dtype '{dtype!r}'") + + def match_json( + self, data: DTypeJSON, *, zarr_format: ZarrFormat + ) -> ZDType[TBaseDType, TBaseScalar]: + for val in self.contents.values(): + try: + return val.from_json(data, zarr_format=zarr_format) + except DataTypeValidationError: + pass + raise ValueError(f"No Zarr data type found that matches {data!r}") diff --git a/src/zarr/core/dtype/wrapper.py b/src/zarr/core/dtype/wrapper.py new file mode 100644 index 0000000000..7be97fa4b4 --- /dev/null +++ b/src/zarr/core/dtype/wrapper.py @@ -0,0 +1,297 @@ +""" +Wrapper for native array data types. + +The ``ZDType`` class is an abstract base class for wrapping native array data types, e.g. NumPy dtypes. +``ZDType`` provides a common interface for working with data types in a way that is independent of the +underlying data type system. + +The wrapper class encapsulates a native data type. Instances of the class can be created from a +native data type instance, and a native data type instance can be created from an instance of the +wrapper class. + +The wrapper class is responsible for: +- Serializing and deserializing a native data type to Zarr V2 or Zarr V3 metadata. + This ensures that the data type can be properly stored and retrieved from array metadata. +- Serializing and deserializing scalar values to Zarr V2 or Zarr V3 metadata. This is important for + storing a fill value for an array in a manner that is valid for the data type. + +You can add support for a new data type in Zarr by subclassing ``ZDType`` wrapper class and adapt its methods +to support your native data type. The wrapper class must be added to a data type registry +(defined elsewhere) before array creation routines or array reading routines can use your new data +type. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import ( + TYPE_CHECKING, + ClassVar, + Generic, + Literal, + Self, + TypeGuard, + TypeVar, + overload, +) + +import numpy as np + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + from zarr.core.dtype.common import DTypeJSON, DTypeSpec_V2, DTypeSpec_V3 + +# This the upper bound for the scalar types we support. It's numpy scalars + str, +# because the new variable-length string dtype in numpy does not have a corresponding scalar type +TBaseScalar = np.generic | str | bytes +# This is the bound for the dtypes that we support. If we support non-numpy dtypes, +# then this bound will need to be widened. +TBaseDType = np.dtype[np.generic] + +# These two type parameters are covariant because we want +# x : ZDType[BaseDType, BaseScalar] = ZDType[SubDType, SubScalar] +# to type check +TScalar_co = TypeVar("TScalar_co", bound=TBaseScalar, covariant=True) +TDType_co = TypeVar("TDType_co", bound=TBaseDType, covariant=True) + + +@dataclass(frozen=True, kw_only=True, slots=True) +class ZDType(ABC, Generic[TDType_co, TScalar_co]): + """ + Abstract base class for wrapping native array data types, e.g. numpy dtypes + + Attributes + ---------- + dtype_cls : ClassVar[type[TDType]] + The wrapped dtype class. This is a class variable. + _zarr_v3_name : ClassVar[str] + The name given to the data type by a Zarr v3 data type specification. This is a + class variable, and it should generally be unique across different data types. + """ + + # this class will create a native data type + # mypy currently disallows class variables to contain type parameters + # but it seems OK for us to use it here: + # https://github.com/python/typing/discussions/1424#discussioncomment-7989934 + dtype_cls: ClassVar[type[TDType_co]] # type: ignore[misc] + _zarr_v3_name: ClassVar[str] + + @classmethod + def _check_native_dtype(cls: type[Self], dtype: TBaseDType) -> TypeGuard[TDType_co]: + """ + Check that a native data type matches the dtype_cls class attribute. Used as a type guard. + + Parameters + ---------- + dtype : TDType + The dtype to check. + + Returns + ------- + Bool + True if the dtype matches, False otherwise. + """ + return type(dtype) is cls.dtype_cls + + @classmethod + @abstractmethod + def from_native_dtype(cls: type[Self], dtype: TBaseDType) -> Self: + """ + Create a ZDType instance from a native data type. The default implementation first performs + a type check via ``cls._check_native_dtype``. If that type check succeeds, the ZDType class + instance is created. + + This method is used when taking a user-provided native data type, like a NumPy data type, + and creating the corresponding ZDType instance from them. + + Parameters + ---------- + dtype : TDType + The native data type object to wrap. + + Returns + ------- + Self + The ZDType that wraps the native data type. + + Raises + ------ + TypeError + If the native data type is not consistent with the wrapped data type. + """ + ... + + @abstractmethod + def to_native_dtype(self: Self) -> TDType_co: + """ + Return an instance of the wrapped data type. This operation inverts ``from_native_dtype``. + + Returns + ------- + TDType + The native data type wrapped by this ZDType. + """ + ... + + @classmethod + @abstractmethod + def _from_json_v2(cls: type[Self], data: DTypeJSON) -> Self: ... + + @classmethod + @abstractmethod + def _from_json_v3(cls: type[Self], data: DTypeJSON) -> Self: ... + + @classmethod + def from_json(cls: type[Self], data: DTypeJSON, *, zarr_format: ZarrFormat) -> Self: + """ + Create an instance of this ZDType from JSON data. + + Parameters + ---------- + data : DTypeJSON + The JSON representation of the data type. The type annotation includes + Mapping[str, object] to accommodate typed dictionaries. + + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + Self + The wrapped data type. + """ + if zarr_format == 2: + return cls._from_json_v2(data) + if zarr_format == 3: + return cls._from_json_v3(data) + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + @overload + def to_json(self, zarr_format: Literal[2]) -> DTypeSpec_V2: ... + + @overload + def to_json(self, zarr_format: Literal[3]) -> DTypeSpec_V3: ... + + @abstractmethod + def to_json(self, zarr_format: ZarrFormat) -> DTypeSpec_V2 | DTypeSpec_V3: + """ + Serialize this ZDType to JSON. + + Parameters + ---------- + zarr_format : ZarrFormat + The zarr format version. + + Returns + ------- + DTypeJSON_V2 | DTypeJSON_V3 + The JSON-serializable representation of the wrapped data type + """ + ... + + @abstractmethod + def _check_scalar(self, data: object) -> bool: + """ + Check that an python object is a valid scalar value for the wrapped data type. + + Parameters + ---------- + data : object + A value to check. + + Returns + ------- + Bool + True if the object is valid, False otherwise. + """ + ... + + @abstractmethod + def cast_scalar(self, data: object) -> TScalar_co: + """ + Cast a python object to the wrapped scalar type. + The type of the provided scalar is first checked for compatibility. + If it's incompatible with the associated scalar type, a ``TypeError`` will be raised. + + Parameters + ---------- + data : object + The python object to cast. + + Returns + ------- + TScalar + The cast value. + """ + + @abstractmethod + def default_scalar(self) -> TScalar_co: + """ + Get the default scalar value for the wrapped data type. This is a method, rather than an + attribute, because the default value for some data types depends on parameters that are + not known until a concrete data type is wrapped. For example, data types parametrized by a + length like fixed-length strings or bytes will generate scalars consistent with that length. + + Returns + ------- + TScalar + The default value for this data type. + """ + ... + + @abstractmethod + def from_json_scalar(self: Self, data: JSON, *, zarr_format: ZarrFormat) -> TScalar_co: + """ + Read a JSON-serializable value as a scalar. + + Parameters + ---------- + data : JSON + A JSON representation of a scalar value. + zarr_format : ZarrFormat + The zarr format version. This is specified because the JSON serialization of scalars + differs between Zarr V2 and Zarr V3. + + Returns + ------- + TScalar + The deserialized scalar value. + """ + ... + + @abstractmethod + def to_json_scalar(self, data: object, *, zarr_format: ZarrFormat) -> JSON: + """ + Serialize a python object to the JSON representation of a scalar. The value will first be + cast to the scalar type associated with this ZDType, then serialized to JSON. + + Parameters + ---------- + data : object + The value to convert. + zarr_format : ZarrFormat + The zarr format version. This is specified because the JSON serialization of scalars + differs between Zarr V2 and Zarr V3. + + Returns + ------- + JSON + The JSON-serialized scalar. + """ + ... + + +def scalar_failed_type_check_msg( + cls_instance: ZDType[TBaseDType, TBaseScalar], bad_scalar: object +) -> str: + """ + Generate an error message reporting that a particular value failed a type check when attempting + to cast that value to a scalar. + """ + return ( + f"The value {bad_scalar!r} failed a type check. " + f"It cannot be safely cast to a scalar compatible with {cls_instance}. " + f"Consult the documentation for {cls_instance} to determine the possible values that can " + "be cast to scalars of the wrapped data type." + ) diff --git a/src/zarr/core/group.py b/src/zarr/core/group.py index 925252ccf0..4c8ced21f4 100644 --- a/src/zarr/core/group.py +++ b/src/zarr/core/group.py @@ -6,7 +6,6 @@ import logging import warnings from collections import defaultdict -from collections.abc import Iterator, Mapping from dataclasses import asdict, dataclass, field, fields, replace from itertools import accumulate from typing import TYPE_CHECKING, Literal, TypeVar, assert_never, cast, overload @@ -42,6 +41,7 @@ ZGROUP_JSON, ZMETADATA_V2_JSON, ChunkCoords, + DimensionNames, NodeType, ShapeLike, ZarrFormat, @@ -49,7 +49,6 @@ ) from zarr.core.config import config from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata -from zarr.core.metadata.v3 import V3JsonEncoder, _replace_special_floats from zarr.core.sync import SyncMixin, sync from zarr.errors import ContainsArrayError, ContainsGroupError, MetadataValidationError from zarr.storage import StoreLike, StorePath @@ -63,6 +62,8 @@ Coroutine, Generator, Iterable, + Iterator, + Mapping, ) from typing import Any @@ -79,7 +80,7 @@ def parse_zarr_format(data: Any) -> ZarrFormat: """Parse the zarr_format field from metadata.""" if data in (2, 3): - return cast(ZarrFormat, data) + return cast("ZarrFormat", data) msg = f"Invalid zarr_format. Expected one of 2 or 3. Got {data}." raise ValueError(msg) @@ -87,7 +88,7 @@ def parse_zarr_format(data: Any) -> ZarrFormat: def parse_node_type(data: Any) -> NodeType: """Parse the node_type field from metadata.""" if data in ("array", "group"): - return cast(Literal["array", "group"], data) + return cast("Literal['array', 'group']", data) raise MetadataValidationError("node_type", "array or group", data) @@ -334,7 +335,7 @@ def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: if self.zarr_format == 3: return { ZARR_JSON: prototype.buffer.from_bytes( - json.dumps(_replace_special_floats(self.to_dict()), cls=V3JsonEncoder).encode() + json.dumps(self.to_dict(), indent=json_indent, allow_nan=False).encode() ) } else: @@ -343,7 +344,7 @@ def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: json.dumps({"zarr_format": self.zarr_format}, indent=json_indent).encode() ), ZATTRS_JSON: prototype.buffer.from_bytes( - json.dumps(self.attributes, indent=json_indent).encode() + json.dumps(self.attributes, indent=json_indent, allow_nan=False).encode() ), } if self.consolidated_metadata: @@ -354,11 +355,11 @@ def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: consolidated_metadata = self.consolidated_metadata.to_dict()["metadata"] assert isinstance(consolidated_metadata, dict) for k, v in consolidated_metadata.items(): - attrs = v.pop("attributes", None) - d[f"{k}/{ZATTRS_JSON}"] = _replace_special_floats(attrs) + attrs = v.pop("attributes", {}) + d[f"{k}/{ZATTRS_JSON}"] = attrs if "shape" in v: # it's an array - d[f"{k}/{ZARRAY_JSON}"] = _replace_special_floats(v) + d[f"{k}/{ZARRAY_JSON}"] = v else: d[f"{k}/{ZGROUP_JSON}"] = { "zarr_format": self.zarr_format, @@ -371,8 +372,7 @@ def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: items[ZMETADATA_V2_JSON] = prototype.buffer.from_bytes( json.dumps( - {"metadata": d, "zarr_consolidated_format": 1}, - cls=V3JsonEncoder, + {"metadata": d, "zarr_consolidated_format": 1}, allow_nan=False ).encode() ) @@ -481,10 +481,11 @@ async def open( By default, consolidated metadata is used if it's present in the store (in the ``zarr.json`` for Zarr format 3 and in the ``.zmetadata`` file - for Zarr format 2). + for Zarr format 2) and the Store supports it. - To explicitly require consolidated metadata, set ``use_consolidated=True``, - which will raise an exception if consolidated metadata is not found. + To explicitly require consolidated metadata, set ``use_consolidated=True``. + In this case, if the Store doesn't support consolidation or consolidated metadata is + not found, a ``ValueError`` exception is raised. To explicitly *not* use consolidated metadata, set ``use_consolidated=False``, which will fall back to using the regular, non consolidated metadata. @@ -494,6 +495,16 @@ async def open( to load consolidated metadata from a non-default key. """ store_path = await make_store_path(store) + if not store_path.store.supports_consolidated_metadata: + # Fail if consolidated metadata was requested but the Store doesn't support it + if use_consolidated: + store_name = type(store_path.store).__name__ + raise ValueError( + f"The Zarr store in use ({store_name}) doesn't support consolidated metadata." + ) + + # if use_consolidated was None (optional), the Store dictates it doesn't want consolidation + use_consolidated = False consolidated_key = ZMETADATA_V2_JSON @@ -611,6 +622,7 @@ def _from_bytes_v2( consolidated_metadata[path].update(v) else: raise ValueError(f"Invalid file type '{kind}' at path '{path}") + group_metadata["consolidated_metadata"] = { "metadata": dict(consolidated_metadata), "kind": "inline", @@ -999,7 +1011,7 @@ async def create_array( order: MemoryOrder | None = None, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfig | ArrayConfigLike | None = None, @@ -1296,6 +1308,8 @@ async def nmembers( async def members( self, max_depth: int | None = 0, + *, + use_consolidated_for_children: bool = True, ) -> AsyncGenerator[ tuple[str, AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata] | AsyncGroup], None, @@ -1313,6 +1327,11 @@ async def members( default, (``max_depth=0``) only immediate children are included. Set ``max_depth=None`` to include all nodes, and some positive integer to consider children within that many levels of the root Group. + use_consolidated_for_children : bool, default True + Whether to use the consolidated metadata of child groups loaded + from the store. Note that this only affects groups loaded from the + store. If the current Group already has consolidated metadata, it + will always be used. Returns ------- @@ -1323,7 +1342,9 @@ async def members( """ if max_depth is not None and max_depth < 0: raise ValueError(f"max_depth must be None or >= 0. Got '{max_depth}' instead") - async for item in self._members(max_depth=max_depth): + async for item in self._members( + max_depth=max_depth, use_consolidated_for_children=use_consolidated_for_children + ): yield item def _members_consolidated( @@ -1353,7 +1374,7 @@ def _members_consolidated( yield from obj._members_consolidated(new_depth, prefix=key) async def _members( - self, max_depth: int | None + self, max_depth: int | None, *, use_consolidated_for_children: bool = True ) -> AsyncGenerator[ tuple[str, AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata] | AsyncGroup], None ]: @@ -1383,7 +1404,11 @@ async def _members( # enforce a concurrency limit by passing a semaphore to all the recursive functions semaphore = asyncio.Semaphore(config.get("async.concurrency")) async for member in _iter_members_deep( - self, max_depth=max_depth, skip_keys=skip_keys, semaphore=semaphore + self, + max_depth=max_depth, + skip_keys=skip_keys, + semaphore=semaphore, + use_consolidated_for_children=use_consolidated_for_children, ): yield member @@ -1429,7 +1454,7 @@ async def create_hierarchy( group already exists at path ``a``, then this function will leave the group at ``a`` as-is. Yields - ------- + ------ tuple[str, AsyncArray | AsyncGroup]. """ # check that all the nodes have the same zarr_format as Self @@ -1744,6 +1769,10 @@ async def move(self, source: str, dest: str) -> None: @dataclass(frozen=True) class Group(SyncMixin): + """ + A Zarr group. + """ + _async_group: AsyncGroup @classmethod @@ -2079,10 +2108,34 @@ def nmembers(self, max_depth: int | None = 0) -> int: return self._sync(self._async_group.nmembers(max_depth=max_depth)) - def members(self, max_depth: int | None = 0) -> tuple[tuple[str, Array | Group], ...]: + def members( + self, max_depth: int | None = 0, *, use_consolidated_for_children: bool = True + ) -> tuple[tuple[str, Array | Group], ...]: """ - Return the sub-arrays and sub-groups of this group as a tuple of (name, array | group) - pairs + Returns an AsyncGenerator over the arrays and groups contained in this group. + This method requires that `store_path.store` supports directory listing. + + The results are not guaranteed to be ordered. + + Parameters + ---------- + max_depth : int, default 0 + The maximum number of levels of the hierarchy to include. By + default, (``max_depth=0``) only immediate children are included. Set + ``max_depth=None`` to include all nodes, and some positive integer + to consider children within that many levels of the root Group. + use_consolidated_for_children : bool, default True + Whether to use the consolidated metadata of child groups loaded + from the store. Note that this only affects groups loaded from the + store. If the current Group already has consolidated metadata, it + will always be used. + + Returns + ------- + path: + A string giving the path to the target, relative to the Group ``self``. + value: AsyncArray or AsyncGroup + The AsyncArray or AsyncGroup that is a child of ``self``. """ _members = self._sync_iter(self._async_group.members(max_depth=max_depth)) @@ -2370,7 +2423,7 @@ def create_array( order: MemoryOrder | None = "C", attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfig | ArrayConfigLike | None = None, @@ -2764,7 +2817,7 @@ def array( order: MemoryOrder | None = "C", attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, storage_options: dict[str, Any] | None = None, overwrite: bool = False, config: ArrayConfig | ArrayConfigLike | None = None, @@ -3234,8 +3287,7 @@ def _ensure_consistent_zarr_format( raise ValueError(msg) return cast( - Mapping[str, GroupMetadata | ArrayV2Metadata] - | Mapping[str, GroupMetadata | ArrayV3Metadata], + "Mapping[str, GroupMetadata | ArrayV2Metadata] | Mapping[str, GroupMetadata | ArrayV3Metadata]", data, ) @@ -3317,6 +3369,7 @@ async def _iter_members_deep( max_depth: int | None, skip_keys: tuple[str, ...], semaphore: asyncio.Semaphore | None = None, + use_consolidated_for_children: bool = True, ) -> AsyncGenerator[ tuple[str, AsyncArray[ArrayV3Metadata] | AsyncArray[ArrayV2Metadata] | AsyncGroup], None ]: @@ -3334,6 +3387,11 @@ async def _iter_members_deep( A tuple of keys to skip when iterating over the possible members of the group. semaphore : asyncio.Semaphore | None An optional semaphore to use for concurrency control. + use_consolidated_for_children : bool, default True + Whether to use the consolidated metadata of child groups loaded + from the store. Note that this only affects groups loaded from the + store. If the current Group already has consolidated metadata, it + will always be used. Yields ------ @@ -3348,8 +3406,19 @@ async def _iter_members_deep( else: new_depth = max_depth - 1 async for name, node in _iter_members(group, skip_keys=skip_keys, semaphore=semaphore): + is_group = isinstance(node, AsyncGroup) + if ( + is_group + and not use_consolidated_for_children + and node.metadata.consolidated_metadata is not None # type: ignore [union-attr] + ): + node = cast("AsyncGroup", node) + # We've decided not to trust consolidated metadata at this point, because we're + # reconsolidating the metadata, for example. + node = replace(node, metadata=replace(node.metadata, consolidated_metadata=None)) yield name, node - if isinstance(node, AsyncGroup) and do_recursion: + if is_group and do_recursion: + node = cast("AsyncGroup", node) to_recurse[name] = _iter_members_deep( node, max_depth=new_depth, skip_keys=skip_keys, semaphore=semaphore ) diff --git a/src/zarr/core/indexing.py b/src/zarr/core/indexing.py index 998fe156a1..c11889f7f4 100644 --- a/src/zarr/core/indexing.py +++ b/src/zarr/core/indexing.py @@ -466,7 +466,7 @@ def replace_ellipsis(selection: Any, shape: ChunkCoords) -> SelectionNormalized: # check selection not too long check_selection_length(selection, shape) - return cast(SelectionNormalized, selection) + return cast("SelectionNormalized", selection) def replace_lists(selection: SelectionNormalized) -> SelectionNormalized: @@ -481,7 +481,7 @@ def replace_lists(selection: SelectionNormalized) -> SelectionNormalized: def ensure_tuple(v: Any) -> SelectionNormalized: if not isinstance(v, tuple): v = (v,) - return cast(SelectionNormalized, v) + return cast("SelectionNormalized", v) class ChunkProjection(NamedTuple): @@ -818,7 +818,7 @@ def ix_(selection: Any, shape: ChunkCoords) -> npt.NDArray[np.intp]: # now get numpy to convert to a coordinate selection selection = np.ix_(*selection) - return cast(npt.NDArray[np.intp], selection) + return cast("npt.NDArray[np.intp]", selection) def oindex(a: npt.NDArray[Any], selection: Selection) -> npt.NDArray[Any]: @@ -948,7 +948,7 @@ def __getitem__(self, selection: OrthogonalSelection | Array) -> NDArrayLikeOrSc new_selection = ensure_tuple(new_selection) new_selection = replace_lists(new_selection) return self.array.get_orthogonal_selection( - cast(OrthogonalSelection, new_selection), fields=fields + cast("OrthogonalSelection", new_selection), fields=fields ) def __setitem__(self, selection: OrthogonalSelection, value: npt.ArrayLike) -> None: @@ -956,7 +956,7 @@ def __setitem__(self, selection: OrthogonalSelection, value: npt.ArrayLike) -> N new_selection = ensure_tuple(new_selection) new_selection = replace_lists(new_selection) return self.array.set_orthogonal_selection( - cast(OrthogonalSelection, new_selection), value, fields=fields + cast("OrthogonalSelection", new_selection), value, fields=fields ) @@ -1050,14 +1050,14 @@ def __getitem__(self, selection: BasicSelection) -> NDArrayLikeOrScalar: fields, new_selection = pop_fields(selection) new_selection = ensure_tuple(new_selection) new_selection = replace_lists(new_selection) - return self.array.get_block_selection(cast(BasicSelection, new_selection), fields=fields) + return self.array.get_block_selection(cast("BasicSelection", new_selection), fields=fields) def __setitem__(self, selection: BasicSelection, value: npt.ArrayLike) -> None: fields, new_selection = pop_fields(selection) new_selection = ensure_tuple(new_selection) new_selection = replace_lists(new_selection) return self.array.set_block_selection( - cast(BasicSelection, new_selection), value, fields=fields + cast("BasicSelection", new_selection), value, fields=fields ) @@ -1105,12 +1105,12 @@ def __init__( nchunks = reduce(operator.mul, cdata_shape, 1) # some initial normalization - selection_normalized = cast(CoordinateSelectionNormalized, ensure_tuple(selection)) + selection_normalized = cast("CoordinateSelectionNormalized", ensure_tuple(selection)) selection_normalized = tuple( np.asarray([i]) if is_integer(i) else i for i in selection_normalized ) selection_normalized = cast( - CoordinateSelectionNormalized, replace_lists(selection_normalized) + "CoordinateSelectionNormalized", replace_lists(selection_normalized) ) # validation @@ -1214,8 +1214,8 @@ def __iter__(self) -> Iterator[ChunkProjection]: class MaskIndexer(CoordinateIndexer): def __init__(self, selection: MaskSelection, shape: ChunkCoords, chunk_grid: ChunkGrid) -> None: # some initial normalization - selection_normalized = cast(tuple[MaskSelection], ensure_tuple(selection)) - selection_normalized = cast(tuple[MaskSelection], replace_lists(selection_normalized)) + selection_normalized = cast("tuple[MaskSelection]", ensure_tuple(selection)) + selection_normalized = cast("tuple[MaskSelection]", replace_lists(selection_normalized)) # validation if not is_mask_selection(selection_normalized, shape): @@ -1311,14 +1311,14 @@ def pop_fields(selection: SelectionWithFields) -> tuple[Fields | None, Selection elif not isinstance(selection, tuple): # single selection item, no fields # leave selection as-is - return None, cast(Selection, selection) + return None, cast("Selection", selection) else: # multiple items, split fields from selection items fields: Fields = [f for f in selection if isinstance(f, str)] fields = fields[0] if len(fields) == 1 else fields selection_tuple = tuple(s for s in selection if not isinstance(s, str)) selection = cast( - Selection, selection_tuple[0] if len(selection_tuple) == 1 else selection_tuple + "Selection", selection_tuple[0] if len(selection_tuple) == 1 else selection_tuple ) return fields, selection @@ -1380,12 +1380,12 @@ def get_indexer( new_selection = ensure_tuple(selection) new_selection = replace_lists(new_selection) if is_coordinate_selection(new_selection, shape): - return CoordinateIndexer(cast(CoordinateSelection, selection), shape, chunk_grid) + return CoordinateIndexer(cast("CoordinateSelection", selection), shape, chunk_grid) elif is_mask_selection(new_selection, shape): - return MaskIndexer(cast(MaskSelection, selection), shape, chunk_grid) + return MaskIndexer(cast("MaskSelection", selection), shape, chunk_grid) else: raise VindexInvalidSelectionError(new_selection) elif is_pure_orthogonal_indexing(pure_selection, len(shape)): - return OrthogonalIndexer(cast(OrthogonalSelection, selection), shape, chunk_grid) + return OrthogonalIndexer(cast("OrthogonalSelection", selection), shape, chunk_grid) else: - return BasicIndexer(cast(BasicSelection, selection), shape, chunk_grid) + return BasicIndexer(cast("BasicSelection", selection), shape, chunk_grid) diff --git a/src/zarr/core/metadata/v2.py b/src/zarr/core/metadata/v2.py index 11f14b37aa..3ac75e0418 100644 --- a/src/zarr/core/metadata/v2.py +++ b/src/zarr/core/metadata/v2.py @@ -1,15 +1,16 @@ from __future__ import annotations -import base64 import warnings from collections.abc import Iterable, Sequence -from enum import Enum from functools import cached_property -from typing import TYPE_CHECKING, Any, TypedDict, cast +from typing import TYPE_CHECKING, Any, TypeAlias, TypedDict, cast import numcodecs.abc from zarr.abc.metadata import Metadata +from zarr.core.chunk_grids import RegularChunkGrid +from zarr.core.dtype import get_data_type_from_json +from zarr.core.dtype.common import OBJECT_CODEC_IDS, DTypeSpec_V2 if TYPE_CHECKING: from typing import Literal, Self @@ -18,18 +19,29 @@ from zarr.core.buffer import Buffer, BufferPrototype from zarr.core.common import ChunkCoords + from zarr.core.dtype.wrapper import ( + TBaseDType, + TBaseScalar, + TDType_co, + TScalar_co, + ZDType, + ) import json -import numbers from dataclasses import dataclass, field, fields, replace import numcodecs import numpy as np from zarr.core.array_spec import ArrayConfig, ArraySpec -from zarr.core.chunk_grids import RegularChunkGrid from zarr.core.chunk_key_encodings import parse_separator -from zarr.core.common import JSON, ZARRAY_JSON, ZATTRS_JSON, MemoryOrder, parse_shapelike +from zarr.core.common import ( + JSON, + ZARRAY_JSON, + ZATTRS_JSON, + MemoryOrder, + parse_shapelike, +) from zarr.core.config import config, parse_indexing_order from zarr.core.metadata.common import parse_attributes @@ -43,16 +55,20 @@ class ArrayV2MetadataDict(TypedDict): attributes: dict[str, JSON] +# Union of acceptable types for v2 compressors +CompressorLikev2: TypeAlias = dict[str, JSON] | numcodecs.abc.Codec | None + + @dataclass(frozen=True, kw_only=True) class ArrayV2Metadata(Metadata): shape: ChunkCoords chunks: ChunkCoords - dtype: np.dtype[Any] - fill_value: int | float | str | bytes | None = 0 + dtype: ZDType[TBaseDType, TBaseScalar] + fill_value: int | float | str | bytes | None = None order: MemoryOrder = "C" filters: tuple[numcodecs.abc.Codec, ...] | None = None dimension_separator: Literal[".", "/"] = "." - compressor: numcodecs.abc.Codec | None = None + compressor: CompressorLikev2 attributes: dict[str, JSON] = field(default_factory=dict) zarr_format: Literal[2] = field(init=False, default=2) @@ -60,12 +76,12 @@ def __init__( self, *, shape: ChunkCoords, - dtype: npt.DTypeLike, + dtype: ZDType[TDType_co, TScalar_co], chunks: ChunkCoords, fill_value: Any, order: MemoryOrder, dimension_separator: Literal[".", "/"] = ".", - compressor: numcodecs.abc.Codec | dict[str, JSON] | None = None, + compressor: CompressorLikev2 = None, filters: Iterable[numcodecs.abc.Codec | dict[str, JSON]] | None = None, attributes: dict[str, JSON] | None = None, ) -> None: @@ -73,18 +89,20 @@ def __init__( Metadata for a Zarr format 2 array. """ shape_parsed = parse_shapelike(shape) - dtype_parsed = parse_dtype(dtype) chunks_parsed = parse_shapelike(chunks) - compressor_parsed = parse_compressor(compressor) order_parsed = parse_indexing_order(order) dimension_separator_parsed = parse_separator(dimension_separator) filters_parsed = parse_filters(filters) - fill_value_parsed = parse_fill_value(fill_value, dtype=dtype_parsed) + fill_value_parsed: TBaseScalar | None + if fill_value is not None: + fill_value_parsed = dtype.cast_scalar(fill_value) + else: + fill_value_parsed = fill_value attributes_parsed = parse_attributes(attributes) object.__setattr__(self, "shape", shape_parsed) - object.__setattr__(self, "dtype", dtype_parsed) + object.__setattr__(self, "dtype", dtype) object.__setattr__(self, "chunks", chunks_parsed) object.__setattr__(self, "compressor", compressor_parsed) object.__setattr__(self, "order", order_parsed) @@ -109,52 +127,12 @@ def shards(self) -> ChunkCoords | None: return None def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: - def _json_convert( - o: Any, - ) -> Any: - if isinstance(o, np.dtype): - if o.fields is None: - return o.str - else: - return o.descr - if isinstance(o, numcodecs.abc.Codec): - codec_config = o.get_config() - - # Hotfix for https://github.com/zarr-developers/zarr-python/issues/2647 - if codec_config["id"] == "zstd" and not codec_config.get("checksum", False): - codec_config.pop("checksum", None) - - return codec_config - if np.isscalar(o): - out: Any - if hasattr(o, "dtype") and o.dtype.kind == "M" and hasattr(o, "view"): - # https://github.com/zarr-developers/zarr-python/issues/2119 - # `.item()` on a datetime type might or might not return an - # integer, depending on the value. - # Explicitly cast to an int first, and then grab .item() - out = o.view("i8").item() - else: - # convert numpy scalar to python type, and pass - # python types through - out = getattr(o, "item", lambda: o)() - if isinstance(out, complex): - # python complex types are not JSON serializable, so we use the - # serialization defined in the zarr v3 spec - return [out.real, out.imag] - return out - if isinstance(o, Enum): - return o.name - raise TypeError - zarray_dict = self.to_dict() - zarray_dict["fill_value"] = _serialize_fill_value(self.fill_value, self.dtype) zattrs_dict = zarray_dict.pop("attributes", {}) json_indent = config.get("json_indent") return { ZARRAY_JSON: prototype.buffer.from_bytes( - json.dumps( - zarray_dict, default=_json_convert, indent=json_indent, allow_nan=False - ).encode() + json.dumps(zarray_dict, indent=json_indent, allow_nan=False).encode() ), ZATTRS_JSON: prototype.buffer.from_bytes( json.dumps(zattrs_dict, indent=json_indent, allow_nan=False).encode() @@ -168,8 +146,33 @@ def from_dict(cls, data: dict[str, Any]) -> ArrayV2Metadata: # Check that the zarr_format attribute is correct. _ = parse_zarr_format(_data.pop("zarr_format")) - # zarr v2 allowed arbitrary keys in the metadata. - # Filter the keys to only those expected by the constructor. + # To resolve a numpy object dtype array, we need to search for an object codec, + # which could be in filters or as a compressor. + # we will reference a hard-coded collection of object codec ids for this search. + + _filters, _compressor = (data.get("filters"), data.get("compressor")) + if _filters is not None: + _filters = cast("tuple[dict[str, JSON], ...]", _filters) + object_codec_id = get_object_codec_id(tuple(_filters) + (_compressor,)) + else: + object_codec_id = get_object_codec_id((_compressor,)) + # we add a layer of indirection here around the dtype attribute of the array metadata + # because we also need to know the object codec id, if any, to resolve the data type + dtype_spec: DTypeSpec_V2 = { + "name": data["dtype"], + "object_codec_id": object_codec_id, + } + dtype = get_data_type_from_json(dtype_spec, zarr_format=2) + + _data["dtype"] = dtype + fill_value_encoded = _data.get("fill_value") + if fill_value_encoded is not None: + fill_value = dtype.from_json_scalar(fill_value_encoded, zarr_format=2) + _data["fill_value"] = fill_value + + # zarr v2 allowed arbitrary keys here. + # We don't want the ArrayV2Metadata constructor to fail just because someone put an + # extra key in the metadata. expected = {x.name for x in fields(cls)} expected |= {"dtype", "chunks"} @@ -194,16 +197,34 @@ def from_dict(cls, data: dict[str, Any]) -> ArrayV2Metadata: def to_dict(self) -> dict[str, JSON]: zarray_dict = super().to_dict() + if isinstance(zarray_dict["compressor"], numcodecs.abc.Codec): + codec_config = zarray_dict["compressor"].get_config() + # Hotfix for https://github.com/zarr-developers/zarr-python/issues/2647 + if codec_config["id"] == "zstd" and not codec_config.get("checksum", False): + codec_config.pop("checksum") + zarray_dict["compressor"] = codec_config + + if zarray_dict["filters"] is not None: + raw_filters = zarray_dict["filters"] + # TODO: remove this when we can stratically type the output JSON data structure + # entirely + if not isinstance(raw_filters, list | tuple): + raise TypeError("Invalid type for filters. Expected a list or tuple.") + new_filters = [] + for f in raw_filters: + if isinstance(f, numcodecs.abc.Codec): + new_filters.append(f.get_config()) + else: + new_filters.append(f) + zarray_dict["filters"] = new_filters - _ = zarray_dict.pop("dtype") - dtype_json: JSON - # In the case of zarr v2, the simplest i.e., '|VXX' dtype is represented as a string - dtype_descr = self.dtype.descr - if self.dtype.kind == "V" and dtype_descr[0][0] != "" and len(dtype_descr) != 0: - dtype_json = tuple(self.dtype.descr) - else: - dtype_json = self.dtype.str - zarray_dict["dtype"] = dtype_json + # serialize the fill value after dtype-specific JSON encoding + if self.fill_value is not None: + fill_value = self.dtype.to_json_scalar(self.fill_value, zarr_format=2) + zarray_dict["fill_value"] = fill_value + + # pull the "name" attribute out of the dtype spec returned by self.dtype.to_json + zarray_dict["dtype"] = self.dtype.to_json(zarr_format=2)["name"] return zarray_dict @@ -292,179 +313,19 @@ def parse_metadata(data: ArrayV2Metadata) -> ArrayV2Metadata: return data -def _parse_structured_fill_value(fill_value: Any, dtype: np.dtype[Any]) -> Any: - """Handle structured dtype/fill value pairs""" - print("FILL VALUE", fill_value, "DT", dtype) - try: - if isinstance(fill_value, list): - return np.array([tuple(fill_value)], dtype=dtype)[0] - elif isinstance(fill_value, tuple): - return np.array([fill_value], dtype=dtype)[0] - elif isinstance(fill_value, bytes): - return np.frombuffer(fill_value, dtype=dtype)[0] - elif isinstance(fill_value, str): - decoded = base64.standard_b64decode(fill_value) - return np.frombuffer(decoded, dtype=dtype)[0] - else: - return np.array(fill_value, dtype=dtype)[()] - except Exception as e: - raise ValueError(f"Fill_value {fill_value} is not valid for dtype {dtype}.") from e - - -def parse_fill_value(fill_value: Any, dtype: np.dtype[Any]) -> Any: +def get_object_codec_id(maybe_object_codecs: Sequence[JSON]) -> str | None: """ - Parse a potential fill value into a value that is compatible with the provided dtype. - - Parameters - ---------- - fill_value : Any - A potential fill value. - dtype : np.dtype[Any] - A numpy dtype. - - Returns - ------- - An instance of `dtype`, or `None`, or any python object (in the case of an object dtype) + Inspect a sequence of codecs / filters for an "object codec", i.e. a codec + that can serialize object arrays to contiguous bytes. Zarr python + maintains a hard-coded set of object codec ids. If any element from the input + has an id that matches one of the hard-coded object codec ids, that id + is returned immediately. """ - - if fill_value is None or dtype.hasobject: - pass - elif dtype.fields is not None: - # the dtype is structured (has multiple fields), so the fill_value might be a - # compound value (e.g., a tuple or dict) that needs field-wise processing. - # We use parse_structured_fill_value to correctly convert each component. - fill_value = _parse_structured_fill_value(fill_value, dtype) - elif not isinstance(fill_value, np.void) and fill_value == 0: - # this should be compatible across numpy versions for any array type, including - # structured arrays - fill_value = np.zeros((), dtype=dtype)[()] - elif dtype.kind == "U": - # special case unicode because of encoding issues on Windows if passed through numpy - # https://github.com/alimanfoo/zarr/pull/172#issuecomment-343782713 - - if not isinstance(fill_value, str): - raise ValueError( - f"fill_value {fill_value!r} is not valid for dtype {dtype}; must be a unicode string" - ) - elif dtype.kind in "SV" and isinstance(fill_value, str): - fill_value = base64.standard_b64decode(fill_value) - elif dtype.kind == "c" and isinstance(fill_value, list) and len(fill_value) == 2: - complex_val = complex(float(fill_value[0]), float(fill_value[1])) - fill_value = np.array(complex_val, dtype=dtype)[()] - else: - try: - if isinstance(fill_value, bytes) and dtype.kind == "V": - # special case for numpy 1.14 compatibility - fill_value = np.array(fill_value, dtype=dtype.str).view(dtype)[()] - else: - fill_value = np.array(fill_value, dtype=dtype)[()] - - except Exception as e: - msg = f"Fill_value {fill_value} is not valid for dtype {dtype}." - raise ValueError(msg) from e - - return fill_value - - -def _serialize_fill_value(fill_value: Any, dtype: np.dtype[Any]) -> JSON: - serialized: JSON - - if fill_value is None: - serialized = None - elif dtype.kind in "SV": - # There's a relationship between dtype and fill_value - # that mypy isn't aware of. The fact that we have S or V dtype here - # means we should have a bytes-type fill_value. - serialized = base64.standard_b64encode(cast(bytes, fill_value)).decode("ascii") - elif isinstance(fill_value, np.datetime64): - serialized = np.datetime_as_string(fill_value) - elif isinstance(fill_value, numbers.Integral): - serialized = int(fill_value) - elif isinstance(fill_value, numbers.Real): - float_fv = float(fill_value) - if np.isnan(float_fv): - serialized = "NaN" - elif np.isinf(float_fv): - serialized = "Infinity" if float_fv > 0 else "-Infinity" - else: - serialized = float_fv - elif isinstance(fill_value, numbers.Complex): - serialized = [ - _serialize_fill_value(fill_value.real, dtype), - _serialize_fill_value(fill_value.imag, dtype), - ] - else: - serialized = fill_value - - return serialized - - -def _default_fill_value(dtype: np.dtype[Any]) -> Any: - """ - Get the default fill value for a type. - - Notes - ----- - This differs from :func:`parse_fill_value`, which parses a fill value - stored in the Array metadata into an in-memory value. This only gives - the default fill value for some type. - - This is useful for reading Zarr format 2 arrays, which allow the fill - value to be unspecified. - """ - if dtype.kind == "S": - return b"" - elif dtype.kind in "UO": - return "" - elif dtype.kind in "Mm": - return dtype.type("nat") - elif dtype.kind == "V": - if dtype.fields is not None: - default = tuple(_default_fill_value(field[0]) for field in dtype.fields.values()) - return np.array([default], dtype=dtype) - else: - return np.zeros(1, dtype=dtype) - else: - return dtype.type(0) - - -def _default_compressor( - dtype: np.dtype[Any], -) -> dict[str, JSON] | None: - """Get the default filters and compressor for a dtype. - - https://numpy.org/doc/2.1/reference/generated/numpy.dtype.kind.html - """ - default_compressor = config.get("array.v2_default_compressor") - if dtype.kind in "biufcmM": - dtype_key = "numeric" - elif dtype.kind in "U": - dtype_key = "string" - elif dtype.kind in "OSV": - dtype_key = "bytes" - else: - raise ValueError(f"Unsupported dtype kind {dtype.kind}") - - return cast(dict[str, JSON] | None, default_compressor.get(dtype_key, None)) - - -def _default_filters( - dtype: np.dtype[Any], -) -> list[dict[str, JSON]] | None: - """Get the default filters and compressor for a dtype. - - https://numpy.org/doc/2.1/reference/generated/numpy.dtype.kind.html - """ - default_filters = config.get("array.v2_default_filters") - if dtype.kind in "biufcmM": - dtype_key = "numeric" - elif dtype.kind in "U": - dtype_key = "string" - elif dtype.kind in "OS": - dtype_key = "bytes" - elif dtype.kind == "V": - dtype_key = "raw" - else: - raise ValueError(f"Unsupported dtype kind {dtype.kind}") - - return cast(list[dict[str, JSON]] | None, default_filters.get(dtype_key, None)) + object_codec_id = None + for maybe_object_codec in maybe_object_codecs: + if ( + isinstance(maybe_object_codec, dict) + and maybe_object_codec.get("id") in OBJECT_CODEC_IDS + ): + return cast("str", maybe_object_codec["id"]) + return object_codec_id diff --git a/src/zarr/core/metadata/v3.py b/src/zarr/core/metadata/v3.py index 9154762648..84872d3dbd 100644 --- a/src/zarr/core/metadata/v3.py +++ b/src/zarr/core/metadata/v3.py @@ -1,28 +1,25 @@ from __future__ import annotations -import warnings -from typing import TYPE_CHECKING, TypedDict, overload +from typing import TYPE_CHECKING, TypedDict from zarr.abc.metadata import Metadata from zarr.core.buffer.core import default_buffer_prototype +from zarr.core.dtype import VariableLengthUTF8, ZDType, get_data_type_from_json +from zarr.core.dtype.common import check_dtype_spec_v3 if TYPE_CHECKING: - from collections.abc import Callable from typing import Self from zarr.core.buffer import Buffer, BufferPrototype from zarr.core.chunk_grids import ChunkGrid from zarr.core.common import JSON, ChunkCoords + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar + import json -from collections.abc import Iterable, Sequence +from collections.abc import Iterable from dataclasses import dataclass, field, replace -from enum import Enum -from typing import Any, Literal, cast - -import numcodecs.abc -import numpy as np -import numpy.typing as npt +from typing import Any, Literal from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec, BytesBytesCodec, Codec from zarr.core.array_spec import ArrayConfig, ArraySpec @@ -32,25 +29,15 @@ JSON, ZARR_JSON, ChunkCoords, + DimensionNames, parse_named_configuration, parse_shapelike, ) from zarr.core.config import config from zarr.core.metadata.common import parse_attributes -from zarr.core.strings import _NUMPY_SUPPORTS_VLEN_STRING -from zarr.core.strings import _STRING_DTYPE as STRING_NP_DTYPE from zarr.errors import MetadataValidationError, NodeTypeValidationError from zarr.registry import get_codec_class -DEFAULT_DTYPE = "float64" - -# Keep in sync with _replace_special_floats -SPECIAL_FLOATS_ENCODED = { - "Infinity": np.inf, - "-Infinity": -np.inf, - "NaN": np.nan, -} - def parse_zarr_format(data: object) -> Literal[3]: if data == 3: @@ -93,7 +80,7 @@ def validate_array_bytes_codec(codecs: tuple[Codec, ...]) -> ArrayBytesCodec: return abcs[0] -def validate_codecs(codecs: tuple[Codec, ...], dtype: DataType) -> None: +def validate_codecs(codecs: tuple[Codec, ...], dtype: ZDType[TBaseDType, TBaseScalar]) -> None: """Check that the codecs are valid for the given dtype""" from zarr.codecs.sharding import ShardingCodec @@ -106,14 +93,11 @@ def validate_codecs(codecs: tuple[Codec, ...], dtype: DataType) -> None: # we need to have special codecs if we are decoding vlen strings or bytestrings # TODO: use codec ID instead of class name codec_class_name = abc.__class__.__name__ - if dtype == DataType.string and not codec_class_name == "VLenUTF8Codec": + # TODO: Fix typing here + if isinstance(dtype, VariableLengthUTF8) and not codec_class_name == "VLenUTF8Codec": # type: ignore[unreachable] raise ValueError( f"For string dtype, ArrayBytesCodec must be `VLenUTF8Codec`, got `{codec_class_name}`." ) - if dtype == DataType.bytes and not codec_class_name == "VLenBytesCodec": - raise ValueError( - f"For bytes dtype, ArrayBytesCodec must be `VLenBytesCodec`, got `{codec_class_name}`." - ) def parse_dimension_names(data: object) -> tuple[str | None, ...] | None: @@ -143,87 +127,6 @@ def parse_storage_transformers(data: object) -> tuple[dict[str, JSON], ...]: ) -class V3JsonEncoder(json.JSONEncoder): - def __init__( - self, - *, - skipkeys: bool = False, - ensure_ascii: bool = True, - check_circular: bool = True, - allow_nan: bool = True, - sort_keys: bool = False, - indent: int | None = None, - separators: tuple[str, str] | None = None, - default: Callable[[object], object] | None = None, - ) -> None: - if indent is None: - indent = config.get("json_indent") - super().__init__( - skipkeys=skipkeys, - ensure_ascii=ensure_ascii, - check_circular=check_circular, - allow_nan=allow_nan, - sort_keys=sort_keys, - indent=indent, - separators=separators, - default=default, - ) - - def default(self, o: object) -> Any: - if isinstance(o, np.dtype): - return str(o) - if np.isscalar(o): - out: Any - if hasattr(o, "dtype") and o.dtype.kind == "M" and hasattr(o, "view"): - # https://github.com/zarr-developers/zarr-python/issues/2119 - # `.item()` on a datetime type might or might not return an - # integer, depending on the value. - # Explicitly cast to an int first, and then grab .item() - out = o.view("i8").item() - else: - # convert numpy scalar to python type, and pass - # python types through - out = getattr(o, "item", lambda: o)() - if isinstance(out, complex): - # python complex types are not JSON serializable, so we use the - # serialization defined in the zarr v3 spec - return _replace_special_floats([out.real, out.imag]) - elif np.isnan(out): - return "NaN" - elif np.isinf(out): - return "Infinity" if out > 0 else "-Infinity" - return out - elif isinstance(o, Enum): - return o.name - # this serializes numcodecs compressors - # todo: implement to_dict for codecs - elif isinstance(o, numcodecs.abc.Codec): - config: dict[str, Any] = o.get_config() - return config - else: - return super().default(o) - - -def _replace_special_floats(obj: object) -> Any: - """Helper function to replace NaN/Inf/-Inf values with special strings - - Note: this cannot be done in the V3JsonEncoder because Python's `json.dumps` optimistically - converts NaN/Inf values to special types outside of the encoding step. - """ - if isinstance(obj, float): - if np.isnan(obj): - return "NaN" - elif np.isinf(obj): - return "Infinity" if obj > 0 else "-Infinity" - elif isinstance(obj, dict): - # Recursively replace in dictionaries - return {k: _replace_special_floats(v) for k, v in obj.items()} - elif isinstance(obj, list): - # Recursively replace in lists - return [_replace_special_floats(item) for item in obj] - return obj - - class ArrayV3MetadataDict(TypedDict): """ A typed dictionary model for zarr v3 metadata. @@ -236,13 +139,13 @@ class ArrayV3MetadataDict(TypedDict): @dataclass(frozen=True, kw_only=True) class ArrayV3Metadata(Metadata): shape: ChunkCoords - data_type: DataType + data_type: ZDType[TBaseDType, TBaseScalar] chunk_grid: ChunkGrid chunk_key_encoding: ChunkKeyEncoding fill_value: Any codecs: tuple[Codec, ...] attributes: dict[str, Any] = field(default_factory=dict) - dimension_names: tuple[str, ...] | None = None + dimension_names: tuple[str | None, ...] | None = None zarr_format: Literal[3] = field(default=3, init=False) node_type: Literal["array"] = field(default="array", init=False) storage_transformers: tuple[dict[str, JSON], ...] @@ -251,45 +154,41 @@ def __init__( self, *, shape: Iterable[int], - data_type: npt.DTypeLike | DataType, + data_type: ZDType[TBaseDType, TBaseScalar], chunk_grid: dict[str, JSON] | ChunkGrid, chunk_key_encoding: ChunkKeyEncodingLike, - fill_value: Any, + fill_value: object, codecs: Iterable[Codec | dict[str, JSON]], attributes: dict[str, JSON] | None, - dimension_names: Iterable[str] | None, + dimension_names: DimensionNames, storage_transformers: Iterable[dict[str, JSON]] | None = None, ) -> None: """ Because the class is a frozen dataclass, we set attributes using object.__setattr__ """ + shape_parsed = parse_shapelike(shape) - data_type_parsed = DataType.parse(data_type) chunk_grid_parsed = ChunkGrid.from_dict(chunk_grid) chunk_key_encoding_parsed = ChunkKeyEncoding.from_dict(chunk_key_encoding) dimension_names_parsed = parse_dimension_names(dimension_names) - if fill_value is None: - fill_value = default_fill_value(data_type_parsed) - # we pass a string here rather than an enum to make mypy happy - fill_value_parsed = parse_fill_value( - fill_value, dtype=cast(ALL_DTYPES, data_type_parsed.value) - ) + # Note: relying on a type method is numpy-specific + fill_value_parsed = data_type.cast_scalar(fill_value) attributes_parsed = parse_attributes(attributes) codecs_parsed_partial = parse_codecs(codecs) storage_transformers_parsed = parse_storage_transformers(storage_transformers) array_spec = ArraySpec( shape=shape_parsed, - dtype=data_type_parsed.to_numpy(), + dtype=data_type, fill_value=fill_value_parsed, config=ArrayConfig.from_dict({}), # TODO: config is not needed here. prototype=default_buffer_prototype(), # TODO: prototype is not needed here. ) codecs_parsed = tuple(c.evolve_from_array_spec(array_spec) for c in codecs_parsed_partial) - validate_codecs(codecs_parsed_partial, data_type_parsed) + validate_codecs(codecs_parsed_partial, data_type) object.__setattr__(self, "shape", shape_parsed) - object.__setattr__(self, "data_type", data_type_parsed) + object.__setattr__(self, "data_type", data_type) object.__setattr__(self, "chunk_grid", chunk_grid_parsed) object.__setattr__(self, "chunk_key_encoding", chunk_key_encoding_parsed) object.__setattr__(self, "codecs", codecs_parsed) @@ -314,19 +213,16 @@ def _validate_metadata(self) -> None: if self.fill_value is None: raise ValueError("`fill_value` is required.") for codec in self.codecs: - codec.validate( - shape=self.shape, dtype=self.data_type.to_numpy(), chunk_grid=self.chunk_grid - ) - - @property - def dtype(self) -> np.dtype[Any]: - """Interpret Zarr dtype as NumPy dtype""" - return self.data_type.to_numpy() + codec.validate(shape=self.shape, dtype=self.data_type, chunk_grid=self.chunk_grid) @property def ndim(self) -> int: return len(self.shape) + @property + def dtype(self) -> ZDType[TBaseDType, TBaseScalar]: + return self.data_type + @property def chunks(self) -> ChunkCoords: if isinstance(self.chunk_grid, RegularChunkGrid): @@ -388,8 +284,13 @@ def encode_chunk_key(self, chunk_coords: ChunkCoords) -> str: return self.chunk_key_encoding.encode_chunk_key(chunk_coords) def to_buffer_dict(self, prototype: BufferPrototype) -> dict[str, Buffer]: - d = _replace_special_floats(self.to_dict()) - return {ZARR_JSON: prototype.buffer.from_bytes(json.dumps(d, cls=V3JsonEncoder).encode())} + json_indent = config.get("json_indent") + d = self.to_dict() + return { + ZARR_JSON: prototype.buffer.from_bytes( + json.dumps(d, allow_nan=False, indent=json_indent).encode() + ) + } @classmethod def from_dict(cls, data: dict[str, JSON]) -> Self: @@ -401,18 +302,31 @@ def from_dict(cls, data: dict[str, JSON]) -> Self: # check that the node_type attribute is correct _ = parse_node_type_array(_data.pop("node_type")) - # check that the data_type attribute is valid - data_type = DataType.parse(_data.pop("data_type")) + data_type_json = _data.pop("data_type") + if not check_dtype_spec_v3(data_type_json): + raise ValueError(f"Invalid data_type: {data_type_json!r}") + data_type = get_data_type_from_json(data_type_json, zarr_format=3) + + # check that the fill value is consistent with the data type + try: + fill = _data.pop("fill_value") + fill_value_parsed = data_type.from_json_scalar(fill, zarr_format=3) + except ValueError as e: + raise TypeError(f"Invalid fill_value: {fill!r}") from e # dimension_names key is optional, normalize missing to `None` _data["dimension_names"] = _data.pop("dimension_names", None) + # attributes key is optional, normalize missing to `None` _data["attributes"] = _data.pop("attributes", None) - return cls(**_data, data_type=data_type) # type: ignore[arg-type] + + return cls(**_data, fill_value=fill_value_parsed, data_type=data_type) # type: ignore[arg-type] def to_dict(self) -> dict[str, JSON]: out_dict = super().to_dict() - + out_dict["fill_value"] = self.data_type.to_json_scalar( + self.fill_value, zarr_format=self.zarr_format + ) if not isinstance(out_dict, dict): raise TypeError(f"Expected dict. Got {type(out_dict)}.") @@ -420,6 +334,15 @@ def to_dict(self) -> dict[str, JSON]: # the metadata document if out_dict["dimension_names"] is None: out_dict.pop("dimension_names") + + # TODO: replace the `to_dict` / `from_dict` on the `Metadata`` class with + # to_json, from_json, and have ZDType inherit from `Metadata` + # until then, we have this hack here, which relies on the fact that to_dict will pass through + # any non-`Metadata` fields as-is. + dtype_meta = out_dict["data_type"] + if isinstance(dtype_meta, ZDType): + out_dict["data_type"] = dtype_meta.to_json(zarr_format=3) # type: ignore[unreachable] + return out_dict def update_shape(self, shape: ChunkCoords) -> Self: @@ -427,299 +350,3 @@ def update_shape(self, shape: ChunkCoords) -> Self: def update_attributes(self, attributes: dict[str, JSON]) -> Self: return replace(self, attributes=attributes) - - -# enum Literals can't be used in typing, so we have to restate all of the V3 dtypes as types -# https://github.com/python/typing/issues/781 - -BOOL_DTYPE = Literal["bool"] -BOOL = np.bool_ -INTEGER_DTYPE = Literal["int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64"] -INTEGER = np.int8 | np.int16 | np.int32 | np.int64 | np.uint8 | np.uint16 | np.uint32 | np.uint64 -FLOAT_DTYPE = Literal["float16", "float32", "float64"] -FLOAT = np.float16 | np.float32 | np.float64 -COMPLEX_DTYPE = Literal["complex64", "complex128"] -COMPLEX = np.complex64 | np.complex128 -STRING_DTYPE = Literal["string"] -STRING = np.str_ -BYTES_DTYPE = Literal["bytes"] -BYTES = np.bytes_ - -ALL_DTYPES = BOOL_DTYPE | INTEGER_DTYPE | FLOAT_DTYPE | COMPLEX_DTYPE | STRING_DTYPE | BYTES_DTYPE - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: BOOL_DTYPE, -) -> BOOL: ... - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: INTEGER_DTYPE, -) -> INTEGER: ... - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: FLOAT_DTYPE, -) -> FLOAT: ... - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: COMPLEX_DTYPE, -) -> COMPLEX: ... - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: STRING_DTYPE, -) -> STRING: ... - - -@overload -def parse_fill_value( - fill_value: complex | str | bytes | np.generic | Sequence[Any] | bool, - dtype: BYTES_DTYPE, -) -> BYTES: ... - - -def parse_fill_value( - fill_value: Any, - dtype: ALL_DTYPES, -) -> Any: - """ - Parse `fill_value`, a potential fill value, into an instance of `dtype`, a data type. - If `fill_value` is `None`, then this function will return the result of casting the value 0 - to the provided data type. Otherwise, `fill_value` will be cast to the provided data type. - - Note that some numpy dtypes use very permissive casting rules. For example, - `np.bool_({'not remotely a bool'})` returns `True`. Thus this function should not be used for - validating that the provided fill value is a valid instance of the data type. - - Parameters - ---------- - fill_value : Any - A potential fill value. - dtype : str - A valid Zarr format 3 DataType. - - Returns - ------- - A scalar instance of `dtype` - """ - data_type = DataType(dtype) - if fill_value is None: - raise ValueError("Fill value cannot be None") - if data_type == DataType.string: - return np.str_(fill_value) - if data_type == DataType.bytes: - return np.bytes_(fill_value) - - # the rest are numeric types - np_dtype = cast(np.dtype[Any], data_type.to_numpy()) - - if isinstance(fill_value, Sequence) and not isinstance(fill_value, str): - if data_type in (DataType.complex64, DataType.complex128): - if len(fill_value) == 2: - decoded_fill_value = tuple( - SPECIAL_FLOATS_ENCODED.get(value, value) for value in fill_value - ) - # complex datatypes serialize to JSON arrays with two elements - return np_dtype.type(complex(*decoded_fill_value)) - else: - msg = ( - f"Got an invalid fill value for complex data type {data_type.value}." - f"Expected a sequence with 2 elements, but {fill_value!r} has " - f"length {len(fill_value)}." - ) - raise ValueError(msg) - msg = f"Cannot parse non-string sequence {fill_value!r} as a scalar with type {data_type.value}." - raise TypeError(msg) - - # Cast the fill_value to the given dtype - try: - # This warning filter can be removed after Zarr supports numpy>=2.0 - # The warning is saying that the future behavior of out of bounds casting will be to raise - # an OverflowError. In the meantime, we allow overflow and catch cases where - # fill_value != casted_value below. - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", category=DeprecationWarning) - casted_value = np.dtype(np_dtype).type(fill_value) - except (ValueError, OverflowError, TypeError) as e: - raise ValueError(f"fill value {fill_value!r} is not valid for dtype {data_type}") from e - # Check if the value is still representable by the dtype - if (fill_value == "NaN" and np.isnan(casted_value)) or ( - fill_value in ["Infinity", "-Infinity"] and not np.isfinite(casted_value) - ): - pass - elif np_dtype.kind == "f": - # float comparison is not exact, especially when dtype str | bytes | np.generic: - if dtype == DataType.string: - return "" - elif dtype == DataType.bytes: - return b"" - else: - np_dtype = dtype.to_numpy() - np_dtype = cast(np.dtype[Any], np_dtype) - return np_dtype.type(0) # type: ignore[misc] - - -# For type checking -_bool = bool - - -class DataType(Enum): - bool = "bool" - int8 = "int8" - int16 = "int16" - int32 = "int32" - int64 = "int64" - uint8 = "uint8" - uint16 = "uint16" - uint32 = "uint32" - uint64 = "uint64" - float16 = "float16" - float32 = "float32" - float64 = "float64" - complex64 = "complex64" - complex128 = "complex128" - string = "string" - bytes = "bytes" - - @property - def byte_count(self) -> int | None: - data_type_byte_counts = { - DataType.bool: 1, - DataType.int8: 1, - DataType.int16: 2, - DataType.int32: 4, - DataType.int64: 8, - DataType.uint8: 1, - DataType.uint16: 2, - DataType.uint32: 4, - DataType.uint64: 8, - DataType.float16: 2, - DataType.float32: 4, - DataType.float64: 8, - DataType.complex64: 8, - DataType.complex128: 16, - } - try: - return data_type_byte_counts[self] - except KeyError: - # string and bytes have variable length - return None - - @property - def has_endianness(self) -> _bool: - return self.byte_count is not None and self.byte_count != 1 - - def to_numpy_shortname(self) -> str: - data_type_to_numpy = { - DataType.bool: "bool", - DataType.int8: "i1", - DataType.int16: "i2", - DataType.int32: "i4", - DataType.int64: "i8", - DataType.uint8: "u1", - DataType.uint16: "u2", - DataType.uint32: "u4", - DataType.uint64: "u8", - DataType.float16: "f2", - DataType.float32: "f4", - DataType.float64: "f8", - DataType.complex64: "c8", - DataType.complex128: "c16", - } - return data_type_to_numpy[self] - - def to_numpy(self) -> np.dtypes.StringDType | np.dtypes.ObjectDType | np.dtype[Any]: - # note: it is not possible to round trip DataType <-> np.dtype - # due to the fact that DataType.string and DataType.bytes both - # generally return np.dtype("O") from this function, even though - # they can originate as fixed-length types (e.g. " DataType: - if dtype.kind in "UT": - return DataType.string - elif dtype.kind == "S": - return DataType.bytes - elif not _NUMPY_SUPPORTS_VLEN_STRING and dtype.kind == "O": - # numpy < 2.0 does not support vlen string dtype - # so we fall back on object array of strings - return DataType.string - dtype_to_data_type = { - "|b1": "bool", - "bool": "bool", - "|i1": "int8", - " DataType: - if dtype is None: - return DataType[DEFAULT_DTYPE] - if isinstance(dtype, DataType): - return dtype - try: - return DataType(dtype) - except ValueError: - pass - try: - dtype = np.dtype(dtype) - except (ValueError, TypeError) as e: - raise ValueError(f"Invalid Zarr format 3 data_type: {dtype}") from e - # check that this is a valid v3 data_type - try: - data_type = DataType.from_numpy(dtype) - except KeyError as e: - raise ValueError(f"Invalid Zarr format 3 data_type: {dtype}") from e - return data_type diff --git a/src/zarr/core/strings.py b/src/zarr/core/strings.py deleted file mode 100644 index ffca0c3b0c..0000000000 --- a/src/zarr/core/strings.py +++ /dev/null @@ -1,86 +0,0 @@ -"""This module contains utilities for working with string arrays across -different versions of Numpy. -""" - -from typing import Any, Union, cast -from warnings import warn - -import numpy as np - -# _STRING_DTYPE is the in-memory datatype that will be used for V3 string arrays -# when reading data back from Zarr. -# Any valid string-like datatype should be fine for *setting* data. - -_STRING_DTYPE: Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"] -_NUMPY_SUPPORTS_VLEN_STRING: bool - - -def cast_array( - data: np.ndarray[Any, np.dtype[Any]], -) -> np.ndarray[Any, Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]]: - raise NotImplementedError - - -try: - # this new vlen string dtype was added in NumPy 2.0 - _STRING_DTYPE = np.dtypes.StringDType() - _NUMPY_SUPPORTS_VLEN_STRING = True - - def cast_array( - data: np.ndarray[Any, np.dtype[Any]], - ) -> np.ndarray[Any, np.dtypes.StringDType | np.dtypes.ObjectDType]: - out = data.astype(_STRING_DTYPE, copy=False) - return cast(np.ndarray[Any, np.dtypes.StringDType], out) - -except AttributeError: - # if not available, we fall back on an object array of strings, as in Zarr < 3 - _STRING_DTYPE = np.dtypes.ObjectDType() - _NUMPY_SUPPORTS_VLEN_STRING = False - - def cast_array( - data: np.ndarray[Any, np.dtype[Any]], - ) -> np.ndarray[Any, Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]]: - out = data.astype(_STRING_DTYPE, copy=False) - return cast(np.ndarray[Any, np.dtypes.ObjectDType], out) - - -def cast_to_string_dtype( - data: np.ndarray[Any, np.dtype[Any]], safe: bool = False -) -> np.ndarray[Any, Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]]: - """Take any data and attempt to cast to to our preferred string dtype. - - data : np.ndarray - The data to cast - - safe : bool - If True, do not issue a warning if the data is cast from object to string dtype. - - """ - if np.issubdtype(data.dtype, np.str_): - # legacy fixed-width string type (e.g. "= 2.", - stacklevel=2, - ) - return cast_array(data) - raise ValueError(f"Cannot cast dtype {data.dtype} to string dtype") diff --git a/src/zarr/core/sync.py b/src/zarr/core/sync.py index d9b4839e8e..ffb04e764d 100644 --- a/src/zarr/core/sync.py +++ b/src/zarr/core/sync.py @@ -6,7 +6,7 @@ import os import threading from concurrent.futures import ThreadPoolExecutor, wait -from typing import TYPE_CHECKING, Any, TypeVar +from typing import TYPE_CHECKING, TypeVar from typing_extensions import ParamSpec diff --git a/src/zarr/dtype.py b/src/zarr/dtype.py new file mode 100644 index 0000000000..6e3789543b --- /dev/null +++ b/src/zarr/dtype.py @@ -0,0 +1,3 @@ +from zarr.core.dtype import ZDType, data_type_registry + +__all__ = ["ZDType", "data_type_registry"] diff --git a/src/zarr/errors.py b/src/zarr/errors.py index 441cdab9a3..4f972a6703 100644 --- a/src/zarr/errors.py +++ b/src/zarr/errors.py @@ -5,6 +5,7 @@ "ContainsArrayAndGroupError", "ContainsArrayError", "ContainsGroupError", + "GroupNotFoundError", "MetadataValidationError", "NodeTypeValidationError", ] @@ -21,6 +22,14 @@ def __init__(self, *args: Any) -> None: super().__init__(self._msg.format(*args)) +class GroupNotFoundError(BaseZarrError, FileNotFoundError): + """ + Raised when a group isn't found at a certain path. + """ + + _msg = "No group found in store {!r} at path {!r}" + + class ContainsGroupError(BaseZarrError): """Raised when a group already exists at a certain path.""" diff --git a/src/zarr/registry.py b/src/zarr/registry.py index 704db3f704..eb345b24b1 100644 --- a/src/zarr/registry.py +++ b/src/zarr/registry.py @@ -6,6 +6,7 @@ from typing import TYPE_CHECKING, Any, Generic, TypeVar from zarr.core.config import BadConfigError, config +from zarr.core.dtype import data_type_registry if TYPE_CHECKING: from importlib.metadata import EntryPoint @@ -43,10 +44,13 @@ def __init__(self) -> None: def lazy_load(self) -> None: for e in self.lazy_load_list: self.register(e.load()) + self.lazy_load_list.clear() - def register(self, cls: type[T]) -> None: - self[fully_qualified_name(cls)] = cls + def register(self, cls: type[T], qualname: str | None = None) -> None: + if qualname is None: + qualname = fully_qualified_name(cls) + self[qualname] = cls __codec_registries: dict[str, Registry[Codec]] = defaultdict(Registry) @@ -58,12 +62,14 @@ def register(self, cls: type[T]) -> None: The registry module is responsible for managing implementations of codecs, pipelines, buffers and ndbuffers and collecting them from entrypoints. The implementation used is determined by the config. + +The registry module is also responsible for managing dtypes. """ def _collect_entrypoints() -> list[Registry[Any]]: """ - Collects codecs, pipelines, buffers and ndbuffers from entrypoints. + Collects codecs, pipelines, dtypes, buffers and ndbuffers from entrypoints. Entry points can either be single items or groups of items. Allowed syntax for entry_points.txt is e.g. @@ -86,6 +92,10 @@ def _collect_entrypoints() -> list[Registry[Any]]: __buffer_registry.lazy_load_list.extend(entry_points.select(group="zarr", name="buffer")) __ndbuffer_registry.lazy_load_list.extend(entry_points.select(group="zarr.ndbuffer")) __ndbuffer_registry.lazy_load_list.extend(entry_points.select(group="zarr", name="ndbuffer")) + + data_type_registry.lazy_load_list.extend(entry_points.select(group="zarr.data_type")) + data_type_registry.lazy_load_list.extend(entry_points.select(group="zarr", name="data_type")) + __pipeline_registry.lazy_load_list.extend(entry_points.select(group="zarr.codec_pipeline")) __pipeline_registry.lazy_load_list.extend( entry_points.select(group="zarr", name="codec_pipeline") @@ -123,12 +133,12 @@ def register_pipeline(pipe_cls: type[CodecPipeline]) -> None: __pipeline_registry.register(pipe_cls) -def register_ndbuffer(cls: type[NDBuffer]) -> None: - __ndbuffer_registry.register(cls) +def register_ndbuffer(cls: type[NDBuffer], qualname: str | None = None) -> None: + __ndbuffer_registry.register(cls, qualname) -def register_buffer(cls: type[Buffer]) -> None: - __buffer_registry.register(cls) +def register_buffer(cls: type[Buffer], qualname: str | None = None) -> None: + __buffer_registry.register(cls, qualname) def get_codec_class(key: str, reload_config: bool = False) -> type[Codec]: @@ -148,7 +158,8 @@ def get_codec_class(key: str, reload_config: bool = False) -> type[Codec]: if len(codec_classes) == 1: return next(iter(codec_classes.values())) warnings.warn( - f"Codec '{key}' not configured in config. Selecting any implementation.", stacklevel=2 + f"Codec '{key}' not configured in config. Selecting any implementation.", + stacklevel=2, ) return list(codec_classes.values())[-1] selected_codec_cls = codec_classes[config_entry] diff --git a/src/zarr/storage/_common.py b/src/zarr/storage/_common.py index d81369f142..f264728cf2 100644 --- a/src/zarr/storage/_common.py +++ b/src/zarr/storage/_common.py @@ -1,8 +1,9 @@ from __future__ import annotations +import importlib.util import json from pathlib import Path -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any, Literal, Self, TypeAlias from zarr.abc.store import ByteRequest, Store from zarr.core.buffer import Buffer, default_buffer_prototype @@ -12,6 +13,12 @@ from zarr.storage._memory import MemoryStore from zarr.storage._utils import normalize_path +_has_fsspec = importlib.util.find_spec("fsspec") +if _has_fsspec: + from fsspec.mapping import FSMap +else: + FSMap = None + if TYPE_CHECKING: from zarr.core.buffer import BufferPrototype @@ -48,9 +55,7 @@ def read_only(self) -> bool: return self.store.read_only @classmethod - async def open( - cls, store: Store, path: str, mode: AccessModeLiteral | None = None - ) -> StorePath: + async def open(cls, store: Store, path: str, mode: AccessModeLiteral | None = None) -> Self: """ Open StorePath based on the provided mode. @@ -67,6 +72,9 @@ async def open( ------ FileExistsError If the mode is 'w-' and the store path already exists. + ValueError + If the mode is not "r" and the store is read-only, or + if the mode is "r" and the store is not read-only. """ await store._ensure_open() @@ -78,6 +86,8 @@ async def open( if store.read_only and mode != "r": raise ValueError(f"Store is read-only but mode is '{mode}'") + if not store.read_only and mode == "r": + raise ValueError(f"Store is not read-only but mode is '{mode}'") match mode: case "w-": @@ -224,7 +234,7 @@ def __eq__(self, other: object) -> bool: return False -StoreLike = Store | StorePath | Path | str | dict[str, Buffer] +StoreLike: TypeAlias = Store | StorePath | FSMap | Path | str | dict[str, Buffer] async def make_store_path( @@ -311,9 +321,18 @@ async def make_store_path( # We deliberate only consider dict[str, Buffer] here, and not arbitrary mutable mappings. # By only allowing dictionaries, which are in-memory, we know that MemoryStore appropriate. store = await MemoryStore.open(store_dict=store_like, read_only=_read_only) + elif _has_fsspec and isinstance(store_like, FSMap): + if path: + raise ValueError( + "'path' was provided but is not used for FSMap store_like objects. Specify the path when creating the FSMap instance instead." + ) + if storage_options: + raise ValueError( + "'storage_options was provided but is not used for FSMap store_like objects. Specify the storage options when creating the FSMap instance instead." + ) + store = FsspecStore.from_mapper(store_like, read_only=_read_only) else: - msg = f"Unsupported type for store_like: '{type(store_like).__name__}'" # type: ignore[unreachable] - raise TypeError(msg) + raise TypeError(f"Unsupported type for store_like: '{type(store_like).__name__}'") result = await StorePath.open(store, path=path_normalized, mode=mode) diff --git a/src/zarr/storage/_fsspec.py b/src/zarr/storage/_fsspec.py index a4730a93d9..4f6929456e 100644 --- a/src/zarr/storage/_fsspec.py +++ b/src/zarr/storage/_fsspec.py @@ -1,9 +1,12 @@ from __future__ import annotations +import json import warnings from contextlib import suppress from typing import TYPE_CHECKING, Any +from packaging.version import parse as parse_version + from zarr.abc.store import ( ByteRequest, OffsetByteRequest, @@ -17,7 +20,9 @@ if TYPE_CHECKING: from collections.abc import AsyncIterator, Iterable + from fsspec import AbstractFileSystem from fsspec.asyn import AsyncFileSystem + from fsspec.mapping import FSMap from zarr.core.buffer import BufferPrototype from zarr.core.common import BytesLike @@ -30,9 +35,45 @@ ) +def _make_async(fs: AbstractFileSystem) -> AsyncFileSystem: + """Convert a sync FSSpec filesystem to an async FFSpec filesystem + + If the filesystem class supports async operations, a new async instance is created + from the existing instance. + + If the filesystem class does not support async operations, the existing instance + is wrapped with AsyncFileSystemWrapper. + """ + import fsspec + + fsspec_version = parse_version(fsspec.__version__) + if fs.async_impl and fs.asynchronous: + # Already an async instance of an async filesystem, nothing to do + return fs + if fs.async_impl: + # Convert sync instance of an async fs to an async instance + fs_dict = json.loads(fs.to_json()) + fs_dict["asynchronous"] = True + return fsspec.AbstractFileSystem.from_json(json.dumps(fs_dict)) + + # Wrap sync filesystems with the async wrapper + if type(fs) is fsspec.implementations.local.LocalFileSystem and not fs.auto_mkdir: + raise ValueError( + f"LocalFilesystem {fs} was created with auto_mkdir=False but Zarr requires the filesystem to automatically create directories" + ) + if fsspec_version < parse_version("2024.12.0"): + raise ImportError( + f"The filesystem '{fs}' is synchronous, and the required " + "AsyncFileSystemWrapper is not available. Upgrade fsspec to version " + "2024.12.0 or later to enable this functionality." + ) + + return fsspec.implementations.asyn_wrapper.AsyncFileSystemWrapper(fs, asynchronous=True) + + class FsspecStore(Store): """ - A remote Store based on FSSpec + Store for remote data based on FSSpec. Parameters ---------- @@ -81,6 +122,7 @@ class FsspecStore(Store): fs: AsyncFileSystem allowed_exceptions: tuple[type[Exception], ...] + path: str def __init__( self, @@ -137,6 +179,38 @@ def from_upath( allowed_exceptions=allowed_exceptions, ) + @classmethod + def from_mapper( + cls, + fs_map: FSMap, + read_only: bool = False, + allowed_exceptions: tuple[type[Exception], ...] = ALLOWED_EXCEPTIONS, + ) -> FsspecStore: + """ + Create a FsspecStore from a FSMap object. + + Parameters + ---------- + fs_map : FSMap + Fsspec mutable mapping object. + read_only : bool + Whether the store is read-only, defaults to False. + allowed_exceptions : tuple, optional + The exceptions that are allowed to be raised when accessing the + store. Defaults to ALLOWED_EXCEPTIONS. + + Returns + ------- + FsspecStore + """ + fs = _make_async(fs_map.fs) + return cls( + fs=fs, + path=fs_map.root, + read_only=read_only, + allowed_exceptions=allowed_exceptions, + ) + @classmethod def from_url( cls, @@ -175,16 +249,7 @@ def from_url( fs, path = url_to_fs(url, **opts) if not fs.async_impl: - try: - from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper - - fs = AsyncFileSystemWrapper(fs, asynchronous=True) - except ImportError as e: - raise ImportError( - f"The filesystem for URL '{url}' is synchronous, and the required " - "AsyncFileSystemWrapper is not available. Upgrade fsspec to version " - "2024.12.0 or later to enable this functionality." - ) from e + fs = _make_async(fs) # fsspec is not consistent about removing the scheme from the path, so check and strip it here # https://github.com/fsspec/filesystem_spec/issues/1722 @@ -194,6 +259,15 @@ def from_url( return cls(fs=fs, path=path, read_only=read_only, allowed_exceptions=allowed_exceptions) + def with_read_only(self, read_only: bool = False) -> FsspecStore: + # docstring inherited + return type(self)( + fs=self.fs, + path=self.path, + allowed_exceptions=self.allowed_exceptions, + read_only=read_only, + ) + async def clear(self) -> None: # docstring inherited try: diff --git a/src/zarr/storage/_local.py b/src/zarr/storage/_local.py index bd5bfc1da2..43e585415d 100644 --- a/src/zarr/storage/_local.py +++ b/src/zarr/storage/_local.py @@ -52,10 +52,10 @@ def _put( with path.open("r+b") as f: f.seek(start) # write takes any object supporting the buffer protocol - f.write(value.as_numpy_array()) # type: ignore[arg-type] + f.write(value.as_buffer_like()) return None else: - view = memoryview(value.as_numpy_array()) # type: ignore[arg-type] + view = value.as_buffer_like() if exclusive: mode = "xb" else: @@ -67,7 +67,7 @@ def _put( class LocalStore(Store): """ - Local file system store. + Store for the local file system. Parameters ---------- @@ -102,6 +102,13 @@ def __init__(self, root: Path | str, *, read_only: bool = False) -> None: ) self.root = root + def with_read_only(self, read_only: bool = False) -> LocalStore: + # docstring inherited + return type(self)( + root=self.root, + read_only=read_only, + ) + async def _open(self) -> None: if not self.read_only: self.root.mkdir(parents=True, exist_ok=True) @@ -253,5 +260,17 @@ async def list_dir(self, prefix: str) -> AsyncIterator[str]: except (FileNotFoundError, NotADirectoryError): pass + async def move(self, dest_root: Path | str) -> None: + """ + Move the store to another path. The old root directory is deleted. + """ + if isinstance(dest_root, str): + dest_root = Path(dest_root) + os.makedirs(dest_root.parent, exist_ok=True) + if os.path.exists(dest_root): + raise FileExistsError(f"Destination root {dest_root} already exists.") + shutil.move(self.root, dest_root) + self.root = dest_root + async def getsize(self, key: str) -> int: return os.path.getsize(self.root / key) diff --git a/src/zarr/storage/_logging.py b/src/zarr/storage/_logging.py index 5f1a97acd9..a2164a418f 100644 --- a/src/zarr/storage/_logging.py +++ b/src/zarr/storage/_logging.py @@ -24,7 +24,7 @@ class LoggingStore(WrapperStore[T_Store]): """ - Store wrapper that logs all calls to the wrapped store. + Store that logs all calls to another wrapped store. Parameters ---------- diff --git a/src/zarr/storage/_memory.py b/src/zarr/storage/_memory.py index b37fc8d5c9..0dc6f13236 100644 --- a/src/zarr/storage/_memory.py +++ b/src/zarr/storage/_memory.py @@ -19,7 +19,7 @@ class MemoryStore(Store): """ - In-memory store. + Store for local memory. Parameters ---------- @@ -54,6 +54,13 @@ def __init__( store_dict = {} self._store_dict = store_dict + def with_read_only(self, read_only: bool = False) -> MemoryStore: + # docstring inherited + return type(self)( + store_dict=self._store_dict, + read_only=read_only, + ) + async def clear(self) -> None: # docstring inherited self._store_dict.clear() @@ -173,8 +180,10 @@ async def list_dir(self, prefix: str) -> AsyncIterator[str]: class GpuMemoryStore(MemoryStore): - """A GPU only memory store that stores every chunk in GPU memory irrespective - of the original location. + """ + Store for GPU memory. + + Stores every chunk in GPU memory irrespective of the original location. The dictionary of buffers to initialize this memory store with *must* be GPU Buffers. diff --git a/src/zarr/storage/_obstore.py b/src/zarr/storage/_obstore.py index 4381acb2ae..047ed07fbb 100644 --- a/src/zarr/storage/_obstore.py +++ b/src/zarr/storage/_obstore.py @@ -4,8 +4,7 @@ import contextlib import pickle from collections import defaultdict -from collections.abc import Iterable -from typing import TYPE_CHECKING, Any, TypedDict +from typing import TYPE_CHECKING, TypedDict from zarr.abc.store import ( ByteRequest, @@ -14,7 +13,6 @@ Store, SuffixByteRequest, ) -from zarr.core.buffer.core import BufferPrototype from zarr.core.config import config if TYPE_CHECKING: @@ -37,7 +35,8 @@ class ObjectStore(Store): - """A Zarr store that uses obstore for fast read/write from AWS, GCP, Azure. + """ + Store that uses obstore for fast read/write from AWS, GCP, Azure. Parameters ---------- @@ -70,6 +69,13 @@ def __init__(self, store: _UpstreamObjectStore, *, read_only: bool = False) -> N super().__init__(read_only=read_only) self.store = store + def with_read_only(self, read_only: bool = False) -> ObjectStore: + # docstring inherited + return type(self)( + store=self.store, + read_only=read_only, + ) + def __str__(self) -> str: return f"object_store://{self.store}" @@ -160,7 +166,7 @@ async def set(self, key: str, value: Buffer) -> None: self._check_writable() - buf = value.to_bytes() + buf = value.as_buffer_like() await obs.put_async(self.store, key, buf) async def set_if_not_exists(self, key: str, value: Buffer) -> None: @@ -168,7 +174,7 @@ async def set_if_not_exists(self, key: str, value: Buffer) -> None: import obstore as obs self._check_writable() - buf = value.to_bytes() + buf = value.as_buffer_like() with contextlib.suppress(obs.exceptions.AlreadyExistsError): await obs.put_async(self.store, key, buf, mode="create") diff --git a/src/zarr/storage/_utils.py b/src/zarr/storage/_utils.py index eda4342f47..145790278c 100644 --- a/src/zarr/storage/_utils.py +++ b/src/zarr/storage/_utils.py @@ -74,11 +74,62 @@ def _join_paths(paths: Iterable[str]) -> str: """ Filter out instances of '' and join the remaining strings with '/'. - Because the root node of a zarr hierarchy is represented by an empty string, + Parameters + ---------- + paths : Iterable[str] + + Returns + ------- + str + + Examples + -------- + >>> _join_paths(["", "a", "b"]) + 'a/b' + >>> _join_paths(["a", "b", "c"]) + 'a/b/c' """ return "/".join(filter(lambda v: v != "", paths)) +def _relativize_path(*, path: str, prefix: str) -> str: + """ + Make a "/"-delimited path relative to some prefix. If the prefix is '', then the path is + returned as-is. Otherwise, the prefix is removed from the path as well as the separator + string "/". + + If ``prefix`` is not the empty string and ``path`` does not start with ``prefix`` + followed by a "/" character, then an error is raised. + + This function assumes that the prefix does not end with "/". + + Parameters + ---------- + path : str + The path to make relative to the prefix. + prefix : str + The prefix to make the path relative to. + + Returns + ------- + str + + Examples + -------- + >>> _relativize_path(path="", prefix="a/b") + 'a/b' + >>> _relativize_path(path="a/b", prefix="a/b/c") + 'c' + """ + if prefix == "": + return path + else: + _prefix = prefix + "/" + if not path.startswith(_prefix): + raise ValueError(f"The first component of {path} does not start with {prefix}.") + return path.removeprefix(f"{prefix}/") + + def _normalize_paths(paths: Iterable[str]) -> tuple[str, ...]: """ Normalize the input paths according to the normalization scheme used for zarr node paths. diff --git a/src/zarr/storage/_wrapper.py b/src/zarr/storage/_wrapper.py index 349048e495..f21d378191 100644 --- a/src/zarr/storage/_wrapper.py +++ b/src/zarr/storage/_wrapper.py @@ -18,7 +18,8 @@ class WrapperStore(Store, Generic[T_Store]): """ - A store class that wraps an existing ``Store`` instance. + Store that wraps an existing Store. + By default all of the store methods are delegated to the wrapped store instance, which is accessible via the ``._store`` attribute of this class. diff --git a/src/zarr/storage/_zip.py b/src/zarr/storage/_zip.py index bbfe6c67aa..5d147deded 100644 --- a/src/zarr/storage/_zip.py +++ b/src/zarr/storage/_zip.py @@ -1,6 +1,7 @@ from __future__ import annotations import os +import shutil import threading import time import zipfile @@ -24,7 +25,7 @@ class ZipStore(Store): """ - Storage class using a ZIP file. + Store using a ZIP file. Parameters ---------- @@ -288,3 +289,15 @@ async def list_dir(self, prefix: str) -> AsyncIterator[str]: if k not in seen: seen.add(k) yield k + + async def move(self, path: Path | str) -> None: + """ + Move the store to another path. + """ + if isinstance(path, str): + path = Path(path) + self.close() + os.makedirs(path.parent, exist_ok=True) + shutil.move(self.path, path) + self.path = path + await self._open() diff --git a/src/zarr/testing/stateful.py b/src/zarr/testing/stateful.py index ede83201ae..f83d942549 100644 --- a/src/zarr/testing/stateful.py +++ b/src/zarr/testing/stateful.py @@ -17,10 +17,18 @@ import zarr from zarr import Array from zarr.abc.store import Store +from zarr.codecs.bytes import BytesCodec from zarr.core.buffer import Buffer, BufferPrototype, cpu, default_buffer_prototype from zarr.core.sync import SyncMixin from zarr.storage import LocalStore, MemoryStore -from zarr.testing.strategies import key_ranges, node_names, np_array_and_chunks, numpy_arrays +from zarr.testing.strategies import ( + basic_indices, + chunk_paths, + key_ranges, + node_names, + np_array_and_chunks, + numpy_arrays, +) from zarr.testing.strategies import keys as zarr_keys MAX_BINARY_SIZE = 100 @@ -108,9 +116,131 @@ def add_array( assume(self.can_add(path)) note(f"Adding array: path='{path}' shape={array.shape} chunks={chunks}") for store in [self.store, self.model]: - zarr.array(array, chunks=chunks, path=path, store=store, fill_value=fill_value) + zarr.array( + array, + chunks=chunks, + path=path, + store=store, + fill_value=fill_value, + # Chose bytes codec to avoid wasting time compressing the data being written + codecs=[BytesCodec()], + ) self.all_arrays.add(path) + @rule() + def clear(self) -> None: + note("clearing") + import zarr + + self._sync(self.store.clear()) + self._sync(self.model.clear()) + + assert self._sync(self.store.is_empty("/")) + assert self._sync(self.model.is_empty("/")) + + self.all_groups.clear() + self.all_arrays.clear() + + zarr.group(store=self.store) + zarr.group(store=self.model) + + # TODO: MemoryStore is broken? + # assert not self._sync(self.store.is_empty("/")) + # assert not self._sync(self.model.is_empty("/")) + + def draw_directory(self, data: DataObject) -> str: + group_st = st.sampled_from(sorted(self.all_groups)) if self.all_groups else st.nothing() + array_st = st.sampled_from(sorted(self.all_arrays)) if self.all_arrays else st.nothing() + array_or_group = data.draw(st.one_of(group_st, array_st)) + if data.draw(st.booleans()) and array_or_group in self.all_arrays: + arr = zarr.open_array(path=array_or_group, store=self.model) + path = data.draw( + st.one_of( + st.sampled_from([array_or_group]), + chunk_paths(ndim=arr.ndim, numblocks=arr.cdata_shape).map( + lambda x: f"{array_or_group}/c/" + ), + ) + ) + else: + path = array_or_group + return path + + @precondition(lambda self: bool(self.all_groups)) + @rule(data=st.data()) + def check_list_dir(self, data: DataObject) -> None: + path = self.draw_directory(data) + note(f"list_dir for {path=!r}") + # Consider .list_dir("path/to/array") for an array with a single chunk. + # The MemoryStore model will return `"c", "zarr.json"` only if the chunk exists + # If that chunk was deleted, then `"c"` is not returned. + # LocalStore will not have this behaviour :/ + # There are similar consistency issues with delete_dir("/path/to/array/c/0/0") + assume(not isinstance(self.store, LocalStore)) + model_ls = sorted(self._sync_iter(self.model.list_dir(path))) + store_ls = sorted(self._sync_iter(self.store.list_dir(path))) + assert model_ls == store_ls, (model_ls, store_ls) + + @precondition(lambda self: bool(self.all_arrays)) + @rule(data=st.data()) + def delete_chunk(self, data: DataObject) -> None: + array = data.draw(st.sampled_from(sorted(self.all_arrays))) + arr = zarr.open_array(path=array, store=self.model) + chunk_path = data.draw(chunk_paths(ndim=arr.ndim, numblocks=arr.cdata_shape, subset=False)) + path = f"{array}/c/{chunk_path}" + note(f"deleting chunk {path=!r}") + self._sync(self.model.delete(path)) + self._sync(self.store.delete(path)) + + @precondition(lambda self: bool(self.all_arrays)) + @rule(data=st.data()) + def overwrite_array_basic_indexing(self, data: DataObject) -> None: + array = data.draw(st.sampled_from(sorted(self.all_arrays))) + model_array = zarr.open_array(path=array, store=self.model) + store_array = zarr.open_array(path=array, store=self.store) + slicer = data.draw(basic_indices(shape=model_array.shape)) + note(f"overwriting array with basic indexer: {slicer=}") + new_data = data.draw( + npst.arrays(shape=np.shape(model_array[slicer]), dtype=model_array.dtype) + ) + model_array[slicer] = new_data + store_array[slicer] = new_data + + @precondition(lambda self: bool(self.all_arrays)) + @rule(data=st.data()) + def resize_array(self, data: DataObject) -> None: + array = data.draw(st.sampled_from(sorted(self.all_arrays))) + model_array = zarr.open_array(path=array, store=self.model) + store_array = zarr.open_array(path=array, store=self.store) + ndim = model_array.ndim + new_shape = tuple( + 0 if oldsize == 0 else newsize + for newsize, oldsize in zip( + data.draw(npst.array_shapes(max_dims=ndim, min_dims=ndim, min_side=0)), + model_array.shape, + strict=True, + ) + ) + + note(f"resizing array from {model_array.shape} to {new_shape}") + model_array.resize(new_shape) + store_array.resize(new_shape) + + @precondition(lambda self: bool(self.all_arrays) or bool(self.all_groups)) + @rule(data=st.data()) + def delete_dir(self, data: DataObject) -> None: + path = self.draw_directory(data) + note(f"delete_dir with {path=!r}") + self._sync(self.model.delete_dir(path)) + self._sync(self.store.delete_dir(path)) + + matches = set() + for node in self.all_groups | self.all_arrays: + if node.startswith(path): + matches.add(node) + self.all_groups = self.all_groups - matches + self.all_arrays = self.all_arrays - matches + # @precondition(lambda self: bool(self.all_groups)) # @precondition(lambda self: bool(self.all_arrays)) # @rule(data=st.data()) @@ -221,13 +351,19 @@ def delete_group_using_del(self, data: DataObject) -> None: # self.check_group_arrays(group) # t1 = time.time() # note(f"Checks took {t1 - t0} sec.") - @invariant() def check_list_prefix_from_root(self) -> None: model_list = self._sync_iter(self.model.list_prefix("")) store_list = self._sync_iter(self.store.list_prefix("")) - note(f"Checking {len(model_list)} keys") - assert sorted(model_list) == sorted(store_list) + note(f"Checking {len(model_list)} expected keys vs {len(store_list)} actual keys") + assert sorted(model_list) == sorted(store_list), ( + sorted(model_list), + sorted(store_list), + ) + + # check that our internal state matches that of the store and model + assert all(f"{path}/zarr.json" in model_list for path in self.all_groups | self.all_arrays) + assert all(f"{path}/zarr.json" in store_list for path in self.all_groups | self.all_arrays) class SyncStoreWrapper(zarr.core.sync.SyncMixin): @@ -326,8 +462,8 @@ def init_store(self) -> None: self.store.clear() @rule(key=zarr_keys(), data=st.binary(min_size=0, max_size=MAX_BINARY_SIZE)) - def set(self, key: str, data: DataObject) -> None: - note(f"(set) Setting {key!r} with {data}") + def set(self, key: str, data: bytes) -> None: + note(f"(set) Setting {key!r} with {data!r}") assert not self.store.read_only data_buf = cpu.Buffer.from_bytes(data) self.store.set(key, data_buf) diff --git a/src/zarr/testing/store.py b/src/zarr/testing/store.py index 867df2121f..970329f393 100644 --- a/src/zarr/testing/store.py +++ b/src/zarr/testing/store.py @@ -58,7 +58,7 @@ async def get(self, store: S, key: str) -> Buffer: @abstractmethod @pytest.fixture - def store_kwargs(self) -> dict[str, Any]: + def store_kwargs(self, *args: Any, **kwargs: Any) -> dict[str, Any]: """Kwargs for instantiating a store""" ... @@ -149,6 +149,58 @@ async def test_read_only_store_raises(self, open_kwargs: dict[str, Any]) -> None ): await store.delete("foo") + async def test_with_read_only_store(self, open_kwargs: dict[str, Any]) -> None: + kwargs = {**open_kwargs, "read_only": True} + store = await self.store_cls.open(**kwargs) + assert store.read_only + + # Test that you cannot write to a read-only store + with pytest.raises( + ValueError, match="store was opened in read-only mode and does not support writing" + ): + await store.set("foo", self.buffer_cls.from_bytes(b"bar")) + + # Check if the store implements with_read_only + try: + writer = store.with_read_only(read_only=False) + except NotImplementedError: + # Test that stores that do not implement with_read_only raise NotImplementedError with the correct message + with pytest.raises( + NotImplementedError, + match=f"with_read_only is not implemented for the {type(store)} store type.", + ): + store.with_read_only(read_only=False) + return + + # Test that you can write to a new store copy + assert not writer._is_open + assert not writer.read_only + await writer.set("foo", self.buffer_cls.from_bytes(b"bar")) + await writer.delete("foo") + + # Test that you cannot write to the original store + assert store.read_only + with pytest.raises( + ValueError, match="store was opened in read-only mode and does not support writing" + ): + await store.set("foo", self.buffer_cls.from_bytes(b"bar")) + with pytest.raises( + ValueError, match="store was opened in read-only mode and does not support writing" + ): + await store.delete("foo") + + # Test that you cannot write to a read-only store copy + reader = store.with_read_only(read_only=True) + assert reader.read_only + with pytest.raises( + ValueError, match="store was opened in read-only mode and does not support writing" + ): + await reader.set("foo", self.buffer_cls.from_bytes(b"bar")) + with pytest.raises( + ValueError, match="store was opened in read-only mode and does not support writing" + ): + await reader.delete("foo") + @pytest.mark.parametrize("key", ["c/0", "foo/c/0.0", "foo/0/0"]) @pytest.mark.parametrize( ("data", "byte_range"), diff --git a/src/zarr/testing/strategies.py b/src/zarr/testing/strategies.py index f2dc38483a..5e070b5387 100644 --- a/src/zarr/testing/strategies.py +++ b/src/zarr/testing/strategies.py @@ -1,10 +1,12 @@ import math import sys +from collections.abc import Callable, Mapping from typing import Any, Literal import hypothesis.extra.numpy as npst import hypothesis.strategies as st import numpy as np +import numpy.typing as npt from hypothesis import event from hypothesis.strategies import SearchStrategy @@ -14,7 +16,8 @@ from zarr.core.array import Array from zarr.core.chunk_grids import RegularChunkGrid from zarr.core.chunk_key_encodings import DefaultChunkKeyEncoding -from zarr.core.common import ZarrFormat +from zarr.core.common import JSON, ZarrFormat +from zarr.core.dtype import get_data_type_from_native_dtype from zarr.core.metadata import ArrayV2Metadata, ArrayV3Metadata from zarr.core.sync import sync from zarr.storage import MemoryStore, StoreLike @@ -30,31 +33,31 @@ ) -@st.composite # type: ignore[misc] -def keys(draw: st.DrawFn, *, max_num_nodes: int | None = None) -> Any: +@st.composite +def keys(draw: st.DrawFn, *, max_num_nodes: int | None = None) -> str: return draw(st.lists(node_names, min_size=1, max_size=max_num_nodes).map("/".join)) -@st.composite # type: ignore[misc] -def paths(draw: st.DrawFn, *, max_num_nodes: int | None = None) -> Any: +@st.composite +def paths(draw: st.DrawFn, *, max_num_nodes: int | None = None) -> str: return draw(st.just("/") | keys(max_num_nodes=max_num_nodes)) -def v3_dtypes() -> st.SearchStrategy[np.dtype]: +def v3_dtypes() -> st.SearchStrategy[np.dtype[Any]]: return ( npst.boolean_dtypes() | npst.integer_dtypes(endianness="=") | npst.unsigned_integer_dtypes(endianness="=") | npst.floating_dtypes(endianness="=") | npst.complex_number_dtypes(endianness="=") - # | npst.byte_string_dtypes(endianness="=") - # | npst.unicode_string_dtypes() - # | npst.datetime64_dtypes() - # | npst.timedelta64_dtypes() + | npst.byte_string_dtypes(endianness="=") + | npst.unicode_string_dtypes(endianness="=") + | npst.datetime64_dtypes(endianness="=") + | npst.timedelta64_dtypes(endianness="=") ) -def v2_dtypes() -> st.SearchStrategy[np.dtype]: +def v2_dtypes() -> st.SearchStrategy[np.dtype[Any]]: return ( npst.boolean_dtypes() | npst.integer_dtypes(endianness="=") @@ -64,7 +67,7 @@ def v2_dtypes() -> st.SearchStrategy[np.dtype]: | npst.byte_string_dtypes(endianness="=") | npst.unicode_string_dtypes(endianness="=") | npst.datetime64_dtypes(endianness="=") - # | npst.timedelta64_dtypes() + | npst.timedelta64_dtypes(endianness="=") ) @@ -75,7 +78,7 @@ def safe_unicode_for_dtype(dtype: np.dtype[np.str_]) -> st.SearchStrategy[str]: return st.text( alphabet=st.characters( - blacklist_categories=["Cs"], # Avoid *technically allowed* surrogates + exclude_categories=["Cs"], # Avoid *technically allowed* surrogates min_codepoint=32, ), min_size=1, @@ -96,14 +99,20 @@ def clear_store(x: Store) -> Store: zarr_key_chars = st.sampled_from( ".-0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ_abcdefghijklmnopqrstuvwxyz" ) -node_names = st.text(zarr_key_chars, min_size=1).filter( - lambda t: t not in (".", "..") and not t.startswith("__") +node_names = ( + st.text(zarr_key_chars, min_size=1) + .filter(lambda t: t not in (".", "..") and not t.startswith("__")) + .filter(lambda name: name.lower() != "zarr.json") ) -short_node_names = st.text(zarr_key_chars, max_size=3, min_size=1).filter( - lambda t: t not in (".", "..") and not t.startswith("__") +short_node_names = ( + st.text(zarr_key_chars, max_size=3, min_size=1) + .filter(lambda t: t not in (".", "..") and not t.startswith("__")) + .filter(lambda name: name.lower() != "zarr.json") ) array_names = node_names -attrs = st.none() | st.dictionaries(_attr_keys, _attr_values) +attrs: st.SearchStrategy[Mapping[str, JSON] | None] = st.none() | st.dictionaries( + _attr_keys, _attr_values +) # st.builds will only call a new store constructor for different keyword arguments # i.e. stores.examples() will always return the same object per Store class. # So we map a clear to reset the store. @@ -111,30 +120,33 @@ def clear_store(x: Store) -> Store: compressors = st.sampled_from([None, "default"]) zarr_formats: st.SearchStrategy[ZarrFormat] = st.sampled_from([3, 2]) # We de-prioritize arrays having dim sizes 0, 1, 2 -array_shapes = npst.array_shapes(max_dims=4, min_side=3) | npst.array_shapes(max_dims=4, min_side=0) +array_shapes = npst.array_shapes(max_dims=4, min_side=3, max_side=5) | npst.array_shapes( + max_dims=4, min_side=0 +) -@st.composite # type: ignore[misc] +@st.composite def dimension_names(draw: st.DrawFn, *, ndim: int | None = None) -> list[None | str] | None: simple_text = st.text(zarr_key_chars, min_size=0) - return draw(st.none() | st.lists(st.none() | simple_text, min_size=ndim, max_size=ndim)) # type: ignore[no-any-return] + return draw(st.none() | st.lists(st.none() | simple_text, min_size=ndim, max_size=ndim)) # type: ignore[arg-type] -@st.composite # type: ignore[misc] +@st.composite def array_metadata( draw: st.DrawFn, *, - array_shapes: st.SearchStrategy[tuple[int, ...]] = npst.array_shapes, + array_shapes: Callable[..., st.SearchStrategy[tuple[int, ...]]] = npst.array_shapes, zarr_formats: st.SearchStrategy[Literal[2, 3]] = zarr_formats, - attributes: st.SearchStrategy[dict[str, Any]] = attrs, + attributes: SearchStrategy[Mapping[str, JSON] | None] = attrs, ) -> ArrayV2Metadata | ArrayV3Metadata: zarr_format = draw(zarr_formats) # separator = draw(st.sampled_from(['/', '\\'])) shape = draw(array_shapes()) ndim = len(shape) chunk_shape = draw(array_shapes(min_dims=ndim, max_dims=ndim)) - dtype = draw(v3_dtypes()) - fill_value = draw(npst.from_dtype(dtype)) + np_dtype = draw(v3_dtypes()) + dtype = get_data_type_from_native_dtype(np_dtype) + fill_value = draw(npst.from_dtype(np_dtype)) if zarr_format == 2: return ArrayV2Metadata( shape=shape, @@ -142,7 +154,7 @@ def array_metadata( dtype=dtype, fill_value=fill_value, order=draw(st.sampled_from(["C", "F"])), - attributes=draw(attributes), + attributes=draw(attributes), # type: ignore[arg-type] dimension_separator=draw(st.sampled_from([".", "/"])), filters=None, compressor=None, @@ -153,7 +165,7 @@ def array_metadata( data_type=dtype, chunk_grid=RegularChunkGrid(chunk_shape=chunk_shape), fill_value=fill_value, - attributes=draw(attributes), + attributes=draw(attributes), # type: ignore[arg-type] dimension_names=draw(dimension_names(ndim=ndim)), chunk_key_encoding=DefaultChunkKeyEncoding(separator="/"), # FIXME codecs=[BytesCodec()], @@ -161,14 +173,14 @@ def array_metadata( ) -@st.composite # type: ignore[misc] +@st.composite def numpy_arrays( draw: st.DrawFn, *, shapes: st.SearchStrategy[tuple[int, ...]] = array_shapes, dtype: np.dtype[Any] | None = None, - zarr_formats: st.SearchStrategy[ZarrFormat] | None = zarr_formats, -) -> Any: + zarr_formats: st.SearchStrategy[ZarrFormat] = zarr_formats, +) -> npt.NDArray[Any]: """ Generate numpy arrays that can be saved in the provided Zarr format. """ @@ -182,7 +194,7 @@ def numpy_arrays( return draw(npst.arrays(dtype=dtype, shape=shapes)) -@st.composite # type: ignore[misc] +@st.composite def chunk_shapes(draw: st.DrawFn, *, shape: tuple[int, ...]) -> tuple[int, ...]: # We want this strategy to shrink towards arrays with smaller number of chunks # 1. st.integers() shrinks towards smaller values. So we use that to generate number of chunks @@ -204,7 +216,7 @@ def chunk_shapes(draw: st.DrawFn, *, shape: tuple[int, ...]) -> tuple[int, ...]: return chunks -@st.composite # type: ignore[misc] +@st.composite def shard_shapes( draw: st.DrawFn, *, shape: tuple[int, ...], chunk_shape: tuple[int, ...] ) -> tuple[int, ...]: @@ -216,9 +228,11 @@ def shard_shapes( return tuple(m * c for m, c in zip(multiples, chunk_shape, strict=True)) -@st.composite # type: ignore[misc] +@st.composite def np_array_and_chunks( - draw: st.DrawFn, *, arrays: st.SearchStrategy[np.ndarray] = numpy_arrays + draw: st.DrawFn, + *, + arrays: st.SearchStrategy[npt.NDArray[Any]] = numpy_arrays(), # noqa: B008 ) -> tuple[np.ndarray, tuple[int, ...]]: # type: ignore[type-arg] """A hypothesis strategy to generate small sized random arrays. @@ -228,14 +242,14 @@ def np_array_and_chunks( return (array, draw(chunk_shapes(shape=array.shape))) -@st.composite # type: ignore[misc] +@st.composite def arrays( draw: st.DrawFn, *, shapes: st.SearchStrategy[tuple[int, ...]] = array_shapes, compressors: st.SearchStrategy = compressors, stores: st.SearchStrategy[StoreLike] = stores, - paths: st.SearchStrategy[str | None] = paths(), # noqa: B008 + paths: st.SearchStrategy[str] = paths(), # noqa: B008 array_names: st.SearchStrategy = array_names, arrays: st.SearchStrategy | None = None, attrs: st.SearchStrategy = attrs, @@ -292,7 +306,7 @@ def arrays( return a -@st.composite # type: ignore[misc] +@st.composite def simple_arrays( draw: st.DrawFn, *, @@ -313,8 +327,8 @@ def is_negative_slice(idx: Any) -> bool: return isinstance(idx, slice) and idx.step is not None and idx.step < 0 -@st.composite # type: ignore[misc] -def end_slices(draw: st.DrawFn, *, shape: tuple[int]) -> Any: +@st.composite +def end_slices(draw: st.DrawFn, *, shape: tuple[int, ...]) -> Any: """ A strategy that slices ranges that include the last chunk. This is intended to stress-test handling of a possibly smaller last chunk. @@ -328,14 +342,28 @@ def end_slices(draw: st.DrawFn, *, shape: tuple[int]) -> Any: return tuple(slicers) -@st.composite # type: ignore[misc] -def basic_indices(draw: st.DrawFn, *, shape: tuple[int], **kwargs: Any) -> Any: +@st.composite +def basic_indices( + draw: st.DrawFn, + *, + shape: tuple[int, ...], + min_dims: int = 0, + max_dims: int | None = None, + allow_newaxis: bool = False, + allow_ellipsis: bool = True, +) -> Any: """Basic indices without unsupported negative slices.""" - strategy = npst.basic_indices(shape=shape, **kwargs).filter( + strategy = npst.basic_indices( + shape=shape, + min_dims=min_dims, + max_dims=max_dims, + allow_newaxis=allow_newaxis, + allow_ellipsis=allow_ellipsis, + ).filter( lambda idxr: ( not ( is_negative_slice(idxr) - or (isinstance(idxr, tuple) and any(is_negative_slice(idx) for idx in idxr)) + or (isinstance(idxr, tuple) and any(is_negative_slice(idx) for idx in idxr)) # type: ignore[redundant-expr] ) ) ) @@ -344,9 +372,9 @@ def basic_indices(draw: st.DrawFn, *, shape: tuple[int], **kwargs: Any) -> Any: return draw(strategy) -@st.composite # type: ignore[misc] +@st.composite def orthogonal_indices( - draw: st.DrawFn, *, shape: tuple[int] + draw: st.DrawFn, *, shape: tuple[int, ...] ) -> tuple[tuple[np.ndarray[Any, Any], ...], tuple[np.ndarray[Any, Any], ...]]: """ Strategy that returns @@ -382,8 +410,8 @@ def orthogonal_indices( def key_ranges( - keys: SearchStrategy = node_names, max_size: int = sys.maxsize -) -> SearchStrategy[list[int]]: + keys: SearchStrategy[str] = node_names, max_size: int = sys.maxsize +) -> SearchStrategy[list[tuple[str, RangeByteRequest]]]: """ Function to generate key_ranges strategy for get_partial_values() returns list strategy w/ form:: @@ -402,3 +430,12 @@ def make_request(start: int, length: int) -> RangeByteRequest: ) key_tuple = st.tuples(keys, byte_ranges) return st.lists(key_tuple, min_size=1, max_size=10) + + +@st.composite +def chunk_paths(draw: st.DrawFn, ndim: int, numblocks: tuple[int, ...], subset: bool = True) -> str: + blockidx = draw( + st.tuples(*tuple(st.integers(min_value=0, max_value=max(0, b - 1)) for b in numblocks)) + ) + subset_slicer = slice(draw(st.integers(min_value=0, max_value=ndim))) if subset else slice(None) + return "/".join(map(str, blockidx[subset_slicer])) diff --git a/src/zarr/testing/utils.py b/src/zarr/testing/utils.py index 0a93b93fdb..afc15d742c 100644 --- a/src/zarr/testing/utils.py +++ b/src/zarr/testing/utils.py @@ -1,7 +1,6 @@ from __future__ import annotations -from collections.abc import Callable, Coroutine -from typing import TYPE_CHECKING, Any, TypeVar, cast +from typing import TYPE_CHECKING, TypeVar, cast import pytest @@ -31,20 +30,20 @@ def has_cupy() -> bool: try: import cupy - return cast(bool, cupy.cuda.runtime.getDeviceCount() > 0) + return cast("bool", cupy.cuda.runtime.getDeviceCount() > 0) except ImportError: return False except cupy.cuda.runtime.CUDARuntimeError: return False -T_Callable = TypeVar("T_Callable", bound=Callable[..., Coroutine[Any, Any, None] | None]) +T = TypeVar("T") # Decorator for GPU tests -def gpu_test(func: T_Callable) -> T_Callable: +def gpu_test(func: T) -> T: return cast( - T_Callable, + "T", pytest.mark.gpu( pytest.mark.skipif(not has_cupy(), reason="CuPy not installed or no GPU available")( func diff --git a/tests/conftest.py b/tests/conftest.py index 74a140c5c7..4d300a1fd4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,6 @@ from __future__ import annotations +import os import pathlib from dataclasses import dataclass, field from typing import TYPE_CHECKING @@ -18,15 +19,19 @@ _parse_chunk_key_encoding, ) from zarr.core.chunk_grids import RegularChunkGrid, _auto_partition -from zarr.core.common import JSON, parse_dtype, parse_shapelike +from zarr.core.common import JSON, DimensionNames, parse_shapelike from zarr.core.config import config as zarr_config +from zarr.core.dtype import ( + get_data_type_from_native_dtype, +) +from zarr.core.dtype.common import HasItemSize from zarr.core.metadata.v2 import ArrayV2Metadata from zarr.core.metadata.v3 import ArrayV3Metadata from zarr.core.sync import sync from zarr.storage import FsspecStore, LocalStore, MemoryStore, StorePath, ZipStore if TYPE_CHECKING: - from collections.abc import Generator, Iterable + from collections.abc import Generator from typing import Any, Literal from _pytest.compat import LEGACY_PATH @@ -35,6 +40,7 @@ from zarr.core.array import CompressorsLike, FiltersLike, SerializerLike, ShardsLike from zarr.core.chunk_key_encodings import ChunkKeyEncoding, ChunkKeyEncodingLike from zarr.core.common import ChunkCoords, MemoryOrder, ShapeLike, ZarrFormat + from zarr.core.dtype.wrapper import ZDType async def parse_store( @@ -188,17 +194,31 @@ def pytest_collection_modifyitems(config: Any, items: Any) -> None: settings.register_profile( - "ci", - max_examples=1000, - deadline=None, + "default", + parent=settings.get_profile("default"), + max_examples=300, suppress_health_check=[HealthCheck.filter_too_much, HealthCheck.too_slow], + deadline=None, + verbosity=Verbosity.verbose, ) settings.register_profile( - "local", + "ci", + parent=settings.get_profile("ci"), max_examples=300, + derandomize=True, # more like regression testing + deadline=None, suppress_health_check=[HealthCheck.filter_too_much, HealthCheck.too_slow], - verbosity=Verbosity.verbose, ) +settings.register_profile( + "nightly", + max_examples=500, + parent=settings.get_profile("ci"), + derandomize=False, + stateful_step_count=100, +) + +settings.load_profile(os.getenv("HYPOTHESIS_PROFILE", "default")) + # TODO: uncomment these overrides when we can get mypy to accept them """ @@ -250,24 +270,29 @@ def create_array_metadata( filters: FiltersLike = "auto", compressors: CompressorsLike = "auto", serializer: SerializerLike = "auto", - fill_value: Any | None = None, + fill_value: Any = 0, order: MemoryOrder | None = None, zarr_format: ZarrFormat, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncoding | ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, ) -> ArrayV2Metadata | ArrayV3Metadata: """ Create array metadata """ - dtype_parsed = parse_dtype(dtype, zarr_format=zarr_format) + dtype_parsed = get_data_type_from_native_dtype(dtype) shape_parsed = parse_shapelike(shape) chunk_key_encoding_parsed = _parse_chunk_key_encoding( chunk_key_encoding, zarr_format=zarr_format ) - + item_size = 1 + if isinstance(dtype_parsed, HasItemSize): + item_size = dtype_parsed.item_size shard_shape_parsed, chunk_shape_parsed = _auto_partition( - array_shape=shape_parsed, shard_shape=shards, chunk_shape=chunks, dtype=dtype_parsed + array_shape=shape_parsed, + shard_shape=shards, + chunk_shape=chunks, + item_size=item_size, ) if order is None: @@ -278,11 +303,11 @@ def create_array_metadata( if zarr_format == 2: filters_parsed, compressor_parsed = _parse_chunk_encoding_v2( - compressor=compressors, filters=filters, dtype=np.dtype(dtype) + compressor=compressors, filters=filters, dtype=dtype_parsed ) return ArrayV2Metadata( shape=shape_parsed, - dtype=np.dtype(dtype), + dtype=dtype_parsed, chunks=chunk_shape_parsed, order=order_parsed, dimension_separator=chunk_key_encoding_parsed.separator, @@ -383,12 +408,12 @@ def meta_from_array( filters: FiltersLike = "auto", compressors: CompressorsLike = "auto", serializer: SerializerLike = "auto", - fill_value: Any | None = None, + fill_value: Any = 0, order: MemoryOrder | None = None, zarr_format: ZarrFormat = 3, attributes: dict[str, JSON] | None = None, chunk_key_encoding: ChunkKeyEncoding | ChunkKeyEncodingLike | None = None, - dimension_names: Iterable[str] | None = None, + dimension_names: DimensionNames = None, ) -> ArrayV3Metadata | ArrayV2Metadata: """ Create array metadata from an array @@ -408,3 +433,12 @@ def meta_from_array( chunk_key_encoding=chunk_key_encoding, dimension_names=dimension_names, ) + + +def skip_object_dtype(dtype: ZDType[Any, Any]) -> None: + if dtype.dtype_cls is type(np.dtype("O")): + msg = ( + f"{dtype} uses the numpy object data type, which is not a valid target for data " + "type resolution" + ) + pytest.skip(msg) diff --git a/tests/package_with_entrypoint-0.1.dist-info/entry_points.txt b/tests/package_with_entrypoint-0.1.dist-info/entry_points.txt index eee724c912..7eb0eb7c86 100644 --- a/tests/package_with_entrypoint-0.1.dist-info/entry_points.txt +++ b/tests/package_with_entrypoint-0.1.dist-info/entry_points.txt @@ -12,3 +12,5 @@ another_buffer = package_with_entrypoint:TestEntrypointGroup.Buffer another_ndbuffer = package_with_entrypoint:TestEntrypointGroup.NDBuffer [zarr.codec_pipeline] another_pipeline = package_with_entrypoint:TestEntrypointGroup.Pipeline +[zarr.data_type] +new_data_type = package_with_entrypoint:TestDataType \ No newline at end of file diff --git a/tests/package_with_entrypoint/__init__.py b/tests/package_with_entrypoint/__init__.py index b818adf8ea..e0d8a52c4d 100644 --- a/tests/package_with_entrypoint/__init__.py +++ b/tests/package_with_entrypoint/__init__.py @@ -1,13 +1,23 @@ -from collections.abc import Iterable +from __future__ import annotations -from numpy import ndarray +from typing import TYPE_CHECKING, Any, Literal, Self + +import numpy as np +import numpy.typing as npt import zarr.core.buffer -from zarr.abc.codec import ArrayBytesCodec, CodecInput, CodecOutput, CodecPipeline +from zarr.abc.codec import ArrayBytesCodec, CodecInput, CodecPipeline from zarr.codecs import BytesCodec -from zarr.core.array_spec import ArraySpec from zarr.core.buffer import Buffer, NDBuffer -from zarr.core.common import BytesLike +from zarr.core.dtype.common import DataTypeValidationError, DTypeJSON, DTypeSpec_V2 +from zarr.core.dtype.npy.bool import Bool + +if TYPE_CHECKING: + from collections.abc import Iterable + from typing import ClassVar, Literal + + from zarr.core.array_spec import ArraySpec + from zarr.core.common import ZarrFormat class TestEntrypointCodec(ArrayBytesCodec): @@ -16,14 +26,14 @@ class TestEntrypointCodec(ArrayBytesCodec): async def encode( self, chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]], - ) -> Iterable[CodecOutput | None]: - pass + ) -> Iterable[Buffer | None]: + return [None] async def decode( self, chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]], - ) -> ndarray: - pass + ) -> npt.NDArray[Any]: + return np.array(1) def compute_encoded_size(self, input_byte_length: int, chunk_spec: ArraySpec) -> int: return input_byte_length @@ -35,13 +45,13 @@ def __init__(self, batch_size: int = 1) -> None: async def encode( self, chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]] - ) -> BytesLike: - pass + ) -> Iterable[Buffer | None]: + return [None] async def decode( self, chunks_and_specs: Iterable[tuple[CodecInput | None, ArraySpec]] - ) -> ndarray: - pass + ) -> Iterable[NDBuffer | None]: + return np.array(1) class TestEntrypointBuffer(Buffer): @@ -64,3 +74,28 @@ class NDBuffer(zarr.core.buffer.NDBuffer): class Pipeline(CodecPipeline): pass + + +class TestDataType(Bool): + """ + This is a "data type" that serializes to "test" + """ + + _zarr_v3_name: ClassVar[Literal["test"]] = "test" # type: ignore[assignment] + + @classmethod + def from_json(cls, data: DTypeJSON, *, zarr_format: Literal[2, 3]) -> Self: + if zarr_format == 2 and data == {"name": cls._zarr_v3_name, "object_codec_id": None}: + return cls() + if zarr_format == 3 and data == cls._zarr_v3_name: + return cls() + raise DataTypeValidationError( + f"Invalid JSON representation of {cls.__name__}. Got {data!r}" + ) + + def to_json(self, zarr_format: ZarrFormat) -> str | DTypeSpec_V2: # type: ignore[override] + if zarr_format == 2: + return {"name": self._zarr_v3_name, "object_codec_id": None} + if zarr_format == 3: + return self._zarr_v3_name + raise ValueError("zarr_format must be 2 or 3") diff --git a/tests/test_api.py b/tests/test_api.py index f03fd53f7a..2a95d7b97c 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -1,13 +1,18 @@ from __future__ import annotations +import re from typing import TYPE_CHECKING +import zarr.codecs +import zarr.storage + if TYPE_CHECKING: import pathlib from zarr.abc.store import Store from zarr.core.common import JSON, MemoryOrder, ZarrFormat +import contextlib import warnings from typing import Literal @@ -24,9 +29,9 @@ create, create_array, create_group, + from_array, group, load, - open, open_group, save, save_array, @@ -34,10 +39,14 @@ ) from zarr.core.buffer import NDArrayLike from zarr.errors import MetadataValidationError -from zarr.storage import MemoryStore +from zarr.storage import LocalStore, MemoryStore, ZipStore from zarr.storage._utils import normalize_path from zarr.testing.utils import gpu_test +if TYPE_CHECKING: + from collections.abc import Callable + from pathlib import Path + def test_create(memory_store: Store) -> None: store = memory_store @@ -70,13 +79,19 @@ def test_create(memory_store: Store) -> None: # TODO: parametrize over everything this function takes @pytest.mark.parametrize("store", ["memory"], indirect=True) -def test_create_array(store: Store) -> None: +def test_create_array(store: Store, zarr_format: ZarrFormat) -> None: attrs: dict[str, JSON] = {"foo": 100} # explicit type annotation to avoid mypy error shape = (10, 10) path = "foo" data_val = 1 array_w = create_array( - store, name=path, shape=shape, attributes=attrs, chunks=shape, dtype="uint8" + store, + name=path, + shape=shape, + attributes=attrs, + chunks=shape, + dtype="uint8", + zarr_format=zarr_format, ) array_w[:] = data_val assert array_w.shape == shape @@ -85,18 +100,27 @@ def test_create_array(store: Store) -> None: @pytest.mark.parametrize("write_empty_chunks", [True, False]) -def test_write_empty_chunks_warns(write_empty_chunks: bool) -> None: +def test_write_empty_chunks_warns(write_empty_chunks: bool, zarr_format: ZarrFormat) -> None: """ Test that using the `write_empty_chunks` kwarg on array access will raise a warning. """ match = "The `write_empty_chunks` keyword argument .*" with pytest.warns(RuntimeWarning, match=match): _ = zarr.array( - data=np.arange(10), shape=(10,), dtype="uint8", write_empty_chunks=write_empty_chunks + data=np.arange(10), + shape=(10,), + dtype="uint8", + write_empty_chunks=write_empty_chunks, + zarr_format=zarr_format, ) with pytest.warns(RuntimeWarning, match=match): - _ = zarr.create(shape=(10,), dtype="uint8", write_empty_chunks=write_empty_chunks) + _ = zarr.create( + shape=(10,), + dtype="uint8", + write_empty_chunks=write_empty_chunks, + zarr_format=zarr_format, + ) @pytest.mark.parametrize("path", ["foo", "/", "/foo", "///foo/bar"]) @@ -113,32 +137,41 @@ def test_open_normalized_path( assert node.path == normalize_path(path) -async def test_open_array(memory_store: MemoryStore) -> None: +async def test_open_array(memory_store: MemoryStore, zarr_format: ZarrFormat) -> None: store = memory_store # open array, create if doesn't exist - z = open(store=store, shape=100) + z = zarr.api.synchronous.open(store=store, shape=100, zarr_format=zarr_format) assert isinstance(z, Array) assert z.shape == (100,) # open array, overwrite # store._store_dict = {} store = MemoryStore() - z = open(store=store, shape=200) + z = zarr.api.synchronous.open(store=store, shape=200, zarr_format=zarr_format) assert isinstance(z, Array) assert z.shape == (200,) # open array, read-only store_cls = type(store) ro_store = await store_cls.open(store_dict=store._store_dict, read_only=True) - z = open(store=ro_store, mode="r") + z = zarr.api.synchronous.open(store=ro_store, mode="r") assert isinstance(z, Array) assert z.shape == (200,) assert z.read_only # path not found with pytest.raises(FileNotFoundError): - open(store="doesnotexist", mode="r") + zarr.api.synchronous.open(store="doesnotexist", mode="r", zarr_format=zarr_format) + + +@pytest.mark.parametrize("store", ["memory", "local", "zip"], indirect=True) +def test_v2_and_v3_exist_at_same_path(store: Store) -> None: + zarr.create_array(store, shape=(10,), dtype="uint8", zarr_format=3) + zarr.create_array(store, shape=(10,), dtype="uint8", zarr_format=2) + msg = f"Both zarr.json (Zarr format 3) and .zarray (Zarr format 2) metadata objects exist at {store}. Zarr v3 will be used." + with pytest.warns(UserWarning, match=re.escape(msg)): + zarr.open(store=store) @pytest.mark.parametrize("store", ["memory"], indirect=True) @@ -161,9 +194,9 @@ async def test_open_group(memory_store: MemoryStore) -> None: assert "foo" in g # open group, overwrite - # g = open_group(store=store) - # assert isinstance(g, Group) - # assert "foo" not in g + g = open_group(store=store, mode="w") + assert isinstance(g, Group) + assert "foo" not in g # open group, read-only store_cls = type(store) @@ -196,22 +229,23 @@ async def test_open_group_unspecified_version( @pytest.mark.parametrize("store", ["local", "memory", "zip"], indirect=["store"]) @pytest.mark.parametrize("n_args", [10, 1, 0]) @pytest.mark.parametrize("n_kwargs", [10, 1, 0]) -def test_save(store: Store, n_args: int, n_kwargs: int) -> None: +@pytest.mark.parametrize("path", [None, "some_path"]) +def test_save(store: Store, n_args: int, n_kwargs: int, path: None | str) -> None: data = np.arange(10) args = [np.arange(10) for _ in range(n_args)] kwargs = {f"arg_{i}": data for i in range(n_kwargs)} if n_kwargs == 0 and n_args == 0: with pytest.raises(ValueError): - save(store) + save(store, path=path) elif n_args == 1 and n_kwargs == 0: - save(store, *args) - array = open(store) + save(store, *args, path=path) + array = zarr.api.synchronous.open(store, path=path) assert isinstance(array, Array) assert_array_equal(array[:], data) else: - save(store, *args, **kwargs) # type: ignore [arg-type] - group = open(store) + save(store, *args, path=path, **kwargs) # type: ignore [arg-type] + group = zarr.api.synchronous.open(store, path=path) assert isinstance(group, Group) for array in group.array_values(): assert_array_equal(array[:], data) @@ -306,7 +340,6 @@ def test_open_with_mode_w_minus(tmp_path: pathlib.Path) -> None: zarr.open(store=tmp_path, mode="w-") -@pytest.mark.parametrize("zarr_format", [2, 3]) def test_array_order(zarr_format: ZarrFormat) -> None: arr = zarr.ones(shape=(2, 2), order=None, zarr_format=zarr_format) expected = zarr.config.get("array.order") @@ -322,17 +355,15 @@ def test_array_order(zarr_format: ZarrFormat) -> None: @pytest.mark.parametrize("order", ["C", "F"]) -@pytest.mark.parametrize("zarr_format", [2, 3]) def test_array_order_warns(order: MemoryOrder | None, zarr_format: ZarrFormat) -> None: with pytest.warns(RuntimeWarning, match="The `order` keyword argument .*"): arr = zarr.ones(shape=(2, 2), order=order, zarr_format=zarr_format) - expected = order or zarr.config.get("array.order") - assert arr.order == expected + assert arr.order == order vals = np.asarray(arr) - if expected == "C": + if order == "C": assert vals.flags.c_contiguous - elif expected == "F": + elif order == "F": assert vals.flags.f_contiguous else: raise AssertionError @@ -354,8 +385,8 @@ def test_array_order_warns(order: MemoryOrder | None, zarr_format: ZarrFormat) - # assert "LazyLoader: " in repr(loader) -def test_load_array(memory_store: Store) -> None: - store = memory_store +def test_load_array(sync_store: Store) -> None: + store = sync_store foo = np.arange(100) bar = np.arange(100, 0, -1) save(store, foo=foo, bar=bar) @@ -370,6 +401,38 @@ def test_load_array(memory_store: Store) -> None: assert_array_equal(bar, array) +@pytest.mark.parametrize("path", ["data", None]) +@pytest.mark.parametrize("load_read_only", [True, False, None]) +def test_load_zip(tmp_path: pathlib.Path, path: str | None, load_read_only: bool | None) -> None: + file = tmp_path / "test.zip" + data = np.arange(100).reshape(10, 10) + + with ZipStore(file, mode="w", read_only=False) as zs: + save(zs, data, path=path) + with ZipStore(file, mode="r", read_only=load_read_only) as zs: + result = zarr.load(store=zs, path=path) + assert isinstance(result, np.ndarray) + assert np.array_equal(result, data) + with ZipStore(file, read_only=load_read_only) as zs: + result = zarr.load(store=zs, path=path) + assert isinstance(result, np.ndarray) + assert np.array_equal(result, data) + + +@pytest.mark.parametrize("path", ["data", None]) +@pytest.mark.parametrize("load_read_only", [True, False]) +def test_load_local(tmp_path: pathlib.Path, path: str | None, load_read_only: bool) -> None: + file = tmp_path / "test.zip" + data = np.arange(100).reshape(10, 10) + + with LocalStore(file, read_only=False) as zs: + save(zs, data, path=path) + with LocalStore(file, read_only=load_read_only) as zs: + result = zarr.load(store=zs, path=path) + assert isinstance(result, np.ndarray) + assert np.array_equal(result, data) + + def test_tree() -> None: pytest.importorskip("rich") g1 = zarr.group() @@ -1053,7 +1116,7 @@ def test_tree() -> None: def test_open_positional_args_deprecated() -> None: store = MemoryStore() with pytest.warns(FutureWarning, match="pass"): - open(store, "w", shape=(1,)) + zarr.api.synchronous.open(store, "w", shape=(1,)) def test_save_array_positional_args_deprecated() -> None: @@ -1094,13 +1157,16 @@ def test_open_falls_back_to_open_group() -> None: assert group.attrs == {"key": "value"} -async def test_open_falls_back_to_open_group_async() -> None: +async def test_open_falls_back_to_open_group_async(zarr_format: ZarrFormat) -> None: # https://github.com/zarr-developers/zarr-python/issues/2309 store = MemoryStore() - await zarr.api.asynchronous.open_group(store, attributes={"key": "value"}) + await zarr.api.asynchronous.open_group( + store, attributes={"key": "value"}, zarr_format=zarr_format + ) group = await zarr.api.asynchronous.open(store=store) assert isinstance(group, zarr.core.group.AsyncGroup) + assert group.metadata.zarr_format == zarr_format assert group.attrs == {"key": "value"} @@ -1136,13 +1202,14 @@ async def test_metadata_validation_error() -> None: ["local", "memory", "zip"], indirect=True, ) -def test_open_array_with_mode_r_plus(store: Store) -> None: +def test_open_array_with_mode_r_plus(store: Store, zarr_format: ZarrFormat) -> None: # 'r+' means read/write (must exist) with pytest.raises(FileNotFoundError): - zarr.open_array(store=store, mode="r+") - zarr.ones(store=store, shape=(3, 3)) + zarr.open_array(store=store, mode="r+", zarr_format=zarr_format) + zarr.ones(store=store, shape=(3, 3), zarr_format=zarr_format) z2 = zarr.open_array(store=store, mode="r+") assert isinstance(z2, Array) + assert z2.metadata.zarr_format == zarr_format result = z2[:] assert isinstance(result, NDArrayLike) assert (result == 1).all() @@ -1191,3 +1258,117 @@ def test_gpu_basic(store: Store, zarr_format: ZarrFormat | None) -> None: # assert_array_equal doesn't check the type assert isinstance(result, type(src)) cp.testing.assert_array_equal(result, src[:10, :10]) + + +def test_v2_without_compressor() -> None: + # Make sure it's possible to set no compressor for v2 arrays + arr = zarr.create(store={}, shape=(1), dtype="uint8", zarr_format=2, compressor=None) + assert arr.compressors == () + + +def test_v2_with_v3_compressor() -> None: + # Check trying to create a v2 array with a v3 compressor fails + with pytest.raises( + ValueError, + match="Cannot use a BytesBytesCodec as a compressor for zarr v2 arrays. Use a numcodecs codec directly instead.", + ): + zarr.create( + store={}, shape=(1), dtype="uint8", zarr_format=2, compressor=zarr.codecs.BloscCodec() + ) + + +def add_empty_file(path: Path) -> Path: + fpath = path / "a.txt" + fpath.touch() + return fpath + + +@pytest.mark.parametrize("create_function", [create_array, from_array]) +@pytest.mark.parametrize("overwrite", [True, False]) +def test_no_overwrite_array(tmp_path: Path, create_function: Callable, overwrite: bool) -> None: # type:ignore[type-arg] + store = zarr.storage.LocalStore(tmp_path) + existing_fpath = add_empty_file(tmp_path) + + assert existing_fpath.exists() + create_function(store=store, data=np.ones(shape=(1,)), overwrite=overwrite) + if overwrite: + assert not existing_fpath.exists() + else: + assert existing_fpath.exists() + + +@pytest.mark.parametrize("create_function", [create_group, group]) +@pytest.mark.parametrize("overwrite", [True, False]) +def test_no_overwrite_group(tmp_path: Path, create_function: Callable, overwrite: bool) -> None: # type:ignore[type-arg] + store = zarr.storage.LocalStore(tmp_path) + existing_fpath = add_empty_file(tmp_path) + + assert existing_fpath.exists() + create_function(store=store, overwrite=overwrite) + if overwrite: + assert not existing_fpath.exists() + else: + assert existing_fpath.exists() + + +@pytest.mark.parametrize("open_func", [zarr.open, open_group]) +@pytest.mark.parametrize("mode", ["r", "r+", "a", "w", "w-"]) +def test_no_overwrite_open(tmp_path: Path, open_func: Callable, mode: str) -> None: # type:ignore[type-arg] + store = zarr.storage.LocalStore(tmp_path) + existing_fpath = add_empty_file(tmp_path) + + assert existing_fpath.exists() + with contextlib.suppress(FileExistsError, FileNotFoundError, ValueError): + open_func(store=store, mode=mode) + if mode == "w": + assert not existing_fpath.exists() + else: + assert existing_fpath.exists() + + +def test_no_overwrite_load(tmp_path: Path) -> None: + store = zarr.storage.LocalStore(tmp_path) + existing_fpath = add_empty_file(tmp_path) + + assert existing_fpath.exists() + with contextlib.suppress(NotImplementedError): + zarr.load(store) + assert existing_fpath.exists() + + +@pytest.mark.parametrize( + "f", + [ + zarr.array, + zarr.create, + zarr.create_array, + zarr.ones, + zarr.ones_like, + zarr.empty, + zarr.empty_like, + zarr.full, + zarr.full_like, + zarr.zeros, + zarr.zeros_like, + ], +) +def test_auto_chunks(f: Callable[..., Array]) -> None: + # Make sure chunks are set automatically across the public API + # TODO: test shards with this test too + shape = (1000, 1000) + dtype = np.uint8 + kwargs = {"shape": shape, "dtype": dtype} + array = np.zeros(shape, dtype=dtype) + store = zarr.storage.MemoryStore() + + if f in [zarr.full, zarr.full_like]: + kwargs["fill_value"] = 0 + if f in [zarr.array]: + kwargs["data"] = array + if f in [zarr.empty_like, zarr.full_like, zarr.empty_like, zarr.ones_like, zarr.zeros_like]: + kwargs["a"] = array + if f in [zarr.create_array]: + kwargs["store"] = store + + a = f(**kwargs) + assert a.chunks == (500, 500) diff --git a/tests/test_array.py b/tests/test_array.py index 5c3c556dfb..28ea812967 100644 --- a/tests/test_array.py +++ b/tests/test_array.py @@ -1,4 +1,5 @@ import dataclasses +import inspect import json import math import multiprocessing as mp @@ -17,22 +18,19 @@ import zarr.api.asynchronous import zarr.api.synchronous as sync_api +from tests.conftest import skip_object_dtype from zarr import Array, AsyncArray, Group from zarr.abc.store import Store from zarr.codecs import ( BytesCodec, GzipCodec, TransposeCodec, - VLenBytesCodec, - VLenUTF8Codec, ZstdCodec, ) from zarr.core._info import ArrayInfo from zarr.core.array import ( CompressorsLike, FiltersLike, - _get_default_chunk_encoding_v2, - _get_default_chunk_encoding_v3, _parse_chunk_encoding_v2, _parse_chunk_encoding_v3, chunks_initialized, @@ -41,17 +39,31 @@ from zarr.core.buffer import NDArrayLike, NDArrayLikeOrScalar, default_buffer_prototype from zarr.core.buffer.cpu import NDBuffer from zarr.core.chunk_grids import _auto_partition +from zarr.core.chunk_key_encodings import ChunkKeyEncodingParams from zarr.core.common import JSON, MemoryOrder, ZarrFormat +from zarr.core.dtype import get_data_type_from_native_dtype +from zarr.core.dtype.common import ENDIANNESS_STR, EndiannessStr +from zarr.core.dtype.npy.common import NUMPY_ENDIANNESS_STR, endianness_from_numpy_str +from zarr.core.dtype.npy.float import Float32, Float64 +from zarr.core.dtype.npy.int import Int16, UInt8 +from zarr.core.dtype.npy.string import VariableLengthUTF8 +from zarr.core.dtype.npy.structured import ( + Structured, +) +from zarr.core.dtype.npy.time import DateTime64, TimeDelta64 +from zarr.core.dtype.wrapper import ZDType from zarr.core.group import AsyncGroup from zarr.core.indexing import BasicIndexer, ceildiv -from zarr.core.metadata.v3 import ArrayV3Metadata, DataType +from zarr.core.metadata.v2 import ArrayV2Metadata from zarr.core.sync import sync from zarr.errors import ContainsArrayError, ContainsGroupError from zarr.storage import LocalStore, MemoryStore, StorePath +from .test_dtype.conftest import zdtype_examples + if TYPE_CHECKING: from zarr.core.array_spec import ArrayConfigLike - from zarr.core.metadata.v2 import ArrayV2Metadata + from zarr.core.metadata.v3 import ArrayV3Metadata @pytest.mark.parametrize("store", ["local", "memory", "zip"], indirect=["store"]) @@ -151,7 +163,7 @@ def test_array_name_properties_no_group( store: LocalStore | MemoryStore, zarr_format: ZarrFormat ) -> None: arr = zarr.create_array( - store=store, shape=(100,), chunks=(10,), zarr_format=zarr_format, dtype="i4" + store=store, shape=(100,), chunks=(10,), zarr_format=zarr_format, dtype=">i4" ) assert arr.path == "" assert arr.name == "/" @@ -177,34 +189,45 @@ def test_array_name_properties_with_group( assert spam.basename == "spam" +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") @pytest.mark.parametrize("store", ["memory"], indirect=True) @pytest.mark.parametrize("specifiy_fill_value", [True, False]) -@pytest.mark.parametrize("dtype_str", ["bool", "uint8", "complex64"]) -def test_array_v3_fill_value_default( - store: MemoryStore, specifiy_fill_value: bool, dtype_str: str +@pytest.mark.parametrize( + "zdtype", zdtype_examples, ids=tuple(str(type(v)) for v in zdtype_examples) +) +def test_array_fill_value_default( + store: MemoryStore, specifiy_fill_value: bool, zdtype: ZDType[Any, Any] ) -> None: """ Test that creating an array with the fill_value parameter set to None, or unspecified, - results in the expected fill_value attribute of the array, i.e. 0 cast to the array's dtype. + results in the expected fill_value attribute of the array, i.e. the default value of the dtype """ shape = (10,) - default_fill_value = 0 if specifiy_fill_value: arr = zarr.create_array( store=store, shape=shape, - dtype=dtype_str, + dtype=zdtype, zarr_format=3, chunks=shape, fill_value=None, ) else: - arr = zarr.create_array( - store=store, shape=shape, dtype=dtype_str, zarr_format=3, chunks=shape - ) + arr = zarr.create_array(store=store, shape=shape, dtype=zdtype, zarr_format=3, chunks=shape) + expected_fill_value = zdtype.default_scalar() + if isinstance(expected_fill_value, np.datetime64 | np.timedelta64): + if np.isnat(expected_fill_value): + assert np.isnat(arr.fill_value) + elif isinstance(expected_fill_value, np.floating | np.complexfloating): + if np.isnan(expected_fill_value): + assert np.isnan(arr.fill_value) + else: + assert arr.fill_value == expected_fill_value + # A simpler check would be to ensure that arr.fill_value.dtype == arr.dtype + # But for some numpy data types (namely, U), scalars might not have length. An empty string + # scalar from a `>U4` array would have dtype `>U`, and arr.fill_value.dtype == arr.dtype will fail. - assert arr.fill_value == np.dtype(dtype_str).type(default_fill_value) - assert arr.fill_value.dtype == arr.dtype + assert type(arr.fill_value) is type(np.array([arr.fill_value], dtype=arr.dtype)[0]) @pytest.mark.parametrize("store", ["memory"], indirect=True) @@ -227,10 +250,13 @@ def test_array_v3_fill_value(store: MemoryStore, fill_value: int, dtype_str: str assert arr.fill_value.dtype == arr.dtype -def test_create_positional_args_deprecated() -> None: - store = MemoryStore() - with pytest.warns(FutureWarning, match="Pass"): - zarr.Array.create(store, (2, 2), dtype="f8") +async def test_create_deprecated() -> None: + with pytest.warns(DeprecationWarning): + with pytest.warns(FutureWarning, match=re.escape("Pass shape=(2, 2) as keyword args")): + await zarr.AsyncArray.create(MemoryStore(), (2, 2), dtype="f8") # type: ignore[call-overload] + with pytest.warns(DeprecationWarning): + with pytest.warns(FutureWarning, match=re.escape("Pass shape=(2, 2) as keyword args")): + zarr.Array.create(MemoryStore(), (2, 2), dtype="f8") def test_selection_positional_args_deprecated() -> None: @@ -321,24 +347,47 @@ def test_serializable_sync_array(store: LocalStore, zarr_format: ZarrFormat) -> @pytest.mark.parametrize("store", ["memory"], indirect=True) -def test_storage_transformers(store: MemoryStore) -> None: +@pytest.mark.parametrize("zarr_format", [2, 3, "invalid"]) +def test_storage_transformers(store: MemoryStore, zarr_format: ZarrFormat | str) -> None: """ Test that providing an actual storage transformer produces a warning and otherwise passes through """ - metadata_dict: dict[str, JSON] = { - "zarr_format": 3, - "node_type": "array", - "shape": (10,), - "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": (1,)}}, - "data_type": "uint8", - "chunk_key_encoding": {"name": "v2", "configuration": {"separator": "/"}}, - "codecs": (BytesCodec().to_dict(),), - "fill_value": 0, - "storage_transformers": ({"test": "should_raise"}), - } - match = "Arrays with storage transformers are not supported in zarr-python at this time." - with pytest.raises(ValueError, match=match): + metadata_dict: dict[str, JSON] + if zarr_format == 3: + metadata_dict = { + "zarr_format": 3, + "node_type": "array", + "shape": (10,), + "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": (1,)}}, + "data_type": "uint8", + "chunk_key_encoding": {"name": "v2", "configuration": {"separator": "/"}}, + "codecs": (BytesCodec().to_dict(),), + "fill_value": 0, + "storage_transformers": ({"test": "should_raise"}), + } + else: + metadata_dict = { + "zarr_format": zarr_format, + "shape": (10,), + "chunks": (1,), + "dtype": "|u1", + "dimension_separator": ".", + "codecs": (BytesCodec().to_dict(),), + "fill_value": 0, + "order": "C", + "storage_transformers": ({"test": "should_raise"}), + } + if zarr_format == 3: + match = "Arrays with storage transformers are not supported in zarr-python at this time." + with pytest.raises(ValueError, match=match): + Array.from_dict(StorePath(store), data=metadata_dict) + elif zarr_format == 2: + # no warning Array.from_dict(StorePath(store), data=metadata_dict) + else: + match = f"Invalid zarr_format: {zarr_format}. Expected 2 or 3" + with pytest.raises(ValueError, match=match): + Array.from_dict(StorePath(store), data=metadata_dict) @pytest.mark.parametrize("test_cls", [Array, AsyncArray[Any]]) @@ -387,12 +436,13 @@ async def test_nchunks_initialized(test_cls: type[Array] | type[AsyncArray[Any]] assert observed == expected -async def test_chunks_initialized() -> None: +@pytest.mark.parametrize("path", ["", "foo"]) +async def test_chunks_initialized(path: str) -> None: """ Test that chunks_initialized accurately returns the keys of stored chunks. """ store = MemoryStore() - arr = zarr.create_array(store, shape=(100,), chunks=(10,), dtype="i4") + arr = zarr.create_array(store, name=path, shape=(100,), chunks=(10,), dtype="i4") chunks_accumulated = tuple( accumulate(tuple(tuple(v.split(" ")) for v in arr._iter_chunk_keys())) @@ -430,48 +480,6 @@ async def test_nbytes_stored_async() -> None: assert result == 902 # the size with all chunks filled. -def test_default_fill_values() -> None: - a = zarr.Array.create(MemoryStore(), shape=5, chunk_shape=5, dtype=" None: - with pytest.raises(ValueError, match="At least one ArrayBytesCodec is required."): - Array.create(MemoryStore(), shape=5, chunks=5, dtype=" None: # regression test for https://github.com/zarr-developers/zarr-python/issues/2328 @@ -493,7 +501,8 @@ def test_info_v2(self, chunks: tuple[int, int], shards: tuple[int, int] | None) result = arr.info expected = ArrayInfo( _zarr_format=2, - _data_type=np.dtype("float64"), + _data_type=arr._async_array._zdtype, + _fill_value=arr.fill_value, _shape=(8, 8), _chunk_shape=chunks, _shard_shape=None, @@ -510,7 +519,8 @@ def test_info_v3(self, chunks: tuple[int, int], shards: tuple[int, int] | None) result = arr.info expected = ArrayInfo( _zarr_format=3, - _data_type=DataType.parse("float64"), + _data_type=arr._async_array._zdtype, + _fill_value=arr.fill_value, _shape=(8, 8), _chunk_shape=chunks, _shard_shape=shards, @@ -535,7 +545,8 @@ def test_info_complete(self, chunks: tuple[int, int], shards: tuple[int, int] | result = arr.info_complete() expected = ArrayInfo( _zarr_format=3, - _data_type=DataType.parse("float64"), + _data_type=arr._async_array._zdtype, + _fill_value=arr.fill_value, _shape=(8, 8), _chunk_shape=chunks, _shard_shape=shards, @@ -570,7 +581,8 @@ async def test_info_v2_async( result = arr.info expected = ArrayInfo( _zarr_format=2, - _data_type=np.dtype("float64"), + _data_type=Float64(), + _fill_value=arr.metadata.fill_value, _shape=(8, 8), _chunk_shape=(2, 2), _shard_shape=None, @@ -595,7 +607,8 @@ async def test_info_v3_async( result = arr.info expected = ArrayInfo( _zarr_format=3, - _data_type=DataType.parse("float64"), + _data_type=arr._zdtype, + _fill_value=arr.metadata.fill_value, _shape=(8, 8), _chunk_shape=chunks, _shard_shape=shards, @@ -622,7 +635,8 @@ async def test_info_complete_async( result = await arr.info_complete() expected = ArrayInfo( _zarr_format=3, - _data_type=DataType.parse("float64"), + _data_type=arr._zdtype, + _fill_value=arr.metadata.fill_value, _shape=(8, 8), _chunk_shape=chunks, _shard_shape=shards, @@ -948,7 +962,10 @@ def test_auto_partition_auto_shards( expected_shards += (cs,) auto_shards, _ = _auto_partition( - array_shape=array_shape, chunk_shape=chunk_shape, shard_shape="auto", dtype=dtype + array_shape=array_shape, + chunk_shape=chunk_shape, + shard_shape="auto", + item_size=dtype.itemsize, ) assert auto_shards == expected_shards @@ -983,53 +1000,81 @@ def test_chunks_and_shards(store: Store) -> None: assert arr_v2.shards is None @staticmethod - @pytest.mark.parametrize( - ("dtype", "fill_value_expected"), [(" None: + @pytest.mark.parametrize("dtype", zdtype_examples) + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + def test_default_fill_value(dtype: ZDType[Any, Any], store: Store) -> None: + """ + Test that the fill value of an array is set to the default value for the dtype object + """ a = zarr.create_array(store, shape=(5,), chunks=(5,), dtype=dtype) - assert a.fill_value == fill_value_expected + if isinstance(dtype, DateTime64 | TimeDelta64) and np.isnat(a.fill_value): + assert np.isnat(dtype.default_scalar()) + else: + assert a.fill_value == dtype.default_scalar() @staticmethod - @pytest.mark.parametrize("dtype", ["uint8", "float32", "str"]) - @pytest.mark.parametrize("empty_value", [None, ()]) - async def test_no_filters_compressors( - store: MemoryStore, dtype: str, empty_value: object, zarr_format: ZarrFormat - ) -> None: + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("dtype", zdtype_examples) + def test_dtype_forms(dtype: ZDType[Any, Any], store: Store, zarr_format: ZarrFormat) -> None: """ - Test that the default ``filters`` and ``compressors`` are removed when ``create_array`` is invoked. + Test that the same array is produced from a ZDType instance, a numpy dtype, or a numpy string """ + skip_object_dtype(dtype) + a = zarr.create_array( + store, name="a", shape=(5,), chunks=(5,), dtype=dtype, zarr_format=zarr_format + ) - arr = await create_array( - store=store, - dtype=dtype, - shape=(10,), + b = zarr.create_array( + store, + name="b", + shape=(5,), + chunks=(5,), + dtype=dtype.to_native_dtype(), zarr_format=zarr_format, - compressors=empty_value, - filters=empty_value, ) - # Test metadata explicitly - if zarr_format == 2: - assert arr.metadata.zarr_format == 2 # guard for mypy - # v2 spec requires that filters be either a collection with at least one filter, or None - assert arr.metadata.filters is None - # Compressor is a single element in v2 metadata; the absence of a compressor is encoded - # as None - assert arr.metadata.compressor is None - - assert arr.filters == () - assert arr.compressors == () - else: - assert arr.metadata.zarr_format == 3 # guard for mypy - if dtype == "str": - assert arr.metadata.codecs == (VLenUTF8Codec(),) - assert arr.serializer == VLenUTF8Codec() + assert a.dtype == b.dtype + + # Structured dtypes do not have a numpy string representation that uniquely identifies them + if not isinstance(dtype, Structured): + if isinstance(dtype, VariableLengthUTF8): + # in numpy 2.3, StringDType().str becomes the string 'StringDType()' which numpy + # does not accept as a string representation of the dtype. + c = zarr.create_array( + store, + name="c", + shape=(5,), + chunks=(5,), + dtype=dtype.to_native_dtype().char, + zarr_format=zarr_format, + ) else: - assert arr.metadata.codecs == (BytesCodec(),) - assert arr.serializer == BytesCodec() + c = zarr.create_array( + store, + name="c", + shape=(5,), + chunks=(5,), + dtype=dtype.to_native_dtype().str, + zarr_format=zarr_format, + ) + assert a.dtype == c.dtype + + @staticmethod + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("dtype", zdtype_examples) + def test_dtype_roundtrip( + dtype: ZDType[Any, Any], store: Store, zarr_format: ZarrFormat + ) -> None: + """ + Test that creating an array, then opening it, gets the same array. + """ + skip_object_dtype(dtype) + a = zarr.create_array(store, shape=(5,), chunks=(5,), dtype=dtype, zarr_format=zarr_format) + b = zarr.open_array(store) + assert a.dtype == b.dtype @staticmethod - @pytest.mark.parametrize("dtype", ["uint8", "float32", "str"]) + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("dtype", ["uint8", "float32", "U3", "S4", "V1"]) @pytest.mark.parametrize( "compressors", [ @@ -1100,13 +1145,121 @@ async def test_v3_chunk_encoding( compressors=compressors, ) filters_expected, _, compressors_expected = _parse_chunk_encoding_v3( - filters=filters, compressors=compressors, serializer="auto", dtype=np.dtype(dtype) + filters=filters, + compressors=compressors, + serializer="auto", + dtype=arr._zdtype, ) assert arr.filters == filters_expected assert arr.compressors == compressors_expected @staticmethod - @pytest.mark.parametrize("dtype", ["uint8", "float32", "str"]) + @pytest.mark.parametrize("name", ["v2", "default", "invalid"]) + @pytest.mark.parametrize("separator", [".", "/"]) + async def test_chunk_key_encoding( + name: str, separator: Literal[".", "/"], zarr_format: ZarrFormat, store: MemoryStore + ) -> None: + chunk_key_encoding = ChunkKeyEncodingParams(name=name, separator=separator) # type: ignore[typeddict-item] + error_msg = "" + if name == "invalid": + error_msg = "Unknown chunk key encoding." + if zarr_format == 2 and name == "default": + error_msg = "Invalid chunk key encoding. For Zarr format 2 arrays, the `name` field of the chunk key encoding must be 'v2'." + if error_msg: + with pytest.raises(ValueError, match=re.escape(error_msg)): + arr = await create_array( + store=store, + dtype="uint8", + shape=(10,), + chunks=(1,), + zarr_format=zarr_format, + chunk_key_encoding=chunk_key_encoding, + ) + else: + arr = await create_array( + store=store, + dtype="uint8", + shape=(10,), + chunks=(1,), + zarr_format=zarr_format, + chunk_key_encoding=chunk_key_encoding, + ) + if isinstance(arr.metadata, ArrayV2Metadata): + assert arr.metadata.dimension_separator == separator + + @staticmethod + @pytest.mark.parametrize( + ("kwargs", "error_msg"), + [ + ({"serializer": "bytes"}, "Zarr format 2 arrays do not support `serializer`."), + ({"dimension_names": ["test"]}, "Zarr format 2 arrays do not support dimension names."), + ], + ) + async def test_create_array_invalid_v2_arguments( + kwargs: dict[str, Any], error_msg: str, store: MemoryStore + ) -> None: + with pytest.raises(ValueError, match=re.escape(error_msg)): + await zarr.api.asynchronous.create_array( + store=store, dtype="uint8", shape=(10,), chunks=(1,), zarr_format=2, **kwargs + ) + + @staticmethod + @pytest.mark.parametrize( + ("kwargs", "error_msg"), + [ + ( + {"dimension_names": ["test"]}, + "dimension_names cannot be used for arrays with zarr_format 2.", + ), + ( + {"chunk_key_encoding": {"name": "default", "separator": "/"}}, + "chunk_key_encoding cannot be used for arrays with zarr_format 2. Use dimension_separator instead.", + ), + ( + {"codecs": "bytes"}, + "codecs cannot be used for arrays with zarr_format 2. Use filters and compressor instead.", + ), + ], + ) + async def test_create_invalid_v2_arguments( + kwargs: dict[str, Any], error_msg: str, store: MemoryStore + ) -> None: + with pytest.raises(ValueError, match=re.escape(error_msg)): + await zarr.api.asynchronous.create( + store=store, dtype="uint8", shape=(10,), chunks=(1,), zarr_format=2, **kwargs + ) + + @staticmethod + @pytest.mark.parametrize( + ("kwargs", "error_msg"), + [ + ( + {"chunk_shape": (1,), "chunks": (2,)}, + "Only one of chunk_shape or chunks can be provided.", + ), + ( + {"dimension_separator": "/"}, + "dimension_separator cannot be used for arrays with zarr_format 3. Use chunk_key_encoding instead.", + ), + ( + {"filters": []}, + "filters cannot be used for arrays with zarr_format 3. Use array-to-array codecs instead", + ), + ( + {"compressor": "blosc"}, + "compressor cannot be used for arrays with zarr_format 3. Use bytes-to-bytes codecs instead", + ), + ], + ) + async def test_invalid_v3_arguments( + kwargs: dict[str, Any], error_msg: str, store: MemoryStore + ) -> None: + kwargs.setdefault("chunks", (1,)) + with pytest.raises(ValueError, match=re.escape(error_msg)): + zarr.create(store=store, dtype="uint8", shape=(10,), zarr_format=3, **kwargs) + + @staticmethod + @pytest.mark.parametrize("dtype", ["uint8", "float32", "str", "U10", "S10", ">M8[10s]"]) @pytest.mark.parametrize( "compressors", [ @@ -1132,7 +1285,7 @@ async def test_v2_chunk_encoding( filters=filters, ) filters_expected, compressor_expected = _parse_chunk_encoding_v2( - filters=filters, compressor=compressors, dtype=np.dtype(dtype) + filters=filters, compressor=compressors, dtype=get_data_type_from_native_dtype(dtype) ) assert arr.metadata.zarr_format == 2 # guard for mypy assert arr.metadata.compressor == compressor_expected @@ -1146,27 +1299,37 @@ async def test_v2_chunk_encoding( assert arr.filters == filters_expected @staticmethod - @pytest.mark.parametrize("dtype", ["uint8", "float32", "str"]) + @pytest.mark.parametrize("dtype", [UInt8(), Float32(), VariableLengthUTF8()]) + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") async def test_default_filters_compressors( - store: MemoryStore, dtype: str, zarr_format: ZarrFormat + store: MemoryStore, dtype: UInt8 | Float32 | VariableLengthUTF8, zarr_format: ZarrFormat ) -> None: """ Test that the default ``filters`` and ``compressors`` are used when ``create_array`` is invoked with ``filters`` and ``compressors`` unspecified. """ + arr = await create_array( store=store, - dtype=dtype, + dtype=dtype, # type: ignore[arg-type] shape=(10,), zarr_format=zarr_format, ) + + sig = inspect.signature(create_array) + if zarr_format == 3: - expected_filters, expected_serializer, expected_compressors = ( - _get_default_chunk_encoding_v3(np_dtype=np.dtype(dtype)) + expected_filters, expected_serializer, expected_compressors = _parse_chunk_encoding_v3( + compressors=sig.parameters["compressors"].default, + filters=sig.parameters["filters"].default, + serializer=sig.parameters["serializer"].default, + dtype=dtype, # type: ignore[arg-type] ) elif zarr_format == 2: - default_filters, default_compressors = _get_default_chunk_encoding_v2( - np_dtype=np.dtype(dtype) + default_filters, default_compressors = _parse_chunk_encoding_v2( + compressor=sig.parameters["compressors"].default, + filters=sig.parameters["filters"].default, + dtype=dtype, # type: ignore[arg-type] ) if default_filters is None: expected_filters = () @@ -1262,9 +1425,11 @@ async def test_data_ignored_params(store: Store) -> None: await create_array(store, data=data, shape=None, dtype=data.dtype, overwrite=True) @staticmethod - @pytest.mark.parametrize("order_config", ["C", "F", None]) + @pytest.mark.parametrize("order", ["C", "F", None]) + @pytest.mark.parametrize("with_config", [True, False]) def test_order( - order_config: MemoryOrder | None, + order: MemoryOrder | None, + with_config: bool, zarr_format: ZarrFormat, store: MemoryStore, ) -> None: @@ -1272,29 +1437,31 @@ def test_order( Test that the arrays generated by array indexing have a memory order defined by the config order value, and that for zarr v2 arrays, the ``order`` field in the array metadata is set correctly. """ - config: ArrayConfigLike = {} - if order_config is None: + config: ArrayConfigLike | None = {} + if order is None: config = {} expected = zarr.config.get("array.order") else: - config = {"order": order_config} - expected = order_config + config = {"order": order} + expected = order + + if not with_config: + # Test without passing config parameter + config = None + + arr = zarr.create_array( + store=store, + shape=(2, 2), + zarr_format=zarr_format, + dtype="i4", + order=order, + config=config, + ) + assert arr.order == expected if zarr_format == 2: - arr = zarr.create_array( - store=store, - shape=(2, 2), - zarr_format=zarr_format, - dtype="i4", - order=expected, - config=config, - ) - # guard for type checking assert arr.metadata.zarr_format == 2 assert arr.metadata.order == expected - else: - arr = zarr.create_array( - store=store, shape=(2, 2), zarr_format=zarr_format, dtype="i4", config=config - ) + vals = np.asarray(arr) if expected == "C": assert vals.flags.c_contiguous @@ -1336,12 +1503,27 @@ async def test_name(store: Store, zarr_format: ZarrFormat, path: str | None) -> for parent_path in parents: # this will raise if these groups were not created _ = await zarr.api.asynchronous.open_group( - store=store, path=parent_path, mode="r", zarr_format=zarr_format + store=store, path=parent_path, zarr_format=zarr_format ) + @staticmethod + @pytest.mark.parametrize("endianness", ENDIANNESS_STR) + def test_default_endianness( + store: Store, zarr_format: ZarrFormat, endianness: EndiannessStr + ) -> None: + """ + Test that that endianness is correctly set when creating an array when not specifying a serializer + """ + dtype = Int16(endianness=endianness) + arr = zarr.create_array(store=store, shape=(1,), dtype=dtype, zarr_format=zarr_format) + byte_order: str = arr[:].dtype.byteorder # type: ignore[union-attr] + assert byte_order in NUMPY_ENDIANNESS_STR + assert endianness_from_numpy_str(byte_order) == endianness # type: ignore[arg-type] + @pytest.mark.parametrize("value", [1, 1.4, "a", b"a", np.array(1)]) @pytest.mark.parametrize("zarr_format", [2, 3]) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_scalar_array(value: Any, zarr_format: ZarrFormat) -> None: arr = zarr.array(value, zarr_format=zarr_format) assert arr[...] == value @@ -1524,7 +1706,7 @@ def test_roundtrip_numcodecs() -> None: BYTES_CODEC = {"name": "bytes", "configuration": {"endian": "little"}} # Read in the array again and check compressor config - root = zarr.open_group(store, mode="r") + root = zarr.open_group(store) metadata = root["test"].metadata.to_dict() expected = (*filters, BYTES_CODEC, *compressors) assert metadata["codecs"] == expected @@ -1580,3 +1762,11 @@ async def test_sharding_coordinate_selection() -> None: result = arr[1, [0, 1]] # type: ignore[index] assert isinstance(result, NDArrayLike) assert (result == np.array([[12, 13, 14, 15], [16, 17, 18, 19]])).all() + + +@pytest.mark.parametrize("store", ["local", "memory", "zip"], indirect=["store"]) +def test_array_repr(store: Store) -> None: + shape = (2, 3, 4) + dtype = "uint8" + arr = zarr.create_array(store, shape=shape, dtype=dtype) + assert str(arr) == f"" diff --git a/tests/test_buffer.py b/tests/test_buffer.py index 33ac0266eb..93b116e908 100644 --- a/tests/test_buffer.py +++ b/tests/test_buffer.py @@ -6,12 +6,13 @@ import pytest import zarr +from zarr.abc.buffer import ArrayLike, BufferPrototype, NDArrayLike +from zarr.buffer import cpu, gpu from zarr.codecs.blosc import BloscCodec from zarr.codecs.crc32c_ import Crc32cCodec from zarr.codecs.gzip import GzipCodec from zarr.codecs.transpose import TransposeCodec from zarr.codecs.zstd import ZstdCodec -from zarr.core.buffer import ArrayLike, BufferPrototype, NDArrayLike, cpu, gpu from zarr.storage import MemoryStore, StorePath from zarr.testing.buffer import ( NDBufferUsingTestNDArrayLike, @@ -148,6 +149,34 @@ async def test_codecs_use_of_gpu_prototype() -> None: assert cp.array_equal(expect, got) +@gpu_test +@pytest.mark.asyncio +async def test_sharding_use_of_gpu_prototype() -> None: + with zarr.config.enable_gpu(): + expect = cp.zeros((10, 10), dtype="uint16", order="F") + + a = await zarr.api.asynchronous.create_array( + StorePath(MemoryStore()) / "test_codecs_use_of_gpu_prototype", + shape=expect.shape, + chunks=(5, 5), + shards=(10, 10), + dtype=expect.dtype, + fill_value=0, + ) + expect[:] = cp.arange(100).reshape(10, 10) + + await a.setitem( + selection=(slice(0, 10), slice(0, 10)), + value=expect[:], + prototype=gpu.buffer_prototype, + ) + got = await a.getitem( + selection=(slice(0, 10), slice(0, 10)), prototype=gpu.buffer_prototype + ) + assert isinstance(got, cp.ndarray) + assert cp.array_equal(expect, got) + + def test_numpy_buffer_prototype() -> None: buffer = cpu.buffer_prototype.buffer.create_zero_length() ndbuffer = cpu.buffer_prototype.nd_buffer.create(shape=(1, 2), dtype=np.dtype("int64")) @@ -155,3 +184,19 @@ def test_numpy_buffer_prototype() -> None: assert isinstance(ndbuffer.as_ndarray_like(), np.ndarray) with pytest.raises(ValueError, match="Buffer does not contain a single scalar value"): ndbuffer.as_scalar() + + +@gpu_test +def test_gpu_buffer_prototype() -> None: + buffer = gpu.buffer_prototype.buffer.create_zero_length() + ndbuffer = gpu.buffer_prototype.nd_buffer.create(shape=(1, 2), dtype=cp.dtype("int64")) + assert isinstance(buffer.as_array_like(), cp.ndarray) + assert isinstance(ndbuffer.as_ndarray_like(), cp.ndarray) + with pytest.raises(ValueError, match="Buffer does not contain a single scalar value"): + ndbuffer.as_scalar() + + +# TODO: the same test for other buffer classes +def test_cpu_buffer_as_scalar() -> None: + buf = cpu.buffer_prototype.nd_buffer.create(shape=(), dtype="int64") + assert buf.as_scalar() == buf.as_ndarray_like()[()] # type: ignore[index] diff --git a/tests/test_codecs/test_blosc.py b/tests/test_codecs/test_blosc.py index c1c5c92329..6e6e9df383 100644 --- a/tests/test_codecs/test_blosc.py +++ b/tests/test_codecs/test_blosc.py @@ -1,7 +1,9 @@ import json +import numcodecs import numpy as np import pytest +from packaging.version import Version import zarr from zarr.abc.store import Store @@ -54,3 +56,20 @@ async def test_blosc_evolve(store: Store, dtype: str) -> None: assert blosc_configuration_json["shuffle"] == "bitshuffle" else: assert blosc_configuration_json["shuffle"] == "shuffle" + + +async def test_typesize() -> None: + a = np.arange(1000000, dtype=np.uint64) + codecs = [zarr.codecs.BytesCodec(), zarr.codecs.BloscCodec()] + z = zarr.array(a, chunks=(10000), codecs=codecs) + data = await z.store.get("c/0", prototype=default_buffer_prototype()) + assert data is not None + bytes = data.to_bytes() + size = len(bytes) + msg = f"Blosc size mismatch. First 10 bytes: {bytes[:20]!r} and last 10 bytes: {bytes[-20:]!r}" + if Version(numcodecs.__version__) >= Version("0.16.0"): + expected_size = 402 + assert size == expected_size, msg + else: + expected_size = 10216 + assert size == expected_size, msg diff --git a/tests/test_codecs/test_codecs.py b/tests/test_codecs/test_codecs.py index b8122b4ac2..468f395254 100644 --- a/tests/test_codecs/test_codecs.py +++ b/tests/test_codecs/test_codecs.py @@ -2,7 +2,7 @@ import json from dataclasses import dataclass -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import numpy as np import pytest @@ -18,32 +18,33 @@ TransposeCodec, ) from zarr.core.buffer import default_buffer_prototype -from zarr.core.indexing import Selection, morton_order_iter +from zarr.core.indexing import BasicSelection, morton_order_iter +from zarr.core.metadata.v3 import ArrayV3Metadata from zarr.storage import StorePath if TYPE_CHECKING: from zarr.abc.store import Store - from zarr.core.buffer import NDArrayLike - from zarr.core.common import MemoryOrder + from zarr.core.buffer.core import NDArrayLikeOrScalar + from zarr.core.common import ChunkCoords, MemoryOrder @dataclass(frozen=True) class _AsyncArrayProxy: - array: AsyncArray + array: AsyncArray[Any] - def __getitem__(self, selection: Selection) -> _AsyncArraySelectionProxy: + def __getitem__(self, selection: BasicSelection) -> _AsyncArraySelectionProxy: return _AsyncArraySelectionProxy(self.array, selection) @dataclass(frozen=True) class _AsyncArraySelectionProxy: - array: AsyncArray - selection: Selection + array: AsyncArray[Any] + selection: BasicSelection - async def get(self) -> NDArrayLike: + async def get(self) -> NDArrayLikeOrScalar: return await self.array.getitem(self.selection) - async def set(self, value: np.ndarray) -> None: + async def set(self, value: np.ndarray[Any, Any]) -> None: return await self.array.setitem(self.selection, value) @@ -101,6 +102,7 @@ async def test_order( read_data = await _AsyncArrayProxy(a)[:, :].get() assert np.array_equal(data, read_data) + assert isinstance(read_data, np.ndarray) if runtime_read_order == "F": assert read_data.flags["F_CONTIGUOUS"] assert not read_data.flags["C_CONTIGUOUS"] @@ -142,6 +144,7 @@ def test_order_implicit( read_data = a[:, :] assert np.array_equal(data, read_data) + assert isinstance(read_data, np.ndarray) if runtime_read_order == "F": assert read_data.flags["F_CONTIGUOUS"] assert not read_data.flags["C_CONTIGUOUS"] @@ -209,7 +212,7 @@ def test_morton() -> None: [3, 2, 1, 6, 4, 5, 2], ], ) -def test_morton2(shape) -> None: +def test_morton2(shape: ChunkCoords) -> None: order = list(morton_order_iter(shape)) for i, x in enumerate(order): assert x not in order[:i] # no duplicates @@ -263,7 +266,10 @@ async def test_dimension_names(store: Store) -> None: dimension_names=("x", "y"), ) - assert (await zarr.api.asynchronous.open_array(store=spath)).metadata.dimension_names == ( + assert isinstance( + meta := (await zarr.api.asynchronous.open_array(store=spath)).metadata, ArrayV3Metadata + ) + assert meta.dimension_names == ( "x", "y", ) @@ -277,7 +283,8 @@ async def test_dimension_names(store: Store) -> None: fill_value=0, ) - assert (await AsyncArray.open(spath2)).metadata.dimension_names is None + assert isinstance(meta := (await AsyncArray.open(spath2)).metadata, ArrayV3Metadata) + assert meta.dimension_names is None zarr_json_buffer = await store.get(f"{path2}/zarr.json", prototype=default_buffer_prototype()) assert zarr_json_buffer is not None assert "dimension_names" not in json.loads(zarr_json_buffer.to_bytes()) diff --git a/tests/test_codecs/test_endian.py b/tests/test_codecs/test_endian.py index c0c4dd4e75..ab64afb1b8 100644 --- a/tests/test_codecs/test_endian.py +++ b/tests/test_codecs/test_endian.py @@ -11,6 +11,7 @@ from .test_codecs import _AsyncArrayProxy +@pytest.mark.filterwarnings("ignore:The endianness of the requested serializer") @pytest.mark.parametrize("store", ["local", "memory"], indirect=["store"]) @pytest.mark.parametrize("endian", ["big", "little"]) async def test_endian(store: Store, endian: Literal["big", "little"]) -> None: @@ -32,6 +33,7 @@ async def test_endian(store: Store, endian: Literal["big", "little"]) -> None: assert np.array_equal(data, readback_data) +@pytest.mark.filterwarnings("ignore:The endianness of the requested serializer") @pytest.mark.parametrize("store", ["local", "memory"], indirect=["store"]) @pytest.mark.parametrize("dtype_input_endian", [">u2", " None: - bstrings = [b"hello", b"world", b"this", b"is", b"a", b"test"] - data = np.array(bstrings).reshape((2, 3)) - assert data.dtype == "|S5" - - sp = StorePath(store, path="string") - a = zarr.create_array( - sp, - shape=data.shape, - chunks=data.shape, - dtype=data.dtype, - fill_value=b"", - compressors=compressor, - ) - assert isinstance(a.metadata, ArrayV3Metadata) # needed for mypy - - # should also work if input array is an object array, provided we explicitly specified - # a bytesting-like dtype when creating the Array - if as_object_array: - data = data.astype("O") - a[:, :] = data - assert np.array_equal(data, a[:, :]) - assert a.metadata.data_type == DataType.bytes - assert a.dtype == "O" - - # test round trip - b = Array.open(sp) - assert isinstance(b.metadata, ArrayV3Metadata) # needed for mypy - assert np.array_equal(data, b[:, :]) - assert b.metadata.data_type == DataType.bytes - assert a.dtype == "O" + assert b.metadata.data_type == get_data_type_from_native_dtype(data.dtype) + assert a.dtype == data.dtype diff --git a/tests/test_config.py b/tests/test_config.py index 1a2453d646..ed778a02ae 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -1,6 +1,6 @@ import os from collections.abc import Iterable -from typing import Any +from typing import TYPE_CHECKING, Any from unittest import mock from unittest.mock import Mock @@ -10,7 +10,7 @@ import zarr import zarr.api from zarr import zeros -from zarr.abc.codec import CodecInput, CodecOutput, CodecPipeline +from zarr.abc.codec import CodecPipeline from zarr.abc.store import ByteSetter, Store from zarr.codecs import ( BloscCodec, @@ -19,10 +19,13 @@ GzipCodec, ShardingCodec, ) +from zarr.core.array import create_array from zarr.core.array_spec import ArraySpec from zarr.core.buffer import NDBuffer +from zarr.core.buffer.core import Buffer from zarr.core.codec_pipeline import BatchedCodecPipeline from zarr.core.config import BadConfigError, config +from zarr.core.dtype import Int8, VariableLengthUTF8 from zarr.core.indexing import SelectorTuple from zarr.registry import ( fully_qualified_name, @@ -43,67 +46,66 @@ TestNDArrayLike, ) +if TYPE_CHECKING: + from zarr.core.dtype.wrapper import ZDType + def test_config_defaults_set() -> None: # regression test for available defaults - assert config.defaults == [ - { - "default_zarr_format": 3, - "array": { - "order": "C", - "write_empty_chunks": False, - "v2_default_compressor": { - "numeric": {"id": "zstd", "level": 0, "checksum": False}, - "string": {"id": "zstd", "level": 0, "checksum": False}, - "bytes": {"id": "zstd", "level": 0, "checksum": False}, - }, - "v2_default_filters": { - "numeric": None, - "string": [{"id": "vlen-utf8"}], - "bytes": [{"id": "vlen-bytes"}], - "raw": None, + assert ( + config.defaults + == [ + { + "default_zarr_format": 3, + "array": { + "order": "C", + "write_empty_chunks": False, + "v2_default_compressor": { + "default": {"id": "zstd", "level": 0, "checksum": False}, + "variable-length-string": {"id": "zstd", "level": 0, "checksum": False}, + }, + "v2_default_filters": { + "default": None, + "variable-length-string": [{"id": "vlen-utf8"}], + }, + "v3_default_filters": {"default": [], "variable-length-string": []}, + "v3_default_serializer": { + "default": {"name": "bytes", "configuration": {"endian": "little"}}, + "variable-length-string": {"name": "vlen-utf8"}, + }, + "v3_default_compressors": { + "default": [ + {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, + ], + "variable-length-string": [ + {"name": "zstd", "configuration": {"level": 0, "checksum": False}} + ], + }, }, - "v3_default_filters": {"numeric": [], "string": [], "bytes": []}, - "v3_default_serializer": { - "numeric": {"name": "bytes", "configuration": {"endian": "little"}}, - "string": {"name": "vlen-utf8"}, - "bytes": {"name": "vlen-bytes"}, + "async": {"concurrency": 10, "timeout": None}, + "threading": {"max_workers": None}, + "json_indent": 2, + "codec_pipeline": { + "path": "zarr.core.codec_pipeline.BatchedCodecPipeline", + "batch_size": 1, }, - "v3_default_compressors": { - "numeric": [ - {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, - ], - "string": [ - {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, - ], - "bytes": [ - {"name": "zstd", "configuration": {"level": 0, "checksum": False}}, - ], + "codecs": { + "blosc": "zarr.codecs.blosc.BloscCodec", + "gzip": "zarr.codecs.gzip.GzipCodec", + "zstd": "zarr.codecs.zstd.ZstdCodec", + "bytes": "zarr.codecs.bytes.BytesCodec", + "endian": "zarr.codecs.bytes.BytesCodec", # compatibility with earlier versions of ZEP1 + "crc32c": "zarr.codecs.crc32c_.Crc32cCodec", + "sharding_indexed": "zarr.codecs.sharding.ShardingCodec", + "transpose": "zarr.codecs.transpose.TransposeCodec", + "vlen-utf8": "zarr.codecs.vlen_utf8.VLenUTF8Codec", + "vlen-bytes": "zarr.codecs.vlen_utf8.VLenBytesCodec", }, - }, - "async": {"concurrency": 10, "timeout": None}, - "threading": {"max_workers": None}, - "json_indent": 2, - "codec_pipeline": { - "path": "zarr.core.codec_pipeline.BatchedCodecPipeline", - "batch_size": 1, - }, - "buffer": "zarr.core.buffer.cpu.Buffer", - "ndbuffer": "zarr.core.buffer.cpu.NDBuffer", - "codecs": { - "blosc": "zarr.codecs.blosc.BloscCodec", - "gzip": "zarr.codecs.gzip.GzipCodec", - "zstd": "zarr.codecs.zstd.ZstdCodec", - "bytes": "zarr.codecs.bytes.BytesCodec", - "endian": "zarr.codecs.bytes.BytesCodec", - "crc32c": "zarr.codecs.crc32c_.Crc32cCodec", - "sharding_indexed": "zarr.codecs.sharding.ShardingCodec", - "transpose": "zarr.codecs.transpose.TransposeCodec", - "vlen-utf8": "zarr.codecs.vlen_utf8.VLenUTF8Codec", - "vlen-bytes": "zarr.codecs.vlen_utf8.VLenBytesCodec", - }, - } - ] + "buffer": "zarr.buffer.cpu.Buffer", + "ndbuffer": "zarr.buffer.cpu.NDBuffer", + } + ] + ) assert config.get("array.order") == "C" assert config.get("async.concurrency") == 10 assert config.get("async.timeout") is None @@ -144,7 +146,7 @@ def test_config_codec_pipeline_class(store: Store) -> None: class MockCodecPipeline(BatchedCodecPipeline): async def write( self, - batch_info: Iterable[tuple[ByteSetter, ArraySpec, SelectorTuple, SelectorTuple]], + batch_info: Iterable[tuple[ByteSetter, ArraySpec, SelectorTuple, SelectorTuple, bool]], value: NDBuffer, drop_axes: tuple[int, ...] = (), ) -> None: @@ -174,7 +176,7 @@ async def write( class MockEnvCodecPipeline(CodecPipeline): pass - register_pipeline(MockEnvCodecPipeline) + register_pipeline(MockEnvCodecPipeline) # type: ignore[type-abstract] with mock.patch.dict( os.environ, {"ZARR_CODEC_PIPELINE__PATH": fully_qualified_name(MockEnvCodecPipeline)} @@ -191,10 +193,9 @@ def test_config_codec_implementation(store: Store) -> None: _mock = Mock() class MockBloscCodec(BloscCodec): - async def _encode_single( - self, chunk_data: CodecInput, chunk_spec: ArraySpec - ) -> CodecOutput | None: + async def _encode_single(self, chunk_bytes: Buffer, chunk_spec: ArraySpec) -> Buffer | None: _mock.call() + return None register_codec("blosc", MockBloscCodec) with config.set({"codecs.blosc": fully_qualified_name(MockBloscCodec)}): @@ -223,9 +224,6 @@ class NewBloscCodec(BloscCodec): @pytest.mark.parametrize("store", ["local", "memory"], indirect=["store"]) def test_config_ndbuffer_implementation(store: Store) -> None: - # has default value - assert fully_qualified_name(get_ndbuffer_class()) == config.defaults[0]["ndbuffer"] - # set custom ndbuffer with TestNDArrayLike implementation register_ndbuffer(NDBufferUsingTestNDArrayLike) with config.set({"ndbuffer": fully_qualified_name(NDBufferUsingTestNDArrayLike)}): @@ -243,9 +241,9 @@ def test_config_ndbuffer_implementation(store: Store) -> None: def test_config_buffer_implementation() -> None: # has default value - assert fully_qualified_name(get_buffer_class()) == config.defaults[0]["buffer"] + assert config.defaults[0]["buffer"] == "zarr.buffer.cpu.Buffer" - arr = zeros(shape=(100), store=StoreExpectingTestBuffer()) + arr = zeros(shape=(100,), store=StoreExpectingTestBuffer()) # AssertionError of StoreExpectingTestBuffer when not using my buffer with pytest.raises(AssertionError): @@ -278,6 +276,27 @@ def test_config_buffer_implementation() -> None: assert np.array_equal(arr_Crc32c[:], data2d) +def test_config_buffer_backwards_compatibility() -> None: + # This should warn once zarr.core is private + # https://github.com/zarr-developers/zarr-python/issues/2621 + with zarr.config.set( + {"buffer": "zarr.core.buffer.cpu.Buffer", "ndbuffer": "zarr.core.buffer.cpu.NDBuffer"} + ): + get_buffer_class() + get_ndbuffer_class() + + +@pytest.mark.gpu +def test_config_buffer_backwards_compatibility_gpu() -> None: + # This should warn once zarr.core is private + # https://github.com/zarr-developers/zarr-python/issues/2621 + with zarr.config.set( + {"buffer": "zarr.core.buffer.gpu.Buffer", "ndbuffer": "zarr.core.buffer.gpu.NDBuffer"} + ): + get_buffer_class() + get_ndbuffer_class() + + @pytest.mark.filterwarnings("error") def test_warning_on_missing_codec_config() -> None: class NewCodec(BytesCodec): @@ -304,28 +323,29 @@ class NewCodec2(BytesCodec): get_codec_class("new_codec") -@pytest.mark.parametrize("dtype", ["int", "bytes", "str"]) -async def test_default_codecs(dtype: str) -> None: - with config.set( - { - "array.v3_default_compressors": { # test setting non-standard codecs - "numeric": [ - {"name": "gzip", "configuration": {"level": 5}}, - ], - "string": [ - {"name": "gzip", "configuration": {"level": 5}}, - ], - "bytes": [ - {"name": "gzip", "configuration": {"level": 5}}, - ], - } - } - ): - arr = await zarr.api.asynchronous.create_array( +@pytest.mark.parametrize("dtype_category", ["variable-length-string", "default"]) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") +async def test_default_codecs(dtype_category: str) -> None: + """ + Test that the default compressors are sensitive to the current setting of the config. + """ + zdtype: ZDType[Any, Any] + if dtype_category == "variable-length-string": + zdtype = VariableLengthUTF8() + else: + zdtype = Int8() + expected_compressors = (GzipCodec(),) + new_conf = { + f"array.v3_default_compressors.{dtype_category}": [ + c.to_dict() for c in expected_compressors + ] + } + with config.set(new_conf): + arr = await create_array( shape=(100,), chunks=(100,), - dtype=np.dtype(dtype), + dtype=zdtype, zarr_format=3, store=MemoryStore(), ) - assert arr.compressors == (GzipCodec(),) + assert arr.compressors == expected_compressors diff --git a/tests/test_dtype/__init__.py b/tests/test_dtype/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_dtype/conftest.py b/tests/test_dtype/conftest.py new file mode 100644 index 0000000000..0be1c60088 --- /dev/null +++ b/tests/test_dtype/conftest.py @@ -0,0 +1,71 @@ +# Generate a collection of zdtype instances for use in testing. +import warnings +from typing import Any + +import numpy as np + +from zarr.core.dtype import data_type_registry +from zarr.core.dtype.common import HasLength +from zarr.core.dtype.npy.structured import Structured +from zarr.core.dtype.npy.time import DateTime64, TimeDelta64 +from zarr.core.dtype.wrapper import ZDType + +zdtype_examples: tuple[ZDType[Any, Any], ...] = () +for wrapper_cls in data_type_registry.contents.values(): + # The Structured dtype has to be constructed with some actual fields + if wrapper_cls is Structured: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + zdtype_examples += ( + wrapper_cls.from_native_dtype(np.dtype([("a", np.float64), ("b", np.int8)])), + ) + elif issubclass(wrapper_cls, HasLength): + zdtype_examples += (wrapper_cls(length=1),) + elif issubclass(wrapper_cls, DateTime64 | TimeDelta64): + zdtype_examples += (wrapper_cls(unit="s", scale_factor=10),) + else: + zdtype_examples += (wrapper_cls(),) + + +def pytest_generate_tests(metafunc: Any) -> None: + """ + This is a pytest hook to parametrize class-scoped fixtures. + + This hook allows us to define class-scoped fixtures as class attributes and then + generate the parametrize calls for pytest. This allows the fixtures to be + reused across multiple tests within the same class. + + For example, if you had a regular pytest class like this: + + class TestClass: + @pytest.mark.parametrize("param_a", [1, 2, 3]) + def test_method(self, param_a): + ... + + Child classes inheriting from ``TestClass`` would not be able to override the ``param_a`` fixture + + this implementation of ``pytest_generate_tests`` allows you to define class-scoped fixtures as + class attributes, which allows the following to work: + + class TestExample: + param_a = [1, 2, 3] + + def test_example(self, param_a): + ... + + # this class will have its test_example method parametrized with the values of TestB.param_a + class TestB(TestExample): + param_a = [1, 2, 100, 10] + + """ + # Iterate over all the fixtures defined in the class + # and parametrize them with the values defined in the class + # This allows us to define class-scoped fixtures as class attributes + # and then generate the parametrize calls for pytest + for fixture_name in metafunc.fixturenames: + if hasattr(metafunc.cls, fixture_name): + params = getattr(metafunc.cls, fixture_name) + if len(params) == 0: + msg = f"{metafunc.cls}.{fixture_name} is empty. Please provide a non-empty sequence of values." + raise ValueError(msg) + metafunc.parametrize(fixture_name, params, scope="class") diff --git a/tests/test_dtype/test_npy/test_bool.py b/tests/test_dtype/test_npy/test_bool.py new file mode 100644 index 0000000000..010dec2e47 --- /dev/null +++ b/tests/test_dtype/test_npy/test_bool.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import numpy as np + +from tests.test_dtype.test_wrapper import BaseTestZDType +from zarr.core.dtype.npy.bool import Bool + + +class TestBool(BaseTestZDType): + test_cls = Bool + + valid_dtype = (np.dtype(np.bool_),) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype(np.uint16), + ) + valid_json_v2 = ({"name": "|b1", "object_codec_id": None},) + valid_json_v3 = ("bool",) + invalid_json_v2 = ( + "|b1", + "bool", + "|f8", + ) + invalid_json_v3 = ( + "|b1", + "|f8", + {"name": "bool", "configuration": {"endianness": "little"}}, + ) + + scalar_v2_params = ((Bool(), True), (Bool(), False)) + scalar_v3_params = ((Bool(), True), (Bool(), False)) + + cast_value_params = ( + (Bool(), "true", np.True_), + (Bool(), True, np.True_), + (Bool(), False, np.False_), + (Bool(), np.True_, np.True_), + (Bool(), np.False_, np.False_), + ) + item_size_params = (Bool(),) diff --git a/tests/test_dtype/test_npy/test_bytes.py b/tests/test_dtype/test_npy/test_bytes.py new file mode 100644 index 0000000000..b7c16f573e --- /dev/null +++ b/tests/test_dtype/test_npy/test_bytes.py @@ -0,0 +1,154 @@ +import numpy as np +import pytest + +from tests.test_dtype.test_wrapper import BaseTestZDType +from zarr.core.dtype.common import UnstableSpecificationWarning +from zarr.core.dtype.npy.bytes import NullTerminatedBytes, RawBytes, VariableLengthBytes + + +class TestNullTerminatedBytes(BaseTestZDType): + test_cls = NullTerminatedBytes + valid_dtype = (np.dtype("|S10"), np.dtype("|S4")) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype("|U10"), + ) + valid_json_v2 = ( + {"name": "|S0", "object_codec_id": None}, + {"name": "|S2", "object_codec_id": None}, + {"name": "|S4", "object_codec_id": None}, + ) + valid_json_v3 = ({"name": "null_terminated_bytes", "configuration": {"length_bytes": 10}},) + invalid_json_v2 = ( + "|S", + "|U10", + "|f8", + ) + invalid_json_v3 = ( + {"name": "fixed_length_ascii", "configuration": {"length_bits": 0}}, + {"name": "numpy.fixed_length_ascii", "configuration": {"length_bits": "invalid"}}, + ) + + scalar_v2_params = ( + (NullTerminatedBytes(length=0), ""), + (NullTerminatedBytes(length=2), "YWI="), + (NullTerminatedBytes(length=4), "YWJjZA=="), + ) + scalar_v3_params = ( + (NullTerminatedBytes(length=0), ""), + (NullTerminatedBytes(length=2), "YWI="), + (NullTerminatedBytes(length=4), "YWJjZA=="), + ) + cast_value_params = ( + (NullTerminatedBytes(length=0), "", np.bytes_("")), + (NullTerminatedBytes(length=2), "ab", np.bytes_("ab")), + (NullTerminatedBytes(length=4), "abcdefg", np.bytes_("abcd")), + ) + item_size_params = ( + NullTerminatedBytes(length=0), + NullTerminatedBytes(length=4), + NullTerminatedBytes(length=10), + ) + + +class TestRawBytes(BaseTestZDType): + test_cls = RawBytes + valid_dtype = (np.dtype("|V10"),) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype("|S10"), + ) + valid_json_v2 = ({"name": "|V10", "object_codec_id": None},) + valid_json_v3 = ( + {"name": "raw_bytes", "configuration": {"length_bytes": 0}}, + {"name": "raw_bytes", "configuration": {"length_bytes": 8}}, + ) + + invalid_json_v2 = ( + "|V", + "|S10", + "|f8", + ) + invalid_json_v3 = ( + {"name": "r10"}, + {"name": "r-80"}, + ) + + scalar_v2_params = ( + (RawBytes(length=0), ""), + (RawBytes(length=2), "YWI="), + (RawBytes(length=4), "YWJjZA=="), + ) + scalar_v3_params = ( + (RawBytes(length=0), ""), + (RawBytes(length=2), "YWI="), + (RawBytes(length=4), "YWJjZA=="), + ) + cast_value_params = ( + (RawBytes(length=0), b"", np.void(b"")), + (RawBytes(length=2), b"ab", np.void(b"ab")), + (RawBytes(length=4), b"abcd", np.void(b"abcd")), + ) + item_size_params = ( + RawBytes(length=0), + RawBytes(length=4), + RawBytes(length=10), + ) + + +class TestVariableLengthBytes(BaseTestZDType): + test_cls = VariableLengthBytes + valid_dtype = (np.dtype("|O"),) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype("|U10"), + ) + valid_json_v2 = ({"name": "|O", "object_codec_id": "vlen-bytes"},) + valid_json_v3 = ("variable_length_bytes",) + invalid_json_v2 = ( + "|S", + "|U10", + "|f8", + ) + invalid_json_v3 = ( + {"name": "fixed_length_ascii", "configuration": {"length_bits": 0}}, + {"name": "numpy.fixed_length_ascii", "configuration": {"length_bits": "invalid"}}, + ) + + scalar_v2_params = ( + (VariableLengthBytes(), ""), + (VariableLengthBytes(), "YWI="), + (VariableLengthBytes(), "YWJjZA=="), + ) + scalar_v3_params = ( + (VariableLengthBytes(), ""), + (VariableLengthBytes(), "YWI="), + (VariableLengthBytes(), "YWJjZA=="), + ) + cast_value_params = ( + (VariableLengthBytes(), "", b""), + (VariableLengthBytes(), "ab", b"ab"), + (VariableLengthBytes(), "abcdefg", b"abcdefg"), + ) + item_size_params = ( + VariableLengthBytes(), + VariableLengthBytes(), + VariableLengthBytes(), + ) + + +@pytest.mark.parametrize( + "zdtype", [NullTerminatedBytes(length=10), RawBytes(length=10), VariableLengthBytes()] +) +def test_unstable_dtype_warning( + zdtype: NullTerminatedBytes | RawBytes | VariableLengthBytes, +) -> None: + """ + Test that we get a warning when serializing a dtype without a zarr v3 spec to json + when zarr_format is 3 + """ + with pytest.raises(UnstableSpecificationWarning): + zdtype.to_json(zarr_format=3) diff --git a/tests/test_dtype/test_npy/test_common.py b/tests/test_dtype/test_npy/test_common.py new file mode 100644 index 0000000000..d39d308112 --- /dev/null +++ b/tests/test_dtype/test_npy/test_common.py @@ -0,0 +1,342 @@ +from __future__ import annotations + +import base64 +import math +import re +import sys +from typing import TYPE_CHECKING, Any, get_args + +import numpy as np +import pytest + +from zarr.core.dtype.common import ENDIANNESS_STR, JSONFloatV2, SpecialFloatStrings +from zarr.core.dtype.npy.common import ( + NumpyEndiannessStr, + bytes_from_json, + bytes_to_json, + check_json_bool, + check_json_complex_float_v2, + check_json_complex_float_v3, + check_json_float_v2, + check_json_float_v3, + check_json_int, + check_json_str, + complex_float_to_json_v2, + complex_float_to_json_v3, + endianness_from_numpy_str, + endianness_to_numpy_str, + float_from_json_v2, + float_from_json_v3, + float_to_json_v2, + float_to_json_v3, +) + +if TYPE_CHECKING: + from zarr.core.common import JSON, ZarrFormat + + +def nan_equal(a: object, b: object) -> bool: + """ + Convenience function for equality comparison between two values ``a`` and ``b``, that might both + be NaN. Returns True if both ``a`` and ``b`` are NaN, otherwise returns a == b + """ + if math.isnan(a) and math.isnan(b): # type: ignore[arg-type] + return True + return a == b + + +json_float_v2_roundtrip_cases: tuple[tuple[JSONFloatV2, float | np.floating[Any]], ...] = ( + ("Infinity", float("inf")), + ("Infinity", np.inf), + ("-Infinity", float("-inf")), + ("-Infinity", -np.inf), + ("NaN", float("nan")), + ("NaN", np.nan), + (1.0, 1.0), +) + +json_float_v3_cases = json_float_v2_roundtrip_cases + + +@pytest.mark.parametrize( + ("data", "expected"), + [(">", "big"), ("<", "little"), ("=", sys.byteorder), ("|", None), ("err", "")], +) +def test_endianness_from_numpy_str(data: str, expected: str | None) -> None: + """ + Test that endianness_from_numpy_str correctly converts a numpy str literal to a human-readable literal value. + This test also checks that an invalid string input raises a ``ValueError`` + """ + if data in get_args(NumpyEndiannessStr): + assert endianness_from_numpy_str(data) == expected # type: ignore[arg-type] + else: + msg = f"Invalid endianness: {data!r}. Expected one of {get_args(NumpyEndiannessStr)}" + with pytest.raises(ValueError, match=re.escape(msg)): + endianness_from_numpy_str(data) # type: ignore[arg-type] + + +@pytest.mark.parametrize( + ("data", "expected"), + [("big", ">"), ("little", "<"), (None, "|"), ("err", "")], +) +def test_endianness_to_numpy_str(data: str | None, expected: str) -> None: + """ + Test that endianness_to_numpy_str correctly converts a human-readable literal value to a numpy str literal. + This test also checks that an invalid string input raises a ``ValueError`` + """ + if data in ENDIANNESS_STR: + assert endianness_to_numpy_str(data) == expected # type: ignore[arg-type] + else: + msg = f"Invalid endianness: {data!r}. Expected one of {ENDIANNESS_STR}" + with pytest.raises(ValueError, match=re.escape(msg)): + endianness_to_numpy_str(data) # type: ignore[arg-type] + + +@pytest.mark.parametrize( + ("data", "expected"), json_float_v2_roundtrip_cases + (("SHOULD_ERR", ""),) +) +def test_float_from_json_v2(data: JSONFloatV2 | str, expected: float | str) -> None: + """ + Test that float_from_json_v2 correctly converts a JSON string representation of a float to a float. + This test also checks that an invalid string input raises a ``ValueError`` + """ + if data != "SHOULD_ERR": + assert nan_equal(float_from_json_v2(data), expected) # type: ignore[arg-type] + else: + msg = f"could not convert string to float: {data!r}" + with pytest.raises(ValueError, match=msg): + float_from_json_v2(data) # type: ignore[arg-type] + + +@pytest.mark.parametrize( + ("data", "expected"), json_float_v3_cases + (("SHOULD_ERR", ""), ("0x", "")) +) +def test_float_from_json_v3(data: JSONFloatV2 | str, expected: float | str) -> None: + """ + Test that float_from_json_v3 correctly converts a JSON string representation of a float to a float. + This test also checks that an invalid string input raises a ``ValueError`` + """ + if data == "SHOULD_ERR": + msg = ( + f"Invalid float value: {data!r}. Expected a string starting with the hex prefix" + " '0x', or one of 'NaN', 'Infinity', or '-Infinity'." + ) + with pytest.raises(ValueError, match=msg): + float_from_json_v3(data) + elif data == "0x": + msg = ( + f"Invalid hexadecimal float value: {data!r}. " + "Expected the '0x' prefix to be followed by 4, 8, or 16 numeral characters" + ) + + with pytest.raises(ValueError, match=msg): + float_from_json_v3(data) + else: + assert nan_equal(float_from_json_v3(data), expected) + + +# note the order of parameters relative to the order of the parametrized variable. +@pytest.mark.parametrize(("expected", "data"), json_float_v2_roundtrip_cases) +def test_float_to_json_v2(data: float | np.floating[Any], expected: JSONFloatV2) -> None: + """ + Test that floats are JSON-encoded properly for zarr v2 + """ + observed = float_to_json_v2(data) + assert observed == expected + + +# note the order of parameters relative to the order of the parametrized variable. +@pytest.mark.parametrize(("expected", "data"), json_float_v3_cases) +def test_float_to_json_v3(data: float | np.floating[Any], expected: JSONFloatV2) -> None: + """ + Test that floats are JSON-encoded properly for zarr v3 + """ + observed = float_to_json_v3(data) + assert observed == expected + + +def test_bytes_from_json(zarr_format: ZarrFormat) -> None: + """ + Test that a string is interpreted as base64-encoded bytes using the ascii alphabet. + This test takes zarr_format as a parameter but doesn't actually do anything with it, because at + present there is no zarr-format-specific logic in the code being tested, but such logic may + exist in the future. + """ + data = "\00" + assert bytes_from_json(data, zarr_format=zarr_format) == base64.b64decode(data.encode("ascii")) + + +def test_bytes_to_json(zarr_format: ZarrFormat) -> None: + """ + Test that bytes are encoded with base64 using the ascii alphabet. + + This test takes zarr_format as a parameter but doesn't actually do anything with it, because at + present there is no zarr-format-specific logic in the code being tested, but such logic may + exist in the future. + """ + + data = b"asdas" + assert bytes_to_json(data, zarr_format=zarr_format) == base64.b64encode(data).decode("ascii") + + +# note the order of parameters relative to the order of the parametrized variable. +@pytest.mark.parametrize(("json_expected", "float_data"), json_float_v2_roundtrip_cases) +def test_complex_to_json_v2( + float_data: float | np.floating[Any], json_expected: JSONFloatV2 +) -> None: + """ + Test that complex numbers are correctly converted to JSON in v2 format. + + This use the same test input as the float tests, but the conversion is tested + for complex numbers with real and imaginary parts equal to the float + values provided in the test cases. + """ + cplx = complex(float_data, float_data) + cplx_npy = np.complex128(cplx) + assert complex_float_to_json_v2(cplx) == (json_expected, json_expected) + assert complex_float_to_json_v2(cplx_npy) == (json_expected, json_expected) + + +# note the order of parameters relative to the order of the parametrized variable. +@pytest.mark.parametrize(("json_expected", "float_data"), json_float_v3_cases) +def test_complex_to_json_v3( + float_data: float | np.floating[Any], json_expected: JSONFloatV2 +) -> None: + """ + Test that complex numbers are correctly converted to JSON in v3 format. + + This use the same test input as the float tests, but the conversion is tested + for complex numbers with real and imaginary parts equal to the float + values provided in the test cases. + """ + cplx = complex(float_data, float_data) + cplx_npy = np.complex128(cplx) + assert complex_float_to_json_v3(cplx) == (json_expected, json_expected) + assert complex_float_to_json_v3(cplx_npy) == (json_expected, json_expected) + + +@pytest.mark.parametrize(("json_expected", "float_data"), json_float_v3_cases) +def test_complex_float_to_json( + float_data: float | np.floating[Any], json_expected: JSONFloatV2, zarr_format: ZarrFormat +) -> None: + """ + Test that complex numbers are correctly converted to JSON in v2 or v3 formats, depending + on the ``zarr_format`` keyword argument. + + This use the same test input as the float tests, but the conversion is tested + for complex numbers with real and imaginary parts equal to the float + values provided in the test cases. + """ + + cplx = complex(float_data, float_data) + cplx_npy = np.complex128(cplx) + if zarr_format == 2: + assert complex_float_to_json_v2(cplx) == (json_expected, json_expected) + assert complex_float_to_json_v2(cplx_npy) == ( + json_expected, + json_expected, + ) + elif zarr_format == 3: + assert complex_float_to_json_v3(cplx) == (json_expected, json_expected) + assert complex_float_to_json_v3(cplx_npy) == ( + json_expected, + json_expected, + ) + else: + raise ValueError("zarr_format must be 2 or 3") # pragma: no cover + + +check_json_float_cases = get_args(SpecialFloatStrings) + (1.0, 2) + + +@pytest.mark.parametrize("data", check_json_float_cases) +def test_check_json_float_v2_valid(data: JSONFloatV2 | int) -> None: + assert check_json_float_v2(data) + + +def test_check_json_float_v2_invalid() -> None: + assert not check_json_float_v2("invalid") + + +@pytest.mark.parametrize("data", check_json_float_cases) +def test_check_json_float_v3_valid(data: JSONFloatV2 | int) -> None: + assert check_json_float_v3(data) + + +def test_check_json_float_v3_invalid() -> None: + assert not check_json_float_v3("invalid") + + +check_json_complex_float_true_cases: tuple[list[JSONFloatV2], ...] = ( + [0.0, 1.0], + [0.0, 1.0], + [-1.0, "NaN"], + ["Infinity", 1.0], + ["Infinity", "NaN"], +) + +check_json_complex_float_false_cases: tuple[object, ...] = ( + 0.0, + "foo", + [0.0], + [1.0, 2.0, 3.0], + [1.0, "_infinity_"], + {"hello": 1.0}, +) + + +@pytest.mark.parametrize("data", check_json_complex_float_true_cases) +def test_check_json_complex_float_v2_true(data: JSON) -> None: + assert check_json_complex_float_v2(data) + + +@pytest.mark.parametrize("data", check_json_complex_float_false_cases) +def test_check_json_complex_float_v2_false(data: JSON) -> None: + assert not check_json_complex_float_v2(data) + + +@pytest.mark.parametrize("data", check_json_complex_float_true_cases) +def test_check_json_complex_float_v3_true(data: JSON) -> None: + assert check_json_complex_float_v3(data) + + +@pytest.mark.parametrize("data", check_json_complex_float_false_cases) +def test_check_json_complex_float_v3_false(data: JSON) -> None: + assert not check_json_complex_float_v3(data) + + +@pytest.mark.parametrize("data", check_json_complex_float_true_cases) +def test_check_json_complex_float_true(data: JSON, zarr_format: ZarrFormat) -> None: + if zarr_format == 2: + assert check_json_complex_float_v2(data) + elif zarr_format == 3: + assert check_json_complex_float_v3(data) + else: + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + +@pytest.mark.parametrize("data", check_json_complex_float_false_cases) +def test_check_json_complex_float_false(data: JSON, zarr_format: ZarrFormat) -> None: + if zarr_format == 2: + assert not check_json_complex_float_v2(data) + elif zarr_format == 3: + assert not check_json_complex_float_v3(data) + else: + raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}") # pragma: no cover + + +def test_check_json_int() -> None: + assert check_json_int(0) + assert not check_json_int(1.0) + + +def test_check_json_str() -> None: + assert check_json_str("0") + assert not check_json_str(1.0) + + +def test_check_json_bool() -> None: + assert check_json_bool(True) + assert check_json_bool(False) + assert not check_json_bool(1.0) + assert not check_json_bool("True") diff --git a/tests/test_dtype/test_npy/test_complex.py b/tests/test_dtype/test_npy/test_complex.py new file mode 100644 index 0000000000..b6a1e799eb --- /dev/null +++ b/tests/test_dtype/test_npy/test_complex.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import math + +import numpy as np + +from tests.test_dtype.test_wrapper import BaseTestZDType +from zarr.core.dtype.npy.complex import Complex64, Complex128 + + +class _BaseTestFloat(BaseTestZDType): + def scalar_equals(self, scalar1: object, scalar2: object) -> bool: + if np.isnan(scalar1) and np.isnan(scalar2): # type: ignore[call-overload] + return True + return super().scalar_equals(scalar1, scalar2) + + +class TestComplex64(_BaseTestFloat): + test_cls = Complex64 + valid_dtype = (np.dtype(">c8"), np.dtype("c8", "object_codec_id": None}, + {"name": "c16"), np.dtype("c16", "object_codec_id": None}, + {"name": " bool: + if np.isnan(scalar1) and np.isnan(scalar2): # type: ignore[call-overload] + return True + return super().scalar_equals(scalar1, scalar2) + + hex_string_params: tuple[tuple[str, float], ...] = () + + def test_hex_encoding(self, hex_string_params: tuple[str, float]) -> None: + """ + Test that hexadecimal strings can be read as NaN values + """ + hex_string, expected = hex_string_params + zdtype = self.test_cls() + observed = zdtype.from_json_scalar(hex_string, zarr_format=3) + assert self.scalar_equals(observed, expected) + + +class TestFloat16(_BaseTestFloat): + test_cls = Float16 + valid_dtype = (np.dtype(">f2"), np.dtype("f2", "object_codec_id": None}, + {"name": "f4"), np.dtype("f4", "object_codec_id": None}, + {"name": "f8"), np.dtype("f8", "object_codec_id": None}, + {"name": "i1", + "int8", + "|f8", + ) + invalid_json_v3 = ( + "|i1", + "|f8", + {"name": "int8", "configuration": {"endianness": "little"}}, + ) + + scalar_v2_params = ((Int8(), 1), (Int8(), -1)) + scalar_v3_params = ((Int8(), 1), (Int8(), -1)) + cast_value_params = ( + (Int8(), 1, np.int8(1)), + (Int8(), -1, np.int8(-1)), + ) + item_size_params = (Int8(),) + + +class TestInt16(BaseTestZDType): + test_cls = Int16 + scalar_type = np.int16 + valid_dtype = (np.dtype(">i2"), np.dtype("i2", "object_codec_id": None}, + {"name": "i4"), np.dtype("i4", "object_codec_id": None}, + {"name": "i8"), np.dtype("i8", "object_codec_id": None}, + {"name": "u2"), np.dtype("u2", "object_codec_id": None}, + {"name": "u4"), np.dtype("u4", "object_codec_id": None}, + {"name": "u8"), np.dtype("u8", "object_codec_id": None}, + {"name": "U10"), np.dtype("U10", "object_codec_id": None}, + {"name": " None: + """ + Test that we get a warning when serializing a dtype without a zarr v3 spec to json + when zarr_format is 3 + """ + with pytest.raises(UnstableSpecificationWarning): + zdtype.to_json(zarr_format=3) diff --git a/tests/test_dtype/test_npy/test_structured.py b/tests/test_dtype/test_npy/test_structured.py new file mode 100644 index 0000000000..e9c9ab11d0 --- /dev/null +++ b/tests/test_dtype/test_npy/test_structured.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np + +from tests.test_dtype.test_wrapper import BaseTestZDType +from zarr.core.dtype import ( + Float16, + Float64, + Int32, + Int64, + Structured, +) + + +class TestStructured(BaseTestZDType): + test_cls = Structured + valid_dtype = ( + np.dtype([("field1", np.int32), ("field2", np.float64)]), + np.dtype([("field1", np.int64), ("field2", np.int32)]), + ) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype("|S10"), + ) + valid_json_v2 = ( + {"name": [["field1", ">i4"], ["field2", ">f8"]], "object_codec_id": None}, + {"name": [["field1", ">i8"], ["field2", ">i4"]], "object_codec_id": None}, + ) + valid_json_v3 = ( + { + "name": "structured", + "configuration": { + "fields": [ + ["field1", "int32"], + ["field2", "float64"], + ] + }, + }, + { + "name": "structured", + "configuration": { + "fields": [ + [ + "field1", + { + "name": "numpy.datetime64", + "configuration": {"unit": "s", "scale_factor": 1}, + }, + ], + [ + "field2", + {"name": "fixed_length_utf32", "configuration": {"length_bytes": 32}}, + ], + ] + }, + }, + ) + invalid_json_v2 = ( + [("field1", "|i1"), ("field2", "|f8")], + [("field1", "|S10"), ("field2", "|f8")], + ) + invalid_json_v3 = ( + { + "name": "structured", + "configuration": { + "fields": [ + ("field1", {"name": "int32", "configuration": {"endianness": "invalid"}}), + ("field2", {"name": "float64", "configuration": {"endianness": "big"}}), + ] + }, + }, + {"name": "invalid_name"}, + ) + + scalar_v2_params = ( + (Structured(fields=(("field1", Int32()), ("field2", Float64()))), "AQAAAAAAAAAAAPA/"), + (Structured(fields=(("field1", Float16()), ("field2", Int32()))), "AQAAAAAA"), + ) + scalar_v3_params = ( + (Structured(fields=(("field1", Int32()), ("field2", Float64()))), "AQAAAAAAAAAAAPA/"), + (Structured(fields=(("field1", Int64()), ("field2", Int32()))), "AQAAAAAAAAAAAPA/"), + ) + + cast_value_params = ( + ( + Structured(fields=(("field1", Int32()), ("field2", Float64()))), + (1, 2.0), + np.array((1, 2.0), dtype=[("field1", np.int32), ("field2", np.float64)]), + ), + ( + Structured(fields=(("field1", Int64()), ("field2", Int32()))), + (3, 4.5), + np.array((3, 4.5), dtype=[("field1", np.int64), ("field2", np.int32)]), + ), + ) + + def scalar_equals(self, scalar1: Any, scalar2: Any) -> bool: + if hasattr(scalar1, "shape") and hasattr(scalar2, "shape"): + return np.array_equal(scalar1, scalar2) + return super().scalar_equals(scalar1, scalar2) + + item_size_params = ( + Structured(fields=(("field1", Int32()), ("field2", Float64()))), + Structured(fields=(("field1", Int64()), ("field2", Int32()))), + ) diff --git a/tests/test_dtype/test_npy/test_time.py b/tests/test_dtype/test_npy/test_time.py new file mode 100644 index 0000000000..e201be5cf6 --- /dev/null +++ b/tests/test_dtype/test_npy/test_time.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import re +from typing import get_args + +import numpy as np +import pytest + +from tests.test_dtype.test_wrapper import BaseTestZDType +from zarr.core.dtype.npy.common import DateTimeUnit +from zarr.core.dtype.npy.time import DateTime64, TimeDelta64, datetime_from_int + + +class _TestTimeBase(BaseTestZDType): + def json_scalar_equals(self, scalar1: object, scalar2: object) -> bool: + # This method gets overridden here to support the equivalency between NaT and + # -9223372036854775808 fill values + nat_scalars = (-9223372036854775808, "NaT") + if scalar1 in nat_scalars and scalar2 in nat_scalars: + return True + return scalar1 == scalar2 + + def scalar_equals(self, scalar1: object, scalar2: object) -> bool: + if np.isnan(scalar1) and np.isnan(scalar2): # type: ignore[call-overload] + return True + return super().scalar_equals(scalar1, scalar2) + + +class TestDateTime64(_TestTimeBase): + test_cls = DateTime64 + valid_dtype = (np.dtype("datetime64[10ns]"), np.dtype("datetime64[us]"), np.dtype("datetime64")) + invalid_dtype = ( + np.dtype(np.int8), + np.dtype(np.float64), + np.dtype("timedelta64[ns]"), + ) + valid_json_v2 = ( + {"name": ">M8", "object_codec_id": None}, + {"name": ">M8[s]", "object_codec_id": None}, + {"name": "m8", "object_codec_id": None}, + {"name": ">m8[s]", "object_codec_id": None}, + {"name": " None: + """ + Test that an invalid unit raises a ValueError. + """ + unit = "invalid" + msg = f"unit must be one of ('Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'μs', 'ns', 'ps', 'fs', 'as', 'generic'), got {unit!r}." + with pytest.raises(ValueError, match=re.escape(msg)): + DateTime64(unit=unit) # type: ignore[arg-type] + with pytest.raises(ValueError, match=re.escape(msg)): + TimeDelta64(unit=unit) # type: ignore[arg-type] + + +def test_time_scale_factor_too_low() -> None: + """ + Test that an invalid unit raises a ValueError. + """ + scale_factor = 0 + msg = f"scale_factor must be > 0, got {scale_factor}." + with pytest.raises(ValueError, match=msg): + DateTime64(scale_factor=scale_factor) + with pytest.raises(ValueError, match=msg): + TimeDelta64(scale_factor=scale_factor) + + +def test_time_scale_factor_too_high() -> None: + """ + Test that an invalid unit raises a ValueError. + """ + scale_factor = 2**31 + msg = f"scale_factor must be < 2147483648, got {scale_factor}." + with pytest.raises(ValueError, match=msg): + DateTime64(scale_factor=scale_factor) + with pytest.raises(ValueError, match=msg): + TimeDelta64(scale_factor=scale_factor) + + +@pytest.mark.parametrize("unit", get_args(DateTimeUnit)) +@pytest.mark.parametrize("scale_factor", [1, 10]) +@pytest.mark.parametrize("value", [0, 1, 10]) +def test_datetime_from_int(unit: DateTimeUnit, scale_factor: int, value: int) -> None: + """ + Test datetime_from_int. + """ + expected = np.int64(value).view(f"datetime64[{scale_factor}{unit}]") + assert datetime_from_int(value, unit=unit, scale_factor=scale_factor) == expected diff --git a/tests/test_dtype/test_wrapper.py b/tests/test_dtype/test_wrapper.py new file mode 100644 index 0000000000..8f461f1a77 --- /dev/null +++ b/tests/test_dtype/test_wrapper.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, ClassVar + +import pytest + +from zarr.core.dtype.common import DTypeSpec_V2, DTypeSpec_V3, HasItemSize + +if TYPE_CHECKING: + from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar, ZDType + + +""" +class _TestZDTypeSchema: + # subclasses define the URL for the schema, if available + schema_url: ClassVar[str] = "" + + @pytest.fixture(scope="class") + def get_schema(self) -> object: + response = requests.get(self.schema_url) + response.raise_for_status() + return json_schema.loads(response.text) + + def test_schema(self, schema: json_schema.Schema) -> None: + assert schema.is_valid(self.test_cls.to_json(zarr_format=2)) +""" + + +class BaseTestZDType: + """ + A base class for testing ZDType subclasses. This class works in conjunction with the custom + pytest collection function ``pytest_generate_tests`` defined in conftest.py, which applies the + following procedure when generating tests: + + At test generation time, for each test fixture referenced by a method on this class + pytest will look for an attribute with the same name as that fixture. Pytest will assume that + this class attribute is a tuple of values to be used for generating a parametrized test fixture. + + This means that child classes can, by using different values for these class attributes, have + customized test parametrization. + + Attributes + ---------- + test_cls : type[ZDType[TBaseDType, TBaseScalar]] + The ZDType subclass being tested. + scalar_type : ClassVar[type[TBaseScalar]] + The expected scalar type for the ZDType. + valid_dtype : ClassVar[tuple[TBaseDType, ...]] + A tuple of valid numpy dtypes for the ZDType. + invalid_dtype : ClassVar[tuple[TBaseDType, ...]] + A tuple of invalid numpy dtypes for the ZDType. + valid_json_v2 : ClassVar[tuple[str | dict[str, object] | list[object], ...]] + A tuple of valid JSON representations for Zarr format version 2. + invalid_json_v2 : ClassVar[tuple[str | dict[str, object] | list[object], ...]] + A tuple of invalid JSON representations for Zarr format version 2. + valid_json_v3 : ClassVar[tuple[str | dict[str, object], ...]] + A tuple of valid JSON representations for Zarr format version 3. + invalid_json_v3 : ClassVar[tuple[str | dict[str, object], ...]] + A tuple of invalid JSON representations for Zarr format version 3. + cast_value_params : ClassVar[tuple[tuple[Any, Any, Any], ...]] + A tuple of (dtype, value, expected) tuples for testing ZDType.cast_value. + """ + + test_cls: type[ZDType[TBaseDType, TBaseScalar]] + scalar_type: ClassVar[type[TBaseScalar]] + valid_dtype: ClassVar[tuple[TBaseDType, ...]] = () + invalid_dtype: ClassVar[tuple[TBaseDType, ...]] = () + + valid_json_v2: ClassVar[tuple[DTypeSpec_V2, ...]] = () + invalid_json_v2: ClassVar[tuple[str | dict[str, object] | list[object], ...]] = () + + valid_json_v3: ClassVar[tuple[DTypeSpec_V3, ...]] = () + invalid_json_v3: ClassVar[tuple[str | dict[str, object], ...]] = () + + # for testing scalar round-trip serialization, we need a tuple of (data type json, scalar json) + # pairs. the first element of the pair is used to create a dtype instance, and the second + # element is the json serialization of the scalar that we want to round-trip. + + scalar_v2_params: ClassVar[tuple[tuple[Any, Any], ...]] = () + scalar_v3_params: ClassVar[tuple[tuple[Any, Any], ...]] = () + cast_value_params: ClassVar[tuple[tuple[Any, Any, Any], ...]] + item_size_params: ClassVar[tuple[ZDType[Any, Any], ...]] + + def json_scalar_equals(self, scalar1: object, scalar2: object) -> bool: + # An equality check for json-encoded scalars. This defaults to regular equality, + # but some classes may need to override this for special cases + return scalar1 == scalar2 + + def scalar_equals(self, scalar1: object, scalar2: object) -> bool: + # An equality check for scalars. This defaults to regular equality, + # but some classes may need to override this for special cases + return scalar1 == scalar2 + + def test_check_dtype_valid(self, valid_dtype: TBaseDType) -> None: + assert self.test_cls._check_native_dtype(valid_dtype) + + def test_check_dtype_invalid(self, invalid_dtype: object) -> None: + assert not self.test_cls._check_native_dtype(invalid_dtype) # type: ignore[arg-type] + + def test_from_dtype_roundtrip(self, valid_dtype: Any) -> None: + zdtype = self.test_cls.from_native_dtype(valid_dtype) + assert zdtype.to_native_dtype() == valid_dtype + + def test_from_json_roundtrip_v2(self, valid_json_v2: DTypeSpec_V2) -> None: + zdtype = self.test_cls.from_json(valid_json_v2, zarr_format=2) + assert zdtype.to_json(zarr_format=2) == valid_json_v2 + + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + def test_from_json_roundtrip_v3(self, valid_json_v3: DTypeSpec_V3) -> None: + zdtype = self.test_cls.from_json(valid_json_v3, zarr_format=3) + assert zdtype.to_json(zarr_format=3) == valid_json_v3 + + def test_scalar_roundtrip_v2(self, scalar_v2_params: tuple[ZDType[Any, Any], Any]) -> None: + zdtype, scalar_json = scalar_v2_params + scalar = zdtype.from_json_scalar(scalar_json, zarr_format=2) + assert self.json_scalar_equals(scalar_json, zdtype.to_json_scalar(scalar, zarr_format=2)) + + def test_scalar_roundtrip_v3(self, scalar_v3_params: tuple[ZDType[Any, Any], Any]) -> None: + zdtype, scalar_json = scalar_v3_params + scalar = zdtype.from_json_scalar(scalar_json, zarr_format=3) + assert self.json_scalar_equals(scalar_json, zdtype.to_json_scalar(scalar, zarr_format=3)) + + def test_cast_value(self, cast_value_params: tuple[ZDType[Any, Any], Any, Any]) -> None: + zdtype, value, expected = cast_value_params + observed = zdtype.cast_scalar(value) + assert self.scalar_equals(expected, observed) + + def test_item_size(self, item_size_params: ZDType[Any, Any]) -> None: + """ + Test that the item_size attribute matches the numpy dtype itemsize attribute, for dtypes + with a fixed scalar size. + """ + if isinstance(item_size_params, HasItemSize): + assert item_size_params.item_size == item_size_params.to_native_dtype().itemsize + else: + pytest.skip(f"Dtype {item_size_params} does not implement HasItemSize") diff --git a/tests/test_dtype_registry.py b/tests/test_dtype_registry.py new file mode 100644 index 0000000000..c7d5f90065 --- /dev/null +++ b/tests/test_dtype_registry.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +import re +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any, get_args + +import numpy as np +import pytest + +import zarr +from tests.conftest import skip_object_dtype +from zarr.core.config import config +from zarr.core.dtype import ( + AnyDType, + Bool, + DataTypeRegistry, + DateTime64, + FixedLengthUTF32, + Int8, + Int16, + TBaseDType, + TBaseScalar, + ZDType, + data_type_registry, + get_data_type_from_json, + parse_data_type, +) + +if TYPE_CHECKING: + from collections.abc import Generator + + from zarr.core.common import ZarrFormat + +from .test_dtype.conftest import zdtype_examples + + +@pytest.fixture +def data_type_registry_fixture() -> DataTypeRegistry: + return DataTypeRegistry() + + +class TestRegistry: + @staticmethod + def test_register(data_type_registry_fixture: DataTypeRegistry) -> None: + """ + Test that registering a dtype in a data type registry works. + """ + data_type_registry_fixture.register(Bool._zarr_v3_name, Bool) + assert data_type_registry_fixture.get(Bool._zarr_v3_name) == Bool + assert isinstance(data_type_registry_fixture.match_dtype(np.dtype("bool")), Bool) + + @staticmethod + def test_override(data_type_registry_fixture: DataTypeRegistry) -> None: + """ + Test that registering a new dtype with the same name works (overriding the previous one). + """ + data_type_registry_fixture.register(Bool._zarr_v3_name, Bool) + + class NewBool(Bool): + def default_scalar(self) -> np.bool_: + return np.True_ + + data_type_registry_fixture.register(NewBool._zarr_v3_name, NewBool) + assert isinstance(data_type_registry_fixture.match_dtype(np.dtype("bool")), NewBool) + + @staticmethod + @pytest.mark.parametrize( + ("wrapper_cls", "dtype_str"), [(Bool, "bool"), (FixedLengthUTF32, "|U4")] + ) + def test_match_dtype( + data_type_registry_fixture: DataTypeRegistry, + wrapper_cls: type[ZDType[TBaseDType, TBaseScalar]], + dtype_str: str, + ) -> None: + """ + Test that match_dtype resolves a numpy dtype into an instance of the correspond wrapper for that dtype. + """ + data_type_registry_fixture.register(wrapper_cls._zarr_v3_name, wrapper_cls) + assert isinstance(data_type_registry_fixture.match_dtype(np.dtype(dtype_str)), wrapper_cls) + + @staticmethod + def test_unregistered_dtype(data_type_registry_fixture: DataTypeRegistry) -> None: + """ + Test that match_dtype raises an error if the dtype is not registered. + """ + outside_dtype_name = "int8" + outside_dtype = np.dtype(outside_dtype_name) + msg = f"No Zarr data type found that matches dtype '{outside_dtype!r}'" + with pytest.raises(ValueError, match=re.escape(msg)): + data_type_registry_fixture.match_dtype(outside_dtype) + + with pytest.raises(KeyError): + data_type_registry_fixture.get(outside_dtype_name) + + @staticmethod + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("zdtype", zdtype_examples) + def test_registered_dtypes_match_dtype(zdtype: ZDType[TBaseDType, TBaseScalar]) -> None: + """ + Test that the registered dtypes can be retrieved from the registry. + """ + skip_object_dtype(zdtype) + assert data_type_registry.match_dtype(zdtype.to_native_dtype()) == zdtype + + @staticmethod + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("zdtype", zdtype_examples) + def test_registered_dtypes_match_json( + zdtype: ZDType[TBaseDType, TBaseScalar], zarr_format: ZarrFormat + ) -> None: + assert ( + data_type_registry.match_json( + zdtype.to_json(zarr_format=zarr_format), zarr_format=zarr_format + ) + == zdtype + ) + + @staticmethod + @pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") + @pytest.mark.parametrize("zdtype", zdtype_examples) + def test_match_dtype_unique( + zdtype: ZDType[Any, Any], + data_type_registry_fixture: DataTypeRegistry, + zarr_format: ZarrFormat, + ) -> None: + """ + Test that the match_dtype method uniquely specifies a registered data type. We create a local registry + that excludes the data type class being tested, and ensure that an instance of the wrapped data type + fails to match anything in the registry + """ + skip_object_dtype(zdtype) + for _cls in get_args(AnyDType): + if _cls is not type(zdtype): + data_type_registry_fixture.register(_cls._zarr_v3_name, _cls) + + dtype_instance = zdtype.to_native_dtype() + + msg = f"No Zarr data type found that matches dtype '{dtype_instance!r}'" + with pytest.raises(ValueError, match=re.escape(msg)): + data_type_registry_fixture.match_dtype(dtype_instance) + + instance_dict = zdtype.to_json(zarr_format=zarr_format) + msg = f"No Zarr data type found that matches {instance_dict!r}" + with pytest.raises(ValueError, match=re.escape(msg)): + data_type_registry_fixture.match_json(instance_dict, zarr_format=zarr_format) + + +# this is copied from the registry tests -- we should deduplicate +here = str(Path(__file__).parent.absolute()) + + +@pytest.fixture +def set_path() -> Generator[None, None, None]: + sys.path.append(here) + zarr.registry._collect_entrypoints() + yield + sys.path.remove(here) + registries = zarr.registry._collect_entrypoints() + for registry in registries: + registry.lazy_load_list.clear() + config.reset() + + +@pytest.mark.usefixtures("set_path") +def test_entrypoint_dtype(zarr_format: ZarrFormat) -> None: + from package_with_entrypoint import TestDataType + + data_type_registry.lazy_load() + instance = TestDataType() + dtype_json = instance.to_json(zarr_format=zarr_format) + assert get_data_type_from_json(dtype_json, zarr_format=zarr_format) == instance + data_type_registry.unregister(TestDataType._zarr_v3_name) + + +@pytest.mark.parametrize( + ("dtype_params", "expected", "zarr_format"), + [ + ("int8", Int8(), 3), + (Int8(), Int8(), 3), + (">i2", Int16(endianness="big"), 2), + ("datetime64[10s]", DateTime64(unit="s", scale_factor=10), 2), + ( + {"name": "numpy.datetime64", "configuration": {"unit": "s", "scale_factor": 10}}, + DateTime64(unit="s", scale_factor=10), + 3, + ), + ], +) +def test_parse_data_type( + dtype_params: Any, expected: ZDType[Any, Any], zarr_format: ZarrFormat +) -> None: + """ + Test that parse_data_type accepts alternative representations of ZDType instances, and resolves + those inputs to the expected ZDType instance. + """ + observed = parse_data_type(dtype_params, zarr_format=zarr_format) + assert observed == expected diff --git a/tests/test_group.py b/tests/test_group.py index 1e4f31b5d6..60a1fcb9bf 100644 --- a/tests/test_group.py +++ b/tests/test_group.py @@ -23,6 +23,8 @@ from zarr.core._info import GroupInfo from zarr.core.buffer import default_buffer_prototype from zarr.core.config import config as zarr_config +from zarr.core.dtype.common import unpack_dtype_json +from zarr.core.dtype.npy.int import UInt8 from zarr.core.group import ( ConsolidatedMetadata, GroupMetadata, @@ -494,7 +496,7 @@ def test_group_child_iterators(store: Store, zarr_format: ZarrFormat, consolidat expected_groups = list(zip(expected_group_keys, expected_group_values, strict=False)) fill_value = 3 - dtype = "uint8" + dtype = UInt8() expected_group_values[0].create_group("subgroup") expected_group_values[0].create_array( @@ -515,7 +517,7 @@ def test_group_child_iterators(store: Store, zarr_format: ZarrFormat, consolidat metadata = { "subarray": { "attributes": {}, - "dtype": dtype, + "dtype": unpack_dtype_json(dtype.to_json(zarr_format=zarr_format)), "fill_value": fill_value, "shape": (1,), "chunks": (1,), @@ -551,7 +553,7 @@ def test_group_child_iterators(store: Store, zarr_format: ZarrFormat, consolidat {"configuration": {"endian": "little"}, "name": "bytes"}, {"configuration": {}, "name": "zstd"}, ), - "data_type": dtype, + "data_type": unpack_dtype_json(dtype.to_json(zarr_format=zarr_format)), "fill_value": fill_value, "node_type": "array", "shape": (1,), @@ -1519,7 +1521,6 @@ def test_create_nodes_concurrency_limit(store: MemoryStore) -> None: # if create_nodes is sensitive to IO latency, # this should take (num_groups * get_latency) seconds # otherwise, it should take only marginally more than get_latency seconds - with zarr_config.set({"async.concurrency": 1}): start = time.time() _ = tuple(sync_group.create_nodes(store=latency_store, nodes=groups)) @@ -1583,14 +1584,12 @@ async def test_create_hierarchy( sync_group.create_hierarchy(store=store, nodes=hierarchy_spec, overwrite=overwrite) ) elif impl == "async": - created = dict( - [ - a - async for a in create_hierarchy( - store=store, nodes=hierarchy_spec, overwrite=overwrite - ) - ] - ) + created = { + k: v + async for k, v in create_hierarchy( + store=store, nodes=hierarchy_spec, overwrite=overwrite + ) + } else: raise ValueError(f"Invalid impl: {impl}") if not overwrite: @@ -2026,9 +2025,7 @@ def test_group_members_concurrency_limit(store: MemoryStore) -> None: # if .members is sensitive to IO latency, # this should take (num_groups * get_latency) seconds # otherwise, it should take only marginally more than get_latency seconds - from zarr.core.config import config - - with config.set({"async.concurrency": 1}): + with zarr_config.set({"async.concurrency": 1}): start = time.time() _ = group_read.members() elapsed = time.time() - start diff --git a/tests/test_info.py b/tests/test_info.py index db0fd0ef76..0abaff9ae7 100644 --- a/tests/test_info.py +++ b/tests/test_info.py @@ -1,11 +1,11 @@ import textwrap -import numpy as np import pytest from zarr.codecs.bytes import BytesCodec from zarr.core._info import ArrayInfo, GroupInfo, human_readable_size from zarr.core.common import ZarrFormat +from zarr.core.dtype.npy.int import Int32 ZARR_FORMATS = [2, 3] @@ -53,7 +53,8 @@ def test_group_info_complete(zarr_format: ZarrFormat) -> None: def test_array_info(zarr_format: ZarrFormat) -> None: info = ArrayInfo( _zarr_format=zarr_format, - _data_type=np.dtype("int32"), + _data_type=Int32(), + _fill_value=0, _shape=(100, 100), _chunk_shape=(10, 100), _order="C", @@ -65,7 +66,8 @@ def test_array_info(zarr_format: ZarrFormat) -> None: assert result == textwrap.dedent(f"""\ Type : Array Zarr format : {zarr_format} - Data type : int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (100, 100) Chunk shape : (10, 100) Order : C @@ -91,7 +93,8 @@ def test_array_info_complete( ) = bytes_things info = ArrayInfo( _zarr_format=zarr_format, - _data_type=np.dtype("int32"), + _data_type=Int32(), + _fill_value=0, _shape=(100, 100), _chunk_shape=(10, 100), _order="C", @@ -106,7 +109,8 @@ def test_array_info_complete( assert result == textwrap.dedent(f"""\ Type : Array Zarr format : {zarr_format} - Data type : int32 + Data type : Int32(endianness='little') + Fill value : 0 Shape : (100, 100) Chunk shape : (10, 100) Order : C diff --git a/tests/test_metadata/test_consolidated.py b/tests/test_metadata/test_consolidated.py index a179982e94..395e036db2 100644 --- a/tests/test_metadata/test_consolidated.py +++ b/tests/test_metadata/test_consolidated.py @@ -18,6 +18,7 @@ open_consolidated, ) from zarr.core.buffer import cpu, default_buffer_prototype +from zarr.core.dtype import parse_data_type from zarr.core.group import ConsolidatedMetadata, GroupMetadata from zarr.core.metadata import ArrayV3Metadata from zarr.core.metadata.v2 import ArrayV2Metadata @@ -503,7 +504,7 @@ async def test_consolidated_metadata_backwards_compatibility( async def test_consolidated_metadata_v2(self): store = zarr.storage.MemoryStore() g = await AsyncGroup.from_store(store, attributes={"key": "root"}, zarr_format=2) - dtype = "uint8" + dtype = parse_data_type("uint8", zarr_format=2) await g.create_array(name="a", shape=(1,), attributes={"key": "a"}, dtype=dtype) g1 = await g.create_group(name="g1", attributes={"key": "g1"}) await g1.create_group(name="g2", attributes={"key": "g2"}) @@ -574,13 +575,56 @@ async def test_use_consolidated_false( assert good.metadata.consolidated_metadata assert sorted(good.metadata.consolidated_metadata.metadata) == ["a", "b"] + async def test_stale_child_metadata_ignored(self, memory_store: zarr.storage.MemoryStore): + # https://github.com/zarr-developers/zarr-python/issues/2921 + # When consolidating metadata, we should ignore any (possibly stale) metadata + # from previous consolidations, *including at child nodes*. + root = await zarr.api.asynchronous.group(store=memory_store, zarr_format=3) + await root.create_group("foo") + await zarr.api.asynchronous.consolidate_metadata(memory_store, path="foo") + await root.create_group("foo/bar/spam") + + await zarr.api.asynchronous.consolidate_metadata(memory_store) + + reopened = await zarr.api.asynchronous.open_consolidated(store=memory_store, zarr_format=3) + result = [x[0] async for x in reopened.members(max_depth=None)] + expected = ["foo", "foo/bar", "foo/bar/spam"] + assert result == expected + + async def test_use_consolidated_for_children_members( + self, memory_store: zarr.storage.MemoryStore + ): + # A test that has *unconsolidated* metadata at the root group, but discovers + # a child group with consolidated metadata. + + root = await zarr.api.asynchronous.create_group(store=memory_store) + await root.create_group("a/b") + # Consolidate metadata at "a/b" + await zarr.api.asynchronous.consolidate_metadata(memory_store, path="a/b") + + # Add a new group a/b/c, that's not present in the CM at "a/b" + await root.create_group("a/b/c") + + # Now according to the consolidated metadata, "a" has children ["b"] + # but according to the unconsolidated metadata, "a" has children ["b", "c"] + group = await zarr.api.asynchronous.open_group(store=memory_store, path="a") + with pytest.warns(UserWarning, match="Object at 'c' not found"): + result = sorted([x[0] async for x in group.members(max_depth=None)]) + expected = ["b"] + assert result == expected + + result = sorted( + [x[0] async for x in group.members(max_depth=None, use_consolidated_for_children=False)] + ) + expected = ["b", "b/c"] + assert result == expected + @pytest.mark.parametrize("fill_value", [np.nan, np.inf, -np.inf]) async def test_consolidated_metadata_encodes_special_chars( memory_store: Store, zarr_format: ZarrFormat, fill_value: float ): root = await group(store=memory_store, zarr_format=zarr_format) - _child = await root.create_group("child", attributes={"test": fill_value}) _time = await root.create_array("time", shape=(12,), dtype=np.float64, fill_value=fill_value) await zarr.api.asynchronous.consolidate_metadata(memory_store) @@ -594,16 +638,44 @@ async def test_consolidated_metadata_encodes_special_chars( "consolidated_metadata" ]["metadata"] - if np.isnan(fill_value): - expected_fill_value = "NaN" - elif np.isneginf(fill_value): - expected_fill_value = "-Infinity" - elif np.isinf(fill_value): - expected_fill_value = "Infinity" + expected_fill_value = _time._zdtype.to_json_scalar(fill_value, zarr_format=2) if zarr_format == 2: - assert root_metadata["child/.zattrs"]["test"] == expected_fill_value assert root_metadata["time/.zarray"]["fill_value"] == expected_fill_value elif zarr_format == 3: - assert root_metadata["child"]["attributes"]["test"] == expected_fill_value assert root_metadata["time"]["fill_value"] == expected_fill_value + + +class NonConsolidatedStore(zarr.storage.MemoryStore): + """A store that doesn't support consolidated metadata""" + + @property + def supports_consolidated_metadata(self) -> bool: + return False + + +async def test_consolidate_metadata_raises_for_self_consolidating_stores(): + """Verify calling consolidate_metadata on a non supporting stores raises an error.""" + + memory_store = NonConsolidatedStore() + root = await zarr.api.asynchronous.create_group(store=memory_store) + await root.create_group("a/b") + + with pytest.raises(TypeError, match="doesn't support consolidated metadata"): + await zarr.api.asynchronous.consolidate_metadata(memory_store) + + +async def test_open_group_in_non_consolidating_stores(): + memory_store = NonConsolidatedStore() + root = await zarr.api.asynchronous.create_group(store=memory_store) + await root.create_group("a/b") + + # Opening a group without consolidatedion works as expected + await AsyncGroup.open(memory_store, use_consolidated=False) + + # let the Store opt out of consolidation + await AsyncGroup.open(memory_store, use_consolidated=None) + + # Opening a group with use_consolidated=True should fail + with pytest.raises(ValueError, match="doesn't support consolidated metadata"): + await AsyncGroup.open(memory_store, use_consolidated=True) diff --git a/tests/test_metadata/test_v2.py b/tests/test_metadata/test_v2.py index 4600a977d4..a2894529aa 100644 --- a/tests/test_metadata/test_v2.py +++ b/tests/test_metadata/test_v2.py @@ -10,6 +10,8 @@ import zarr.storage from zarr.core.buffer import cpu from zarr.core.buffer.core import default_buffer_prototype +from zarr.core.dtype.npy.float import Float32, Float64 +from zarr.core.dtype.npy.int import Int16 from zarr.core.group import ConsolidatedMetadata, GroupMetadata from zarr.core.metadata import ArrayV2Metadata from zarr.core.metadata.v2 import parse_zarr_format @@ -19,8 +21,6 @@ from zarr.abc.codec import Codec -import numcodecs - def test_parse_zarr_format_valid() -> None: assert parse_zarr_format(2) == 2 @@ -33,8 +33,8 @@ def test_parse_zarr_format_invalid(data: Any) -> None: @pytest.mark.parametrize("attributes", [None, {"foo": "bar"}]) -@pytest.mark.parametrize("filters", [None, (numcodecs.GZip(),)]) -@pytest.mark.parametrize("compressor", [None, numcodecs.GZip()]) +@pytest.mark.parametrize("filters", [None, [{"id": "gzip", "level": 1}]]) +@pytest.mark.parametrize("compressor", [None, {"id": "gzip", "level": 1}]) @pytest.mark.parametrize("fill_value", [None, 0, 1]) @pytest.mark.parametrize("order", ["C", "F"]) @pytest.mark.parametrize("dimension_separator", [".", "/", None]) @@ -86,7 +86,7 @@ def test_filters_empty_tuple_warns() -> None: "zarr_format": 2, "shape": (1,), "chunks": (1,), - "dtype": "uint8", + "dtype": "|u1", "order": "C", "compressor": None, "filters": (), @@ -128,7 +128,7 @@ async def v2_consolidated_metadata( "chunks": [730], "compressor": None, "dtype": " None: expected = ArrayV2Metadata( attributes={"key": "value"}, shape=(8,), - dtype="float64", + dtype=Float64(), chunks=(8,), fill_value=0.0, order="C", @@ -316,3 +316,23 @@ def test_zstd_checksum() -> None: arr.metadata.to_buffer_dict(default_buffer_prototype())[".zarray"].to_bytes() ) assert "checksum" not in metadata["compressor"] + + +@pytest.mark.parametrize("fill_value", [np.void((0, 0), np.dtype([("foo", "i4"), ("bar", "i4")]))]) +def test_structured_dtype_fill_value_serialization(tmp_path, fill_value): + zarr_format = 2 + group_path = tmp_path / "test.zarr" + root_group = zarr.open_group(group_path, mode="w", zarr_format=zarr_format) + dtype = np.dtype([("foo", "i4"), ("bar", "i4")]) + root_group.create_array( + name="structured_dtype", + shape=(100, 100), + chunks=(100, 100), + dtype=dtype, + fill_value=fill_value, + ) + + zarr.consolidate_metadata(root_group.store, zarr_format=zarr_format) + root_group = zarr.open_group(group_path, mode="r") + observed = root_group.metadata.consolidated_metadata.metadata["structured_dtype"].fill_value + assert observed == fill_value diff --git a/tests/test_metadata/test_v3.py b/tests/test_metadata/test_v3.py index a47cbf43bb..4f385afa6d 100644 --- a/tests/test_metadata/test_v3.py +++ b/tests/test_metadata/test_v3.py @@ -11,16 +11,16 @@ from zarr.core.buffer import default_buffer_prototype from zarr.core.chunk_key_encodings import DefaultChunkKeyEncoding, V2ChunkKeyEncoding from zarr.core.config import config +from zarr.core.dtype import get_data_type_from_native_dtype +from zarr.core.dtype.npy.string import _NUMPY_SUPPORTS_VLEN_STRING +from zarr.core.dtype.npy.time import DateTime64 from zarr.core.group import GroupMetadata, parse_node_type from zarr.core.metadata.v3 import ( ArrayV3Metadata, - DataType, - default_fill_value, parse_dimension_names, - parse_fill_value, parse_zarr_format, ) -from zarr.errors import MetadataValidationError +from zarr.errors import MetadataValidationError, NodeTypeValidationError if TYPE_CHECKING: from collections.abc import Sequence @@ -54,15 +54,27 @@ ) complex_dtypes = ("complex64", "complex128") -vlen_dtypes = ("string", "bytes") - -dtypes = (*bool_dtypes, *int_dtypes, *float_dtypes, *complex_dtypes, *vlen_dtypes) +flexible_dtypes = ("str", "bytes", "void") +if _NUMPY_SUPPORTS_VLEN_STRING: + vlen_string_dtypes = ("T",) +else: + vlen_string_dtypes = ("O",) + +dtypes = ( + *bool_dtypes, + *int_dtypes, + *float_dtypes, + *complex_dtypes, + *flexible_dtypes, + *vlen_string_dtypes, +) @pytest.mark.parametrize("data", [None, 1, 2, 4, 5, "3"]) def test_parse_zarr_format_invalid(data: Any) -> None: with pytest.raises( - ValueError, match=f"Invalid value for 'zarr_format'. Expected '3'. Got '{data}'." + MetadataValidationError, + match=f"Invalid value for 'zarr_format'. Expected '3'. Got '{data}'.", ): parse_zarr_format(data) @@ -88,7 +100,8 @@ def test_parse_node_type_invalid(node_type: Any) -> None: @pytest.mark.parametrize("data", [None, "group"]) def test_parse_node_type_array_invalid(data: Any) -> None: with pytest.raises( - ValueError, match=f"Invalid value for 'node_type'. Expected 'array'. Got '{data}'." + NodeTypeValidationError, + match=f"Invalid value for 'node_type'. Expected 'array'. Got '{data}'.", ): parse_node_type_array(data) @@ -108,90 +121,19 @@ def parse_dimension_names_valid(data: Sequence[str] | None) -> None: assert parse_dimension_names(data) == data -@pytest.mark.parametrize("dtype_str", dtypes) -def test_default_fill_value(dtype_str: str) -> None: - """ - Test that parse_fill_value(None, dtype) results in the 0 value for the given dtype. - """ - dtype = DataType(dtype_str) - fill_value = default_fill_value(dtype) - if dtype == DataType.string: - assert fill_value == "" - elif dtype == DataType.bytes: - assert fill_value == b"" - else: - assert fill_value == dtype.to_numpy().type(0) - - -@pytest.mark.parametrize( - ("fill_value", "dtype_str"), - [ - (True, "bool"), - (False, "bool"), - (-8, "int8"), - (0, "int16"), - (1e10, "uint64"), - (-999, "float32"), - (1e32, "float64"), - (float("NaN"), "float64"), - (np.nan, "float64"), - (np.inf, "float64"), - (-1 * np.inf, "float64"), - (0j, "complex64"), - ], -) -def test_parse_fill_value_valid(fill_value: Any, dtype_str: str) -> None: - """ - Test that parse_fill_value(fill_value, dtype) casts fill_value to the given dtype. - """ - parsed = parse_fill_value(fill_value, dtype_str) - - if np.isnan(fill_value): - assert np.isnan(parsed) - else: - assert parsed == DataType(dtype_str).to_numpy().type(fill_value) - - -@pytest.mark.parametrize("fill_value", ["not a valid value"]) -@pytest.mark.parametrize("dtype_str", [*int_dtypes, *float_dtypes, *complex_dtypes]) -def test_parse_fill_value_invalid_value(fill_value: Any, dtype_str: str) -> None: - """ - Test that parse_fill_value(fill_value, dtype) raises ValueError for invalid values. - This test excludes bool because the bool constructor takes anything. - """ - with pytest.raises(ValueError): - parse_fill_value(fill_value, dtype_str) - - -@pytest.mark.parametrize("fill_value", [[1.0, 0.0], [0, 1], complex(1, 1), np.complex64(0)]) +@pytest.mark.parametrize("fill_value", [[1.0, 0.0], [0, 1]]) @pytest.mark.parametrize("dtype_str", [*complex_dtypes]) -def test_parse_fill_value_complex(fill_value: Any, dtype_str: str) -> None: +def test_jsonify_fill_value_complex(fill_value: Any, dtype_str: str) -> None: """ Test that parse_fill_value(fill_value, dtype) correctly handles complex values represented as length-2 sequences """ - dtype = DataType(dtype_str) - if isinstance(fill_value, list): - expected = dtype.to_numpy().type(complex(*fill_value)) - else: - expected = dtype.to_numpy().type(fill_value) - assert expected == parse_fill_value(fill_value, dtype_str) - - -@pytest.mark.parametrize("fill_value", [[1.0, 0.0, 3.0], [0, 1, 3], [1]]) -@pytest.mark.parametrize("dtype_str", [*complex_dtypes]) -def test_parse_fill_value_complex_invalid(fill_value: Any, dtype_str: str) -> None: - """ - Test that parse_fill_value(fill_value, dtype) correctly rejects sequences with length not - equal to 2 - """ - match = ( - f"Got an invalid fill value for complex data type {dtype_str}." - f"Expected a sequence with 2 elements, but {fill_value} has " - f"length {len(fill_value)}." - ) - with pytest.raises(ValueError, match=re.escape(match)): - parse_fill_value(fill_value=fill_value, dtype=dtype_str) + zarr_format = 3 + dtype = get_data_type_from_native_dtype(dtype_str) + expected = dtype.to_native_dtype().type(complex(*fill_value)) + observed = dtype.from_json_scalar(fill_value, zarr_format=zarr_format) + assert observed == expected + assert dtype.to_json_scalar(observed, zarr_format=zarr_format) == tuple(fill_value) @pytest.mark.parametrize("fill_value", [{"foo": 10}]) @@ -201,8 +143,9 @@ def test_parse_fill_value_invalid_type(fill_value: Any, dtype_str: str) -> None: Test that parse_fill_value(fill_value, dtype) raises TypeError for invalid non-sequential types. This test excludes bool because the bool constructor takes anything. """ - with pytest.raises(ValueError, match=r"fill value .* is not valid for dtype .*"): - parse_fill_value(fill_value, dtype_str) + dtype_instance = get_data_type_from_native_dtype(dtype_str) + with pytest.raises(TypeError, match=f"Invalid type: {fill_value}"): + dtype_instance.from_json_scalar(fill_value, zarr_format=3) @pytest.mark.parametrize( @@ -221,14 +164,14 @@ def test_parse_fill_value_invalid_type_sequence(fill_value: Any, dtype_str: str) This test excludes bool because the bool constructor takes anything, and complex because complex values can be created from length-2 sequences. """ - match = f"Cannot parse non-string sequence {fill_value} as a scalar with type {dtype_str}" - with pytest.raises(TypeError, match=re.escape(match)): - parse_fill_value(fill_value, dtype_str) + dtype_instance = get_data_type_from_native_dtype(dtype_str) + with pytest.raises(TypeError, match=re.escape(f"Invalid type: {fill_value}")): + dtype_instance.from_json_scalar(fill_value, zarr_format=3) @pytest.mark.parametrize("chunk_grid", ["regular"]) @pytest.mark.parametrize("attributes", [None, {"foo": "bar"}]) -@pytest.mark.parametrize("codecs", [[BytesCodec()]]) +@pytest.mark.parametrize("codecs", [[BytesCodec(endian=None)]]) @pytest.mark.parametrize("fill_value", [0, 1]) @pytest.mark.parametrize("chunk_key_encoding", ["v2", "default"]) @pytest.mark.parametrize("dimension_separator", [".", "/", None]) @@ -245,7 +188,7 @@ def test_metadata_to_dict( storage_transformers: tuple[dict[str, JSON]] | None, ) -> None: shape = (1, 2, 3) - data_type = DataType.uint8 + data_type_str = "uint8" if chunk_grid == "regular": cgrid = {"name": "regular", "configuration": {"chunk_shape": (1, 1, 1)}} @@ -269,7 +212,7 @@ def test_metadata_to_dict( "node_type": "array", "shape": shape, "chunk_grid": cgrid, - "data_type": data_type, + "data_type": data_type_str, "chunk_key_encoding": cke, "codecs": tuple(c.to_dict() for c in codecs), "fill_value": fill_value, @@ -313,50 +256,32 @@ def test_json_indent(indent: int): assert d == json.dumps(json.loads(d), indent=indent).encode() -# @pytest.mark.parametrize("fill_value", [-1, 0, 1, 2932897]) -# @pytest.mark.parametrize("precision", ["ns", "D"]) -# async def test_datetime_metadata(fill_value: int, precision: str) -> None: -# metadata_dict = { -# "zarr_format": 3, -# "node_type": "array", -# "shape": (1,), -# "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": (1,)}}, -# "data_type": f" None: +@pytest.mark.parametrize("fill_value", [-1, 0, 1, 2932897]) +@pytest.mark.parametrize("precision", ["ns", "D"]) +async def test_datetime_metadata(fill_value: int, precision: str) -> None: + dtype = DateTime64(unit=precision) metadata_dict = { "zarr_format": 3, "node_type": "array", "shape": (1,), "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": (1,)}}, - "data_type": " None: metadata_dict = { @@ -366,10 +291,11 @@ async def test_invalid_fill_value_raises(data_type: str, fill_value: float) -> N "chunk_grid": {"name": "regular", "configuration": {"chunk_shape": (1,)}}, "data_type": data_type, "chunk_key_encoding": {"name": "default", "separator": "."}, - "codecs": (), + "codecs": ({"name": "bytes"},), "fill_value": fill_value, # this is not a valid fill value for uint8 } - with pytest.raises(ValueError, match=r"fill value .* is not valid for dtype .*"): + # multiple things can go wrong here, so we don't match on the error message. + with pytest.raises(TypeError): ArrayV3Metadata.from_dict(metadata_dict) @@ -397,17 +323,3 @@ async def test_special_float_fill_values(fill_value: str) -> None: elif fill_value == "-Infinity": assert np.isneginf(m.fill_value) assert d["fill_value"] == "-Infinity" - - -@pytest.mark.parametrize("dtype_str", dtypes) -def test_dtypes(dtype_str: str) -> None: - dt = DataType(dtype_str) - np_dtype = dt.to_numpy() - if dtype_str not in vlen_dtypes: - # we can round trip "normal" dtypes - assert dt == DataType.from_numpy(np_dtype) - assert dt.byte_count == np_dtype.itemsize - assert dt.has_endianness == (dt.byte_count > 1) - else: - # return type for vlen types may vary depending on numpy version - assert dt.byte_count is None diff --git a/tests/test_properties.py b/tests/test_properties.py index d48dfe2fef..b8d50ef0b1 100644 --- a/tests/test_properties.py +++ b/tests/test_properties.py @@ -1,4 +1,3 @@ -import dataclasses import json import numbers from typing import Any @@ -76,6 +75,7 @@ def deep_equal(a: Any, b: Any) -> bool: return a == b +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") @given(data=st.data(), zarr_format=zarr_formats) def test_array_roundtrip(data: st.DataObject, zarr_format: int) -> None: nparray = data.draw(numpy_arrays(zarr_formats=st.just(zarr_format))) @@ -83,6 +83,7 @@ def test_array_roundtrip(data: st.DataObject, zarr_format: int) -> None: assert_array_equal(nparray, zarray[:]) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") @given(array=arrays()) def test_array_creates_implicit_groups(array): path = array.path @@ -102,7 +103,10 @@ def test_array_creates_implicit_groups(array): # this decorator removes timeout; not ideal but it should avoid intermittent CI failures + + @settings(deadline=None) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") @given(data=st.data()) def test_basic_indexing(data: st.DataObject) -> None: zarray = data.draw(simple_arrays()) @@ -118,6 +122,7 @@ def test_basic_indexing(data: st.DataObject) -> None: @given(data=st.data()) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_oindex(data: st.DataObject) -> None: # integer_array_indices can't handle 0-size dimensions. zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=4, min_side=1))) @@ -139,6 +144,7 @@ def test_oindex(data: st.DataObject) -> None: @given(data=st.data()) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_vindex(data: st.DataObject) -> None: # integer_array_indices can't handle 0-size dimensions. zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=4, min_side=1))) @@ -162,6 +168,7 @@ def test_vindex(data: st.DataObject) -> None: @given(store=stores, meta=array_metadata()) # type: ignore[misc] +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") async def test_roundtrip_array_metadata_from_store( store: Store, meta: ArrayV2Metadata | ArrayV3Metadata ) -> None: @@ -181,6 +188,7 @@ async def test_roundtrip_array_metadata_from_store( @given(data=st.data(), zarr_format=zarr_formats) +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_roundtrip_array_metadata_from_json(data: st.DataObject, zarr_format: int) -> None: """ Verify that JSON serialization and deserialization of metadata is lossless. @@ -209,8 +217,8 @@ def test_roundtrip_array_metadata_from_json(data: st.DataObject, zarr_format: in zarray_dict = json.loads(buffer_dict[ZARR_JSON].to_bytes().decode()) metadata_roundtripped = ArrayV3Metadata.from_dict(zarray_dict) - orig = dataclasses.asdict(metadata) - rt = dataclasses.asdict(metadata_roundtripped) + orig = metadata.to_dict() + rt = metadata_roundtripped.to_dict() assert deep_equal(orig, rt), f"Roundtrip mismatch:\nOriginal: {orig}\nRoundtripped: {rt}" @@ -239,6 +247,29 @@ def test_roundtrip_array_metadata_from_json(data: st.DataObject, zarr_format: in # assert_array_equal(nparray, zarray[:]) +def serialized_complex_float_is_valid( + serialized: tuple[numbers.Real | str, numbers.Real | str], +) -> bool: + """ + Validate that the serialized representation of a complex float conforms to the spec. + + The specification requires that a serialized complex float must be either: + - A JSON number, or + - One of the strings "NaN", "Infinity", or "-Infinity". + + Args: + serialized: The value produced by JSON serialization for a complex floating point number. + + Returns: + bool: True if the serialized value is valid according to the spec, False otherwise. + """ + return ( + isinstance(serialized, tuple) + and len(serialized) == 2 + and all(serialized_float_is_valid(x) for x in serialized) + ) + + def serialized_float_is_valid(serialized: numbers.Real | str) -> bool: """ Validate that the serialized representation of a float conforms to the spec. @@ -259,6 +290,7 @@ def serialized_float_is_valid(serialized: numbers.Real | str) -> bool: @given(meta=array_metadata()) # type: ignore[misc] +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_array_metadata_meets_spec(meta: ArrayV2Metadata | ArrayV3Metadata) -> None: """ Validate that the array metadata produced by the library conforms to the relevant spec (V2 vs V3). @@ -294,11 +326,11 @@ def test_array_metadata_meets_spec(meta: ArrayV2Metadata | ArrayV3Metadata) -> N assert asdict_dict["zarr_format"] == 3 # version-agnostic validations - if meta.dtype.kind == "f": + dtype_native = meta.dtype.to_native_dtype() + if dtype_native.kind == "f": assert serialized_float_is_valid(asdict_dict["fill_value"]) - elif meta.dtype.kind == "c": + elif dtype_native.kind == "c": # fill_value should be a two-element array [real, imag]. - assert serialized_float_is_valid(asdict_dict["fill_value"].real) - assert serialized_float_is_valid(asdict_dict["fill_value"].imag) - elif meta.dtype.kind == "M" and np.isnat(meta.fill_value): - assert asdict_dict["fill_value"] == "NaT" + assert serialized_complex_float_is_valid(asdict_dict["fill_value"]) + elif dtype_native.kind in ("M", "m") and np.isnat(meta.fill_value): + assert asdict_dict["fill_value"] == -9223372036854775808 diff --git a/tests/test_regression/__init__.py b/tests/test_regression/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_regression/scripts/__init__.py b/tests/test_regression/scripts/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_regression/scripts/v2.18.py b/tests/test_regression/scripts/v2.18.py new file mode 100644 index 0000000000..39e1c5210c --- /dev/null +++ b/tests/test_regression/scripts/v2.18.py @@ -0,0 +1,81 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "zarr==2.18", +# "numcodecs==0.15" +# ] +# /// + +import argparse + +import zarr +from zarr._storage.store import BaseStore + + +def copy_group( + *, node: zarr.hierarchy.Group, store: zarr.storage.BaseStore, path: str, overwrite: bool +) -> zarr.hierarchy.Group: + result = zarr.group(store=store, path=path, overwrite=overwrite) + result.attrs.put(node.attrs.asdict()) + for key, child in node.items(): + child_path = f"{path}/{key}" + if isinstance(child, zarr.hierarchy.Group): + copy_group(node=child, store=store, path=child_path, overwrite=overwrite) + elif isinstance(child, zarr.core.Array): + copy_array(node=child, store=store, overwrite=overwrite, path=child_path) + return result + + +def copy_array( + *, node: zarr.core.Array, store: BaseStore, path: str, overwrite: bool +) -> zarr.core.Array: + result = zarr.create( + shape=node.shape, + dtype=node.dtype, + fill_value=node.fill_value, + chunks=node.chunks, + compressor=node.compressor, + filters=node.filters, + order=node.order, + dimension_separator=node._dimension_separator, + store=store, + path=path, + overwrite=overwrite, + ) + result.attrs.put(node.attrs.asdict()) + result[:] = node[:] + return result + + +def copy_node( + node: zarr.hierarchy.Group | zarr.core.Array, store: BaseStore, path: str, overwrite: bool +) -> zarr.hierarchy.Group | zarr.core.Array: + if isinstance(node, zarr.hierarchy.Group): + return copy_group(node=node, store=store, path=path, overwrite=overwrite) + elif isinstance(node, zarr.core.Array): + return copy_array(node=node, store=store, path=path, overwrite=overwrite) + else: + raise TypeError(f"Unexpected node type: {type(node)}") # pragma: no cover + + +def cli() -> None: + parser = argparse.ArgumentParser( + description="Copy a zarr hierarchy from one location to another" + ) + parser.add_argument("source", type=str, help="Path to the source zarr hierarchy") + parser.add_argument("destination", type=str, help="Path to the destination zarr hierarchy") + args = parser.parse_args() + + src, dst = args.source, args.destination + root_src = zarr.open(src, mode="r") + result = copy_node(node=root_src, store=zarr.NestedDirectoryStore(dst), path="", overwrite=True) + + print(f"successfully created {result} at {dst}") + + +def main() -> None: + cli() + + +if __name__ == "__main__": + main() diff --git a/tests/test_regression/test_regression.py b/tests/test_regression/test_regression.py new file mode 100644 index 0000000000..34c48a6933 --- /dev/null +++ b/tests/test_regression/test_regression.py @@ -0,0 +1,156 @@ +import subprocess +from dataclasses import dataclass +from itertools import product +from pathlib import Path +from typing import TYPE_CHECKING + +import numcodecs +import numpy as np +import pytest +from numcodecs import LZ4, LZMA, Blosc, GZip, VLenBytes, VLenUTF8, Zstd + +import zarr +from zarr.core.array import Array +from zarr.core.chunk_key_encodings import V2ChunkKeyEncoding +from zarr.core.dtype.npy.bytes import VariableLengthBytes +from zarr.core.dtype.npy.string import VariableLengthUTF8 +from zarr.storage import LocalStore + +if TYPE_CHECKING: + from zarr.core.dtype import ZDTypeLike + + +def runner_installed() -> bool: + """ + Check if a PEP-723 compliant python script runner is installed. + """ + try: + subprocess.check_output(["uv", "--version"]) + return True # noqa: TRY300 + except FileNotFoundError: + return False + + +@dataclass(kw_only=True) +class ArrayParams: + values: np.ndarray[tuple[int], np.dtype[np.generic]] + fill_value: np.generic | str | int | bytes + filters: tuple[numcodecs.abc.Codec, ...] = () + compressor: numcodecs.abc.Codec + + +basic_codecs = GZip(), Blosc(), LZ4(), LZMA(), Zstd() +basic_dtypes = "|b", ">i2", ">i4", ">f4", ">f8", "c8", "c16", "M8[10us]", "m8[4ps]" +string_dtypes = "U4" +bytes_dtypes = ">S1", "V10", " Array: + dest = tmp_path / "in" + store = LocalStore(dest) + array_params: ArrayParams = request.param + compressor = array_params.compressor + chunk_key_encoding = V2ChunkKeyEncoding(separator="/") + dtype: ZDTypeLike + if array_params.values.dtype == np.dtype("|O") and array_params.filters == (VLenUTF8(),): + dtype = VariableLengthUTF8() # type: ignore[assignment] + elif array_params.values.dtype == np.dtype("|O") and array_params.filters == (VLenBytes(),): + dtype = VariableLengthBytes() + else: + dtype = array_params.values.dtype + z = zarr.create_array( + store, + shape=array_params.values.shape, + dtype=dtype, + chunks=array_params.values.shape, + compressors=compressor, + filters=array_params.filters, + fill_value=array_params.fill_value, + order="C", + chunk_key_encoding=chunk_key_encoding, + write_data=True, + zarr_format=2, + ) + z[:] = array_params.values + return z + + +# TODO: make this dynamic based on the installed scripts +script_paths = [Path(__file__).resolve().parent / "scripts" / "v2.18.py"] + + +@pytest.mark.skipif(not runner_installed(), reason="no python script runner installed") +@pytest.mark.parametrize( + "source_array", array_cases, indirect=True, ids=tuple(map(str, array_cases)) +) +@pytest.mark.parametrize("script_path", script_paths) +def test_roundtrip(source_array: Array, tmp_path: Path, script_path: Path) -> None: + out_path = tmp_path / "out" + copy_op = subprocess.run( + [ + "uv", + "run", + script_path, + str(source_array.store).removeprefix("file://"), + str(out_path), + ], + capture_output=True, + text=True, + ) + assert copy_op.returncode == 0 + out_array = zarr.open_array(store=out_path, mode="r", zarr_format=2) + assert source_array.metadata.to_dict() == out_array.metadata.to_dict() + assert np.array_equal(source_array[:], out_array[:]) diff --git a/tests/test_store/test_core.py b/tests/test_store/test_core.py index 87d0e6e40d..e9c9319ad3 100644 --- a/tests/test_store/test_core.py +++ b/tests/test_store/test_core.py @@ -4,11 +4,38 @@ import pytest from _pytest.compat import LEGACY_PATH +import zarr from zarr import Group from zarr.core.common import AccessModeLiteral, ZarrFormat from zarr.storage import FsspecStore, LocalStore, MemoryStore, StoreLike, StorePath from zarr.storage._common import contains_array, contains_group, make_store_path -from zarr.storage._utils import _join_paths, _normalize_path_keys, _normalize_paths, normalize_path +from zarr.storage._utils import ( + _join_paths, + _normalize_path_keys, + _normalize_paths, + _relativize_path, + normalize_path, +) + + +@pytest.fixture( + params=["none", "temp_dir_str", "temp_dir_path", "store_path", "memory_store", "dict"] +) +def store_like(request): + if request.param == "none": + yield None + elif request.param == "temp_dir_str": + with tempfile.TemporaryDirectory() as temp_dir: + yield temp_dir + elif request.param == "temp_dir_path": + with tempfile.TemporaryDirectory() as temp_dir: + yield Path(temp_dir) + elif request.param == "store_path": + yield StorePath(store=MemoryStore(store_dict={}), path="/") + elif request.param == "memory_store": + yield MemoryStore(store_dict={}) + elif request.param == "dict": + yield {} @pytest.mark.parametrize("path", ["foo", "foo/bar"]) @@ -127,17 +154,6 @@ async def test_make_store_path_fsspec(monkeypatch) -> None: assert isinstance(store_path.store, FsspecStore) -@pytest.mark.parametrize( - "store_like", - [ - None, - tempfile.TemporaryDirectory().name, - Path(tempfile.TemporaryDirectory().name), - StorePath(store=MemoryStore(store_dict={}), path="/"), - MemoryStore(store_dict={}), - {}, - ], -) async def test_make_store_path_storage_options_raises(store_like: StoreLike) -> None: with pytest.raises(TypeError, match="storage_options"): await make_store_path(store_like, storage_options={"foo": "bar"}) @@ -197,7 +213,7 @@ def test_valid() -> None: Test that path normalization works as expected """ paths = ["a", "b", "c", "d", "", "//a///b//"] - assert _normalize_paths(paths) == tuple([normalize_path(p) for p in paths]) + assert _normalize_paths(paths) == tuple(normalize_path(p) for p in paths) @staticmethod @pytest.mark.parametrize("paths", [("", "/"), ("///a", "a")]) @@ -221,3 +237,34 @@ def test_normalize_path_keys(): """ data = {"a": 10, "//b": 10} assert _normalize_path_keys(data) == {normalize_path(k): v for k, v in data.items()} + + +@pytest.mark.parametrize( + ("path", "prefix", "expected"), + [ + ("a", "", "a"), + ("a/b/c", "a/b", "c"), + ("a/b/c", "a", "b/c"), + ], +) +def test_relativize_path_valid(path: str, prefix: str, expected: str) -> None: + """ + Test the normal behavior of the _relativize_path function. Prefixes should be removed from the + path argument. + """ + assert _relativize_path(path=path, prefix=prefix) == expected + + +def test_relativize_path_invalid() -> None: + path = "a/b/c" + prefix = "b" + msg = f"The first component of {path} does not start with {prefix}." + with pytest.raises(ValueError, match=msg): + _relativize_path(path="a/b/c", prefix="b") + + +def test_invalid_open_mode() -> None: + store = MemoryStore() + zarr.create((100,), store=store, zarr_format=2, path="a") + with pytest.raises(ValueError, match="Store is not read-only but mode is 'r'"): + zarr.open_array(store=store, path="a", zarr_format=2, mode="r") diff --git a/tests/test_store/test_fsspec.py b/tests/test_store/test_fsspec.py index 08cf2f286d..1a989525e3 100644 --- a/tests/test_store/test_fsspec.py +++ b/tests/test_store/test_fsspec.py @@ -3,22 +3,31 @@ import json import os import re -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any +import numpy as np import pytest from packaging.version import parse as parse_version import zarr.api.asynchronous +from zarr import Array from zarr.abc.store import OffsetByteRequest from zarr.core.buffer import Buffer, cpu, default_buffer_prototype from zarr.core.sync import _collect_aiterator, sync from zarr.storage import FsspecStore +from zarr.storage._fsspec import _make_async from zarr.testing.store import StoreTests if TYPE_CHECKING: + import pathlib from collections.abc import Generator + from pathlib import Path import botocore.client + import s3fs + + from zarr.core.common import JSON + # Warning filter due to https://github.com/boto/boto3/issues/3889 pytestmark = [ @@ -109,10 +118,13 @@ async def test_basic() -> None: data = b"hello" await store.set("foo", cpu.Buffer.from_bytes(data)) assert await store.exists("foo") - assert (await store.get("foo", prototype=default_buffer_prototype())).to_bytes() == data + buf = await store.get("foo", prototype=default_buffer_prototype()) + assert buf is not None + assert buf.to_bytes() == data out = await store.get_partial_values( prototype=default_buffer_prototype(), key_ranges=[("foo", OffsetByteRequest(1))] ) + assert out[0] is not None assert out[0].to_bytes() == data[1:] @@ -121,7 +133,7 @@ class TestFsspecStoreS3(StoreTests[FsspecStore, cpu.Buffer]): buffer_cls = cpu.Buffer @pytest.fixture - def store_kwargs(self, request) -> dict[str, str | bool]: + def store_kwargs(self) -> dict[str, str | bool]: try: from fsspec import url_to_fs except ImportError: @@ -133,7 +145,7 @@ def store_kwargs(self, request) -> dict[str, str | bool]: return {"fs": fs, "path": path} @pytest.fixture - def store(self, store_kwargs: dict[str, str | bool]) -> FsspecStore: + async def store(self, store_kwargs: dict[str, Any]) -> FsspecStore: return self.store_cls(**store_kwargs) async def get(self, store: FsspecStore, key: str) -> Buffer: @@ -168,7 +180,11 @@ async def test_fsspec_store_from_uri(self, store: FsspecStore) -> None: "anon": False, } - meta = {"attributes": {"key": "value"}, "zarr_format": 3, "node_type": "group"} + meta: dict[str, JSON] = { + "attributes": {"key": "value"}, + "zarr_format": 3, + "node_type": "group", + } await store.set( "zarr.json", @@ -179,7 +195,11 @@ async def test_fsspec_store_from_uri(self, store: FsspecStore) -> None: ) assert dict(group.attrs) == {"key": "value"} - meta["attributes"]["key"] = "value-2" + meta = { + "attributes": {"key": "value-2"}, + "zarr_format": 3, + "node_type": "group", + } await store.set( "directory-2/zarr.json", self.buffer_cls.from_bytes(json.dumps(meta).encode()), @@ -189,7 +209,11 @@ async def test_fsspec_store_from_uri(self, store: FsspecStore) -> None: ) assert dict(group.attrs) == {"key": "value-2"} - meta["attributes"]["key"] = "value-3" + meta = { + "attributes": {"key": "value-3"}, + "zarr_format": 3, + "node_type": "group", + } await store.set( "directory-3/zarr.json", self.buffer_cls.from_bytes(json.dumps(meta).encode()), @@ -216,7 +240,7 @@ def test_from_upath(self) -> None: assert result.fs.asynchronous assert result.path == f"{test_bucket_name}/foo/bar" - def test_init_raises_if_path_has_scheme(self, store_kwargs) -> None: + def test_init_raises_if_path_has_scheme(self, store_kwargs: dict[str, Any]) -> None: # regression test for https://github.com/zarr-developers/zarr-python/issues/2342 store_kwargs["path"] = "s3://" + store_kwargs["path"] with pytest.raises( @@ -237,7 +261,7 @@ def test_init_warns_if_fs_asynchronous_is_false(self) -> None: with pytest.warns(UserWarning, match=r".* was not created with `asynchronous=True`.*"): self.store_cls(**store_kwargs) - async def test_empty_nonexistent_path(self, store_kwargs) -> None: + async def test_empty_nonexistent_path(self, store_kwargs: dict[str, Any]) -> None: # regression test for https://github.com/zarr-developers/zarr-python/pull/2343 store_kwargs["path"] += "/abc" store = await self.store_cls.open(**store_kwargs) @@ -252,44 +276,143 @@ async def test_delete_dir_unsupported_deletes(self, store: FsspecStore) -> None: await store.delete_dir("test_prefix") +def array_roundtrip(store: FsspecStore) -> None: + """ + Round trip an array using a Zarr store + + Args: + store: FsspecStore + """ + data = np.ones((3, 3)) + arr = zarr.create_array(store=store, overwrite=True, data=data) + assert isinstance(arr, Array) + # Read set values + arr2 = zarr.open_array(store=store) + assert isinstance(arr2, Array) + np.testing.assert_array_equal(arr[:], data) + + @pytest.mark.skipif( parse_version(fsspec.__version__) < parse_version("2024.12.0"), reason="No AsyncFileSystemWrapper", ) -def test_wrap_sync_filesystem(): +def test_wrap_sync_filesystem(tmp_path: pathlib.Path) -> None: """The local fs is not async so we should expect it to be wrapped automatically""" from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper - store = FsspecStore.from_url("local://test/path") - + store = FsspecStore.from_url(f"file://{tmp_path}", storage_options={"auto_mkdir": True}) assert isinstance(store.fs, AsyncFileSystemWrapper) assert store.fs.async_impl + array_roundtrip(store) + + +@pytest.mark.skipif( + parse_version(fsspec.__version__) >= parse_version("2024.12.0"), + reason="No AsyncFileSystemWrapper", +) +def test_wrap_sync_filesystem_raises(tmp_path: pathlib.Path) -> None: + """The local fs is not async so we should expect it to be wrapped automatically""" + with pytest.raises(ImportError, match="The filesystem .*"): + FsspecStore.from_url(f"file://{tmp_path}", storage_options={"auto_mkdir": True}) @pytest.mark.skipif( parse_version(fsspec.__version__) < parse_version("2024.12.0"), reason="No AsyncFileSystemWrapper", ) -def test_no_wrap_async_filesystem(): - """An async fs should not be wrapped automatically; fsspec's https filesystem is such an fs""" +def test_no_wrap_async_filesystem() -> None: + """An async fs should not be wrapped automatically; fsspec's s3 filesystem is such an fs""" from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper - store = FsspecStore.from_url("https://test/path") - + store = FsspecStore.from_url( + f"s3://{test_bucket_name}/foo/spam/", + storage_options={"endpoint_url": endpoint_url, "anon": False, "asynchronous": True}, + read_only=False, + ) assert not isinstance(store.fs, AsyncFileSystemWrapper) assert store.fs.async_impl + array_roundtrip(store) + + +@pytest.mark.skipif( + parse_version(fsspec.__version__) < parse_version("2024.12.0"), + reason="No AsyncFileSystemWrapper", +) +def test_open_fsmap_file(tmp_path: pathlib.Path) -> None: + min_fsspec_with_async_wrapper = parse_version("2024.12.0") + current_version = parse_version(fsspec.__version__) + + fs = fsspec.filesystem("file", auto_mkdir=True) + mapper = fs.get_mapper(tmp_path) + + if current_version < min_fsspec_with_async_wrapper: + # Expect ImportError for older versions + with pytest.raises( + ImportError, + match=r"The filesystem .* is synchronous, and the required AsyncFileSystemWrapper is not available.*", + ): + array_roundtrip(mapper) + else: + # Newer versions should work + array_roundtrip(mapper) + + +@pytest.mark.skipif( + parse_version(fsspec.__version__) < parse_version("2024.12.0"), + reason="No AsyncFileSystemWrapper", +) +def test_open_fsmap_file_raises(tmp_path: pathlib.Path) -> None: + fsspec = pytest.importorskip("fsspec.implementations.local") + fs = fsspec.LocalFileSystem(auto_mkdir=False) + mapper = fs.get_mapper(tmp_path) + with pytest.raises(ValueError, match="LocalFilesystem .*"): + array_roundtrip(mapper) + + +@pytest.mark.parametrize("asynchronous", [True, False]) +def test_open_fsmap_s3(asynchronous: bool) -> None: + s3_filesystem = s3fs.S3FileSystem( + asynchronous=asynchronous, endpoint_url=endpoint_url, anon=False + ) + mapper = s3_filesystem.get_mapper(f"s3://{test_bucket_name}/map/foo/") + array_roundtrip(mapper) + + +def test_open_s3map_raises() -> None: + with pytest.raises(TypeError, match="Unsupported type for store_like:.*"): + zarr.open(store=0, mode="w", shape=(3, 3)) + s3_filesystem = s3fs.S3FileSystem(asynchronous=True, endpoint_url=endpoint_url, anon=False) + mapper = s3_filesystem.get_mapper(f"s3://{test_bucket_name}/map/foo/") + with pytest.raises( + ValueError, match="'path' was provided but is not used for FSMap store_like objects" + ): + zarr.open(store=mapper, path="bar", mode="w", shape=(3, 3)) + with pytest.raises( + ValueError, + match="'storage_options was provided but is not used for FSMap store_like objects", + ): + zarr.open(store=mapper, storage_options={"anon": True}, mode="w", shape=(3, 3)) + + +@pytest.mark.parametrize("asynchronous", [True, False]) +def test_make_async(asynchronous: bool) -> None: + s3_filesystem = s3fs.S3FileSystem( + asynchronous=asynchronous, endpoint_url=endpoint_url, anon=False + ) + fs = _make_async(s3_filesystem) + assert fs.asynchronous @pytest.mark.skipif( parse_version(fsspec.__version__) < parse_version("2024.12.0"), reason="No AsyncFileSystemWrapper", ) -async def test_delete_dir_wrapped_filesystem(tmpdir) -> None: +async def test_delete_dir_wrapped_filesystem(tmp_path: Path) -> None: from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper from fsspec.implementations.local import LocalFileSystem wrapped_fs = AsyncFileSystemWrapper(LocalFileSystem(auto_mkdir=True)) - store = FsspecStore(wrapped_fs, read_only=False, path=f"{tmpdir}/test/path") + store = FsspecStore(wrapped_fs, read_only=False, path=f"{tmp_path}/test/path") assert isinstance(store.fs, AsyncFileSystemWrapper) assert store.fs.asynchronous diff --git a/tests/test_store/test_local.py b/tests/test_store/test_local.py index d9d941c6f0..7974d0d633 100644 --- a/tests/test_store/test_local.py +++ b/tests/test_store/test_local.py @@ -1,18 +1,18 @@ from __future__ import annotations -from typing import TYPE_CHECKING +import pathlib +import re +import numpy as np import pytest import zarr +from zarr import create_array from zarr.core.buffer import Buffer, cpu from zarr.storage import LocalStore from zarr.testing.store import StoreTests from zarr.testing.utils import assert_bytes_equal -if TYPE_CHECKING: - import pathlib - class TestLocalStore(StoreTests[LocalStore, cpu.Buffer]): store_cls = LocalStore @@ -28,7 +28,7 @@ async def set(self, store: LocalStore, key: str, value: Buffer) -> None: (store.root / key).write_bytes(value.to_bytes()) @pytest.fixture - def store_kwargs(self, tmpdir) -> dict[str, str]: + def store_kwargs(self, tmpdir: str) -> dict[str, str]: return {"root": str(tmpdir)} def test_store_repr(self, store: LocalStore) -> None: @@ -48,14 +48,14 @@ async def test_empty_with_empty_subdir(self, store: LocalStore) -> None: (store.root / "foo/bar").mkdir(parents=True) assert await store.is_empty("") - def test_creates_new_directory(self, tmp_path: pathlib.Path): + def test_creates_new_directory(self, tmp_path: pathlib.Path) -> None: target = tmp_path.joinpath("a", "b", "c") assert not target.exists() store = self.store_cls(root=target) zarr.group(store=store) - def test_invalid_root_raises(self): + def test_invalid_root_raises(self) -> None: """ Test that a TypeError is raised when a non-str/Path type is used for the `root` argument """ @@ -63,9 +63,9 @@ def test_invalid_root_raises(self): TypeError, match=r"'root' must be a string or Path instance. Got an instance of instead.", ): - LocalStore(root=0) + LocalStore(root=0) # type: ignore[arg-type] - async def test_get_with_prototype_default(self, store: LocalStore): + async def test_get_with_prototype_default(self, store: LocalStore) -> None: """ Ensure that data can be read via ``store.get`` if the prototype keyword argument is unspecified, i.e. set to ``None``. """ @@ -74,3 +74,38 @@ async def test_get_with_prototype_default(self, store: LocalStore): await self.set(store, key, data_buf) observed = await store.get(key, prototype=None) assert_bytes_equal(observed, data_buf) + + @pytest.mark.parametrize("ndim", [0, 1, 3]) + @pytest.mark.parametrize( + "destination", ["destination", "foo/bar/destintion", pathlib.Path("foo/bar/destintion")] + ) + async def test_move( + self, tmp_path: pathlib.Path, ndim: int, destination: pathlib.Path | str + ) -> None: + origin = tmp_path / "origin" + if isinstance(destination, str): + destination = str(tmp_path / destination) + else: + destination = tmp_path / destination + + print(type(destination)) + store = await LocalStore.open(root=origin) + shape = (4,) * ndim + chunks = (2,) * ndim + data = np.arange(4**ndim) + if ndim > 0: + data = data.reshape(*shape) + array = create_array(store, data=data, chunks=chunks or "auto") + + await store.move(destination) + + assert store.root == pathlib.Path(destination) + assert pathlib.Path(destination).exists() + assert not origin.exists() + assert np.array_equal(array[...], data) + + store2 = await LocalStore.open(root=origin) + with pytest.raises( + FileExistsError, match=re.escape(f"Destination root {destination} already exists") + ): + await store2.move(destination) diff --git a/tests/test_store/test_memory.py b/tests/test_store/test_memory.py index e520c7d054..4fc3f6e698 100644 --- a/tests/test_store/test_memory.py +++ b/tests/test_store/test_memory.py @@ -1,9 +1,10 @@ from __future__ import annotations import re -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import numpy as np +import numpy.typing as npt import pytest import zarr @@ -31,16 +32,16 @@ async def get(self, store: MemoryStore, key: str) -> Buffer: return store._store_dict[key] @pytest.fixture(params=[None, True]) - def store_kwargs( - self, request: pytest.FixtureRequest - ) -> dict[str, str | dict[str, Buffer] | None]: - kwargs = {"store_dict": None} + def store_kwargs(self, request: pytest.FixtureRequest) -> dict[str, Any]: + kwargs: dict[str, Any] if request.param is True: - kwargs["store_dict"] = {} + kwargs = {"store_dict": {}} + else: + kwargs = {"store_dict": None} return kwargs @pytest.fixture - def store(self, store_kwargs: str | dict[str, Buffer] | None) -> MemoryStore: + async def store(self, store_kwargs: dict[str, Any]) -> MemoryStore: return self.store_cls(**store_kwargs) def test_store_repr(self, store: MemoryStore) -> None: @@ -55,13 +56,13 @@ def test_store_supports_listing(self, store: MemoryStore) -> None: def test_store_supports_partial_writes(self, store: MemoryStore) -> None: assert store.supports_partial_writes - def test_list_prefix(self, store: MemoryStore) -> None: + async def test_list_prefix(self, store: MemoryStore) -> None: assert True @pytest.mark.parametrize("dtype", ["uint8", "float32", "int64"]) @pytest.mark.parametrize("zarr_format", [2, 3]) async def test_deterministic_size( - self, store: MemoryStore, dtype, zarr_format: ZarrFormat + self, store: MemoryStore, dtype: npt.DTypeLike, zarr_format: ZarrFormat ) -> None: a = zarr.empty( store=store, @@ -85,23 +86,23 @@ class TestGpuMemoryStore(StoreTests[GpuMemoryStore, gpu.Buffer]): store_cls = GpuMemoryStore buffer_cls = gpu.Buffer - async def set(self, store: GpuMemoryStore, key: str, value: Buffer) -> None: + async def set(self, store: GpuMemoryStore, key: str, value: gpu.Buffer) -> None: # type: ignore[override] store._store_dict[key] = value async def get(self, store: MemoryStore, key: str) -> Buffer: return store._store_dict[key] @pytest.fixture(params=[None, True]) - def store_kwargs( - self, request: pytest.FixtureRequest - ) -> dict[str, str | dict[str, Buffer] | None]: - kwargs = {"store_dict": None} + def store_kwargs(self, request: pytest.FixtureRequest) -> dict[str, Any]: + kwargs: dict[str, Any] if request.param is True: - kwargs["store_dict"] = {} + kwargs = {"store_dict": {}} + else: + kwargs = {"store_dict": None} return kwargs @pytest.fixture - def store(self, store_kwargs: str | dict[str, gpu.Buffer] | None) -> GpuMemoryStore: + async def store(self, store_kwargs: dict[str, Any]) -> GpuMemoryStore: return self.store_cls(**store_kwargs) def test_store_repr(self, store: GpuMemoryStore) -> None: @@ -116,15 +117,15 @@ def test_store_supports_listing(self, store: GpuMemoryStore) -> None: def test_store_supports_partial_writes(self, store: GpuMemoryStore) -> None: assert store.supports_partial_writes - def test_list_prefix(self, store: GpuMemoryStore) -> None: + async def test_list_prefix(self, store: GpuMemoryStore) -> None: assert True def test_dict_reference(self, store: GpuMemoryStore) -> None: - store_dict = {} + store_dict: dict[str, Any] = {} result = GpuMemoryStore(store_dict=store_dict) assert result._store_dict is store_dict - def test_from_dict(self): + def test_from_dict(self) -> None: d = { "a": gpu.Buffer.from_bytes(b"aaaa"), "b": cpu.Buffer.from_bytes(b"bbbb"), diff --git a/tests/test_store/test_object.py b/tests/test_store/test_object.py index 943564abc8..4d9e8fcc1f 100644 --- a/tests/test_store/test_object.py +++ b/tests/test_store/test_object.py @@ -4,7 +4,7 @@ import pytest obstore = pytest.importorskip("obstore") -import pytest + from hypothesis.stateful import ( run_state_machine_as_test, ) diff --git a/tests/test_store/test_stateful.py b/tests/test_store/test_stateful.py index a17d7a55be..c0997c3df3 100644 --- a/tests/test_store/test_stateful.py +++ b/tests/test_store/test_stateful.py @@ -15,6 +15,7 @@ ] +@pytest.mark.filterwarnings("ignore::zarr.core.dtype.common.UnstableSpecificationWarning") def test_zarr_hierarchy(sync_store: Store): def mk_test_instance_sync() -> ZarrHierarchyStateMachine: return ZarrHierarchyStateMachine(sync_store) diff --git a/tests/test_store/test_zip.py b/tests/test_store/test_zip.py index 0237258ab1..24b25ed315 100644 --- a/tests/test_store/test_zip.py +++ b/tests/test_store/test_zip.py @@ -10,7 +10,9 @@ import pytest import zarr +from zarr import create_array from zarr.core.buffer import Buffer, cpu, default_buffer_prototype +from zarr.core.group import Group from zarr.storage import ZipStore from zarr.testing.store import StoreTests @@ -32,7 +34,7 @@ class TestZipStore(StoreTests[ZipStore, cpu.Buffer]): buffer_cls = cpu.Buffer @pytest.fixture - def store_kwargs(self, request) -> dict[str, str | bool]: + def store_kwargs(self) -> dict[str, str | bool]: fd, temp_path = tempfile.mkstemp() os.close(fd) os.unlink(temp_path) @@ -40,12 +42,14 @@ def store_kwargs(self, request) -> dict[str, str | bool]: return {"path": temp_path, "mode": "w", "read_only": False} async def get(self, store: ZipStore, key: str) -> Buffer: - return store._get(key, prototype=default_buffer_prototype()) + buf = store._get(key, prototype=default_buffer_prototype()) + assert buf is not None + return buf async def set(self, store: ZipStore, key: str, value: Buffer) -> None: return store._set(key, value) - def test_store_read_only(self, store: ZipStore, store_kwargs: dict[str, Any]) -> None: + def test_store_read_only(self, store: ZipStore) -> None: assert not store.read_only async def test_read_only_store_raises(self, store_kwargs: dict[str, Any]) -> None: @@ -109,7 +113,7 @@ def test_api_integration(self, store: ZipStore) -> None: async def test_store_open_read_only( self, store_kwargs: dict[str, Any], read_only: bool ) -> None: - if read_only == "r": + if read_only: # create an empty zipfile with zipfile.ZipFile(store_kwargs["path"], mode="w"): pass @@ -129,9 +133,25 @@ def test_externally_zipped_store(self, tmp_path: Path) -> None: zarr_path = tmp_path / "foo.zarr" root = zarr.open_group(store=zarr_path, mode="w") root.require_group("foo") - root["foo"]["bar"] = np.array([1]) - shutil.make_archive(zarr_path, "zip", zarr_path) + assert isinstance(foo := root["foo"], Group) # noqa: RUF018 + foo["bar"] = np.array([1]) + shutil.make_archive(str(zarr_path), "zip", zarr_path) zip_path = tmp_path / "foo.zarr.zip" zipped = zarr.open_group(ZipStore(zip_path, mode="r"), mode="r") assert list(zipped.keys()) == list(root.keys()) - assert list(zipped["foo"].keys()) == list(root["foo"].keys()) + assert isinstance(group := zipped["foo"], Group) + assert list(group.keys()) == list(group.keys()) + + async def test_move(self, tmp_path: Path) -> None: + origin = tmp_path / "origin.zip" + destination = tmp_path / "some_folder" / "destination.zip" + + store = await ZipStore.open(path=origin, mode="a") + array = create_array(store, data=np.arange(10)) + + await store.move(str(destination)) + + assert store.path == destination + assert destination.exists() + assert not origin.exists() + assert np.array_equal(array[...], np.arange(10)) diff --git a/tests/test_strings.py b/tests/test_strings.py deleted file mode 100644 index dca0570a25..0000000000 --- a/tests/test_strings.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Tests for the strings module.""" - -import numpy as np -import pytest - -from zarr.core.strings import _NUMPY_SUPPORTS_VLEN_STRING, _STRING_DTYPE, cast_to_string_dtype - - -def test_string_defaults() -> None: - if _NUMPY_SUPPORTS_VLEN_STRING: - assert _STRING_DTYPE == np.dtypes.StringDType() - else: - assert _STRING_DTYPE == np.dtypes.ObjectDType() - - -def test_cast_to_string_dtype() -> None: - d1 = np.array(["a", "b", "c"]) - assert d1.dtype == np.dtype(" Iterator[StorePath]: +async def store() -> StorePath: return StorePath(await MemoryStore.open()) @@ -41,33 +45,6 @@ def test_simple(store: StorePath) -> None: assert np.array_equal(data, a[:, :]) -@pytest.mark.parametrize("store", ["memory"], indirect=True) -@pytest.mark.parametrize( - ("dtype", "fill_value"), - [ - ("bool", False), - ("int64", 0), - ("float64", 0.0), - ("|S1", b""), - ("|U1", ""), - ("object", ""), - (str, ""), - ], -) -def test_implicit_fill_value(store: MemoryStore, dtype: str, fill_value: Any) -> None: - arr = zarr.create(store=store, shape=(4,), fill_value=None, zarr_format=2, dtype=dtype) - assert arr.metadata.fill_value is None - assert arr.metadata.to_dict()["fill_value"] is None - result = arr[:] - if dtype is str: - # special case - numpy_dtype = np.dtype(object) - else: - numpy_dtype = np.dtype(dtype) - expected = np.full(arr.shape, fill_value, dtype=numpy_dtype) - np.testing.assert_array_equal(result, expected) - - def test_codec_pipeline() -> None: # https://github.com/zarr-developers/zarr-python/issues/2243 store = MemoryStore() @@ -86,14 +63,16 @@ def test_codec_pipeline() -> None: @pytest.mark.parametrize( - ("dtype", "expected_dtype", "fill_value", "fill_value_encoding"), + ("dtype", "expected_dtype", "fill_value", "fill_value_json"), [ - ("|S", "|S0", b"X", "WA=="), - ("|V", "|V0", b"X", "WA=="), + ("|S1", "|S1", b"X", "WA=="), + ("|V1", "|V1", b"X", "WA=="), ("|V10", "|V10", b"X", "WAAAAAAAAAAAAA=="), ], ) -async def test_v2_encode_decode(dtype, expected_dtype, fill_value, fill_value_encoding) -> None: +async def test_v2_encode_decode( + dtype: str, expected_dtype: str, fill_value: bytes, fill_value_json: str +) -> None: with config.set( { "array.v2_default_filters.bytes": [{"id": "vlen-bytes"}], @@ -114,8 +93,8 @@ async def test_v2_encode_decode(dtype, expected_dtype, fill_value, fill_value_en "chunks": [3], "compressor": None, "dtype": expected_dtype, - "fill_value": fill_value_encoding, - "filters": [{"id": "vlen-bytes"}] if dtype == "|S" else None, + "fill_value": fill_value_json, + "filters": None, "order": "C", "shape": [3], "zarr_format": 2, @@ -124,41 +103,27 @@ async def test_v2_encode_decode(dtype, expected_dtype, fill_value, fill_value_en assert serialized == expected data = zarr.open_array(store=store, path="foo")[:] - expected = np.full((3,), b"X", dtype=dtype) - np.testing.assert_equal(data, expected) + np.testing.assert_equal(data, np.full((3,), b"X", dtype=dtype)) -@pytest.mark.parametrize("dtype_value", [["|S", b"Y"], ["|U", "Y"], ["O", b"Y"]]) -def test_v2_encode_decode_with_data(dtype_value): - dtype, value = dtype_value - with config.set( - { - "array.v2_default_filters": { - "string": [{"id": "vlen-utf8"}], - "bytes": [{"id": "vlen-bytes"}], - }, - } - ): - expected = np.full((3,), value, dtype=dtype) - a = zarr.create( - shape=(3,), - zarr_format=2, - dtype=dtype, - ) - a[:] = expected - data = a[:] - np.testing.assert_equal(data, expected) - - -@pytest.mark.parametrize("dtype", [str, "str"]) -async def test_create_dtype_str(dtype: Any) -> None: - arr = zarr.create(shape=3, dtype=dtype, zarr_format=2) - assert arr.dtype.kind == "O" - assert arr.metadata.to_dict()["dtype"] == "|O" - assert arr.metadata.filters == (numcodecs.vlen.VLenBytes(),) - arr[:] = [b"a", b"bb", b"ccc"] - result = arr[:] - np.testing.assert_array_equal(result, np.array([b"a", b"bb", b"ccc"], dtype="object")) +@pytest.mark.parametrize( + ("dtype", "value"), + [ + (NullTerminatedBytes(length=1), b"Y"), + (FixedLengthUTF32(length=1), "Y"), + (VariableLengthUTF8(), "Y"), + ], +) +def test_v2_encode_decode_with_data(dtype: ZDType[Any, Any], value: str) -> None: + expected = np.full((3,), value, dtype=dtype.to_native_dtype()) + a = zarr.create( + shape=(3,), + zarr_format=2, + dtype=dtype, + ) + a[:] = expected + data = a[:] + np.testing.assert_equal(data, expected) @pytest.mark.parametrize("filters", [[], [numcodecs.Delta(dtype=" None: @pytest.mark.filterwarnings("ignore") @pytest.mark.parametrize("store", ["memory"], indirect=True) -def test_create_array_defaults(store: Store): +def test_create_array_defaults(store: Store) -> None: """ Test that passing compressor=None results in no compressor. Also test that the default value of the compressor parameter does produce a compressor. """ g = zarr.open(store, mode="w", zarr_format=2) + assert isinstance(g, Group) arr = g.create_array("one", dtype="i8", shape=(1,), chunks=(1,), compressor=None) assert arr._async_array.compressor is None assert not (arr.filters) @@ -195,12 +161,13 @@ def test_create_array_defaults(store: Store): ) -@pytest.mark.parametrize("array_order", ["C", "F"]) -@pytest.mark.parametrize("data_order", ["C", "F"]) -@pytest.mark.parametrize("memory_order", ["C", "F"]) -def test_v2_non_contiguous( - array_order: Literal["C", "F"], data_order: Literal["C", "F"], memory_order: Literal["C", "F"] -) -> None: +@pytest.mark.parametrize("numpy_order", ["C", "F"]) +@pytest.mark.parametrize("zarr_order", ["C", "F"]) +def test_v2_non_contiguous(numpy_order: Literal["C", "F"], zarr_order: Literal["C", "F"]) -> None: + """ + Make sure zarr v2 arrays save data using the memory order given to the zarr array, + not the memory order of the original numpy array. + """ store = MemoryStore() arr = zarr.create_array( store, @@ -212,26 +179,29 @@ def test_v2_non_contiguous( filters=None, compressors=None, overwrite=True, - order=array_order, - config={"order": memory_order}, + order=zarr_order, ) - # Non-contiguous write - a = np.arange(arr.shape[0] * arr.shape[1]).reshape(arr.shape, order=data_order) + # Non-contiguous write, using numpy memory order + a = np.arange(arr.shape[0] * arr.shape[1]).reshape(arr.shape, order=numpy_order) arr[6:9, 3:6] = a[6:9, 3:6] # The slice on the RHS is important np.testing.assert_array_equal(arr[6:9, 3:6], a[6:9, 3:6]) + buf = sync(store.get("2.1", default_buffer_prototype())) + assert buf is not None np.testing.assert_array_equal( a[6:9, 3:6], - np.frombuffer( - sync(store.get("2.1", default_buffer_prototype())).to_bytes(), dtype="float64" - ).reshape((3, 3), order=array_order), + np.frombuffer(buf.to_bytes(), dtype="float64").reshape((3, 3), order=zarr_order), ) - if memory_order == "F": - assert (arr[6:9, 3:6]).flags.f_contiguous + # After writing and reading from zarr array, order should be same as zarr order + sub_arr = arr[6:9, 3:6] + assert isinstance(sub_arr, np.ndarray) + if zarr_order == "F": + assert (sub_arr).flags.f_contiguous else: - assert (arr[6:9, 3:6]).flags.c_contiguous + assert (sub_arr).flags.c_contiguous + # Contiguous write store = MemoryStore() arr = zarr.create_array( store, @@ -243,59 +213,28 @@ def test_v2_non_contiguous( compressors=None, filters=None, overwrite=True, - order=array_order, - config={"order": memory_order}, + order=zarr_order, ) - # Contiguous write - a = np.arange(9).reshape((3, 3), order=data_order) - if data_order == "F": - assert a.flags.f_contiguous - else: - assert a.flags.c_contiguous + a = np.arange(9).reshape((3, 3), order=numpy_order) arr[6:9, 3:6] = a np.testing.assert_array_equal(arr[6:9, 3:6], a) + # After writing and reading from zarr array, order should be same as zarr order + sub_arr = arr[6:9, 3:6] + assert isinstance(sub_arr, np.ndarray) + if zarr_order == "F": + assert (sub_arr).flags.f_contiguous + else: + assert (sub_arr).flags.c_contiguous -def test_default_compressor_deprecation_warning(): +def test_default_compressor_deprecation_warning() -> None: with pytest.warns(DeprecationWarning, match="default_compressor is deprecated"): - zarr.storage.default_compressor = "zarr.codecs.zstd.ZstdCodec()" - - -@pytest.mark.parametrize( - "dtype_expected", - [ - ["b", "zstd", None], - ["i", "zstd", None], - ["f", "zstd", None], - ["|S1", "zstd", "vlen-bytes"], - ["|U1", "zstd", "vlen-utf8"], - ], -) -def test_default_filters_and_compressor(dtype_expected: Any) -> None: - with config.set( - { - "array.v2_default_compressor": { - "numeric": {"id": "zstd", "level": "0"}, - "string": {"id": "zstd", "level": "0"}, - "bytes": {"id": "zstd", "level": "0"}, - }, - "array.v2_default_filters": { - "numeric": [], - "string": [{"id": "vlen-utf8"}], - "bytes": [{"id": "vlen-bytes"}], - }, - } - ): - dtype, expected_compressor, expected_filter = dtype_expected - arr = zarr.create(shape=(3,), path="foo", store={}, zarr_format=2, dtype=dtype) - assert arr.metadata.compressor.codec_id == expected_compressor - if expected_filter is not None: - assert arr.metadata.filters[0].codec_id == expected_filter + zarr.storage.default_compressor = "zarr.codecs.zstd.ZstdCodec()" # type: ignore[attr-defined] @pytest.mark.parametrize("fill_value", [None, (b"", 0, 0.0)], ids=["no_fill", "fill"]) -def test_structured_dtype_roundtrip(fill_value, tmp_path) -> None: +def test_structured_dtype_roundtrip(fill_value: float | bytes, tmp_path: Path) -> None: a = np.array( [(b"aaa", 1, 4.2), (b"bbb", 2, 8.4), (b"ccc", 3, 12.6)], dtype=[("foo", "S3"), ("bar", "i4"), ("baz", "f8")], @@ -338,35 +277,18 @@ def test_structured_dtype_roundtrip(fill_value, tmp_path) -> None: np.dtype([("x", "i4"), ("y", "i4")]), np.array([(1, 2)], dtype=[("x", "i4"), ("y", "i4")])[0], ), - ( - "BQAAAA==", - np.dtype([("val", "i4")]), - np.array([(5,)], dtype=[("val", "i4")])[0], - ), - ( - {"x": 1, "y": 2}, - np.dtype([("location", "O")]), - np.array([({"x": 1, "y": 2},)], dtype=[("location", "O")])[0], - ), - ( - {"x": 1, "y": 2, "z": 3}, - np.dtype([("location", "O")]), - np.array([({"x": 1, "y": 2, "z": 3},)], dtype=[("location", "O")])[0], - ), ], ids=[ "tuple_input", "list_input", "bytes_input", - "string_input", - "dictionary_input", - "dictionary_input_extra_fields", ], ) def test_parse_structured_fill_value_valid( fill_value: Any, dtype: np.dtype[Any], expected_result: Any ) -> None: - result = _parse_structured_fill_value(fill_value, dtype) + zdtype = Structured.from_native_dtype(dtype) + result = zdtype.cast_scalar(fill_value) assert result.dtype == expected_result.dtype assert result == expected_result if isinstance(expected_result, np.void): @@ -374,33 +296,8 @@ def test_parse_structured_fill_value_valid( assert result[name] == expected_result[name] -@pytest.mark.parametrize( - ( - "fill_value", - "dtype", - ), - [ - (("Alice", 30), np.dtype([("name", "U10"), ("age", "i4"), ("city", "U20")])), - (b"\x01\x00\x00\x00", np.dtype([("x", "i4"), ("y", "i4")])), - ("this_is_not_base64", np.dtype([("val", "i4")])), - ("hello", np.dtype([("age", "i4")])), - ({"x": 1, "y": 2}, np.dtype([("location", "i4")])), - ], - ids=[ - "tuple_list_wrong_length", - "bytes_wrong_length", - "invalid_base64", - "wrong_data_type", - "wrong_dictionary", - ], -) -def test_parse_structured_fill_value_invalid(fill_value: Any, dtype: np.dtype[Any]) -> None: - with pytest.raises(ValueError): - _parse_structured_fill_value(fill_value, dtype) - - @pytest.mark.parametrize("fill_value", [None, b"x"], ids=["no_fill", "fill"]) -def test_other_dtype_roundtrip(fill_value, tmp_path) -> None: +def test_other_dtype_roundtrip(fill_value: None | bytes, tmp_path: Path) -> None: a = np.array([b"a\0\0", b"bb", b"ccc"], dtype="V7") array_path = tmp_path / "data.zarr" za = zarr.create( diff --git a/tests/test_zarr.py b/tests/test_zarr.py index 2aa62e4231..f49873132e 100644 --- a/tests/test_zarr.py +++ b/tests/test_zarr.py @@ -1,3 +1,5 @@ +import pytest + import zarr @@ -9,3 +11,19 @@ def test_exports() -> None: for export in __all__: getattr(zarr, export) + + +def test_print_debug_info(capsys: pytest.CaptureFixture[str]) -> None: + """ + Ensure that print_debug_info does not raise an error + """ + from importlib.metadata import version + + from zarr import __version__, print_debug_info + + print_debug_info() + captured = capsys.readouterr() + # test that at least some of what we expect is + # printed out + assert f"zarr: {__version__}" in captured.out + assert f"numpy: {version('numpy')}" in captured.out