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"content": "<p>Here's an ~ official ~ release announcement for <a href=\"https://neuromatch.social/tags/numpydantic\" class=\"mention hashtag\" rel=\"tag\">#<span>numpydantic</span></a></p><p>repo: <a href=\"https://github.com/p2p-ld/numpydantic\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">github.com/p2p-ld/numpydantic</span><span class=\"invisible\"></span></a><br>docs: <a href=\"https://numpydantic.readthedocs.io\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">numpydantic.readthedocs.io</span><span class=\"invisible\"></span></a></p><p>Problems: <span class=\"h-card\" translate=\"no\"><a href=\"https://fosstodon.org/@pydantic\" class=\"u-url mention\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">@<span>pydantic</span></a></span> is great for modeling data!! but at the moment it doesn't support array data out of the box. Often array shape and dtype are as important as whether something is an array at all, but there isn't a good way to specify and validate that with the Python type system. Many data formats and standards couple their implementation very tightly with their schema, making them less flexible, less interoperable, and more difficult to maintain than they could be. The existing tools for parameterized array types like nptyping and jaxtyping tie their annotations to a specific array library, rather than allowing array specifications that can be abstract across implementations.</p><p><code>numpydantic</code> is a super small, few-dep, and well-tested package that provides generic array annotations for pydantic models. Specify an array along with its shape and dtype and then use that model with any array library you'd like! Extending support for new array libraries is just subclassing - no PRs or monkeypatching needed. The type has some magic under the hood that uses pydantic validators to give a uniform array interface to things that don't usually behave like arrays - pass a path to a video file, that's an array. pass a path to an HDF5 file and a nested array within it, that's an array. We take advantage of the rest of pydantic's features too, including generating rich JSON schema and smart array dumping.</p><p>This is a standalone part of my work with <span class=\"h-card\" translate=\"no\"><a href=\"https://fosstodon.org/@linkml\" class=\"u-url mention\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">@<span>linkml</span></a></span> <a href=\"https://linkml.io/linkml/schemas/arrays.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">arrays</a> and rearchitecting neurobio data formats like NWB to be dead simple to use and extend, integrating with the tools you already use and across the experimental process - specify your data in a simple <code>yaml</code> format, and get back high quality data modeling code that is standards-compliant out of the box and can be used with arbitrary backends. One step towards the wild exuberance of FAIR data that is just as comfortable in the scattered scripts of real experimental work as it is in carefully curated archives and high performance computing clusters. Longer term I'm trying to abstract away data store implementations to bring content-addressed p2p data stores right into the python interpreter as simply as if something was born in local memory. </p><p>plenty of <a href=\"https://numpydantic.readthedocs.io/en/latest/todo.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">todos</a>, but hope ya like it.</p><p><a href=\"https://neuromatch.social/tags/linkml\" class=\"mention hashtag\" rel=\"tag\">#<span>linkml</span></a> <a href=\"https://neuromatch.social/tags/python\" class=\"mention hashtag\" rel=\"tag\">#<span>python</span></a> <a href=\"https://neuromatch.social/tags/NewWork\" class=\"mention hashtag\" rel=\"tag\">#<span>NewWork</span></a> <a href=\"https://neuromatch.social/tags/pydantic\" class=\"mention hashtag\" rel=\"tag\">#<span>pydantic</span></a> <a href=\"https://neuromatch.social/tags/ScientificSoftware\" class=\"mention hashtag\" rel=\"tag\">#<span>ScientificSoftware</span></a></p>",
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"en": "<p>Here's an ~ official ~ release announcement for <a href=\"https://neuromatch.social/tags/numpydantic\" class=\"mention hashtag\" rel=\"tag\">#<span>numpydantic</span></a></p><p>repo: <a href=\"https://github.com/p2p-ld/numpydantic\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">github.com/p2p-ld/numpydantic</span><span class=\"invisible\"></span></a><br>docs: <a href=\"https://numpydantic.readthedocs.io\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">numpydantic.readthedocs.io</span><span class=\"invisible\"></span></a></p><p>Problems: <span class=\"h-card\" translate=\"no\"><a href=\"https://fosstodon.org/@pydantic\" class=\"u-url mention\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">@<span>pydantic</span></a></span> is great for modeling data!! but at the moment it doesn't support array data out of the box. Often array shape and dtype are as important as whether something is an array at all, but there isn't a good way to specify and validate that with the Python type system. Many data formats and standards couple their implementation very tightly with their schema, making them less flexible, less interoperable, and more difficult to maintain than they could be. The existing tools for parameterized array types like nptyping and jaxtyping tie their annotations to a specific array library, rather than allowing array specifications that can be abstract across implementations.</p><p><code>numpydantic</code> is a super small, few-dep, and well-tested package that provides generic array annotations for pydantic models. Specify an array along with its shape and dtype and then use that model with any array library you'd like! Extending support for new array libraries is just subclassing - no PRs or monkeypatching needed. The type has some magic under the hood that uses pydantic validators to give a uniform array interface to things that don't usually behave like arrays - pass a path to a video file, that's an array. pass a path to an HDF5 file and a nested array within it, that's an array. We take advantage of the rest of pydantic's features too, including generating rich JSON schema and smart array dumping.</p><p>This is a standalone part of my work with <span class=\"h-card\" translate=\"no\"><a href=\"https://fosstodon.org/@linkml\" class=\"u-url mention\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">@<span>linkml</span></a></span> <a href=\"https://linkml.io/linkml/schemas/arrays.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">arrays</a> and rearchitecting neurobio data formats like NWB to be dead simple to use and extend, integrating with the tools you already use and across the experimental process - specify your data in a simple <code>yaml</code> format, and get back high quality data modeling code that is standards-compliant out of the box and can be used with arbitrary backends. One step towards the wild exuberance of FAIR data that is just as comfortable in the scattered scripts of real experimental work as it is in carefully curated archives and high performance computing clusters. Longer term I'm trying to abstract away data store implementations to bring content-addressed p2p data stores right into the python interpreter as simply as if something was born in local memory. </p><p>plenty of <a href=\"https://numpydantic.readthedocs.io/en/latest/todo.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">todos</a>, but hope ya like it.</p><p><a href=\"https://neuromatch.social/tags/linkml\" class=\"mention hashtag\" rel=\"tag\">#<span>linkml</span></a> <a href=\"https://neuromatch.social/tags/python\" class=\"mention hashtag\" rel=\"tag\">#<span>python</span></a> <a href=\"https://neuromatch.social/tags/NewWork\" class=\"mention hashtag\" rel=\"tag\">#<span>NewWork</span></a> <a href=\"https://neuromatch.social/tags/pydantic\" class=\"mention hashtag\" rel=\"tag\">#<span>pydantic</span></a> <a href=\"https://neuromatch.social/tags/ScientificSoftware\" class=\"mention hashtag\" rel=\"tag\">#<span>ScientificSoftware</span></a></p>"
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"name": "[This and the following images aren't very screen reader friendly with a lot of code in them. I'll describe what's going on in brackets and then put the text below.\n\nIn this image: a demonstration of the basic usage of numpydantic, declaring an \"array\" field on a pydantic model with an NDArray class with a shape and dtype specification. The model can then be used with a number of different array libraries and data formats, including validation.]\n\nNumpydantic allows you to do this:\n\nfrom pydantic import BaseModel\nfrom numpydantic import NDArray, Shape\n\nclass MyModel(BaseModel):\n array: NDArray[Shape[\"3 x, 4 y, * z\"], int]\n\nAnd use it with your favorite array library:\n\nimport numpy as np\nimport dask.array as da\nimport zarr\n\n# numpy\nmodel = MyModel(array=np.zeros((3, 4, 5), dtype=int))\n# dask\nmodel = MyModel(array=da.zeros((3, 4, 5), dtype=int))\n# hdf5 datasets\nmodel = MyModel(array=('data.h5', '/nested/dataset'))\n# zarr arrays\nmodel = MyModel(array=zarr.zeros((3,4,5), dtype=int))\nmodel = MyModel(array='data.zarr')\nmodel = MyModel(array=('data.zarr', '/nested/dataset'))\n# video files\nmodel = MyModel(array=\"data.mp4\")\n\n\n\n",
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