mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-11-10 00:34:29 +00:00
Use dask instead of trying to make a dataframe
compress arrays when serializing to json
This commit is contained in:
parent
50e816bad4
commit
d16b4757e1
8 changed files with 551 additions and 302 deletions
600
nwb_linkml/poetry.lock
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nwb_linkml/poetry.lock
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@ -68,6 +68,42 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
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tests = ["attrs[tests-no-zope]", "zope-interface"]
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tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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[[package]]
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name = "blosc2"
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version = "2.2.7"
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description = "Python wrapper for the C-Blosc2 library"
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optional = false
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python-versions = "<4,>=3.8"
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files = [
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{file = "blosc2-2.2.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:85c76f7025a325831f0610c2848cee46a88fe5dc89813e62d00b3bc8a28c3fed"},
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{file = "blosc2-2.2.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8cdc1017a7f97769b7c4cfb772294aba0fdeb894efa8bf31d68b0ad2b859169b"},
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{file = "blosc2-2.2.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0e0e5bf5fa458af42aaa76deb5e901da751ead215448c2b3d2f046005cf88d"},
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{file = "blosc2-2.2.7-cp39-cp39-win_amd64.whl", hash = "sha256:05c7eafd31598072797b92f7de67ae575b57caa5879b75d699e8c138517aee22"},
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{file = "blosc2-2.2.7.tar.gz", hash = "sha256:7b6d805446c560b26003d1f907e7bd368edd82d232833e9f3b617e2c88108d5e"},
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]
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[package.dependencies]
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msgpack = "*"
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ndindex = ">=1.4"
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numpy = ">=1.20.3"
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py-cpuinfo = "*"
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[[package]]
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name = "certifi"
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version = "2023.7.22"
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@ -201,6 +237,17 @@ files = [
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[package.dependencies]
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colorama = {version = "*", markers = "platform_system == \"Windows\""}
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[[package]]
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name = "cloudpickle"
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version = "2.2.1"
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description = "Extended pickling support for Python objects"
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optional = false
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python-versions = ">=3.6"
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files = [
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{file = "cloudpickle-2.2.1-py3-none-any.whl", hash = "sha256:61f594d1f4c295fa5cd9014ceb3a1fc4a70b0de1164b94fbc2d854ccba056f9f"},
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{file = "cloudpickle-2.2.1.tar.gz", hash = "sha256:d89684b8de9e34a2a43b3460fbca07d09d6e25ce858df4d5a44240403b6178f5"},
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]
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[[package]]
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name = "colorama"
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version = "0.4.6"
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@ -295,13 +342,13 @@ yaml = ["PyYAML (>=3.10)"]
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[[package]]
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name = "curies"
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version = "0.6.3"
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version = "0.6.4"
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description = "Idiomatic conversion between URIs and compact URIs (CURIEs)."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "curies-0.6.3-py3-none-any.whl", hash = "sha256:fb4e99b011f34fb61f7aa38e0bb32c6c6c529c252d27ad2c8a7031687c6894ea"},
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{file = "curies-0.6.3.tar.gz", hash = "sha256:37f4763c387a674adcfa5e93d222315d646647a2df198a60382ff877b72963df"},
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{file = "curies-0.6.4-py3-none-any.whl", hash = "sha256:d19e2f61d8a37560e96a5676a5f98cf056f4e2206e281bdcca74934e7623b9d8"},
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{file = "curies-0.6.4.tar.gz", hash = "sha256:c896e6d8d7f469c9ce52f781c8c35d25c46619e977cc6c932f34d98bf33e3749"},
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]
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[package.dependencies]
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@ -397,6 +444,35 @@ files = [
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{file = "dash_table-5.0.0.tar.gz", hash = "sha256:18624d693d4c8ef2ddec99a6f167593437a7ea0bf153aa20f318c170c5bc7308"},
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]
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[[package]]
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name = "dask"
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version = "2023.9.2"
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description = "Parallel PyData with Task Scheduling"
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optional = false
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python-versions = ">=3.9"
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files = [
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{file = "dask-2023.9.2-py3-none-any.whl", hash = "sha256:5742bc3752a8caad1c8bbb24aa2986424c73c4c819d5ac5a9544c6f373d0094b"},
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{file = "dask-2023.9.2.tar.gz", hash = "sha256:34e04ed79870cd01119217562f417c6ce575b10fb8fdcd866e9c5cd10c088944"},
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]
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[package.dependencies]
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click = ">=8.0"
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cloudpickle = ">=1.5.0"
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fsspec = ">=2021.09.0"
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importlib-metadata = ">=4.13.0"
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packaging = ">=20.0"
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partd = ">=1.2.0"
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pyyaml = ">=5.3.1"
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toolz = ">=0.10.0"
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[package.extras]
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array = ["numpy (>=1.21)"]
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complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=7.0)"]
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dataframe = ["dask[array]", "pandas (>=1.3)"]
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diagnostics = ["bokeh (>=2.4.2)", "jinja2 (>=2.10.3)"]
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distributed = ["distributed (==2023.9.2)"]
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test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
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[[package]]
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name = "deprecated"
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version = "1.2.14"
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@ -467,6 +543,41 @@ files = [
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{file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
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]
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[[package]]
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name = "fsspec"
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version = "2023.9.2"
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description = "File-system specification"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "fsspec-2023.9.2-py3-none-any.whl", hash = "sha256:603dbc52c75b84da501b9b2ec8c11e1f61c25984c4a0dda1f129ef391fbfc9b4"},
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]
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[package.extras]
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abfs = ["adlfs"]
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adl = ["adlfs"]
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arrow = ["pyarrow (>=1)"]
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dask = ["dask", "distributed"]
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devel = ["pytest", "pytest-cov"]
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dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
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fuse = ["fusepy"]
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gcs = ["gcsfs"]
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git = ["pygit2"]
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github = ["requests"]
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gs = ["gcsfs"]
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gui = ["panel"]
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hdfs = ["pyarrow (>=1)"]
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http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
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libarchive = ["libarchive-c"]
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oci = ["ocifs"]
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s3 = ["s3fs"]
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sftp = ["paramiko"]
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smb = ["smbprotocol"]
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ssh = ["paramiko"]
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tqdm = ["tqdm"]
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[[package]]
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name = "future-fstrings"
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version = "1.2.0"
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@ -901,13 +1012,13 @@ linkml-runtime = ">=1.1.6"
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[[package]]
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name = "linkml-runtime"
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version = "1.5.6"
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version = "1.5.7"
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description = "Runtime environment for LinkML, the Linked open data modeling language"
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optional = false
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python-versions = ">=3.7.6,<4.0.0"
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files = [
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{file = "linkml_runtime-1.5.6-py3-none-any.whl", hash = "sha256:b869f7adf340b7df73f4fefa28a402de5719bbd7d172590e8a7ae9c25e179d77"},
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{file = "linkml_runtime-1.5.6.tar.gz", hash = "sha256:eb5f506f7ec08c44e49578d0ecc80e2d3d922756c2430e06906ee9502cca1b4c"},
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{file = "linkml_runtime-1.5.7-py3-none-any.whl", hash = "sha256:8ea32b5f0de3637c2339edfbb88008bb08a9e2ff36d1aada20e002058263418f"},
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{file = "linkml_runtime-1.5.7.tar.gz", hash = "sha256:c202799f01571b0c202bb521957f281b1052fabad106419c8e1368802088d6a4"},
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]
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[package.dependencies]
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@ -925,6 +1036,17 @@ pyyaml = "*"
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rdflib = ">=6.0.0"
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requests = "*"
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[[package]]
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name = "locket"
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version = "1.0.0"
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description = "File-based locks for Python on Linux and Windows"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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files = [
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{file = "locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3"},
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]
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[[package]]
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name = "markdown-it-py"
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version = "3.0.0"
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@ -1019,6 +1141,85 @@ files = [
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{file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
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]
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[[package]]
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name = "msgpack"
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version = "1.0.6"
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description = "MessagePack serializer"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "msgpack-1.0.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f4321692e7f299277e55f322329b2c972d93bb612d85f3fda8741bec5c6285ce"},
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|
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[[package]]
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[package.dependencies]
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|
||||
{file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pyyaml"
|
||||
version = "6.0.1"
|
||||
|
|
@ -2287,6 +2405,17 @@ files = [
|
|||
[package.extras]
|
||||
doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "toolz"
|
||||
version = "0.12.0"
|
||||
description = "List processing tools and functional utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.5"
|
||||
files = [
|
||||
{file = "toolz-0.12.0-py3-none-any.whl", hash = "sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f"},
|
||||
{file = "toolz-0.12.0.tar.gz", hash = "sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.8.0"
|
||||
|
|
@ -2298,17 +2427,6 @@ files = [
|
|||
{file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2023.3"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
|
||||
{file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "uri-template"
|
||||
version = "1.3.0"
|
||||
|
|
@ -2517,4 +2635,4 @@ tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-depends", "pyt
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.11,<3.13"
|
||||
content-hash = "0f2d9fc76cf3788fbdefc6f7b06afb7267c5fe2967970389907a5a9c4864334a"
|
||||
content-hash = "6c8e41c25a97368f31f190b4d76c3dd839a3b2b364c8653dcdd00ffc258a2547"
|
||||
|
|
|
|||
|
|
@ -30,7 +30,9 @@ pytest-cov = {version = "^4.1.0", optional = true}
|
|||
coveralls = {version = "^3.3.1", optional = true}
|
||||
pytest-profiling = {version = "^1.7.0", optional = true}
|
||||
pydantic-settings = "^2.0.3"
|
||||
pandas = "^2.1.1"
|
||||
dask = "^2023.9.2"
|
||||
blosc2 = "^2.2.7"
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
tests = [
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
Utility functions for introspection on python annotations
|
||||
"""
|
||||
import typing
|
||||
from typing import Any, List
|
||||
|
||||
|
||||
def unwrap_optional(annotation):
|
||||
|
|
@ -12,6 +13,17 @@ def unwrap_optional(annotation):
|
|||
annotation = args[0]
|
||||
return annotation
|
||||
|
||||
def get_inner_types(annotation) -> List[Any]:
|
||||
types = []
|
||||
args = typing.get_args(annotation)
|
||||
for arg in args:
|
||||
# try and get inner types to see if we're a terminal type
|
||||
inner_args = typing.get_args(arg)
|
||||
if len(inner_args) == 0:
|
||||
types.append(arg)
|
||||
else:
|
||||
types.extend(get_inner_types(arg))
|
||||
return types
|
||||
|
||||
def take_outer_type(annotation):
|
||||
if typing.get_origin(annotation) is list:
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@
|
|||
Mapping functions for handling HDMF classes like DynamicTables
|
||||
"""
|
||||
import pdb
|
||||
import warnings
|
||||
from typing import List, Type, Optional, Any
|
||||
import ast
|
||||
from nwb_linkml.types import DataFrame
|
||||
|
|
@ -10,52 +11,114 @@ from pydantic import create_model, BaseModel
|
|||
from nwb_linkml.maps import dtype
|
||||
import numpy as np
|
||||
from nwb_linkml.types.hdf5 import HDF5_Path
|
||||
from nwb_linkml.types.ndarray import NDArray
|
||||
from nwb_linkml.types.ndarray import NDArray, NDArrayProxy
|
||||
from nwb_linkml.annotations import get_inner_types
|
||||
import dask.array as da
|
||||
import nptyping
|
||||
|
||||
def model_from_dynamictable(group:h5py.Group, base:Optional[BaseModel] = None) -> Type[DataFrame]:
|
||||
"""
|
||||
Create a pydantic model from a dynamic table
|
||||
"""
|
||||
colnames = group.attrs['colnames']
|
||||
types = {}
|
||||
for col in colnames:
|
||||
idxname = col + '_index'
|
||||
if idxname in group.keys():
|
||||
idx = group.get(idxname)[0]
|
||||
dset = group.get(col)
|
||||
item = dset[idx]
|
||||
else:
|
||||
dset = group.get(col)
|
||||
item = dset[0]
|
||||
# read the first entry to see what we got
|
||||
# idxname = col + '_index'
|
||||
# if idxname in group.keys():
|
||||
# idx = group.get(idxname)[0]
|
||||
# dset = group.get(col)
|
||||
# item = dset[idx]
|
||||
# else:
|
||||
# dset = group.get(col)
|
||||
# item = dset[0]
|
||||
# # read the first entry to see what we got
|
||||
#
|
||||
# if isinstance(item, bytes):
|
||||
# item = item.decode('utf-8')
|
||||
# if isinstance(item, str):
|
||||
# # try to see if this is actually a list or smth encoded as a string
|
||||
# try:
|
||||
# item = ast.literal_eval(item)
|
||||
# except (ValueError, SyntaxError):
|
||||
# pass
|
||||
|
||||
if isinstance(item, bytes):
|
||||
item = item.decode('utf-8')
|
||||
if isinstance(item, str):
|
||||
# try to see if this is actually a list or smth encoded as a string
|
||||
try:
|
||||
item = ast.literal_eval(item)
|
||||
except (ValueError, SyntaxError):
|
||||
pass
|
||||
# Get a nptypes type for the array
|
||||
#pdb.set_trace()
|
||||
|
||||
# type_ = type(item)
|
||||
# type_ = dtype.np_to_python.get(type_, type_)
|
||||
# if type_ is h5py.h5r.Reference:
|
||||
# #type_ = HDF5_Path
|
||||
# type_ = 'String'
|
||||
# elif type_ is np.ndarray:
|
||||
# item: np.ndarray
|
||||
# type_ = dtype.flat_to_npytyping[item.dtype.name]
|
||||
|
||||
#if type_ is not np.void:
|
||||
#type_ = NDArray[Any, getattr(nptyping, dtype.flat_to_npytyping[item.dtype.name])]
|
||||
|
||||
#nptype = nptyping.typing_.name_per_dtype[group[col].dtype.type]
|
||||
nptype = group[col].dtype.type
|
||||
type_ = Optional[NDArray[Any, nptype]]
|
||||
|
||||
type_ = type(item)
|
||||
type_ = dtype.np_to_python.get(type_, type_)
|
||||
if type_ is h5py.h5r.Reference:
|
||||
type_ = HDF5_Path
|
||||
elif type_ is np.ndarray:
|
||||
type_ = NDArray
|
||||
|
||||
if type_ is not np.void:
|
||||
# FIXME: handling nested column types that appear only in some versions?
|
||||
types[col] = (List[type_ | None], ...)
|
||||
#types[col] = (List[type_ | None], ...)
|
||||
types[col] = (type_, None)
|
||||
|
||||
if base is None:
|
||||
base = DataFrame
|
||||
#base = DataFrame
|
||||
base = BaseModel
|
||||
else:
|
||||
base = (DataFrame, base)
|
||||
base = (BaseModel, base)
|
||||
#base = (DataFrame, base)
|
||||
|
||||
|
||||
model = create_model(group.name.split('/')[-1], **types, __base__=base)
|
||||
return model
|
||||
|
||||
|
||||
def dynamictable_to_model(
|
||||
group:h5py.Group,
|
||||
model:Optional[Type[DataFrame]]=None,
|
||||
base:Optional[BaseModel] = None) -> BaseModel:
|
||||
"""
|
||||
Instantiate a dynamictable model
|
||||
|
||||
Calls :func:`.model_from_dynamictable` if model is not provided.
|
||||
"""
|
||||
if model is None:
|
||||
model = model_from_dynamictable(group, base)
|
||||
|
||||
items = {}
|
||||
for col, col_type in model.model_fields.items():
|
||||
if col not in group.keys():
|
||||
if col in group.attrs:
|
||||
items[col] = group.attrs[col]
|
||||
continue
|
||||
|
||||
if col_type.annotation is HDF5_Path:
|
||||
items[col] = [HDF5_Path(group[d].name) for d in group[col][:]]
|
||||
else:
|
||||
try:
|
||||
items[col] = da.from_array(group[col])
|
||||
except NotImplementedError:
|
||||
if str in get_inner_types(col_type.annotation):
|
||||
# dask can't handle this, we just arrayproxy it
|
||||
items[col] = NDArrayProxy(h5f_file=group.file.filename, path=group[col].name)
|
||||
else:
|
||||
warnings.warn(f"Dask cant handle object type arrays like {col} in {group.name}. Skipping")
|
||||
# pdb.set_trace()
|
||||
# # can't auto-chunk with "object" type
|
||||
# items[col] = da.from_array(group[col], chunks=-1)
|
||||
|
||||
return model.model_construct(hdf5_path = group.name,
|
||||
name = group.name.split('/')[-1],
|
||||
**items)
|
||||
|
||||
|
||||
|
||||
|
||||
def dynamictable_to_df(group:h5py.Group,
|
||||
model:Optional[Type[DataFrame]]=None,
|
||||
base:Optional[BaseModel] = None) -> DataFrame:
|
||||
|
|
|
|||
|
|
@ -116,25 +116,3 @@ class DataFrame(BaseModel, pd.DataFrame):
|
|||
for k, v in out.items()
|
||||
}
|
||||
return nxt(self.__class__(**out))
|
||||
|
||||
|
||||
class DataFrameProxy(DataFrame):
|
||||
def __init__(self, hdf5_file: str, hdf5_path:str, **kwargs):
|
||||
# pdb.set_trace()
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.hdf5_file = hdf5_file
|
||||
self.hdf5_path = hdf5_path
|
||||
|
||||
|
||||
def _load(self):
|
||||
cols = {}
|
||||
with h5py.File(self.hdf5_file, 'r') as h5f:
|
||||
obj = h5f.get(self.hdf5_path)
|
||||
for col in self.model_fields.keys():
|
||||
if col in obj.keys():
|
||||
setattr(self, col, obj[col][:])
|
||||
|
||||
def __getitem__(self, item:str):
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ Extension of nptyping NDArray for pydantic that allows for JSON-Schema serializa
|
|||
|
||||
* Order to store data in (row first)
|
||||
"""
|
||||
import base64
|
||||
import pdb
|
||||
from pathlib import Path
|
||||
from typing import (
|
||||
|
|
@ -12,6 +13,8 @@ from typing import (
|
|||
Generic,
|
||||
TypeVar
|
||||
)
|
||||
import sys
|
||||
from copy import copy
|
||||
|
||||
from pydantic_core import core_schema
|
||||
from pydantic import (
|
||||
|
|
@ -24,6 +27,8 @@ from pydantic.json_schema import JsonSchemaValue
|
|||
|
||||
import numpy as np
|
||||
import h5py
|
||||
from dask.array.core import Array as DaskArray
|
||||
import blosc2
|
||||
|
||||
from nptyping import NDArray as _NDArray
|
||||
from nptyping.ndarray import NDArrayMeta
|
||||
|
|
@ -32,6 +37,7 @@ from nptyping.shape_expression import check_shape
|
|||
|
||||
from nwb_linkml.maps.dtype import np_to_python
|
||||
|
||||
|
||||
class NDArray(_NDArray):
|
||||
"""
|
||||
Following the example here: https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types
|
||||
|
|
@ -56,7 +62,11 @@ class NDArray(_NDArray):
|
|||
assert value.dtype == dtype, f"Invalid dtype! expected {dtype}, got {value.dtype}"
|
||||
return value
|
||||
def validate_array(value: Any) -> np.ndarray:
|
||||
assert cls.__instancecheck__(value), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}'
|
||||
if isinstance(value, np.ndarray):
|
||||
assert cls.__instancecheck__(value), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}'
|
||||
elif isinstance(value, DaskArray):
|
||||
assert shape is Any or check_shape(value.shape, shape), f'Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}'
|
||||
|
||||
return value
|
||||
|
||||
def coerce_list(value: Any) -> np.ndarray:
|
||||
|
|
@ -106,36 +116,60 @@ class NDArray(_NDArray):
|
|||
)
|
||||
|
||||
|
||||
def array_to_list(instance: np.ndarray | DaskArray) -> list|dict:
|
||||
if isinstance(instance, DaskArray):
|
||||
arr = instance.__array__()
|
||||
else:
|
||||
arr = instance
|
||||
|
||||
# If we're larger than 16kB then compress array!
|
||||
if sys.getsizeof(arr) > 16 * 1024:
|
||||
packed = blosc2.pack_array2(arr)
|
||||
packed = base64.b64encode(packed)
|
||||
ret= {
|
||||
'array': packed,
|
||||
'shape': copy(arr.shape),
|
||||
'dtype': copy(arr.dtype.name),
|
||||
'unpack_fns': ['base64.b64decode', 'blosc2.unpack_array2']
|
||||
}
|
||||
return ret
|
||||
else:
|
||||
return arr.tolist()
|
||||
|
||||
|
||||
|
||||
|
||||
return core_schema.json_or_python_schema(
|
||||
json_schema=list_schema,
|
||||
python_schema=core_schema.chain_schema(
|
||||
[
|
||||
core_schema.no_info_plain_validator_function(coerce_list),
|
||||
core_schema.is_instance_schema(np.ndarray),
|
||||
core_schema.union_schema([
|
||||
core_schema.is_instance_schema(cls=np.ndarray),
|
||||
core_schema.is_instance_schema(cls=DaskArray)
|
||||
]),
|
||||
core_schema.no_info_plain_validator_function(validate_dtype),
|
||||
core_schema.no_info_plain_validator_function(validate_array)
|
||||
]
|
||||
),
|
||||
serialization=core_schema.plain_serializer_function_ser_schema(
|
||||
lambda instance: instance.tolist(),
|
||||
array_to_list,
|
||||
when_used='json'
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class NDArrayProxy():
|
||||
"""
|
||||
Thin proxy to numpy arrays stored within hdf5 files,
|
||||
only read into memory when accessed, but otherwise
|
||||
passthrough all attempts to access attributes.
|
||||
"""
|
||||
def __init__(self, h5f_file: Path, path: str):
|
||||
def __init__(self, h5f_file: Path|str, path: str):
|
||||
"""
|
||||
Args:
|
||||
h5f_file (:class:`pathlib.Path`): Path to source HDF5 file
|
||||
path (str): Location within HDF5 file where this array is located
|
||||
"""
|
||||
self.h5f_file = h5f_file
|
||||
self.h5f_file = Path(h5f_file)
|
||||
self.path = path
|
||||
|
||||
def __getattr__(self, item):
|
||||
|
|
@ -148,3 +182,20 @@ class NDArrayProxy():
|
|||
return obj[slice]
|
||||
def __setitem__(self, slice, value):
|
||||
raise NotImplementedError(f"Cant write into an arrayproxy yet!")
|
||||
|
||||
|
||||
@classmethod
|
||||
def __get_pydantic_core_schema__(
|
||||
cls,
|
||||
_source_type: _NDArray,
|
||||
_handler: Callable[[Any], core_schema.CoreSchema],
|
||||
|
||||
) -> core_schema.CoreSchema:
|
||||
# return core_schema.no_info_after_validator_function(
|
||||
# serialization=core_schema.plain_serializer_function_ser_schema(
|
||||
# lambda instance: instance.tolist(),
|
||||
# when_used='json'
|
||||
# )
|
||||
# )
|
||||
|
||||
return NDArray_.__get_pydantic_core_schema__(cls, _source_type, _handler)
|
||||
|
|
|
|||
17
nwb_linkml/tests/test_annotations.py
Normal file
17
nwb_linkml/tests/test_annotations.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
import pytest
|
||||
|
||||
from typing import List, Optional, Union
|
||||
from types import NoneType
|
||||
from nwb_linkml.annotations import get_inner_types
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
('annotation', 'inner_types'),
|
||||
[
|
||||
(List[str | None], (str, NoneType))
|
||||
]
|
||||
)
|
||||
def test_get_inner_types(annotation, inner_types):
|
||||
got_inner_types = get_inner_types(annotation)
|
||||
assert len(got_inner_types) == len(inner_types)
|
||||
for got, expected in zip(got_inner_types, inner_types):
|
||||
assert got is expected
|
||||
|
|
@ -1,16 +1,24 @@
|
|||
import pdb
|
||||
|
||||
import pytest
|
||||
import h5py
|
||||
import time
|
||||
|
||||
from nwb_linkml.maps.hdmf import model_from_dynamictable, dynamictable_to_df
|
||||
from nwb_linkml.maps.hdmf import model_from_dynamictable, dynamictable_to_model
|
||||
|
||||
NWBFILE = '/Users/jonny/Dropbox/lab/p2p_ld/data/nwb/sub-738651046_ses-760693773.nwb'
|
||||
|
||||
@pytest.mark.skip()
|
||||
def test_make_dynamictable():
|
||||
h5f = h5py.File(NWBFILE, 'r')
|
||||
group = h5f['intervals']['drifting_gratings_presentations']
|
||||
group = h5f['units']
|
||||
|
||||
start_time = time.time()
|
||||
model = model_from_dynamictable(group)
|
||||
data = dynamictable_to_df(group, model)
|
||||
data = dynamictable_to_model(group, model)
|
||||
ser = data.model_dump_json()
|
||||
end_time = time.time()
|
||||
total_time = end_time - start_time
|
||||
pdb.set_trace()
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue