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Merge branch 'main' into educational-resources
2 parents c9e659c + 17e7a1e commit ce52539

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Lines changed: 12 additions & 9 deletions

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.github/workflows/ndex.yml

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@@ -14,7 +14,7 @@ jobs:
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- uses: actions/checkout@master
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- uses: actions/setup-python@v2
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with:
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python-version: "3.13"
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python-version: "3.14"
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- name: Install dependencies
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run: |
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pip install tox tox-uv

.github/workflows/tests.yml

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@@ -25,7 +25,7 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [ "3.13", "3.10" ]
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python-version: [ "3.14", "3.10" ]
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tox-command: [ "lint", "pyroma", "mypy" ]
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steps:
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- uses: actions/checkout@v4
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# We only test documentation on the latest version
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# sphinx 8.0 / sphinx-rtd-theme 3.0 discontinued Python 3.9 support
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# a year early, which prompted re-thinking about this.
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python-version: [ "3.13" ]
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python-version: [ "3.14" ]
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steps:
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- uses: actions/checkout@v4
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- name: "Install uv"
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strategy:
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matrix:
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os: [ ubuntu-latest ]
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python-version: [ "3.13", "3.10" ]
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python-version: [ "3.14", "3.10" ]
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steps:
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- uses: actions/checkout@v4
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- name: "Install uv"

.github/workflows/update_web.yml

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@@ -14,10 +14,10 @@ jobs:
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- uses: actions/checkout@master
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with:
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persist-credentials: false # otherwise, the token used is the GITHUB_TOKEN, instead of your personal token
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fetch-depth: 0 # otherwise, you will failed to push refs to dest repo
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fetch-depth: 0 # otherwise, you will fail to push refs to dest repo
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- uses: actions/setup-python@v2
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with:
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python-version: "3.13"
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python-version: "3.14"
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- name: Install dependencies
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run: |
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pip install tox tox-uv

scripts/generate_cl_mesh_mappings.py

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@@ -33,7 +33,7 @@
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if has_mesh_id:
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continue
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matches = gilda.ground(data["name"])
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matches = gilda.ground(data["name"]) # type:ignore[no-untyped-call]
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if not matches:
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if data["name"].endswith(" cells"):
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matches = gilda.ground(data["name"].replace(" cells", "")) # type:ignore[no-untyped-call]

scripts/generate_vaccine_mappings.py

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@@ -2,6 +2,7 @@
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import click
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import pyobo
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import ssslm
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from curies.vocabulary import exact_match, lexical_matching_process
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from pyobo.sources.cpt import iter_terms
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from sssom_pydantic import MappingTool, SemanticMapping
@@ -19,11 +20,13 @@ def main() -> None:
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append_lexical_predictions("cpt", ["mesh", "vo"], mapping_tool=provenance)
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preds = []
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grounder = pyobo.get_grounder(["mesh", "vo"], versions=["2023", None])
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grounder: ssslm.Grounder = pyobo.get_grounder(["mesh", "vo"], versions=["2023", None])
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for term in iter_terms():
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texts = [term.name, *(s.name for s in term.synonyms)]
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for text in texts:
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for scored_match in grounder.get_matches(text + " vaccine"): # type:ignore[no-untyped-call]
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if not text:
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continue
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for scored_match in grounder.get_matches(text + " vaccine"):
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pred = SemanticMapping(
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subject=term.reference,
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predicate=exact_match,

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