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[pre-commit.ci] auto fixes from pre-commit.com hooks
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.github/workflows/publish-pypi.yml

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@@ -52,4 +52,4 @@ jobs:
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# This uses the trusted publisher workflow so no token is required.
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/run-tests.yml

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- name: Test with tox
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run: |
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tox
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tox

README.md

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@@ -82,7 +82,7 @@ dataset = garr.build_genomicarray(
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```
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> [!NOTE]
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> - The aggregate function is expected to return either a scalar value or a 1-dimensional NumPy ndarray. If the later, users need to specify the expected dimension of the return array. e.g.
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> - The aggregate function is expected to return either a scalar value or a 1-dimensional NumPy ndarray. If the later, users need to specify the expected dimension of the return array. e.g.
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> ```python
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> feature_annotation_options=garr.FeatureAnnotationOptions(
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> aggregate_function = my_custom_func,

assets/genarr.svg

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src/genomicarrays/GenomicArrayDatasetSlice.py

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from dataclasses import dataclass
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from typing import Any
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import anndata
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import genomicranges as gr
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import pandas as pd
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import summarizedexperiment as se
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__author__ = "Jayaram Kancherla"
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__copyright__ = "Jayaram Kancherla"

src/genomicarrays/build_genomicarray.py

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@@ -315,4 +315,6 @@ def _write_intervals_to_tiledb(outpath, intervals, bwpath, bwidx, agg_func, tota
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def _wrapper_extract_bwinfo(args):
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"""Wrapper for multiprocessing multiple files and intervals."""
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counts_uri, input_intervals, bwpath, idx, agg_func, total_length, outsize_per_feature = args
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return _write_intervals_to_tiledb(counts_uri, input_intervals, bwpath, idx, agg_func, total_length, outsize_per_feature)
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return _write_intervals_to_tiledb(
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counts_uri, input_intervals, bwpath, idx, agg_func, total_length, outsize_per_feature
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)

src/genomicarrays/build_options.py

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interval. The aggregate function is expected to
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return either a scalar value or a 1-dimensional
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NumPy `ndarray`.
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Defaults to None.
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expected_agg_function_length:

tests/agg_test.py

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def aggfunc(x):
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print(x)
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return np.array([np.nanmin(x), np.nansum(x)])
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return np.array([np.nanmin(x), np.nansum(x)])

tests/test_ingest.py

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sfp = tiledb.open(f"{tempdir}/sample_metadata", "r")
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samples = sfp.df[:]
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assert len(samples) == 2
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assert len(samples) == 2

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