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Scalene: An exception of type UnicodeDecodeError occurred. Arguments:
('ascii', b'/* PrismJS 1.26.0\nhttps://prismjs.com/download.html#themes=prism&languages=markup+css+clike+javascript+python&plugins=normalize-whitespace */\n/// <reference lib="WebWorker"/>\n\nvar _self =\n  typeof window !== "undefined"\n    ? window // if in browser\n    : typeof WorkerGlobalScope !== "undefined" &&\n      self instanceof WorkerGlobalScope\n    ? self // if in worker\n    : {}; // if in node js\n\n/**\n * Prism: Lightweight, robust, elegant syntax highlighting\n *\n * @license MIT <https://opensource.org/licenses/MIT>\n * @author Lea Verou <https://lea.verou.me>\n * @namespace\n * @public\n */\nvar Prism = (function (_self) {\n  // Private helper vars\n  var lang = /(?:^|\\s)lang(?:uage)?-([\\w-]+)(?=\\s|$)/i;\n  var uniqueId = 0;\n\n  // The grammar object for plaintext\n  var plainTextGrammar = {};\n\n  var _ = {\n    /**\n     * By default, Prism will attempt to highlight all code elements (by calling {@link Prism.highl

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git checkout dask-hvg
python run_hvg.py
# 544.9354245662689
git checkout dask-hvg-w-pandas
python run_hvg.py
# 130.07193517684937

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# conda deactivate

mamba env remove -yn anndata-min-deps-test
mamba create -yn anndata-min-deps-test "python=3.9"

PACKAGES=`python3 ci/scripts/min-deps.py pyproject.toml --extra dev test`

# conda activate anndata-min-deps-test
# conda run -n anndata-min-deps-test pip install cupy-cuda12x

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import zarr
import pandas as pd, numpy as np

from anndata.experimental import read_elem, write_elem
from anndata._core.sparse_dataset import SparseDataset

def _df_index(df: zarr.Group) -> np.ndarray:
    index_key = df.attrs["_index"]
    return read_elem(df[index_key])

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# Docs: https://docs.h5py.org/en/stable/vds.html

import h5py
import numpy as np

def virtual_concatenate(datasets: list[h5py.Dataset]) -> h5py.VirtualLayout:
    """Concatenate datasets along the first axis."""
    vds = h5py.VirtualLayout(shape=sum(d.shape[0] for d in datasets), dtype=datasets[0].dtype)
    offset = 0
    for d in datasets: