Skip to main content

Cell painting gallery data handling and validation

Project description

Cell painting gallery data handling and validation

Getting started

Install cpgdata package

pip install cpgdata

Sync pre-generated index files

cpg index sync -o "path to save index files"

Example of using the index for filtering files to download from the Cell painting gallery

from pathlib import Path
from pprint import pprint

import polars as pl
from cpgdata.utils import download_s3_files, parallel

index_dir = Path("path to dir containing index files")
index_files = [file for file in index_dir.glob("*.parquet")]
df = pl.scan_parquet(index_files)

df = (
    df
    .filter(pl.col("dataset_id").eq("cpg0016-jump"))
    .filter(pl.col("source_id").eq("source_4"))
    .filter(pl.col("leaf_node").str.contains("Cells.csv"))
    .select(pl.col("key"))
    .collect()
)

# print first 10 results
pprint(df.to_dicts()[0:10])

# Download filtered files
download_keys = list(df.to_dict()["key"])
parallel(download_keys, download_s3_files, ["cellpainting-gallery", Path("path to save downloaded files")], jobs=20)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cpgdata-0.4.0.tar.gz (11.4 kB view hashes)

Uploaded Source

Built Distribution

cpgdata-0.4.0-py3-none-any.whl (15.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page