aircheckdata: AIRCHECK Parquet Dataset Loader

A lightweight Python package and CLI tool for listing and loading AIRCHECK datasets, with built-in support for column selection, progress tracking, and automatic local caching. This is the Pythonic way to programmatically access datasets that are also available for download via the AIRCHECK website. Before using any dataset, please ensure you have read and agreed to the dataset agreement HitGen End User License Agreement (EULA)

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✅ Best Practices

• Use virtual environments to avoid dependency conflicts:

  python -m venv .venv
  source .venv/bin/activate  # On Windows use .venv\Scripts\activate

• Always validate that your code respects data privacy and licensing terms.

• Avoid storing large datasets in version control. Let aircheckdata handle caching.

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📦 Installation

You can install the package from PyPI:

pip install aircheckdata

For development and testing (optional):

pip install -e ".[dev]"

Installation verification (optional)

Verify that the installation was successful by running unit tests

pytest tests/

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🔧 Usage in a Python Project (Virtual Environment)

aircheckdata can be used directly from your Python environment to:

• List pre-configured datasets
• View available columns and metadata
• Load datasets with optional filtering and progress indicators

Quick Start

List Datasets

from aircheckdata import list_datasets

datasets = list_datasets()
for name, desc in datasets.items():
    print(f"{name}: {desc}")

View Available Columns

from aircheckdata import get_columns

columns = get_columns('HitGen','WDR91')
names = [item["name"] for item in columns]
print("Column Names: \n", names)

Load dataset

from aircheckdata import load_dataset

df = load_dataset('HitGen','WDR91', columns=['ECFP6','ECFP4','LABEL'])  # Download specified data columns with progressbar or
df = load_dataset('HitGen','WDR91', columns=['ECFP6','ECFP4','LABEL'],show_progress=False) # Download specified data columns with without progressbar, this is more memory efficient and faster
df = load_dataset()  # Download once, then cache locally (by default it loads HitGen WDR91 Target)
print(df.head())

Advanced Usage

# Load only selected columns
df = load_dataset('WDR91', columns=['ECFP6', 'ECFP4', 'LABEL'])

# Show progress while loading
df = load_dataset('WDR91', show_progress=True)

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💻 CLI Usage

The aircheckdata CLI enables quick access to datasets via command-line:

aircheckdata --help

Options and Examples

Option	Description
list	List all available datasets
columns Provider Name "Target Name"	Select columns to load or list columns of a dataset

Examples

# List datasets
aircheckdata list


# View available columns for Distinct Target (defaults to HitGen WDR91 if no provider and Target name is given)
# aircheckdata columns
airctest columns <Provider Name> <Target Name>
airctest columns HitGen "WDR12"

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📜 License and Terms of Use

This package is distributed under the MIT License. However, the datasets it provides access to are subject to the HitGen End User License Agreement (EULA).

⚠️ By using any dataset accessed via aircheckdata, you agree to abide by the HitGen EULA.

Please refer to the full license terms and conditions here: 👉 https://www.aircheck.ai/docs/HitGen.pdf

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📚 Pre-configured Datasets

Currently available datasets include:

• WDR91: A curated Parquet dataset provided by HitGen
• WDR12: A curated Parquet dataset provided by HitGen
• SETDB1: A curated Parquet dataset provided by HitGen
• LRRK2: A curated Parquet dataset provided by HitGen
• DCAF7: A curated Parquet dataset provided by HitGen
• Chicken PLCZ1: A curated Parquet dataset provided by HitGen
• Chicken PLCZ1 known inhibitor: A curated Parquet dataset provided by HitGen
• Human PLCZ1 (D202R OR H170A&H215A): A curated Parquet dataset provided by HitGen
• Human PLCZ1 (D202R OR H170A&H215A) known inhibitor: A curated Parquet dataset provided by HitGen
• PLCZ1 (Chicken or Human mutants): A curated Parquet dataset provided by HitGen
• PLCZ1 off target His-PLCD1;2:756: A curated Parquet dataset provided by HitGen

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🛠 Requirements

• Python 3.7+

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