Skip to main content

OECT (Organic Electrochemical Transistor) data processing infrastructure for experiment management, feature engineering, and analysis

Project description

OECT-Infra

A comprehensive data processing infrastructure for OECT (Organic Electrochemical Transistor) experiments

PyPI version Python 3.11+ License: MIT

Overview

OECT-Infra is an end-to-end platform that transforms raw experimental data into high-performance structured formats, providing standardized feature engineering, visualization, and reporting capabilities for OECT research.

Key Features

  • 🔄 Data Conversion: Parallel batch conversion from CSV/JSON to standardized HDF5 format
  • 📊 Lazy-Loading API: Efficient access to experimental metadata and measurement data with intelligent caching
  • 🔧 Feature Engineering:
    • V1: Extract transfer characteristics (gm, Von, |I|, etc.) in columnar HDF5 format
    • V2: Advanced DAG-based extraction with YAML configs, Parquet storage, and HuggingFace-style API
  • 📁 Unified Data Catalog: SQLite-based indexing with bidirectional file↔database sync
  • 📈 Visualization: High-performance plotting with animation/video export
  • 📄 Automated Reporting: Configurable PowerPoint generation for stability analysis
  • 📉 Degradation Analysis: 17+ power law models with multi-metric comparison framework

Installation

pip install oect-infra

Requirements

  • Python 3.11 or higher
  • Core dependencies: h5py, pandas, numpy, matplotlib, pydantic, scipy, scikit-learn, PyYAML

Quick Start

Using the Unified Interface

from infra.catalog import UnifiedExperimentManager

# Initialize manager
manager = UnifiedExperimentManager('catalog_config.yaml')

# Get an experiment
exp = manager.get_experiment(chip_id="#20250804008", device_id="3")

# Access data
transfer_data = exp.get_transfer_data()
features = exp.get_features(['gm_max_forward', 'Von_forward'])

# Visualization
fig = exp.plot_transfer_evolution()

Using the Command-Line Interface

# Initialize catalog system
catalog init --auto-config

# Scan and index HDF5 files
catalog scan --path data/raw --recursive

# Synchronize data
catalog sync --direction both

# Query experiments
catalog query --chip "#20250804008" --output table

# Extract Features V2
catalog v2 extract-batch --feature-config v2_ml_ready --workers 4

Features V2 Extraction

# Single experiment with V2
exp = manager.get_experiment(chip_id="#20250804008", device_id="3")
result_df = exp.extract_features_v2('v2_transfer_basic', output_format='dataframe')

# Batch extraction
experiments = manager.search(chip_id="#20250804008")
result = manager.batch_extract_features_v2(
    experiments=experiments,
    feature_config='v2_ml_ready',
    save_format='parquet',
    n_workers=4
)

Architecture

Layered Design

Core Foundation (L0)

  • csv2hdf: Data conversion
  • experiment: Data access
  • oect_transfer: Transfer characteristics analysis
  • features: Feature storage

Business Application (L1)

  • features_version: Feature workflows V1
  • features_v2: Feature engineering V2 system
  • visualization: Plotting tools

Application Integration (L2)

  • catalog: Unified management
  • stability_report: Report generation

Data Flow Pipeline

CSV/JSON → csv2hdf → Raw HDF5 → experiment (lazy-loading)
         → [V1] oect_transfer & features_version → Feature HDF5
         → [V2] features_v2 (DAG compute graph) → Feature Parquet
         → catalog (indexing + workflow metadata) → visualization/stability_report

Configuration

OECT-Infra uses YAML configuration files. Create a catalog_config.yaml:

roots:
  raw_data: "data/raw"
  features_v1: "data/features"
  features_v2: "data/features_v2"

database:
  path: "catalog.db"

sync:
  conflict_strategy: "keep_newer"

Documentation

  • Complete Documentation
  • Package documentation included in the installed package
  • See infra/ subdirectory for detailed module documentation

Examples

Check out example notebooks in the source repository:

  • Example notebooks and scripts included in package
  • Comprehensive API documentation in module docstrings

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use OECT-Infra in your research, please cite:

@software{oect_infra,
  author = {lidonghao},
  title = {OECT-Infra: Data Processing Infrastructure for OECT Experiments},
  year = {2025},
  url = {https://github.com/Durian-leader/oect-infra-package}
}

Support

For issues and questions:

Acknowledgments

This project was developed for OECT (Organic Electrochemical Transistor) research, providing tools for efficient data management, analysis, and visualization in materials science and electrochemistry research.

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

oect_infra-1.0.6.tar.gz (300.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

oect_infra-1.0.6-py3-none-any.whl (365.3 kB view details)

Uploaded Python 3

File details

Details for the file oect_infra-1.0.6.tar.gz.

File metadata

  • Download URL: oect_infra-1.0.6.tar.gz
  • Upload date:
  • Size: 300.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.9

File hashes

Hashes for oect_infra-1.0.6.tar.gz
Algorithm Hash digest
SHA256 9e294c0780f747f5ce332e44b83f551b0ca9488751eaee14470a31d1f297ab0d
MD5 78d7d34459fc5dbde3088ff3ba86c098
BLAKE2b-256 febe0f619489f2bdf8e107da880071d83f4f3bb112ab9dbe7d803863195f4527

See more details on using hashes here.

File details

Details for the file oect_infra-1.0.6-py3-none-any.whl.

File metadata

  • Download URL: oect_infra-1.0.6-py3-none-any.whl
  • Upload date:
  • Size: 365.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.9

File hashes

Hashes for oect_infra-1.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 b37801d74edcf91e77236822fc2de4d2a7adf2b4b9dff8f6cc1eac2b0baa9fee
MD5 e065584622a6f14ebfad9f1823608809
BLAKE2b-256 e30d0fc6930b505579050994d127009815678a4a297030b740bcf6d595fb311e

See more details on using hashes here.

Supported by

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