Metadata-Version: 2.1
Name: normet
Version: 0.1.2
Summary: Normet for automated air quality intervention studies
Author: Dr. Congbo Song and other MEDAL group members
License: MIT
Project-URL: homepage, https://github.com/dsncas/NORmet
Keywords: Atmospheric Science,Air Quality,Machine Learning,Causal Analysis
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Requires-Python: >=3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: joblib
Requires-Dist: flaml
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: ruptures
Requires-Dist: scikit-learn
Requires-Dist: statsmodels
Requires-Dist: cdsapi
Requires-Dist: pyreadr
Requires-Dist: wget
Requires-Dist: xarray

normet
======

**normet** is a Python package to conduct automated data curation, automated machine learning-based meteorology/weather normalisation and causal analysis on air quality interventions for atmospheric science, air pollution and policy analysis. The main aim of this package is to provide a Swiss army knife enabling rapid automated-air quality intervention studies, and contributing to cross-disciplinary studies with public health, economics, policy, etc.

Installation
============

.. code-block:: bash

   conda create -n normet python=3.9
   conda activate normet

This package depends on AutoML from flaml. Install FLAML first:

.. code-block:: bash

   conda install flaml -c conda-forge

Install normet from source:

.. code-block:: bash

   git clone https://github.com/dsncas/normet.git
   cd normet
   python setup.py install

(optional) for jupyter notebook:

.. code-block:: bash

   conda install jupyter

Main Features
=============

Here are a few of the functions that normet implemented:

  - Automated data curation. Download air quality data and re-analysis data at any time in any area.
  - Automated machine learning. Help to select the 'best' ML model for the dataset and model training.
  - Partial dependency. Look at the drivers (both interactive and noninteractive) of changes in air pollutant concentrations and feature importance.
  - Weather normalisation. Decoupling emission-related air pollutant concentrations from meteorological effects.
  - Change point detection. Detect the change points caused by policy interventions.
  - Causal inference for air quality interventions. Attribution of changes in air pollutant concentrations to air quality policy interventions.

Documentation
=============

You can find Demo and tutorials of the functions `here <https://normet.readthedocs.io>`_.
