normet
======
.. image:: ../statics/logo.svg
:align: right
:height: 131.5
**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 jupyter
conda activate normet
This package depends on AutoML from flaml. Install FLAML first:
.. code-block:: bash
conda install flaml -c conda-forge
Install normet using pip:
.. code-block:: bash
pip install normet
Or install normet from source:
.. code-block:: bash
git clone https://github.com/dsncas/normet.git
cd normet/python
python setup.py install
Main Features
=============
Here are a few of the functions that normet implemented:
- Automated machine learning. Help to select the 'best' ML model for the dataset and model training.
- Partial dependency. Look at the drivers of changes in air pollutant concentrations and feature importance.
- Weather normalisation. Decoupling emission-related air pollutant concentrations from meteorological effects.
- 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>`_.
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"description": "normet\n======\n\n.. image:: ../statics/logo.svg\n :align: right\n :height: 131.5\n\n**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.\n\nInstallation\n============\n\n.. code-block:: bash\n\n conda create -n normet jupyter\n conda activate normet\n\nThis package depends on AutoML from flaml. Install FLAML first:\n\n.. code-block:: bash\n\n conda install flaml -c conda-forge\n\nInstall normet using pip:\n\n.. code-block:: bash\n\n pip install normet\n\nOr install normet from source:\n\n.. code-block:: bash\n\n git clone https://github.com/dsncas/normet.git\n cd normet/python\n python setup.py install\n\nMain Features\n=============\n\nHere are a few of the functions that normet implemented:\n\n - Automated machine learning. Help to select the 'best' ML model for the dataset and model training.\n - Partial dependency. Look at the drivers of changes in air pollutant concentrations and feature importance.\n - Weather normalisation. Decoupling emission-related air pollutant concentrations from meteorological effects.\n - Causal inference for air quality interventions. Attribution of changes in air pollutant concentrations to air quality policy interventions.\n\nDocumentation\n=============\n\nYou can find Demo and tutorials of the functions `here <https://normet.readthedocs.io>`_.\n",
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