README.md

MLAir - Machine Learning on Air Data

MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine learning (ML) models for the analysis and forecasting of meteorological and air quality time series. You can find the docs here.

• MLAir - Machine Learning on Air Data
• Installation
  • openSUSE Leap 15.1
  • Ubuntu 20.04.1
  • macOS & windows
• How to start with MLAir
  • Example 1
  • Example 2
  • Example 3
• Default Workflow
• Customised Run Module and Workflow
• Custom Model
  • How to create a customised model?
  • How to plug in the customised model into the workflow?
  • Specials for Branched Models
  • How to access my customised model?
• Data Handlers
• Special Remarks
  • Workaround to skip geographical plot
  • Special instructions for installation on Jülich HPC systems
  • Security using JOIN
  • Known Issues
    • Problem with multiprocessing

Installation

MLAir is based on several python frameworks. To work properly, you have to install all packages from the requirements.txt file. Additionally to support the geographical plotting part it is required to install geo packages built for your operating system. Unfortunately, the names of these package may differ for different systems. In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS. If the installation is still not working, we recommend skipping the geographical plot. We have put together a small workaround here. For special instructions to install MLAir on the Juelich HPC systems, see here.

• Make sure to have the python3.6 version installed (We are already using python3.8, but will refer to python3.6 here as this was used for long time and is therefore tested well.)
• (geo) A c++ compiler is required for the installation of the program cartopy
• (geo) Install proj and GEOS on your machine using the console.
• Install the python3.6 develop libraries.
• Install all requirements from requirements.txt preferably in a virtual environment. You can use pip install -r requirements.txt to install all requirements at once. Note, we recently updated the version of Cartopy and there seems to be an ongoing issue when installing numpy and Cartopy at the same time. If you run into trouble, you could use cat requirements.txt | cut -f1 -d"#" | sed '/^\s*$/d' | xargs -L 1 pip install instead or first install numpy with pip install numpy==<version_from_reqs> followed be the default installation of requirements. For the latter, you can also use grep numpy requirements.txt | xargs pip install.
• Installation of MLAir:
  • Either clone MLAir from the gitlab repository and use it without installation (beside the requirements)
  • or download the distribution file (current version) and install it via pip install <dist_file>.whl. In this case, you can simply import MLAir in any python script inside your virtual environment using import mlair.

openSUSE Leap 15.1

• c++ compiler

sudo zypper install gcc-c++

• geo packages

sudo zypper install proj geos-devel

• depending on the pre-installed packages it could be required to install further packages

sudo zypper install libproj-devel binutils gdal-devel graphviz graphviz-gnome

• python develop libraries

sudo zypper install python3-devel

Ubuntu 20.04.1

• c++ compiler

sudo apt install build-essential

• geo packages

sudo apt install proj-bin libgeos-dev libproj-dev

• depending on the pre-installed packages it could be required to install further packages

sudo apt install graphviz libgeos++-dev

• python develop libraries

sudo apt install python3.6-dev

macOS & windows

The installation on macOS is not tested yet. The following commands are possibly needed:

brew install geos

sudo port install graphviz

The installation on Windows is not tested yet.