Notebooks from the teaching sessions and the workshops of the 2024 Summer School for Astrostatistics in Crete. Our Slack Workspace: 2024 Summer School Slack Workspace.
The school material is a collection of Jupyter notebooks, organized in one folder per subject. The data are also provided in the repository. You can view and use the material by either:
- Installing the package (see "Installing" below)
- Manually downloading the material and use it (see "Downloading without installation" below)
We suggest to install the package in a new, clean virtual environment to avoid conflicts with existing software, and
to ensure you are using package versions that were found compatible with the school notebooks. Here we give instructions
for a conda environment:
conda create --channel conda-forge --name astrostat24 python=3.9.16
conda activate astrostat24
conda install nb_conda=2.2.1
export SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True
git clone https://github.com/astrostatistics-in-crete/2024_summer_school.git
cd 2024_summer_school
pip install .
The nb_conda package is automatically registering all conda environments to
the Jupyter kernels. Alternatively, you can run python -m ipykernel install --user --name=astrostat24 and a new
kernel will appear next time you run Jupyter.
You can download the material in two ways:
- Download the material using the Code -> Download ZIP button.
- Running
git clone https://github.com/astrostatistics-in-crete/2024_summer_school.git(providedgitis installed in your system)
Successfully viewing and using the notebooks depends on the Python version, and the ability to install all dependencies. It is advisable to create a new clean Python 3.9.16 environment and work from there. To avoid complications with dependencies we suggest to install the package (see "Installing above").
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Andrews, Jeff - University of Florida (USA)
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Bonfini, Paolo - Alma Sistemi (Italy)
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Kovlakas, Kostantinos - Institute of Space Sciences (Spain)
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Maravelias, Grigoris - National Observatory of Athens & FORTH (Greece)
All the material provided here (notebooks and scripts) is licenced under the GNU GPLv3.
The notebooks have adopted publicly available material from several sources that are properly credited. All the references to published papers, data, and software tools are properly addressed within each notebook.
If the material you learned through this summer school directly and significantly contributed to your work, we invite you to include the following acknowledgement in your manuscript:
We wish to thank the "Summer School for Astrostatistics in Crete" for providing training on the statistical methods adopted in this work.