For discussions during and after the hackathon, please use the chat room.

During the hackathon, the data will be available here. We will move the data to a permanent, public storage location after the hackathon.

Direct download files for compressed archive files:

• annotations.zip
• seismic_data.zip
• timelapse_images.zip
• timelapse_images_fast.zip
• timeseries_derived_data_products.zip

The code you will need for the hackathon lives in this repository.

• docs.zip
• Azure Intro

To use the framework, you require the following tools as prerequisites:

• Python 3.7
• git
• gcc

We recommend Anaconda or Miniconda, the latter being a minimal (but sufficient) version of the Anaconda distribution. The following instructions will be based on Miniconda, but you can use any other Python environment.

After the installation of Anaconda, open a terminal (on Windows Anaconda Prompt) and create a new environment by typing:

conda create -n permafrost python==3.7 git curl numcodecs -y
conda activate permafrost
pip install --pre poetry

On Linux, do the following:

git clone https://gitlab.ethz.ch/tec/public/employees/matthias-meyer/permafrostanalytics
cd permafrostanalytics
poetry install -vvv

As Windows has issues with the Python packages we use, we require some additional steps:

git clone https://gitlab.ethz.ch/tec/public/employees/matthias-meyer/permafrostanalytics
cd permafrostanalytics
conda install -n permafrost pyyaml pytorch torchvision cpuonly -c pytorch
conda install -n permafrost -c conda-forge xarray-extras -y
poetry install -vvv

python ideas/visualization/plot_timeseries.py

The script connects to the data repository on Azure. If you want to use the script without an internet connection, you can download the files once and run the script with the local option as shown below:

python utils/download_files.py -f timeseries_derived_data_products.zip
python ideas/visualization/plot_timeseries.py --local

In case your default browser does not render the page correctly, consider trying another browser. We recommend Mozilla Firefox, which delivers the most stable performance. On Windows, you can switch between default programs using Control Panel → Programs → Default Programs → Set your default programs.

Conda: In case conda should throw errors, try updating it to its newest version:

conda update -n base -c defaults conda

Long installation time: If the installation script is stuck for a prolonged period of time (> 30 seconds), simply try pressing the Enter key; it occasionally waits for affirmation which are not displayed properly.

Reset: If you would like to setup a new Python environment (e.g. in case your package management is corrupted because you installed new packets which are not compatible anymore, or due to a MemoryError()), you can quickly reset by removing the permafrost environment and starting from scratch:

conda deactivate
conda remove -n permafrost --all

If you prefer to work with GitHub, we have mirrored the repository here and you can clone it:

git clone https://github.com/ETHZ-TEC/permafrostanalytics

There are more examples in the ideas folder and some tutorials in the tutorial folder which we will partially go through during the hackathon.

We encourage you to create your own idea in this repository. To do so, create a new git fork of the GitHub repository and add a folder with the name of your idea in /ideas. You can also share any code in this folder if you want to open source it. To make your idea publicly available for the discussion with others, we suggest you create a pull request after you are finished so we can include your idea directly inside the main repository.

1. Login with your GitHub account (or create a new one).
2. On our GitHub repository in the upper right corner, click "Fork" to create a linked copy of this repository. This will allow you to easily merge your changes back to our own repository.
3. Clone your own (new) repository and add content:

git clone https://github.com/{youruser_name}/permafrostanalytics
cd permafrostanalytics
mkdir ideas/youridea_name
cd ideas/youridea_name
...
git add ideas/youridea_name
git commit -m "Added new idea youridea_name"
git push

4. Lastly, create a new pull request by going to your repository on GitHub, then choosing the second tab "Pull requests" below the repository title. There, you can use the green button on the upper right corner to directly create a request that your changes should be integrated into our own GitHub repository.

For more information on how to fork a repository, we refer to the official GitHub documentation.

• Weber et al. A decade of detailed observations (2008-2018) in steep bedrock permafrost at Matterhorn Hörnligrat (Zermatt, CH). Earth System Science Data, 2019. Link.
• Weber et al. Data supplement to "A decade of detailed observations (2008-2018) in steep bedrock permafrost at Matterhorn Hörnligrat (Zermatt, CH)". Pangea, 2019. Link.
• Data Science Virtual Machines. Microsoft, 2019. Link.

• Using the scripts from this repo, most of the data from here can be used plug and play.
• If you have access to the arclink service, you can use it to download seismic streams. There is an explanation in the file ideas/visualization/plot_spectrogram.py.

• If you want to dig deeper you can also have a look at the code for our data management package stuett.

Check out the many open-source tools available online. The ones we use and found most useful are

• python
• xarray
• dask
• pytorch
• plotly/dash
• zarr
• pandas
• obspy/obsplus