The Rosetta mission was the first mission designed to orbiting and landing on a comet. The objective🎯 of the mission was to study the way in which the Solar System evolved, and to do so (and after some changes in the mission) the decision of visit the comet 67P/Churyumov-Gerasimenko☄️ was taken.
The objective of this project is to generate a 3D visualization of the comet 67P/Churyumov-Gerasimenko by utilizing data from the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS), which is a camera system onboard the orbiter Rosetta. This involves defining a coordinate system for the comet, which has a unique shape, and deriving a shape model that contains three-dimensional information of the comet.
The shape model, which includes vertices (positional vectors with X, Y, and Z coordinates), edges (links between vertices), and faces (areas enclosed by edges, defined by a list of vertex indices), enables the rendering, visualization, and manipulation of 3D objects representing the comet.
Additionally, shape models of comet 67P have also been derived using data from the Navigation Cameras (NAVCAM), originally intended for engineering purposes to determine the spacecraft's orientation in space, but which have also provided valuable scientific insights.
The project aims to leverage these shape models for creating detailed and accurate 3D visualizations of comet 67P.
The outcome of this project is a GIF image with a file size of 16.9 MiB, which showcases comet 67P/Churyumov-Gerasimenko rotating around its center. This visualization shows the 3D shape model derived from data obtained by the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS) aboard the Rosetta orbiter, as well as insights from the Navigation Cameras (NAVCAM). The GIF effectively demonstrates the comet's unique structure and dynamics by providing a comprehensive 360-degree view, enhancing our understanding and appreciation of its complex geometry and surface features.
In order to get the best out of the template:
- Don't remove any lines from the
.gitignorefile we provide - Make sure your results can be reproduced by following a data engineering convention
- Don't commit data to your repository
- Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in
conf/local/
Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.
To install them, run:
pip install -r src/requirements.txt
You can run your Kedro project with:
kedro run
Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:
kedro test
To configure the coverage threshold, go to the .coveragerc file.
To generate or update the dependency requirements for your project:
python -m piptools compile --upgrade --resolver backtracking -o src/requirements.lock src/requirements.txt -v
pip install -r src/requirements.lock
This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.
After this, if you'd like to update your project requirements, please update src/requirements.txt and re-run kedro build-reqs.
Note: Using
kedro jupyterorkedro ipythonto run your notebook provides these variables in scope:catalog,context,pipelinesandsession.Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run
pip install -r src/requirements.txtyou will not need to take any extra steps before you use them.
To use Jupyter notebooks in your Kedro project, you need to install Jupyter:
pip install jupyter
After installing Jupyter, you can start a local notebook server:
kedro jupyter notebook
To use JupyterLab, you need to install it:
pip install jupyterlab
You can also start JupyterLab:
kedro jupyter lab
And if you want to run an IPython session:
kedro ipython
You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.
By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:
kedro jupyter convert <filepath_to_my_notebook>
Note: The name of the Python file matches the name of the original notebook.
Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:
kedro jupyter convert --all
To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.
Note: Your output cells will be retained locally.