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Project Nahual

The blog

In this blog I will explain my journey on this project and maybe share some other thoughts if they ever come to my mind.

The project

The project is in its first steps and best described by the blog article below. Have a look and maybe let you inspire yourself.

Me, myself and I

Who is “I”? I’m Jannik Baumgart. I am from germany, currently working at a medtech company and am a space nerd by heart.

  • On plans

    So, sometimes life goes not exactly like you plan it to be. On closer look, it rarely goes as expected. Depending on how you feel about live and curiosity, this is usually a good thing. In the last blog post I told you about the plans I had with the WSA-ENLIL model. Spoiler alert: plans changed.

    In the first steps of Project Nahual, I familiarized myself with solar wind with the help of the WSA-ENLIL model. To start an actual simulation of an artificial Marsian magnetic shield, I tried to get my hands on the source code of this WSA-ENLIL model. I doug through the internet without success. The only option would be to request a run with specific parameters, though defining such specific parameters (e.g. ones who actually weren’t intended by the creator) is impossible without an extensive documentation or the source code. I found this option at the Community coordinated modeling center (CCMC) run by NASA.

    In an oldschool attempt I wrote an email describing my issue to the CCMC model hosts. And someone answered! Someone from NASA wrote me an email. This absolutely made my week and is still absolutely awesome. Unfortunately they could not help me with my request of the WSA-ENLIL model. It is not open source, only model runs can be requested. But they told me about an alternative, the Space Weather Modeling Framework (SWMF) created and maintained openly with the University of Michigan. Welp. So now they not only answered, they also supported me. And the SWMF apparently is an absolutely powerful tool. The documentation is insanely good and I immediatly had the feeling, that it will make my simulation efforts a whole lot easier. I’m currently doing baby steps with a program designed for super computers by absolute expterts in their field.

    It took me quite a while to get the model up and running on my system. This is also a work in progress for me, since the options are basically unlimited. However to again show you something I had the model produce 3D-data of the earths magnetosphere. Enjoy, but don’t use it for any scientific purposes. I am by no means sure, I gave the model physically authentic input.

    So after releasing the last blog post, I would’ve never guessed, where my project would lead me. You can be sure, that I love this path, though. My plan for the next one is a bit vague. Since in the meantime some people of the real life told me “I have no idea, what you are doing there”, I’d like to give some insight about how using this model actually looks. But we will see about the future.

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  • On Project Nahual

    What the project is all about and what it will be

    Solar wind barrages onto our atmosphere every second. It consists of charged particles, which get heavily influenced by the magnetic fields of earth and sun. The interaction between solar wind and the earthly magnetic field results in the wonderful phenomenon of polar lights. A well more hidden effect of the earthly magnetic field is its protection aspect. It deflects and redirects the charged solar wind particles, which otherwise might strip away our atmosphere. Once again we can thank our planet for the Home it provides to us.

    A picture of northern lights by wikipedia user Varjisakka under creative commons licence
    Northern lights by Varjisakka under creative commons licence

    When we look up to the sky, we find a planet which lived this fate. Our neighborly planet Mars is assumed to have a significant atmosphere a long time ago. It might have lost it due to many erosion effects, of which the drag of the solar wind is one of.

    As I learned about the eroded and lost natural Mars atmosphere I was very sad, because it essentially makes any future terraforming efforts worthless. Without a magnetic field any artificial atmosphere on Mars would erode just like its natural one.

    For a long time I thought terraforming Mars would be impossible and our closest neighbor cannot be a Home for us. One day in the far past of my life I heard from an idea, which stuck with me. James Green from NASA proposed at the NASA Planetary Science Vision 2050 Workshop the concept of a powerful magnet placed at the L1-Lagrange point between Sun and Mars. It would act as a shield for the Martian atmosphere. I was intrigued, kept the idea for a long time and began to search for more but did not find satisfying results.

    An artificial magnetosphere of sufficient size generated at L1 – a point where the gravitational pull of Mars and the sun are at a rough equilibrium — allows Mars to be well protected by what is known as the magnetotail. The L1 point for Mars is about 673,920 miles (or 320 Mars radii) away from the planet. In this image, Green’s team simulated the passage of a hypothetical extreme Interplanetary Coronal Mass Ejection at Mars. By staying inside the magnetotail of the artificial magnetosphere, the Martian atmosphere lost an order of magnitude less material than it would have otherwise. (J. Green)
    Magnet shield proposed by James Green from NASA at the NASA Planetary Science Vision 2050 Workshop – article link

    While there are proposals for this topic, as far as I know nobody actually analyzed the solar wind as source of the problem. Any designs orientate heavily at the structure of the earthly magnetic field. I want to deep dive into the plasma dynamics of the solar wind to maybe find a more efficient solution. The scope of Project Nahual is a comprehensive proposal for a Martian solar magnet shield.

    For a first step I wanted to have an accurate description about solar wind. The most promising dataset is the one given by the WSA-Enlil model from the NOAA. I created a visualizer, which goes beyond the basic 2D one provided. However like I mentioned in the last blog article I will probably need a dataset specific for this purpose as well as a deep understanding of the model itself to advance in this project.

    Apart from technical problems like having a magnetic field, which might need the size of a planet, or modeling the solar wind accurately enough, a realization would require huge investments. Because of this a practical project scope is far out of reach for me. Nevertheless I want to add to our understanding of our solar system. In the spirit of the always welcoming space community I want to share my ideas and my progress, even if I possibly will not find a feasible or even doable solution.

    Join me, ask me, discuss with me or just read. Our fascination for the stars shines bright and I am happy to be here.

    All sources, articles and pictures to download: On Project

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  • On graphing the stars

    Try getting a new look on the sky and find a new graphing library for python.

    Hello dear reader and a warm welcome to my first blog post to this project and also my first blog post ever. I am Jannik Baumgart and I want to bring life and light to a project, which kept me busy for a long time and will do so in the future.

    While the Project Nahual will be a secret for a little bit longer, I want to share insights I learn on the way. This brings me to the topic “On graphing the stars”. Part of Nahual is the detailed modelling of solar wind, a phenomenon happening every second in our solar system and a reason for polar lights. Because looking at the sky to find data of a mostly invisible phenomenon is not very promissing, our Cyberspace seemed like the easier way.

    Lo and behold! Since solar wind is also “weather” in some context the National Oceanic and Atmospheric Administration (NOAA) of the USA provides a solar wind prediction ( The data of their prediction is a product of the WSA-ENLIL model, which in turn takes satellite data and calculates the solar wind plasma velocity, density and polarity around the sun and our earth. They also provide a 2D visualization, which I already really like.

    However since I plan to model changes in the solar wind, the basic visualization of precalculated data is not sufficient for me. Also moving in a 3-dimensional room with 2-dimensional view always takes brain capacity. Thankfully the NOAA provides the data to download. Because of this I was keen to build a visualization myself.

    The challenge: visualize complex data in a 3-dimensional room with spheric coordinates over a timeframe.

    Since as a hobby-analyst python is my home, my first stop was matplotlib, the standard graphing library for data in python. It is powerful and fits most needs, but definitely gave me a hard time. Between clunky function calls and misleading error messages, I repeatedly looked for alternatives and finally found the Plotly library.

    Especially the functions are easy to use and provide the opportunity to transform massive datasets to easily comprehensible graphs. To let you follow me here, I provided everything relevant as a download file.

    For the following plotly example I provided drastically shortened and reformatted data (to save us all download time). It is now in cartesian and tidy format. With mainly the (optional) visual tweaking we get a wonderful 3D visualization out of plotly. This python code produces an HTML version. Down below is also a rendered video of the full dataset.

    import pandas
    path = "wsa_enlil_data_cme_2022-02-03.csv"
    data = pandas.read_csv(path)
    scatter_default = {"x": "x_coord",
                       "y": "y_coord",
                       "z": "z_coord",
                       "animation_frame": "absolute_time",
                       "color": "velocity_[m/s]",
                       "range_color": [230000, 680000],
                       "size": "density_[kg/m3]",
                       "template": "plotly_dark",
                       "color_continuous_scale": "jet",
    layout_default = {"title": {"text": "solar wind velocity"},
                      "scene_camera": {"up":{"x": 0, "y": 0, "z": 1},
                                       "center": {"x": 0, "y": 0, "z": 0},
                                       "eye": {"x": 0.8, "y": -0.4, "z": 0.25}
    traces_default = {"marker": {"opacity": 1,
                                 "line": {"width": 0}
                      "selector": {"mode": "markers"}
    fig =, **scatter_default)
    Solar wind prediction visualization

    On a closer look on either the code or the video you can see, that this visualization is not only in 3D, it also shows the solar wind plasma velocity and density together. While the color represents the velocity, the size of the datapoints represents the density.

    Maybe now you also wondered why there is also only a sphere as well as a vertical and a horizontal plane. Unfortunately this is currently all the 3D data we get from the download. This is also an advantage of the chosen plot type. In the basic visualization you don’t know on which data you rely on and which data is just not there. In this example you can even differentiate the outer data points and could do so in the middle, when modifying the view. For the project I will need more datapoints. You will probably see a deep dive to the WSA-ENLIL model from me in the future.

    In short: Python Plotly is pretty awesome when you come from matplotlib. The WSA-ENLIL model is awesome and the NOAA and the NASA are awesome to provide this for free. However there is still very much to do and you will get to see it, if you are interested.

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