Table of Contents
This interactive website was initially created for the 2022 NASA Space Apps Challenge (October 1-2). As a team of 2, Warren Wu and I worked on a challenge called "Creative Data Display With the Parker Solar Probe," but alas, our project was too ambitious to finish in 48 hours. So after the challenge ended, I decided to keep working on it on my own to steadily bring our original vision to fruition - that is, a vision of helping educate the public on the Parker Solar Probe's mission through an all-encompassing, interactive experience packed with visuals.
The website is best viewed on larger, horizontal screens (computers, laptops, tablets, etc.). Currently, it does has some responsiveness for viewing on smaller, vertical screens (i.e. phones); however, the viewing experience there is not quite ideal. Future improvements could certainly include added responsiveness.
This section simply displays one of two logos that I designed - one is for larger, horizontal displays and the other for smaller, vertical displays.
This section loads the most recent image of the sun from the SDO (Solar Dynamics Observatory) website's "The Sun Now". These images are supposed to be updated every 15 minutes, though I have experienced some glitches where the images disappear or are formatted weirdly. The user can click on the image to cycle amongst images of different wavelengths.
This section uses a 3D model loaded directly from NASA's website. When we first made the website, we actually downloaded the 3D model file and used Three.js to display it to our liking; however, it refused to work once uploaded onto GitHub, so I had to replace it.
This section provides some preliminary context on the corona - the part of the sun that the Parker Solar Probe is intended to explore. It covers information on the corona itself, the Alfvén surface (or the outer boundary of the corona), and the solar weather events that arise from the corona.
This section displays the images captured by the WISPR's inner & outer cameras for all 12 available orbits, along with a position display showing the PSP's position, it's distance from the sun (AU), and the date. Interactivity is integrated using the wispr_display.js file. Now, the slider acts as the main control point, representing the range of indices for the list of fits data that corresponds to the selected orbit. When you drag the slider, it finds the fits data (i.e. date, position, distance) at the index of the slider, updating the position display accordingly. It's a bit more complicated for the images, because not every fits data point corresponds with an inner/outer image. First, it searches the PNG_match_lists for an inner and/or outer image date matching the fits date. If one is found, it uses the index of the image date to get the name of the image from PNG_lists, which is needed to load the image from the WISPR website. Therefore, it should be noted that the images won't necessarily change every time you change the slider. And as for the play button, it simply automates dragging the slider at a specified speed (fps).
The WISPR images displayed here are loaded from https://wispr.nrl.navy.mil/data/rel/pngs/ (I was unable to upload all of the images directly to the repository, as the file size was too large). As a result, the images can sometimes lag, and I have yet to find a workable solution other than enabling the user to lower/raise the frame rate as needed. Now, in order to obtain the PNG_match_lists and PNG_lists mentioned above, I first downloaded all of the PNGs. Then, I ran a python script (get_wispr_png_names.py) to compile all of the PNG names into text files for each orbit & camera, and then compile those text files into all-inner-pngs.txt and all-outer-pngs.txt which I copy & pasted into wispr_display.js (I know, very fancy). I used another python script (process_wispr_fits_and_png_data.py) to find the matching dates between the fits & png data, and export them into text files for each camera, which I copy & pasted into wispr_display.js.
In order to retrieve the date & position data, I first used wget to download the L3 fits data from https://wispr.nrl.navy.mil/data/rel/fits/L3/. Then, I ran a python script (get_wispr_fits.py) using Astropy to extract the data I wanted, calculated the distances using the position data, and stored it all in text files for each of the orbits. Then, I ran another python script (read_wisprtext.py) to format all of the data into a gigantic 3D array which I then copy & pasted into my wispr_display.js file (later, when I was working on the SWEAP display, I realized that I could write the data directly into a JS file, so I did that for the SWEAP data, but did not feel the need to change it for the WISPR data). Finally, for the background images with the orbits, I used matplotlib to plot the PSP's position, and highlighted a different orbit for each image - generated by running process_wispr_fits_and_png_data.py.
This section visualizes data on the three most common particles in the solar wind - protons, electrons, and alphas - which are what SWEAP is measuring. There is a position display similar to the one in the WISPR section, but using data from SWEAP, which also includes the speed of the PSP. And finally, there are 3 units from which the user can pick: metric, imperial, and wacky (these will be explained further below).
Interactivity is controlled by sweap_display.js (by the way, the slider/play button here work in essentially the same way as described above for WISPR), and all data analysis/extraction was done using pySPEDAS in pyspedas_data_analysis.ipynb, which contains more thorough documentation.
The controls on the left allow the user to switch between protons, electrons, and alphas. Whenever you switch modes, it will try to keep the date & time the same as it was before, but if that's impossible, it will simply go to the start of the current orbit. This is done by taking the date/time before switching and checking if it's in the list of the dates for the new mode (...date_list.js files stored in sweap_data and generated using read_sweaptext.py). The position display here uses data from the SPC dataset spc_list.js. It had to be different from the WISPR position display because for SWEAP, I was able to export one data point for every minute (for all data used in this section), which wouldn't have been compatible with the WISPR fits data. The reason the orbits look slightly different here is merely due to the usage of a different coordinate system. For the units here, I chose not to have the distance units (AU) change, so as to keep it consistent with the WISPR section. For speed, I converted it to KMPH for metric, MPH for imperial, and MACH NUMBER for wacky (speed of sound = 2.91545 km/s), which by the way, is supposed to wacky because sound cannot travel in the vacuum of space.
For SPC, I used pySPEDAS to download the cdf files from NASA's website, quickly extract data from them, and export the data (date/time, proton velocity, proton density, proton temperature, x & y coordinates, distance, and spacecraft velocity) into text files which were compiled into spc_list.js using read_sweaptext.py. In the protons display, I visualized the data by (1) scaling the proton velocity according to the actual velocty (2) making the number of protons on screen at any time match the density in protons/cm^2 (3) changing the color of the protons based on the temperature. I also changed the radius of the sun in the center based on how far the PSP is from it (r = 32 / (distance in AU / 0.075)). Below are the different units that I used:
| Data Type | Original | Metric | Imperial | Wacky |
|---|---|---|---|---|
| Temperature | K | C | F | Death Valleys (1 death valley = 329.817 K) |
| Density | 1/cm^2 | 1/cm^2 | 1/in^2 | 1/penny (area of penny = 2.85 cm^2) |
| Velocity | km/s | kmph | mph | Mach Number (Mach 1 = 0.343 km/s) |
For SPAN-e, I used pySPEDAS to download the cdf files from NASA's website, quickly extract data from them, and export the data (date/time, differential energy flux for each of the 32 energy bins, and total electron density) into text files which were compiled into spane_list.js using read_sweaptext.py. In the electrons display, I visualized the data for each of the 32 energy bins by plotting the energy flux on a logarithmic scale. I've also included a short blurb (with additional resources) to help the reader understand basically what energy flux represents here.
| Data Type | Original | Metric | Imperial | Wacky |
|---|---|---|---|---|
| Total Energy Flux | 1/cm^2-s-ster | 1/cm^2-s-ster | 1/in^2-s-ster | 1/eardrum-s-ster (area of eardrum = 1.555 cm^2) |
For SPAN-i, I used pySPEDAS to download the cdf files from NASA's website, extract data from them (using datatypes spi_sf0a_mom_inst & spi_sf0a_l3_mom), and export it (date/time, proton velocity, proton density, proton temperature) into text files which were compiled into spani_list.js using read_sweaptext.py. In doing so, I found that there was no data available for orbit 1. For the alphas display, I visualized the data by (1) scaling the alpha velocity according to the actual velocty (2) making the number of alphas on screen at any time match the density in alphas/cm^2 (3) changing the color of the alphas based on the temperature. Overall, very similar to the protons display, but here the display shows that the instrument is pointed to the side, not directly at the sun. Below are the different units that I used:
| Data Type | Original | Metric | Imperial | Wacky |
|---|---|---|---|---|
| Temperature | K | C | F | Death Valleys (1 death valley = 329.817 K) |
| Density | 1/cm^3 | 1/cm^3 | 1/in^3 | 1/acorn (volume of acorn = 2.5 cm^3) |
| Velocity | km/s | kmph | mph | Mach Number (Mach 1 = 0.343 km/s) |
This section visualizes magnetic and electric field data from the FIELDS instrument. Similarly to the SWEAP display, this section contains a controls box, a position display (which runs using the same data as SWEAP), a play button, and a canvas display for data visualization.
Interactivity is controlled by fields_display.js, and all data analysis/extraction was done using pySPEDAS in pyspedas_data_analysis.ipynb, which contains more thorough documentation on that matter.
These displays are almost identical to those of SWEAP. The controls on the left allow the user to switch between magnetic and electric field. Whenever you switch modes, it should keep the date & time the same as it was before, as the two datasets encompass the same timeframes. The position display here uses data from the SPC dataset spc_list.js. By exporting one FIELDS data point for each minute, I was able to make it easily compatible with the position and velocity data from SPC. All that had to be done is take the date/time from the FIELDS data and check if it's in the spcdate_list.js, generated using read_sweaptext.py. For the units here, I chose not to have the distance units (AU) change, so as to keep it consistent with the WISPR section. For speed, I converted it to KMPH for metric, MPH for imperial, and MACH NUMBER for wacky (speed of sound = 2.91545 km/s), which by the way, is supposed to wacky because sound cannot travel in the vacuum of space.
For magnetic field data, I used pySPEDAS to download the cdf files from NASA's website (datatype = mag_rtn), extract the data from them, and export it (magnetic field components (nT) in the RTN coordinate system) into text files which were then compiled as fields_list.js using read_fieldstext.py. I visualized this data by (1) scaling vector magnitudes logarithmically (base 2) to encompass the range of data [-554.0 nT, 786.6 nT] (2) splitting the display into a 3D view, a 2D side view, and a 2D top view (3) drawing axes to represent the RTN coordinates system, with bolded axes signifying positive values and the sun as a reference point (4) plotting the magnetic field's RTN components on the axes and labeling their values in nT. I also calculated the magnitude of the total magnetic field and displayed it in the bottom left.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
- Courtesy of NASA/SDO and the AIA, EVE, and HMI science teams.
- Parker Solar Probe was designed, built, and is now operated by the Johns Hopkins Applied Physics Laboratory as part of NASA’s Living with a Star (LWS) program (contract NNN06AA01C). Support from the LWS management and technical team has played a critical role in the success of the Parker Solar Probe mission.
- The Wide-Field Imager for Parker Solar Probe (WISPR) instrument was designed, built, and is now operated by the US Naval Research Laboratory in collaboration with Johns Hopkins University/Applied Physics Laboratory, California Institute of Technology/Jet Propulsion Laboratory, University of Gottingen, Germany, Centre Spatiale de Liege, Belgium and University of Toulouse/Research Institute in Astrophysics and Planetology.
- We acknowledge the NASA Parker Solar Probe Mission and the SWEAP team led by J. Kasper for use of data.
- The FIELDS experiment on the Parker Solar Probe spacecraft was designed and developed under NASA contract NNN06AA01C.
- README template by othneildrew