Solar System

This directory deals with information, diagram, data about our solar system.

Motivation

The pure number regarding our solar system's objects rarley give us an idea about is sizing, and what that sizes imply. Just the distances are so big, that it becomes difficult to draw them with proper scaling. You can not have the same scale for the objects'

• distances from sun to planet - with its Million km, e.g. it goes better just to focus the inner or the outer planets
  • Mercury: 58 Mio km
  • Pluto: 5.906 Mio km. Pluto is more than 100 times farther from Sun than Mercury.
• sizes: planets' diameters
  • Mercury: 4.879 km
  • Jupiter: 142.984 km. The diameter of Mercury fits about 30 times into the diameter of Jupiter.

Distances and planets' diameters are scalled different. The diagram shows the planets' diameter 2.500 times bigger than the distances from sun. That means they should be 2.500 times smaller than actually drawn, but if so - you will get for all of them just a point, unable to compare the planets' sizing.

The distances, with its Million km, we will match with the speed of light

• 299.792 km / sec, equivalent to
• 0,299 Mio km / sec

So we find the light needs more than eight minutes to reach the Earth, and more the four hours traveling to Neptune.

Assuming the Sun will turn off, we (at Earth) will realize it after more than eight minutes.

Technology used

The coding has been done with a jupyter-lab notebook running at an older (2019) laptop as our server, accessible from remote throuh our intranet LAN

• Acer Aspire E5-575G, with
  • Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz,
  • 4 core,
  • 32 GB RAM,
  • NVNe SSD 1 TB,
  • OS: Manjaro,

using Python with pandas, numpy and matplotlib

Data used

Originally our Jupyter notebook acesses the data via pandas and the mysql.connector from a mariadb - testing the jupyter-lab instead of the jupyter notebook, and kernels for octave, gnuplot and mariadb, this project was a pmy "pilot" on the server's setup - and it works within our LAN.

You can take the csv file and use pandas.read_csv to work with the data - as done here.

object	diameter__km	distance_from_sun__Mio_km	matplotlib_color
Mercury	4_879	57.90	orangered
Venus	12_104	108.20	darkorange
Earth	12_756	149.60	blue
Moon	3_475	0.38	brown
Mars	6_792	228.00	darkred
Jupiter	142_984	778.50	peru
Saturn	120_536	1_432.00	burlywood
Uranus	51_118	2_867.00	lightblue
Neptune	49_528	4_515.00	cornflowerblue
Pluto	2_376	5_906.40	dimgray
Sun	1392700	0.00	yellow

Note: see file solar_system.csv, use ; as separator

Code

is a pure jupyter-lab notebook importing some python libraries (pandas, numpy, datetime, matplotlib) and a collection of 3 helper funtion from solar_system_helpers.py (in the same directory as the notebook).

    def get_data_from_csv(fn : str, 
                          df : pd.DataFrame) 
        -> pd.DataFrame:
    
    def gen_planets_as_circles(df: pd.DataFrame) 
        -> (np.array, np.array):
    
    def gen_diagram(fig: plt.figure, 
                    ax: plt.axes, 
                    vals: np.array, 
                    df: pd.DataFrame) 
        -> plt.figure:

Result

We produce a small diagram of the solar systems' planets, see the notebook.