Graph für potentielle Energie im Gravitationsfeld

mit matplotlib, NumPy, pandas, SciPy, SymPy und weiteren mathematischen Programmbibliotheken.
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Beiträge: 1
Registriert: Sonntag 16. April 2023, 14:07

Hey ho,
Wie der Titel schon verrät, will ich den Graphen für die potentielle Energie im Gravitationsfeld errechnen lassen. Jedoch sieht mein Graph immer wieder so aus: [img]data:image/png;base64,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[/img]
Bin ein absolut blutiger Anfänger und finde hier meinen Fehler nicht.


Der Code:

import numpy as np
import matplotlib.pyplot as plt #imports matplotlib's library
%matplotlib inline


G = 6.67*10**-11
m1 = 1.989*10**30
m2 = 5.97*10**24
r = np.linspace(1,5900*10**9,20)
U = -(G*m1*m2)/r

MyFigure, ax = plt.subplots()
ax.plot(r,U)
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Beiträge: 13061
Registriert: Samstag 2. Juni 2018, 10:21
Wohnort: 127.0.0.1
Kontaktdaten:

@Addu: Welchen Fehler? Was hattest Du denn erwartet zu sehen? Du hast halt im Ergebnis einen Wert der ziemlich stark von den anderen Abweicht was die Grössenordung angeht, und damit sind die Unterschiede zwischen allen anderen Werten im Graph nicht mehr sichtbar. Kannst den Ausreisser ja mal weg lassen beim plotten.

Edit: Oder eine logarithmische Skala verwenden.
„All religions are the same: religion is basically guilt, with different holidays.” — Cathy Ladman
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