diff --git a/README.txt b/README.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ee3d16984fa0e5acc390d52463f099eaf1be3b7
--- /dev/null
+++ b/README.txt
@@ -0,0 +1,7 @@
+'particle_simulation.py', ehemals 'geodyn_final.py', ist unser vorzeigecode für die partikelsimulation.
+
+'solarsystem.py', ehemals 'geodyn_update.py', ist unser vorzeigecode für die sonnensystemsimulation.
+
+'_particle_sim_1.py', ehemals 'geodyn_new.py', wird nicht mehr benötigt und wurde deswegen umbenannt. die aktuelle version dieses codes ist 'particle_simulation.py'.
+
+'_solarsystem.py', ehemals 'geodyn.py', ist die alte version von 'geodyn_update.py' und wird nicht mehr benötigt.
\ No newline at end of file
diff --git a/geodyn_new.py b/_particle_sim_1.py
similarity index 99%
rename from geodyn_new.py
rename to _particle_sim_1.py
index 917b1586288e19cd347b4305f83d0b0810643b82..ffe5f89c562a8ceb0a5d0af9f537c2415400dd38 100644
--- a/geodyn_new.py
+++ b/_particle_sim_1.py
@@ -2,7 +2,7 @@ from random import random
 
 from sklearn.metrics import mean_poisson_deviance
 #from geodyn import simulation
-from geodyn_update import Particle
+from solarsystem import Particle
 import numpy as np
 from numpy import empty, sqrt, sign
 import matplotlib.pyplot as plt
diff --git a/geodyn.py b/_solarsystem.py
similarity index 100%
rename from geodyn.py
rename to _solarsystem.py
diff --git a/geodyn.ipynb b/geodyn.ipynb
deleted file mode 100644
index 80f5d693bd9719bca4353c700994e3850b872423..0000000000000000000000000000000000000000
--- a/geodyn.ipynb
+++ /dev/null
@@ -1,489 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dbdda0b5-5c6b-4a92-bf95-e5ac3d1ea5e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from typing import Tuple\n",
-    "from numpy import sqrt\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.animation as animation\n",
-    "\n",
-    "# Objektorientiertes Klassifizieren\n",
-    "class Particle():\n",
-    "    def __init__(self, name, pos_x, pos_y, vel_x, vel_y, mass, color='blue') -> None:\n",
-    "        self.name = name\n",
-    "        self.pos_x = pos_x\n",
-    "        self.pos_y = pos_y\n",
-    "        self.vel_x = vel_x\n",
-    "        self.vel_y = vel_y\n",
-    "        self.mass = mass\n",
-    "        self.force_x = 0.0\n",
-    "        self.force_y = 0.0\n",
-    "        self.color = color\n",
-    "        self.pos_x_history = [pos_x]\n",
-    "        self.pos_y_history = [pos_y]\n",
-    "\n",
-    "    def get_force(self) -> Tuple:\n",
-    "        return (self.force_x, self.force_y)\n",
-    "\n",
-    "    def get_position(self) -> Tuple:\n",
-    "        return (self.pos_x, self.pos_y)\n",
-    "\n",
-    "    def get_velocity(self) -> Tuple:\n",
-    "        return (self.vel_x, self.vel_y)\n",
-    "\n",
-    "    def get_acceleration(self) -> Tuple:\n",
-    "        return (self.force_x/self.mass, self.force_y/self.mass)\n",
-    "\n",
-    "\n",
-    "def calc_force(objects) -> None:\n",
-    "    G = 6.67e-11\n",
-    "    for object in objects:\n",
-    "        object.force_x = 0\n",
-    "        object.force_y = 0\n",
-    "        for neighbor in objects:\n",
-    "            if neighbor != object:\n",
-    "                force = G * object.mass * neighbor.mass / ((object.pos_x - neighbor.pos_x)**2 + (object.pos_y - neighbor.pos_y)**2)\n",
-    "                direction = (-(object.pos_x - neighbor.pos_x), - (object.pos_y - neighbor.pos_y))\n",
-    "                alpha = direction[0]/direction[1]\n",
-    "                object.force_x += (-(object.pos_x - neighbor.pos_x) / abs((object.pos_x - neighbor.pos_x))) * force/sqrt(1+1/(alpha**2))\n",
-    "                object.force_y += (-(object.pos_y - neighbor.pos_y) / abs((object.pos_y - neighbor.pos_y))) * force/sqrt(1+alpha**2)\n",
-    "\n",
-    "\n",
-    "def calc_velocity(objects, dt) -> None:\n",
-    "    for object in objects:\n",
-    "        object.vel_x += (object.force_x/object.mass) * dt\n",
-    "        object.vel_y += (object.force_y/object.mass) * dt\n",
-    "\n",
-    "\n",
-    "def calc_position(objects, dt) -> None:\n",
-    "    for object in objects:\n",
-    "        object.pos_x += object.vel_x * dt\n",
-    "        object.pos_y += object.vel_y * dt\n",
-    "\n",
-    "\n",
-    "def simulation(objects, dt, timesteps):\n",
-    "    memory = [[] for object in objects]\n",
-    "    for t in range(timesteps):\n",
-    "        calc_force(objects)\n",
-    "        calc_velocity(objects, dt)\n",
-    "        calc_position(objects, dt)\n",
-    "        for object in objects:\n",
-    "            object.pos_x_history.append(object.pos_x)\n",
-    "            object.pos_y_history.append(object.pos_x)\n",
-    "\n",
-    "        for i in range(len(objects)):\n",
-    "            memory[i].append(objects[i].get_position())\n",
-    "    return memory\n",
-    "\n",
-    "\n",
-    "def plotting(data, render_points, string, legend, answ='no'):\n",
-    "    plt.figure()\n",
-    "    for object in data:\n",
-    "        plt.plot([i[0] for i in object[::len(object)//render_points]], [i[1] for i in object[::len(object)//render_points]])\n",
-    "    plt.xlabel('x Position [m]', fontsize=14)\n",
-    "    plt.xticks(fontsize=14)\n",
-    "    plt.ylabel('y Position [m]', fontsize=14)\n",
-    "    plt.yticks(fontsize=14)\n",
-    "    plt.title(string, fontsize=16, fontweight='bold')\n",
-    "    plt.legend(legend)\n",
-    "    if answ != 'no':\n",
-    "        plt.axis([-0.025e11, 0.025e11, -0.025e11, 0.025e11])\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    particles = [Particle(\"Sol\", 0, 0, 0, 0, 1.99e30, 'orange'),\n",
-    "                 Particle(\"Mercurio\", 0.57e11, 3, 0, 4.74e4, 0.33e24, 'brown'),\n",
-    "                 Particle('Amore', 1.08e11, 2, 0, 3.5e4, 4.875e24, 'magenta'),\n",
-    "                 Particle(\"Dirt\", 1.496e11, 1, 0, 3e4, 5.97e24, 'blue'),\n",
-    "                 Particle(\"reddot\", 2.28e11, 4, 0, 2.41e4, 0.642e24, 'red'),\n",
-    "                 Particle(\"Gas\", 1, 6.64e11, -1.303e4, 0, 1.898e27, 'cyan'),\n",
-    "                 Particle(\"Ring\", 14.32e11, 5, 0, 0.97e4, 568e24, 'yellow'),\n",
-    "                 Particle(\"Sunaru\", 28.67e11, 6, 0, 0.68e4, 86.8e24, 'pink'),\n",
-    "                 Particle(\"Oceanboy\", 45.15e11, 7, 0, 0.54e4, 102e24, 'grey')]\n",
-    "    # Particle(\"Dirt\", 1.496e11, 1, 0, 3e4, 5.97e24, 'blue')\n",
-    "    # Particle(\"Gas\", 6.64e14, 2, 0, 1.303e4, 1.898e27, 'cyan')\n",
-    "    # Particle(\"Mun\", 1.499e11, 4, 0, 4e4, 7.34e22, 'gray')\n",
-    "    # Erde: 1.496e11 m ; 5.97e24 kg\n",
-    "    # Jupiter: 6.64e14 m ; 1,898e27 kg\n",
-    "\n",
-    "\n",
-    "    dt = 86400\n",
-    "    timesteps = 365*11\n",
-    "    render_points = 2000\n",
-    "\n",
-    "\n",
-    "    data = simulation(particles, dt, timesteps)\n",
-    "    plotting(data, render_points, 'Umlaufbahnen', legend=[i.name for i in particles])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c2bf7567-c580-4b50-8f38-942c7eeec12e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'\\nif __name__ == \\'__main__\\':\\n    particles = [Particle(\"Sol\",        0,              0,                      0,                      0,          1.99e30,    \\'orange\\'),\\n                 Particle(\"Mercurio\",   0.57e11,        3,                      0,                      4.74e4,     0.33e24,    \\'brown\\'),\\n                 Particle(\\'Amore\\',      1.08e11,        2,                      0,                      3.5e4,      4.875e24,   \\'magenta\\'),\\n                 Particle(\"Dirt\",       1.496e11,       1,                      0,                      3e4,        5.97e24,    \\'blue\\'),\\n                 Particle(\"reddot\",     2.28e11,        4,                      0,                      2.41e4,     0.642e24,   \\'red\\'),\\n                 Particle(\"Gas\",        1,              6.64e11,                -1.303e4,               0,          1.898e27,   \\'cyan\\'),\\n                 Particle(\"Ring\",       14.32e11,       5,                      0,                      0.97e4,     568e24,     \\'yellow\\'),\\n                 Particle(\"Sunaru\",     28.67e11,       6,                      0,                      0.68e4,     86.8e24,    \\'pink\\'),\\n                 Particle(\"Oceanboy\",   45.15e11,       7,                      0,                      0.54e4,     102e24,     \\'grey\\'),\\n                 Particle(\"Mun\",        1.499e11,       8,                      0,                      3.1e4,      7.34e22,    \\'gray\\'),\\n                 Particle(\"Io\",         -4.21e8,        6.64e11+1,              -1.303e4,               1.7e4,      8.93e22,    \\'violet\\'),\\n                 Particle(\"Europa\",     6.71e8,         6.64e11+2,              -1.303e4,               -1.3e4,     4.8e22,     \\'bordeaux\\'),\\n                 Particle(\"Ganymede\",   12,             6.64e11-1.07e9,         -1.303e4-1.08e4,        0,          14.8e22,    \\'gray\\'),\\n                 Particle(\"Callisto\",   13,             6.64e11+1.88e9,         -1.303e4-0.82e4,        0,          10.8e22,    \\'gray\\')]\\n\\n    # Particle(\"Dirt\", 1.496e11, 1, 0, 3e4, 5.97e24, \\'blue\\')\\n    # Particle(\"Gas\", 6.64e14, 2, 0, 1.303e4, 1.898e27, \\'cyan\\')\\n    # Particle(\"Mun\", 1.499e11, 4, 0, 4e4, 7.34e22, \\'gray\\')\\n    # Erde: 1.496e11 m ; 5.97e24 kg\\n    # Jupiter: 6.64e14 m ; 1.898e27 kg\\n    # Io: 4.21e8 m ; 8.93e22 kg\\n    # Europa: 6.71e8 m ; 4.8e22 kg\\n    # Ganymede: 1.07e9 m ; 14.8e22 kg\\n    # Callisto:1.88e9 m ; 10.8e22 kg\\n'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import List, Tuple\n",
-    "\n",
-    "import numpy as np\n",
-    "from numpy import sqrt, sign\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.animation as animation\n",
-    "from mpl_toolkits import mplot3d\n",
-    "\n",
-    "'''\n",
-    "Aufgabenstellung:\n",
-    "Simulation der Bahnbewegung aller 8 im Sonnensystem befindlichen Planeten in 2D inklusive ein paar Monde\n",
-    "'''\n",
-    "\n",
-    "# Das Programm ist objektorientiert geschrieben, daher hier zunächst die Init-Funktion der Klasse.\n",
-    "# Außerdem werden Funktionen geschrieben zum Zurückgeben der Kraft, Position, Geschwindigkeit und Beschleunigung.\n",
-    "class Particle():\n",
-    "    def __init__(self, name, pos_x, pos_y, pos_z, vel_x, vel_y, vel_z, mass, color) -> None:\n",
-    "        self.name = name\n",
-    "        self.pos_x = pos_x\n",
-    "        self.pos_y = pos_y\n",
-    "        self.pos_z = pos_z\n",
-    "        self.vel_x = vel_x\n",
-    "        self.vel_y = vel_y\n",
-    "        self.vel_z = vel_z\n",
-    "        self.mass = mass\n",
-    "        self.force_x = 0.0\n",
-    "        self.force_y = 0.0\n",
-    "        self.force_z = 0.0\n",
-    "        self.color = color\n",
-    "        self.pos_x_history = [pos_x]\n",
-    "        self.pos_y_history = [pos_y]\n",
-    "        self.pos_z_history = [pos_z]\n",
-    "\n",
-    "    # Rückgabe Kraft in x-, y- und z- Richtung\n",
-    "    def get_force(self) -> Tuple:\n",
-    "        return (self.force_x, self.force_y, self.force_z)\n",
-    "\n",
-    "    # Rückgabe Position in x-, y- und z- Richtung\n",
-    "    def get_position(self) -> Tuple:\n",
-    "        return (self.pos_x, self.pos_y, self.pos_z)\n",
-    "\n",
-    "    # Rückgabe Geschwindigkeit in x-, y- und z- Richtung\n",
-    "    def get_velocity(self) -> Tuple:\n",
-    "        return (self.vel_x, self.vel_y, self.vel_z)\n",
-    "\n",
-    "    # Rückgabe Beschleunigung in x-, y- und z- Richtung\n",
-    "    def get_acceleration(self) -> Tuple:\n",
-    "        return (self.force_x/self.mass, self.force_y/self.mass, self.force_z/self.mass)\n",
-    "\n",
-    "\n",
-    "# Berechnung der gesamt wirkenden Kraft auf einen Körper mittels Gravitationsgesetz:\n",
-    "def calc_force(objects) -> None:\n",
-    "    G = 6.67e-11  # Gravitationskonstante, N · m2 / kg2\n",
-    "    for object in objects:\n",
-    "        # Anfangskraft auf Körper = 0\n",
-    "        object.force_x = 0\n",
-    "        object.force_y = 0\n",
-    "        object.force_z = 0\n",
-    "\n",
-    "        # Jeder weitere Körper übt die Gravitationskraft auf den betrachteten Körper aus\n",
-    "        for neighbor in objects:\n",
-    "            if neighbor != object:  # Für jeden anderen Körper, der nicht der betrachtete Körper selber ist\n",
-    "                # Gesamt ausgeübte Gravitationskraft der Nachbarkörper auf den betrachteten Körper\n",
-    "                force = G * object.mass * neighbor.mass / ((object.pos_x - neighbor.pos_x) ** 2 +\n",
-    "                                                           (object.pos_y - neighbor.pos_y) ** 2 +\n",
-    "                                                           (object.pos_z - neighbor.pos_z) ** 2)\n",
-    "                # Richtung der Kraft in x-, y- und z- Richtung\n",
-    "                direction = (-(object.pos_x - neighbor.pos_x), - (object.pos_y - neighbor.pos_y), - (object.pos_z - neighbor.pos_z))\n",
-    "                if direction[1] and direction[0] != 0:\n",
-    "                    alpha = direction[0] / direction[1]\n",
-    "                else:\n",
-    "                    alpha = 0.001\n",
-    "                if direction[2] and direction[0] != 0:\n",
-    "                    beta = direction[0] / direction[2]\n",
-    "                else:\n",
-    "                    beta = 0.001\n",
-    "                if direction[1] and direction[2] != 0:\n",
-    "                    gamma = direction[1] / direction[2]\n",
-    "                else:\n",
-    "                    gamma = 0.001\n",
-    "\n",
-    "\n",
-    "                # Berechnung der Kraft in x-, y- und z- Richtung\n",
-    "                object.force_x += np.sign(direction[0]) * force / sqrt(1 + 1 / (alpha ** 2) + 1 / (beta ** 2))\n",
-    "                object.force_y += np.sign(direction[1]) * force / sqrt(1 + alpha ** 2 + 1 / (gamma ** 2))\n",
-    "                object.force_z += np.sign(direction[2]) * force / sqrt(1 + beta ** 2 + gamma ** 2 )\n",
-    "\n",
-    "\n",
-    "# Funktion zum Berechnen der Geschwindigkeit in x- und y-Richtung\n",
-    "# Zum Berechnen der neuen Geschwindigkeit v in jedem Zeitschritt dt wird benutzt, dass\n",
-    "# F=m*a bzw a = F/m und a = v*dt gilt, also v=F/m *dt. Jede Kraftänderung führt zu einer Geschwindigkeitsänderung,\n",
-    "# welche die vorherige Geschwindigkeit beeinflusst. Daher wird die neue Geschwindigkeit auf die alte drauf gerechnet\n",
-    "def calc_velocity(objects, dt) -> None:\n",
-    "    for object in objects:\n",
-    "        object.vel_x += (object.force_x/object.mass) * dt\n",
-    "        object.vel_y += (object.force_y/object.mass) * dt\n",
-    "        object.vel_z += (object.force_z / object.mass) * dt\n",
-    "\n",
-    "\n",
-    "# Außerdem wird noch die Position nach jedem Zeitschritt dt benötigt. Dabei wird ausgenutzt, dass sich die\n",
-    "# Strecke pos in jedem Zeitschritt dt näherungsweise linear ändert mit pos = v * dt. Dies gilt nicht mehr, wenn\n",
-    "# zwei Körper sehr dicht beieinander sind. In dem aktuellen Problem betrachten wir aber die Bewegung der Planeten\n",
-    "# um die Sonne, wenn man den Zeitschritt dt also hinreichend klein wählt, kann man die lineare Änderung der Strecke\n",
-    "# annehmen, da sich zwei Planeten nicht so dicht kommen.\n",
-    "# Jede Positionsänderung wird auf die vorherige Position draufgerechnet.\n",
-    "#\n",
-    "# Funktion zum Berechnen der Position in x-, y- und z- Richtung\n",
-    "def calc_position(objects, dt) -> None:\n",
-    "    for object in objects:\n",
-    "        object.pos_x += object.vel_x * dt\n",
-    "        object.pos_y += object.vel_y * dt\n",
-    "        object.pos_z += object.vel_z * dt\n",
-    "\n",
-    "\n",
-    "# Mit der Funktion simulation() wird die Simulation durchgeführt. Für jeden Körper wird zu jedem Zeitschritt\n",
-    "# die aufeinanderwirkende Kraft und die daraus resultierende Positionsänderung berechnet\n",
-    "def simulation(objects, dt, timesteps) -> List:\n",
-    "    memory = [[] for object in objects]\n",
-    "    for t in range(timesteps):\n",
-    "        calc_force(objects)\n",
-    "        calc_velocity(objects, dt)\n",
-    "        calc_position(objects, dt)\n",
-    "        for object in objects:\n",
-    "            object.pos_x_history.append(object.pos_x)\n",
-    "            object.pos_y_history.append(object.pos_x)\n",
-    "            object.pos_z_history.append(object.pos_x)\n",
-    "\n",
-    "        for i in range(len(objects)):\n",
-    "            memory[i].append(objects[i].get_position())\n",
-    "    return memory\n",
-    "\n",
-    "\n",
-    "# Mit der Funktion plotting3D() werden die Positionen dann in 3D geplottet\n",
-    "def plotting3D(data, string, legend, render_points, answ='no') -> None:\n",
-    "    fig = plt.figure()\n",
-    "    ax = fig.add_subplot(projection='3d')\n",
-    "    for object in data:\n",
-    "        ax.plot([i[0] for i in object[::len(object)//render_points]], [i[1] for i in object[::len(object)//render_points]], [i[2] for i in object[::len(object)//render_points]])\n",
-    "    plt.xlabel('x Position [m]', fontsize=14)\n",
-    "    plt.xticks(fontsize=14)\n",
-    "    plt.ylabel('y Position [m]', fontsize=14)\n",
-    "    plt.yticks(fontsize=14)\n",
-    "    ax.set_zlabel('z Position [m]', fontsize=14)\n",
-    "    ax.tick_params(axis='z', labelsize=14)\n",
-    "    plt.title(string + '3D', fontsize=16, fontweight='bold')\n",
-    "    plt.legend(legend)\n",
-    "    ax.set_zlim([-4.5e12, 4.5e12])\n",
-    "    if answ != 'no':\n",
-    "        plt.axis([-0.025e11, 0.025e11, -0.025e11, 0.025e11])\n",
-    "    plt.show()\n",
-    "\n",
-    "# Mit der Funktion plotting2D() werden die Positionen dann in 2D geplottet\n",
-    "def plotting2D(data, string, legend, render_points, answ='no') -> None:\n",
-    "    for object in data:\n",
-    "        plt.plot([i[0] for i in object[::len(object) // render_points]],\n",
-    "                 [i[1] for i in object[::len(object) // render_points]])\n",
-    "    plt.xlabel('x Position [m]', fontsize=14)\n",
-    "    plt.xticks(fontsize=14)\n",
-    "    plt.ylabel('y Position [m]', fontsize=14)\n",
-    "    plt.yticks(fontsize=14)\n",
-    "    plt.title(string + '2D', fontsize=16, fontweight='bold')\n",
-    "    plt.legend(legend)\n",
-    "    if answ != 'no':\n",
-    "        plt.axis([-0.025e11, 0.025e11, -0.025e11, 0.025e11])\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Das Programm ist objektorientiert geschrieben, wobei das Objekt particles ist. In diesem Objekt sind der Name,\n",
-    "# die Positionen, Geschwindigkeiten und Masse festgelegt und ihm wird eine Farbe zugeordnet.\n",
-    "\n",
-    "# Particle(name, pos_x [m], pos_y [m], pos_z [m], vel_x [m/s], vel_y [m/s], vel_z [m/s], mass [kg], colour)\n",
-    "if __name__ == '__main__':\n",
-    "    particles = [Particle(\"Sonne\",      0,              0,                      0,                  0,                      0,          0,      1.99e30,    'orange'),\n",
-    "                 Particle(\"Merkur\",     0.57e11,        1,                      7e9,                0,                      4.74e4,     0,      0.33e24,    'brown'),\n",
-    "                 Particle('Venus',      1.08e11,        2,                      6.4e9,              0,                      3.5e4,      0,      4.875e24,   'magenta'),\n",
-    "                 Particle(\"Erde\",       1.496e11,       3,                      1,                  0,                      3e4,        0,      5.97e24,    'blue'),\n",
-    "                 Particle(\"Mars\",       2.28e11,        4,                      7.2e9,              0,                      2.41e4,     0,      0.642e24,   'red'),\n",
-    "                 Particle(\"Jupiter\",    0,              6.64e11,                0,                  -1.3026e4,              0,          -0.296e4,      1.898e27,   'cyan'),\n",
-    "                 Particle(\"Saturn\",     14.32e11,       5,                      6.25e10,            0,                      0.97e4,     0,      568e24,     'yellow'),\n",
-    "                 Particle(\"Uranus\",     28.67e11,       6,                      3.9e10,             0,                      0.68e4,     0,      86.8e24,    'pink'),\n",
-    "                 Particle(\"Neptun\",     45.15e11,       7,                      1.4e11,             0,                      0.54e4,     0,      102e24,     'grey'),\n",
-    "                 Particle(\"Mun\",        1.496e11+3.829e8, 8,                    3.4e7,              0,                3e4+0.107e4,        0,      7.346e22,    'gray')]\n",
-    "                 # Particle(\"Io\",         -4.21e8,       6.64e11+1,              10,                 -1.303e4,               1.7e4,      0,      8.93e22,    'violet'),\n",
-    "                 # Particle(\"Europa\",     6.71e8,        6.64e11+2,              11,                 -1.303e4,               -1.3e4,     0,      4.8e22,     'bordeaux'),\n",
-    "                 # Particle(\"Ganymede\",   0,             6.64e11-1.07e9,         12,                 -1.303e4-1.08e4,        0,          0,      14.8e22,    'gray'),\n",
-    "                 # Particle(\"Callisto\",   0,             6.64e11+1.88e9,         13,                 -1.303e4+0.82e4,        0,          0,      10.8e22,    'gray')]\n",
-    "\n",
-    "# Ekliptikwerte:\n",
-    "# Merkur: 7°\n",
-    "# Venus: 3,4°\n",
-    "# Erde: 0°\n",
-    "# Mond: 5,1°\n",
-    "# Mars: 1,8°\n",
-    "# Jupiter: 1,3° --> z_max = 1.5e10m --> v_z = cos(90-1.3)*v_ges\n",
-    "# Saturn: 2,5°\n",
-    "# Uranus: 0,8°\n",
-    "# Neptun: 1,8°\n",
-    "\n",
-    "\n",
-    "\n",
-    "    # Zeitschrittgröße dt\n",
-    "    # 24h = 86400s -> gut für Planeten, aber nicht für Mond\n",
-    "    # Der Mond braucht 27,3 Tage um Erde\n",
-    "    dt = 86400\n",
-    "    # dt = 3600  # 1h\n",
-    "\n",
-    "    # Insgesamt betrachtete Zeit -> 10 Jahre: 365 * 10\n",
-    "    timesteps = 365 // 4\n",
-    "    # timesteps = 365 * 24 * 4\n",
-    "    render_points = timesteps\n",
-    "\n",
-    "\n",
-    "    # Durchführung der Simulation\n",
-    "    data = simulation(particles, dt, timesteps)\n",
-    "\n",
-    "    # Plotten der Daten in 2D\n",
-    "    plotting2D(data, 'Umlaufbahnen', legend=[i.name for i in particles], render_points=min(timesteps, render_points))\n",
-    "\n",
-    "    # Plotten der Daten in 3D\n",
-    "    plotting3D(data, 'Umlaufbahnen', legend=[i.name for i in particles], render_points=min(timesteps, render_points))\n",
-    "\n",
-    "'''\n",
-    "    # Animation\n",
-    "    # Define the meta data for the movie\n",
-    "    metadata = dict(title='Movie Test', artist='Matplotlib',\n",
-    "                    comment='Jupiter moons')\n",
-    "    writer = animation.FFMpegWriter() # fps=15, metadata=metadata)\n",
-    "\n",
-    "    color = ['orange', 'brown', 'magenta', 'red', 'blue', 'red', 'cyan', 'yellow', 'pink', 'grey', 'gray', 'violet', 'black', 'gray', 'gray']\n",
-    "    # Initialize the movie\n",
-    "    fig = plt.figure()\n",
-    "    ax = fig.add_subplot(projection='3d')\n",
-    "    ax.set_xlabel('x Position [m]', fontsize=14)\n",
-    "    plt.xticks(fontsize=14)\n",
-    "    ax.set_ylabel('y Position [m]', fontsize=14)\n",
-    "    plt.yticks(fontsize=14)\n",
-    "    plt.title('Animation', fontsize=16, fontweight='bold')\n",
-    "    # plt.axis([1.492e11, 1.5e11, -4e10, 4e10])\n",
-    "    plt.axis([0.5e11, 1.55e11, -4e8, 1.5e11])\n",
-    "    ax.set_zlim([-4e8, 4e8])\n",
-    "    plt.legend([i.name for i in particles])\n",
-    "    # Update the frames for the movie\n",
-    "    for j in range(len(data[0][:])):\n",
-    "        for i in range(len(data)):\n",
-    "            ax.scatter(data[i][j][0], data[i][j][1], data[i][j][2], c=color[i], s=2)\n",
-    "            plt.pause(0.00000000001)\n",
-    "'''\n",
-    "'''\n",
-    "if __name__ == '__main__':\n",
-    "    particles = [Particle(\"Sol\",        0,              0,                      0,                      0,          1.99e30,    'orange'),\n",
-    "                 Particle(\"Mercurio\",   0.57e11,        3,                      0,                      4.74e4,     0.33e24,    'brown'),\n",
-    "                 Particle('Amore',      1.08e11,        2,                      0,                      3.5e4,      4.875e24,   'magenta'),\n",
-    "                 Particle(\"Dirt\",       1.496e11,       1,                      0,                      3e4,        5.97e24,    'blue'),\n",
-    "                 Particle(\"reddot\",     2.28e11,        4,                      0,                      2.41e4,     0.642e24,   'red'),\n",
-    "                 Particle(\"Gas\",        1,              6.64e11,                -1.303e4,               0,          1.898e27,   'cyan'),\n",
-    "                 Particle(\"Ring\",       14.32e11,       5,                      0,                      0.97e4,     568e24,     'yellow'),\n",
-    "                 Particle(\"Sunaru\",     28.67e11,       6,                      0,                      0.68e4,     86.8e24,    'pink'),\n",
-    "                 Particle(\"Oceanboy\",   45.15e11,       7,                      0,                      0.54e4,     102e24,     'grey'),\n",
-    "                 Particle(\"Mun\",        1.499e11,       8,                      0,                      3.1e4,      7.34e22,    'gray'),\n",
-    "                 Particle(\"Io\",         -4.21e8,        6.64e11+1,              -1.303e4,               1.7e4,      8.93e22,    'violet'),\n",
-    "                 Particle(\"Europa\",     6.71e8,         6.64e11+2,              -1.303e4,               -1.3e4,     4.8e22,     'bordeaux'),\n",
-    "                 Particle(\"Ganymede\",   12,             6.64e11-1.07e9,         -1.303e4-1.08e4,        0,          14.8e22,    'gray'),\n",
-    "                 Particle(\"Callisto\",   13,             6.64e11+1.88e9,         -1.303e4-0.82e4,        0,          10.8e22,    'gray')]\n",
-    "\n",
-    "    # Particle(\"Dirt\", 1.496e11, 1, 0, 3e4, 5.97e24, 'blue')\n",
-    "    # Particle(\"Gas\", 6.64e14, 2, 0, 1.303e4, 1.898e27, 'cyan')\n",
-    "    # Particle(\"Mun\", 1.499e11, 4, 0, 4e4, 7.34e22, 'gray')\n",
-    "    # Erde: 1.496e11 m ; 5.97e24 kg\n",
-    "    # Jupiter: 6.64e14 m ; 1.898e27 kg\n",
-    "    # Io: 4.21e8 m ; 8.93e22 kg\n",
-    "    # Europa: 6.71e8 m ; 4.8e22 kg\n",
-    "    # Ganymede: 1.07e9 m ; 14.8e22 kg\n",
-    "    # Callisto:1.88e9 m ; 10.8e22 kg\n",
-    "'''\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e044973d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/geodyn_final.py b/particle_simulation.py
similarity index 97%
rename from geodyn_final.py
rename to particle_simulation.py
index a5d8b2e07ddba3f8204d965027a7cbd3515e0384..77b9c09f09296a4ec75a005e3017d34da8e0fcbb 100644
--- a/geodyn_final.py
+++ b/particle_simulation.py
@@ -2,7 +2,7 @@ from random import random
 
 from sklearn.metrics import mean_poisson_deviance
 #from geodyn import simulation
-from geodyn_update import Particle
+from solarsystem import Particle
 import numpy as np
 from numpy import empty, sqrt, sign
 import matplotlib.pyplot as plt
@@ -346,12 +346,9 @@ class Cluster():
             ax.plot3D(body.pos_x_history,
                      body.pos_y_history, body.pos_z_history,
                      color=body.color)
-        plt.xlabel('x [m]', fontsize=16)
-        plt.ylabel('y [m]', fontsize=16)
-        plt.zlabel('z [m]', fontsize=16)
-        plt.tick_params(axis='x', labelsize=16)
-        plt.tick_params(axis='y', labelsize=16)
-        plt.tick_params(axis='z', labelsize=16)
+        ax.set_xlabel('x [m]')
+        ax.set_ylabel('y [m]')
+        ax.set_zlabel('z [m]')
         ax.set_xlim3d(-30000,70000)
         ax.set_ylim3d(-100000,100000)
         ax.set_zlim3d(-100000,100000)
@@ -395,11 +392,11 @@ class Cluster():
 
 # Initialization, simulation and plotting
 
-solarsystem = Cluster(timesteps=int(365 * 24 * 100))
-solarsystem.create_bodies(num_bodies=30, min_mass=10 ** 2, max_mass=10 ** 7, max_radius=10 ** 4, max_vel=10 ** (-5), s='2D')
+solarsystem = Cluster(timesteps=int(365*24*100))
+solarsystem.create_bodies(num_bodies=30, min_mass=10 ** 2, max_mass=10 ** 7, max_radius=10 ** 4, max_vel=10 ** (-5), s='3D')
 solarsystem.simulation(dt=86400 / 24)
 solarsystem.plot2d()
-# solarsystem.plot3d()
+solarsystem.plot3d()
 solarsystem._return_collisions()
 # solarsystem.animation2d()
 # solarsystem._return_collisions()
\ No newline at end of file
diff --git a/geodyn_update.py b/solarsystem.py
similarity index 100%
rename from geodyn_update.py
rename to solarsystem.py