Initial commit

.gitignore

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+BTs

distance.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+distances = []
+time = []
+
+for i in range (1,401):
+    num = str(i).rjust(5, '0')
+    file = "BTs/"+str(args.density)+"/boom."+num+".bt"
+    data = pd.read_csv(file, sep=' ', names=columns)
+    xPosAU = data["X"].mean()
+    xPos = xPosAU*1.5e8
+    yPosAU = data["Y"].mean()
+    yPos = xPosAU*1.5e8
+    zPosAU = data["Z"].mean()
+    zPos = xPosAU*1.5e8
+    distance = math.sqrt((xPos**2)+(yPos**2)+(zPos**2))
+    time.append(i)
+    distances.append(distance)
+
+print(min(distances))
+
+ax=plt.axes()
+# ax.set_yscale("log")
+ax.plot(time, distances)
+plt.show()

distanceVDensity.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+deviations = []
+distances = []
+time = []
+densities = ["300","350","400","450","500","550","600","650","700"]
+breakUpDistances = []
+
+for density in densities:
+    for i in range (1,320):
+        num = str(i).rjust(5, '0')
+        file = "BTs/High-Res-"+density+"/boom."+num+".bt"
+        data = pd.read_csv(file, sep=' ', names=columns)
+        data["X"] = data["X"]*1.5e8
+        data["Y"] = data["Y"]*1.5e8
+        data["Z"] = data["Z"]*1.5e8
+        data["X2"] = data["X"]**2
+        data["Y2"] = data["Y"]**2
+        data["Z2"] = data["Z"]**2
+        data["distance2"] = data["X2"]+data["Y2"]+data["Z2"]
+        data["distance"] = data["distance2"]**0.5
+        deviation = data["distance"].std()
+        distance = data["distance"].mean()
+        time.append(i)
+        deviations.append(deviation)
+        distances.append(distance)
+
+
+    del time[-1]
+    del time[-1]
+    del distances[-1]
+    del distances[-1]
+    firstDeriv=np.diff(deviations)
+    secondDeriv=np.diff(firstDeriv)
+
+    max_index=np.argmax(secondDeriv)
+
+    print(distances[max_index])
+    breakUpDistances.append(distances[max_index])
+
+ax=plt.axes()
+# ax.set_yscale("log")
+ax.plot(densities, breakUpDistances)
+plt.show()
+

energy.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+Kenergies = []
+Genergies = []
+time = []
+
+for i in range (1,401):
+    num = str(i).rjust(5, '0')
+    file = "BTs/"+str(args.density)+"/boom."+num+".bt"
+    data = pd.read_csv(file, sep=' ', names=columns)
+    data["X"] = data["X"]*1.5e8
+    data["Y"] = data["Y"]*1.5e8
+    data["Z"] = data["Z"]*1.5e8
+    data["X2"] = data["X"]**2
+    data["Y2"] = data["Y"]**2
+    data["Z2"] = data["Z"]**2
+    data["distance2"] = data["X2"]+data["Y2"]+data["Z2"]
+    data["distance"] = data["distance2"]**0.5
+    data["vX"] = data["vX"]*29880
+    data["vY"] = data["vY"]*29880
+    data["vZ"] = data["vZ"]*29880
+    data["vX2"] = data["vX"]**2
+    data["vY2"] = data["vY"]**2
+    data["vZ2"] = data["vZ"]**2
+    data["Mass"] = data["Mass"]*1.989e30
+    data["vel2"] = data["X"]+data["Y"]+data["Z"]**2
+    data["vel"] = data["distance2"]**0.5
+    data["KE"] = 0.5*data["Mass"]*data["vel2"]
+    data["GPE"] = (6.67e-11*1.89e27*data["Mass"])/(data["distance"])
+    # data["Energy"] = data["KE"]+data["GPE"]
+    # energy = data["Energy"].sum()
+    time.append(i)
+    KE = data["KE"].sum()
+    Kenergies.append(KE)
+    GPE = data["GPE"].sum()
+    Genergies.append(GPE)
+
+ax=plt.axes()
+# ax.set_yscale("log")
+ax.plot(time, Kenergies, label="Kinetic")
+ax.plot(time, Genergies, label="Gravitational")
+ax.legend()
+plt.show()

getDistance.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+parser.add_argument("--frame", "-f")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+
+
+num = str(args.frame).rjust(5, '0')
+file = "BTs/"+str(args.density)+"/boom."+num+".bt"
+data = pd.read_csv(file, sep=' ', names=columns)
+data["X"] = data["X"]*1.5e8
+data["Y"] = data["Y"]*1.5e8
+data["Z"] = data["Z"]*1.5e8
+data["X2"] = data["X"]**2
+data["Y2"] = data["Y"]**2
+data["Z2"] = data["Z"]**2
+data["distance2"] = data["X2"]+data["Y2"]+data["Z2"]
+data["distance"] = data["distance2"]**0.5
+
+distance = data["distance"].mean()
+
+print(distance)

position.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits import mplot3d
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+xposs = []
+yposs = []
+zposs = []
+
+for i in range (1,401):
+    num = str(i).rjust(5, '0')
+    file = "BTs/"+str(args.density)+"/boom."+num+".bt"
+    data = pd.read_csv(file, sep=' ', names=columns)
+    data["X"] = data["X"]*1.5e8
+    data["Y"] = data["Y"]*1.5e8
+    data["Z"] = data["Z"]*1.5e8
+    xpos = data["X"].mean()
+    ypos = data["Y"].mean()
+    zpos = data["Z"].mean()
+    xposs.append(xpos)
+    yposs.append(ypos)
+    zposs.append(zpos)
+
+theta = np.linspace(0, 2 * np.pi, 100)
+phi = np.linspace(0, np.pi, 50)
+theta, phi = np.meshgrid(theta, phi)
+r = 69911
+
+x = r * np.sin(phi) * np.cos(theta)
+y = r * np.sin(phi) * np.sin(theta)
+z = r * np.cos(phi)
+
+
+ax=plt.axes(projection='3d')
+ax.set_box_aspect([1, 1, 1])
+max_range = 0.5*np.max([np.max(np.abs(xposs)), np.max(np.abs(yposs)), np.max(np.abs(zposs))])
+ax.set_xlim(-max_range, max_range)
+ax.set_ylim(-max_range, max_range)
+ax.set_zlim(-max_range, max_range)
+ax.plot(xposs, yposs, zposs)
+ax.plot_surface(x, y, z, alpha=0.3)
+plt.show()
+

seperation.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+deviations = []
+time = []
+
+for i in range (1,401):
+    num = str(i).rjust(5, '0')
+    file = "BTs/"+str(args.density)+"/boom."+num+".bt"
+    data = pd.read_csv(file, sep=' ', names=columns)
+    data["X"] = data["X"]*1.5e8
+    data["Y"] = data["Y"]*1.5e8
+    data["Z"] = data["Z"]*1.5e8
+    data["X2"] = data["X"]**2
+    data["Y2"] = data["Y"]**2
+    data["Z2"] = data["Z"]**2
+    data["distance2"] = data["X2"]+data["Y2"]+data["Z2"]
+    data["distance"] = data["distance2"]**0.5
+    deviation = data["distance"].std()
+    time.append(i)
+    deviations.append(deviation)
+
+ax=plt.axes()
+# ax.set_yscale("log")
+ax.plot(time, deviations)
+plt.show()

seperationDerivative.py

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+import math
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--density", "-d")
+
+args=parser.parse_args()
+
+columns = ["ID", "idx", "Mass", "Radius", "X", "Y", "Z", "vX", "vY", "vZ", "sX", "sY", "sZ", "Colour"]
+deviations = []
+time = []
+
+for i in range (1,401):
+    num = str(i).rjust(5, '0')
+    file = "BTs/"+args.density+"/boom."+num+".bt"
+    data = pd.read_csv(file, sep=' ', names=columns)
+    data["X"] = data["X"]*1.5e8
+    data["Y"] = data["Y"]*1.5e8
+    data["Z"] = data["Z"]*1.5e8
+    data["X2"] = data["X"]**2
+    data["Y2"] = data["Y"]**2
+    data["Z2"] = data["Z"]**2
+    data["distance2"] = data["X2"]+data["Y2"]+data["Z2"]
+    data["distance"] = data["distance2"]**0.5
+    deviation = data["distance"].std()
+    time.append(i)
+    deviations.append(deviation)
+
+del time[-1]
+del time[-1]
+firstDeriv=np.diff(deviations)
+secondDeriv=np.diff(firstDeriv)
+
+ax=plt.axes()
+# ax.set_yscale("log")
+ax.plot(time, secondDeriv)
+plt.show()