def core_density_plot(data,
out_file=None,
show=True,
retrieve=False,
color='k'):
nbody = Bodies()
nbody.copy(data)
core_density = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
center = center_of_mass(nbody.r[i], nbody.m[i])
distances[i] = nbody[i].sort_by_radius(center=center)
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.1:
confined_mass += nbody.m[i, j]
j += 1
core_density[i] = 3 * confined_mass / 4 / np.pi / distances[i, j]**3
plt.semilogy(nbody.t, core_density, color=color)
plt.xlabel('Time, s')
plt.ylabel('Core density, kg m$^{-3}$')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return core_density
def half_mass_radius_center_off_plot(data,
out_file=None,
show=True,
retrieve=False,
color='k'):
nbody = Bodies()
nbody.copy(data)
half_mass_radius = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
distances[i] = nbody[i].sort_by_radius()
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.5:
confined_mass += nbody.m[i, j]
j += 1
half_mass_radius[i] = distances[i, j]
plt.plot(nbody.t, half_mass_radius, color=color)
plt.xlabel('Time, s')
plt.ylabel('Half mass radius, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return half_mass_radius
def potential_energy_plot_other(data,
out_file=None,
show=True,
retrieve=False,
color='b'):
nbody = Bodies()
nbody.copy(data)
all_potential_energy = np.zeros(nbody.m.shape[0])
for j in range(nbody.m.shape[1]):
if j % 10 == 0:
print j
for k in range(j):
distances = np.sqrt(
np.sum(np.power(nbody.r[:, j, :] - nbody.r[:, k, :], 2),
axis=1))
all_potential_energy -= constants.G * nbody.m[:,
j] * nbody.m[:,
k] / distances
plt.plot(nbody.t, all_potential_energy, color=color)
plt.xlabel('Time, s')
plt.ylabel('Potential energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_potential_energy
def potential_energy_center_off_plot(data,
out_file=None,
show=True,
retrieve=False,
color='b'):
nbody = Bodies()
nbody.copy(data)
all_potential_energy = np.zeros(nbody.m.shape[0])
potential_energy = np.zeros(nbody.m.shape[1])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
#distances[i] = np.sqrt(np.sum(nbody.r[i, :, :] ** 2, axis=1))
# Turiu zvaigzdziu atstumus iki centro
distances[i] = nbody[i].sort_by_radius()
confined_mass = 0
for j in range(nbody.m.shape[1]):
potential_energy[j] = -constants.G * confined_mass * nbody.m[
i, j] / distances[i, j]
confined_mass += nbody.m[i, j]
all_potential_energy[i] = np.sum(potential_energy)
plt.plot(nbody.t, all_potential_energy, color=color)
plt.xlabel('Time')
plt.ylabel('Potential energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_potential_energy
def potential_energy_center_off_plot(data, out_file=None, show=True, retrieve=False, color='b'):
nbody = Bodies()
nbody.copy(data)
all_potential_energy = np.zeros(nbody.m.shape[0])
potential_energy = np.zeros(nbody.m.shape[1])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
#distances[i] = np.sqrt(np.sum(nbody.r[i, :, :] ** 2, axis=1))
# Turiu zvaigzdziu atstumus iki centro
distances[i] = nbody[i].sort_by_radius()
confined_mass = 0
for j in range(nbody.m.shape[1]):
potential_energy[j] = -constants.G * confined_mass * nbody.m[i, j] / distances[i, j]
confined_mass += nbody.m[i, j]
all_potential_energy[i] = np.sum(potential_energy)
plt.plot(nbody.t, all_potential_energy, color=color)
plt.xlabel('Time')
plt.ylabel('Potential energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_potential_energy
def total_energy_plot(data,
out_file=None,
show=True,
show_ratio=False,
color='0.5'):
nbody = Bodies()
nbody.copy(data)
all_kinetic_energy = kinetic_energy_plot(data,
out_file=None,
show=False,
retrieve=True)
all_potential_energy = potential_energy_plot_other(data,
out_file=None,
show=False,
retrieve=True)
if show_ratio is True:
plt.clf()
plt.plot(nbody.t,
all_kinetic_energy / all_potential_energy,
color=color)
else:
plt.plot(nbody.t,
all_kinetic_energy + all_potential_energy,
color=color)
plt.ylabel('Energy')
plt.xlabel('Time, s')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
def core_density_plot(data, out_file=None, show=True, retrieve=False, color='k'):
nbody = Bodies()
nbody.copy(data)
core_density = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
center = center_of_mass(nbody.r[i], nbody.m[i])
distances[i] = nbody[i].sort_by_radius(center=center)
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.1:
confined_mass += nbody.m[i, j]
j += 1
core_density[i] = 3 * confined_mass / 4 / np.pi / distances[i, j]**3
plt.semilogy(nbody.t, core_density, color=color)
plt.xlabel('Time, s')
plt.ylabel('Core density, kg m$^{-3}$')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return core_density
def center_of_mass_plot(data,
out_file=None,
show=True,
retrieve=False,
color='k'):
nbody = Bodies()
nbody.copy(data)
centers_of_mass = np.zeros((nbody.m.shape[0], 3))
for i in range(nbody.m.shape[0]):
centers_of_mass[i] = center_of_mass(nbody.r[i], nbody.m[i])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(centers_of_mass[:, 0],
centers_of_mass[:, 1],
zs=centers_of_mass[:, 2],
c=color,
depthshade=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return center_of_mass
def half_mass_radius_center_off_plot(data, out_file=None, show=True, retrieve=False, color='k'):
nbody = Bodies()
nbody.copy(data)
half_mass_radius = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
distances[i] = nbody[i].sort_by_radius()
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.5:
confined_mass += nbody.m[i, j]
j += 1
half_mass_radius[i] = distances[i, j]
plt.plot(nbody.t, half_mass_radius, color=color)
plt.xlabel('Time, s')
plt.ylabel('Half mass radius, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return half_mass_radius
def mean_distance_plot(data,
out_file=None,
show=True,
retrieve=False,
color='k'):
nbody = Bodies()
nbody.copy(data)
mean_distance = np.zeros(nbody.m.shape[0])
#mass = np.sum(nbody[0].m)
#rad = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
center = center_of_mass(nbody.r[i], nbody.m[i])
mean_distance[i] = np.mean(np.sqrt(
np.sum((nbody.r[i, :, :] - center)**2, axis=1)),
axis=0)
#rad[i] = analytic_collapse.radius_at_t(nbody.t[i], 1e14, 3./4./np.pi*mass/1e14**3)
plt.plot(nbody.t, mean_distance, color=color)
#plt.plot(nbody.t, rad, color='b')
plt.xlabel('Time, s')
plt.ylabel('Mean distance, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return mean_distance
def uniform_distribution(N, R, koeff=1./3.):
#koeff nulemia pasiskirstyma.
x_stars, y_stars, z_stars = vector_projection(N, np.power(np.random.sample(N), koeff) * R)
bodies = Bodies()
bodies.r = np.transpose(np.array([x_stars, y_stars, z_stars]))
bodies.m = np.ones(N) * constants.SOLAR_MASS
bodies.v = np.zeros(bodies.r.shape)
M = np.sum(bodies.m)
ro = M/(4./3.*np.pi*R**3)
print "Expected collapse time : {:e}".format(np.sqrt(3*np.pi/32./constants.G/ro))
return bodies, 'dist{:.3}'.format(koeff)
def plummer(N, r_pl):
M_min = 0.08 # min zvaigzdes mase
M_max = 100 # max zvaigzdes mase
mStars = IMF_salpeter(N, M_min, M_max) * constants.SOLAR_MASS
rStars = np.sort(distance_plummer(N, r_pl))
xStars, yStars, zStars = vector_projection(N, rStars)
vStars = velocity_kepler(N, mStars, rStars)
v_xStars, v_yStars, v_zStars = vector_projection(N, vStars)
bodies = Bodies()
bodies.r = np.transpose(np.array([xStars, yStars, zStars]))
bodies.m = np.array(mStars)
bodies.v = np.transpose(np.array([v_xStars, v_yStars, v_zStars]))
return bodies, 'plummer'
def mean_distance_center_off_plot(data, out_file=None, show=True, retrieve=False, color='k'):
nbody = Bodies()
nbody.copy(data)
mean_distance = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
mean_distance[i] = np.mean(np.sqrt(np.sum(nbody.r[i, :, :] ** 2, axis=1)), axis=0)
plt.plot(nbody.t, mean_distance, color=color)
plt.xlabel('Time, s')
plt.ylabel('Mean distance, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return mean_distance
def kinetic_energy_plot(data, out_file=None, show=True, retrieve=False, color='r'):
nbody = Bodies()
nbody.copy(data)
all_kinetic_energy = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
all_kinetic_energy[i] = np.sum(np.sum(nbody.v[i] ** 2, axis=1) * nbody.m[i] / 2.)
plt.plot(nbody.t, all_kinetic_energy, color=color)
plt.xlabel('Time, s')
plt.ylabel('Kinetic energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_kinetic_energy
def total_energy_plot(data, out_file=None, show=True, show_ratio=False, color='0.5'):
nbody = Bodies()
nbody.copy(data)
all_kinetic_energy = kinetic_energy_plot(data, out_file=None, show=False, retrieve=True)
all_potential_energy = potential_energy_plot_other(data, out_file=None, show=False, retrieve=True)
if show_ratio is True:
plt.clf()
plt.plot(nbody.t, all_kinetic_energy / all_potential_energy, color=color)
else:
plt.plot(nbody.t, all_kinetic_energy + all_potential_energy, color=color)
plt.ylabel('Energy')
plt.xlabel('Time, s')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
def center_of_mass_plot(data, out_file=None, show=True, retrieve=False, color='k'):
nbody = Bodies()
nbody.copy(data)
centers_of_mass = np.zeros((nbody.m.shape[0], 3))
for i in range(nbody.m.shape[0]):
centers_of_mass[i] = center_of_mass(nbody.r[i], nbody.m[i])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(centers_of_mass[:, 0], centers_of_mass[:, 1], zs=centers_of_mass[:, 2], c=color, depthshade=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return center_of_mass
def lagrange_radius_plot(data, out_file=None, show=True):
nbody = Bodies()
nbody.copy(data)
lag_radii_10 = np.zeros(nbody.m.shape[0])
lag_radii_25 = np.zeros(nbody.m.shape[0])
lag_radii_50 = np.zeros(nbody.m.shape[0])
lag_radii_80 = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
center = center_of_mass(nbody.r[i], nbody.m[i])
distances[i] = nbody[i].sort_by_radius(center=center)
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.1:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_10[i] = distances[i, j]
while confined_mass <= total_mass * 0.25:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_25[i] = distances[i, j]
while confined_mass <= total_mass * 0.5:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_50[i] = distances[i, j]
while confined_mass <= total_mass * 0.8:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_80[i] = distances[i, j]
plt.plot(nbody.t, lag_radii_10, color='r', label='10% mass radii')
plt.plot(nbody.t, lag_radii_25, color='b', label='25% mass radii')
plt.plot(nbody.t, lag_radii_50, color='g', label='50% mass radii')
plt.plot(nbody.t, lag_radii_80, color='k', label='80% mass radii')
plt.xlabel('Time, s')
plt.ylabel('Lagrange radii, m')
plt.xlim((0, nbody.t[-1]))
plt.legend(frameon=False, loc=0)
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
def lagrange_radius_plot(data, out_file=None, show=True):
nbody = Bodies()
nbody.copy(data)
lag_radii_10 = np.zeros(nbody.m.shape[0])
lag_radii_25 = np.zeros(nbody.m.shape[0])
lag_radii_50 = np.zeros(nbody.m.shape[0])
lag_radii_80 = np.zeros(nbody.m.shape[0])
distances = np.zeros(nbody.m.shape)
for i in range(nbody.m.shape[0]):
total_mass = np.sum(nbody.m[i])
center = center_of_mass(nbody.r[i], nbody.m[i])
distances[i] = nbody[i].sort_by_radius(center=center)
confined_mass = 0
j = 0
while confined_mass <= total_mass * 0.1:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_10[i] = distances[i, j]
while confined_mass <= total_mass * 0.25:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_25[i] = distances[i, j]
while confined_mass <= total_mass * 0.5:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_50[i] = distances[i, j]
while confined_mass <= total_mass * 0.8:
confined_mass += nbody.m[i, j]
j += 1
lag_radii_80[i] = distances[i, j]
plt.plot(nbody.t, lag_radii_10, color='r', label='10% mass radii')
plt.plot(nbody.t, lag_radii_25, color='b', label='25% mass radii')
plt.plot(nbody.t, lag_radii_50, color='g', label='50% mass radii')
plt.plot(nbody.t, lag_radii_80, color='k', label='80% mass radii')
plt.xlabel('Time, s')
plt.ylabel('Lagrange radii, m')
plt.xlim((0, nbody.t[-1]))
plt.legend(frameon=False, loc=0)
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
def getShip(self, i, shipfile):
#method to intitialize a ship into the simulation
#takes a timestep and a file containing info on the ship and sets up the required properties
with open(shipfile, 'r') as f:
for line in f:
for word in line.split():
self.ships.append(word)
k = 0
while self.ships[k] != 'end':
if self.ships[k] == 'new':
self.names.append(self.ships[k + 1])
self.masses.append(float(self.ships[k + 3]))
self.positions.append(float(self.ships[k + 5]))
self.sizes.append(float(self.ships[k + 7]))
self.colors.append(self.ships[k + 9])
self.moons.append(self.ships[k + 11])
self.ships.append(self.ships[k + 13])
self.surfaces.append(float(self.ships[k + 15]))
deltaV = float(self.ships[k + 17])
angle = float(self.ships[k + 19])
goal = self.ships[k + 21]
k += 1
self.objects.append(
Bodies(self.names[-1], self.masses[-1], self.positions[-1],
self.sizes[-1], self.colors[-1], self.moons[-1],
self.ships[-1], i, self.surfaces[-1]))
self.objects[-1].goal = goal
for j in self.objects:
if self.objects[-1].moon == j.name:
previousLenght = self.distance(j, self.objects[0], i - 1,
False)
previousDirection = self.direction(j, self.objects[0], i - 1)
planetLength = self.distance(j, self.objects[0], i, False)
planetDirection = self.direction(j, self.objects[0], i)
self.objects[-1].position = np.vstack(
(self.objects[-1].position,
previousDirection * self.objects[-1].start +
previousDirection * previousLenght))
self.objects[-1].position = np.vstack(
(self.objects[-1].position,
planetDirection * self.objects[-1].start +
planetDirection * planetLength))
planetTan = self.normalize(
self.direction(self.objects[-1], j, i))
sunVel = math.sqrt(self.G * self.objects[0].mass / self.distance(
self.objects[-1], self.objects[0], i, False)) * self.normalize(
self.direction(self.objects[-1], self.objects[0], i))
planetVel = planetTan * 460
launchVel = self.setShipVel(self.objects[-1], i, deltaV, True, angle)
self.objects[-1].velocity = np.vstack(
(self.objects[-1].velocity, sunVel + planetVel + launchVel))
self.objects[-1].acs = np.vstack(
(self.objects[-1].acs, self.acceleration(self.objects[-1], i - 1)))
def potential_energy_plot_other(data, out_file=None, show=True, retrieve=False, color='b'):
nbody = Bodies()
nbody.copy(data)
all_potential_energy = np.zeros(nbody.m.shape[0])
for j in range(nbody.m.shape[1]):
if j % 10 == 0:
print j
for k in range(j):
distances = np.sqrt(np.sum(np.power(nbody.r[:, j, :] - nbody.r[:, k, :], 2), axis=1))
all_potential_energy -= constants.G*nbody.m[:, j]*nbody.m[:, k]/distances
plt.plot(nbody.t, all_potential_energy, color=color)
plt.xlabel('Time, s')
plt.ylabel('Potential energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_potential_energy
def uniform_sphere(N, R):
phi = np.random.random((N)) * 2.0 * np.pi
costheta = (np.random.random((N)) - 0.5) * 2.0
u = np.random.random((N))
theta = np.arccos(costheta)
r = R * u**(1.0/3.0)
x_stars = r * np.sin(theta) * np.cos(phi)
y_stars = r * np.sin(theta) * np.sin(phi)
z_stars = r * np.cos(theta)
bodies = Bodies()
bodies.r = np.transpose(np.array([x_stars, y_stars, z_stars]))
bodies.m = np.ones(N) * constants.SOLAR_MASS
bodies.v = np.zeros(bodies.r.shape)
M = np.sum(bodies.m)
ro = M/(4./3.*np.pi*R**3)
print "Expected collapse time : {:e}".format(np.sqrt(3*np.pi/32./constants.G/ro))
return bodies, 'sphere'
def kinetic_energy_plot(data,
out_file=None,
show=True,
retrieve=False,
color='r'):
nbody = Bodies()
nbody.copy(data)
all_kinetic_energy = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
all_kinetic_energy[i] = np.sum(
np.sum(nbody.v[i]**2, axis=1) * nbody.m[i] / 2.)
plt.plot(nbody.t, all_kinetic_energy, color=color)
plt.xlabel('Time, s')
plt.ylabel('Kinetic energy')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return all_kinetic_energy
def mean_distance_plot(data, out_file=None, show=True, retrieve=False, color='k'):
nbody = Bodies()
nbody.copy(data)
mean_distance = np.zeros(nbody.m.shape[0])
#mass = np.sum(nbody[0].m)
#rad = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
center = center_of_mass(nbody.r[i], nbody.m[i])
mean_distance[i] = np.mean(np.sqrt(np.sum((nbody.r[i, :, :] - center) ** 2, axis=1)), axis=0)
#rad[i] = analytic_collapse.radius_at_t(nbody.t[i], 1e14, 3./4./np.pi*mass/1e14**3)
plt.plot(nbody.t, mean_distance, color=color)
#plt.plot(nbody.t, rad, color='b')
plt.xlabel('Time, s')
plt.ylabel('Mean distance, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return mean_distance
def mean_distance_center_off_plot(data,
out_file=None,
show=True,
retrieve=False,
color='k'):
nbody = Bodies()
nbody.copy(data)
mean_distance = np.zeros(nbody.m.shape[0])
for i in range(nbody.m.shape[0]):
mean_distance[i] = np.mean(np.sqrt(np.sum(nbody.r[i, :, :]**2,
axis=1)),
axis=0)
plt.plot(nbody.t, mean_distance, color=color)
plt.xlabel('Time, s')
plt.ylabel('Mean distance, m')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
if retrieve is True:
return mean_distance
def addRandomCelestial(self, i):
#method that takes a timestep and adds a body with random properties to the system at that timestep
mass = random.randrange(10**10, 10**17)
size = 1000000000
color = 'y'
self.objects.append(
Bodies('asteroid', mass, 1, size, color, 'no', 'True', i, 100000))
xpos = random.randrange(-1e14, 1e14)
ypos = random.randrange(-1e14, 1e14)
self.objects[-1].position = np.vstack(
(self.objects[-1].position, np.array([xpos, ypos])))
self.objects[-1].position = np.vstack(
(self.objects[-1].position, np.array([xpos, ypos])))
xvel = random.randrange(-150000, 150000)
yvel = random.randrange(-150000, 150000)
self.objects[-1].velocity = np.vstack(
(self.objects[-1].velocity, np.array([xvel, yvel])))
self.objects[-1].acs = np.vstack(
(self.objects[-1].acs, self.acceleration(self.objects[-1], i - 1)))
plt.clf()
plt.plot(nbody.t, all_kinetic_energy / all_potential_energy, color=color)
else:
plt.plot(nbody.t, all_kinetic_energy + all_potential_energy, color=color)
plt.ylabel('Energy')
plt.xlabel('Time, s')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
import analytic_collapse
from matplotlib.colors import colorConverter
if __name__ == "__main__":
bodies = Bodies()
bodies.from_pickle(np.load("plummer_N700_T1000_E1e+11_d2e+14.pkl"))
bodies.t = np.load('plummer_N700_T1000_E1e+11_d2e+14_time.npy')
#print colorConverter.colors
#for i in range(len(bodies[0].color)):
#print bodies[0].color[i][:3]
#print colorConverter.colors[tuple(bodies[0].color[i][:3])]
#print colorConverter.colors.get('k') == bodies[0].color[i][:3]
#center_of_mass_plot(bodies)
#core_density_plot(bodies)
#mean_distance_center_off_plot(bodies, color='r', show=False)
#mean_distance_plot(bodies)
#half_mass_radius_center_off_plot(bodies, color='r', show=False)
#half_mass_radius_plot(bodies)
#lagrange_radius_plot(bodies)
plt.plot(nbody.t,
all_kinetic_energy + all_potential_energy,
color=color)
plt.ylabel('Energy')
plt.xlabel('Time, s')
plt.xlim((0, nbody.t[-1]))
if out_file is not None:
plt.savefig(out_file)
if show is True:
plt.show()
import analytic_collapse
from matplotlib.colors import colorConverter
if __name__ == "__main__":
bodies = Bodies()
bodies.from_pickle(np.load("plummer_N700_T1000_E1e+11_d2e+14.pkl"))
bodies.t = np.load('plummer_N700_T1000_E1e+11_d2e+14_time.npy')
#print colorConverter.colors
#for i in range(len(bodies[0].color)):
#print bodies[0].color[i][:3]
#print colorConverter.colors[tuple(bodies[0].color[i][:3])]
#print colorConverter.colors.get('k') == bodies[0].color[i][:3]
#center_of_mass_plot(bodies)
#core_density_plot(bodies)
#mean_distance_center_off_plot(bodies, color='r', show=False)
#mean_distance_plot(bodies)
#half_mass_radius_center_off_plot(bodies, color='r', show=False)
#half_mass_radius_plot(bodies)
#lagrange_radius_plot(bodies)
def __init__(self, launchShips, date1, date2, date3, ship1, ship2, ship3,
addAsteriods, testAlignement, universe):
self.G = 6.67408 * 10**-11
self.launchShips = launchShips
self.date1 = date1
self.date2 = date2
self.date3 = date3
self.ship1 = ship1
self.ship1 = ship1
self.ship1 = ship1
self.addAsteriods = addAsteriods
self.testAlignement = testAlignement
self.objects = []
self.ships = []
self.info = []
self.names = []
self.masses = []
self.positions = []
self.sizes = []
self.colors = []
self.moons = []
self.count = 2
self.reset = None
self.switch = None
self.arived = None
self.flyby = None
self.ships = []
self.surfaces = []
self.alignements = []
self.alignement = None
self.crashes = []
self.crashed = []
self.asteroidFactor = 0.0001
with open(universe, 'r') as f:
for line in f:
for word in line.split():
self.info.append(word)
k = 0
while self.info[k] != 'end': #reads values from text file
if self.info[k] == 'new':
self.names.append(self.info[k + 1])
self.masses.append(float(self.info[k + 3]))
self.positions.append(float(self.info[k + 5]))
self.sizes.append(float(self.info[k + 7]))
self.colors.append(self.info[k + 9])
self.moons.append(self.info[k + 11])
self.ships.append(self.info[k + 13])
self.surfaces.append(float(self.info[k + 15]))
if self.info[k] == 'details':
self.stepLength = int(self.info[k + 2])
self.steps = int(self.info[k + 4])
k += 1
for i in range(0, len(
self.names)): #initilisez the objects and placing moons
self.objects.append(
Bodies(self.names[i], self.masses[i], self.positions[i],
self.sizes[i], self.colors[i], self.moons[i],
self.ships[i], 0, self.surfaces[i]))
if self.objects[i].moon != "no":
for j in self.objects:
if self.objects[i].moon == j.name:
planetLength = self.distance(j, self.objects[0], 0,
False)
planetDirection = self.direction(j, self.objects[0], 0)
self.objects[
i].position = planetDirection * self.objects[
i].start + planetDirection * planetLength
for i in self.objects: #initilising velocities
i.velocity = np.array([0, 0])
if i == self.objects[0]:
self.zeroPoint = np.array([0, 0])
continue
else:
i.velocity = i.velocity + math.sqrt(
self.G * self.objects[0].mass / self.distance(
i, self.objects[0], 0, False)) * self.normalize(
self.direction(i, self.objects[0], 0))
if i.moon != "no":
for j in self.objects:
if i == j or i.moon != j.name:
continue
else:
i.velocity = i.velocity + math.sqrt(
self.G * j.mass / self.distance(j, i, 0, False)
) * self.normalize(self.direction(i, j, 0))
i.zeroPoint = self.absDirection(i.velocity, 0, 0)
self.launch = self.steps
