def main():
if len(sys.argv) != 4:
print("Wrong number of arguments.")
print("Usage: " + sys.argv[0] + "<input file>" + " <output file>" +
"method of time integration:either 'euler' or 'verlet'")
quit()
else:
outfile_name = sys.argv[2]
infile_name = sys.argv[1]
# Open output file
outfile = open(outfile_name, "w")
#read in input file
infile = open(infile_name, "r")
#Get the specific constants for the particle from the input file
#read in first line from input file
constants = infile.readline()
#split first line up
tokens = constants.split(",")
#set up initial parameters for particles
D = tokens[0]
alpha = tokens[2]
r_e = tokens[1]
dt = 0.01
numstep = 20
time = 0.0
#set up the initial state of the particles
p1 = Particle3D.file_read(infile)
p2 = Particle3D.file_read(infile)
#create a list of particles p1,p2
particles = [p1, p2]
sep_vector = p1.separation(p2)
#calculate the magnitude of the separation between p1 and p2
distance = np.linalg.norm(sep_vector)
#get the total initial energy of the system
energy = Particle3D.kinetic_energy(p1) + Particle3D.kinetic_energy(
p2) + pot_energy_morse(distance, D, alpha, r_e)
#write the initial time, initial separation and initial total energy to the output file
outfile.write("{0:f} {1:f} {2:12.8f}\n".format(time, distance, energy))
# Initialise data lists for plotting later
time_list = []
distance_list = []
energy_list = []
def kinetic_energy_dw(Plist):
no_parts = len(Plist)
kinetic_energy = np.zeros(no_parts)
for i in range(no_parts):
kinetic_energy[i] = Particle3D.kinetic_energy(Plist[i])
return kinetic_energy
def kinetic_energy_dw(Plist):
"""
Method to return kinetic energy, which is given by:
1/2*m*v^2
:param particlei: Particle3D instance
:return: kinetic energy of body as a float
"""
no_parts = len(Plist)
kinetic_energy = np.zeros(no_parts)
for i in range(no_parts):
kinetic_energy[i] = Particle3D.kinetic_energy(Plist[i])
return kinetic_energy
def main():
if len(sys.argv) != 4:
print("Wrong number of arguments.")
print("Usage: " + sys.argv[0] + "<input file>" + " <output file>" +
" method of time integration: either euler or verlet")
quit()
else:
outfile_name = sys.argv[2]
infile_name = sys.argv[1]
# Open output file
outfile = open(outfile_name, "w")
#read in input file
infile = open(infile_name, "r")
#Get the specific constants for the particle from the input file
#read in first line from input file
constants = infile.readline()
#split first line up
tokens = constants.split(",")
#set up initial parameters for particles
D = tokens[0]
alpha = tokens[2]
r_e = tokens[1]
dt = 0.01
numstep = 2000
time = 0.0
#set up the initial state of the particles
p1 = Particle3D.file_read(infile)
p2 = Particle3D.file_read(infile)
sep_vector = p1.separation(p2)
#calculate the magnitude of the separation between p1 and p2
distance = np.linalg.norm(sep_vector)
#get the total initial energy of the system
energy = Particle3D.kinetic_energy(p1) + Particle3D.kinetic_energy(
p2) + pot_energy_morse(distance, D, alpha, r_e)
#write the initial time, initial separation and initial total energy to the output file
outfile.write("{0:f} {1:f} {2:12.8f}\n".format(time, distance, energy))
tot_time = dt * numstep
#list to hold timesteps
dt_list = []
#list to hold energy fluctuations
fluc_list = []
# Start the time integration loop
for dt in np.linspace(0.001, 0.15, 500):
# Initialise data lists for plotting later
time_list = []
distance_list = []
energy_list = []
numstep = int(tot_time / dt)
for i in range(numstep):
#use time integration method to get the new separations and new total energy after the time step
if sys.argv[3] == 'euler':
new_data = symp_euler(p1, p2, dt, D, alpha, r_e)
else:
new_data = v_verlet(p1, p2, dt, D, alpha, r_e)
#get new separation
sep = new_data[0]
#get new total energy
new_energy = new_data[1]
# Output particle information
outfile.write("{0:f} {1:f} {2:12.8f}\n".format(
time, sep, new_energy))
# Append information to data lists
time_list.append(time)
distance_list.append(sep)
energy_list.append(new_energy)
# Increase time
time = time + dt
#find fluctuation
fluctuation = abs(
(max(energy_list) - min(energy_list)) / np.mean(energy_list))
#add the dt and the corresponding energy fluctuation to lists
dt_list.append(dt)
fluc_list.append(fluctuation)
# Post-simulation:
#find the maximum dt such that the energy fluctuation is less than 0.001
for i in range(len(fluc_list)):
if fluc_list[len(fluc_list) - 1 - i] < 0.001:
print("the largest energy fluctuation less than 0.001 is:" +
str(fluc_list[len(fluc_list) - 1 - i]))
print(
"the maximum timestep that can be used so that the energy fluctuation is less than 0.001 is:"
+ str(dt_list[len(fluc_list) - 1 - i]))
break
#plot of dt vs energy fluctuation
if sys.argv[3] == 'euler':
pyplot.title('Symplectic Euler: energy fluctuation vs timestep')
else:
pyplot.title('Velocity verlet: energy fluctuation vs timestep')
pyplot.xlabel('timestep')
pyplot.ylabel('energy fluctuation')
pyplot.plot(dt_list, fluc_list)
pyplot.show()
# Close output file
outfile.close()
def main():
if len(sys.argv) != 3:
print("Wrong number of arguments.")
print("Usage: " + sys.argv[0] + " <input file> " + " <output file> ")
quit()
else:
outfile_name = sys.argv[2]
infile_name = sys.argv[1]
# Open output file
outfile = open(outfile_name, "w")
#read in input file
infile = open(infile_name, "r")
#Get the specific constants for the particle from the input file
#read in first line from input file
constants = infile.readline()
#split first line up
tokens = constants.split(",")
#set up initial parameters for particles
D = float(tokens[0])
alpha = float(tokens[2])
r_e = float(tokens[1])
dt = 0.01
numstep = 2000
time = 0.0
#set up the initial state of the particles
p1 = Particle3D.file_read(infile)
p2 = Particle3D.file_read(infile)
sep_vector = p1.separation(p2)
#calculate the magnitude of the separation between p1 and p2
distance = np.linalg.norm(sep_vector)
#get the total initial energy of the system
energy = Particle3D.kinetic_energy(p1) + Particle3D.kinetic_energy(
p2) + morse_pot(distance, D, alpha, r_e)
#write the initial time, initial separation and initial total energy to the output file
outfile.write("{0:f} {1:f} {2:12.8f}\n".format(time, distance, energy))
# Initialise data lists for plotting later
time_list = []
distance_list = []
energy_list = []
# Start the time integration loop
for i in range(numstep):
#use symplectic euler to get the new separations and new total energy after the time step
new_data = symp_euler(p1, p2, dt, D, alpha, r_e)
#get new separations
sep = new_data[0]
#get new total energy
new_energy = new_data[1]
# Output particle information
outfile.write("{0:f} {1:f} {2:12.8f}\n".format(time, sep, new_energy))
# Append information to data lists
time_list.append(time)
distance_list.append(sep)
energy_list.append(new_energy)
# Increase time
time = time + dt
# Post-simulation:
# Close output file
outfile.close()
# Plot particle trajectory to screen
pyplot.title('Symplectic Euler: separation vs time')
pyplot.xlabel('Time')
pyplot.ylabel('Separation')
pyplot.plot(time_list, distance_list)
pyplot.show()
# Plot particle energy to screen
pyplot.title('Symplectic Euler: total energy vs time')
pyplot.xlabel('Time')
pyplot.ylabel('Energy')
pyplot.plot(time_list, energy_list)
pyplot.show()