def plot_system_temperature(dir):
items = os.listdir(dir)
log_files = []
for name in items:
if name[:13] == "log.boundary_":
log_files.append(name)
log_files = sorted(log_files)
n_files = len(log_files)
for file in log_files:
with open(dir + file, "r") as infile:
contents = infile.readlines()
i = 0
while i < len(contents):
if "on 4 procs for 5000 steps with" in contents[i]:
words = contents[i].split(" ")
n_atoms = int(words[-2])
break
i += 1
log = lammps_logfile.File(dir + file)
time = log.get("v_time")
Ek = log.get("KinEng")
T = log.get("Temp")
std = np.std(T[int(1 / 5 * len(T)):])
plt.plot(time,
T,
label=rf'# of atoms: {n_atoms}. $\sigma_T = {std:.4f}$')
plt.legend()
plt.xlabel(r"Time $[\tau]$")
plt.ylabel(r"Temperature $[Tk_b/\epsilon]$")
plt.show()
def plot_pressure(dir):
dir_files = os.listdir(dir)
log_files = []
for file in dir_files:
if file[:6] == "log.T_":
log_files.append(file)
n_files = len(log_files)
mean_temp = np.zeros(n_files)
mean_press = np.zeros(n_files)
for i, filename in enumerate(log_files):
log = lammps_logfile.File(dir + filename)
temp = log.get("Temp")
press = log.get("Press")
equil = int(len(temp) * 0.5)
mean_temp[i] = np.mean(temp[equil:-1])
mean_press[i] = np.mean(press[equil:-1])
idx = np.argsort(mean_temp)
plt.plot(mean_temp[idx], mean_press[idx], label='Simulation results')
plt.plot(mean_temp[idx],
idea_gas_law(mean_temp[idx]),
label='Ideal gas law')
plt.ylabel(r"Pressure $[P\sigma^3 / \epsilon]$")
plt.xlabel(r"Temperature $[ Tk_b/\epsilon]$")
plt.legend()
plt.show()
def plot_radial_distribution(dir,
n_time_steps=5000,
step_window=100,
n_bins=50,
n_info_lines=3):
dir_files = os.listdir(dir)
log_files = []
for file in dir_files:
if file[:7] == "log.rdf":
log_files.append(file)
# log_files = np.sort(log_files)
log_files = natsort.natsorted(log_files)
print(log_files)
# T_init_vals = list(range(20, 300, 20))
for i, file in enumerate(log_files):
# T_init = float(file[10:])
T = int(file[10:])
# T = T_init_vals[i]
log = lammps_logfile.File(dir + f"log.rdf_T_{T}")
# T = log.get("Temp")
# idx = len(T) / 2
bins, radials = read_rdf_dump(dir, file)
# T_mean = np.mean(T)
plt.plot(bins, radials / radials[-1], label=f"T={T:d}")
plt.legend(loc=1)
plt.xlabel(r"$r$")
plt.ylabel(r"$g(r)$")
plt.show()
def plot_radial_distribution(dir,
n_time_steps = 5000,
step_window = 100,
n_bins = 50,
n_info_lines = 3):
dir_files = os.listdir(dir)
log_files = []
for file in dir_files:
if file[:7] == "tmp.rdf":
log_files.append(file)
log_files = np.sort(log_files)
# print(log_files)
T_init_vals = [0.1, 1, 5]
print(T_init_vals)
for i, file in enumerate(log_files):
T_init = float(file[10:])
log = lammps_logfile.File(dir + f"log.rdf_T_{T_init_vals[i]}")
T = log.get("Temp")
idx = len(T) / 2
bins, radials = read_rdf_dump(dir, file)
T_mean = np.mean(T)
plt.plot(bins, radials/radials[-1], label=f"T={T_mean:.2f}")
plt.legend()
plt.xlabel(r"$r$")
plt.ylabel(r"$g(r)$")
plt.show()
def plot_thermostat(dir):
log_be = lammps_logfile.File(dir + "log.berendsen")
log_nh = lammps_logfile.File(dir + "log.nosehoover")
T_be = log_be.get("Temp")
T_nh = log_nh.get("Temp")
t = np.linspace(0, len(T_be)*0.005, len(T_be))
plt.plot(t, T_be, label='Berendsen')
plt.plot(t, T_nh, label='Nosé-Hoover')
plt.xlabel(r"t")
plt.ylabel(r"T")
plt.legend()
plt.show()
def plotTemp(dir, filename):
log = lammps_logfile.File(dir + filename)
time = log.get("v_time", run_num=2)
temp = log.get("c_moving_temp", run_num=2)
plt.plot(time, temp)
plt.xlabel(r"$t/\tau$")
plt.ylabel(r"$T/T_0$")
plt.show()
def update_timings(self, t0):
t1 = time.time()
self.elapsed_time = t1 - t0
try:
log = lammps_logfile.File(
os.path.join(self.path, self.outfile_name))
remaining_time = log.get('CPULeft', run_num=-1)
except:
remaining_time = None
if remaining_time is None:
self.remaining_time = 0.0
else:
self.remaining_time = round(remaining_time[-1])
def diffusion(dir, filename):
log = lammps_logfile.File(dir + filename)
dt = 0.005
time = np.array(log.get("v_time", run_num=2))
msd = np.array(log.get("c_my_msd[4]", run_num=2))
timeFinal = time[-1]
coeff = np.polyfit(time, msd, deg=1)[0]
D = coeff / (6 * timeFinal)
print(f"Estimated diffusion coefficient: D={D:.3f}")
plt.plot(time, msd)
plt.xlabel(r"$t$")
plt.ylabel(r"$\langle r^2(t) \rangle $")
plt.show()
def plot_diffusion(dir):
dir_files = os.listdir(dir)
log_files = []
for file in dir_files:
if file[:7] == "log.msd":
log_files.append(file)
msd_vals = []
T_vals = []
dt = 1.0
log_files = natsort.natsorted(log_files)
for i, filename in enumerate(log_files):
log = lammps_logfile.File(dir + filename)
msd = log.get("c_msd[4]", run_num=0)
# print(msd)
msd_vals.append(msd)
T_vals.append(str(filename[10:]))
t_vals = np.linspace(0, len(msd_vals[0]) * 10, len(msd_vals[0]))
for i in range(len(log_files)):
plt.plot(t_vals, msd_vals[i], label=f'T={T_vals[i]}')
plt.xlabel(r"$t$ [fs]")
plt.ylabel(r"$\langle r^2(t) \rangle$ [A$^2$]")
plt.legend()
plt.show()
D_vals = []
idx = 20
for i in range(len(log_files)):
D_vals.append(
np.polyfit(t_vals[-idx:], msd_vals[i][-idx:], deg=1)[0] / 6)
plt.scatter(T_vals, D_vals)
plt.xlabel(r"$T$ [K]")
plt.ylabel(r"$D(T)$ [A$^2$/fs]")
plt.show()
def plot_diffusion(dir):
dir_files = os.listdir(dir)
log_files = []
for file in dir_files:
if file[:7] == "log.msd":
log_files.append(file)
msd_vals = []
T_vals = []
dt = 0.005 # in units tau = 2.1569e3 fs
log_files = np.sort(log_files)
for i, filename in enumerate(log_files):
log = lammps_logfile.File(dir + filename)
msd = log.get("c_msd[4]")
msd_vals.append(msd)
T_vals.append(str(filename[10:]))
t_vals = np.linspace(0, len(msd_vals[0]) * 100 * dt, len(msd_vals[0]))
for i in range(len(log_files)):
plt.plot(t_vals, msd_vals[i], label=f'T={T_vals[i]}')
plt.xlabel(r"$t$ $[\tau]$")
plt.ylabel(r"$\langle r^2(t) \rangle$ $[\sigma^2]$")
plt.legend()
plt.show()
D_vals = []
for i in range(len(log_files)):
D_vals.append(np.polyfit(t_vals[-5:], msd_vals[i][-5:], deg=1)[0] / 6)
plt.scatter(T_vals, D_vals)
plt.xlabel(r"$T$ $[\epsilon/k_B]$")
plt.ylabel(r"$D(T)$ $[\sigma^2/ \tau]$")
plt.show()
def energy_vs_dt(dir):
items = os.listdir(dir)
log_files = []
for name in items:
if name[:6] == "log.dt":
log_files.append(name)
log_files = sorted(log_files)
n_files = len(log_files)
for file in log_files:
log = lammps_logfile.File(dir + file)
dt = float(file[7:])
time = log.get("v_time")
etot = log.get("TotEng")
plt.plot(time, etot, label=rf"$\Delta t = {dt}$")
plt.legend()
plt.xlabel(r"Time $[\tau]$")
plt.ylabel(r"Energy $[\epsilon]$")
plt.show()
def plot_pressure_grid(dir):
dir += "press_vs_temp_density/"
temps = [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0]
press = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0]
n_temps = len(temps)
n_rhos = len(press)
dirs = os.listdir(dir)
dirs = sorted(dirs)
press_grid = np.zeros((n_rhos, n_temps))
for i, d in enumerate(dirs):
log_files = os.listdir(dir + d)
n_files = len(log_files)
mean_temp = np.zeros(n_files)
mean_press = np.zeros(n_files)
density = float(d[3:])
for j, filename in enumerate(log_files):
log = lammps_logfile.File(dir + d + "/" + filename)
temp = log.get("Temp")
press = log.get("Press")
equil = int(len(temp) * 7 / 10)
mean_temp[j] = np.mean(temp[equil:-1])
mean_press[j] = np.mean(press[equil:-1])
idx = np.argsort(mean_temp)
press_grid[i, :] = mean_press[idx]
if density != 0.5 and density != 1.0:
plt.plot(mean_temp[idx],
mean_press[idx],
label=rf'$\rho={density}$')
plt.xlabel(r"Pressure $[P\sigma^3 / \epsilon]$")
plt.ylabel(r"Temperature $[ Tk_b/\epsilon]$")
plt.legend()
plt.show()
import os
import matplotlib.pyplot as plt
import lammps_logfile
log = lammps_logfile.File(os.path.join(os.path.dirname(__file__), "logfiles", "crack_log.lammps"))
print("Log keywords: ", log.get_keywords())
x = log.get("Time")
y = log.get("Pyy")
plt.figure(figsize=(6,6))
plt.subplot(221)
plt.plot(x, y)
plt.xlabel("$t$")
plt.ylabel("$p_{yy}$")
plt.subplot(222)
for i in range(log.get_num_partial_logs()):
x = log.get("Time", run_num=i)
y = log.get("Pyy", run_num=i)
plt.plot(x, y)
plt.xlabel("$t$")
plt.ylabel("$p_{yy}$")
plt.subplot(223)
x = log.get("Time")
y = log.get("Temp")
plt.plot(x, y)
plt.xlabel("$t$")
r_vec = np.zeros(n_files)
phi_vec = np.zeros(n_files)
vcm_vec = np.zeros(n_files)
for i, filename in enumerate(log_files):
idx_k = filename.index("_r")
idx_phi = filename.index("phi")
r = filename[idx_k + 2:idx_k + 5]
phi = filename[idx_phi + 3:idx_phi + 7]
r_vec[i] = r
phi_vec[i] = phi
log = lammps_logfile.File(data_dir + filename)
vcm = log.get("v_velocity_cm", run_num=0)
if type(vcm) is np.ndarray:
avg_vcm = np.mean(vcm)
vcm_vec[i] = avg_vcm
else:
print(f"no vcm data in file {filename}")
# exit()
sorted_idx = np.argsort(r_vec)
r_vec = r_vec[sorted_idx]
phi_vec = phi_vec[sorted_idx]
vcm_vec = vcm_vec[sorted_idx]
# print(r_vec)
# print(phi_vec)
print(vcm_vec)
def setUp(self):
self.log = lammps_logfile.File(os.path.join(os.path.dirname(__file__), "data", "log.lammps"))
def silicon_diffusion(dir):
files_dir = os.listdir(dir)
log_files = []
for file in files_dir:
if "log.T" in file:
log_files.append(file)
log_files = np.sort(log_files)
# data_dict = {}
# avg_temps = []
msd_vals = []
T_vals = []
for file in log_files:
log = lammps_logfile.File(os.path.join(dir, file))
T = log.get("Temp")
# size = T_temp.shape[0]
# size_2 = size//2
# T = T_temp[size_2:]
t = log.get("Time")#[size_2:]
msd = log.get("c_msd[4]")#[size_2:]
#
avg_temp = int(np.mean(T[int(len(T)/4):]))
# avg_temps.append(avg_temp)
# data_dict["T_" + str(avg_temp)] = T
# data_dict["msd_" + str(avg_temp)] = msd
msd_vals.append(msd)
T_vals.append(T[0])
# plt.plot(t, msd, label=f'T={avg_temp}')
n_files = len(log_files)
for i in range(n_files):
plt.plot(t, msd_vals[i], label=f'T={T_vals[i]}')
plt.legend()
plt.xlabel(r"$t$")
plt.ylabel(r"$\langle r^2(t) \rangle$")
plt.show()
plt.clf()
diffusion = np.zeros(n_files)
# plt.plot()
#
idx1 = int(len(t)/2)
idx2 = int(len(t)/10)
for i in range(n_files):
# diffusion[i] = np.polyfit(t[:-5], data_dict["msd_" + str(avg_temps[i])][:-5], deg=1)[0]/6
# diffusion[i] = np.polyfit(t[:-idx], msd_vals[i][:-idx], deg=1)[0] / 6
diffusion[i] = np.polyfit(t[-idx1:-idx2], msd_vals[i][-idx1:-idx2], deg=1)[0] / 6
#
a, b = np.polyfit(T_vals, diffusion, deg=1)
T_fit_vals = np.linspace(0, 8000, len(T_vals))
# plt.plot(T_vals, np.linspace(0, T_vals[-1], len(T_vals))*a + b, label='Line fit')
# plt.plot(T_fit_vals, T_fit_vals*a + b)
# plt.scatter(-b/a, 0, label=rf'Approximate melting point $T = {-b/a:.0f}K$')
plt.scatter(T_vals, diffusion, c="g")#, label="Simulated results")
plt.xlabel("T")
plt.ylabel("D(T)")
# plt.plot([1687, 1687], [-2.5, diffusion.max()], "b--", label=r"Experimental melting point $T = 1687$K")
# plt.plot([3538, 3538], [0, diffusion.max()], "r--", label="Experimental boiling point")
plt.legend()
plt.show()
