def temperature_evolution(*args, labels, title, save=False):
"""Plot MSD for the given simnames"""
fig = plt.figure(figsize=(8, 8), dpi=150)
ax = fig.add_subplot(1, 1, 1)
df1, df2 = extract.extract_def(args[0])
strain = np.linspace(0, 1.718, df1.shape[0])
for index, filename in enumerate(args):
df1, df2 = extract.extract_def(filename)
ax.plot(strain, df1.iloc[:, 7], label=labels[index], linewidth=2)
ax.legend(fontsize=16)
ax = set_x_props(ax, 'Strain')
ax = set_y_props(ax, 'Temperature')
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
textstr = '\n'.join(('Parallel electric field', 'Electric field = 1.2'))
ax.text(0.40,
0.70,
textstr,
transform=ax.transAxes,
fontsize=14,
verticalalignment='top',
bbox=props)
ax.set_xlim(0, 1.71)
if save:
plt.tight_layout()
full_path = imagesavepath + 'temperature-' + title + '.png'
plt.savefig(full_path, dpi=300)
def energy_all_evolution(*args, labels, title, save=False):
"""Plot energy for the given simnames"""
energy = [9, 12, 13, 14]
ene_label = ['Bonded-energy', 'Ecoul', 'Van-der-Waal', 'Total']
df1, df2 = extract.extract_def(args[0])
strain = np.linspace(0, 1.718, df1.shape[0])
label_fname = ''
for element in labels:
label_fname += element
for i in range(4):
fig = plt.figure(figsize=(8, 8), dpi=150)
ax = fig.add_subplot(1, 1, 1)
for index, filename in enumerate(args):
df1, df2 = extract.extract_def(filename)
ene_temp = (df1.iloc[:, energy[i]] - df1.iloc[0, energy[i]]).values
ax.plot(strain, ene_temp, label=labels[index], linewidth=2)
ax.legend(fontsize=16)
ax.set_title(title, fontsize=20)
ax = set_x_props(ax, 'Strain')
ax = set_y_props(ax, ene_label[i])
ax.set_xlim(0, 1.71)
if save:
plt.tight_layout()
full_path = imagesavepath + 'v20/energy_during_deformation/32C_128DP_energy_' + str(
ene_label[i]) + label_fname + '.png'
plt.savefig(full_path, dpi=300)
def return_mean_stress(*args):
"""Read stress for the each of the given files and return the mean stress.
Typically used for finding out the mean stress for a deformation of
the same system along different directions or at different equilibration
times.
Args:
*args (str): file name for the deformation.
Returns:
m_stress (list): a list containing the mean stress for the given deformations.
"""
df1, df2 = extract.extract_def(args[0])
m_stress = np.zeros((df1.shape[0], len(args)))
for index, arg in enumerate(args):
df1, df2 = extract.extract_def(arg)
df1 = df1.values
df2 = df2.values
deformation_along = np.argmax(
np.array([np.std(df1[:, 1]),
np.std(df1[:, 2]),
np.std(df1[:, 3])]))
m_stress[:, index] = df1[:, deformation_along + 1]
m_stress = np.mean(m_stress, axis=1)
return m_stress
def energy_evolution_single(simname, save=False):
"""
Find out energy evolution during the deformation of the given simulation.
Args:
simname (string): Name of the simulation
save (bool): Whether to save the plot
"""
def1, def2 = extract.extract_def(simname)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
#epair = (def1.iloc[:,8] - def1.iloc[0,8]).values
ebond = (def1.iloc[:, 9] - def1.iloc[0, 9]).values
ecoul = (def1.iloc[:, 12] - def1.iloc[0, 12]).values
evdwl = (def1.iloc[:, 13] - def1.iloc[0, 13]).values
etotal = (def1.iloc[:, 14] - def1.iloc[0, 14]).values
strain = np.linspace(0, 1.718, len(etotal))
#ax.plot(strain, epair, label = 'pairwise')
ax.plot(strain, ebond, label='bonded')
ax.plot(strain, ecoul, label='coulombic')
ax.plot(strain, evdwl, label='Van der Waals')
ax.plot(strain, etotal, label='total')
ax = set_x_props(ax, 'Strain')
ax = set_y_props(ax, 'Energy')
ax.set_xlim(0, 1.71)
ax.legend(fontsize=14)
plt.tight_layout()
if save:
curr_name = simname + '_energy_evolution_deformation.png'
full_path = imagesavepath + curr_name
plt.savefig(full_path)
def return_mean_temperature(*args):
"""Read temperature for the each of the given files and return the mean temperature.
Typically used for finding out the mean temperature for a deformation of
the same system along different directions or at different equilibration
times.
Args:
*args (str): file name for the deformation.
Returns:
m_temperature (list): a list containing the mean stress for the given deformations.
"""
df1, df2 = extract.extract_def(args[0])
m_temperature = np.zeros((df1.shape[0], len(args)))
for index, arg in enumerate(args):
df1, df2 = extract.extract_def(arg)
df1 = df1.values
df2 = df2.values
m_temperature[:, index] = df1[:, 7]
m_temperature = np.mean(m_temperature, axis=1)
return m_temperature
def energy_evolution_comparison(simname, ids, save=False):
"""
Compare energy evolution along different directions for the
given simulation.
Args:
simname (string): Name of the simulation
ids: if '2_x', '3_x' then [2,3]
save (bool): Whether to save the plot
"""
dirs = ['x', 'y', 'z', 'x', 'y', 'z']
df1 = {}
df2 = {}
col_nums = [8, 9, 12, 13, 14, 17, 7, 15, 16]
col_labels = [
'pairwise', 'bonded', 'coulombic', 'Van der Waals', 'total', 'density',
'temp', 'pe', 'ke'
]
for index in range(6):
fname = simname + '_' + str(ids[int(index / 3)]) + '_' + dirs[index]
df1[index], df2[index] = extract.extract_def(fname)
fig = plt.figure(figsize=(20, 18))
strain = np.arange(0, 1.718, 0.001)
for index in range(9):
ax = fig.add_subplot(3, 3, index + 1)
for inner_index in range(6):
#curr_energy = (df1[inner_index].iloc[:,col_nums[index]] - df1[inner_index].iloc[0,col_nums[index]]).values
#ax.plot(strain, curr_energy, label = dirs[inner_index])
ax.plot(strain,
df1[inner_index].iloc[:, col_nums[index]],
label=dirs[inner_index])
ax.legend(fontsize=16)
ax.set_title(col_labels[index], fontsize=20)
ax = set_x_props(ax, 'Strain')
ax = set_y_props(ax, 'Energy')
ax.set_xlim(0, 1.71)
if save:
curr_name = simname + '_energy_evolution_deformation_comparison.png'
full_path = imagesavepath + curr_name
plt.tight_layout()
plt.savefig(full_path)
def understand_variation(*args,
labels,
smoothed=False,
save=False,
msd=False,
deformation_strain_end=1.72002,
plot_strain_end=1.0,
units='lj'):
"""
Plot data for repeats of same simulation to understand intra-model
variation.
Args:
fname (str) : Names of the simulation.
smoothed (=False) : Apply a Savgol filter to smooth curves
"""
fig1 = plt.figure(figsize=[8, 8], dpi=100)
for index, arg in enumerate(args):
df1, df2 = extract.extract_def(arg)
df1 = df1.values
df2 = df2.values
deformation_along = np.argmax(
np.array([np.std(df1[:, 1]),
np.std(df1[:, 2]),
np.std(df1[:, 3])]))
plt.figure(1)
till = len(df1[:, deformation_along + 1])
strain = np.linspace(0, deformation_strain_end, till)
if smoothed:
if units == 'real':
y_noise = df1[:till, deformation_along + 1] * 1000
elif units == 'lj':
y_noise = df1[:till, deformation_along + 1]
y = signal.savgol_filter(
y_noise,
357, # window size used for filtering
1,
mode='nearest') # order of polynomial
y = np.asarray(y)
plt.plot(strain[:till], y.T, linewidth=2, label=arg)
else:
#strain=np.arange(0,1.718,0.00)
if units == 'real':
plt.plot(strain[:till],
df1[:till, deformation_along + 1] * 1000,
linewidth=2,
label=arg)
elif units == 'lj':
plt.plot(strain[:till],
df1[:till, deformation_along + 1],
linewidth=2,
label=labels[index])
if msd:
plt.figure(2)
ax2 = plt.gca()
ax2.plot(strain, df2[:, 4], label='positive')
ax2.plot(strain, df2[:, 8], label='negative')
ax2.plot(strain, df2[:, 12], label='ions')
ax2.plot(strain,
df2[:, 16],
label='neutral',
linestyle=':',
linewidth=2)
#ax2.plot(strain,df2[:,20],label=arg,linewidth=2)
#ax3.plot(df2[:,0],df2[:,19],label='positive')
#ax3.plot(df2[:,0],df2[:,22],label='negative')
#ax3.plot(df2[:,0],df2[:,25],label='ions')
#ax3.plot(df2[:,0],df2[:,28],label='neutral')
#plt.figure(3)
#plt.plot(strain,df2[:,35],label=arg)
plt.figure(1)
plt.xlabel('Strain', fontsize=20, fontfamily='serif')
if units == 'real':
plt.ylabel('Stress (MPa)', fontsize=20, fontfamily='serif')
elif units == 'lj':
plt.ylabel('Stress', fontsize=20, fontfamily='serif')
ax = plt.gca()
ax = set_strain_props(ax)
fontProperties = {'family': 'serif', 'size': 16}
ax.set_yticklabels(ax.get_yticks(), fontProperties)
plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%g'))
plt.legend(fontsize=16)
ax.set_xlim(0, plot_strain_end)
plt.tight_layout()
if msd:
ax2 = set_strain_props(ax2)
plt.ylabel('MSD ($\AA ^2$)', fontsize=20, fontfamily='serif')
plt.legend()
plt.close()
plt.figure(3)
plt.xlabel('Strain', fontsize=20, fontfamily='Serif')
plt.ylabel('Non Gaussian Parameter', fontsize=20, fontfamily='Serif')
plt.legend()
plt.close()
full_path = imagesavepath + str(args[0])[:-4]
if save:
if smoothed:
fname = full_path + '_smoothed.png'
print(fname)
fig1.savefig(fname, dpi=300)
else:
fname = full_path + '.png'
print(fname)
fig1.savefig(fname, dpi=300)
