def test_plot_scatter():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# The default line style is iterating over color, line, and marker with
# hollow types.
linestyle = mpltex.linestyle_generator(colors=[],
lines=['-',':'],
markers=['o','s'],
hollow_styles=[False, False, True, True],
)
for i in range(8):
y = np.random.normal(size=10).cumsum()
x = np.arange(10)
ax.plot(x, y, label=str(i), **linestyle.next())
ax.locator_params(nbins=5)
ax.set_xlabel('Number of steps')
ax.set_ylabel('Distance')
ax.legend(loc='best', ncol=4)
fig.tight_layout(pad=0.1)
fig.savefig('test_special_custom_linestyle_generator')
def test_plot_scatter():
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# The default line style is iterating over color, line, and marker with
# hollow types.
linestyle = mpltex.linestyle_generator(
colors=[],
lines=['-', ':'],
markers=['o', 's'],
hollow_styles=[False, False, True, True],
)
for i in range(8):
y = np.random.normal(size=10).cumsum()
x = np.arange(10)
ax.plot(x, y, label=str(i), **next(linestyle))
ax.locator_params(nbins=5)
ax.set_xlabel('Number of steps')
ax.set_ylabel('Distance')
ax.legend(loc='best', ncol=4)
fig.tight_layout(pad=0.35)
fig.savefig('test_special_custom_linestyle_generator')
def plot_fig():
data = np.loadtxt("all.dat",
dtype={'names': ('folder', 'x', 'dA/dx', 'err', 'A'),
'formats': ('S20', 'f4', 'f4', 'f4', 'f4')})
xcut = 27.5 # free energy beyong 27.5 is too large
x0 = 28.36 # the top layer of Cu
x, y = [], []
for i in range(len(data)):
tx = data[i][1]
ty = data[i][4]
if tx < xcut:
x.append(x0-tx)
y.append(data[i][4])
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-',':'], markers=['o','s'],
hollow_styles=[False, False, True, True],
)
ax.plot(x, y, label="",
**linestyle.next())
ax.set_xlim([0, 9])
#ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6])
ax.set_xlabel("z$_I$ ($\AA$)")
ax.set_ylabel("Free Energy (kcal/mol)")
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
def plot_fig():
data = np.loadtxt("dA.dat")
data = data.transpose()
x, y = data[0], data[1]
y_int = integrate.cumtrapz(y, x, initial=0)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-',':'], markers=['o','s'],
hollow_styles=[False, False, True, True],
)
fig, ax = plt.subplots(1)
ax.plot(x[::50], y_int[::50], label="FE",
**linestyle.next())
ax.set_xlabel("r$_{C-O}$ ($\AA$)")
ax.set_ylabel("FE (eV)")
ax.legend(loc='best')
ax2 = ax.twinx()
ax2.plot(x[::50], y[::50], label="PMF",
**linestyle.next())
ax2.set_ylabel("PMF (eV/$\AA$)", color="green")
ax2.legend(loc='lower left')
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
o = open("all.dat", "w")
for i in range(len(x)):
o.write("%12.4f%12.4f%12.4f\n"%
(x[i], y[i], y_int[i]))
o.close()
def plot_fig_fe_pmf():
data = get_fe()
x, y, yerr = data[0], data[1], data[2]
y_int = integrate.cumtrapz(y, x, initial=0)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-', ':'],
markers=['o', 's'],
hollow_styles=[False, False, True, True],
)
fig, ax = plt.subplots(1)
ax.plot(x, y_int, label="FE", **linestyle.next())
ax.set_xlabel("r$_{C-O}$ ($\AA$)")
ax.set_ylabel("FE (eV)")
ax.legend(loc='best')
ax2 = ax.twinx()
ax2.errorbar(x, y, yerr=yerr, label="PMF", **linestyle.next())
ax2.set_ylabel("PMF (eV/$\AA$)", color="green")
ax2.legend(loc='lower left')
fig.tight_layout(pad=0.1)
fig.savefig("01-fe-pmf.png", dpi=600)
o = open("all.dat", "w")
o.write('# CV PMF PMF(Error) Int\n')
for i in range(len(x)):
o.write("%12.4f%12.4f%12.4f%12.4f\n" % (x[i], y[i], yerr[i], y_int[i]))
o.close()
def plot_fig():
data = np.loadtxt("all.dat",
dtype={
'names': ('folder', 'x', 'dA/dx', 'err', 'A'),
'formats': ('S20', 'f4', 'f4', 'f4', 'f4')
})
xcut = 27.5 # free energy beyong 27.5 is too large
x0 = 28.36 # the top layer of Cu
x, y = [], []
for i in range(len(data)):
tx = data[i][1]
ty = data[i][4]
if tx < xcut:
x.append(x0 - tx)
y.append(data[i][4])
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-', ':'],
markers=['o', 's'],
hollow_styles=[False, False, True, True],
)
ax.plot(x, y, label="", **linestyle.next())
ax.set_xlim([0, 9])
#ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6])
ax.set_xlabel("z$_I$ ($\AA$)")
ax.set_ylabel("Free Energy (kcal/mol)")
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
def plot_fig():
data = np.loadtxt("fe.dat")
data = data.transpose()
x, y, yerr = data[0], data[1], data[2]
y_int = integrate.cumtrapz(y, x, initial=0)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-',':'], markers=['o','s'],
hollow_styles=[False, False, True, True],
)
fig, ax = plt.subplots(1)
ax.plot(x, y_int, label="FE",
**linestyle.next())
ax.set_xlabel("r$_{C-O}$ ($\AA$)")
ax.set_ylabel("FE (eV)")
ax.legend(loc='best')
ax2 = ax.twinx()
ax2.errorbar(x, y, yerr=yerr, label="PMF",
**linestyle.next())
ax2.set_ylabel("PMF (eV/$\AA$)", color="green")
ax2.legend(loc='lower left')
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
o = open("all.dat", "w")
for i in range(len(x)):
o.write("%12.4f%12.4f%12.4f%12.4f\n"%
(x[i], y[i], yerr[i], y_int[i]))
o.close()
def plot_density():
#base_dir = 'benchmark/BCC'
data_dir = ['B0.5_C25_scft/scft_out_943.mat',
'B0.5_C25_fts/fts_out_50000.mat',
'B25_C0.5_scft/scft_out_788.mat',
'B25_C0.5_fts/fts_out_50000.mat',
]
is_cl = [False, True, False, True]
labels = ['SCFT, $B=0.5, C=25$',
'CL, $B=0.5, C=25$',
'SCFT, $B=25, C=0.5$',
'CL, $B=25, C=0.5$',
]
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(lines=['-'], markers=[])
i = 0
for f in data_dir:
print 'Processing ', f
try:
mat = loadmat(f)
except:
print 'Missing datafile', f
continue
x = mat['x']
x = x.reshape(x.size)
if is_cl[i]:
phi = mat['phi_avg'].real
phi = phi.reshape(phi.size)
else:
phi = mat['phi']
phi = phi.reshape(phi.size)
y = np.arange(-1, 1, 0.01)
phi = cheb_interpolation_1d(y, phi)
yp = 0.5 * (y + 1) * (x.max() - x.min())
ax.plot(yp, phi, label=labels[i], **linestyle.next())
i += 1
#ax.set_yscale('log')
ax.locator_params(nbins=5)
ax.set_xlabel('$x$')
ax.set_ylabel('$\phi$')
#ax.set_xlim([0.4, 0.6])
#ax.set_ylim([1.71, 1.76])
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig('density_profile')
def my_plot(name):
fig, ax = plt.subplots(1)
linestyles = mpltex.linestyle_generator(markers=[])
#ax.plot(t, t, label='$t$', **next(linestyles))
ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(0.05))
ax.plot(strain, pz,linewidth=1, **next(linestyles),label="Pz")
ax.plot(strain, py,linewidth=1, **next(linestyles),label="Py")
ax.plot(strain, px,linewidth=1, **next(linestyles),label="Px")
ax.set_xlabel('Strain')
ax.set_ylabel('Stress')
ax.legend(loc='best', ncol=2)
plt.grid()
fig.tight_layout(pad=0.1)
fig.savefig("plot"+name[:-4])
def plot_fig():
data = np.loadtxt("all_wf.dat",
dtype={
'names': ('folder', 'x', 'dA/dx', 'err', 'A', 'wf'),
'formats': ('S20', 'f4', 'f4', 'f4', 'f4', 'f4')
})
xcut = 27.5 # free energy beyong 27.5 is too large
x0 = 28.36 # the top layer of Cu
x, y, wf = [], [], []
for i in range(len(data)):
tx = data[i][1]
ty = data[i][4]
twf = data[i][5]
if tx < xcut:
x.append(x0 - tx)
y.append(ty)
wf.append(-twf)
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-', ':'],
markers=['o', 's'],
hollow_styles=[False, False, True, True],
)
ax.plot(x, y, label="Free Energy", **linestyle.next())
#ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6])
ax.set_xlabel("z$_I$ ($\AA$)")
ax.set_ylabel("Free Energy (eV)")
ax.legend(loc='upper right', bbox_to_anchor=(0.88, 0.97))
ax2 = ax.twinx()
ax2.plot(x, wf, label="Work Function", **linestyle.next())
ax2.set_ylim([-5, -3])
ax2.set_ylabel("Work Function (eV)", color="green")
ax2.tick_params(axis='y', colors='green')
ax2.legend(loc='upper right', bbox_to_anchor=(0.93, 0.87))
ax.set_xlim([0, 9])
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
def plot_timeseries(t, data, ylabel, figname):
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(lines=['-'], markers=[],
hollow_styles=[])
i = 0
for d in data:
if t.size > 0:
ax.plot(t, d, **linestyle.next())
else:
ax.plot(d, **linestyle.next())
i += 1
ax.locator_params(nbins=5)
ax.set_xlabel('$t$')
ax.set_ylabel(ylabel)
fig.tight_layout(pad=0.1)
fig.savefig(figname)
plt.close(fig)
def plot_spatial(x, data, ylabel, labels, figname):
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(lines=['-'], markers=['o'],
hollow_styles=[])
i = 0
for d in data:
if len(data) > 1:
ax.plot(x, d, label=labels[i], **linestyle.next())
else:
ax.plot(x, d, **linestyle.next())
i += 1
ax.locator_params(nbins=5)
ax.set_xlabel('$z$')
ax.set_ylabel(ylabel)
if len(data) > 1:
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig(figname)
plt.close(fig)
def plot_fig():
data = np.loadtxt(
"all_wf.dat",
dtype={"names": ("folder", "x", "dA/dx", "err", "A", "wf"), "formats": ("S20", "f4", "f4", "f4", "f4", "f4")},
)
xcut = 27.5 # free energy beyong 27.5 is too large
x0 = 28.36 # the top layer of Cu
x, y, wf = [], [], []
for i in range(len(data)):
tx = data[i][1]
ty = data[i][4]
twf = data[i][5]
if tx < xcut:
x.append(x0 - tx)
y.append(ty)
wf.append(-twf)
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"], lines=["-", ":"], markers=["o", "s"], hollow_styles=[False, False, True, True]
)
ax.plot(x, y, label="Free Energy", **linestyle.next())
# ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6])
ax.set_xlabel("z$_I$ ($\AA$)")
ax.set_ylabel("Free Energy (eV)")
ax.legend(loc="upper right", bbox_to_anchor=(0.88, 0.97))
ax2 = ax.twinx()
ax2.plot(x, wf, label="Work Function", **linestyle.next())
ax2.set_ylim([-5, -3])
ax2.set_ylabel("Work Function (eV)", color="green")
ax2.tick_params(axis="y", colors="green")
ax2.legend(loc="upper right", bbox_to_anchor=(0.93, 0.87))
ax.set_xlim([0, 9])
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
def plot_fig_fe():
data = get_fe()
x, y, yerr = data[0], data[1], data[2]
y_int = integrate.cumtrapz(y, x, initial=0)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-', ':'],
markers=['o', 's'],
hollow_styles=[False, False, True, True],
)
fig, ax = plt.subplots(1)
ax.plot(x, y_int, label="FE", **linestyle.next())
ax.set_xlabel("r$_{C-O}$ ($\AA$)")
ax.set_ylabel("FE (eV)")
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig("02-fe.png", dpi=600)
def plot_iw():
#base_dir = 'benchmark/BCC'
data_dir = [#'B0.5_C25_fts_fixphi_run1/fts_out_50000.mat',
'B0.5_C25_fts_fixscftphi/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run1/fts_out_50000.mat',
#'B0.5_C25_fts_fixscftphi_run2/fts_out_50000.mat',
#'B0.5_C25_fts_fixscftphi_run3/fts_out_100000.mat',
'B0.5_C25_fts_fixscftphi_run4/fts_out_100000.mat',
'B0.5_C25_fts_fixscftphi_run5/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run6/fts_out_100000.mat',
#'B0.5_C25_fts_fixotherphi/fts_out_50000.mat',
#'B0.5_C25_fts_fixphi_Lx128/fts_out_50000.mat',
]
is_cl = [True, True, True, True, True]
labels = [#'$\phi_{CL}, L_x=64$',
'$\phi_{SCFT}, \lambda\Delta t=10^{-6}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=3 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-4}, t=3 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-4}, t=8 \\times 10^4$',
'$\phi_{SCFT}, \lambda\Delta t=10^{-5}, t=8 \\times 10^4$',
'$\phi_{SCFT}, \lambda\Delta t=10^{-5}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-2}, t=8 \\times 10^4$',
#'$\phi_{other}, L_x=64$',
#'$\phi_{CL}, L_x=128$'
]
B = [0.5, 0.5, 0.5, 0.5, 0.5]
C = [25, 25, 25, 25, 25]
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(lines=['-'], markers=['-o'],
hollow_styles=[])
i = 0
for f in data_dir:
print 'Processing ', f
try:
mat = loadmat(f)
except:
print 'Missing datafile', f
continue
x = mat['x']
x = x.reshape(x.size)
if is_cl[i]:
iw_avg = mat['iw_avg'].real
iw_avg = iw_avg.reshape(iw_avg.size)
phi_avg = mat['phi_avg'].real
phi_avg = phi_avg.reshape(phi_avg.size)
y = np.arange(-1, 1, 0.01)
#iw_avg = cheb_interpolation_1d(y, iw_avg)
phi_avg = cheb_interpolation_1d(y, phi_avg)
yp = 0.5 * (y + 1) * (x.max() - x.min())
ax.plot(x, iw_avg, label=labels[i], **linestyle.next())
i += 1
#ax.set_yscale('log')
ax.locator_params(nbins=5)
ax.set_xlabel('$x$')
ax.set_ylabel('$<iw>$')
#ax.set_xlim([0.4, 0.6])
#ax.set_ylim([1.71, 1.76])
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig('iw_profile')
mpl.use('Agg')
import matplotlib.pyplot as plt
import mpltex
@mpltex.acs_decorator
def plot_fig():
<<<<<<< HEAD
data = np.loadtxt("all.xvg")
=======
data = np.loadtxt("gofr.dat")
>>>>>>> 2fa3d11b619e5b1e1192c57a96f67798715b647c
data = data.transpose()
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "green", "red"],
lines=['-',':'], markers=['o','s'],
hollow_styles=[False, False, True, True],
)
<<<<<<< HEAD
ax.plot(data[0][::10]*10, data[1][::10], label="k = 500",
**linestyle.next())
ax.plot(data[2][::10]*10, data[3][::10] + 1.2, label="k = 1000",
**linestyle.next())
ax.plot(data[4][::10]*10, data[5][::10] + 3.3, label="k = 1500",
**linestyle.next())
ax.set_xlim([1.8, 6.7])
ax.set_xlabel("r$_{Li-N}$ ($\AA$)")
ax.set_ylabel("Free Energy (kcal/mol)")
ax.legend(loc='best')
=======
def plot_force():
#base_dir = 'benchmark/BCC'
data_dir = [#'B0.5_C25_fts_fixphi_run1/fts_out_50000.mat',
#'B0.5_C25_fts_fixscftphi/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run1/fts_out_50000.mat',
#'B0.5_C25_fts_fixscftphi_run2/fts_out_50000.mat',
#'B0.5_C25_fts_fixscftphi_run3/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run4/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run5/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run6/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_run7/fts_out_100000.mat',
'B25_C0.5_fts_fixclphi_update_phi_imag/fts_out_100000.mat',
#'B25_C0.5_fts_fixphi/fts_out_50000.mat',
#'B25_C0.5_fts_fixphi_run1/fts_out_50000.mat',
#'B25_C0.5_fts_fixscftphi/fts_out_100000.mat',
#'B25_C0.5_fts_fixscftphi_update_phi_imag/fts_out_150000.mat',
#'B25_C0.5_fts_fixscftphi_update_phi_imag_run1/fts_out_100000.mat',
#'B25_C0.5_fts_fixscftphi_update_phi_imag_run2/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_update_phi_imag_run1/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_update_phi_imag_run2/fts_out_100000.mat',
#'B0.5_C25_fts_fixscftphi_update_phi_imag_run3/fts_out_200000.mat',
#'B0.5_C25_fts_fixotherphi/fts_out_50000.mat',
#'B0.5_C25_fts_fixphi_Lx128/fts_out_50000.mat',
]
is_cl = [True, True, True, True, True]
labels = [#'$\phi_{CL}, L_x=64$',
'$\phi_{CL}, \lambda\Delta t=10^{-3}, t=8 \\times 10^4$',
#'$\phi_{CL}, \lambda\Delta t=10^{-3}, t=3 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=1 \\times 10^5$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=6 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-4}, t=5 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=1.8 \\times 10^5$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-3}, t=3 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-4}, t=3 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-4}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-5}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-5}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-2}, t=8 \\times 10^4$',
#'$\phi_{SCFT}, \lambda\Delta t=10^{-6}, t=8 \\times 10^4$',
#'$\phi_{other}, L_x=64$',
#'$\phi_{CL}, L_x=128$'
]
B = [0.5, 0.5, 0.5, 0.5, 0.5]
C = [25, 25, 25, 25, 25]
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(lines=['-'], markers=['o'],
hollow_styles=[])
i = 0
for f in data_dir:
print 'Processing ', f
try:
mat = loadmat(f)
except:
print 'Missing datafile', f
continue
x = mat['x']
x = x.reshape(x.size)
if is_cl[i]:
iw_avg = mat['iw_avg'].real
iw_avg = iw_avg.reshape(iw_avg.size)
phi = mat['phi'].real
phi = phi.reshape(phi.size)
y = np.arange(-1, 1, 0.01)
force = C[i] * phi - iw_avg / B[i]
print "\tmean force: ", 0.5 * cheb_quadrature_clencurt(force)
#force = cheb_interpolation_1d(y, force)
#yp = 0.5 * (y + 1) * (x.max() - x.min())
ax.plot(x, force, label=labels[i], **linestyle.next())
i += 1
#ax.set_yscale('log')
ax.locator_params(nbins=5)
ax.set_xlabel('$x$')
ax.set_ylabel('$<\\frac{\delta H}{\delta \phi}>_{\phi}$')
#ax.set_xlim([0.4, 0.6])
#ax.set_ylim([1.71, 1.76])
ax.legend(loc='best')
fig.tight_layout(pad=0.1)
fig.savefig('force_profile')
def plot_fig():
fig, ax = plt.subplots(1)
linestyle = mpltex.linestyle_generator(
colors=["black", "black", "red", "red"],
lines=['-', ':'],
markers=['o', 'o', '^', '^'],
hollow_styles=[False, False, True, True],
)
# plot data 1
data = np.loadtxt("gofr_1.dat")
data = data.transpose()
x, y = [], []
for i in range(len(data[0])):
if i <= 50:
x.append(data[0][i])
y.append(data[1][i])
else:
if i % 10 == 0:
x.append(data[0][i])
y.append(data[1][i])
ax.plot(x, y, label="I$^-$(bulk)-H", **linestyle.next())
ax2 = ax.twinx()
ax2.plot(data[0][::10], data[2][::10], **linestyle.next())
# plot data 2
data = np.loadtxt("gofr_2.dat")
data = data.transpose()
x, y = [], []
for i in range(len(data[0])):
if i <= 50:
x.append(data[0][i])
y.append(data[1][i])
else:
if i % 10 == 0:
x.append(data[0][i])
y.append(data[1][i])
ax.plot(x, y, label="I*(interface)-H", **linestyle.next())
# add annodate
ax2.plot(data[0][::5], data[2][::5], **linestyle.next())
ax2.plot([3.0, 8.0], [5.0, 5.0], ls="dotted", lw=0.5, color="black")
ax2.text(7.0, 5.2, "N = 5")
# set up axis
ax.set_xlabel("r$_{I-H}$ ($\AA$)")
ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6])
ax.set_ylabel("g(r)")
ax.legend(loc='upper right', bbox_to_anchor=(0.85, 0.95))
ax2.set_ylim([0.0, 8.0])
ax2.set_xlim([1.0, 8.0])
ax2.set_ylabel("$\int$g(r) (N)")
# output
fig.tight_layout(pad=0.1)
fig.savefig("test.png", dpi=600)
bbox_inches=bbox)
if __name__ == "__main__":
gns_display = [
'email-Enron', 'email-Eu', 'contact-primary-school',
'contact-high-school', 'NDC-classes', 'NDC-substances', 'DAWN',
'congress-bills', 'tags-ask-ubuntu', 'tags-math-sx',
'threads-ask-ubuntu', 'threads-math-sx', 'coauth-MAG-History',
'coauth-MAG-Geology', 'coauth-DBLP'
]
linestyle_map = defaultdict(str)
dotstyle_map = defaultdict(str)
linestyles = mpltex.linestyle_generator()
dotstyles = mpltex.linestyle_generator(lines=[])
for gn in gns_display:
if gn == "DAWN": # I don't like pink
next(linestyles)
next(dotstyles)
linestyle_map[gn] = next(linestyles)
dotstyle_map[gn] = next(dotstyles)
""" Legnd """
save_legend(n_cols=3)
""" Dot plot """
for neg in ["hub", "clique"]:
for imb in [10]:
plot_dots(neg_type=neg, imb=imb, seed=0)
""" MI Correlation plot """
for imb in [10, 5, 2, 1]: