fc = FigureCollection(pagesize=(3, 5.5),
figure_style="science",
col=1,
row=9)
# fc.fc_param["figure.lpad"] = 0.02
# fc.fc_param["figure.rpad"] = 0.0
fc.fc_param["figure.tpad"] = 0.05
fc.fc_param["figure.bpad"] = 0.05
# fc.fc_param["annotation.location"] = (0,0)
fig1, num1 = fc.add_figure(loc=(0, 0, 1, 3), label=True)
fig1.add_file_figure("../img/scheme-EDL.pdf")
fig2, num1 = fc.add_figure(loc=(0, 3, 1, 3), label=True)
fig2.set_plot_func(plot_ph_dep, MD=False)
fig3, num2 = fc.add_figure(loc=(0, 6, 1, 3), label=True)
fig3.set_plot_func(plot_ph_dep, MD=True)
org.figure(
fc.save_all("../img/2d-ph-dependency+MD_1.pdf", outline=False),
label="fig:res-EDL",
caption=
("(a) Scheme of the interface between the 2D material "
"and the aqueous phase. "
r"(b) $(\Delta\cos\theta)^{\mathrm{EDL}}$ "
"as a function of "
r"$\sigma_{\mathrm{2D}}$ with varied solute concentrations. "
r"The concentration $c_{0}$ varies from "
r"$10^{0}$ to $10^{-7}$ mol$\cdot\mathrm{L}^{-1}$ "
r"(c) Overall change of contact angle $(\Delta \cos \theta)^{\mathrm{Orien+EDL}}$ "
"combining the orientation and EDL effects, "
"with varied solute concentrations as in (b)."),
attributes=[("latex", ":width 0.5\linewidth")])
# ax2.set_xlim(ax1.get_xlim())
# ax2.set_xticks(c_atom_to_sigma(ax2_ticks)/10**13)
# ax2.set_xticklabels(list(map(str, ax2_ticks)))
# # ax2.plot(charge_per_atom, potential_tot, alpha=0)
# ax2.set_xlabel("Unit charge per atom", labelpad=10)
# # print(ax1.get_ylim())
# # print(ax1.get_yticks())
# ax3.set_yticks(ax1.get_yticks())
# ax3.set_ylim(ax1.get_ylim())
# ax3_yticks = ax1.get_yticks()/A_c/const.N_A*10**6
# ax3.set_yticklabels(list(map(lambda a: "%.1f"%a, ax3_yticks)))
# # ax3.plot(sigma/10**13, potential_tot/A_c/const.N_A*1000, alpha=0.0)
# ax3.set_ylabel(r"$\Delta\gamma_{\mathrm{WG}}$ [mJ$\cdot$m$^{-2}$]", labelpad=-2)
# org.figure(plt.savefig("../img/e-vdw.png"))
if __name__ == "__main__":
matplotlib.style.use("science")
fig = plt.figure(figsize=(3, 3))
plot_Phi_charge(fig, error=True)
org.figure(plt.savefig("../img/e-vdw-2.pdf"))
fig = plt.figure(figsize=(3, 3))
plot_fitting(fig)
org.figure(plt.savefig("../img/e-Phi-fitting.pdf"))
def org(self):
'''Print an org-represention of the plate and data.
'''
from pycse.orgmode import table, figure, headline, print_redirect, link
import datetime
import json
import time
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('[%Y-%m-%d %a %H:%M:%S]')
CWD = os.getcwd()
os.chdir(os.path.join(self.base,
self.directory))
try:
import matplotlib
matplotlib.use('TkAgg')
A, B = self.metadata['A'], self.metadata['B']
print(f'#+TITLE: {A}-{B}')
print(f'#+CREATED: {st}')
print('#+STARTUP: showall')
print()
print('Generated report. Do not edit. Changes may be lost.\n')
headline('Summary',
tags=[A, B],
body=str(self))
link('file+sys', f'./primary_data/{A}col{B}rowdata.xls')
link('file+sys', './images', 'Image directory')
link('file+sys', './auxillary_data', 'auxillary_data')
print()
headline('Hydrogen data')
headline('Hydrogen production plots', level=2)
plt.figure()
self.data['mmolH'].plot_mmolH_grid_window()
plt.tight_layout()
figure('espyranto/kinetics-grid.png',
name='fig-kinetics-grid',
attributes=[['org', ':width 600']],
caption=f'Hydrogen umolH in each well for {A}-{B}.')
plt.clf()
print()
headline('Max production', level=2)
ncols, nrows = self.ncols, len(self.data['mmolH'].mmolH) // self.ncols
maxh = np.max(self.data['mmolH'].mmolH, axis=1).reshape(nrows, ncols)
data = np.round(maxh, 2)
# Add row and column labels
col_labels = self.A.reshape(nrows, ncols)[0][None, :]
row_labels = np.concatenate(([['']],
self.B.reshape(nrows, ncols)[:, 0][:, None]))
data = np.concatenate((col_labels, data))
data = np.concatenate((row_labels, data), axis=1)
data = list(data)
data.insert(1, None)
data[0][0] = f'{B}\{A}'
table(data, name='tab-maxh', caption='Maxh µmol H_{2} produced.')
print()
self.data['mmolH'].plot_mmolH_max();
plt.tight_layout()
figure('espyranto/maxh.png',
name='fig-maxh',
attributes=[['org', ':width 600']],
caption=f'Maximum hydrogen produced for {A}-{B}.')
plt.clf()
print()
tot_conc = (self.A + self.B)
# The nself.where deals with the case where A = B = 0.0
x_A = self.A / np.where(tot_conc > 0, tot_conc, 1)
plt.figure()
plt.scatter(x_A, np.max(self.data['mmolH'].mmolH, axis=1),
tot_conc * 20)
plt.xlabel(f'$x_{{{A}}}$');
plt.ylabel('$\mu molH$');
print()
figure('espyranto/maxh-scatter.png',
name='fig-maxh-scatter',
attributes=[['org', ':width 600']],
caption=f'Scatter plot for maximum hydrogen produced for {A}-{B}.')
print()
headline('Max rate', level=2)
# Heatmap of max rates
print()
self.data['mmolH'].plot_mmolH_max_derivative_window()
figure('espyranto/maxrate.png',
name='fig-maxrate',
attributes=[['org', ':width 600']],
caption=f'Maximum rate (window) of hydrogen produced for {A}-{B}.')
# Scatter plot
print()
mmolH = self.data['mmolH']
t = np.arange(0, len(mmolH[0])) * mmolH.timestep / 3600
rate_data = []
for row in self.data['mmolH'].mmolH:
ys = mmolH.smooth_window(row)
dydx = np.gradient(ys, t, edge_order=2)
rate_data += [np.max(dydx)]
# Scatter plot
plt.figure()
plt.scatter(x_A, rate_data, tot_conc * 20)
plt.xlabel(f'$x_{{{A}}}$');
plt.ylabel('$\mu molH/hr$');
print()
figure('espyranto/maxrate-scatter.png',
name='fig-maxrate-scatter',
attributes=[['org', ':width 600']],
caption=f'Scatter plot for maximum rate of hydrogen production for {A}-{B}.')
print()
# For making a table
rate_data = np.round(rate_data, 2)
rate_data = rate_data.reshape((nrows, ncols))
rate_data = np.concatenate((col_labels, rate_data))
rate_data = np.concatenate((row_labels, rate_data), axis=1)
rate_data = list(rate_data)
rate_data.insert(1, None)
rate_data[0][0] = f'{B}\{A}'
table(rate_data, name='tab-maxrate', caption='Max rate µmol/hr H_{2} produced.')
print()
headline('Metadata')
print()
print(f'''#+name: metadata
#+BEGIN_SRC json
{json.dumps(self.metadata, sort_keys=True, indent=2)}
#+END_SRC
''')
finally:
os.chdir(CWD)
plt.close('all')
"{:.3f}\n".format(f_esem),
r"$\sigma_{0}$",
"={:.1f}".format(sigma_i_esem * 10),
r"$\times 10^{12}$",
r" $e\cdot$cm$^{-2}$",
)))
ax.set_xlabel(r"$\sigma_{\mathrm{2D}}$ ($10^{13}$ $e\cdot$cm$^{-2}$)")
ax.set_ylabel(r"$\Delta\cos\theta$")
ax.legend(loc=0, frameon=True)
ax.set_xlim(-2, 2)
ax.set_ylim(-0.05, 0.5)
fig.tight_layout()
matplotlib.style.use("science")
fig = plt.figure(figsize=(4.0, 3.0))
if __name__ == "__main__":
plot_fitting_f(fig)
org.figure(plt.savefig("../img/plot-fitting.pdf"),
attributes=[("latex", ":width 0.95\linewidth")],
label="fig:f-nc-exp",
caption=("Theoretical and fitted experimental data of "
r"$\Delta\cos\theta$ "
"as a function of "
r"$\sigma_{\mathrm{2D}}$. "
"The electrowetting data are extracted from Ref. "
"[[cite:hong_mechanism_2016]]; "
"the ESEM data are extracted from Ref. "
"[[cite:ashraf_doping-induced_2016]]. "))
def test_fig5():
figure("pycse/tests/test_orgmode.png", caption="test",
name='fig', attributes=[('org', ':width 300')])
assert sys.stdout.getvalue() == ('#+attr_org: :width 300\n'
'#+name: fig\n#+caption: test\n'
'[[file:pycse/tests/test_orgmode.png]]\n')
d_sys[:, 1],
label=r"%d$\times10^{-3}$ $e$/atom" % (c))
ax.set_ylabel(r"$\rho_{\mathrm{w}}$ (kg$\cdot$m$^{-3}$)")
ax.set_xlabel(r"$z$ (nm)")
ax.set_xlim(0, 1.5)
ax.legend(loc=0)
elif what is "charge":
for index, c in enumerate(charge_per_atom):
c_sys = numpy.genfromtxt(f_charge_base.format(name[index]),
delimiter=(12, 17),
skip_header=19)
ax.plot(c_sys[:, 0] - z_gr,
c_sys[:, 1],
label=r"%d$\times10^{-3}$ $e$/atom" % (c))
ax.set_ylabel(r"$\delta_{\mathrm{w}}$ ($e\cdot$nm$^{-3}$)")
ax.set_xlabel(r"$z$ (nm)")
ax.set_xlim(0, 1.5)
ax.legend(loc=0)
fig.tight_layout(pad=0)
if __name__ == "__main__":
fig = plt.figure()
plot_den(fig, what="mass")
org.figure(plt.savefig("img/density_m.pdf"))
plt.cla()
fig = plt.figure()
plot_den(fig, what="charge")
org.figure(plt.savefig("img/density_c.pdf"))
def test_fig4():
figure("pycse/tests/test_orgmode.png", caption="test", name='fig')
assert sys.stdout.getvalue() == ('#+name: fig\n#+caption: test\n'
'[[file:pycse/tests/test_orgmode.png]]\n')
def test_fig3():
figure("pycse/tests/test_orgmode.png", caption="test")
result = '#+caption: test\n[[file:pycse/tests/test_orgmode.png]]\n'
assert sys.stdout.getvalue() == result
def test_fig2():
figure("pycse/tests/test_orgmode.png")
assert sys.stdout.getvalue() == '[[file:pycse/tests/test_orgmode.png]]\n'
def test_fig():
figure("test")
assert sys.stdout.getvalue() == '[[file:test]]\n'
elif what is "charge":
for index, c in enumerate(charge_per_atom):
c_sys = numpy.genfromtxt(f_charge_base.format(name[index]),
delimiter=(12, 17),
skip_header=19)
zz = numpy.linspace(c_sys[:, 0].min(), c_sys[:, 0].max(), 50000)
f_y = interp1d(c_sys[:, 0], c_sys[:, 1], kind="cubic")
yy = f_y(zz)
# ax.plot(c_sys[:, 0] - z_gr, c_sys[:, 1],
# label=r"%d$\times10^{-3}$ $e$/atom" % (c) )
ax.plot(zz - z_gr, yy, label=r"%s $e$/atom" % (label_name[index]))
ax.set_ylabel(r"$\delta_{\mathrm{L}}$ ($e\cdot$nm$^{-3}$)")
ax.set_xlabel(r"$z$ (nm)")
ax.set_xlim(0, 1)
ax.legend(loc=0, title=r"$\sigma_{\mathrm{2D}}$")
fig.tight_layout(pad=0.05)
if __name__ == "__main__":
matplotlib.style.use("science")
fig = plt.figure(figsize=(2.5, 2.5))
plot_den(fig, what="mass")
org.figure(plt.savefig("../img/density_m_small.pdf"))
plt.cla()
fig = plt.figure(figsize=(2.5, 2.5))
plot_den(fig, what="charge")
org.figure(plt.savefig("../img/density_c_small.pdf"))
