def copy_mpp(args):
out = outdir_path_helper(args.out)
df = read_xcsv(args.jv, read_csv_kwargs=dict(index_col=0))[0]
df.index.name = 'index'
df['P'] = -df.j_tot_nc * df.V
df = df[df.concentration_factor > 0]
df_mpp = df.sort_values('P', ascending=False).drop_duplicates(CASE_KEYS)
df_jsc = df[df.V.abs() < 1e-10].drop_duplicates(CASE_KEYS)
# df1 = df1.sort_values('index')
df1 = pd.concat([df_mpp, df_jsc])
for index, row in df1.iterrows():
filename = row['filename']
base = osp.dirname(filename)
for oldname in [
filename,
filename.rpartition('.plot_meta.yaml')[0] + '.csv.0',
osp.join(base, 'submit.yaml')
]:
newname = osp.join(out, oldname)
mkdirp(osp.dirname(newname))
print(oldname, newname)
shutil.copyfile(oldname, newname)
to_xcsv(df_mpp, out + "JV_mpp.csv")
to_xcsv(df1, out + "JV_mpp_and_jsc.csv")
def plot_jv_old(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
df = read_xcsv(args.jv)[0]
df = df[df['IB_sigma_ci'] == 1e-12]
mus = list(df['IB_mobility'].unique())
mus.sort(key=float)
fig, ax = plt.subplots()
j_min = 0
for mu in mus:
df2 = df[((df['IB_mobility'] == mu) &
(df['concentration_factor'] == 1e3))]
j = df2['j_tot_nc'] / df2['concentration_factor']
j_min = min(j_min, j.min())
ax.plot(df2['V'], j, label=r"$\mu={}$".format(mu), marker='.')
ax.set_xlabel(r'applied bias ($\mathrm{V}$)')
ax.set_ylabel(r'$J_{\mathrm{tot}} / X$ ($\mathrm{mA/cm^2}$)')
#ax.set_ylim([j_min*1.05, 0])
ax.set_ylim([-55, -48])
ax.legend()
fig.tight_layout()
savefig(fig, out + 'JV mus')
def extract_jv(args):
out_dir = outdir_path_helper(args.out_dir)
input_dirs = args.input_dirs
rows = []
for input_dir in tqdm.tqdm(input_dirs):
submit = h5yaml.load(os.path.join(input_dir, 'submit.yaml'))
spar = submit['parameters']
for f in os.listdir(input_dir):
m = voltage_step_re.match(f)
if not m:
continue
V_ext = m.group(1)
filename = os.path.join(input_dir, f)
tree = h5yaml.load(filename)
integrals = tree['integrals']
d = dict(IB_mobility=float(spar['IB_mobility']),
IB_thickness=float(spar['IB_thickness']),
IB_sigma_ci=float(spar['IB_sigma_ci']),
IB_sigma_iv=float(spar['IB_sigma_iv']),
concentration_factor=float(spar['concentration_factor']),
V=float(V_ext),
j_tot_nc=(integrals['avg_j_CB:n_contact'] +
integrals['avg_j_VB:n_contact']))
for k, v in integrals.items():
d["integrals:" + k] = v
d['filename'] = filename
rows.append(d)
df = pd.DataFrame(rows)
to_xcsv(df, out_dir + "JV.csv")
def plot_detail_j(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
spdir = args.spatial_input_dir
SILLY = args.silly
jv = read_xcsv(args.jv_mpp)[0]
fig = plt.figure(figsize=(4.8, 6.4), dpi=100)
gs0 = GridSpec(ncols=1,
nrows=2,
figure=fig,
left=0.18,
right=0.95,
top=0.97,
bottom=0.10,
wspace=0.00,
hspace=0.25,
height_ratios=[1, 1])
ax0 = fig.add_subplot(gs0[0, 0])
ax1 = fig.add_subplot(gs0[1, 0])
sigma_ci_to_ls = {
2e-13: 'solid',
1e-12: 'dashed',
}
mu_to_color = {0.001: COLOR['blue'], 30.0: COLOR['red']}
jv1 = jv.query("(IB_sigma_ci == 2e-13) &" "(IB_mobility == 100.0)")
assert len(jv1.index) == 1
df = common_load_spatial_data(spdir, jv1['filename'].iloc[0])
df_add_drift_diffusion_terms(df)
ax = ax0
plot_detail_j_subplot(
locals(),
ax=ax0,
concentration_factor=jv1['concentration_factor'].iloc[0])
ax = ax1
jv["j_IB_max_abs"] = jv["filename"].apply(
partial(extract_max_abs_IB_current, spdir))
for sigma_ci in [2e-13, 1e-12]:
jv1 = jv[jv.IB_sigma_ci == sigma_ci]
jv1 = jv1.sort_values("IB_mobility")
ax.plot(jv1.IB_mobility,
jv1.j_IB_max_abs / jv1.concentration_factor,
color='black',
label=SIGMA_CI_TO_LABEL[sigma_ci],
linestyle=SIGMA_CI_TO_LS[sigma_ci],
marker=SIGMA_CI_TO_MARKER[sigma_ci])
ax.set_ylim([0, 10.3])
ax.set_xlabel(r'IB mobility ($\mathrm{cm^2/V/s}$)')
ax.set_ylabel(r'$\max(|j_{I}| / X)$ ($\mathrm{cm^2/V/s}$)')
ax.set_xscale('log')
ax.legend()
to_xcsv(jv, out + "marti02_final_Jdd_maxIB.csv")
savefig(fig, out + 'marti02_final_Jdd')
def plot_fs(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
spdir = args.spatial_input_dir
SILLY = args.silly
jv = read_xcsv(args.jv_mpp)[0]
fig = plt.figure(figsize=(4.8, 3.6), dpi=100)
gs0 = GridSpec(ncols=1,
nrows=1,
figure=fig,
left=0.18,
right=0.95,
top=0.97,
bottom=0.16,
wspace=0.00,
hspace=0.00,
height_ratios=[1])
ax0 = fig.add_subplot(gs0[0, 0])
sigma_ci_to_ls = {
2e-13: 'solid',
1e-12: 'dashed',
}
mu_to_color = {0.001: COLOR['blue'], 30.0: COLOR['red']}
ax = ax0
for sigma_ci in [2e-13, 1e-12]:
jv1 = jv[jv.IB_sigma_ci == sigma_ci]
jv1 = jv1.sort_values('IB_mobility', ascending=False)
# if not SILLY:
# jv1 = jv1[jv1.IB_mobility.isin([0.001, 30])]
ls = sigma_ci_to_ls[sigma_ci]
# position doesn't matter, will be manually adjusted in inkscape
ax.text(
0.07,
0.93,
SIGMA_CI_TO_LABEL[sigma_ci],
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,
# fontsize='small'
)
for index, row in jv1.iterrows():
try:
color = mu_to_color[row['IB_mobility']]
except KeyError:
continue
df = common_load_spatial_data(spdir, row['filename'])
df = extract_IB_region_only(df)
df = df[df.x.between(200, 1000)] # Jacob request; was (250, 1000)
ax.plot(df['x'],
df['f_IB'],
color=color,
linestyle=ls,
label='_nolegend_')
if sigma_ci == 2e-13:
mu_label = (r'$\mu_{{I}} = {:.7g}'
'\;\mathrm{{cm^2/V/s}}$'.format(
row['IB_mobility']))
ax.plot([], [], label=mu_label, color=color)
# ax.plot([], [], linestyle=ls, color='black',
# label=SIGMA_CI_TO_LABEL[sigma_ci])
if not SILLY:
ax.legend()
ax.set_ylabel(r'IB filling fraction')
ax.set_xlim([200, 1000])
ax.set_ylim([0.23, 0.56])
ax.set_xlabel(r"position ($\mathrm{nm}$)")
savefig(fig, out + 'marti02_final_f_IB')
def plot_bands(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
spdir = args.spatial_input_dir
# python3 -m ecd_thesis2018.marti2002_plot plot-bands --usetex --out out.marti2002_plot/ --jv out.marti2002_plot_datafiles/JV_mpp_and_jsc.csv --spatial-input-dir out.marti2002_plot_datafiles/
# thesis2018=d0332dc0382ea2b5cd609c931e7b7970c218bd29
# simudo=682f18db136c86b6342e6fbbd2ff7a4df3ea67c1
jv = read_xcsv(args.jv)[0]
jv = jv[jv.V.abs() < 1e-10] # short circuit
fig = plt.figure(figsize=(4.8, 2.6), dpi=100)
gs0 = GridSpec(ncols=1,
nrows=1,
figure=fig,
left=0.14,
right=0.95,
top=0.97,
bottom=0.22,
wspace=0.00,
hspace=0.00,
height_ratios=[1])
ax0 = fig.add_subplot(gs0[0, 0])
jv1 = jv[(jv.IB_sigma_ci == 1e-12) & (jv['IB_mobility'] == 10.0)]
(_, row), = list(jv1.iterrows())
df = common_load_spatial_data(spdir, row['filename'])
print(row['filename'])
df_mask_IB(df)
XS = 1e-3
ax = ax0
for band_name, band in BANDS.items():
ax.plot(df['x'] * XS,
df['Ephi_' + band_name],
label='_nolegend_',
color=band['color'],
alpha=0.5)
ax.text(0.5,
0.5,
r"$\mathcal{{E}}_{}$".format(band['sym']),
color='black') # as my soul
for band_name, band in BANDS.items():
ax.plot(df['x'] * XS,
df['qfl_' + band_name],
label='$w_{{{}}}$'.format(band['sym']),
color=band['color'],
linestyle='dashed')
ax.text(0.5, 0.5, "$w_{}$".format(band['sym']), color=band['color'])
# print(df['qfl_VB'])
ax.set_xlabel(r'position ($\mathrm{\mu m}$)')
ax.set_ylabel(r'Energy ($\mathrm{eV}$)')
ax.set_xlim(np.array([0 - 10, (df['x'].max() + 10)]) * XS)
ax.set_ylim([-1.63, 1.76])
# ax.legend()
# fig.tight_layout()
savefig(fig, out + 'marti02_final_bands')
def plot_jv(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
df = read_xcsv(args.jv)[0]
fig = plt.figure(figsize=(4.8, 6.4), dpi=100)
gs0 = GridSpec(ncols=1,
nrows=2,
figure=fig,
left=0.2,
right=0.95,
top=0.97,
bottom=0.1,
wspace=0.00,
hspace=0.25,
height_ratios=[1, 1])
ax0 = fig.add_subplot(gs0[0, 0])
ax1 = fig.add_subplot(gs0[1, 0])
def _ll(ax, *args, **kwargs):
ax.plot([], [], *args, **kwargs)
sigma_ci_to_ls = SIGMA_CI_TO_LS
sigma_ci_to_label = SIGMA_CI_TO_LABEL
ax = ax0
for sigma_ci, mu in itertools.product([2e-13, 1e-12], [0.001, 300.0]):
df1 = df[(df.IB_mobility == mu) & (df.IB_sigma_ci == sigma_ci) &
(df.concentration_factor > 0)]
color = COLOR['red' if mu == 300.0 else 'blue']
ls = sigma_ci_to_ls[sigma_ci]
j = df1['j_tot_nc'] / df1['concentration_factor']
ax.plot(df1.V,
j,
color=color,
linestyle=ls,
label='_nolegend_'
# label="${}$ ${}$".format(sigma_ci, mu)
)
# by Jacob's request
# _ll(ax, color='black', linestyle='solid',
# label=sigma_ci_to_label[2e-13])
# _ll(ax, color='black', linestyle='dotted',
# label=sigma_ci_to_label[1e-12])
_ll(ax, color=COLOR['red'], label=r'$\mu_{I} = 300\;\mathrm{cm^2/V/s}$')
_ll(ax, color=COLOR['blue'], label=r'$\mu_{I} = 0.001\;\mathrm{cm^2/V/s}$')
ax.set_ylim([-55, -48.5])
ax.set_xlim([0, 1.35])
ax.legend()
ax.set_xlabel(r'applied bias ($\mathrm{V}$)')
ax.set_ylabel(r'$J_{\mathrm{tot}} / X$ ($\mathrm{mA/cm^2}$)')
ax = ax1
df1 = df[(df.V == 0.0) & (df.concentration_factor > 0)]
# mus = list(sorted(df['IB_mobility'].unique()))
for sigma_ci in [2e-13, 1e-12]:
df2 = df1[df1['IB_sigma_ci'] == sigma_ci]
df2 = df2.sort_values('IB_mobility')
j = df2['j_tot_nc'] / df2['concentration_factor']
ls = sigma_ci_to_ls[sigma_ci]
ax.plot(df2.IB_mobility,
j,
linestyle=ls,
color='black',
label=sigma_ci_to_label[sigma_ci],
marker=SIGMA_CI_TO_MARKER[sigma_ci])
# ax.set_xlim([1e-3, 300])
ax.set_ylim([-54.5, -53])
ax.set_xscale('log')
ax.legend()
ax.yaxis.set_major_locator(MultipleLocator(0.5))
ax.set_xlabel(r'IB mobility ($\mathrm{cm^2/V/s}$)')
ax.set_ylabel(r'$J_{SC} / X$ ($\mathrm{mA/cm^2}$)')
savefig(fig, out + 'marti02_final_JVs')
def make_fs_plot(args):
savefig = args.matplotlib_savefig
out = outdir_path_helper(args.out)
V = args.V
input_dir = args.input_dir
input_dir = input_dir.rstrip('/') + '/'
fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()
fig3, ax3 = plt.subplots()
fig4, ax4 = plt.subplots()
fig5, ax5 = plt.subplots()
x_unit = r'$\mathrm{nm}$'
for mu_str in ['0.001', '0.01', '0.1', '1', '30', '100', '300']:
mdir = 'marti02 X=1e3 mu={} s_ci=1e-12 s_iv=2e-13'.format(mu_str)
base = os.path.join(input_dir, mdir,
'a parameter={} csvplot'.format(V))
df = pd.read_csv(base + '.csv.0')
df['x'] = df['coord_x'] * 1e3
df = extract_IB_region_only(df, None)
df = df[df['x'].between(250, 1000)] # Jacob request
label = r'$\mu={}$'.format(mu_str)
N_IB = 1e17
ax.plot(df['x'], df['u_IB'] / N_IB, label=label, alpha=0.6)
grad_qfl = np.gradient(df['qfl_IB'], df['x'])
ax2.plot(df['x'], grad_qfl, label=label, alpha=0.6)
ax3.plot(df['x'], df['E'], label=label, alpha=0.6)
ax4.plot(df['x'], df['qfl_IB'], label=label, alpha=0.6)
ax5.plot(df['x'], df['qfl_IB'] - df['Ephi_IB'], label=label, alpha=0.6)
for a in (ax, ax2, ax3, ax4, ax5):
a.set_xlabel(r'position ({})'.format(x_unit))
a.grid(True)
ax.set_ylabel(r'IB filling fraction')
ax2.set_ylabel(r'$\nabla w_{\mathrm{IB}}$ ($\mathrm{eV/nm}$)')
ax3.set_ylabel(r'electric field ($\mathrm{V/cm}$)')
ax4.set_ylabel(r'$w_{\mathrm{IB}}$ ($\mathrm{eV}$)')
ax5.set_ylabel(
r'$w_{\mathrm{IB}} + eV - \mathcal{E}_{\mathrm{IB}}$ ($\mathrm{eV}$)')
ax2.set_yscale('symlog', linthreshy=1e-8)
ax3.set_yscale('symlog', linthreshy=1e-8)
# ax4.set_yscale('symlog', linthreshy=1e-8)
# ax.set_ylim([0, 1])
ax.legend()
ax2.legend()
ax3.legend()
ax4.legend()
ax5.legend()
fig.tight_layout()
fig2.tight_layout()
fig3.tight_layout()
fig4.tight_layout()
fig5.tight_layout()
savefig(fig, input_dir + 'IBfs V={}'.format(V))
savefig(fig2, input_dir + 'IBgradqfls V={}'.format(V))
savefig(fig3, input_dir + 'IBE V={}'.format(V))
savefig(fig4, input_dir + 'IBqfls V={}'.format(V))
savefig(fig5, input_dir + 'IBphiqfl V={}'.format(V))