ax.yaxis.set_label_text("Normalized Scattering Cross Section")
if 1.33 <= nd < 1.34:
ax.set_facecolor(np.array([230, 241, 255]) / 255)
# frame = leg.get_frame()
# frame.set_facecolor( np.array([230, 241, 255])/255 )
# plt.xlabel("Wavelength [nm]")
# plt.ylabel("Normalized Scattering Cross Section")
if plot_make_big:
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
del mng
fig.tight_layout()
plt.subplots_adjust(hspace=0)
vs.saveplot(plot_file("AllScattNorm.png"), overwrite=True)
#%% PLOT EFFIENCIENCY
colors = [
sc(np.linspace(0, 1,
len(s) + 3))[3:] for sc, s in zip(series_colors, series)
]
fig, axes = plt.subplots(2, 1, sharex=True)
fig.suptitle(plot_title)
axes[1].yaxis.tick_right()
axes[1].yaxis.set_label_position("right")
for ax, s, d, t, p, n, sc, psl, tsl, pc, pls, tls in zip(
axes, series, data, theory, params, index, series_column, series_label,
mdt[:, 1] / max(mdt[:, 1]),
linestyle='dashed',
color=c,
label=f"Marian Exp {d} nm")
plt.style.use('default')
for mdt, d, c in zip(marian_mie_data, diameters, colors):
plt.plot(mdt[:, 0],
mdt[:, 1] / max(mdt[:, 1]),
linestyle="dotted",
color=c,
label=f"Marian Mie {d} nm")
plt.xlabel("Wavelength [nm]")
plt.ylabel("Normalized Scattering [a.u.]")
plt.xlim([450, 650])
plt.legend(ncol=4, framealpha=1)
vs.saveplot(water_file("", "AllScatt.png"), overwrite=True)
#%% 1st: PLOT 103 nm ALL: VACUUM, WATER, MARIAN EXPERIMENTAL, MARIAN MIE
# This makes up the first figures I've made, the ones that confused me
colors = (c for c in plab.cm.Reds(np.linspace(0, 1, 2 + 5))[2:])
plt.figure()
plt.title("Scattering of Au sphere (103 nm diameter)")
scatt = vacuum_data[-1][:, 1] #* np.pi * (diameters[-1]**2) / 4
plt.plot(vacuum_data[-1][:, 0],
scatt / max(scatt),
linestyle="solid",
color=next(colors),
label="Vacuum")
scatt = water_data[-1][:, 1] #* np.pi * (diameters[-1]**2) / 4
max_wlen_theory = wlens[np.argmax(scatt_eff_theory)]
dif_max_wlen = [ml - max_wlen_theory for ml in max_wlen[0]]
#%% WAVELENGTH MAXIMUM DIFFERENCE VS TIME FACTOR CELL
second_time_factor = [p["second_time_factor"] for p in params[0]]
plt.title("Difference in scattering maximum for " + plot_title)
plt.plot(second_time_factor, dif_max_wlen, '.', markersize=12)
plt.grid(True)
plt.legend(["Data", r"Fit $f(r)=a_0 e^{-a_1 r} + a_2$"])
plt.xlabel("Second time factor")
plt.ylabel(
"Difference in wavelength $\lambda_{max}^{MEEP}-\lambda_{max}^{MIE}$ [nm]")
vs.saveplot(plot_file("WLenDiff.png"), overwrite=True)
#%% GET ENLAPSED TIME COMPARED
second_time_factor = [[p["second_time_factor"] for p in par] for par in params]
enlapsed_time = [[p["enlapsed"] for p in par] for par in params]
total_enlapsed_time = [[sum(p["enlapsed"]) for p in par] for par in params]
first_second_time_factor = []
second_second_time_factor = []
first_build_time = []
first_sim_time = []
second_flux_time = []
second_build_time = []
second_sim_time = []
for enl, stf in zip(enlapsed_time, second_time_factor):
data = []
params = []
for s in series:
data.append(np.loadtxt(file(s, "Results.txt")))
params.append(vs.retrieve_footer(file(s, "Results.txt")))
header = vs.retrieve_header(file(s, "Results.txt"))
params = [vu.fix_params_dict(p) for p in params]
r = [p["r"] for p in params]
from_um_factor = [p["from_um_factor"] for p in params]
#%% GET MAX WAVELENGTH
max_wlen = [data[i][np.argmax(data[i][:, 1]), 0] for i in range(len(data))]
#%% PLOT
colors = plab.cm.Blues(np.linspace(0, 1, len(series) + 3))[3:]
plt.figure()
for s, d, c, p in zip(series, data, colors, params):
scatt = d[:, 1] * np.pi * (p["r"] * p["from_um_factor"])**2
scatt = scatt
plt.plot(d[:, 0], scatt, linestyle='solid', color=c, label=series_label(s))
plt.xlabel("Wavelength [nm]")
plt.ylabel("Scattering Cross Section [$\mu$m$^2$]")
plt.legend()
vs.saveplot(file("", "AllScatt.png"), overwrite=True)
def exponential_fit(X, A, b, C):
return A * np.exp(-b * X) + C
rsq, parameters = va.nonlinear_fit(np.array(resolution),
np.array(dif_max_wlen),
exponential_fit,
par_units=["nm", "", "nm"])
plt.title("Difference in scattering maximum for Au 103 nm sphere in vacuum")
plt.legend(["Data", r"Fit $f(r)=a_0 e^{-a_1 r} + a_2$"])
plt.xlabel("Resolution")
plt.ylabel(
"Difference in wavelength $\lambda_{max}^{MEEP}-\lambda_{max}^{MIE}$")
vs.saveplot(plot_file("WLenDiff.png"), overwrite=True)
#%% GET ENLAPSED TIME
enlapsed_time = [params[0][i]["enlapsed"] for i in range(len(data[0]))]
total_enlapsed_time = [sum(et) for et in enlapsed_time]
def quartic_fit(X, A, b):
return A * (X)**4 + b
rsq, parameters = va.nonlinear_fit(np.array(resolution),
np.array(total_enlapsed_time),
quartic_fit,
par_units=["s", "s"])
markersize=12,
color=colors[i][1],
label=label)
plt.grid(True, axis="x", which="minor")
plt.grid(True, axis="x", which="major")
plt.legend(loc="lower right")
plt.xlabel(r"Wavelength difference $\lambda_{MEEP} - \lambda_{MIE}$ [nm]")
plt.title(plot_title)
ax = fig.axes[0]
ax.xaxis.set_minor_locator(AutoMinorLocator())
plt.show()
fig.set_size_inches([6.4, 4.07])
plt.tight_layout()
vs.saveplot(plot_file("WlenDif.png"), overwrite=True)
#%%
colors = [
sc(np.linspace(0, 1,
len(s) + 3))[3:] for sc, s in zip(series_colors, series)
]
fig = plt.figure()
for i in range(len(data)):
for j in range(len(data[i])):
if j == 0: label = series_label[i]
else: label = None
plt.plot(marian_wlen_diff[i][j],
f"{2 * r[i][j] * from_um_factor[i][j] * 1e3:.0f} nm",
ax.set_title(t)
ax.plot(wl, f(eps), lst, color=col, label=l)
ax.xaxis.set_label_text(r"Wavelength [$\mu$m]")
ax.yaxis.set_label_text(y)
ax.legend()
ax.set_xlim(*wlen_range_jc)
max_value.append(max(f(eps)))
min_value.append(min(f(eps)))
for ax in axes:
ax.set_ylim([
min(min_value) - .1 * (max(max_value) - min(min_value)),
max(max_value) + .1 * (max(max_value) - min(min_value))
])
vs.saveplot(plot_file(f"{material}DrudeLorentzJCFit.png"), overwrite=True)
#%%
for ax in axes:
ax.set_ylim([-50, 50])
if material == "Au":
ax.set_xlim([.4, .8])
elif material == "Ag":
ax.set_xlim([.25, .8])
else:
raise ValueError("Material not recognized for zoom in plot")
vs.saveplot(plot_file(f"{material}DrudeLorentzJCFitZoom.png"), overwrite=True)
#%%
