def structure_graph(structure, **kwargs):
global defaults
options = copy.copy(defaults)
options.update(kwargs)
current_x = 0
x = []
conduction = []
valence = []
for layer in structure:
x.append(current_x)
conduction.append(layer.Ec)
conduction.append(layer.Ec)
valence.append(layer.Ev)
valence.append(layer.Ev)
current_x += layer.width
x.append(current_x)
# x = np.array(x)
# conduction = np.array(conduction)
# valence = np.array(valence)
g = Graph(
GraphData(x, conduction, label="Conduction Band", linewidth=2, color="black"),
GraphData(x, valence, label="Valence Band", linewidth=2, color="black"),
**options
)
return g
def potentials_graph(x, Ve=None, Vhh=None, Vlh=None, color="grey", **kwargs):
global defaults
options = copy.copy(defaults)
options.update(kwargs)
data = []
normalise_psi = lambda p: p * q * 5e-6
if Ve is not None: data.append(GraphData(x, Ve, color=color, linewidth=2, label="Ve"))
if Vlh is not None: data.append(GraphData(x, Vlh, color=color, linewidth=2, dashes=[1, 1], label="Vlh"))
if Vhh is not None: data.append(GraphData(x, Vhh, color=color, linewidth=2, label="Vhh"))
g = Graph(data, **options)
return g
def split_schrodinger_graph(schrodinger_result,
trim_levels_beyond=1e-2,
linewidth=1,
scale=0.03,
suppress_invert=False,
probability_density=False,
wfalpha=0.8,
potentialalpha=0.8,
**kwargs):
options = copy.copy(defaults)
options["square"] = False
defaults.update(kwargs)
potentials = schrodinger_result["potentials"]
wavefunctions = schrodinger_result["wavefunctions"]
energy_levels = schrodinger_result["E"]
x = schrodinger_result["x"]
if 'EU' in energy_levels.keys():
energy_levels['Ehh'] = energy_levels['EU']
energy_levels['Elh'] = energy_levels['EU']
wavefunctions["psi_hh"] = wavefunctions["psi_g1"]
wavefunctions["psi_lh"] = wavefunctions["psi_g2"]
conduction_data = [GraphData(x, potentials["Ve"], linewidth=2, color="grey", alpha=potentialalpha)]
valence_data = [
GraphData(x, potentials["Vlh"], linewidth=2, color="grey", alpha=potentialalpha, dashes=[1, 1], label="Vlh"),
GraphData(x, potentials["Vhh"], linewidth=2, color="grey", alpha=potentialalpha, label="Vhh")
]
# normalise_psi = lambda p:p/numpy.sqrt(numpy.trapz(x=x*1e9,y=p**2))*q
conduction_data.extend(
prepare_wavefunction_data(x, energy_levels["Ee"], wavefunctions["psi_e"], trim_levels_beyond, linewidth, "blue",
scale, suppress_invert, alpha=wfalpha, square=probability_density))
valence_data.extend(
prepare_wavefunction_data(x, energy_levels["Ehh"], wavefunctions["psi_hh"], trim_levels_beyond, linewidth,
"green",
scale, suppress_invert, alpha=wfalpha, square=probability_density))
valence_data.extend(
prepare_wavefunction_data(x, energy_levels["Elh"], wavefunctions["psi_lh"], trim_levels_beyond, linewidth,
"red",
scale, suppress_invert, alpha=wfalpha, square=probability_density))
g = Graph(valence_data, subplot=212, **options)
del options["xlabel"]
g.add_subplot(conduction_data, subplot=211, **options)
# g.axis.get_xaxis().set_ticklabels([])
return g
def prepare_wavefunction_data(x,
E,
Psi,
trim_levels_beyond,
linewidth,
color,
scale,
suppress_invert=False,
square=False,
alpha=1,
label="untitled"):
data = []
for e, psi in zip(E, Psi):
norm_psi = normalise_psi(psi if not square else psi**2)
trim_to = norm_psi**2 / q**2 > trim_levels_beyond**2
trimmed_x = x[trim_to]
norm_psi = norm_psi[trim_to]
invert = -1 if norm_psi[
0] < 0 and not suppress_invert and not square else 1
linekwargs = {"linewidth": linewidth, "alpha": alpha, "label": label}
data.append(
GraphData(trimmed_x,
e + norm_psi * scale * invert,
color=color,
**linekwargs))
return data
E /= 1.6e-19
idx = np.argmin(abs(E - 2))
print(round(E[idx] * 1000), round(nn[idx] * 1000), round(kk[idx] * 1000))
out = [round(E[idx] * 1000), round(nn[idx] * 1000), round(kk[idx] * 1000)]
data = [2000.0, 3629.0, 308.0]
print(data == out)
curves = [
GraphData(E,
adachi_alpha_data / 100,
label="adachi-Generated Data",
linewidth=2,
color="red"),
GraphData(E,
solcore_alpha_data / 100,
label="Default Solcore Data",
linewidth=2,
color="grey"),
]
g = Graph(curves,
yscale="log",
legend="best",
xlim=(0, 6),
ylim=(1000, 1e7)).draw()