def test_sample_range(self):
x = np.linspace(400, 1010, 2000)
abs_spec = np.column_stack((x, bandgap(x, 600, 1000)))
ems_spec = thermodynamic_emission(abs_spec, T=300, mu=0.1)
dist = Distribution(ems_spec[:, 0], ems_spec[:, 1])
xmin = dist.sample(0)
assert np.isclose(xmin, x.min())
xmax = dist.sample(1)
assert np.isclose(xmax, x.max())
y_at_xmin = dist.lookup(xmin)
assert np.isclose(y_at_xmin, 0.0)
y_at_xmax = dist.lookup(xmax)
assert np.isclose(y_at_xmin, 0.0)
def test_step_sample(self):
""" Sampling a step function should only return two value.
"""
nmedge = 600.0
nmmin, nmmax = (400.0, 800.0)
spacing = 1.0
x = np.arange(nmmin, nmmax + spacing, spacing)
abs_spec = np.column_stack((x, bandgap(x, nmedge, 1.0))) # ends a mid range.
dist = Distribution(abs_spec[:, 0], abs_spec[:, 1], hist=True)
xmin = dist.sample(0)
assert np.isclose(xmin, nmmin)
xmax = dist.sample(1) # The probabiliity of getting a value > nmedge is zero
assert np.isclose(xmax, nmedge)
pmin = dist.lookup(nmmin)
pmax = dist.lookup(nmmax)
assert pmin >= 0.0 and pmin <= dist.lookup(nmmin+spacing)
assert pmax == 1.0
values = dist.sample(np.linspace(dist.lookup(599-spacing), dist.lookup(600+spacing), 10000))
assert len(set(values)) == 3
def test2():
x = np.arange(400, 801, dtype=np.float)
size = (l, w, d) = (4.8, 1.8, 0.250) # cm-1
lsc = LSC(size, wavelength_range=x)
lsc.add_luminophore(
"Fluro Red",
np.column_stack((x, fluro_red.absorption(x) * 11.387815)), # cm-1
np.column_stack((x, fluro_red.emission(x))),
quantum_yield=0.95,
)
lsc.add_absorber("PMMA", 0.02) # cm-1
def lamp_spectrum(x):
""" Fit to an experimentally measured lamp spectrum with long wavelength filter.
"""
def g(x, a, p, w):
return a * np.exp(-(((p - x) / w)**2))
a1 = 0.53025700136646192
p1 = 512.91400020614333
w1 = 93.491838802960473
a2 = 0.63578999789955015
p2 = 577.63100003089369
w2 = 66.031706473985736
return g(x, a1, p1, w1) + g(x, a2, p2, w2)
lamp_dist = Distribution(x, lamp_spectrum(x))
wavelength_callable = lambda: lamp_dist.sample(np.random.uniform())
position_callable = lambda: rectangular_mask(l / 2, w / 2)
lsc.add_light(
"Oriel Lamp + Filter",
(0.0, 0.0, 0.5 * d + 0.01), # put close to top surface
rotation=(np.radians(180), (1, 0,
0)), # normal and into the top surface
wavelength=wavelength_callable, # wavelength delegate callable
position=position_callable, # uniform surface illumination
)
lsc.add_solar_cell({"left", "right", "near", "far"})
lsc.add_air_gap_mirror(lambertian=False)
lsc.show()
throw = 300
lsc.simulate(throw, emit_method="redshift")
lsc.report()
def test1():
x = np.arange(400, 801, dtype=np.float)
size = (l, w, d) = (4.8, 1.8, 0.250) # cm-1
lsc = LSC(size, wavelength_range=x)
lsc.add_luminophore(
"Fluro Red",
np.column_stack((x, fluro_red.absorption(x) * 11.387815)), # cm-1
np.column_stack((x, fluro_red.emission(x))),
quantum_yield=0.95,
)
lsc.add_absorber("PMMA", 0.02) # cm-1
def lamp_spectrum(x):
""" Fit to an experimentally measured lamp spectrum with long wavelength filter.
"""
def g(x, a, p, w):
return a * np.exp(-(((p - x) / w)**2))
a1 = 0.53025700136646192
p1 = 512.91400020614333
w1 = 93.491838802960473
a2 = 0.63578999789955015
p2 = 577.63100003089369
w2 = 66.031706473985736
return g(x, a1, p1, w1) + g(x, a2, p2, w2)
lamp_dist = Distribution(x, lamp_spectrum(x))
wavelength_callable = lambda: lamp_dist.sample(np.random.uniform())
position_callable = lambda: rectangular_mask(l / 2, w / 2)
lsc.add_light(
"Oriel Lamp + Filter",
(0.0, 0.0, 0.5 * d + 0.01), # put close to top surface
rotation=(np.radians(180), (1, 0,
0)), # normal and into the top surface
wavelength=wavelength_callable, # wavelength delegate callable
position=position_callable, # uniform surface illumination
)
lsc.show()
throw = 2500
lsc.simulate(throw, emit_method="redshift")
edge = lsc.spectrum(facets={"left", "right", "near", "far"}, source="all")
escape = lsc.spectrum(facets={"top", "bottom"}, source="all")
lost = lsc.spectrum(source="all", events={"absorb"})
import matplotlib.pyplot as plt
plt.hist(edge, bins=np.linspace(400, 800, 10), label="edge")
plt.hist(escape, bins=np.linspace(400, 800, 10), label="escape")
plt.hist(lost, bins=np.linspace(400, 800, 10), label="lost")
plt.show()
# number of lamp rays hitting the top surface
incident = lsc.spectrum(source={"Oriel Lamp + Filter"},
kind="first",
facets={"top"})
hitting = len(incident)
counts = {"edge": len(edge), "escape": len(escape), "lost": len(lost)}
fractions = {
"edge": counts["edge"] / hitting,
"escape": counts["escape"] / hitting,
"lost": counts["lost"] / hitting,
}
print(counts, "sum", np.sum(list(counts.values())))
print(fractions, "sum", np.sum(list(fractions.values())))