def test_isotropic_float():
result = irradiance.isotropic(40, 100)
assert_allclose(result, 88.30222215594891)
def test_isotropic_series(irrad_data):
result = irradiance.isotropic(40, irrad_data['dhi'])
assert_allclose(result, [0, 35.728402, 104.601328, 54.777191], atol=1e-4)
def test_isotropic_float():
result = irradiance.isotropic(40, 100)
assert_allclose(result, 88.30222215594891)
def test_isotropic_series(irrad_data):
result = irradiance.isotropic(40, irrad_data['dhi'])
assert_allclose(result, [0, 35.728402, 104.601328, 54.777191], atol=1e-4)
def test_isotropic_series():
irradiance.isotropic(40, irrad_data['DHI'])
def test_isotropic_float():
irradiance.isotropic(40, 100)
def test_isotropic_series():
irradiance.isotropic(40, irrad_data['dhi'])
def test_isotropic_float():
irradiance.isotropic(40, 100)
# %%
# So as the array is packed tighter (decreasing ``k``), the average masking
# angle increases.
#
# Next we'll recreate Figure 5. Note that the y-axis here is the ratio of
# diffuse plane of array irradiance (after accounting for shading) to diffuse
# horizontal irradiance. This means that the deviation from 100% is due to the
# combination of self-shading and the fact that being at a tilt blocks off
# the portion of the sky behind the row. The first effect is modeled with
# :py:func:`pvlib.shading.sky_diffuse_passias` and the second with
# :py:func:`pvlib.irradiance.isotropic`.
plt.figure()
for k in [1, 1.5, 2, 10]:
gcr = 1 / k
psi = shading.masking_angle_passias(surface_tilt, gcr)
shading_loss = shading.sky_diffuse_passias(psi)
transposition_ratio = irradiance.isotropic(surface_tilt, dhi=1.0)
relative_diffuse = transposition_ratio * (1 - shading_loss) * 100 # %
plt.plot(surface_tilt, relative_diffuse, label=f'k={k}')
plt.xlabel('Inclination angle [degrees]')
plt.ylabel('Relative diffuse irradiance [%]')
plt.ylim(0, 105)
plt.legend()
plt.show()
# %%
# As ``k`` decreases, GCR increases, so self-shading loss increases and
# collected diffuse irradiance decreases.