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test_irradiance.py
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test_irradiance.py
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import datetime
from collections import OrderedDict
import warnings
import numpy as np
from numpy import array, nan
import pandas as pd
import pytest
from numpy.testing import (assert_almost_equal,
assert_allclose)
from pvlib import irradiance
from .conftest import (
assert_frame_equal,
assert_series_equal,
requires_ephem,
requires_numba
)
# fixtures create realistic test input data
# test input data generated at Location(32.2, -111, 'US/Arizona', 700)
# test input data is hard coded to avoid dependencies on other parts of pvlib
@pytest.fixture
def times():
# must include night values
return pd.date_range(start='20140624', freq='6H', periods=4,
tz='US/Arizona')
@pytest.fixture
def irrad_data(times):
return pd.DataFrame(np.array(
[[0., 0., 0.],
[79.73860422, 316.1949056, 40.46149818],
[1042.48031487, 939.95469881, 118.45831879],
[257.20751138, 646.22886049, 62.03376265]]),
columns=['ghi', 'dni', 'dhi'], index=times)
@pytest.fixture
def ephem_data(times):
return pd.DataFrame(np.array(
[[124.0390863, 124.0390863, -34.0390863, -34.0390863,
352.69550699, -2.36677158],
[82.85457044, 82.97705621, 7.14542956, 7.02294379,
66.71410338, -2.42072165],
[10.56413562, 10.56725766, 79.43586438, 79.43274234,
144.76567754, -2.47457321],
[72.41687122, 72.46903556, 17.58312878, 17.53096444,
287.04104128, -2.52831909]]),
columns=['apparent_zenith', 'zenith', 'apparent_elevation',
'elevation', 'azimuth', 'equation_of_time'],
index=times)
@pytest.fixture
def dni_et(times):
return np.array(
[1321.1655834833093, 1321.1655834833093, 1321.1655834833093,
1321.1655834833093])
@pytest.fixture
def relative_airmass(times):
return pd.Series([np.nan, 7.58831596, 1.01688136, 3.27930443], times)
# setup for et rad test. put it here for readability
timestamp = pd.Timestamp('20161026')
dt_index = pd.DatetimeIndex([timestamp])
doy = timestamp.dayofyear
dt_date = timestamp.date()
dt_datetime = datetime.datetime.combine(dt_date, datetime.time(0))
dt_np64 = np.datetime64(dt_datetime)
value = 1383.636203
@pytest.mark.parametrize('testval, expected', [
(doy, value),
(np.float64(doy), value),
(dt_date, value),
(dt_datetime, value),
(dt_np64, value),
(np.array([doy]), np.array([value])),
(pd.Series([doy]), np.array([value])),
(dt_index, pd.Series([value], index=dt_index)),
(timestamp, value)
])
@pytest.mark.parametrize('method', [
'asce', 'spencer', 'nrel', pytest.param('pyephem', marks=requires_ephem)])
def test_get_extra_radiation(testval, expected, method):
out = irradiance.get_extra_radiation(testval, method=method)
assert_allclose(out, expected, atol=10)
def test_get_extra_radiation_epoch_year():
out = irradiance.get_extra_radiation(doy, method='nrel', epoch_year=2012)
assert_allclose(out, 1382.4926804890767, atol=0.1)
@requires_numba
def test_get_extra_radiation_nrel_numba(times):
with warnings.catch_warnings():
# don't warn on method reload or num threads
warnings.simplefilter("ignore")
result = irradiance.get_extra_radiation(
times, method='nrel', how='numba', numthreads=4)
# and reset to no-numba state
irradiance.get_extra_radiation(times, method='nrel')
assert_allclose(result,
[1322.332316, 1322.296282, 1322.261205, 1322.227091])
def test_get_extra_radiation_invalid():
with pytest.raises(ValueError):
irradiance.get_extra_radiation(300, method='invalid')
def test_get_ground_diffuse_simple_float():
result = irradiance.get_ground_diffuse(40, 900)
assert_allclose(result, 26.32000014911496)
def test_get_ground_diffuse_simple_series(irrad_data):
ground_irrad = irradiance.get_ground_diffuse(40, irrad_data['ghi'])
assert ground_irrad.name == 'diffuse_ground'
def test_get_ground_diffuse_albedo_0(irrad_data):
ground_irrad = irradiance.get_ground_diffuse(
40, irrad_data['ghi'], albedo=0)
assert (0 == ground_irrad).all()
def test_get_ground_diffuse_albedo_series(times):
albedo = pd.Series(0.2, index=times)
ground_irrad = irradiance.get_ground_diffuse(
45, pd.Series(1000, index=times), albedo)
expected = albedo * 0.5 * (1 - np.sqrt(2) / 2.) * 1000
expected.name = 'diffuse_ground'
assert_series_equal(ground_irrad, expected)
def test_grounddiffuse_albedo_invalid_surface(irrad_data):
with pytest.raises(KeyError):
irradiance.get_ground_diffuse(
40, irrad_data['ghi'], surface_type='invalid')
def test_get_ground_diffuse_albedo_surface(irrad_data):
result = irradiance.get_ground_diffuse(40, irrad_data['ghi'],
surface_type='sand')
assert_allclose(result, [0, 3.731058, 48.778813, 12.035025], 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_klucher_series_float():
# klucher inputs
surface_tilt, surface_azimuth = 40.0, 180.0
dhi, ghi = 100.0, 900.0
solar_zenith, solar_azimuth = 20.0, 180.0
# expect same result for floats and pd.Series
expected = irradiance.klucher(
surface_tilt, surface_azimuth,
pd.Series(dhi), pd.Series(ghi),
pd.Series(solar_zenith), pd.Series(solar_azimuth)
) # 94.99429931664851
result = irradiance.klucher(
surface_tilt, surface_azimuth, dhi, ghi, solar_zenith, solar_azimuth
)
assert_allclose(result, expected[0])
def test_klucher_series(irrad_data, ephem_data):
result = irradiance.klucher(40, 180, irrad_data['dhi'], irrad_data['ghi'],
ephem_data['apparent_zenith'],
ephem_data['azimuth'])
# pvlib matlab 1.4 does not contain the max(cos_tt, 0) correction
# so, these values are different
assert_allclose(result, [0., 36.789794, 109.209347, 56.965916], atol=1e-4)
# expect same result for np.array and pd.Series
expected = irradiance.klucher(
40, 180, irrad_data['dhi'].values, irrad_data['ghi'].values,
ephem_data['apparent_zenith'].values, ephem_data['azimuth'].values
)
assert_allclose(result, expected, atol=1e-4)
def test_haydavies(irrad_data, ephem_data, dni_et):
result = irradiance.haydavies(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'])
# values from matlab 1.4 code
assert_allclose(result, [0, 27.1775, 102.9949, 33.1909], atol=1e-4)
def test_haydavies_components(irrad_data, ephem_data, dni_et):
expected = pd.DataFrame(np.array(
[[0, 27.1775, 102.9949, 33.1909],
[0, 27.1775, 30.1818, 27.9837],
[0, 0, 72.8130, 5.2071],
[0, 0, 0, 0]]).T,
columns=['sky_diffuse', 'isotropic', 'circumsolar', 'horizon'],
index=irrad_data.index
)
# pandas
result = irradiance.haydavies(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
return_components=True)
assert_frame_equal(result, expected, check_less_precise=4)
# numpy
result = irradiance.haydavies(
40, 180, irrad_data['dhi'].values, irrad_data['dni'].values, dni_et,
ephem_data['apparent_zenith'].values, ephem_data['azimuth'].values,
return_components=True)
assert_allclose(result['sky_diffuse'], expected['sky_diffuse'], atol=1e-4)
assert_allclose(result['isotropic'], expected['isotropic'], atol=1e-4)
assert_allclose(result['circumsolar'], expected['circumsolar'], atol=1e-4)
assert_allclose(result['horizon'], expected['horizon'], atol=1e-4)
assert isinstance(result, dict)
# scalar
result = irradiance.haydavies(
40, 180, irrad_data['dhi'].values[-1], irrad_data['dni'].values[-1],
dni_et[-1], ephem_data['apparent_zenith'].values[-1],
ephem_data['azimuth'].values[-1], return_components=True)
assert_allclose(result['sky_diffuse'], expected['sky_diffuse'][-1],
atol=1e-4)
assert_allclose(result['isotropic'], expected['isotropic'][-1],
atol=1e-4)
assert_allclose(result['circumsolar'], expected['circumsolar'][-1],
atol=1e-4)
assert_allclose(result['horizon'], expected['horizon'][-1], atol=1e-4)
assert isinstance(result, dict)
def test_reindl(irrad_data, ephem_data, dni_et):
result = irradiance.reindl(
40, 180, irrad_data['dhi'], irrad_data['dni'], irrad_data['ghi'],
dni_et, ephem_data['apparent_zenith'], ephem_data['azimuth'])
# values from matlab 1.4 code
assert_allclose(result, [0., 27.9412, 104.1317, 34.1663], atol=1e-4)
def test_king(irrad_data, ephem_data):
result = irradiance.king(40, irrad_data['dhi'], irrad_data['ghi'],
ephem_data['apparent_zenith'])
assert_allclose(result, [0, 44.629352, 115.182626, 79.719855], atol=1e-4)
def test_perez(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], relative_airmass)
expected = pd.Series(np.array(
[0., 31.46046871, np.nan, 45.45539877]),
index=irrad_data.index)
assert_series_equal(out, expected, check_less_precise=2)
def test_perez_components(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], relative_airmass,
return_components=True)
expected = pd.DataFrame(np.array(
[[0., 31.46046871, np.nan, 45.45539877],
[0., 26.84138589, np.nan, 31.72696071],
[0., 0., np.nan, 4.47966439],
[0., 4.62212181, np.nan, 9.25316454]]).T,
columns=['sky_diffuse', 'isotropic', 'circumsolar', 'horizon'],
index=irrad_data.index
)
expected_for_sum = expected['sky_diffuse'].copy()
expected_for_sum.iloc[2] = 0
sum_components = out.iloc[:, 1:].sum(axis=1)
sum_components.name = 'sky_diffuse'
assert_frame_equal(out, expected, check_less_precise=2)
assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
def test_perez_negative_horizon():
times = pd.date_range(start='20190101 11:30:00', freq='1H',
periods=5, tz='US/Central')
# Avoid test dependencies on functionality not being tested by hard-coding
# the inputs. This data corresponds to Goodwin Creek in the afternoon on
# 1/1/2019.
# dni_e is slightly rounded from irradiance.get_extra_radiation
# airmass from atmosphere.get_relative_airmas
inputs = pd.DataFrame(np.array(
[[158, 19, 1, 0, 0],
[249, 165, 136, 93, 50],
[57.746951, 57.564205, 60.813841, 66.989435, 75.353368],
[171.003315, 187.346924, 202.974357, 216.725599, 228.317233],
[1414, 1414, 1414, 1414, 1414],
[1.869315, 1.859981, 2.044429, 2.544943, 3.900136]]).T,
columns=['dni', 'dhi', 'solar_zenith',
'solar_azimuth', 'dni_extra', 'airmass'],
index=times
)
out = irradiance.perez(34, 180, inputs['dhi'], inputs['dni'],
inputs['dni_extra'], inputs['solar_zenith'],
inputs['solar_azimuth'], inputs['airmass'],
model='allsitescomposite1990',
return_components=True)
# sky_diffuse can be less than isotropic under certain conditions as
# horizon goes negative
expected = pd.DataFrame(np.array(
[[281.410185, 152.20879, 123.867898, 82.836412, 43.517015],
[166.785419, 142.24475, 119.173875, 83.525150, 45.725931],
[113.548755, 16.09757, 9.956174, 3.142467, 0],
[1.076010, -6.13353, -5.262151, -3.831230, -2.208923]]).T,
columns=['sky_diffuse', 'isotropic', 'circumsolar', 'horizon'],
index=times
)
expected_for_sum = expected['sky_diffuse'].copy()
sum_components = out.iloc[:, 1:].sum(axis=1)
sum_components.name = 'sky_diffuse'
assert_frame_equal(out, expected, check_less_precise=2)
assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
def test_perez_arrays(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'].values, dni.values,
dni_et, ephem_data['apparent_zenith'].values,
ephem_data['azimuth'].values,
relative_airmass.values)
expected = np.array(
[0., 31.46046871, np.nan, 45.45539877])
assert_allclose(out, expected, atol=1e-2)
assert isinstance(out, np.ndarray)
def test_perez_scalar():
# copied values from fixtures
out = irradiance.perez(40, 180, 118.45831879, 939.95469881,
1321.1655834833093, 10.56413562, 144.76567754,
1.01688136)
# this will fail. out is ndarry with ndim == 0. fix in future version.
# assert np.isscalar(out)
assert_allclose(out, 109.084332)
@pytest.mark.parametrize('model', ['isotropic', 'klucher', 'haydavies',
'reindl', 'king', 'perez'])
def test_sky_diffuse_zenith_close_to_90(model):
# GH 432
sky_diffuse = irradiance.get_sky_diffuse(
30, 180, 89.999, 230,
dni=10, ghi=51, dhi=50, dni_extra=1360, airmass=12, model=model)
assert sky_diffuse < 100
def test_get_sky_diffuse_model_invalid():
with pytest.raises(ValueError):
irradiance.get_sky_diffuse(
30, 180, 0, 180, 1000, 1100, 100, dni_extra=1360, airmass=1,
model='invalid')
def test_get_sky_diffuse_missing_dni_extra():
msg = 'dni_extra is required'
with pytest.raises(ValueError, match=msg):
irradiance.get_sky_diffuse(
30, 180, 0, 180, 1000, 1100, 100, airmass=1,
model='haydavies')
def test_get_sky_diffuse_missing_airmass(irrad_data, ephem_data, dni_et):
# test assumes location is Tucson, AZ
# calculated airmass should be the equivalent to fixture airmass
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.get_sky_diffuse(
40, 180, ephem_data['apparent_zenith'], ephem_data['azimuth'], dni,
irrad_data['ghi'], irrad_data['dhi'], dni_et, model='perez')
expected = pd.Series(np.array(
[0., 31.46046871, np.nan, 45.45539877]),
index=irrad_data.index)
assert_series_equal(out, expected, check_less_precise=2)
def test_campbell_norman():
expected = pd.DataFrame(np.array(
[[863.859736967, 653.123094076, 220.65905025]]),
columns=['ghi', 'dni', 'dhi'],
index=[0])
out = irradiance.campbell_norman(
pd.Series([10]), pd.Series([0.5]), pd.Series([109764.21013135818]),
dni_extra=1400)
assert_frame_equal(out, expected)
def test_get_total_irradiance(irrad_data, ephem_data, dni_et,
relative_airmass):
models = ['isotropic', 'klucher',
'haydavies', 'reindl', 'king', 'perez']
for model in models:
total = irradiance.get_total_irradiance(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=relative_airmass,
model=model,
surface_type='urban')
assert total.columns.tolist() == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
@pytest.mark.parametrize('model', ['isotropic', 'klucher',
'haydavies', 'reindl', 'king', 'perez'])
def test_get_total_irradiance_albedo(
irrad_data, ephem_data, dni_et, relative_airmass, model):
albedo = pd.Series(0.2, index=ephem_data.index)
total = irradiance.get_total_irradiance(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=relative_airmass,
model=model,
albedo=albedo)
assert total.columns.tolist() == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
@pytest.mark.parametrize('model', ['isotropic', 'klucher',
'haydavies', 'reindl', 'king', 'perez'])
def test_get_total_irradiance_scalars(model):
total = irradiance.get_total_irradiance(
32, 180,
10, 180,
dni=1000, ghi=1100,
dhi=100,
dni_extra=1400, airmass=1,
model=model,
surface_type='urban')
assert list(total.keys()) == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
# test that none of the values are nan
assert np.isnan(np.array(list(total.values()))).sum() == 0
def test_get_total_irradiance_missing_dni_extra():
msg = 'dni_extra is required'
with pytest.raises(ValueError, match=msg):
irradiance.get_total_irradiance(
32, 180,
10, 180,
dni=1000, ghi=1100,
dhi=100,
model='haydavies')
def test_get_total_irradiance_missing_airmass():
total = irradiance.get_total_irradiance(
32, 180,
10, 180,
dni=1000, ghi=1100,
dhi=100,
dni_extra=1400,
model='perez')
assert list(total.keys()) == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
def test_poa_components(irrad_data, ephem_data, dni_et, relative_airmass):
aoi = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['azimuth'])
gr_sand = irradiance.get_ground_diffuse(40, irrad_data['ghi'],
surface_type='sand')
diff_perez = irradiance.perez(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'], relative_airmass)
out = irradiance.poa_components(
aoi, irrad_data['dni'], diff_perez, gr_sand)
expected = pd.DataFrame(np.array(
[[0., -0., 0., 0.,
0.],
[35.19456561, 0., 35.19456561, 31.4635077,
3.73105791],
[956.18253696, 798.31939281, 157.86314414, 109.08433162,
48.77881252],
[90.99624896, 33.50143401, 57.49481495, 45.45978964,
12.03502531]]),
columns=['poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse'],
index=irrad_data.index)
assert_frame_equal(out, expected)
@pytest.mark.parametrize('pressure,expected', [
(93193, [[830.46567, 0.79742, 0.93505],
[676.09497, 0.63776, 3.02102]]),
(None, [[868.72425, 0.79742, 1.01664],
[680.66679, 0.63776, 3.28463]]),
(101325, [[868.72425, 0.79742, 1.01664],
[680.66679, 0.63776, 3.28463]])
])
def test_disc_value(pressure, expected):
# see GH 449 for pressure=None vs. 101325.
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
tz='America/Phoenix')
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
out = irradiance.disc(ghi, zenith, times, pressure=pressure)
expected_values = np.array(expected)
expected = pd.DataFrame(expected_values, columns=columns, index=times)
# check the pandas dataframe. check_less_precise is weird
assert_frame_equal(out, expected, check_less_precise=True)
# use np.assert_allclose to check values more clearly
assert_allclose(out.values, expected_values, atol=1e-5)
def test_disc_overirradiance():
columns = ['dni', 'kt', 'airmass']
ghi = np.array([3000])
solar_zenith = np.full_like(ghi, 0)
times = pd.date_range(start='2016-07-19 12:00:00', freq='1s',
periods=len(ghi), tz='America/Phoenix')
out = irradiance.disc(ghi=ghi, solar_zenith=solar_zenith,
datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[8.72544336e+02, 1.00000000e+00, 9.99493933e-01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_disc_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith and/or max_airmass keep these results reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[0., 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[277.50185968, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith keeps this result reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_airmass=100)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 36.39544757]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow zenith to be close to 90 and airmass to be infinite
# and we get crazy values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[6.68577449e+03, 1.16046346e-02, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow min cos zenith to be 0, zenith to be close to 90,
# and airmass to be very big and we get even higher DNI values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[7.21238390e+03, 1., 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_dirint_value():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure)
assert_almost_equal(dirint_data.values,
np.array([868.8, 699.7]), 1)
def test_dirint_nans():
times = pd.date_range(start='2014-06-24T12-0700', periods=5, freq='6H')
ghi = pd.Series([np.nan, 1038.62, 1038.62, 1038.62, 1038.62], index=times)
zenith = pd.Series([10.567, np.nan, 10.567, 10.567, 10.567], index=times)
pressure = pd.Series([93193., 93193., np.nan, 93193., 93193.], index=times)
temp_dew = pd.Series([10, 10, 10, np.nan, 10], index=times)
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
temp_dew=temp_dew)
assert_almost_equal(dirint_data.values,
np.array([np.nan, np.nan, np.nan, np.nan, 893.1]), 1)
def test_dirint_tdew():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
temp_dew=10)
assert_almost_equal(dirint_data.values,
np.array([882.1, 672.6]), 1)
def test_dirint_no_delta_kt():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
use_delta_kt_prime=False)
assert_almost_equal(dirint_data.values,
np.array([861.9, 670.4]), 1)
def test_dirint_coeffs():
coeffs = irradiance._get_dirint_coeffs()
assert coeffs[0, 0, 0, 0] == 0.385230
assert coeffs[0, 1, 2, 1] == 0.229970
assert coeffs[3, 2, 6, 3] == 1.032260
def test_dirint_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirint(ghi, solar_zenith, times)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected)
out = irradiance.dirint(ghi, solar_zenith, times, max_zenith=90)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0,
max_zenith=90)
expected = pd.Series([0.0, 144.264507], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0,
max_zenith=100)
expected = pd.Series([0.0, 144.264507], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
def test_gti_dirint():
times = pd.DatetimeIndex(
['2014-06-24T06-0700', '2014-06-24T09-0700', '2014-06-24T12-0700'])
poa_global = np.array([20, 300, 1000])
aoi = np.array([100, 70, 10])
zenith = np.array([80, 45, 20])
azimuth = np.array([90, 135, 180])
surface_tilt = 30
surface_azimuth = 180
# test defaults
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth)
expected_col_order = ['ghi', 'dni', 'dhi']
expected = pd.DataFrame(array(
[[21.05796198, 0., 21.05796198],
[291.40037163, 63.41290679, 246.56067523],
[931.04078010, 695.94965324, 277.06172442]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test ignore calculate_gt_90
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
calculate_gt_90=False)
expected_no_90 = expected.copy()
expected_no_90.iloc[0, :] = np.nan
assert_frame_equal(output, expected_no_90)
# test pressure input
pressure = 93193.
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
pressure=pressure)
expected = pd.DataFrame(array(
[[21.05796198, 0., 21.05796198],
[293.21310935, 63.27500913, 248.47092131],
[932.46756378, 648.05001357, 323.49974813]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test albedo input
albedo = 0.05
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
albedo=albedo)
expected = pd.DataFrame(array(
[[21.3592591, 0., 21.3592591],
[294.4985420, 66.25848451, 247.64671830],
[941.7943404, 727.50552952, 258.16276278]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test with albedo as a Series
albedo = pd.Series(0.05, index=times)
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
albedo=albedo)
assert_frame_equal(output, expected)
# test temp_dew input
temp_dew = np.array([70, 80, 20])
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
temp_dew=temp_dew)
expected = pd.DataFrame(array(
[[21.05796198, 0., 21.05796198],
[295.06070190, 38.20346345, 268.0467738],
[931.79627208, 689.81549269, 283.5817439]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
def test_erbs():
index = pd.DatetimeIndex(['20190101']*3 + ['20190620'])
ghi = pd.Series([0, 50, 1000, 1000], index=index)
zenith = pd.Series([120, 85, 10, 10], index=index)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[9.67192672e+01, 4.15703604e+01, 4.05723511e-01],
[7.94205651e+02, 2.17860117e+02, 7.18132729e-01],
[8.42001578e+02, 1.70790318e+02, 7.68214312e-01]]),
columns=['dni', 'dhi', 'kt'], index=index)
out = irradiance.erbs(ghi, zenith, index)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def test_boland():
index = pd.DatetimeIndex(['20190101']*3 + ['20190620'])
ghi = pd.Series([0, 50, 1000, 1000], index=index)
zenith = pd.Series([120, 85, 10, 10], index=index)
expected = pd.DataFrame(np.array(
[[0.0, 0.0, 0.0],
[81.9448546, 42.8580353, 0.405723511],
[723.764990, 287.230626, 0.718132729],
[805.020419, 207.209650, 0.768214312]]),
columns=['dni', 'dhi', 'kt'], index=index)
out = irradiance.boland(ghi, zenith, index)
assert np.allclose(out, expected)
def test_erbs_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'dhi', 'kt']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
# max_zenith keeps these results reasonable
out = irradiance.erbs(ghi=1.0, zenith=89.99999,
datetime_or_doy=times, min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0., 1., 1.]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# 4-5 9s will produce bad behavior without max_zenith limit
out = irradiance.erbs(ghi=1.0, zenith=89.99999,
datetime_or_doy=times, max_zenith=100)
expected = pd.DataFrame(np.array(
[[6.00115286e+03, 9.98952601e-01, 1.16377640e-02]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# 1-2 9s will produce bad behavior without either limit
out = irradiance.erbs(ghi=1.0, zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[4.78419761e+03, 1.65000000e-01, 1.00000000e+00]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# check default behavior under hardest condition
out = irradiance.erbs(ghi=1.0, zenith=90, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0., 1., 0.01163776]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = OrderedDict()
expected['dni'] = 8.42358014e+02
expected['dhi'] = 1.70439297e+02
expected['kt'] = 7.68919470e-01
out = irradiance.erbs(ghi, zenith, doy)
for k, v in out.items():
assert_allclose(v, expected[k], 5)
def test_dirindex(times):
ghi = pd.Series([0, 0, 1038.62, 254.53], index=times)
ghi_clearsky = pd.Series(
np.array([0., 79.73860422, 1042.48031487, 257.20751138]),
index=times
)
dni_clearsky = pd.Series(
np.array([0., 316.1949056, 939.95469881, 646.22886049]),
index=times
)
zenith = pd.Series(
np.array([124.0390863, 82.85457044, 10.56413562, 72.41687122]),
index=times
)
pressure = 93193.
tdew = 10.
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky,
zenith, times, pressure=pressure,
temp_dew=tdew)
dirint_close_values = irradiance.dirint(ghi, zenith, times,
pressure=pressure,
use_delta_kt_prime=True,
temp_dew=tdew).values
expected_out = np.array([np.nan, 0., 748.31562753, 630.72592644])
tolerance = 1e-8
assert np.allclose(out, expected_out, rtol=tolerance, atol=0,
equal_nan=True)
tol_dirint = 0.2
assert np.allclose(
out.values,
dirint_close_values,
rtol=tol_dirint,
atol=0,
equal_nan=True)
def test_dirindex_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
ghi_clearsky = pd.Series([0, 1], index=times)
dni_clearsky = pd.Series([0, 5], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, max_zenith=90)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0, max_zenith=100)
expected = pd.Series([nan, 5.], index=times)
assert_series_equal(out, expected)
def test_dni():
ghi = pd.Series([90, 100, 100, 100, 100])
dhi = pd.Series([100, 90, 50, 50, 50])
zenith = pd.Series([80, 100, 85, 70, 85])
clearsky_dni = pd.Series([50, 50, 200, 50, 300])
dni = irradiance.dni(ghi, dhi, zenith,
clearsky_dni=clearsky_dni, clearsky_tolerance=2)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 400,
146.190220008, 573.685662283]))
dni = irradiance.dni(ghi, dhi, zenith)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 573.685662283,
146.190220008, 573.685662283]))
@pytest.mark.parametrize(
'surface_tilt,surface_azimuth,solar_zenith,' +
'solar_azimuth,aoi_expected,aoi_proj_expected',
[(0, 0, 0, 0, 0, 1),
(30, 180, 30, 180, 0, 1),
(30, 180, 150, 0, 180, -1),
(90, 0, 30, 60, 75.5224878, 0.25),
(90, 0, 30, 170, 119.4987042, -0.4924038)])
def test_aoi_and_aoi_projection(surface_tilt, surface_azimuth, solar_zenith,
solar_azimuth, aoi_expected,
aoi_proj_expected):
aoi = irradiance.aoi(surface_tilt, surface_azimuth, solar_zenith,
solar_azimuth)
assert_allclose(aoi, aoi_expected, atol=1e-5)
aoi_projection = irradiance.aoi_projection(
surface_tilt, surface_azimuth, solar_zenith, solar_azimuth)
assert_allclose(aoi_projection, aoi_proj_expected, atol=1e-6)
def test_aoi_projection_precision():
# GH 1185 -- test that aoi_projection does not exceed 1.0, and when
# given identical inputs, the returned projection is very close to 1.0
# scalars
zenith = 89.26778228223463
azimuth = 60.932028605997004
projection = irradiance.aoi_projection(zenith, azimuth, zenith, azimuth)
assert projection <= 1
assert np.isclose(projection, 1)
# arrays
zeniths = np.array([zenith])
azimuths = np.array([azimuth])
projections = irradiance.aoi_projection(zeniths, azimuths,
zeniths, azimuths)
assert all(projections <= 1)