8000 Fix clearsky Ineichen rounding by cedricleroy · Pull Request #808 · pvlib/pvlib-python · GitHub
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Fix clearsky Ineichen rounding #808

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Fix clearsky Ineichen rounding #808

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2f124d9
handle warnings from temperature model tests (#796)
cwhanse Oct 23, 2019
e94d923
replace Pandas item() implementation with numpy's using .values (#797)
CameronTStark Oct 24, 2019
b99ded7
mark xfail of test_get_psm3 (#803)
CameronTStark Oct 29, 2019
10d54de
coefficient estimation method following DeSoto(2006) (#784)
tylunel Oct 31, 2019
945c339
change tools._scalar_out/_array_out arg name to avoid collision with …
CameronTStark Oct 31, 2019
a349bea
Implement IEC 61853 IAM calculations for diffuse irradiance (#793)
adriesse Nov 2, 2019
01e4769
Fix clearsky Ineichen rounding
cedricleroy Nov 3, 2019
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Fix clearsky Ineichen rounding
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@cedricleroy
cedricleroy committed Nov 3, 2019
commit 01e4769c8d76ded26a5cb93979d18074b87830ef
2 changes: 1 addition & 1 deletion pvlib/clearsky.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
ghi = cg1 * dni_extra * cos_zenith * tl / tl * np.fmax(ghi, 0)

# BncI = "normal beam clear sky radiation"
b = 0.664 + 0.163/fh1
b = 0.664 + 0.16268/fh1
bnci = b * np.exp(-0.09 * airmass_absolute * (tl - 1))
bnci = dni_extra * np.fmax(bnci, 0)

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70 changes: 36 additions & 34 deletions pvlib/test/test_clearsky.py
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Expand Up @@ -34,11 +34,11 @@ def test_ineichen_series():
expected = pd.DataFrame(np.
array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 65.49426624, 321.16092181, 25.54562017],
[ 704.6968125 , 888.90147035, 87.73601277],
[1044.1230677 , 953.24925854, 107.03109696],
[ 853.02065704, 922.06124712, 96.42909484],
[ 251.99427693, 655.44925241, 53.9901349 ],
[ 65.49426624, 321.03665146, 25.56107792],
[ 704.6968125 , 888.55751839, 87.97474001],
[1044.1230677 , 952.88040806, 107.39369589],
[ 853.02065704, 921.70446345, 96.72185108],
[ 251.99427693, 655.19563211, 54.0667509 ],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['ghi', 'dni', 'dhi'],
Expand All @@ -62,11 +62,11 @@ def test_ineichen_series_perez_enhancement():
expected = pd.DataFrame(np.
array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 91.1249279 , 321.16092171, 51.17628184],
[ 716.46580547, 888.9014706 , 99.50500553],
[1053.42066073, 953.24925905, 116.3286895 ],
[ 863.54692748, 922.06124652, 106.9553658 ],
[ 271.06382275, 655.44925213, 73.05968076],
[ 91.1249279 , 321.03665146, 51.1917396 ],
[ 716.46580547, 888.55751839, 99.7437328 ],
[1053.42066073, 952.88040806, 116.69128857],
[ 863.54692748, 921.70446345, 107.24812191],
[ 271.06382275, 655.1956321 , 73.13629663],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['ghi', 'dni', 'dhi'],
Expand All @@ -79,8 +79,8 @@ def test_ineichen_series_perez_enhancement():
def test_ineichen_scalar_input():
expected = OrderedDict()
expected['ghi'] = 1038.159219
expected['dni'] = 942.2081860378344
expected['dhi'] = 110.26529293612793
expected['dni'] = 941.843607
expected['dhi'] = 110.624333

out = clearsky.ineichen(10., 1., 3.)
for k, v in expected.items():
Expand All @@ -107,8 +107,8 @@ def test_ineichen_nans():
expected['dhi'] = np.full(length, np.nan)

expected['ghi'][length-1] = 1042.72590228
expected['dni'][length-1] = 946.35279683
expected['dhi'][length-1] = 110.75033088
expected['dni'][length-1] = 945.986614
expected['dhi'][length-1] = 111.11095

out = clearsky.ineichen(apparent_zenith, airmass_absolute,
linke_turbidity, dni_extra=dni_extra)
Expand All @@ -134,30 +134,30 @@ def test_ineichen_arrays():
[ 94.81136442, 64.38555328, 43.72365587]]]))

expected['dni'] = (np.
array([[[1014.38807396, 942.20818604, 861.11344424],
[1014.38807396, 942.20818604, 861.11344424],
[1014.38807396, 942.20818604, 861.11344424]],
array([[[1014.38807396, 941.8436073 , 860.78024436],
[1014.38807396, 941.8436073 , 860.78024436],
[1014.38807396, 941.8436073 , 860.78024436]],

[[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235],
[ 687.61305142, 419.14891162, 255.50098235]],
[[ 687.34698591, 418.98672583, 255.40211861],
[ 687.34698591, 418.98672583, 255.40211861],
[ 687.34698591, 418.98672583, 255.40211861]],

[[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012],
[ 458.62196014, 186.46177428, 75.80970012]]]))
[[ 458.44450061, 186.38962462, 75.78036626],
[ 458.44450061, 186.38962462, 75.78036626],
[ 458.44450061, 186.38962462, 75.78036626]]]))

expected['dhi'] = (np.
array([[[ 81.38267402, 111.96631281, 153.04382915],
[ 62.3427452 , 85.77117175, 117.23837487],
[ 14.13195304, 19.44274618, 26.57578203]],
array([[[ 81.38267402, 112.33089156, 153.37702903],
[ 62.3427452 , 86.05045527, 117.49362079],
[ 14.13195304, 19.50605461, 26.63364159]],

[[ 85.87736039, 206.04588395, 249.82982258],
[ 65.78587472, 157.84030442, 191.38074731],
[ 14.91244713, 35.77949226, 43.38249342]],
[[ 86.1434259 , 206.20806974, 249.92868632],
[ 65.98969272, 157.96454595, 191.45648133],
[ 14.95864893, 35.80765553, 43.39966093]],

[[ 87.37492676, 184.31984947, 175.98479873],
[ 66.93307711, 141.19719644, 134.81217714],
[ 15.17249681, 32.00680597, 30.5594396 ]]]))
[[ 87.55238629, 184.39199913, 176.01413259],
[ 67.069019 , 141.25246629, 134.83464818],
[ 15.20331233, 32.01933462, 30.56453337]]]))

apparent_zenith = np.linspace(0, 80, 3)
airmass_absolute = np.linspace(1, 10, 3)
Expand All @@ -169,12 +169,14 @@ def test_ineichen_arrays():
out = clearsky.ineichen(apparent_zenith, airmass_absolute, linke_turbidity)

for k, v in expected.items():
print(expected[k])
print(out[k])
assert_allclose(expected[k], out[k])


def test_ineichen_dni_extra():
expected = pd.DataFrame(
np.array([[1042.72590228, 946.35279683, 110.75033088]]),
np.array([[1042.72590228, 945.98661437, 111.11095021]]),
columns=['ghi', 'dni', 'dhi'])

out = clearsky.ineichen(10, 1, 3, dni_extra=pd.Series(1370))
Expand All @@ -183,7 +185,7 @@ def test_ineichen_dni_extra():

def test_ineichen_altitude():
expected = pd.DataFrame(
np.array([[1134.24312405, 994.95377835, 154.40492924]]),
np.array([[1134.24312405, 994.48564998, 154.86594569]]),
columns=['ghi', 'dni', 'dhi'])

out = clearsky.ineichen(10, 1, 3, altitude=pd.Series(2000))
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