def test_detect_clearsky_iterations(detect_clearsky_data):
expected, cs = detect_clearsky_data
alpha = 1.0448
with pytest.warns(RuntimeWarning):
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi']*alpha, cs.index, 10, max_iterations=1)
assert (clear_samples[:'2012-04-01 10:41:00'] == True).all()
assert (clear_samples['2012-04-01 10:42:00':] == False).all()
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi']*alpha, cs.index, 10, max_iterations=20)
assert_series_equal(expected['Clear or not'], clear_samples,
check_dtype=False, check_names=False)
def test_detect_clearsky_kwargs(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 10,
mean_diff=1000, max_diff=1000, lower_line_length=-1000,
upper_line_length=1000, var_diff=10, slope_dev=1000)
assert clear_samples.all()
def test_detect_clearsky_arrays(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(expected['GHI'].values,
cs['ghi'].values,
times=cs.index,
window_length=10)
assert isinstance(clear_samples, np.ndarray)
assert (clear_samples == expected['Clear or not'].values).all()
def test_detect_clearsky_irregular_times(detect_clearsky_data):
expected, cs = detect_clearsky_data
times = cs.index.values.copy()
times[0] += 10**9
times = pd.DatetimeIndex(times)
with pytest.raises(NotImplementedError):
clear_samples = clearsky.detect_clearsky(
expected['GHI'].values, cs['ghi'].values, times, 10)
def test_detect_clearsky_components(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples, components, alpha = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 10, return_components=True)
assert_series_equal(expected['Clear or not'], clear_samples,
check_dtype=False, check_names=False)
assert isinstance(components, OrderedDict)
assert np.allclose(alpha, 0.95345573579557108)
def test_detect_clearsky_irregular_times(detect_clearsky_data):
expected, cs = detect_clearsky_data
times = cs.index.values.copy()
times[0] += 10**9
times = pd.DatetimeIndex(times)
with pytest.raises(NotImplementedError):
clear_samples = clearsky.detect_clearsky(
expected['GHI'].values, cs['ghi'].values, times, 10)
def test_detect_clearsky(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(expected['GHI'], cs['ghi'],
cs.index, 10)
assert_series_equal(expected['Clear or not'],
clear_samples,
check_dtype=False,
check_names=False)
def test_detect_clearsky_components(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples, components, alpha = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 10, return_components=True)
assert_series_equal(expected['Clear or not'], clear_samples,
check_dtype=False, check_names=False)
assert isinstance(components, OrderedDict)
assert np.allclose(alpha, 0.9633903181941296)
def test_detect_clearsky_window(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 3)
expected = expected['Clear or not'].copy()
expected.iloc[-3:] = True
assert_series_equal(expected, clear_samples,
check_dtype=False, check_names=False)
def test_detect_clearsky_window(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 3)
expected = expected['Clear or not'].copy()
expected.iloc[-3:] = True
assert_series_equal(expected, clear_samples,
check_dtype=False, check_names=False)
def test_detect_clearsky_iterations(detect_clearsky_data):
expected, cs = detect_clearsky_data
alpha = 1.0448
with pytest.warns(RuntimeWarning):
clear_samples = clearsky.detect_clearsky(expected['GHI'],
cs['ghi'] * alpha,
cs.index,
10,
max_iterations=1)
assert (clear_samples[:'2012-04-01 10:41:00'] == True).all()
assert (clear_samples['2012-04-01 10:42:00':] == False).all()
clear_samples = clearsky.detect_clearsky(expected['GHI'],
cs['ghi'] * alpha,
cs.index,
10,
max_iterations=20)
assert_series_equal(expected['Clear or not'],
clear_samples,
check_dtype=False,
check_names=False)
def test_detect_clearsky_time_interval(detect_clearsky_data):
expected, cs = detect_clearsky_data
u = np.arange(0, len(cs), 2)
cs2 = cs.iloc[u]
expected2 = expected.iloc[u]
clear_samples = clearsky.detect_clearsky(expected2['GHI'],
cs2['ghi'],
window_length=6)
assert_series_equal(expected2['Clear or not'],
clear_samples,
check_dtype=False,
check_names=False)
def test_detect_clearsky_kwargs(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(expected['GHI'],
cs['ghi'],
cs.index,
10,
mean_diff=1000,
max_diff=1000,
lower_line_length=-1000,
upper_line_length=1000,
var_diff=10,
slope_dev=1000)
assert clear_samples.all()
def test_detect_clearsky_arrays(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(
expected['GHI'].values, cs['ghi'].values, cs.index, 10)
assert isinstance(clear_samples, np.ndarray)
assert (clear_samples == expected['Clear or not'].values).all()
def test_detect_clearsky_missing_index(detect_clearsky_data):
expected, cs = detect_clearsky_data
with pytest.raises(ValueError):
clearsky.detect_clearsky(expected['GHI'].values, cs['ghi'].values)
def test_detect_clearsky(detect_clearsky_data):
expected, cs = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(
expected['GHI'], cs['ghi'], cs.index, 10)
assert_series_equal(expected['Clear or not'], clear_samples,
check_dtype=False, check_names=False)
def time_detect_clearsky(self, ndays):
clearsky.detect_clearsky(
self.measured, self.clearsky, self.times, self.window_length
)
def find_clearsky_poa(df,
lat,
lon,
irradiance_poa_key='irradiance_poa_o_###',
mounting='fixed',
tilt=0,
azimuth=180,
altitude=0):
loc = Location(lat, lon, altitude=altitude)
CS = loc.get_clearsky(df.index)
df['csghi'] = CS.ghi
df['csdhi'] = CS.dhi
df['csdni'] = CS.dni
if mounting.lower() == "fixed":
sun = get_solarposition(df.index, lat, lon)
fixedpoa = get_total_irradiance(tilt, azimuth, sun.zenith, sun.azimuth,
CS.dni, CS.ghi, CS.dhi)
df['cspoa'] = fixedpoa.poa_global
if mounting.lower() == "tracking":
sun = get_solarposition(df.index, lat, lon)
# default to axis_tilt=0 and axis_azimuth=180
tracker_data = singleaxis(sun.apparent_zenith,
sun.azimuth,
axis_tilt=tilt,
axis_azimuth=azimuth,
max_angle=50,
backtrack=True,
gcr=0.35)
track = get_total_irradiance(tracker_data['surface_tilt'],
tracker_data['surface_azimuth'],
sun.zenith, sun.azimuth, CS.dni, CS.ghi,
CS.dhi)
df['cspoa'] = track.poa_global
# the following code is assuming clear sky poa has been generated per pvlib, aligned in the same
# datetime index, and daylight savings or any time shifts were previously corrected
# the inputs below were tuned for POA at a 15 minute frequency
# note that detect_clearsky has a scaling factor but I still got slightly different results when I scaled measured poa first
df['poa'] = df[irradiance_poa_key] / df[irradiance_poa_key].quantile(
0.98) * df.cspoa.quantile(0.98)
# inputs for detect_clearsky
measured = df.poa.copy()
clear = df.cspoa.copy()
dur = 60
lower_line_length = -41.416
upper_line_length = 77.789
var_diff = .00745
mean_diff = 80
max_diff = 90
slope_dev = 3
is_clear_results = detect_clearsky(measured.values,
clear.values,
df.index,
dur,
mean_diff,
max_diff,
lower_line_length,
upper_line_length,
var_diff,
slope_dev,
return_components=True)
clearSeries = pd.Series(index=df.index, data=is_clear_results[0])
clearSeries = clearSeries.reindex(index=df.index, method='ffill', limit=3)
return clearSeries
