def test_nans():
apparent_zenith = np.array([10, np.nan, 10])
apparent_azimuth = np.array([180, 180, np.nan])
with np.errstate(invalid='ignore'):
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = {'tracker_theta': np.array([0, nan, nan]),
'aoi': np.array([10, nan, nan]),
'surface_azimuth': np.array([90, nan, nan]),
'surface_tilt': np.array([0, nan, nan])}
for k, v in expect.items():
assert_allclose(tracker_data[k], v)
# repeat with Series because nans can differ
apparent_zenith = pd.Series(apparent_zenith)
apparent_azimuth = pd.Series(apparent_azimuth)
with np.errstate(invalid='ignore'):
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame(np.array(
[[ 0., 10., 90., 0.],
[nan, nan, nan, nan],
[nan, nan, nan, nan]]),
columns=['tracker_theta', 'aoi', 'surface_azimuth', 'surface_tilt'])
assert_frame_equal(tracker_data, expect)
def test_axis_azimuth():
apparent_zenith = pd.Series([30])
apparent_azimuth = pd.Series([90])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=90,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 30, 'surface_azimuth': 180,
'surface_tilt': 0, 'tracker_theta': 0},
index=[0], dtype=np.float64)
expect = expect[SINGLEAXIS_COL_ORDER]
assert_frame_equal(expect, tracker_data)
apparent_zenith = pd.Series([30])
apparent_azimuth = pd.Series([180])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=90,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 0, 'surface_azimuth': 180,
'surface_tilt': 30, 'tracker_theta': 30},
index=[0], dtype=np.float64)
expect = expect[SINGLEAXIS_COL_ORDER]
assert_frame_equal(expect, tracker_data)
def test_axis_tilt():
apparent_zenith = pd.Series([30])
apparent_azimuth = pd.Series([135])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=30, axis_azimuth=180,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 7.286245, 'surface_azimuth': 142.65730,
'surface_tilt': 35.98741,
'tracker_theta': -20.88121},
index=[0], dtype=np.float64)
expect = expect[SINGLEAXIS_COL_ORDER]
assert_frame_equal(expect, tracker_data)
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=30, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 47.6632, 'surface_azimuth': 50.96969,
'surface_tilt': 42.5152, 'tracker_theta': 31.6655},
index=[0], dtype=np.float64)
expect = expect[SINGLEAXIS_COL_ORDER]
assert_frame_equal(expect, tracker_data)
def test_backtrack():
apparent_zenith = pd.Series([80])
apparent_azimuth = pd.Series([90])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=False,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 0, 'surface_azimuth': 90,
'surface_tilt': 80, 'tracker_theta': 80},
index=[0], dtype=np.float64)
assert_frame_equal(expect, tracker_data)
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 52.5716, 'surface_azimuth': 90,
'surface_tilt': 27.42833, 'tracker_theta': 27.4283},
index=[0], dtype=np.float64)
assert_frame_equal(expect, tracker_data)
def test_index_mismatch():
apparent_zenith = pd.Series([30])
apparent_azimuth = pd.Series([90,180])
with pytest.raises(ValueError):
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=90,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
def test_arrays_multi():
apparent_zenith = np.array([[10, 10], [10, 10]])
apparent_azimuth = np.array([[180, 180], [180, 180]])
# singleaxis should fail for num dim > 1
with pytest.raises(ValueError):
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
def test_arrays():
apparent_zenith = np.array([10])
apparent_azimuth = np.array([180])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
assert isinstance(tracker_data, dict)
expect = {'tracker_theta': 0, 'aoi': 10, 'surface_azimuth': 90,
'surface_tilt': 0}
for k, v in expect.items():
assert_allclose(tracker_data[k], v)
def test_max_angle():
apparent_zenith = pd.Series([60])
apparent_azimuth = pd.Series([90])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=45, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'aoi': 15, 'surface_azimuth': 90,
'surface_tilt': 45, 'tracker_theta': 45},
index=[0], dtype=np.float64)
assert_frame_equal(expect, tracker_data)
def test_horizon_tilted():
# GH 569
solar_azimuth = np.array([0, 180, 359])
solar_zenith = np.full_like(solar_azimuth, 45)
solar_azimuth = pd.Series(solar_azimuth)
solar_zenith = pd.Series(solar_zenith)
out = tracking.singleaxis(solar_zenith, solar_azimuth, axis_tilt=90,
axis_azimuth=180, backtrack=False, max_angle=180)
expected = pd.DataFrame(np.array(
[[ 180., 45., 0., 90.],
[ 0., 45., 180., 90.],
[ 179., 45., 359., 90.]]),
columns=['tracker_theta', 'aoi', 'surface_azimuth', 'surface_tilt'])
assert_frame_equal(out, expected)
def test_solar_noon():
index = pd.date_range(start='20180701T1200', freq='1s', periods=1)
apparent_zenith = pd.Series([10], index=index)
apparent_azimuth = pd.Series([180], index=index)
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth,
axis_tilt=0, axis_azimuth=0,
max_angle=90, backtrack=True,
gcr=2.0/7.0)
expect = pd.DataFrame({'tracker_theta': 0, 'aoi': 10,
'surface_azimuth': 90, 'surface_tilt': 0},
index=index, dtype=np.float64)
expect = expect[SINGLEAXIS_COL_ORDER]
assert_frame_equal(expect, tracker_data)
def test_horizon_flat():
# GH 569
solar_azimuth = np.array([0, 180, 359])
solar_zenith = np.array([100, 45, 100])
solar_azimuth = pd.Series(solar_azimuth)
solar_zenith = pd.Series(solar_zenith)
# depending on platform and numpy versions this will generate
# RuntimeWarning: invalid value encountered in > < >=
out = tracking.singleaxis(solar_zenith, solar_azimuth, axis_tilt=0,
axis_azimuth=180, backtrack=False, max_angle=180)
expected = pd.DataFrame(np.array(
[[ nan, nan, nan, nan],
[ 0., 45., 270., 0.],
[ nan, nan, nan, nan]]),
columns=['tracker_theta', 'aoi', 'surface_azimuth', 'surface_tilt'])
assert_frame_equal(out, expected)
