def test_collection_stitch():
"""Does Collection.stitch() work?"""
lc = LightCurve(time=np.arange(1, 5), flux=np.ones(4))
lc2 = LightCurve(time=np.arange(5, 16), flux=np.ones(11))
lcc = LightCurveCollection([lc, lc2])
lc_stitched = lcc.stitch()
assert len(lc_stitched.flux) == 15
lc_stitched2 = lcc.stitch(corrector_func=lambda x: x * 2)
assert_array_equal(lc_stitched.flux * 2, lc_stitched2.flux)
def test_collection_init():
lc = LightCurve(time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5))
lc2 = LightCurve(time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15))
lcc = LightCurveCollection([lc, lc2])
assert len(lcc) == 2
assert lcc.data == [lc, lc2]
str(lcc) # Does repr work?
lcc.plot()
plt.close("all")
def test_accessor_tess_sector():
lc0 = TessLightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc0.meta["SECTOR"] = 14
lc1 = TessLightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lc1.meta["SECTOR"] = 26
lcc = LightCurveCollection([lc0, lc1])
assert (lcc.sector == [14, 26]).all()
# The sector accessor can be used to generate boolean array
# to support filter collection by sector
assert ((lcc.sector == 26) == [False, True]).all()
assert ((lcc.sector < 20) == [True, False]).all()
# boundary condition: some lightcurve objects do not have sector
lc2 = LightCurve(
time=np.arange(15, 20),
flux=np.arange(15, 20),
flux_err=np.arange(15, 20),
targetid=23456,
)
lcc.append(lc2)
# expecting [14, 26, np.nan], need 2 asserts to do it.
assert (lcc.sector[:-1] == [14, 26]).all()
assert np.isnan(lcc.sector[-1])
# The sector accessor can be used to generate boolean array
# to support filter collection by sector
assert ((lcc.sector == 26) == [False, True, False]).all()
assert ((lcc.sector < 20) == [True, False, False]).all()
# ensure it works for TPFs too.
with warnings.catch_warnings():
warnings.simplefilter("ignore", LightkurveWarning)
# Ignore "A Kepler data product is being opened using the `TessTargetPixelFile` class"
# the test only cares about the SECTOR header that it sets.
tpf = TessTargetPixelFile(filename_tpf_all_zeros)
tpf.hdu[0].header["SECTOR"] = 23
tpf2 = TessTargetPixelFile(filename_tpf_one_center)
# tpf2 has no sector defined
tpf3 = TessTargetPixelFile(filename_tpf_one_center)
tpf3.hdu[0].header["SECTOR"] = 1
tpfc = TargetPixelFileCollection([tpf, tpf2, tpf3])
assert (tpfc.sector == [23, None, 1]).all()
def test_accessor_k2_campaign():
# scaled down version of tess sector test, as they share the same codepath
lc0 = KeplerLightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc0.meta["CAMPAIGN"] = 2
lc1 = KeplerLightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lc1.meta["CAMPAIGN"] = 1
lcc = LightCurveCollection([lc0, lc1])
assert (lcc.campaign == [2, 1]).all()
# ensure it works for TPFs too.
tpf0 = KeplerTargetPixelFile(filename_tpf_all_zeros)
tpf0.hdu[0].header["CAMPAIGN"] = 2
tpf1 = KeplerTargetPixelFile(filename_tpf_one_center)
tpf1.hdu[0].header["CAMPAIGN"] = 1
tpfc = TargetPixelFileCollection([tpf0, tpf1])
assert (tpfc.campaign == [2, 1]).all()
def test_stitch_repr():
"""Regression test for #884."""
lc = search_lightcurve("Pi Men", mission="TESS", author="SPOC",
sector=1).download()
# The line below used to raise `ValueError: Unable to parse format string
# "{:10d}" for entry "70445.0" in column "cadenceno"`
LightCurveCollection((lc, lc)).stitch().__repr__()
def test_collection_getitem_by_slices():
"""Tests Collection.__getitem__ by slices"""
lc0 = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc1 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lc2 = LightCurve(
time=np.arange(15, 20),
flux=np.arange(15, 20),
flux_err=np.arange(15, 20),
targetid=23456,
)
lcc = LightCurveCollection([lc0, lc1, lc2])
assert lcc[:2].data == [lc0, lc1]
assert lcc[1:999].data == [lc1, lc2]
def test_collection_append():
"""Does Collection.append() work?"""
lc = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=500,
)
lc2 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=100,
)
lcc = LightCurveCollection([lc])
lcc.append(lc2)
assert len(lcc) == 2
def test_unmergeable_columns():
"""Regression test for #954."""
lc1 = LightCurve(data={'time': [1, 2, 3], 'x': [1, 2, 3]})
lc2 = LightCurve(data={
'time': [1, 2, 3],
'x': [1, 2, 3] * u.electron / u.second
})
with pytest.warns(LightkurveWarning,
match="column types are incompatible"):
LightCurveCollection([lc1, lc2]).stitch()
def test_collection_getitem():
"""Tests Collection.__getitem__"""
lc = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc2 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lcc = LightCurveCollection([lc])
lcc.append(lc2)
assert (lcc[0] == lc).all()
assert (lcc[1] == lc2).all()
with pytest.raises(IndexError):
lcc[50]
def test_collection_getitem_by_boolean_array():
"""Tests Collection.__getitem__ , case the argument is a mask, i.e, indexed by boolean array"""
lc0 = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc1 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lc2 = LightCurve(
time=np.arange(15, 20),
flux=np.arange(15, 20),
flux_err=np.arange(15, 20),
targetid=23456,
)
lcc = LightCurveCollection([lc0, lc1, lc2])
lcc_f = lcc[[True, False, True]]
assert lcc_f.data == [lc0, lc2]
assert type(lcc_f) is LightCurveCollection
# boundary case: 1 element
lcc_f = lcc[[False, True, False]]
assert lcc_f.data == [lc1]
# boundary case: no element
lcc_f = lcc[[False, False, False]]
assert lcc_f.data == []
# other array-like input: tuple
lcc_f = lcc[(True, False, True)]
assert lcc_f.data == [lc0, lc2]
# other array-like input: ndarray
lcc_f = lcc[np.array([True, False, True])]
assert lcc_f.data == [lc0, lc2]
# boundary case: mask length not matching - shorter
with pytest.raises(IndexError):
lcc[[True, False]]
# boundary case: mask length not matching - longer
with pytest.raises(IndexError):
lcc[[True, False, True, True]]
def test_collection_getitem_by_other_array():
"""Tests Collection.__getitem__ , case the argument an non-boolean array"""
lc0 = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc1 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lc2 = LightCurve(
time=np.arange(15, 20),
flux=np.arange(15, 20),
flux_err=np.arange(15, 20),
targetid=23456,
)
lcc = LightCurveCollection([lc0, lc1, lc2])
# case: an int array-like, follow ndarray behavior
lcc_f = lcc[[2, 0]]
assert lcc_f.data == [lc2, lc0]
lcc_f = lcc[np.array([2, 0])]
assert lcc_f.data == [lc2, lc0]
# support other int types in np too
lcc_f = lcc[np.array([np.int64(2), np.uint8(0)])]
assert lcc_f.data == [lc2, lc0]
# boundary condition: True / False is interpreted as 1/0 in an bool/int mixed array-like
lcc_f = lcc[[True, False, 2]]
assert lcc_f.data == [lc1, lc0, lc2]
# boundary condition: some index is out of bound
with pytest.raises(IndexError):
lcc[[2, 99]]
# boundary conditions: array-like of neither bool or int, follow ndarray behavior
with pytest.raises(IndexError):
lcc[["abc", "def"]]
with pytest.raises(IndexError):
lcc[[True, "def"]]
def test_collection_setitem():
"""Tests Collection. __setitem__"""
lc = LightCurve(
time=np.arange(1, 5),
flux=np.arange(1, 5),
flux_err=np.arange(1, 5),
targetid=50000,
)
lc2 = LightCurve(
time=np.arange(10, 15),
flux=np.arange(10, 15),
flux_err=np.arange(10, 15),
targetid=120334,
)
lcc = LightCurveCollection([lc])
lcc.append(lc2)
lc3 = LightCurve(time=[1], targetid=55)
lcc[1] = lc3
assert lcc[1].time == lc3.time
lcc.append(lc2)
assert (lcc[2].time == lc2.time).all()
with pytest.raises(IndexError):
lcc[51] = 10
