def test_estimate_background():
"""Verifies tpf.estimate_background()."""
# Create a TPF with 100 electron/second in every pixel
tpf = read(filename_tpf_all_zeros) + 100.0
# The resulting background should be 100 e/s/pixel
bg = tpf.estimate_background(aperture_mask="all")
assert_array_equal(bg.flux.value, 100)
assert bg.flux.unit == tpf.flux.unit / u.pixel
def test_parse_numeric_aperture_masks():
"""Regression test for #694: float or int aperture masks should be
interpreted as boolean masks."""
tpf = read(filename_tpf_one_center)
mask = tpf._parse_aperture_mask(np.zeros(tpf.shape[1:], dtype=float))
assert mask.dtype == bool
mask = tpf._parse_aperture_mask(np.zeros(tpf.shape[1:], dtype=int))
assert mask.dtype == bool
def test_get_header():
"""Test the basic functionality of ``tpf.get_header()``"""
tpf = read(filename_tpf_one_center)
assert tpf.get_header()["CHANNEL"] == tpf.get_keyword("CHANNEL")
assert tpf.get_header(0)["MISSION"] == tpf.get_keyword("MISSION")
assert tpf.get_header(ext=2)["EXTNAME"] == "APERTURE"
# ``tpf.header`` is deprecated
with pytest.warns(LightkurveDeprecationWarning, match="deprecated"):
tpf.header
def test_read():
# define paths to k2 and tess data
k2_path = os.path.join(TESTDATA, "test-tpf-star.fits")
tess_path = os.path.join(TESTDATA,
"tess25155310-s01-first-cadences.fits.gz")
# Ensure files are read in as the correct object
k2tpf = read(k2_path)
assert isinstance(k2tpf, KeplerTargetPixelFile)
tesstpf = read(tess_path)
assert isinstance(tesstpf, TessTargetPixelFile)
# Open should fail if the filetype is not recognized
try:
read(os.path.join(PACKAGEDIR, "data", "lightkurve.mplstyle"))
except LightkurveError:
pass
# Can you instantiate with a path?
assert isinstance(KeplerTargetPixelFile(k2_path), KeplerTargetPixelFile)
assert isinstance(TessTargetPixelFile(tess_path), TessTargetPixelFile)
# Can open take a quality_bitmask argument?
assert read(k2_path, quality_bitmask="hard").quality_bitmask == "hard"
def test_tpf_meta():
"""Can we access meta data using tpf.meta?"""
tpf = read(filename_tpf_one_center)
assert tpf.meta.get("MISSION") == "K2"
assert tpf.meta["MISSION"] == "K2"
assert tpf.meta.get("mission", None) is None # key is case in-sensitive
assert tpf.meta.get("CHANNEL") == 45
# ensure meta is read-only view of the underlying self.hdu[0].header
with pytest.raises(TypeError):
tpf.meta["CHANNEL"] = 44
with pytest.raises(TypeError):
tpf.meta["KEY-NEW"] = 44
def test_centroid_methods_consistency():
"""Are the centroid methods consistent for a well behaved target?"""
pixels = read(filename_synthetic_flat)
centr_moments = pixels.estimate_centroids(method="moments")
centr_quadratic = pixels.estimate_centroids(method="quadratic")
# check that the maximum relative difference doesnt exceed 1%
assert (np.max(
np.abs(centr_moments[0] - centr_quadratic[0]) / centr_moments[0]) <
1e-2)
assert (np.max(
np.abs(centr_moments[1] - centr_quadratic[1]) / centr_moments[1]) <
1e-2)
def test_aperture_photometry_nan():
"""Regression test for #648.
When FLUX or FLUX_ERR is entirely NaN in a TPF, the resulting light curve
should report NaNs in that cadence rather than zero."""
tpf = read(filename_tpf_one_center)
tpf.hdu[1].data["FLUX"][2] = np.nan
tpf.hdu[1].data["FLUX_ERR"][2] = np.nan
lc = tpf.to_lightcurve(aperture_mask="all")
assert ~np.isnan(lc.flux[1])
assert ~np.isnan(lc.flux_err[1])
assert np.isnan(lc.flux[2])
assert np.isnan(lc.flux_err[2])
def test_fluxmode():
"""This should verify the median flux use in an aperture"""
tpf = read(filename_tpf_one_center)
lc_n = tpf.extract_aperture_photometry(aperture_mask="all")
lc_sum = tpf.extract_aperture_photometry(aperture_mask="all",
flux_method="sum")
lc_med = tpf.extract_aperture_photometry(aperture_mask="all",
flux_method="median")
lc_mean = tpf.extract_aperture_photometry(aperture_mask="all",
flux_method="mean")
assert lc_n.flux.value[0] == np.nansum(tpf.flux.value[0])
assert lc_sum.flux.value[0] == np.nansum(tpf.flux.value[0])
assert lc_med.flux.value[0] == np.nanmedian(tpf.flux.value[0])
assert lc_mean.flux.value[0] == np.nanmean(tpf.flux.value[0])
def test_tpf_meta():
"""Can we access meta data using tpf.meta?"""
tpf = read(filename_tpf_one_center)
assert tpf.meta.get("MISSION") == "K2"
assert tpf.meta["MISSION"] == "K2"
assert tpf.meta.get("mission", None) is None # key is case in-sensitive
assert tpf.meta.get("CHANNEL") == 45
# ensure meta is read-only view of the underlying self.hdu[0].header
with pytest.raises(TypeError):
tpf.meta["CHANNEL"] = 44
with pytest.raises(TypeError):
tpf.meta["KEY-NEW"] = 44
# needed for current internal HduToMetaMapping-based meta, ensuring it has
# a friendly __repr__() and __str__()
expected = collections.OrderedDict(tpf.meta).__repr__()
assert tpf.meta.__repr__() == expected
assert tpf.meta.__str__() == expected
def test_animate():
tpf = read(filename_tpf_one_center)
tpf.animate()
def test_cutout_quality_masking():
"""Regression test for #813: Does tpf.cutout() maintain the quality mask?"""
tpf = read(filename_tpf_one_center, quality_bitmask=8192)
tpfcut = tpf.cutout()
assert len(tpf) == len(tpfcut)
def test_filenotfound():
"""Regression test for #540; ensure lk.read() yields `FileNotFoundError`."""
with pytest.raises(FileNotFoundError):
read("DOESNOTEXIST")
