def test_lc_baseline_offset_fewbins(self):
times = np.arange(0, 4, 1)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 4.005]]
lc = Lightcurve(times, counts, gti=gti)
with pytest.warns(UserWarning) as record:
lc.baseline(10000, 0.01, offset_correction=True)
assert np.any(["Too few bins to perform baseline offset correction"
in r.message.args[0] for r in record])
def test_lc_baseline_offset_fewbins(self):
times = np.arange(0, 4, 1)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 4.005]]
lc = Lightcurve(times, counts, gti=gti)
with pytest.warns(UserWarning) as record:
lc.baseline(10000, 0.01, offset_correction=True)
assert np.any(["Too few bins to perform baseline offset correction"
in r.message.args[0] for r in record])
def test_lc_baseline(self):
times = np.arange(0, 100, 0.01)
counts = np.random.normal(100, 0.1, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01)
assert np.all(lc.counts - baseline < 1)
def test_lc_baseline(self):
times = np.arange(0, 100, 0.01)
counts = np.random.normal(100, 0.1, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01)
assert np.all(lc.counts - baseline < 1)
def test_lc_baseline_offset(self):
times = np.arange(0, 100, 0.01)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01, offset_correction=True)
assert np.isclose(np.std(lc.counts - baseline), input_stdev, rtol=0.1)
def test_lc_baseline_offset(self):
times = np.arange(0, 100, 0.01)
input_stdev = 0.1
counts = np.random.normal(100, input_stdev, len(times)) + \
0.001 * times
gti = [[-0.005, 50.005], [59.005, 100.005]]
good = create_gti_mask(times, gti)
counts[np.logical_not(good)] = 0
lc = Lightcurve(times, counts, gti=gti)
baseline = lc.baseline(10000, 0.01, offset_correction=True)
assert np.isclose(np.std(lc.counts - baseline), input_stdev, rtol=0.1)
