def test_find_peaks_raises_typeerror_if_mask_is_wrong_type():
x = np.arange(0, 3200)
stddev = 8
x0 = x.min() + 0.5 * x.ptp()
g = models.Gaussian1D(mean=x0, stddev=stddev)
y = g(x)
mask = np.zeros_like(y)
with pytest.raises(TypeError):
peaks_detected, _ = tracing.find_peaks(y, np.ones_like(y) * stddev, mask=mask)
def test_find_peaks():
x = np.arange(0, 3200)
y = np.zeros_like(x, dtype=float)
n_peaks = 20
stddev = 8.
peaks = np.linspace(
x.min() + 0.05 * x.ptp(), x.max() - 0.05 * x.ptp(), n_peaks)
for x0 in peaks:
g = models.Gaussian1D(mean=x0, stddev=stddev)
y += g(x)
peaks_detected, _ = tracing.find_peaks(y, np.ones_like(y) * stddev)
np.testing.assert_allclose(peaks_detected, peaks, atol=0.5)
def recalc_apertures(self):
"""
Recalculate the apertures based on the current set of fitter inputs.
This will redo the aperture detection. Then it calls to each registered
listener function and calls it with a list of N locations and N limits.
"""
max_apertures = self.max_apertures
if not isinstance(max_apertures, int):
max_apertures = int(max_apertures)
# TODO: find_peaks might not be best considering we have no
# idea whether sources will be extended or not
widths = np.arange(3, 20)
peaks_and_snrs = tracing.find_peaks(self.profile,
widths,
mask=self.prof_mask
& DQ.not_signal,
variance=1.0,
reject_bad=False,
min_snr=3,
min_frac=0.2)
if peaks_and_snrs.size == 0:
self.locations = []
self.all_limits = []
else:
# Reverse-sort by SNR and return only the locations
self.locations = np.array(
sorted(peaks_and_snrs.T, key=lambda x: x[1],
reverse=True)[:max_apertures]).T[0]
self.all_limits = tracing.get_limits(np.nan_to_num(self.profile),
self.prof_mask,
peaks=self.locations,
threshold=self.threshold,
method=self.sizing_method)
for listener in self.recalc_listeners:
listener(self.locations, self.all_limits)
for l in self.listeners:
for i, loclim in enumerate(zip(self.locations, self.all_limits)):
loc = loclim[0]
lim = loclim[1]
l.handle_aperture(i + 1, loc, lim[0], lim[1])
def test_find_peaks(noise):
x = np.arange(0, 3200)
y = np.zeros_like(x, dtype=float)
n_peaks = 20
stddev = 4.
peaks = np.linspace(x.min() + 0.05 * x.ptp(),
x.max() - 0.05 * x.ptp(), n_peaks)
for x0 in peaks:
g = models.Gaussian1D(mean=x0, stddev=stddev, amplitude=100)
y += g(x)
np.random.seed(0)
y += (np.random.random(x.size) - 0.5) * noise
peaks_detected, _ = tracing.find_peaks(y, np.ones_like(y) * stddev)
np.testing.assert_allclose(peaks_detected, peaks, atol=1)