def center_of_mass(self):
"""
Calculates the position of the source across all cadences using `muchbettermoments` and `self.best_aperture`.
Finds the brightest pixel in a (`height`, `width`) region summed up over all cadence.
Searches a smaller (3x3) region around this pixel at each cadence and uses `muchbettermoments` to find the maximum.
"""
self.x_com = []
self.y_com = []
summed_pixels = np.sum(self.aperture * self.tpf, axis=0)
brightest = np.where(summed_pixels == np.max(summed_pixels))
cen = [brightest[0][0], brightest[1][0]]
if cen[0] < 3.0:
cen[0] = 3
if cen[1] < 3.0:
cen[1] = 3
if cen[0] + 3 > np.shape(self.tpf[0])[0]:
cen[0] = np.shape(self.tpf[0])[0] - 3
if cen[1] + 3 > np.shape(self.tpf[0])[1]:
cen[1] = np.shape(self.tpf[0])[1] - 3
for a in range(len(self.tpf)):
data = self.tpf[a, cen[0] - 3:cen[0] + 2, cen[1] - 3:cen[1] + 2]
c_0 = quadratic_2d(data)
c_frame = [cen[0] + c_0[0], cen[1] + c_0[1]]
self.x_com.append(c_frame[0])
self.y_com.append(c_frame[1])
return
def test_recover_center_of_gaussian_wo_noise():
u = np.linspace(0, 8, 9)
x, y = np.meshgrid(u, u)
true_center = [3.45, 4.73]
z = 10 * np.exp(-(x - true_center[1])**2 - (y - true_center[0])**2)
center = quadratic_2d(z)
np.testing.assert_almost_equal(center, true_center, decimal=1)
def isolated_center(x, y, image):
"""Finds the centroid of each isolated source with quadratic_2d.
Parameters
----------
x : array-like
Initial guesses of x positions for sources.
y : array-like
Initial guesses of y positions for sources.
image : np.ndarray
FFI flux data.
Returns
-------
cenx : array-like
Controid x coordinates for all good input sources.
ceny : array-like
Centroid y coordinates for all good input sources.
good : list
Indexes into input arrays corresponding to good sources.
"""
cenx, ceny, good = [], [], []
print("Finding isolated centers of sources")
for i in range(len(x)):
if x[i] > 0. and y[i] > 0.:
tpf = Cutout2D(image, position=(x[i], y[i]), size=(7,7), mode='partial')
origin = tpf.origin_original
cen = quadratic_2d(tpf.data)
cenx.append(cen[0]+origin[0]); ceny.append(cen[1]+origin[1])
good.append(i)
cenx, ceny = np.array(cenx), np.array(ceny)
return cenx, ceny, good
def isolated_center(x, y, image):
""" Finds the center of each isolated TPF with quadratic_2d """
cenx, ceny, good = [], [], []
for i in range(len(x)):
if x[i] > 0. and y[i] > 0.:
tpf = Cutout2D(image,
position=(x[i], y[i]),
size=(7, 7),
mode='partial')
cen = quadratic_2d(tpf.data)
cenx.append(cen[0])
ceny.append(cen[1])
good.append(i)
cenx, ceny = np.array(cenx), np.array(ceny)
return cenx, ceny, good
