def fit_2dgaussian(array, cy=None, cx=None, fwhm=4):
""" Fitting a 2D Gaussian to the 2D distribution of the data with photutils.
Parameters
----------
array : array_like
Input frame with a single point source, approximately at the center.
cy : int
Y integer position of source in the array for extracting a subframe.
cx : int
X integer position of source in the array for extracting a subframe.
fwhm : float
Expected FWHM of the gaussian.
Returns
-------
x : float
Source centroid x position on input array from fitting.
y : float
Source centroid y position on input array from fitting.
"""
if cy and cx and fwhm:
subimage, suby, subx = get_square(array, size=6 * int(fwhm), y=cy, x=cx, position=True)
x, y = morphology.centroid_2dg(subimage)
x += subx
y += suby
else:
x, y = morphology.centroid_2dg(array)
return y, x
def centroid_2dg(self):
"""
This function ...
:return:
"""
return centroid_2dg(self._data)
def centroidg2d(image_data): cx=np.zeros(64) cy=np.zeros(64) for i in range(64): cx[i],cy[i]=centroid_2dg(image_data3[i,13:18,13:18]) cx[i]+=13 cy[i]+=13 return cx,cy
def centroidg2d(image_data): cx=np.zeros(len(image_data)) cy=np.zeros(len(image_data)) for i in range(len(image_data)): print i cx[i],cy[i]=centroid_2dg(image_data[i,13:18,13:18]) cx[i]+=13 cy[i]+=13 return cx,cy
def centroid(data, coord, box_size=10, wcs=None):
coord = [np.float(coord[0]), np.float(coord[1])]
## make cutout mask
mask = get_mask(data, coord, box_size)
# first, try gaussian
xc, yc = centroid_2dg(data, mask=mask)
if np.isnan((xc, yc)).any():
print 'Failed to centroid'
xc, yc = coord
if wcs:
ra, dec = pix2wcs([xc, yc], wcs)
tbl = Table([[xc], [yc], [ra], [dec]],
names=['xcen', 'ycen', 'ra', 'dec'])
else:
tbl = Table([[xc], [yc]], names=['xcen', 'ycen'])
return tbl
def calc_centroids(section):
y2, x2 = centroid_2dg(section)
print 'VALUE ', section[int(y2+.5),int(x2+.5)]
# print y2,x2
return x2+.5, y2+.5
def recenter(image, pos, window_size=15, method="2dg"):
"""
Recenter each star in each frame of the image cube before performing
aperture photometry to take care of slight misalignments between frames
because of atmospheric turbulence and tracking/pointing errors.
Parameters
----------
image : numpy array
2D image
pos : list
List of (x,y) tuples for star positions
window_size : int
Window size in which to fit the gaussian to the star to calculate
new center
method : string
Method used to find center of the star. Options are 1d Gaussian fit,
2d gaussian fit or com (center of mass)
Returns
-------
xcen, ycen : float
Source x and y centers
"""
pos = np.asarray(pos)
ny, nx = image.shape
window_size = int(window_size)
nstars = pos.shape[0]
star_pos = np.zeros([nstars, 2], dtype=np.float32)
for i in range(nstars):
x, y = pos[i][0], pos[i][1]
xmin, xmax = int(x) - int(window_size / 2), int(x) + int(window_size / 2) + 1
ymin, ymax = int(y) - int(window_size / 2), int(y) + int(window_size / 2) + 1
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax > nx:
xmax = nx
if ymax > ny:
ymax = ny
if method == "1dg":
xcen, ycen = centroid_1dg(image[ymin:ymax, xmin:xmax])
elif method == "2dg":
xcen, ycen = centroid_2dg(image[ymin:ymax, xmin:xmax])
elif method == "com":
xcen, ycen = centroid_com(image[ymin:ymax, xmin:xmax])
if (np.abs(xmin + xcen - x)) > 3.0 or (np.abs(ymin + ycen - y)) > 3.0:
star_pos[i, 0] = x
star_pos[i, 1] = y
else:
star_pos[i, 0] = xmin + xcen
star_pos[i, 1] = ymin + ycen
return star_pos
