def _peak_gauss_method(
i,
npix,
u,
v,
filt,
index_mask,
pixelsize,
innerpix,
innerpix_center,
fw_splodge=0.7,
hole_diam=0.8,
):
mf = np.zeros([npix, npix])
n_holes = index_mask.n_holes
l_B = np.sqrt(u**2 + v**2)
minbl = np.min(l_B) * filt[0]
if n_holes >= 15:
sampledisk_r = minbl / 2 / filt[0] * pixelsize * npix * 0.9
else:
sampledisk_r = minbl / 2.0 / filt[0] * pixelsize * npix * fw_splodge
xspot = float(np.round(v[i] * pixelsize * npix + npix / 2.0))
yspot = float(np.round(u[i] * pixelsize * npix + npix / 2.0))
mf = plot_circle(mf, xspot, yspot, sampledisk_r, display=False)
mf = np.roll(mf, npix // 2, axis=0)
mf = np.roll(mf, npix // 2, axis=1)
X = [np.arange(npix), np.arange(npix), 1]
splodge_fwhm = hole_diam / filt[0] * pixelsize * npix / 1.9
param = {
"A": 1,
"x0": -npix // 2 + yspot,
"y0": -npix // 2 + xspot,
"fwhm_x": splodge_fwhm,
"fwhm_y": splodge_fwhm,
"theta": 0,
}
gauss = gauss_2d_asym(X, param)
gauss = np.roll(gauss, npix // 2, axis=0)
gauss = np.roll(gauss, npix // 2, axis=1)
mfg = gauss / np.sum(gauss)
mf_gain_flat = mfg.ravel()
mf_gain_centered = norm_max(np.fft.fftshift(mfg).ravel())
mf_flat = norm_max(mf.ravel())
mf_centered = norm_max(np.fft.fftshift(mf).ravel())
mf_flat[innerpix] = 0.0
mf_gain_flat[innerpix] = 0.0
mf_centered[innerpix_center] = 0.0
mf_gain_centered[innerpix_center] = 0.0
mf = {
"flat": mf_flat,
"centered": mf_centered,
"gain_f": mf_gain_flat,
"gain_c": mf_gain_centered,
}
return dict2class(mf)
def _peak_gauss_method(i,
npix,
u,
v,
filt,
index_mask,
pixelsize,
innerpix,
innerpix_center,
fw_splodge=0.7,
hole_diam=0.8):
mf = np.zeros([npix, npix])
n_holes = index_mask.n_holes
l_B = np.sqrt(u**2 + v**2)
minbl = np.min(l_B) * filt[0]
if n_holes >= 15:
sampledisk_r = minbl / 2 / filt[0] * pixelsize * npix * 0.9
else:
sampledisk_r = minbl / 2. / \
filt[0] * pixelsize * npix * fw_splodge
xspot = float(np.round((v[i] * pixelsize * npix + npix / 2.)))
yspot = float(np.round((u[i] * pixelsize * npix + npix / 2.)))
mf = plot_circle(mf, xspot, yspot, sampledisk_r, display=False)
mf = np.roll(mf, npix // 2, axis=0)
mf = np.roll(mf, npix // 2, axis=1)
X = [np.arange(npix), np.arange(npix), 1]
splodge_fwhm = hole_diam / filt[0] * pixelsize * npix / 1.9
param = {
'A': 1,
'x0': -npix // 2 + yspot,
'y0': -npix // 2 + xspot,
'fwhm_x': splodge_fwhm,
'fwhm_y': splodge_fwhm,
'theta': 0
}
gauss = gauss_2d_asym(X, param)
gauss = np.roll(gauss, npix // 2, axis=0)
gauss = np.roll(gauss, npix // 2, axis=1)
mfg = gauss / np.sum(gauss)
mf_gain_flat = mfg.ravel()
mf_gain_centered = norm_max(np.fft.fftshift(mfg).ravel())
mf_flat = norm_max(mf.ravel())
mf_centered = norm_max(np.fft.fftshift(mf).ravel())
mf_flat[innerpix] = 0.
mf_gain_flat[innerpix] = 0.
mf_centered[innerpix_center] = 0.
mf_gain_centered[innerpix_center] = 0.
mf = {
'flat': mf_flat,
'centered': mf_centered,
'gain_f': mf_gain_flat,
'gain_c': mf_gain_centered
}
return dict2class(mf)
def _peak_fft_method(i, npix, xy_coords, wl, index_mask, pixelsize,
innerpix, innerpix_center):
mf = np.zeros([npix, npix])
n_holes = index_mask.n_holes
bl2h_ix = index_mask.bl2h_ix
npix = mf.shape[0]
sum_xy = np.sum(xy_coords, axis=0)/n_holes
shift_fact = np.ones([n_holes, 2])
shift_fact[:, 0] = sum_xy[0]
shift_fact[:, 1] = sum_xy[1]
xy_coords2 = xy_coords.copy()
xy_coords2 -= shift_fact
for j in range(len(wl)):
xyh = xy_coords2[bl2h_ix[1, i], :]/wl[j]*pixelsize * \
npix + npix//2
delta = xyh-np.floor(xyh)
ap1 = np.zeros([npix, npix])
x1 = int(xyh[1])
y1 = int(xyh[0])
ap1[x1, y1] = (1.-delta[0]) * (1.-delta[1])
ap1[x1, y1+1] = delta[0]*(1.-delta[1])
ap1[x1+1, y1] = (1.-delta[0])*delta[1]
ap1[x1+1, y1+1] = delta[0]*delta[1]
xyh = xy_coords2[bl2h_ix[0, i], :]/wl[j]*pixelsize * \
npix + npix//2
delta = xyh-np.floor(xyh)
ap2 = np.zeros([npix, npix])
x2 = int(xyh[1])
y2 = int(xyh[0])
ap2[x2, y2] = (1.-delta[0])*(1.-delta[1])
ap2[x2, y2+1] = delta[0]*(1.-delta[1])
ap2[x2+1, y2] = (1.-delta[0])*delta[1]
ap2[x2+1, y2+1] = delta[0]*delta[1]
n_elts = npix**2
tmf = (np.fft.fft2(ap1)/n_elts *
np.conj(np.fft.fft2(ap2)/n_elts))
tmf = np.fft.fft2(tmf)
mf = mf+np.real(tmf)
mf_flat = norm_max(mf.ravel())
mf_centered = norm_max(np.fft.fftshift(mf).ravel())
mf_centered[innerpix_center] = 0.0
mf_flat[innerpix] = 0.0
dic_mf = {'flat': mf_flat,
'centered': mf_centered}
return dic_mf
def _peak_one_method(i, npix, u, v, pixelsize, innerpix, innerpix_center):
mf = np.zeros([npix, npix])
uv = np.array([v[i], u[i]]) * pixelsize * npix
uv = (uv + npix) % npix
uv_int = np.array(np.round(uv), dtype=int)
mf[uv_int[0], uv_int[1]] = 1
mf = np.roll(mf, [0, 0])
mf_flat = norm_max(mf.ravel())
mf_centered = norm_max(np.fft.fftshift(mf).ravel())
mf_flat[innerpix] = 0.0
mf_centered[innerpix_center] = 0.0
dic_mf = {"flat": mf_flat, "centered": mf_centered}
return dic_mf
def _peak_square_method(i, npix, u, v, pixelsize, innerpix, innerpix_center):
mf = np.zeros([npix, npix])
uv = np.array([v[i], u[i]]) * pixelsize * npix
uv = (uv + npix) % npix
uv_int = np.array(np.floor(uv), dtype=int)
uv_frac = uv - uv_int
mf[uv_int[0], uv_int[1]] = (1 - uv_frac[0]) * (1 - uv_frac[1])
mf[uv_int[0], (uv_int[1] + 1) % npix] = (1 - uv_frac[0]) * uv_frac[1]
mf[(uv_int[0] + 1) % npix, uv_int[1]] = uv_frac[0] * (1 - uv_frac[1])
mf[(uv_int[0] + 1) % npix, (uv_int[1] + 1) % npix] = uv_frac[0] * uv_frac[1]
mf = np.roll(mf, [0, 0])
mf_flat = norm_max(mf.ravel())
mf_centered = norm_max(np.fft.fftshift(mf).ravel())
mf_flat[innerpix] = 0.0
mf_centered[innerpix_center] = 0.0
dic_mf = {"flat": mf_flat, "centered": mf_centered}
return dic_mf
