def make_standard_scao_constpsf():
waves = [1.2, 1.6, 2.15]
nmRms = [310, 310, 310] # achieves the 40-18-6 Strehls for MAORY
offset = [0, 0]
psfs = []
for wave, rms in zip(waves, nmRms):
psf = aniso.AnalyticalScaoPsf(pixelSize=0.004,
N=256,
wavelength=wave,
nmRms=rms)
psf.shift_off_axis(offset[0], offset[1])
psfs += [psf.hdu]
keys = {
"AUTHOR": "Kieran Leschinski",
"DATE_CRE": "2019-07-30",
"DATE_MOD": "2019-07-30",
"SOURCE": "AnisoCADO",
"STATUS": "Best guess for a MAORY ConstantPSF with AnisoCADO",
"ETYPE": "CONSTPSF",
"ECAT": (-1, "Field constant. No layer catalogue"),
"EDATA": (1, "PSFs begin from EXT 1"),
"XOFFSET": (offset[0], "[arcsec] offset from NGS"),
"YOFFSET": (offset[1], "[arcsec] offset from NGS"),
}
pri = fits.PrimaryHDU()
pri.header.update(keys)
hdus = fits.HDUList([pri] + psfs)
hdus.writeto("MCAO_ConstPSF_40_18_6.fits", clobber=True)
for i in range(1, 4):
print(hdus[i].header["WAVE0"], hdus[i].header["STREHL"])
print(hdus.info())
def generate_anisocado_strehl_map_for_micado():
# 310 nm --> 40 in Ks, 18 in H, 6 in J
nmRms = np.linspace(0, 700, 36)
waves = np.linspace(0.8, 2.4, 9)
offset = [0, 0]
psfs = []
srs = np.zeros((len(nmRms), len(waves)))
for y, wave in enumerate(waves):
sr = 1
for x, rms in enumerate(nmRms):
if sr > 0.001:
psf = aniso.AnalyticalScaoPsf(pixelSize=0.004,
N=256,
wavelength=wave,
nmRms=rms)
psf.shift_off_axis(offset[0], offset[1])
psfs += [psf.hdu]
sr = psf.strehl_ratio
srs[x, y] = sr
print(wave, rms, srs[x, y])
hdu = fits.ImageHDU(data=srs)
hdu.writeto("AnisoCADO_rms_map.fits", clobber=True)
plt.imshow(srs)
plt.colorbar()
plt.show()
def get_kernel(self, fov):
# called by .apply_to() from the base PSF class
if self._kernel is None:
if isinstance(fov, FieldOfViewBase):
pixel_scale = fov.header["CDELT1"] * u.deg.to(u.arcsec)
elif isinstance(fov, float):
pixel_scale = fov
n = self.meta["psf_side_length"]
wave = self.wavelength
self._psf_object = aniso.AnalyticalScaoPsf(pixelSize=pixel_scale,
N=n,
wavelength=wave,
nmRms=self.nmRms)
if np.any(self.meta["offset"]):
self._psf_object.shift_off_axis(self.meta["offset"][0],
self.meta["offset"][1])
self._kernel = self._psf_object.psf_latest
self._kernel /= np.sum(self._kernel)
if self.meta["rounded_edges"]:
self._kernel = pu.round_kernel_edges(self._kernel)
return self._kernel
def make_standard_scao_constpsf():
waves = [1.2, 1.6, 2.15]
psfs = []
for wave in waves:
psf = aniso.AnalyticalScaoPsf(pixelSize=0.004, N=256, wavelength=wave)
psf.shift_off_axis(0, 5)
psfs += [psf.hdu]
keys = {"AUTHOR" : "Kieran Leschinski",
"DATE_CRE" : "2019-07-30",
"DATE_MOD" : "2019-07-30",
"SOURCE" : "AnisoCADO",
"STATUS" : "Best guess for a standard observations",
"ETYPE" : "CONSTPSF",
"ECAT" : (-1, "Field constant. No layer catalogue"),
"EDATA" : (1, "PSFs begin from EXT 1"),
"XOFFSET": (0, "[arcsec] offset from NGS"),
"YOFFSET": (5, "[arcsec] offset from NGS"),
}
pri = fits.PrimaryHDU()
pri.header.update(keys)
hdus = fits.HDUList([pri] + psfs)
hdus.writeto("SCAO_ConstPSF_5off.fits", clobber=True)
print(hdus.info())
def make_psf(
psf_wavelength: float = 2.15,
shift: Tuple[int] = (0, 14),
N: int = 511,
transform: Callable[[np.ndarray], np.ndarray] = lambda x: x
) -> scopesim.effects.Effect:
"""
create a psf effect for scopesim to be as close as possible to how an anisocado PSF is used in simcado
:param psf_wavelength:
:param shift:
:param N: ? Size of kernel?
:param transform: function to apply to the psf array
:return: effect object you can plug into OpticalTrain
"""
hdus = anisocado.misc.make_simcado_psf_file([shift], [psf_wavelength],
pixelSize=pixel_scale.value,
N=N)
image = hdus[2]
image.data = np.squeeze(
image.data
) # remove leading dimension, we're only looking at a single picture, not a stack
# re-sample to shift center
actual_center = np.array(centroid_quadratic(image.data, fit_boxsize=5))
expected_center = np.array(center_of_image(image.data))
xshift, yshift = expected_center - actual_center
resampled = upsample_image(image.data, xshift=xshift, yshift=yshift).real
image.data = resampled
image.data = transform(image.data)
filename = tempfile.NamedTemporaryFile('w', suffix='.fits').name
image.writeto(filename)
# noinspection PyTypeChecker
tmp_psf = anisocado.AnalyticalScaoPsf(N=N, wavelength=psf_wavelength)
strehl = tmp_psf.strehl_ratio
# Todo: passing a filename that does not end in .fits causes a weird parsing error
return scopesim.effects.FieldConstantPSF(
name=Config.instance().psf_name,
filename=filename,
wavelength=psf_wavelength,
psf_side_length=N,
strehl_ratio=strehl,
)
model = make_anisocado_model() like = likelyhood(detections, model) y, x = np.indices(detections.shape) plt.figure() model.x_0 = 15 plt.imshow(like) # %% # %% # %% import anisocado psf = anisocado.AnalyticalScaoPsf() img = psf.psf_latest # %% # %% Ns = np.logspace(2, 8, 1000) plt.figure() plt.loglog(Ns, [psf_to_sigma(img, N, 10, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma(img, N, 5, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma(img, N, 2, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma(img, N, 0, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma_d(img, N, 10, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma_d(img, N, 5, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma_d(img, N, 2, 0) for N in Ns]) plt.loglog(Ns, [psf_to_sigma_d(img, N, 0, 0) for N in Ns])
pixelSize=pixel_scale,
N=half_length)
image = hdus[2]
image.data = np.squeeze(
image.data
) # remove leading dimension, we're only looking at a single picture, not a stack
x, y = image.data.shape
#TODO psf array is offset by one, hence need the -1 after coordinates?
image.data = image.data * Gaussian2D(x_stddev=5, y_stddev=5)(
*np.mgrid[-x / 2:x / 2:x * 1j, -y / 2:y / 2:y * 1j] - 1)
filename = tempfile.NamedTemporaryFile('w', suffix='.fits').name
image.writeto(filename)
tmp_psf = anisocado.AnalyticalScaoPsf(N=half_length, wavelength=psf_wavelength)
strehl = tmp_psf.strehl_ratio
# Todo: passing a filename that does not end in .fits causes a weird parsing error
psf_effect = scopesim.effects.FieldConstantPSF(
name='mypsf.fits',
filename=filename,
wavelength=psf_wavelength,
psf_side_length=half_length,
strehl_ratio=strehl,
)
N1d = 30
N = N1d**2
spectral_types = ['A0V'] * N
border = 32