def _assert_valid(self, data, expected_data):
tested_data = {key: data[key] for key in expected_data}
self.assertEqual(tested_data, expected_data)
self.assertEqual(data["t"].size, data["gh"].shape[1])
self.assertEqual(data["nm"].shape[0], data["gh"].shape[0])
self.assertEqual(data["nm"].shape[1], 2)
self.assertEqual(data["nm"][..., 0].min(), data["degree_min"])
self.assertEqual(data["nm"][..., 0].max(), data["degree_max"])
self.assertTrue(aabs(data["nm"][..., 1]).max() <= data["degree_max"])
def __init__(self, indices, coefficients, **kwargs):
SHCoefficients.__init__(self, **kwargs)
n_idx, m_idx = indices[..., 0], indices[..., 1]
self._degree = n_idx.max()
self._index = stack((
aabs(m_idx) + (n_idx * (n_idx + 1)) // 2,
(m_idx < 0).astype('int'),
n_idx,
), 1)
self._coeff = coefficients
def __init__(self, indices, coefficients, **kwargs):
SHCoefficients.__init__(self, **kwargs)
n_idx, m_idx = indices[..., 0], indices[..., 1]
self._degree = n_idx.max()
self._index = stack((
aabs(m_idx) + (n_idx*(n_idx + 1))//2,
(m_idx < 0).astype('int'),
n_idx,
), 1)
self._coeff = coefficients
def _assert_valid(self, data, expected_data):
tested_data = {key: data[key] for key in expected_data}
assert_allclose(data["t"],
[data["epoch"], data["epoch"] + WMM_VALIDITY_PERIOD])
self.assertEqual(tested_data, expected_data)
self.assertEqual(data["t"].size, data["gh"].shape[1])
self.assertEqual(data["nm"].shape[0], data["gh"].shape[0])
self.assertEqual(data["nm"].shape[1], 2)
self.assertEqual(data["nm"][..., 0].min(), data["degree_min"])
self.assertEqual(data["nm"][..., 0].max(), data["degree_max"])
self.assertTrue(aabs(data["nm"][..., 1]).max() <= data["degree_max"])
def _assert_valid(self, data, expected_data):
tested_data = {
key: data[key] for key in expected_data
}
self.assertEqual(tested_data, expected_data)
self.assertEqual(data["t"].size, data["gh"].shape[1])
self.assertEqual(data["nm"].shape[0], data["gh"].shape[0])
self.assertEqual(data["nm"].shape[1], 2)
self.assertEqual(data["nm"][..., 0].min(), data["degree_min"])
self.assertEqual(data["nm"][..., 0].max(), data["degree_max"])
self.assertTrue(aabs(data["nm"][..., 1]).max() <= data["degree_max"])
def _assert_valid(self, data, expected_data):
tested_data = {
key: data[key] for key in expected_data
}
assert_allclose(
data["t"], [data["epoch"], data["epoch"] + WMM_VALIDITY_PERIOD]
)
self.assertEqual(tested_data, expected_data)
self.assertEqual(data["t"].size, data["gh"].shape[1])
self.assertEqual(data["nm"].shape[0], data["gh"].shape[0])
self.assertEqual(data["nm"].shape[1], 2)
self.assertEqual(data["nm"][..., 0].min(), data["degree_min"])
self.assertEqual(data["nm"][..., 0].max(), data["degree_max"])
self.assertTrue(aabs(data["nm"][..., 1]).max() <= data["degree_max"])
def _extend( data , sgm , I , eps = 1e-6 , clock_wise = True ) :
"""
extend hull
find a point closely located to the boundary segement but not part
of the footprint polygon
eps is the absolute tolerance of the distance below which the
point is considered part of the bounary
"""
if len(I) < 1 : return None
# distance from the line segment and parametric projection of the point
# to the boundary segement
dist , parm = _evalPoint2LineDistAndParmam( data[I] ,_getLineCoef( data[sgm] , clock_wise ) )
# select points below the threshold and projected on the boundary segment
idx = aand( aabs( dist ) < eps , aand( parm >= 0 , parm <= 1 ) ).nonzero()[0]
return None if ( len(idx) < 1 ) else I[idx[0]]
def _extend( data , sgm , I , eps = 1e-6 , clock_wise = True ) :
"""
extend hull
find a point closely located to the boundary segement but not part
of the footprint polygon
eps is the absolute tolerance of the distance below which the
point is considered part of the bounary
"""
if len(I) < 1 : return None
# distance from the line segment and parametric projection of the point
# to the boundary segement
dist , parm = _evalPoint2LineDistAndParmam( data[I] ,_getLineCoef( data[sgm] , clock_wise ) )
# select points below the threshold and projected on the boundary segment
idx = aand( aabs( dist ) < eps , aand( parm >= 0 , parm <= 1 ) ).nonzero()[0]
return None if ( len(idx) < 1 ) else I[idx[0]]
def generalPSF(mask, x, y, a, r, wl, T=None, gamma=None, sigma=None):
'''
Returns the diffraction-limited PSF generated by a given pupil, convolved with
a Voigt profile specified by the parameters gamma and sigma.
Parameters
==========
mask : array_like (2D)
The actual pupil of the telescope
x, y : array_like
Coordinate array of the image
a : scalar (float)
Aperture
r : scalar (float)
Distance from the optics to the object
wl : scalar (float) / array_like
Wavelength(s)
T : scalar (float), required when wl is array_like
Black body temperature
gamma : scalar (float)
Parameter of the Lorentz profile (in arcsec)
sigma : scalar (float)
Parameter of the Gaussian profile (in arcsec)
Note: The convolution of the Lorentz and the Gaussian profiles lead to the
===== Voigt profile
'''
if not hasattr(wl, '__len__'):
wl = [wl]
multiple_wl = False
else:
multiple_wl = True
if T == None:
raise ValueError('Black body temperature must be provided')
psf_bol = []
N = 0.
x = asarray(x)
y = asarray(y)
Nx = len(x)
Ny = len(y)
dx = x[1] - x[0]
dy = y[1] - y[0]
if any(x[1:] - x[:-1] != dx) or any(y[1:] - y[:-1] != dy):
warn("Coordinates must be equidistant")
if not (gamma is None and sigma is None):
X = roll(arange(-floor(Nx / 2.) * dx, Nx / 2. * dx, dx),
-int(floor(Nx / 2.)))
Y = roll(arange(-floor(Ny / 2.) * dy, Ny / 2. * dy, dy),
-int(floor(Ny / 2.)))
R, theta, z = coordCyl(X, Y, [0], [X[0], Y[0], 0], [0, 0, 1])
R = sqrt(R[0][:, :, 0]**2 + R[1][:, :, 0]**2)
nonIdeal = ones((Nx, Ny), dtype=complex)
if gamma == 0.:
gamma = None
if sigma == 0.:
sigma = None
if not gamma is None:
gamma = r * arctan(gamma * 2. * pi / (360. * 3600.))
nonIdeal *= fft2(.5 * gamma / (pi * (R**2 + .25 * gamma**2)))
if not sigma is None:
sigma = r * arctan(sigma * 2. * pi / (360. * 3600.))
nonIdeal *= fft2(
exp(-R**2 / (2. * sigma**2)) / (sqrt(2. * pi) * sigma))
for l in wl:
Lx = l / (Nx * sin(dx / r))
Ly = l / (Ny * sin(dy / r))
s = (1 + 2 * int(floor(.5 * a / Lx)), 1 + 2 * int(floor(.5 * a / Ly)))
mask_resampled = resample(mask, s)
mask_padded = real(zeroPadding(mask_resampled, (Nx, Ny)))
#mask_shifted = (-1)**sum(meshgrid(arange(Nx), arange(Ny)))*mask_padded
psf_val = aabs(fft2(mask_padded))**2
#psf_val = psf_val/psf_val[int(floor(Nx/2.)),int(floor(Ny/2.))]
psf_val = psf_val / asum(psf_val)
if multiple_wl:
pl = planck(l, T)
else:
pl = 1.
psf_bol += [pl * psf_val]
N += pl
if gamma is None and sigma is None:
return sum(psf_bol) / N
else:
nonIdeal /= nonIdeal[0, 0]
psf_nonIdeal = real(ifft2(fft2(sum(psf_bol) / N) * nonIdeal))
#return aabs(ifft2(nonIdeal))
#print(mean(psf_nonIdeal))
return psf_nonIdeal