def test_interpolate(self):
shapeletsInterp = Shapelets(interpolation=True)
x, y = 0.99, 0
beta = 0.5
flux_full = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
flux_interp = shapeletsInterp.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
npt.assert_almost_equal(flux_interp, flux_full, decimal=10)
class TestShapeletSet(object):
"""
class to test Shapelets
"""
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
def test_shapelet_set(self):
"""
:return:
"""
n_max = 2
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
input = np.array(0.)
input += output
output = self.shapeletSet.function(self.x,
self.y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output[10] == 0.47957022395315946
output = self.shapeletSet.function(1,
1,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
n_max = -1
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0
beta = 1.
amp = 1
shapelets = Shapelets(precalc=False, stable_cut=False)
output = shapelets.function(np.array(1),
np.array(1),
amp,
beta,
0,
0,
center_x=0,
center_y=0)
npt.assert_almost_equal(0.2075537487102974, output, decimal=8)
def test_shapelet_basis(self):
num_order = 5
beta = 1
numPix = 10
kernel_list = self.shapeletSet.shapelet_basis_2d(
num_order, beta, numPix)
assert kernel_list[0][4, 4] == 0.43939128946772255
def test_decomposition(self):
"""
:return:
"""
n_max = 2
beta = 10.
deltaPix = 1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(100, deltaPix, 1)
input = self.shapeletSet.function(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
amp_out = self.shapeletSet.decomposition(input,
x,
y,
n_max,
beta,
deltaPix,
center_x=0,
center_y=0)
for i in range(len(amp)):
npt.assert_almost_equal(amp_out[i], amp[i], decimal=4)
def test_function_split(self):
n_max = 2
beta = 10.
deltaPix = 0.1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(10, deltaPix, 1)
function_set = self.shapeletSet.function_split(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
test_flux = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
print(np.shape(function_set))
print(np.shape(test_flux))
assert function_set[0][10] == test_flux[10]
def test_interpolate(self):
shapeletsInterp = Shapelets(interpolation=True)
x, y = 0.99, 0
beta = 0.5
flux_full = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
flux_interp = shapeletsInterp.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
npt.assert_almost_equal(flux_interp, flux_full, decimal=10)
def test_hermval(self):
x = np.linspace(0, 2000, 2001)
n_array = [1, 2, 3, 0, 1]
import numpy.polynomial.hermite as hermite
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
shape_true = out_true * np.exp(-x**2 / 2.)
shape_approx = out_approx * np.exp(-x**2 / 2.)
npt.assert_almost_equal(shape_approx, shape_true, decimal=6)
x = 2
n_array = [1, 2, 3, 0, 1]
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
npt.assert_almost_equal(out_approx, out_true, decimal=6)
x = 2001
n_array = [1, 2, 3, 0, 1]
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
shape_true = out_true * np.exp(-x**2 / 2.)
shape_approx = out_approx * np.exp(-x**2 / 2.)
npt.assert_almost_equal(shape_approx, shape_true, decimal=6)
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
class TestShapeletSet(object):
"""
class to test Shapelets
"""
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
def test_shapelet_set(self):
"""
:return:
"""
n_max = 2
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
input = np.array(0.)
input += output
output = self.shapeletSet.function(self.x,
self.y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output[10] == 0.47957022395315946
output = self.shapeletSet.function(1,
1,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
def test_shapelet_basis(self):
num_order = 5
beta = 1
numPix = 10
kernel_list = self.shapeletSet.shapelet_basis_2d(
num_order, beta, numPix)
assert kernel_list[0][4, 4] == 0.43939128946772255
def test_decomposition(self):
"""
:return:
"""
n_max = 2
beta = 10.
deltaPix = 1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(100, deltaPix, 1)
input = self.shapeletSet.function(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
amp_out = self.shapeletSet.decomposition(input,
x,
y,
n_max,
beta,
deltaPix,
center_x=0,
center_y=0)
for i in range(len(amp)):
npt.assert_almost_equal(amp_out[i], amp[i], decimal=4)
def test_function_split(self):
n_max = 2
beta = 10.
deltaPix = 0.1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(10, deltaPix, 1)
function_set = self.shapeletSet.function_split(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
test_flux = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
print(np.shape(function_set))
print(np.shape(test_flux))
assert function_set[0][10] == test_flux[10]
def test_interpolate(self):
shapeletsInterp = Shapelets(interpolation=True)
x, y = 0.99, 0
beta = 0.5
flux_full = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
flux_interp = shapeletsInterp.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
npt.assert_almost_equal(flux_interp, flux_full, decimal=10)
def get_source_iter(self, param, num_order, beta, x_grid, y_grid, kwargs_source, x_pos, y_pos, sigma, cov_param=None):
"""
:param param:
:param num_order:
:param beta:
:return:
"""
if not self.kwargs_options['source_type'] == 'NONE':
new = {'I0_sersic': param[0], 'center_x': 0, 'center_y': 0}
kwargs_source_new = kwargs_source.copy()
kwargs_source_new.update(new)
source = self.get_surface_brightness(x_grid, y_grid, **kwargs_source_new)
else:
source = np.zeros_like(x_grid)
x_center = kwargs_source['center_x']
y_center = kwargs_source['center_y']
num_clumps = self.kwargs_options.get('num_clumps', 0)
num_param_shapelets = (num_order+2)*(num_order+1)/2
if not self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets':
numShapelets_clump = 1
else:
num_order_clump = self.kwargs_options.get('num_order_clump', 1)
numShapelets_clump = (num_order_clump+2)*(num_order_clump+1)/2
shapelets = Shapelets(interpolation=False, precalc=False)
error_map_source = np.zeros_like(x_grid)
n1 = 0
n2 = 0
basis_functions = np.zeros((len(param), len(x_grid)))
for i in range(len(param)-num_param_shapelets-num_clumps*numShapelets_clump, len(param)-num_clumps*numShapelets_clump):
source += shapelets.function(x_grid, y_grid, param[i], beta, n1, n2, center_x=0, center_y=0)
basis_functions[i, :] = shapelets.function(x_grid, y_grid, 1, beta, n1, n2, center_x=0, center_y=0)
if n1 == 0:
n1 = n2 + 1
n2 = 0
else:
n1 -= 1
n2 += 1
if self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Gaussian':
for i in range(num_clumps):
j = i + len(param) - num_clumps*numShapelets_clump
source += self.gaussian.function(x_grid, y_grid, amp=param[j], sigma_x=sigma[i], sigma_y=sigma[i], center_x=x_pos[i]-x_center, center_y=y_pos[i]-y_center)
elif self.kwargs_options.get('source_clump_type', 'Gaussian') == 'Shapelets':
i = len(param)-num_clumps*numShapelets_clump
for j in range(0, num_clumps):
H_x, H_y = self.shapelets.pre_calc(x_grid, y_grid, sigma[j], num_order, x_pos[j]-x_center, y_pos[j]-y_center)
n1 = 0
n2 = 0
for k in range(0, numShapelets_clump):
kwargs_source_shapelet = {'center_x': x_pos[j], 'center_y': y_pos[j], 'n1': n1, 'n2': n2, 'beta': sigma[j], 'amp': param[i]}
source += self.shapelets.function(H_x, H_y, **kwargs_source_shapelet)
if n1 == 0:
n1 = n2 + 1
n2 = 0
else:
n1 -= 1
n2 += 1
i += 1
else:
raise ValueError("clump_type %s not valid." %(self.kwargs_options['source_clump_type']))
if cov_param is not None:
error_map_source = np.zeros_like(x_grid)
for i in range(len(error_map_source)):
error_map_source[i] = basis_functions[:, i].T.dot(cov_param).dot(basis_functions[:,i])
return util.array2image(source), util.array2image(error_map_source)