def test_kernel_normalization_mode(self):
"""
Test that an error is raised if mode is invalid.
"""
with pytest.raises(ValueError):
kernel = CustomKernel(np.ones(3))
kernel.normalize(mode='invalid')
def test_normalize_peak(self):
"""
Check if normalize works with peak mode.
"""
custom = CustomKernel([1, 2, 3, 2, 1])
custom.normalize(mode='peak')
assert custom.array.max() == 1
def test_custom_2D_kernel(self):
"""
Check CustomKernel against Box2DKernel.
"""
# Define one dimensional array:
array = np.ones((5, 5))
custom = CustomKernel(array)
custom.normalize()
box = Box2DKernel(5)
c2 = convolve(delta_pulse_2D, custom, boundary='fill')
c1 = convolve(delta_pulse_2D, box, boundary='fill')
assert_almost_equal(c1, c2, decimal=12)
def test_custom_2D_kernel_zerosum(self):
"""
Check if CustomKernel works when the input array/list
sums to zero.
"""
array = [[0, -1, 0], [-1, 4, -1], [0, -1, 0]]
custom = CustomKernel(array)
with pytest.warns(AstropyUserWarning, match=r'kernel cannot be '
r'normalized because it sums to zero'):
custom.normalize()
assert custom.truncation == 0.
assert custom._kernel_sum == 0.
def test_custom_2D_kernel_list(self):
"""
Check if CustomKernel works with lists.
"""
custom = CustomKernel([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
assert custom.is_bool is True
def test_kernel_normalization(self):
"""
Test that repeated normalizations do not change the kernel [#3747].
"""
kernel = CustomKernel(np.ones(5))
kernel.normalize()
data = np.copy(kernel.array)
kernel.normalize()
assert_allclose(data, kernel.array)
kernel.normalize()
assert_allclose(data, kernel.array)
def use_custom_lsf(self,FWHM='6.5',grating='G130M',life_position='1',cen_wave='1300A'):
if FWHM=='COS':
instr_config=dict(name='COS',grating=grating,life_position=life_position,cen_wave=cen_wave)
coslsf=LSF(instr_config)
s,data=coslsf.load_COS_data()
#if 1150A 1200A 1250A 1300A 1350A 1400A 1450A
kernel = CustomKernel(data[cen_wave].data)
else:
COS_kernel=np.double(FWHM)/2.355 #6.5 pixels
#window_size_number_of_points=np.round(FWHM /(2.355*np.abs(velgrid[2]-velgrid[1])))
# Create kernel
kernel = Gaussian1DKernel(stddev=COS_kernel)
self.kernel=kernel
Paso 3:
Convolucionar la imagen modelo con el PSF, para obtener la imagen vista por el instrumento
'''
# Leer imagen modelo
image = io.imread('PDS70c.jpg',
as_gray=True) #Cargar como escala de grises, no como RGB
image = resize(image, (image.shape[0] // 2, image.shape[1] // 2),
anti_aliasing=True)
print('Tamaño de la matriz de la imagen: {}'.format(image.shape))
#Límites para truncar el kernel de convolución
zoom = 1 # zoom factor to truncate and display kernel
kern_min = int((dom_size - 1) / 2 - (dom_size - 1) / (2 * zoom))
kern_max = int((dom_size - 1) / 2 + (dom_size - 1) / (2 * zoom))
kernel = CustomKernel(beam[kern_min:kern_max + 1, kern_min:kern_max + 1])
print('Tamaño de la matriz del kernel de convolución: {}'.format(kernel.shape))
#Ejecutar la convolución:
#im_conv = convolve(image, kernel)
im_conv = convolve_fft(image, kernel)
'''
Paso 4:
Crear, desplegar y guardar las figuras.
'''
fig = plt.figure(figsize=(10, 10))
ax1 = plt.subplot(2, 2, 1)
ax2 = plt.subplot(2, 2, 2)
ax3 = plt.subplot(2, 2, 3)
ax4 = plt.subplot(2, 2, 4)
def test_custom_kernel_odd_error(self):
"""
Check if CustomKernel raises if the array size is odd.
"""
with pytest.raises(KernelSizeError):
CustomKernel([1, 1, 1, 1])