def __init__(self, data):
self.x = data
self.x2 = self.x ** 2
self.i = self.x.cumsum(axis=0).cumsum(axis=1)
self.i2 = self.x2.cumsum(axis=0).cumsum(axis=1)
self.i_pad = pad2d(self.i, 1)
self.i2_pad = pad2d(self.i2, 1)
def __init__(self, data):
self.x = data
self.x2 = self.x**2
self.i = self.x.cumsum(axis=0).cumsum(axis=1)
self.i2 = self.x2.cumsum(axis=0).cumsum(axis=1)
self.i_pad = pad2d(self.i, 1)
self.i2_pad = pad2d(self.i2, 1)
def window(data, pos, pixel_rad):
# Check that the input array has two dimensions.
data = np.array(data)
if data.ndim != 2:
raise ValueError('The input array must be 2D.')
# If the pixel radius size is one repat the value for the 2nd dimension.
pixel_rad = np.array(pixel_rad)
if pixel_rad.size == 1:
pixel_rad = np.repeat(pixel_rad, 2)
if pixel_rad.size not in (1, 2):
raise ValueError('The pixel radius must have a size of 1 or 2.')
# Check that the array position has a size of two.
pos = np.array(pos) % np.array(data.shape)
if pos.size != 2:
raise ValueError('The array position must have a size of 2.')
# Check if the pixel radius is within the bounds of the input array.
if (np.any(pixel_rad < 1)
or np.any(np.array(data.shape) / 2 - pixel_rad < 0)):
raise ValueError('The pixel radius values must have a value of at '
'least 1 and at most half the size of the input '
'array. Array size: ' + str(data.shape))
# Return window selection.
return pad2d(data, pixel_rad)[[
slice(a, a + 2 * b + 1) for a, b in zip(pos, pixel_rad)
]]
def psf_var_convolve(image, psf):
# Function to select slices of an image.
def select_slices(image_shape, sub_shape):
ranges = np.array([np.arange(i) for i in image_shape])
limits = np.array([ranges.T + sub_shape / 2 + 1,
ranges.T + 1.5 * sub_shape + 1]).T
return np.array([np.array([slice(*i), slice(*j)]) for i in limits[0]
for j in limits[1]])
# Function to convolve a PSF with a sub-image.
def get_convolve(sub_image, psf):
return np.multiply(sub_image, np.rot90(psf, 2)).sum()
# Pad image borders by PSF size.
image_pad = pad2d(image, psf.shape[1:])
# Get sub-image slices of the padded image.
slices = select_slices(image.shape, data2np(psf.shape[1:]))
# Convolve sub-images with PSFs.
image_conv = np.array([[get_convolve(image_pad[list(x)], y)]
for x, y in zip(slices, psf)])
return np.reshape(image_conv, image.shape)
def window(data, pos, pixel_rad):
# Check that the input array has two dimensions.
data = np.array(data)
if data.ndim != 2:
raise ValueError('The input array must be 2D.')
# If the pixel radius size is one repat the value for the 2nd dimension.
pixel_rad = np.array(pixel_rad)
if pixel_rad.size == 1:
pixel_rad = np.repeat(pixel_rad, 2)
if pixel_rad.size not in (1, 2):
raise ValueError('The pixel radius must have a size of 1 or 2.')
# Check that the array position has a size of two.
pos = np.array(pos) % np.array(data.shape)
if pos.size != 2:
raise ValueError('The array position must have a size of 2.')
# Check if the pixel radius is within the bounds of the input array.
if (np.any(pixel_rad < 1) or np.any(np.array(data.shape) / 2 -
pixel_rad < 0)):
raise ValueError('The pixel radius values must have a value of at '
'least 1 and at most half the size of the input '
'array. Array size: ' + str(data.shape))
# Return window selection.
return pad2d(data, pixel_rad)[[slice(a, a + 2 * b + 1) for a, b in
zip(pos, pixel_rad)]]
def get_l2norm(data, layout, pixel_rad=9):
radius = data.shape[0] / layout[0] / 2
centres = image.image_centres(data.shape, layout)
cube = image_file_io.gen_data_cube(data, centres, pixel_rad)
cube = transform.cube2map(np.array([np_adjust.pad2d(x, radius - pixel_rad)
for x in cube]), layout)
diff = data - cube
l2norm = np.linalg.norm(diff) / np.sum(diff > 0) * np.prod(data.shape)
print ' - L2 Norm of noise from data:', l2norm
return l2norm
def _pad_data(self):
self.pad_data = pad2d(self.data, self.pixel_rad)
def _pad_data(self):
self.pad_data = pad2d(self.data, self.pixel_rad)
