convolution.py		convolution.py
	# coding=utf-8		# coding=utf-8
	import numpy as np		import numpy as np
	try:		try:
	from pyfftw.interfaces.numpy_fft import rfftn, irfftn, fftshift		from pyfftw.interfaces.numpy_fft import rfftn, irfftn, fftshift, rfft2, irfft2
	except ImportError:		except ImportError:
	from numpy.fft import rfftn, irfftn, fftshift		from numpy.fft import rfftn, irfftn, fftshift, rfft2, irfft2
	def convolve_nd_valid(cube, psf, compute_fourier=True):		def convolve_nd_valid(cube, psf, compute_fourier=True):
	"""		"""
	using fft convolution of n-dim arrays M & N		using fft convolution of n-dim arrays M & N
	Returns a cube of dimensions (M-N+1)³		Returns a cube of dimensions (M-N+1)³
	"""		"""
	if compute_fourier:		if compute_fourier:
	s1 = np.array(cube.shape)		s1 = np.array(cube.shape)
	s2 = np.array(psf.shape)		s2 = np.array(psf.shape)
	skipping to change at line 92		skipping to change at line 92
	using provided Point Spread Function.		using provided Point Spread Function.
	"""		"""
	# Compute needed padding		# Compute needed padding
	cubep, boxcube = padding(cube, axes=[1, 2])		cubep, boxcube = padding(cube, axes=[1, 2])
	size = np.array(np.shape(cubep)[slice(1, 3)])		size = np.array(np.shape(cubep)[slice(1, 3)])
	if compute_fourier:		if compute_fourier:
	psfp, boxpsf = padding(psf, axes=[1, 2])		psfp, boxpsf = padding(psf, axes=[1, 2])
	fftpsf = np.fft.rfftn(psfp, s=size, axes=[1, 2])		fftpsf = rfftn(psfp, s=size, axes=[1, 2])
	else:		else:
	fftpsf = psf		fftpsf = psf
	fftimg = np.fft.rfftn(cubep, s=size, axes=[1, 2])		fftimg = rfftn(cubep, s=size, axes=[1, 2])
	#Convolution		#Convolution
	fft = np.fft.fftshift(np.fft.irfftn(fftimg * fftpsf, s=size, axes=[1, 2 ]), axes=[1, 2]).real		fft = np.real(fftshift(irfftn(fftimg * fftpsf, s=size, axes=[1, 2]), ax es=[1, 2]))
	# Remove padding		# Remove padding
	cube_conv = fft[boxcube]		cube_conv = fft[boxcube]
	return cube_conv, fftpsf		return cube_conv, fftpsf
	def convolve_2d(image, psf, compute_fourier=True):		def convolve_2d(image, psf, compute_fourier=True):
	"""		"""
	Compute fft of image And of PSF		Compute fft of image And of PSF
	Accepts fftPSF already computed with do_PSF=False		Accepts fftPSF already computed with do_PSF=False
	skipping to change at line 123		skipping to change at line 123
	# Compute needed padding		# Compute needed padding
	cubep, boxcube = padding(image)		cubep, boxcube = padding(image)
	if compute_fourier:		if compute_fourier:
	psfp, boxpsf = padding(psf)		psfp, boxpsf = padding(psf)
	fftpsf = np.fft.rfft2(psfp)		fftpsf = np.fft.rfft2(psfp)
	else:		else:
	fftpsf = psf		fftpsf = psf
	fftimg = np.fft.rfft2(cubep)		fftimg = rfft2(cubep)
	# Convolution		# Convolution
	fft = np.fft.fftshift(np.fft.irfft2(fftimg * fftpsf)).real		fft = np.real(fftshift(irfft2(fftimg * fftpsf)))
	# Remove padding		# Remove padding
	cube_conv = fft[boxcube]		cube_conv = fft[boxcube]
	return cube_conv, fftpsf		return cube_conv, fftpsf
	def convolve_1d(data, psf, compute_fourier=True, axis=0):		def convolve_1d(data, psf, compute_fourier=True, axis=0):
	"""		"""
	Convolve data with PSF only along one dimension specified by axis (defa ult: 0)		Convolve data with PSF only along one dimension specified by axis (defa ult: 0)
	PSF can be the PSF data or its Fourier transform		PSF can be the PSF data or its Fourier transform
	skipping to change at line 151		skipping to change at line 151
	axis = np.array([axis])		axis = np.array([axis])
	# Compute needed padding		# Compute needed padding
	cubep, boxcube = padding(data, axes=axis)		cubep, boxcube = padding(data, axes=axis)
	# Get the size of the axis		# Get the size of the axis
	size = np.array(np.shape(cubep)[slice(axis, axis + 1)])		size = np.array(np.shape(cubep)[slice(axis, axis + 1)])
	if compute_fourier:		if compute_fourier:
	psfp, boxpsf = padding(psf, axes=axis)		psfp, boxpsf = padding(psf, axes=axis)
	fftpsf = np.fft.rfftn(psfp, s=size, axes=axis)		fftpsf = rfftn(psfp, s=size, axes=axis)
	else:		else:
	fftpsf = psf		fftpsf = psf
	fftimg = np.fft.rfftn(cubep, s=size, axes=axis)		fftimg = rfftn(cubep, s=size, axes=axis)
	# Convolution		# Convolution
	fft = np.fft.fftshift(np.fft.irfftn(fftimg * fftpsf, s=size, axes=axis) , axes=axis).real		fft = np.real(fftshift(irfftn(fftimg * fftpsf, s=size, axes=axis), axes =axis))
	# Remove padding		# Remove padding
	cube_conv = fft[boxcube]		cube_conv = fft[boxcube]
	return cube_conv, fftpsf		return cube_conv, fftpsf
	def padding(cube, axes=None):		def padding(cube, axes=None):
	"""		"""
	Computes padding needed for a cube to make sure it has		Computes padding needed for a cube to make sure it has
	a power of 2 shape along dimensions of passed axes (default [0,1])		a power of 2 shape along dimensions of passed axes (default [0,1])
End of changes. 10 change blocks.
	10 lines changed or deleted		10 lines changed or added
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