https://github.com/jyhmiinlin/pynufft
Revision 891984064999c1158e15ae7bfeaed7bfeb8e1837 authored by LElgueddari on 10 May 2019, 09:14:34 UTC, committed by Jyh-Miin Lin on 10 May 2019, 09:14:34 UTC
* Initial commit of documentation fixing * [NUFFT_HSA]: Adding verbosity level and warning instead of prints * [NUFFT_hsa]: now pep8 compliant * [Helper]: Adding the verbosity * [NUFFT_hsa]: Adding the verbosity on the helper * [NUFFT_hsa]: pycodestyle issues * [nufft_cpu]: pycodestyle compatibility
1 parent 6742ec5
Tip revision: 891984064999c1158e15ae7bfeaed7bfeb8e1837 authored by LElgueddari on 10 May 2019, 09:14:34 UTC
Cleaning some part of the code and removing useless prints (#18)
Cleaning some part of the code and removing useless prints (#18)
Tip revision: 8919840
.pynufft_cpu.py
"""
Class NUFFT on Numpy/Scipy
=================================
"""
import numpy
import scipy.sparse
import numpy.fft
import scipy.signal
import scipy.linalg
import scipy.special
from src._helper.helper import *
class NUFFT:
def __init__(self):
self.dtype=numpy.complex64
pass
def plan(self, om, Nd, Kd, Jd):
self.debug = 0 # debug
n_shift = tuple(0*x for x in Nd)
self.st = plan(om, Nd, Kd, Jd)
self.Nd = self.st['Nd'] # backup
self.sn = self.st['sn'] # backup
self.ndims=len(self.st['Nd']) # dimension
self.linear_phase(n_shift)
# calculate the linear phase thing
self.st['pH'] = self.st['p'].getH().tocsr()
self.st['pHp']= self.st['pH'].dot(self.st['p'])
self.NdCPUorder, self.KdCPUorder, self.nelem = preindex_copy(self.st['Nd'], self.st['Kd'])
# self.st['W'] = self.pipe_density()
self.shape = (self.st['M'], numpy.prod(self.st['Nd']))
# print('untrimmed',self.st['pHp'].nnz)
# self.truncate_selfadjoint(1e-1)
# print('trimmed', self.st['pHp'].nnz)
def _matvec(self, x_vec):
'''
dot operation provided for scipy.sparse.linalg
wrapper of self.forward()
'''
x2 = numpy.reshape(x_vec, self.st['Nd'], order='F')
return self.forward(x2)
def solver(self,y, solver=None, *args, **kwargs):
import src._solver.solver_cpu
return src._solver.solver_cpu.solver(self, y, solver, *args, **kwargs)
def _pipe_density(self,maxiter):
'''
Create the density function by iterative solution
Generate pHp matrix
'''
# W = pipe_density(self.st['p'])
# sampling density function
try:
if maxiter < self.last_iter:
W = self.st['W']
else: #maxiter > self.last_iter
W = self.st['W']
for pp in range(0,maxiter - self.last_iter):
# E = self.st['p'].dot(V1.dot(W))
E = self.forward(self.adjoint(W))
W = (W/E)
self.last_iter = maxiter
except:
W = numpy.ones((self.st['M'],),dtype=self.dtype)
# V1= self.st['p'].getH()
# VVH = V.dot(V.getH())
for pp in range(0,maxiter):
# E = self.st['p'].dot(V1.dot(W))
E = self.forward(self.adjoint(W))
W = (W/E)
self.last_iter = maxiter
return W
# density of the k-space, reduced size
def linear_phase(self, n_shift):
'''
This method shifts the interpolation matrix self.st['p0']
and create a shifted interpolation matrix self.st['p']
Parameters
----------
n_shift : tuple
Input shifts. integers
Raises
------
ValueError
If `s` and `axes` have different length.
IndexError
If an element of `axes` is larger than than the number of axes of `a`.
'''
om = self.st['om']
M = self.st['M']
final_shifts = tuple(
numpy.array(n_shift) +
numpy.array(
self.st['Nd']) /
2)
phase = numpy.exp(
1.0j *
numpy.sum(
om *
numpy.tile(
final_shifts,
(M,
1)),
1))
# add-up all the linear phasees in all axes,
self.st['p'] = scipy.sparse.diags(phase, 0).dot(self.st['p0'])
return 0 # shifted sparse matrix
def truncate_selfadjoint(self, tol):
indix=numpy.abs(self.st['pHp'].data)< tol
self.st['pHp'].data[indix]=0
self.st['pHp'].eliminate_zeros()
def forward(self, x):
'''
This method computes the Non-Uniform Fast Fourier Transform.
input:
x: Nd array
output:
y: (M,) array
'''
y = self.k2y(self.xx2k(self.x2xx(x)))
return y
def adjoint(self, y):
'''
backward method (not inverse, see solver() method) computes the adjoint transform
(conjugate transpose, or Hermitian transpose) of forward
Non-Uniform Fast Fourier Transform.
input:
y: non-uniform data, (M,) array
output:
x: adjoint image, Nd array
'''
x = self.xx2x(self.k2xx(self.y2k(y)))
return x
def selfadjoint(self, x):
# x2 = self.adjoint(self.forward(x))
x2 = self.xx2x(self.k2xx(self.k2y2k(self.xx2k(self.x2xx(x)))))
# x2 = self.k2xx(self.k2y2k(self.xx2k(x)))
return x2
def x2xx(self, x):
'''
scaling of the image, generate Nd
input:
x: 2D image
output:
xx: scaled 2D image
'''
xx = x * self.st['sn']
return xx
def xx2k(self, xx):
'''
fft of the image
input:
xx: scaled 2D image
output:
k: k-space grid
'''
# dd = numpy.size(self.st['Kd'])
output_x = numpy.zeros(self.st['Kd'], dtype=self.dtype,order='C')
# output_x[crop_slice_ind(xx.shape)] = xx
output_x.flat[self.KdCPUorder]=xx.flat[self.NdCPUorder]
k = numpy.fft.fftn(output_x, self.st['Kd'], range(0, self.ndims))
return k
def k2vec(self,k):
k_vec = numpy.reshape(k, (numpy.prod(self.st['Kd']), ), order='C')
return k_vec
def vec2y(self,k_vec):
'''
gridding:
'''
y = self.st['p'].dot(k_vec)
return y
def k2y(self, k):
'''
2D k-space grid to 1D array
input:
k: k-space grid,
output:
y: non-Cartesian data
'''
y = self.vec2y(self.k2vec(k)) #numpy.reshape(self.st['p'].dot(Xk), (self.st['M'], ), order='F')
return y
def y2vec(self, y):
'''
regridding non-uniform data, (unsorted vector)
'''
# k_vec = self.st['p'].getH().dot(y)
k_vec = self.st['pH'].dot(y)
return k_vec
def vec2k(self, k_vec):
'''
Sorting the vector to k-spectrum Kd array
'''
k = numpy.reshape(k_vec, self.st['Kd'], order='C')
return k
def y2k(self, y):
'''
input:
y: non-uniform data, (M,) array
output:
k: k-spectrum on the Kd grid (Kd array)
'''
k_vec = self.y2vec(y)
k = self.vec2k(k_vec)
return k
def k2xx(self, k):
'''
Transform regular k-space (Kd array) to scaled image xx (Nd array)
'''
# dd = numpy.size(self.st['Kd'])
k = numpy.fft.ifftn(k, self.st['Kd'], range(0, self.ndims))
xx= numpy.zeros(self.st['Nd'],dtype=dtype, order='C')
xx.flat[self.NdCPUorder]=k.flat[self.KdCPUorder]
# xx = xx[crop_slice_ind(self.st['Nd'])]
return xx
def xx2x(self, xx):
'''
Rescaled image xx (Nd array) to x (Nd array)
'''
x = self.x2xx(xx)
return x
def k2y2k(self, k):
'''
gridding and regridding of k-spectrum
input
k: Kd array,
output
k: Kd array
'''
Xk = self.k2vec(k)
k = self.st['pHp'].dot(Xk)
k = self.vec2k(k)
return k
def test_installation():
'''
Test the installation
'''
import pkg_resources
PYNUFFT_PATH = pkg_resources.resource_filename('pynufft', './')
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
import os.path
print('Does pynufft.py exist? ',os.path.isfile(PYNUFFT_PATH+'pynufft.py'))
print('Does om1D.npz exist?',os.path.isfile(DATA_PATH+'om1D.npz'))
print('Does om2D.npz exist?',os.path.isfile(DATA_PATH+'om2D.npz'))
print('Does om3D.npz exist?',os.path.isfile(DATA_PATH+'om3D.npz'))
print('Does phantom_3D_128_128_128.npz exist?', os.path.isfile(DATA_PATH+'phantom_3D_128_128_128.npz'))
print('Does phantom_256_256.npz exist?', os.path.isfile(DATA_PATH+'phantom_256_256.npz'))
print('Does 1D_example.py exist?', os.path.isfile(PYNUFFT_PATH+'example/1D_example.py'))
print('Does 2D_example.py exist?', os.path.isfile(PYNUFFT_PATH+'example/1D_example.py'))
def test_2D():
import pkg_resources
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import numpy
import matplotlib.pyplot
# load example image
# image = numpy.loadtxt(DATA_PATH +'phantom_256_256.txt')
image = scipy.misc.ascent()
image = scipy.misc.imresize(image, (256,256))
image=image.astype(numpy.float)/numpy.max(image[...])
#numpy.save('phantom_256_256',image)
matplotlib.pyplot.imshow(image, cmap=matplotlib.cm.gray)
matplotlib.pyplot.show()
print('loading image...')
Nd = (256, 256) # image size
print('setting image dimension Nd...', Nd)
Kd = (512, 512) # k-space size
print('setting spectrum dimension Kd...', Kd)
Jd = (6, 6) # interpolation size
print('setting interpolation size Jd...', Jd)
# load k-space points
# om = numpy.loadtxt(DATA_PATH+'om.txt')
om = numpy.load(DATA_PATH+'om2D.npz')['arr_0']
print('setting non-uniform coordinates...')
matplotlib.pyplot.plot(om[::10,0],om[::10,1],'o')
matplotlib.pyplot.title('non-uniform coordinates')
matplotlib.pyplot.xlabel('axis 0')
matplotlib.pyplot.ylabel('axis 1')
matplotlib.pyplot.show()
NufftObj = NUFFT()
NufftObj.plan(om, Nd, Kd, Jd)
y = NufftObj.forward(image)
print('setting non-uniform data')
print('y is an (M,) list',type(y), y.shape)
# kspectrum = NufftObj.xx2k( NufftObj.solver(y,solver='bicgstab',maxiter = 100))
image_restore = NufftObj.solver(y, solver='cg',maxiter=10)
shifted_kspectrum = numpy.fft.fftshift( numpy.fft.fftn( numpy.fft.fftshift(image_restore)))
print('getting the k-space spectrum, shape =',shifted_kspectrum.shape)
print('Showing the shifted k-space spectrum')
matplotlib.pyplot.imshow( shifted_kspectrum.real, cmap = matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=-100, vmax=100))
matplotlib.pyplot.title('shifted k-space spectrum')
matplotlib.pyplot.show()
image2 = NufftObj.solver(y, 'dc', maxiter = 25)
image3 = NufftObj.solver(y, 'L1TVLAD',maxiter=100, rho= 1)
image4 = NufftObj.solver( y,'L1TVOLS',maxiter=100, rho= 1)
matplotlib.pyplot.subplot(1,3,1)
matplotlib.pyplot.imshow(image, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
matplotlib.pyplot.subplot(1,3,2)
matplotlib.pyplot.imshow(image3.real, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
matplotlib.pyplot.subplot(1,3,3)
matplotlib.pyplot.imshow(image4.real, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
matplotlib.pyplot.show()
# matplotlib.pyplot.imshow(image2.real, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
# matplotlib.pyplot.show()
maxiter =25
counter = 1
for solver in ('dc','bicg','bicgstab','cg', 'gmres','lgmres', 'lsmr', 'lsqr'):
if 'lsqr' == solver:
image2 = NufftObj.solver(y, solver,iter_lim=maxiter)
else:
image2 = NufftObj.solver(y, solver,maxiter=maxiter)
# image2 = NufftObj.solver(y, solver='bicgstab',maxiter=30)
matplotlib.pyplot.subplot(2,4,counter)
matplotlib.pyplot.imshow(image2.real, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
matplotlib.pyplot.title(solver)
print(counter, solver)
counter += 1
matplotlib.pyplot.show()
def test_asoperator():
import pkg_resources
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import numpy
import matplotlib.pyplot
# load example image
# image = numpy.loadtxt(DATA_PATH +'phantom_256_256.txt')
image = scipy.misc.face(gray=True)
image = scipy.misc.imresize(image, (256,256))
image=image.astype(numpy.float)/numpy.max(image[...])
#numpy.save('phantom_256_256',image)
matplotlib.pyplot.imshow(image, cmap=matplotlib.cm.gray)
matplotlib.pyplot.show()
print('loading image...')
Nd = (256, 256) # image size
print('setting image dimension Nd...', Nd)
Kd = (512, 512) # k-space size
print('setting spectrum dimension Kd...', Kd)
Jd = (6, 6) # interpolation size
print('setting interpolation size Jd...', Jd)
# load k-space points
# om = numpy.loadtxt(DATA_PATH+'om.txt')
om = numpy.load(DATA_PATH+'om2D.npz')['arr_0']
print('setting non-uniform coordinates...')
matplotlib.pyplot.plot(om[::10,0],om[::10,1],'o')
matplotlib.pyplot.title('non-uniform coordinates')
matplotlib.pyplot.xlabel('axis 0')
matplotlib.pyplot.ylabel('axis 1')
matplotlib.pyplot.show()
NufftObj = NUFFT()
NufftObj.plan(om, Nd, Kd, Jd)
print('NufftObj.dtype=',NufftObj.dtype, ' NufftObj.shape=',NufftObj.shape)
y1 = NufftObj.forward(image)
x_vec= numpy.reshape(image, (numpy.prod(NufftObj.st['Nd']), ) , order='F')
y2 = NufftObj._matvec(x_vec)
A = scipy.sparse.linalg.LinearOperator(NufftObj.shape, matvec=NufftObj._matvec, rmatvec=NufftObj._adjoint, dtype=NufftObj.dtype)
# scipy.sparse.linalg.aslinearoperator(A)
print(type(A))
# y1 = A.matvec(x_vec)
print('y1.shape',numpy.shape(y1))
import time
t0=time.time()
KKK = scipy.sparse.linalg.lsqr(A, y1, )
print(numpy.shape(KKK))
print(time.time() - t0)
x2 = numpy.reshape(KKK[0], NufftObj.st['Nd'], order='F')
matplotlib.pyplot.subplot(2,1,1)
matplotlib.pyplot.imshow(x2.real,cmap=matplotlib.cm.gray)
matplotlib.pyplot.subplot(2,1,2)
matplotlib.pyplot.imshow(image,cmap=matplotlib.cm.gray)
matplotlib.pyplot.show()
print('y1 y2 close? ', numpy.allclose(y1, y2))
def test_multidimension():
for ndims in range(1, 6):
Nd= ()
Kd=()
Jd=()
om = numpy.random.randn(2,ndims)
for pp in range(0, ndims):
Nd += (128,)
Kd += (256,)
Jd += (4,)
# Nd =tuple([slice(0, 16) for ss in range(0, ndims)]) # image size
# print('setting image dimension Nd...', Nd)
# Kd = tuple([slice(0, 32) for ss in range(0, ndims)]) # k-space size
# print('setting spectrum dimension Kd...', Kd)
# Jd = tuple([slice(0, 6) for ss in range(0, ndims)]) # interpolation size
# print('setting interpolation size Jd...', Jd)
NufftObj = NUFFT()
NufftObj.plan(om, Nd, Kd, Jd)
print(ndims,'-dimensional NUFFT created!')
# y = NufftObj.forward(image)
if __name__ == '__main__':
"""
Test the module pynufft
"""
test_2D()
# test_multidimension()
# test_asoperator()
# test_installation()
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