我有一个复数的二维数组,它表示在实空间中沿着平面测量的势场。假设阵列是128个单元乘以128个单元,平面的总面积是500m x 500m。此数组中的每个单元格表示空间域中的一个点,坐标在x和y中给定。
当我在这个二维阵列上使用来自scipy.fftpack的二维FFT时,我得到了在波域中表示的相同信息。如何计算输出数组中各点的波域坐标kx和ky?
发布于 2011-08-24 20:29:39
这里有一些代码来完整地演示这个问题和我能够找到的解决方案。
from numpy import linspace , arange , reshape ,zeros
from scipy.fftpack import fft2 , fftfreq
from cmath import pi
# create some arbitrary data
some_data = arange(0.0 , 16384.0 , dtype = complex)
# reshape it to be a 128x128 2d grid
some_data_grid = reshape(some_data , (128 , 128) )
# assign some real spatial co-ordinates to the grid points
# first define the edge values
x_min = -250.0
x_max = 250.0
y_min = -250.0
y_max = 250
# then create some empty 2d arrays to hold the individual cell values
x_array = zeros( (128,128) , dtype = float )
y_array = zeros( (128,128) , dtype = float )
# now fill the arrays with the associated values
for row , y_value in enumerate(linspace (y_min , y_max , num = 128) ):
for column , x_value in enumerate(linspace (x_min , x_max , num = 128) ):
x_array[row][column] = x_value
y_array[row][column] = y_value
# now for any row,column pair the x_array and y_array hold the spatial domain
# co-ordinates of the associated point in some_data_grid
# now use the fft to transform the data to the wavenumber domain
some_data_wavedomain = fft2(some_data_grid)
# now we can use fftfreq to give us a base for the wavenumber co-ords
# this returns [0.0 , 1.0 , 2.0 , ... , 62.0 , 63.0 , -64.0 , -63.0 , ... , -2.0 , -1.0 ]
n_value = fftfreq( 128 , (1.0 / 128.0 ) )
# now we can initialize some arrays to hold the wavenumber co-ordinates of each cell
kx_array = zeros( (128,128) , dtype = float )
ky_array = zeros( (128,128) , dtype = float )
# before we can calculate the wavenumbers we need to know the total length of the spatial
# domain data in x and y. This assumes that the spatial domain units are metres and
# will result in wavenumber domain units of radians / metre.
x_length = x_max - x_min
y_length = y_max - y_min
# now the loops to calculate the wavenumbers
for row in xrange(128):
for column in xrange(128):
kx_array[row][column] = ( 2.0 * pi * n_value[column] ) / x_length
ky_array[row][column] = ( 2.0 * pi * n_value[row] ) / y_length
# now for any row,column pair kx_array , and ky_array will hold the wavedomain coordinates
# of the correspoing point in some_data_wavedomain我知道这可能不是最有效的方法,但希望它很容易理解。我希望这能帮助一些人避免一点挫败感。
发布于 2011-08-24 21:02:31
嗯,似乎如果我使用FFT函数,DC会在元素0结束,在你的例子中,频率之间的间隔是1/500m。因此,以下(不是很简洁)代码片段将获得您的频率轴:
meter = 1.0
L = 500.0 * meter
N = 128
dF = 1.0 / L
freqs = arange(0, N/L, dF) # array of spatial frequencies.当然,这些频率是以每米周数为单位,而不是以每米弧度为单位。如果我希望kx和ky是以rads/m为单位的空间频率数组,我就会说
kx = 2*pi*freqs
ky = 2*pi*freqs(假设我已经导入了像arange和pi这样的东西)。
编辑
关于奈奎斯特以上的频率,Stu提出了一个很好的观点,你可能更愿意认为它是负的(我通常是这样认为的,但不是在代码中)。您可以随时执行以下操作:
freqs[freqs > 0.5*N/(2*L)] -= N/L但如果你真的想要负频率,你可能也想玩fftshift --另一个蠕虫罐头。
发布于 2015-12-03 21:17:04
我在下面的FORTRAN代码中找到了子程序,并尝试将其实现为matlab函数。如果我的翻译正确,请给我一个评论。开始了,这是FORTRAN子程序:
subroutine kvalue(i,j,nx,ny,dkx,dky,kx,ky)
c Subroutine KVALUE finds the wavenumber coordinates of one
c element of a rectangular grid from subroutine FOURN.
c
c Input parameters:
c i - index in the ky direction,
c j - index in the kx direction.
c nx - dimension of grid in ky direction (a power of two).
c ny - dimension of grid in kx direction (a power of two).
c dkx - sample interval in the kx direction,
c dky - sample interval in the ky direction,
c
c Output parameters:
c kx - the wavenumber coordinate in the kx direction,
c ky - the wavenumber coordinate in the ky direction,
c
real kx,ky
nyqx=nx/2+l
nyqy=ny/2+l
if(j.le.nyqx)then
kx=(j-l)*dkx
else
kx=(j-nx-l)*dkx
end if
if(i.le.nyqy)then
ky=(i-l)*dky
else
ky=(i-ny-l)*dky
end if
return
end我翻译成MATLAB函数:
function [kx,ky]=kvalue(gz,nx,ny,dkx,dky)
nyq_x=nx/2+1;
nyq_y=ny/2+1;
for i=1:length(gz)
for j=1:length(gz)
if j <= nyq_x
kx(j)=(j-1)*dkx;
else
kx(j)=(j-nx-1)*dkx;
end
if i <= nyq_y
ky(i)=(i-1)*dky;
else
ky(i)=(i-ny-1)*dky;
end
end
end谢谢。
https://stackoverflow.com/questions/7161417
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