wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-02 00:29:00 +08:00
Code Issues Packages Projects Releases Wiki Activity

Remove anfft and fftw dependency

Browse Source
This commit is contained in:
François Orieux
2013-10-28 11:53:23 +01:00
parent e25ccf61f6
commit 4d1df18241
1 changed files with 6 additions and 36 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/deconvolution/uft.py
+6 -36
View File
@@ -34,9 +34,6 @@ equal to
.. math:: \frac{1}{\sqrt{n}} \sum_i x_i.
If the anfft module is present, his function are used. anfft wrap fftw
C library. Otherwise, numpy fft functions are used.
You must keep in mind that the transform are applied from the last
axes. this is a fftw convention for performance reason (c order
array). If you want more sofisticated use, you must use directly the
@@ -52,16 +49,7 @@ References
# code:
import logging
import numpy as np
try:
import anfft
ANFFTMOD = True
except ImportError:
logging.info("Installation of the anfft package improve preformance"
" by using fftw library.")
ANFFTMOD = False
__author__ = "Fran��ois Orieux"
__copyright__ = "Copyright (C) 2011, 2012, 2013 F. Orieux <orieux@iap.fr>"
@@ -152,10 +140,7 @@ def ufftn(inarray, dim=None):
if not dim:
dim = inarray.ndim
if ANFFTMOD:
outarray = anfft.fftn(inarray, k=dim)
else:
outarray = np.fft.fftn(inarray, axes=range(-dim, 0))
outarray = np.fft.fftn(inarray, axes=range(-dim, 0))
return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))
@@ -179,10 +164,7 @@ def uifftn(inarray, dim=None):
if not dim:
dim = inarray.ndim
if ANFFTMOD:
outarray = anfft.ifftn(inarray, k=dim)
else:
outarray = np.fft.ifftn(inarray, axes=range(-dim, 0))
outarray = np.fft.ifftn(inarray, axes=range(-dim, 0))
return outarray * np.sqrt(np.prod(inarray.shape[-dim:]))
@@ -209,10 +191,7 @@ def urfftn(inarray, dim=None):
if not dim:
dim = inarray.ndim
if ANFFTMOD:
outarray = anfft.rfftn(inarray, k=dim)
else:
outarray = np.fft.rfftn(inarray, axes=range(-dim, 0))
outarray = np.fft.rfftn(inarray, axes=range(-dim, 0))
return outarray / np.sqrt(np.prod(inarray.shape[-dim:]))
@@ -239,10 +218,7 @@ def uirfftn(inarray, dim=None):
if not dim:
dim = inarray.ndim
if ANFFTMOD:
outarray = anfft.irfftn(inarray, k=dim)
else:
outarray = np.fft.irfftn(inarray, axes=range(-dim, 0))
outarray = np.fft.irfftn(inarray, axes=range(-dim, 0))
return outarray * np.sqrt(np.prod(inarray.shape[-dim:-1]) *
(inarray.shape[-1] - 1) * 2)
@@ -427,15 +403,9 @@ def ir2tf(imp_resp, shape, dim=None, real=True):
for i, s in enumerate(imp_resp.shape)])
if real:
if ANFFTMOD:
return anfft.rfftn(irpadded, k=dim)
else:
return np.fft.rfftn(irpadded, axes=range(-dim, 0))
return np.fft.rfftn(irpadded, axes=range(-dim, 0))
else:
if ANFFTMOD:
return anfft.fftn(irpadded, k=dim)
else:
return np.fft.fftn(irpadded, axes=range(-dim, 0))
return np.fft.fftn(irpadded, axes=range(-dim, 0))
def laplacian(ndim, shape):