[python-dtcwt] 184/497: move existing lowlevel code to explicit numpy backend
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Tue Jul 21 18:06:02 UTC 2015
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to branch debian/sid
in repository python-dtcwt.
commit 3fdd1136db6b6141dc8db630871f742df48d10af
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date: Mon Nov 11 12:26:15 2013 +0000
move existing lowlevel code to explicit numpy backend
---
dtcwt/backend/__init__.py | 0
dtcwt/backend/numpy/__init__.py | 0
dtcwt/{ => backend/numpy}/lowlevel.py | 0
dtcwt/lowlevel.py | 327 +---------------------------------
dtcwt/opencl/lowlevel.py | 2 +-
5 files changed, 2 insertions(+), 327 deletions(-)
diff --git a/dtcwt/backend/__init__.py b/dtcwt/backend/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dtcwt/backend/numpy/__init__.py b/dtcwt/backend/numpy/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dtcwt/lowlevel.py b/dtcwt/backend/numpy/lowlevel.py
similarity index 100%
copy from dtcwt/lowlevel.py
copy to dtcwt/backend/numpy/lowlevel.py
diff --git a/dtcwt/lowlevel.py b/dtcwt/lowlevel.py
index 878e767..c440bf9 100644
--- a/dtcwt/lowlevel.py
+++ b/dtcwt/lowlevel.py
@@ -1,326 +1 @@
-import numpy as np
-from six.moves import xrange
-
-def asfarray(X):
- """Similar to :py:func:`numpy.asfarray` except that this function tries to
- preserve the original datatype of X if it is already a floating point type
- and will pass floating point arrays through directly without copying.
-
- """
- X = np.asanyarray(X)
- return np.asfarray(X, dtype=X.dtype)
-
-def appropriate_complex_type_for(X):
- """Return an appropriate complex data type depending on the type of X. If X
- is already complex, return that, if it is floating point return a complex
- type of the appropriate size and if it is integer, choose an complex
- floating point type depending on the result of :py:func:`numpy.asfarray`.
-
- """
- X = asfarray(X)
-
- if np.issubsctype(X.dtype, np.complex64) or np.issubsctype(X.dtype, np.complex128):
- return X.dtype
- elif np.issubsctype(X.dtype, np.float32):
- return np.complex64
- elif np.issubsctype(X.dtype, np.float64):
- return np.complex128
-
- # God knows, err on the side of caution
- return np.complex128
-
-def as_column_vector(v):
- """Return *v* as a column vector with shape (N,1).
-
- """
- v = np.atleast_2d(v)
- if v.shape[0] == 1:
- return v.T
- else:
- return v
-
-def _centered(arr, newsize):
- # Return the center newsize portion of the array.
- # (Shamelessly cribbed from scipy.)
- newsize = np.asanyarray(newsize)
- currsize = np.array(arr.shape)
- startind = (currsize - newsize) // 2
- endind = startind + newsize
- myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
- return arr[tuple(myslice)]
-
-# This is to allow easy replacement of these later with, possibly, GPU versions
-_rfft = np.fft.rfft
-_irfft = np.fft.irfft
-
-def _column_convolve(X, h):
- """Convolve the columns of *X* with *h* returning only the 'valid' section,
- i.e. those values unaffected by zero padding. Irrespective of the ftype of
- *h*, the output will have the dtype of *X* appropriately expanded to a
- floating point type if necessary.
-
- We assume that h is small and so direct convolution is the most efficient.
-
- """
- Xshape = np.asanyarray(X.shape)
- h = h.flatten().astype(X.dtype)
- h_size = h.shape[0]
-
- full_size = X.shape[0] + h_size - 1
- Xshape[0] = full_size
-
- out = np.zeros(Xshape, dtype=X.dtype)
- for idx in xrange(h_size):
- out[idx:(idx+X.shape[0]),...] += X * h[idx]
-
- outShape = Xshape.copy()
- outShape[0] = abs(X.shape[0] - h_size) + 1
- return _centered(out, outShape)
-
-def reflect(x, minx, maxx):
- """Reflect the values in matrix *x* about the scalar values *minx* and
- *maxx*. Hence a vector *x* containing a long linearly increasing series is
- converted into a waveform which ramps linearly up and down between *minx* and
- *maxx*. If *x* contains integers and *minx* and *maxx* are (integers + 0.5), the
- ramps will have repeated max and min samples.
-
- .. codeauthor:: Rich Wareham <rjw57 at cantab.net>, Aug 2013
- .. codeauthor:: Nick Kingsbury, Cambridge University, January 1999.
-
- """
-
- # Copy x to avoid in-place modification
- y = np.array(x, copy=True)
-
- # Reflect y in maxx.
- t = y > maxx
- y[t] = (2*maxx - y[t]).astype(y.dtype)
-
- while np.any(y < minx):
- # Reflect y in minx.
- t = y < minx
- y[t] = (2*minx - y[t]).astype(y.dtype)
-
- # Reflect y in maxx.
- t = y > maxx
- y[t] = (2*maxx - y[t]).astype(y.dtype)
-
- return y
-
-def colfilter(X, h):
- """Filter the columns of image *X* using filter vector *h*, without decimation.
- If len(h) is odd, each output sample is aligned with each input sample
- and *Y* is the same size as *X*. If len(h) is even, each output sample is
- aligned with the mid point of each pair of input samples, and Y.shape =
- X.shape + [1 0].
-
- :param X: an image whose columns are to be filtered
- :param h: the filter coefficients.
- :returns Y: the filtered image.
-
- .. codeauthor:: Rich Wareham <rjw57 at cantab.net>, August 2013
- .. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
- .. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
-
- """
-
- # Interpret all inputs as arrays
- X = asfarray(X)
- h = as_column_vector(h)
-
- r, c = X.shape
- m = h.shape[0]
- m2 = np.fix(m*0.5)
-
- # Symmetrically extend with repeat of end samples.
- # Use 'reflect' so r < m2 works OK.
- xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
-
- # Perform filtering on the columns of the extended matrix X(xe,:), keeping
- # only the 'valid' output samples, so Y is the same size as X if m is odd.
- Y = _column_convolve(X[xe,:], h)
-
- return Y
-
-def coldfilt(X, ha, hb):
- """Filter the columns of image X using the two filters ha and hb =
- reverse(ha). ha operates on the odd samples of X and hb on the even
- samples. Both filters should be even length, and h should be approx linear
- phase with a quarter sample advance from its mid pt (i.e. :math:`|h(m/2)| >
- |h(m/2 + 1)|`).
-
- .. code-block:: text
-
- ext top edge bottom edge ext
- Level 1: ! | ! | !
- odd filt on . b b b b a a a a a a a a b b b b
- odd filt on . a a a a b b b b b b b b a a a a
- Level 2: ! | ! | !
- +q filt on x b b a a a a b b
- -q filt on o a a b b b b a a
-
- The output is decimated by two from the input sample rate and the results
- from the two filters, Ya and Yb, are interleaved to give Y. Symmetric
- extension with repeated end samples is used on the composite X columns
- before each filter is applied.
-
- Raises ValueError if the number of rows in X is not a multiple of 4, the
- length of ha does not match hb or the lengths of ha or hb are non-even.
-
- .. codeauthor:: Rich Wareham <rjw57 at cantab.net>, August 2013
- .. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
- .. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
-
- """
- # Make sure all inputs are arrays
- X = asfarray(X)
- ha = asfarray(ha)
- hb = asfarray(hb)
-
- r, c = X.shape
- if r % 4 != 0:
- raise ValueError('No. of rows in X must be a multiple of 4')
-
- if ha.shape != hb.shape:
- raise ValueError('Shapes of ha and hb must be the same')
-
- if ha.shape[0] % 2 != 0:
- raise ValueError('Lengths of ha and hb must be even')
-
- m = ha.shape[0]
- m2 = np.fix(m*0.5)
-
- # Set up vector for symmetric extension of X with repeated end samples.
- xe = reflect(np.arange(-m, r+m), -0.5, r-0.5)
-
- # Select odd and even samples from ha and hb. Note that due to 0-indexing
- # 'odd' and 'even' are not perhaps what you might expect them to be.
- hao = as_column_vector(ha[0:m:2])
- hae = as_column_vector(ha[1:m:2])
- hbo = as_column_vector(hb[0:m:2])
- hbe = as_column_vector(hb[1:m:2])
- t = np.arange(5, r+2*m-2, 4)
- r2 = r/2;
- Y = np.zeros((r2,c), dtype=X.dtype)
-
- if np.sum(ha*hb) > 0:
- s1 = slice(0, r2, 2)
- s2 = slice(1, r2, 2)
- else:
- s2 = slice(0, r2, 2)
- s1 = slice(1, r2, 2)
-
- # Perform filtering on columns of extended matrix X(xe,:) in 4 ways.
- Y[s1,:] = _column_convolve(X[xe[t-1],:],hao) + _column_convolve(X[xe[t-3],:],hae)
- Y[s2,:] = _column_convolve(X[xe[t],:],hbo) + _column_convolve(X[xe[t-2],:],hbe)
-
- return Y
-
-def colifilt(X, ha, hb):
- """ Filter the columns of image X using the two filters ha and hb =
- reverse(ha). ha operates on the odd samples of X and hb on the even
- samples. Both filters should be even length, and h should be approx linear
- phase with a quarter sample advance from its mid pt (i.e `:math:`|h(m/2)| >
- |h(m/2 + 1)|`).
-
- .. code-block:: text
-
- ext left edge right edge ext
- Level 2: ! | ! | !
- +q filt on x b b a a a a b b
- -q filt on o a a b b b b a a
- Level 1: ! | ! | !
- odd filt on . b b b b a a a a a a a a b b b b
- odd filt on . a a a a b b b b b b b b a a a a
-
- The output is interpolated by two from the input sample rate and the
- results from the two filters, Ya and Yb, are interleaved to give Y.
- Symmetric extension with repeated end samples is used on the composite X
- columns before each filter is applied.
-
- .. codeauthor:: Rich Wareham <rjw57 at cantab.net>, August 2013
- .. codeauthor:: Cian Shaffrey, Cambridge University, August 2000
- .. codeauthor:: Nick Kingsbury, Cambridge University, August 2000
-
- """
- # Make sure all inputs are arrays
- X = asfarray(X)
- ha = asfarray(ha)
- hb = asfarray(hb)
-
- r, c = X.shape
- if r % 2 != 0:
- raise ValueError('No. of rows in X must be a multiple of 2')
-
- if ha.shape != hb.shape:
- raise ValueError('Shapes of ha and hb must be the same')
-
- if ha.shape[0] % 2 != 0:
- raise ValueError('Lengths of ha and hb must be even')
-
- m = ha.shape[0]
- m2 = np.fix(m*0.5)
-
- Y = np.zeros((r*2,c), dtype=X.dtype)
- if not np.any(np.nonzero(X[:])[0]):
- return Y
-
- if m2 % 2 == 0:
- # m/2 is even, so set up t to start on d samples.
- # Set up vector for symmetric extension of X with repeated end samples.
- # Use 'reflect' so r < m2 works OK.
- xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
-
- t = np.arange(3, r+m, 2)
- if np.sum(ha*hb) > 0:
- ta = t
- tb = t - 1
- else:
- ta = t - 1
- tb = t
-
- # Select odd and even samples from ha and hb. Note that due to 0-indexing
- # 'odd' and 'even' are not perhaps what you might expect them to be.
- hao = as_column_vector(ha[0:m:2])
- hae = as_column_vector(ha[1:m:2])
- hbo = as_column_vector(hb[0:m:2])
- hbe = as_column_vector(hb[1:m:2])
-
- s = np.arange(0,r*2,4)
-
- Y[s,:] = _column_convolve(X[xe[tb-2],:],hae)
- Y[s+1,:] = _column_convolve(X[xe[ta-2],:],hbe)
- Y[s+2,:] = _column_convolve(X[xe[tb ],:],hao)
- Y[s+3,:] = _column_convolve(X[xe[ta ],:],hbo)
- else:
- # m/2 is odd, so set up t to start on b samples.
- # Set up vector for symmetric extension of X with repeated end samples.
- # Use 'reflect' so r < m2 works OK.
- xe = reflect(np.arange(-m2, r+m2, dtype=np.int), -0.5, r-0.5)
-
- t = np.arange(2, r+m-1, 2)
- if np.sum(ha*hb) > 0:
- ta = t
- tb = t - 1
- else:
- ta = t - 1
- tb = t
-
- # Select odd and even samples from ha and hb. Note that due to 0-indexing
- # 'odd' and 'even' are not perhaps what you might expect them to be.
- hao = as_column_vector(ha[0:m:2])
- hae = as_column_vector(ha[1:m:2])
- hbo = as_column_vector(hb[0:m:2])
- hbe = as_column_vector(hb[1:m:2])
-
- s = np.arange(0,r*2,4)
-
- Y[s,:] = _column_convolve(X[xe[tb],:],hao)
- Y[s+1,:] = _column_convolve(X[xe[ta],:],hbo)
- Y[s+2,:] = _column_convolve(X[xe[tb],:],hae)
- Y[s+3,:] = _column_convolve(X[xe[ta],:],hbe)
-
- return Y
-
-# vim:sw=4:sts=4:et
-
+from dtcwt.backend.numpy.lowlevel import *
diff --git a/dtcwt/opencl/lowlevel.py b/dtcwt/opencl/lowlevel.py
index 49ae4c8..8de1501 100644
--- a/dtcwt/opencl/lowlevel.py
+++ b/dtcwt/opencl/lowlevel.py
@@ -17,7 +17,7 @@ from six.moves import xrange
import struct
import functools
-from dtcwt.lowlevel import asfarray, as_column_vector, reflect, _column_convolve
+from dtcwt.lowlevel import asfarray, as_column_vector, reflect
# note that this decorator ignores **kwargs
# From https://wiki.python.org/moin/PythonDecoratorLibrary#Alternate_memoize_as_nested_functions
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/python-dtcwt.git
More information about the debian-science-commits
mailing list