[python-dtcwt] 09/497: remove non-optimisations for Python

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/sid
in repository python-dtcwt.

commit e7e36fe2c70257d1753ceae87ed199f682521736
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Tue Aug 6 16:57:40 2013 +0100

    remove non-optimisations for Python
---
 dtcwt/lowlevel.py    | 15 ++++++---------
 dtcwt/transform2d.py | 19 +++++++++----------
 2 files changed, 15 insertions(+), 19 deletions(-)

diff --git a/dtcwt/lowlevel.py b/dtcwt/lowlevel.py
index baba922..6dde05b 100644
--- a/dtcwt/lowlevel.py
+++ b/dtcwt/lowlevel.py
@@ -54,16 +54,13 @@ def colfilter(X, h):
     m = h.shape[0]
     m2 = np.fix(m*0.5)
 
-    if np.any(np.nonzero(X[:])[0]):
-        # 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)
+    # 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 = convolve2d(X[xe,:], h, 'valid')
-    else:
-        Y = np.zeros((r+1-(m%2), c))
+    # 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 = convolve2d(X[xe,:], h, 'valid')
 
     return Y
 
diff --git a/dtcwt/transform2d.py b/dtcwt/transform2d.py
index fdc5673..4b706d9 100644
--- a/dtcwt/transform2d.py
+++ b/dtcwt/transform2d.py
@@ -315,16 +315,15 @@ def c2q(w,gain):
     sw = w.shape
     x = np.zeros((w.shape[0]*2, w.shape[1]*2))
 
-    if np.any(w[:] != 0) and np.any(gain[:] != 0):
-        sc = np.sqrt(0.5) * gain
-        P = w[:,:,0]*sc[0] + w[:,:,1]*sc[1]
-        Q = w[:,:,0]*sc[0] - w[:,:,1]*sc[1]
-
-        # Recover each of the 4 corners of the quads.
-        x[0::2, 0::2] = np.real(P)  # a = (A+C)*sc
-        x[0::2, 1::2] = np.imag(P)  # b = (B+D)*sc
-        x[1::2, 0::2] = np.imag(Q)  # c = (B-D)*sc
-        x[1::2, 1::2] = -np.real(Q) # d = (C-A)*sc
+    sc = np.sqrt(0.5) * gain
+    P = w[:,:,0]*sc[0] + w[:,:,1]*sc[1]
+    Q = w[:,:,0]*sc[0] - w[:,:,1]*sc[1]
+
+    # Recover each of the 4 corners of the quads.
+    x[0::2, 0::2] = np.real(P)  # a = (A+C)*sc
+    x[0::2, 1::2] = np.imag(P)  # b = (B+D)*sc
+    x[1::2, 0::2] = np.imag(Q)  # c = (B-D)*sc
+    x[1::2, 1::2] = -np.real(Q) # d = (C-A)*sc
 
     return x
 

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