[python-dtcwt] 243/497: utils: re-write reflect to be non-iterative

[Ghislain Vaillant]

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

commit 4ba866fb19ff25825d9c19927ca9cf5062a9da89
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Mon Jan 27 14:46:29 2014 +0000

    utils: re-write reflect to be non-iterative
    
    dtcwt.utils.reflect uses an iterative method to clip the input within
    the bounds specified. This in effect makes the runtime proportional to
    the magnitude of the input which is an unexpected performance
    characteristic.
    
    Taking the implementation from OpenCL, rewrite reflect() to use a
    non-iterative, if far more opaque, method.
---
 dtcwt/utils.py | 29 +++++++++--------------------
 1 file changed, 9 insertions(+), 20 deletions(-)

diff --git a/dtcwt/utils.py b/dtcwt/utils.py
index dcfcd78..c338eef 100644
--- a/dtcwt/utils.py
+++ b/dtcwt/utils.py
@@ -100,29 +100,18 @@ def reflect(x, minx, maxx):
     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
+    """
+    x = np.asanyarray(x)
+    rng = maxx - minx
+    rng_by_2 = 2 * rng
+    mod = np.fmod(x - minx, rng_by_2)
+    normed_mod = np.where(mod < 0, mod + rng_by_2, mod)
+    out = np.where(normed_mod >= rng, rng_by_2 - normed_mod, normed_mod) + minx
+    return np.array(out, dtype=x.dtype)
 
 # note that this decorator ignores **kwargs
 # From https://wiki.python.org/moin/PythonDecoratorLibrary#Alternate_memoize_as_nested_functions

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