[python-dtcwt] 321/497: docs/variant: use modern API for transform

[Ghislain Vaillant]

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

commit b01fc44ac97a9940cf6387e34cc243db05270308
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Sat Feb 1 12:50:08 2014 +0000

    docs/variant: use modern API for transform
---
 docs/variant.rst | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/docs/variant.rst b/docs/variant.rst
index d988e72..48cc85f 100644
--- a/docs/variant.rst
+++ b/docs/variant.rst
@@ -53,13 +53,17 @@ Working on the Lena image, the standard 2-D DTCWT achieves perfect reconstructio
 .. plot::
     :include-source: true
 
-    image = datasets.lena()
+    import dtcwt.backend.backend_numpy as backend
+
+    # Use the standard 2-D DTCWT
+    transform = backend.Transform2d(biort='near_sym_b', qshift='qshift_b')
 
-    # Perform the standard 2-D DTCWT
-    Yl, Yh = dtcwt.dtwavexfm2(image, nlevels=4, biort='near_sym_b', qshift='qshift_b')
+    # Forward transform
+    image = datasets.lena()
+    image_t = transform.forward(image)
 
-    # Perform the inverse transform
-    Z = dtcwt.dtwaveifm2(Yl, Yh, biort='near_sym_b', qshift='qshift_b')
+    # Inverse transform
+    Z = transform.inverse(image_t).value
 
     # Show the error
     imshow(Z-image, cmap=cm.gray)
@@ -73,13 +77,17 @@ Using the modified wavelets yields the following result:
 .. plot::
     :include-source: true
 
-    image = datasets.lena()
+    import dtcwt.backend.backend_numpy as backend
+
+    # Use the modified 2-D DTCWT
+    transform = backend.Transform2d(biort='near_sym_b_bp', qshift='qshift_b_bp')
 
-    # Perform the standard 2-D DTCWT
-    Yl, Yh = dtcwt.dtwavexfm2(image, nlevels=4, biort='near_sym_b_bp', qshift='qshift_b_bp')
+    # Forward transform
+    image = datasets.lena()
+    image_t = transform.forward(image)
 
-    # Perform the inverse transform
-    Z = dtcwt.dtwaveifm2(Yl, Yh, biort='near_sym_b_bp', qshift='qshift_b_bp')
+    # Inverse transform
+    Z = transform.inverse(image_t).value
 
     # Show the error
     imshow(Z-image, cmap=cm.gray)

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