[python-dtcwt] 03/38: docs: replace Lena with mandrill

[Ghislain Vaillant]

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commit a2de16ba73ad388bc14c12f1701549a2cd465716
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Mon Aug 3 12:03:38 2015 +0100

    docs: replace Lena with mandrill
---
 docs/2dtransform.rst | 16 ++++++++--------
 docs/variant.rst     | 18 +++++++++---------
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/docs/2dtransform.rst b/docs/2dtransform.rst
index 28dff58..e5bcfd3 100644
--- a/docs/2dtransform.rst
+++ b/docs/2dtransform.rst
@@ -9,28 +9,28 @@ wavelet coefficients:
     :include-source: true
 
     # Load the Lena image
-    lena = datasets.lena()
+    mandrill = datasets.mandrill()
 
-    # Show lena
+    # Show mandrill
     figure(1)
-    imshow(lena, cmap=cm.gray, clim=(0,1))
+    imshow(mandrill, cmap=cm.gray, clim=(0,1))
 
     import dtcwt
     transform = dtcwt.Transform2d()
 
     # Compute two levels of dtcwt with the defaul wavelet family
-    lena_t = transform.forward(lena, nlevels=2)
+    mandrill_t = transform.forward(mandrill, nlevels=2)
 
     # Show the absolute images for each direction in level 2.
     # Note that the 2nd level has index 1 since the 1st has index 0.
     figure(2)
-    for slice_idx in range(lena_t.highpasses[1].shape[2]):
+    for slice_idx in range(mandrill_t.highpasses[1].shape[2]):
         subplot(2, 3, slice_idx)
-        imshow(np.abs(lena_t.highpasses[1][:,:,slice_idx]), cmap=cm.spectral, clim=(0, 1))
+        imshow(np.abs(mandrill_t.highpasses[1][:,:,slice_idx]), cmap=cm.spectral, clim=(0, 1))
 
     # Show the phase images for each direction in level 2.
     figure(3)
-    for slice_idx in range(lena_t.highpasses[1].shape[2]):
+    for slice_idx in range(mandrill_t.highpasses[1].shape[2]):
         subplot(2, 3, slice_idx)
-        imshow(np.angle(lena_t.highpasses[1][:,:,slice_idx]), cmap=cm.hsv, clim=(-np.pi, np.pi))
+        imshow(np.angle(mandrill_t.highpasses[1][:,:,slice_idx]), cmap=cm.hsv, clim=(-np.pi, np.pi))
 
diff --git a/docs/variant.rst b/docs/variant.rst
index c334739..e923eeb 100644
--- a/docs/variant.rst
+++ b/docs/variant.rst
@@ -9,16 +9,16 @@ supports a selection of variant transforms.
 Rotational symmetry modified wavelet transform
 ----------------------------------------------
 
-For some applications, one may prefer the subband responses to be more rotationally similar. 
+For some applications, one may prefer the subband responses to be more rotationally similar.
 
-In the original 2-D DTCWT, the 45 and 135 degree subbands have passbands whose centre frequencies 
-are somewhat further from the origin than those of the other four subbands. This results from 
-the combination of two highpass 1-D wavelet filters to produce 2-D wavelets. The remaining 
-subbands combine highpass and lowpass 1-D filters, and hence their centre frequencies are a 
+In the original 2-D DTCWT, the 45 and 135 degree subbands have passbands whose centre frequencies
+are somewhat further from the origin than those of the other four subbands. This results from
+the combination of two highpass 1-D wavelet filters to produce 2-D wavelets. The remaining
+subbands combine highpass and lowpass 1-D filters, and hence their centre frequencies are a
 factor of approximately sqrt(1.8) closer to the origin of the frequency plane.
 
-The dtwavexfm2b() function employs an alternative bandpass 1-D filter in place of the highpass 
-filter for the appropriate subbands. The image below illustrates the relevant differences in impulse 
+The dtwavexfm2b() function employs an alternative bandpass 1-D filter in place of the highpass
+filter for the appropriate subbands. The image below illustrates the relevant differences in impulse
 and frequency responses[1].
 
 .. figure:: modified_wavelets.png
@@ -62,7 +62,7 @@ Working on the Lena image, the standard 2-D DTCWT achieves perfect reconstructio
     transform = dtcwt.Transform2d(biort='near_sym_b', qshift='qshift_b')
 
     # Forward transform
-    image = datasets.lena()
+    image = datasets.mandrill()
     image_t = transform.forward(image)
 
     # Inverse transform
@@ -86,7 +86,7 @@ Using the modified wavelets yields the following result:
     transform = dtcwt.Transform2d(biort='near_sym_b_bp', qshift='qshift_b_bp')
 
     # Forward transform
-    image = datasets.lena()
+    image = datasets.mandrill()
     image_t = transform.forward(image)
 
     # Inverse transform

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