[python-dtcwt] 397/497: update usage example of 1d transform

[Ghislain Vaillant]

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

commit 6004650f1294c55812640dd9e5e798c5f8da60dc
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Mon Feb 10 17:12:12 2014 +0000

    update usage example of 1d transform
---
 docs/1dtransform.rst | 36 +++++++++++++++++++++++-------------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/docs/1dtransform.rst b/docs/1dtransform.rst
index 2343f78..349fd1b 100644
--- a/docs/1dtransform.rst
+++ b/docs/1dtransform.rst
@@ -3,40 +3,50 @@
 
 This example generates two 1D random walks and demonstrates reconstructing them
 using the forward and inverse 1D transforms. Note that
-:py:func:`dtcwt.dtwavexfm` and :py:func:`dtcwt.dtwaveifm` will transform
-columns of an input array independently::
+:py:func`dtcwt.Transform1d.forward` and :py:func:`dtcwt.Transform1d.inverse`
+will transform columns of an input array independently
 
-    import numpy as np
-    from matplotlib.pyplot import *
+.. plot::
+    :include-source: true
+
+    from matplotlib.pylab import *
+    import dtcwt
 
     # Generate a 300x2 array of a random walk
     vecs = np.cumsum(np.random.rand(300,2) - 0.5, 0)
 
     # Show input
-    figure(1)
+    figure()
     plot(vecs)
     title('Input')
 
-    import dtcwt
+    # 1D transform, 5 levels
+    transform = dtcwt.Transform1d()
+    vecs_t = transform.forward(vecs, nlevels=5)
 
-    # 1D transform
-    Yl, Yh = dtcwt.dtwavexfm(vecs)
+    # Show level 2 highpass coefficient magnitudes
+    figure()
+    plot(np.abs(vecs_t.highpasses[1]))
+    title('Level 2 wavelet coefficient magnitudes')
+
+    # Show last level lowpass image
+    figure()
+    plot(vecs_t.lowpass)
+    title('Lowpass signals')
 
     # Inverse
-    vecs_recon = dtcwt.dtwaveifm(Yl, Yh)
+    vecs_recon = transform.inverse(vecs_t)
 
     # Show output
-    figure(2)
+    figure()
     plot(vecs_recon)
     title('Output')
 
     # Show error
-    figure(3)
+    figure()
     plot(vecs_recon - vecs)
     title('Reconstruction error')
 
     print('Maximum reconstruction error: {0}'.format(np.max(np.abs(vecs - vecs_recon))))
 
-    show()
-
 

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