Origin: upstream, https://bitbucket.org/nigma/pywt/changeset/75bee65cd484
Description: Format float arrays in doctests to fix representation issues
 accross Python versions.
Last-Update: 2012-05-22

--- a/doc/source/ref/wavelets.rst
+++ b/doc/source/ref/wavelets.rst
@@ -174,6 +174,9 @@

   .. sourcecode:: python

+    >>> def format_array(arr):
+    ...     return "[%s]" % ", ".join(["%.14f" % x for x in arr])
+
     >>> import pywt
     >>> wavelet = pywt.Wavelet('db1')
     >>> print wavelet
@@ -184,10 +187,10 @@
       Orthogonal:     True
       Biorthogonal:   True
       Symmetry:       asymmetric
-    >>> print wavelet.dec_lo, wavelet.dec_hi
-    [0.70710678118654757, 0.70710678118654757] [-0.70710678118654757, 0.70710678118654757]
-    >>> print wavelet.rec_lo, wavelet.rec_hi
-    [0.70710678118654757, 0.70710678118654757] [0.70710678118654757, -0.70710678118654757]
+    >>> print format_array(wavelet.dec_lo), format_array(wavelet.dec_hi)
+    [0.70710678118655, 0.70710678118655] [-0.70710678118655, 0.70710678118655]
+    >>> print format_array(wavelet.rec_lo), format_array(wavelet.rec_hi)
+    [0.70710678118655, 0.70710678118655] [0.70710678118655, -0.70710678118655]


 Approximating wavelet and scaling functions - ``Wavelet.wavefun()``
--- a/doc/source/regression/wavelet.rst
+++ b/doc/source/regression/wavelet.rst
@@ -78,14 +78,17 @@
 corresponds to lowpass and highpass decomposition filters and lowpass and
 highpass reconstruction filters respectively:

-    >>> w.dec_lo
-    [0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688]
-    >>> w.dec_hi
-    [-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656]
-    >>> w.rec_lo
-    [0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656]
-    >>> w.rec_hi
-    [0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688]
+    >>> def print_array(arr):
+    ...     print "[%s]" % ", ".join(["%.14f" % x for x in arr])
+
+    >>> print_array(w.dec_lo)
+    [0.03522629188210, -0.08544127388224, -0.13501102001039, 0.45987750211933, 0.80689150931334, 0.33267055295096]
+    >>> print_array(w.dec_hi)
+    [-0.33267055295096, 0.80689150931334, -0.45987750211933, -0.13501102001039, 0.08544127388224, 0.03522629188210]
+    >>> print_array(w.rec_lo)
+    [0.33267055295096, 0.80689150931334, 0.45987750211933, -0.13501102001039, -0.08544127388224, 0.03522629188210]
+    >>> print_array(w.rec_hi)
+    [0.03522629188210, 0.08544127388224, -0.13501102001039, -0.45987750211933, 0.80689150931334, -0.33267055295096]

 Another way to get the filters data is to use the :attr:`~Wavelet.filter_bank`
 attribute, which returns all four filters in a tuple: