Merge pull request #150 from grlee77/doctest_fixes

merging this as is for now.  Discuss potential improvements for a future release in #151

Author: grlee77

.travis.yml

@@ -56,4 +56,4 @@ before_install:
   - set -o pipefail
 
 script:
-  - python -u $OPTIMIZE runtests.py -g -m full --coverage
+  - python -u $OPTIMIZE runtests.py -g -m full --coverage --doctests

doc/source/dev/testing.rst

@@ -28,7 +28,7 @@ Tests are implemented with `nose`_, so use one of:
 
     $ nosetests pywt
 
-    >>> pywt.test()
+    >>> pywt.test()  # doctest: +SKIP
 
 
 Running tests with Tox

doc/source/ref/idwt-inverse-discrete-wavelet-transform.rst

@@ -20,7 +20,7 @@ Single level ``idwt``
     >>> import pywt
     >>> (cA, cD) = pywt.dwt([1,2,3,4,5,6], 'db2', 'smooth')
     >>> print pywt.idwt(cA, cD, 'db2', 'smooth')
-    [ 1.  2.  3.  4.  5.  6.]
+    array([ 1.,  2.,  3.,  4.,  5.,  6.])
 
   One of the neat features of :func:`idwt` is that one of the *cA* and *cD*
   arguments can be set to ``None``. In that situation the reconstruction will be
@@ -36,7 +36,7 @@ Single level ``idwt``
     >>> A = pywt.idwt(cA, None, 'db2', 'smooth')
     >>> D = pywt.idwt(None, cD, 'db2', 'smooth')
     >>> print A + D
-    [ 1.  2.  3.  4.  5.  6.]
+    array([ 1.,  2.,  3.,  4.,  5.,  6.])
 
 
 Multilevel reconstruction using ``waverec``

doc/source/ref/wavelets.rst

@@ -134,17 +134,17 @@ Built-in wavelets - ``wavelist()``
 
     >>> import pywt
     >>> wavelet = pywt.Wavelet('db1')
-    >>> print wavelet
+    >>> print(wavelet)
     Wavelet db1
       Family name:    Daubechies
       Short name:     db
       Filters length: 2
       Orthogonal:     True
       Biorthogonal:   True
       Symmetry:       asymmetric
-    >>> print format_array(wavelet.dec_lo), format_array(wavelet.dec_hi)
+    >>> 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)
+    >>> print(format_array(wavelet.rec_lo), format_array(wavelet.rec_hi))
     [0.70710678118655, 0.70710678118655] [0.70710678118655, -0.70710678118655]
 
 

doc/source/regression/dwt-idwt.rst

@@ -18,9 +18,9 @@ the ``db2`` wavelet. It's simple..
 And the approximation and details coefficients are in ``cA`` and ``cD``
 respectively:
 
-    >>> print cA
+    >>> print(cA)
     [ 5.65685425  7.39923721  0.22414387  3.33677403  7.77817459]
-    >>> print cD
+    >>> print(cD)
     [-2.44948974 -1.60368225 -4.44140056 -0.41361256  1.22474487]
 
 Inverse Discrete Wavelet Transform
@@ -29,7 +29,7 @@ Inverse Discrete Wavelet Transform
 Now let's do an opposite operation
 - :func:`Inverse Discrete Wavelet Transform <idwt>`:
 
-    >>> print pywt.idwt(cA, cD, 'db2')
+    >>> print(pywt.idwt(cA, cD, 'db2'))
     [ 3.  7.  1.  1. -2.  5.  4.  6.]
 
 Voilà! That's it!
@@ -44,9 +44,9 @@ border effect handling:
 
     >>> w = pywt.Wavelet('sym3')
     >>> cA, cD = pywt.dwt(x, wavelet=w, mode='constant')
-    >>> print cA
+    >>> print(cA)
     [ 4.38354585  3.80302657  7.31813271 -0.58565539  4.09727044  7.81994027]
-    >>> print cD
+    >>> print(cD)
     [-1.33068221 -2.78795192 -3.16825651 -0.67715519 -0.09722957 -0.07045258]
 
 Note that the output coefficients arrays length depends not only on the input
@@ -91,9 +91,9 @@ doing :func:`DWT <dwt>` and :func:`IDWT <idwt>`. Otherwise, it will produce
     >>> x
     [3, 7, 1, 1, -2, 5, 4, 6]
     >>> cA, cD = pywt.dwt(x, wavelet=w, mode='periodization')
-    >>> print pywt.idwt(cA, cD, 'sym3', 'symmetric') # invalid mode
+    >>> print(pywt.idwt(cA, cD, 'sym3', 'symmetric')) # invalid mode
     [ 1.  1. -2.  5.]
-    >>> print pywt.idwt(cA, cD, 'sym3', 'periodization')
+    >>> print(pywt.idwt(cA, cD, 'sym3', 'periodization'))
     [ 3.  7.  1.  1. -2.  5.  4.  6.]
 
 
@@ -107,21 +107,21 @@ Now some tips & tricks. Passing ``None`` as one of the coefficient arrays
 parameters is similar to passing a *zero-filled* array. The results are simply
 the same:
 
-    >>> print pywt.idwt([1,2,0,1], None, 'db2', 'symmetric')
+    >>> print(pywt.idwt([1,2,0,1], None, 'db2', 'symmetric'))
     [ 1.19006969  1.54362308  0.44828774 -0.25881905  0.48296291  0.8365163 ]
 
-    >>> print pywt.idwt([1, 2, 0, 1], [0, 0, 0, 0], 'db2', 'symmetric')
+    >>> print(pywt.idwt([1, 2, 0, 1], [0, 0, 0, 0], 'db2', 'symmetric'))
     [ 1.19006969  1.54362308  0.44828774 -0.25881905  0.48296291  0.8365163 ]
 
-    >>> print pywt.idwt(None, [1, 2, 0, 1], 'db2', 'symmetric')
+    >>> print(pywt.idwt(None, [1, 2, 0, 1], 'db2', 'symmetric'))
     [ 0.57769726 -0.93125065  1.67303261 -0.96592583 -0.12940952 -0.22414387]
 
-    >>> print pywt.idwt([0, 0, 0, 0], [1, 2, 0, 1], 'db2', 'symmetric')
+    >>> print(pywt.idwt([0, 0, 0, 0], [1, 2, 0, 1], 'db2', 'symmetric'))
     [ 0.57769726 -0.93125065  1.67303261 -0.96592583 -0.12940952 -0.22414387]
 
 Remember that only one argument at a time can be ``None``:
 
-    >>> print pywt.idwt(None, None, 'db2', 'symmetric')
+    >>> print(pywt.idwt(None, None, 'db2', 'symmetric'))
     Traceback (most recent call last):
     ...
     ValueError: At least one coefficient parameter must be specified.
@@ -133,7 +133,7 @@ Coefficients data size in :attr:`idwt`
 When doing the :func:`IDWT <idwt>` transform, usually the coefficient arrays
 must have the same size.
 
-    >>> print pywt.idwt([1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'symmetric')
+    >>> print(pywt.idwt([1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'symmetric'))
     Traceback (most recent call last):
     ...
     ValueError: Coefficients arrays must have the same size.

doc/source/regression/modes.rst

@@ -18,7 +18,7 @@ Import :mod:`pywt` first
 
 List of available signal extension :ref:`modes <Modes>`:
 
-    >>> print pywt.Modes.modes
+    >>> print(pywt.Modes.modes)
     ['zero', 'constant', 'symmetric', 'periodic', 'smooth', 'periodization']
 
 
@@ -27,10 +27,11 @@ Test that :func:`dwt` and :func:`idwt` can be performed using every mode:
     >>> x = [1,2,1,5,-1,8,4,6]
     >>> for mode in pywt.Modes.modes:
     ...     cA, cD = pywt.dwt(x, 'db2', mode)
-    ...     print "Mode:", mode
-    ...     print "cA:", format_array(cA)
-    ...     print "cD:", format_array(cD)
-    ...     print "Reconstruction:", pywt.idwt(cA, cD, 'db2', mode)
+    ...     print("Mode: %s" % mode)
+    ...     print("cA: " + format_array(cA))
+    ...     print("cD: " + format_array(cD))
+    ...     print("Reconstruction: " + format_array(
+    ...         pywt.idwt(cA, cD, 'db2', mode)))
     Mode: zero
     cA: [-0.03468  1.73309  3.40612  6.32929  6.95095]
     cD: [-0.12941 -2.156   -5.95035 -1.21545 -1.8625 ]
@@ -70,10 +71,11 @@ You can also refer to modes via :ref:`Modes <Modes>` class attributes:
     >>> for mode_name in ['zero', 'constant', 'symmetric', 'periodic', 'smooth', 'periodization']:
     ...     mode = getattr(pywt.Modes, mode_name)
     ...     cA, cD = pywt.dwt([1,2,1,5,-1,8,4,6], 'db2', mode)
-    ...     print "Mode:", mode, "(%s)" % mode_name
-    ...     print "cA:", format_array(cA)
-    ...     print "cD:", format_array(cD)
-    ...     print "Reconstruction:", pywt.idwt(cA, cD, 'db2', mode)
+    ...     print("Mode: %d (%s)" % (mode, mode_name))
+    ...     print("cA: " + format_array(cA))
+    ...     print("cD: " + format_array(cD))
+    ...     print("Reconstruction: " + format_array(
+    ...         pywt.idwt(cA, cD, 'db2', mode)))
     Mode: 0 (zero)
     cA: [-0.03468  1.73309  3.40612  6.32929  6.95095]
     cD: [-0.12941 -2.156   -5.95035 -1.21545 -1.8625 ]
@@ -103,20 +105,20 @@ You can also refer to modes via :ref:`Modes <Modes>` class attributes:
 The default mode is :ref:`symmetric <Modes.symmetric>`:
 
     >>> cA, cD = pywt.dwt(x, 'db2')
-    >>> print cA
+    >>> print(cA)
     [ 1.76776695  1.73309178  3.40612438  6.32928585  7.77817459]
-    >>> print cD
+    >>> print(cD)
     [-0.61237244 -2.15599552 -5.95034847 -1.21545369  1.22474487]
-    >>> print pywt.idwt(cA, cD, 'db2')
+    >>> print(pywt.idwt(cA, cD, 'db2'))
     [ 1.  2.  1.  5. -1.  8.  4.  6.]
 
 
 And using a keyword argument:
 
     >>> cA, cD = pywt.dwt(x, 'db2', mode='symmetric')
-    >>> print cA
+    >>> print(cA)
     [ 1.76776695  1.73309178  3.40612438  6.32928585  7.77817459]
-    >>> print cD
+    >>> print(cD)
     [-0.61237244 -2.15599552 -5.95034847 -1.21545369  1.22474487]
-    >>> print pywt.idwt(cA, cD, 'db2')
+    >>> print(pywt.idwt(cA, cD, 'db2'))
     [ 1.  2.  1.  5. -1.  8.  4.  6.]

doc/source/regression/multilevel.rst

@@ -12,13 +12,13 @@ Multilevel DWT decomposition
 >>> x = [3, 7, 1, 1, -2, 5, 4, 6]
 >>> db1 = pywt.Wavelet('db1')
 >>> cA3, cD3, cD2, cD1 = pywt.wavedec(x, db1)
->>> print cA3
+>>> print(cA3)
 [ 8.83883476]
->>> print cD3
+>>> print(cD3)
 [-0.35355339]
->>> print cD2
+>>> print(cD2)
 [ 4.  -3.5]
->>> print cD1
+>>> print(cD1)
 [-2.82842712  0.         -4.94974747 -1.41421356]
 
 >>> pywt.dwt_max_level(len(x), db1)
@@ -31,7 +31,7 @@ Multilevel IDWT reconstruction
 ------------------------------
 
 >>> coeffs = pywt.wavedec(x, db1)
->>> print pywt.waverec(coeffs, db1)
+>>> print(pywt.waverec(coeffs, db1))
 [ 3.  7.  1.  1. -2.  5.  4.  6.]
 
 
@@ -40,21 +40,21 @@ Multilevel SWT decomposition
 
 >>> x = [3, 7, 1, 3, -2, 6, 4, 6]
 >>> (cA2, cD2), (cA1, cD1) = pywt.swt(x, db1, level=2)
->>> print cA1
+>>> print(cA1)
 [ 7.07106781  5.65685425  2.82842712  0.70710678  2.82842712  7.07106781
   7.07106781  6.36396103]
->>> print cD1
+>>> print(cD1)
 [-2.82842712  4.24264069 -1.41421356  3.53553391 -5.65685425  1.41421356
  -1.41421356  2.12132034]
->>> print cA2
+>>> print(cA2)
 [  7.    4.5   4.    5.5   7.    9.5  10.    8.5]
->>> print cD2
+>>> print(cD2)
 [ 3.   3.5  0.  -4.5 -3.   0.5  0.   0.5]
 
 >>> [(cA2, cD2)] = pywt.swt(cA1, db1, level=1, start_level=1)
->>> print cA2
+>>> print(cA2)
 [  7.    4.5   4.    5.5   7.    9.5  10.    8.5]
->>> print cD2
+>>> print(cD2)
 [ 3.   3.5  0.  -4.5 -3.   0.5  0.   0.5]
 
 >>> coeffs = pywt.swt(x, db1)

doc/source/regression/wavelet.rst

@@ -26,7 +26,7 @@ The :func:`wavelist` function with family name passed as an argument is used to
 obtain the list of wavelet names in each family.
 
     >>> for family in pywt.families():
-    ...     print "%s family:" % family, ', '.join(pywt.wavelist(family))
+    ...     print("%s family: " % family + ', '.join(pywt.wavelist(family)))
     haar family: haar
     db family: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20
     sym family: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20
@@ -62,7 +62,7 @@ First, let's try printing a :class:`Wavelet` object. This shows a brief
 information about its name, its family name and some properties like
 orthogonality and symmetry.
 
-    >>> print w
+    >>> print(w)
     Wavelet db3
       Family name:    Daubechies
       Short name:     db
@@ -79,7 +79,7 @@ corresponds to lowpass and highpass decomposition filters and lowpass and
 highpass reconstruction filters respectively:
 
     >>> def print_array(arr):
-    ...     print "[%s]" % ", ".join(["%.14f" % x for x in 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]
@@ -101,11 +101,11 @@ Other Wavelet's properties are:
 
     Wavelet :attr:`~Wavelet.name`, :attr:`~Wavelet.short_family_name` and :attr:`~Wavelet.family_name`:
 
-        >>> print w.name
+        >>> print(w.name)
         db3
-        >>> print w.short_family_name
+        >>> print(w.short_family_name)
         db
-        >>> print w.family_name
+        >>> print(w.family_name)
         Daubechies
 
     - Decomposition (:attr:`~Wavelet.dec_len`) and reconstruction
@@ -125,7 +125,7 @@ Other Wavelet's properties are:
 
     - Symmetry (:attr:`~Wavelet.symmetry`):
 
-        >>> print w.symmetry
+        >>> print(w.symmetry)
         asymmetric
 
     - Number of vanishing moments for the scaling function *phi*
@@ -166,7 +166,7 @@ Now when we know a bit about the builtin Wavelets, let's see how to create
 Note that such custom wavelets **will not** have all the properties set
 to correct values:
 
-    >>> print my_wavelet
+    >>> print(my_wavelet)
     Wavelet My Haar Wavelet
       Family name:
       Short name:
@@ -180,7 +180,7 @@ to correct values:
     >>> my_wavelet.orthogonal = True
     >>> my_wavelet.biorthogonal = True
 
-    >>> print my_wavelet
+    >>> print(my_wavelet)
     Wavelet My Haar Wavelet
       Family name:
       Short name: