DOC: minor formatting fixes

1.) consistently use double backticks
2.) consistently use a single space after periods

Author: grlee77

pywt/_dwt.py

@@ -26,7 +26,7 @@ def dwt_max_level(data_len, filter_len):
     data_len : int
         Input data length.
     filter_len : int, str or Wavelet
-        The wavelet filter length.  Alternatively, the name of a discrete
+        The wavelet filter length. Alternatively, the name of a discrete
         wavelet or a Wavelet object can be specified.
 
     Returns
@@ -38,8 +38,8 @@ def dwt_max_level(data_len, filter_len):
     -----
     The rational for the choice of levels is the maximum level where at least
     one coefficient in the output is uncorrupted by edge effects caused by
-    signal extension.  Put another way, decomposition stops when the signal
-    becomes shorter than the FIR filter length for a given wavelet.  This
+    signal extension. Put another way, decomposition stops when the signal
+    becomes shorter than the FIR filter length for a given wavelet. This
     corresponds to:
 
     .. max_level = floor(log2(data_len/(filter_len - 1)))
@@ -66,7 +66,7 @@ def dwt_max_level(data_len, filter_len):
             filter_len = Wavelet(filter_len).dec_len
         else:
             raise ValueError(
-                ("'{}', is not a recognized discrete wavelet.  A list of "
+                ("'{}', is not a recognized discrete wavelet. A list of "
                  "supported wavelet names can be obtained via "
                  "pywt.wavelist(kind='discrete')").format(filter_len))
     elif not (isinstance(filter_len, Number) and filter_len % 1 == 0):
@@ -198,11 +198,11 @@ def idwt(cA, cD, wavelet, mode='symmetric', axis=-1):
     Parameters
     ----------
     cA : array_like or None
-        Approximation coefficients.  If None, will be set to array of zeros
-        with same shape as `cD`.
+        Approximation coefficients. If None, will be set to array of zeros
+        with same shape as ``cD``.
     cD : array_like or None
-        Detail coefficients.  If None, will be set to array of zeros
-        with same shape as `cA`.
+        Detail coefficients. If None, will be set to array of zeros
+        with same shape as ``cA``.
     wavelet : Wavelet object or name
         Wavelet to use
     mode : str, optional (default: 'symmetric')
@@ -224,9 +224,9 @@ def idwt(cA, cD, wavelet, mode='symmetric', axis=-1):
     >>> pywt.idwt(cA, cD, 'db2', 'smooth')
     array([ 1.,  2.,  3.,  4.,  5.,  6.])
 
-    One of the neat features of `idwt` is that one of the ``cA`` and ``cD``
-    arguments can be set to None.  In that situation the reconstruction will be
-    performed using only the other one.  Mathematically speaking, this is
+    One of the neat features of ``idwt`` is that one of the ``cA`` and ``cD``
+    arguments can be set to None. In that situation the reconstruction will be
+    performed using only the other one. Mathematically speaking, this is
     equivalent to passing a zero-filled array as one of the arguments.
 
     >>> (cA, cD) = pywt.dwt([1,2,3,4,5,6], 'db2', 'smooth')
@@ -300,7 +300,7 @@ def downcoef(part, data, wavelet, mode='symmetric', level=1):
 
     Partial Discrete Wavelet Transform data decomposition.
 
-    Similar to `pywt.dwt`, but computes only one set of coefficients.
+    Similar to ``dwt``, but computes only one set of coefficients.
     Useful when you need only approximation or only details at the given level.
 
     Parameters
@@ -316,9 +316,9 @@ def downcoef(part, data, wavelet, mode='symmetric', level=1):
     wavelet : Wavelet object or name
         Wavelet to use
     mode : str, optional
-        Signal extension mode, see `Modes`.  Default is 'symmetric'.
+        Signal extension mode, see ``Modes``. Default is 'symmetric'.
     level : int, optional
-        Decomposition level.  Default is 1.
+        Decomposition level. Default is 1.
 
     Returns
     -------
@@ -362,7 +362,7 @@ def upcoef(part, coeffs, wavelet, level=1, take=0):
     wavelet : Wavelet object or name
         Wavelet to use
     level : int, optional
-        Multilevel reconstruction level.  Default is 1.
+        Multilevel reconstruction level. Default is 1.
     take : int, optional
         Take central part of length equal to 'take' from the result.
         Default is 0.

pywt/_extensions/_pywt.pyx

@@ -148,7 +148,7 @@ class _DeprecatedMODES(_Modes):
         every time MODES is used.
 
         N.B. have to use __getattribute__ as well as __getattr__
-        to ensure warning on e.g. `MODES.symmetric`.
+        to ensure warning on e.g. ``MODES.symmetric``.
         """
         if not attr.startswith('_'):
             warnings.warn(_DeprecatedMODES.msg, DeprecationWarning)
@@ -200,7 +200,7 @@ def wavelist(family=None, kind='all'):
 
     kind : {'all', 'continuous', 'discrete'}, optional
         Whether to return only wavelet names of discrete or continuous
-        wavelets, or all wavelets.  Default is ``'all'``.
+        wavelets, or all wavelets. Default is ``'all'``.
         Ignored if ``family`` is specified.
 
     Returns
@@ -309,7 +309,7 @@ def DiscreteContinuousWavelet(name=u"", object filter_bank=None):
     a valid name from the wavelist() list.
     To create a custom wavelet object, filter_bank parameter must
     be specified. It can be either a list of four filters or an object
-    that a `filter_bank` attribute which returns a list of four
+    that a ``filter_bank`` attribute which returns a list of four
     filters - just like the Wavelet instance itself.
 
     For a ContinuousWavelet, filter_bank cannot be used and must remain unset.
@@ -337,7 +337,7 @@ cdef public class Wavelet [type WaveletType, object WaveletObject]:
     a valid name from the wavelist() list.
     To create a custom wavelet object, filter_bank parameter must
     be specified. It can be either a list of four filters or an object
-    that a `filter_bank` attribute which returns a list of four
+    that a ``filter_bank`` attribute which returns a list of four
     filters - just like the Wavelet instance itself.
 
     """
@@ -360,7 +360,7 @@ cdef public class Wavelet [type WaveletType, object WaveletObject]:
             if self.w is NULL:
                 if self.name in wavelist(kind='continuous'):
                     raise ValueError("The `Wavelet` class is for discrete "
-                          "wavelets, %s is a continuous wavelet.  Use "
+                          "wavelets, %s is a continuous wavelet. Use "
                           "pywt.ContinuousWavelet instead" % self.name)
                 else:
                     raise ValueError("Invalid wavelet name '%s'." % self.name)
@@ -548,8 +548,8 @@ cdef public class Wavelet [type WaveletType, object WaveletObject]:
         """
         wavefun(self, level=8)
 
-        Calculates approximations of scaling function (`phi`) and wavelet
-        function (`psi`) on xgrid (`x`) at a given level of refinement.
+        Calculates approximations of scaling function (``phi``) and wavelet
+        function (``psi``) on xgrid (``x``) at a given level of refinement.
 
         Parameters
         ----------
@@ -700,7 +700,7 @@ cdef public class ContinuousWavelet [type ContinuousWaveletType, object Continuo
                 if base_name == 'fbsp':
                     msg = (
                         "Wavelets of family {0}, without parameters "
-                        "specified in the name are deprecated.  The name "
+                        "specified in the name are deprecated. The name "
                         "should take the form {0}M-B-C where M is the spline "
                         "order and B, C are floats representing the bandwidth "
                         "frequency and center frequency, respectively "
@@ -1030,7 +1030,7 @@ cpdef np.dtype _check_dtype(data):
                 # half-precision input converted to single precision
                 dt = np.dtype('float32')
             elif dt == np.complex256:
-                # complex256 is not supported.  run at reduced precision
+                # complex256 is not supported. run at reduced precision
                 dt = np.dtype('complex128')
             else:
                 # integer input was always accepted; convert to float64

pywt/_functions.py

@@ -64,7 +64,7 @@ def integrate_wavelet(wavelet, precision=8):
     Parameters
     ----------
     wavelet : Wavelet instance or str
-        Wavelet to integrate.  If a string, should be the name of a wavelet.
+        Wavelet to integrate. If a string, should be the name of a wavelet.
     precision : int, optional
         Precision that will be used for wavelet function
         approximation computed with the wavefun(level=precision)
@@ -126,7 +126,7 @@ def central_frequency(wavelet, precision=8):
     Parameters
     ----------
     wavelet : Wavelet instance, str or tuple
-        Wavelet to integrate.  If a string, should be the name of a wavelet.
+        Wavelet to integrate. If a string, should be the name of a wavelet.
     precision : int, optional
         Precision that will be used for wavelet function
         approximation computed with the wavefun(level=precision)
@@ -166,11 +166,11 @@ def scale2frequency(wavelet, scale, precision=8):
     Parameters
     ----------
     wavelet : Wavelet instance or str
-        Wavelet to integrate.  If a string, should be the name of a wavelet.
+        Wavelet to integrate. If a string, should be the name of a wavelet.
     scale : scalar
     precision : int, optional
         Precision that will be used for wavelet function approximation computed
-        with ``wavelet.wavefun(level=precision)``.  Default is 8.
+        with ``wavelet.wavefun(level=precision)``. Default is 8.
 
     Returns
     -------
@@ -184,7 +184,7 @@ def qmf(filt):
     """
     Returns the Quadrature Mirror Filter(QMF).
 
-    The magnitude response of QMF is mirror image about `pi/2` of that of the
+    The magnitude response of QMF is mirror image about ``pi/2`` of that of the
     input filter.
 
     Parameters

pywt/_multidim.py

@@ -30,10 +30,10 @@ def dwt2(data, wavelet, mode='symmetric', axes=(-2, -1)):
     data : array_like
         2D array with input data
     wavelet : Wavelet object or name string, or 2-tuple of wavelets
-        Wavelet to use.  This can also be a tuple containing a wavelet to
+        Wavelet to use. This can also be a tuple containing a wavelet to
         apply along each axis in ``axes``.
     mode : str or 2-tuple of strings, optional
-        Signal extension mode, see Modes (default: 'symmetric').  This can
+        Signal extension mode, see Modes (default: 'symmetric'). This can
         also be a tuple of modes specifying the mode to use on each axis in
         ``axes``.
     axes : 2-tuple of ints, optional
@@ -44,7 +44,7 @@ def dwt2(data, wavelet, mode='symmetric', axes=(-2, -1)):
     -------
     (cA, (cH, cV, cD)) : tuple
         Approximation, horizontal detail, vertical detail and diagonal
-        detail coefficients respectively.  Horizontal refers to array axis 0
+        detail coefficients respectively. Horizontal refers to array axis 0
         (or ``axes[0]`` for user-specified ``axes``).
 
     Examples
@@ -84,13 +84,13 @@ def idwt2(coeffs, wavelet, mode='symmetric', axes=(-2, -1)):
     ----------
     coeffs : tuple
         (cA, (cH, cV, cD)) A tuple with approximation coefficients and three
-        details coefficients 2D arrays like from `dwt2`.  If any of these
+        details coefficients 2D arrays like from `dwt2`. If any of these
         components are set to ``None``, it will be treated as zeros.
     wavelet : Wavelet object or name string, or 2-tuple of wavelets
-        Wavelet to use.  This can also be a tuple containing a wavelet to
+        Wavelet to use. This can also be a tuple containing a wavelet to
         apply along each axis in ``axes``.
     mode : str or 2-tuple of strings, optional
-        Signal extension mode, see Modes (default: 'symmetric').  This can
+        Signal extension mode, see Modes (default: 'symmetric'). This can
         also be a tuple of modes specifying the mode to use on each axis in
         ``axes``.
     axes : 2-tuple of ints, optional
@@ -127,11 +127,11 @@ def dwtn(data, wavelet, mode='symmetric', axes=None):
     data : array_like
         n-dimensional array with input data.
     wavelet : Wavelet object or name string, or tuple of wavelets
-        Wavelet to use.  This can also be a tuple containing a wavelet to
+        Wavelet to use. This can also be a tuple containing a wavelet to
         apply along each axis in ``axes``.
     mode : str or tuple of string, optional
         Signal extension mode used in the decomposition,
-        see Modes (default: 'symmetric').  This can also be a tuple of modes
+        see Modes (default: 'symmetric'). This can also be a tuple of modes
         specifying the mode to use on each axis in ``axes``.
     axes : sequence of ints, optional
         Axes over which to compute the DWT. Repeated elements mean the DWT will
@@ -229,11 +229,11 @@ def idwtn(coeffs, wavelet, mode='symmetric', axes=None):
         Dictionary as in output of ``dwtn``. Missing or ``None`` items
         will be treated as zeros.
     wavelet : Wavelet object or name string, or tuple of wavelets
-        Wavelet to use.  This can also be a tuple containing a wavelet to
+        Wavelet to use. This can also be a tuple containing a wavelet to
         apply along each axis in ``axes``.
     mode : str or list of string, optional
         Signal extension mode used in the decomposition,
-        see Modes (default: 'symmetric').  This can also be a tuple of modes
+        see Modes (default: 'symmetric'). This can also be a tuple of modes
         specifying the mode to use on each axis in ``axes``.
     axes : sequence of ints, optional
         Axes over which to compute the IDWT. Repeated elements mean the IDWT