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in repository python-dtcwt.
commit 3662c6a5f6ca3e68d5f7e1a109231fb42ac7e573
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date: Fri Aug 9 11:02:40 2013 +0100
some trivial documentation fixes
Mostly little grammar/syntax fixes
---
docs/gettingstarted.rst | 2 +-
docs/index.rst | 2 +-
dtcwt/transform1d.py | 4 ++--
dtcwt/transform2d.py | 6 +++---
4 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index 8998b43..fbf8ce7 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -25,7 +25,7 @@ This will also write test-coverage information to the ``cover/`` directory.
Simple usage
````````````
-Once installed, you are most likely to use one of four functions:
+Once installed, you are most likely to use one of these functions:
* :py:func:`dtcwt.dtwavexfm` -- 1D DT-CWT transform.
* :py:func:`dtcwt.dtwaveifm` -- Inverse 1D DT-CWT transform.
diff --git a/docs/index.rst b/docs/index.rst
index afe8551..a36b0c7 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -7,7 +7,7 @@ transforms and their inverse in Python.
The interface is intentionally similar to the existing MATLAB dual-tree complex
wavelet transform toolbox provided by `Prof. Nick Kingsbury
<http://www-sigproc.eng.cam.ac.uk/~ngk/>`_. This library is intended to ease
-the porting of algorithms written in using this toolbox from MATLAB to Python.
+the porting of algorithms written using this toolbox from MATLAB to Python.
The original toolbox is copyrighted and there are some restrictions on use
which are outlined in the file
diff --git a/dtcwt/transform1d.py b/dtcwt/transform1d.py
index 71af13a..3314388 100644
--- a/dtcwt/transform1d.py
+++ b/dtcwt/transform1d.py
@@ -11,7 +11,7 @@ def dtwavexfm(X, nlevels=3, biort=DEFAULT_BIORT, qshift=DEFAULT_QSHIFT, include_
"""Perform a *n*-level DTCWT decompostion on a 1D column vector *X* (or on
the columns of a matrix *X*).
- :param X: 1D real matrix/Image or matrix of 1D columns of shape (N, M)
+ :param X: 1D real array or 2D real array whose columns are to be transformed
:param nlevels: Number of levels of wavelet decomposition
:param biort: Level 1 wavelets to use. See :py:func:`biort`.
:param qshift: Level >= 2 wavelets to use. See :py:func:`qshift`.
@@ -109,7 +109,7 @@ def dtwaveifm(Yl, Yh, biort=DEFAULT_BIORT, qshift=DEFAULT_QSHIFT, gain_mask=None
:param qshift: Level >= 2 wavelets to use. See :py:func:`qshift`.
:param gain_mask: Gain to be applied to each subband.
- :returns Z: Reconstructed real signal vector (or matrix).
+ :returns Z: Reconstructed real array.
The *l*-th element of *gain_mask* is gain for wavelet subband at level l.
If gain_mask[l] == 0, no computation is performed for band *l*. Default
diff --git a/dtcwt/transform2d.py b/dtcwt/transform2d.py
index 05fc8c0..83ef4dd 100644
--- a/dtcwt/transform2d.py
+++ b/dtcwt/transform2d.py
@@ -10,13 +10,13 @@ from dtcwt.lowlevel import colfilter, coldfilt, colifilt
def dtwavexfm2(X, nlevels=3, biort=DEFAULT_BIORT, qshift=DEFAULT_QSHIFT, include_scale=False):
"""Perform a *n*-level DTCWT-2D decompostion on a 2D matrix *X*.
- :param X: 2D real matrix/Image of shape (N, M)
+ :param X: 2D real array
:param nlevels: Number of levels of wavelet decomposition
:param biort: Level 1 wavelets to use. See :py:func:`biort`.
:param qshift: Level >= 2 wavelets to use. See :py:func:`qshift`.
:returns Yl: The real lowpass image from the final level
- :returns Yh: A tuple containing the (N, M, 6) shape complex highpass subimages for each level.
+ :returns Yh: A tuple containing the complex highpass subimages for each level.
:returns Yscale: If *include_scale* is True, a tuple containing real lowpass coefficients for every scale.
If *biort* or *qshift* are strings, they are used as an argument to the
@@ -164,7 +164,7 @@ def dtwaveifm2(Yl,Yh,biort=DEFAULT_BIORT,qshift=DEFAULT_QSHIFT,gain_mask=None):
:param qshift: Level >= 2 wavelets to use. See :py:func:`qshift`.
:param gain_mask: Gain to be applied to each subband.
- :returns Z: Reconstructed real image matrix.
+ :returns Z: Reconstructed real array
The (*d*, *l*)-th element of *gain_mask* is gain for subband with direction
*d* at level *l*. If gain_mask[d,l] == 0, no computation is performed for
--
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