Merge pull request #1040 from DimitriPapadopoulos/codespell

DOC: Typos found by codespell

Running [codespell](https://github.com/codespell-project/codespell) manually for now, but could be automated as suggested in systemd/systemd#15436 if there is interest.

Changelog

@@ -342,7 +342,7 @@ Bug fixes
   reviewed by PM)
 * Safer warning registry manipulation when checking for overflows (pr/753)
   (CM, reviewed by MB)
-* Correctly write .annot files with duplicate lables (pr/763) (Richard Nemec
+* Correctly write .annot files with duplicate labels (pr/763) (Richard Nemec
   with CM)
 
 Maintenance
@@ -997,7 +997,7 @@ visiting the URL::
 * Bugfix: Removed left-over print statement in extension code.
 * Bugfix: Prevent saving of bogus 'None.nii' images when the filename
   was previously assign, before calling NiftiImage.save() (Closes: #517920).
-* Bugfix: Extension length was to short for all `edata` whos length matches
+* Bugfix: Extension length was to short for all `edata` whose length matches
   n*16-8, for all integer n.
 
 0.20090205.1 (Thu, 5 Feb 2009)
@@ -1017,7 +1017,7 @@ visiting the URL::
   automatically dumped into this extension.
   Embedded meta data is not loaded automatically, since this has security
   implications, because code from the file header is actually executed.
-  The documentation explicitely mentions this risk.
+  The documentation explicitly mentions this risk.
 * Added :class:`~nifti.extensions.NiftiExtensions`. This is a container-like
   handler to access and manipulate NIfTI1 header extensions.
 * Exposed :class:`~nifti.image.MemMappedNiftiImage` in the root module.
@@ -1223,7 +1223,7 @@ visiting the URL::
 * Does not depend on libfslio anymore.
 * Up to seven-dimensional dataset are supported (as much as NIfTI can do).
 * The complete NIfTI header dataset is modifiable.
-* Most image properties are accessable via class attributes and accessor
+* Most image properties are accessible via class attributes and accessor
   methods.
 * Improved documentation (but still a long way to go).
 

doc/misc/pylintrc

@@ -79,7 +79,7 @@ output-format=colorized
 # Include message's id in output
 include-ids=yes
 
-# Tells wether to display a full report or only the messages
+# Tells whether to display a full report or only the messages
 reports=yes
 
 [MISCELLANEOUS]

doc/source/coordinate_systems.rst

@@ -530,7 +530,7 @@ then the image affine matrix $A$ is:
 
 Why the extra row of $[0, 0, 0, 1]$?  We need this row because we have
 rephrased the combination of rotations / zooms and translations as a
-transformation in *homogenous coordinates* (see `wikipedia homogenous
+transformation in *homogeneous coordinates* (see `wikipedia homogeneous
 coordinates`_).  This is a trick that allows us to put the translation part
 into the same matrix as the rotations / zooms, so that both translations and
 rotations / zooms can be applied by matrix multiplication.  In order to make

doc/source/devel/data_pkg_discuss.rst

@@ -369,7 +369,7 @@ Discovery
 
     Revsion ids could for example be hashes of the package instantiation
     (package contents), so they could be globally unique to the contents,
-    whereever the contents was when the identifier was made.  However, *tags*
+    wherever the contents was when the identifier was made.  However, *tags*
     are just names that someone has attached to a particular revsion id.  If
     there is more than one person providing versions of a particular package,
     there may not be agreement on the revsion that a particular tag is attached

doc/source/devel/devguide.rst

@@ -64,7 +64,7 @@ necessary and the branch gets tagged when a package version is released.
 Maintenance (as well as backport) releases or branches off from the respective
 packaging tag.
 
-There might be additonal branches for each developer, prefixed with intials.
+There might be additional branches for each developer, prefixed with initials.
 Alternatively, several GitHub (or elsewhere) clones might be used.
 
 
@@ -99,7 +99,7 @@ Changelog
 =========
 
 The changelog is located in the toplevel directory of the source tree in the
-`Changelog` file. The content of this file should be formated as restructured
+`Changelog` file. The content of this file should be formatted as restructured
 text to make it easy to put it into manual appendix and on the website.
 
 This changelog should neither replicate the VCS commit log nor the

doc/source/dicom/dicom_intro.rst

@@ -686,7 +686,7 @@ For example, there is a DIMSE service called "C-ECHO" that requests confirmation
 from the responding application that the echo message arrived.
 
 The definition of the DIMSE services specifies, for a particular DIMSE service,
-whether the DIMSE commend set should be followed by a data set.
+whether the DIMSE command set should be followed by a data set.
 
 In particular, the data set will be a full Information Object Definition's worth
 of data.

doc/source/dicom/dicom_mosaic.rst

@@ -124,7 +124,7 @@ Data scaling
 SPM gets the DICOM scaling, offset for the image ('RescaleSlope',
 'RescaleIntercept').  It writes these scalings into the nifti_ header.
 Then it writes the raw image data (unscaled) to disk.  Obviously these
-will have the corrent scalings applied when the nifti image is read again.
+will have the current scalings applied when the nifti image is read again.
 
 A comment in the code here says that the data are not scaled by the
 maximum amount.  I assume by this they mean that the DICOM scaling may

doc/source/dicom/dicom_orientation.rst

@@ -350,7 +350,7 @@ constant, to the voxel coordinate for the slice (the value for the slice
 index).
 
 Our DICOM might have the 'SliceLocation' field (0020,1041).
-'SliceLocation' seems to be proportianal to slice location, at least for
+'SliceLocation' seems to be proportional to slice location, at least for
 some GE and Philips DICOMs I was looking at.  But, there is a more
 reliable way (that doesn't depend on this field), and uses only the very
 standard 'ImageOrientationPatient' and 'ImagePositionPatient' fields.
@@ -385,7 +385,7 @@ unit change in the slice voxel coordinate.  So, the
 addition of two vectors $T^j = \mathbf{a} + \mathbf{b}$, where
 $\mathbf{a}$ is the position of the first voxel in some slice (here
 slice 1, therefore $\mathbf{a} = T^1$) and $\mathbf{b}$ is $d$ times the
-third colum of $A$.  Obviously $d$ can be negative or positive. This
+third column of $A$.  Obviously $d$ can be negative or positive. This
 leads to various ways of recovering something that is proportional to
 $d$ plus a constant.  The algorithm suggested in this `ITK post on
 ordering slices`_ - and the one used by SPM - is to take the inner

doc/source/dicom/siemens_csa.rst

@@ -124,7 +124,7 @@ Each item
 
 Now there's a different length check from CSA1.  ``item_len`` is given
 just by ``xx[1]``.  If ``item_len`` > ``csa_max_pos - csa_position``
-(the remaining bytes in the header), then we just read the remaning
+(the remaining bytes in the header), then we just read the remaining
 bytes in the header (as above) into ``value`` below, as uint8, move the
 filepointer to the next 4 byte boundary, and give up reading. 
 

doc/source/gettingstarted.rst

@@ -14,7 +14,7 @@ Getting Started
 ***************
 
 NiBabel supports an ever growing collection of neuroimaging file formats. Every
-file format format has its own features and pecularities that need to be taken
+file format format has its own features and peculiarities that need to be taken
 care of to get the most out of it. To this end, NiBabel offers both high-level
 format-independent access to neuroimages, as well as an API with various levels
 of format-specific access to all available information in a particular file
@@ -109,7 +109,7 @@ True
 
 In this case, we used the identity matrix as the affine transformation. The
 image header is initialized from the provided data array (i.e. shape, dtype)
-and all other values are set to resonable defaults.
+and all other values are set to reasonable defaults.
 
 Saving this new image to a file is trivial:
 

doc/source/gitwash/configure_git.rst

@@ -142,7 +142,7 @@ and it gives graph / text output something like this (but with color!)::
     | * 4aff2a8 - fixed bug 35, and added a test in test_bugfixes (2 weeks ago) [Hugo]
     |/  
     * a7ff2e5 - Added notes on discussion/proposal made during Data Array Summit. (2 weeks ago) [Corran Webster]
-    * 68f6752 - Initial implimentation of AxisIndexer - uses 'index_by' which needs to be changed to a call on an Axes object - this is all very sketchy right now. (2 weeks ago) [Corr
+    * 68f6752 - Initial implementation of AxisIndexer - uses 'index_by' which needs to be changed to a call on an Axes object - this is all very sketchy right now. (2 weeks ago) [Corr
     *   376adbd - Merge pull request #46 from terhorst/master (2 weeks ago) [Jonathan Terhorst]
     |\  
     | * b605216 - updated joshu example to current api (3 weeks ago) [Jonathan Terhorst]

doc/source/links_names.txt

@@ -217,7 +217,7 @@
 .. _`wikipedia affine transform`: https://en.wikipedia.org/wiki/Affine_transformation
 .. _`wikipedia linear transform`: https://en.wikipedia.org/wiki/Linear_transformation
 .. _`wikipedia rotation matrix`: https://en.wikipedia.org/wiki/Rotation_matrix
-.. _`wikipedia homogenous coordinates`: https://en.wikipedia.org/wiki/Homogeneous_coordinates
+.. _`wikipedia homogeneous coordinates`: https://en.wikipedia.org/wiki/Homogeneous_coordinates
 .. _`wikipedia axis angle`: https://en.wikipedia.org/wiki/Axis_angle
 .. _`wikipedia Euler angles`: https://en.wikipedia.org/wiki/Euler_angles
 .. _`Mathworld Euler angles`: http://mathworld.wolfram.com/EulerAngles.html

doc/source/old/design.txt

@@ -71,7 +71,7 @@ We think of an image as being the association of:
 
    For simplicity, we want the transformation (above) to be spatial.
    Because the images are always at least 3D, and the transform is
-   spatial, this means that the tranformation is always exactly 3D.  We
+   spatial, this means that the transformation is always exactly 3D.  We
    have to know which of the N image dimensions are spatial. For
    example, if we have a 4D (space and time) image, we need to know
    which of the 4 dimensions are spatial.  We could ask the image to

doc/source/old/orientation.txt

@@ -12,7 +12,7 @@ Affines as orientation
 ----------------------
 
 Orientations are expressed by 4 by 4 affine arrays.  4x4 affine arrays
-give, in homogenous coordinates, the relationship between the
+give, in homogeneous coordinates, the relationship between the
 coordinates in the voxel array, and millimeters.  Let is say that I have
 a simple affine like this:
 
@@ -26,7 +26,7 @@ And I have a voxel coordinate:
 
 then the millimeter coordinate for that voxel is given by:
 
->>> # add extra 1 for homogenous coordinates
+>>> # add extra 1 for homogeneous coordinates
 >>> homogenous_coord = np.concatenate((coord, [1]))
 >>> mm_coord = np.dot(aff, homogenous_coord)[:3]
 >>> mm_coord

nibabel/affines.py

@@ -34,7 +34,7 @@ def apply_affine(aff, pts):
     Parameters
     ----------
     aff : (N, N) array-like
-        Homogenous affine, for 3D points, will be 4 by 4. Contrary to first
+        Homogeneous affine, for 3D points, will be 4 by 4. Contrary to first
         appearance, the affine will be applied on the left of `pts`.
     pts : (..., N-1) array-like
         Points, where the last dimension contains the coordinates of each
@@ -87,7 +87,7 @@ def apply_affine(aff, pts):
 def to_matvec(transform):
     """Split a transform into its matrix and vector components.
 
-    The tranformation must be represented in homogeneous coordinates and is
+    The transformation must be represented in homogeneous coordinates and is
     split into its rotation matrix and translation vector components.
 
     Parameters
@@ -104,7 +104,7 @@ def to_matvec(transform):
     matrix : (N-1, M-1) array
         Matrix component of `transform`
     vector : (M-1,) array
-        Vector compoent of `transform`
+        Vector component of `transform`
 
     See Also
     --------
@@ -145,7 +145,7 @@ def from_matvec(matrix, vector=None):
     Returns
     -------
     xform : array
-        An (N+1, M+1) homogenous transform matrix.
+        An (N+1, M+1) homogeneous transform matrix.
 
     See Also
     --------
@@ -269,7 +269,7 @@ def voxel_sizes(affine):
     1)[:3]``.  The world coordinate vector of voxel vector (1, 0, 0) is
     ``v1_ax1 = affine.dot((1, 0, 0, 1))[:3]``.  The final 1 in the voxel
     vectors and the ``[:3]`` at the end are because the affine works on
-    homogenous coodinates.  The translations part of the affine is ``trans =
+    homogeneous coordinates.  The translations part of the affine is ``trans =
     affine[:3, 3]``, and the rotations, zooms and shearing part of the affine
     is ``rzs = affine[:3, :3]``. Because of the final 1 in the input voxel
     vector, ``v0 == rzs.dot((0, 0, 0)) + trans``, and ``v1_ax1 == rzs.dot((1,

nibabel/analyze.py

@@ -385,7 +385,7 @@ def from_header(klass, header=None, check=True):
                     # safely discard fields with names not known to this header
                     # type on the basis they are from the wrong Analyze dialect
                     pass
-            # set any fields etc that are specific to this format (overriden by
+            # set any fields etc that are specific to this format (overridden by
             # sub-classes)
             obj._clean_after_mapping()
         # Fallback basic conversion always done.

nibabel/batteryrunners.py

@@ -37,7 +37,7 @@
 (very bad problem).  The levels follow the log levels from the logging
 module (e.g 40 equivalent to "error" level, 50 to "critical").  The
 ``error`` can be one of ``None`` if no error to suggest, or an Exception
-class that the user might consider raising for this sitation.  The
+class that the user might consider raising for this situation.  The
 ``problem_msg`` and ``fix_msg`` are human readable strings that should
 explain what happened.
 

nibabel/casting.py

@@ -1,4 +1,4 @@
-""" Utilties for casting numpy values in various ways
+""" Utilities for casting numpy values in various ways
 
 Most routines work round some numpy oddities in floating point precision and
 casting.  Others work round numpy casting to and from python ints
@@ -132,7 +132,7 @@ def shared_range(flt_type, int_type):
     Returns
     -------
     mn : object
-        Number of type `flt_type` that is the minumum value in the range of
+        Number of type `flt_type` that is the minimum value in the range of
         `int_type`, such that ``mn.astype(int_type)`` >= min of `int_type`
     mx : object
         Number of type `flt_type` that is the maximum value in the range of

nibabel/cifti2/tests/test_cifti2io_axes.py

@@ -79,7 +79,7 @@ def check_Conte69(brain_model):
 
 def check_rewrite(arr, axes, extension='.nii'):
     """
-    Checks wheter writing the Cifti2 array to disc and reading it back in gives the same object
+    Checks whether writing the Cifti2 array to disc and reading it back in gives the same object
 
     Parameters
     ----------

nibabel/cmdline/diff.py

@@ -73,7 +73,7 @@ def get_opt_parser():
 def are_values_different(*values):
     """Generically compare values, return True if different
 
-    Note that comparison is targetting reporting of comparison of the headers
+    Note that comparison is targeting reporting of comparison of the headers
     so has following specifics:
     - even a difference in data types is considered a difference, i.e. 1 != 1.0
     - nans are considered to be the "same", although generally nan != nan
@@ -94,7 +94,7 @@ def are_values_different(*values):
         except TypeError as exc:
             str_exc = str(exc)
             # Not implemented in numpy 1.7.1
-            if "not supported" in str_exc or "ot implemented" in str_exc:
+            if "not supported" in str_exc or "not implemented" in str_exc:
                 value0_nans = None
             else:
                 raise

nibabel/cmdline/tck2trk.py

@@ -30,7 +30,7 @@ def main():
     try:
         nii = nib.load(args.anatomy)
     except Exception:
-        parser.error("Expecting anatomical image as first agument.")
+        parser.error("Expecting anatomical image as first argument.")
 
     for tractogram in args.tractograms:
         tractogram_format = nib.streamlines.detect_format(tractogram)

nibabel/dataobj_images.py

@@ -27,9 +27,9 @@ def __init__(self, dataobj, header=None, extra=None, file_map=None):
         Parameters
         ----------
         dataobj : object
-           Object containg image data.  It should be some object that retuns an
-           array from ``np.asanyarray``.  It should have ``shape`` and ``ndim``
-           attributes or properties
+           Object containing image data.  It should be some object that returns
+           an array from ``np.asanyarray``.  It should have ``shape`` and
+           ``ndim`` attributes or properties
         header : None or mapping or header instance, optional
            metadata for this image format
         extra : None or mapping, optional

nibabel/ecat.py

@@ -661,7 +661,7 @@ def data_from_fileobj(self, frame=0, orientation=None):
 
 
 class EcatImageArrayProxy(object):
-    """ Ecat implemention of array proxy protocol
+    """ Ecat implementation of array proxy protocol
 
     The array proxy allows us to freeze the passed fileobj and
     header such that it returns the expected data array.
@@ -989,7 +989,7 @@ def to_file_map(self, file_map=None):
             # Write frame images
             self._write_data(image, imgf, pos + 2, endianness='>')
 
-            # Move to dictionnary offset and write dictionnary entry
+            # Move to dictionary offset and write dictionary entry
             self._write_data(mlist[index], imgf, entry_pos, endianness='>')
 
             entry_pos = entry_pos + 16

nibabel/filename_parser.py

@@ -275,7 +275,7 @@ def splitext_addext(filename,
        Extension, where extension is not in `addexts` - e.g. ``.ext`` in
        example above
     addext : str
-       Any suffixes appearing in `addext` occuring at end of filename
+       Any suffixes appearing in `addext` occurring at end of filename
 
     Examples
     --------

nibabel/fileslice.py

@@ -416,7 +416,7 @@ def optimize_slicer(slicer, dim_len, all_full, is_slowest, stride,
         # full, but reversed
         if slicer == slice(dim_len - 1, None, -1):
             return slice(None), slice(None, None, -1)
-        # Not full, mabye continuous
+        # Not full, maybe continuous
         is_int = False
     else:  # int
         if slicer < 0:  # make negative offsets positive

nibabel/gifti/gifti.py

@@ -331,7 +331,7 @@ class GiftiDataArray(xml.XmlSerializable):
         The Endianness to store the data array.  Should correspond to the
         machine endianness.  Default is system byteorder.
     coordsys : :class:`GiftiCoordSystem` instance
-        Input and output coordinate system with tranformation matrix between
+        Input and output coordinate system with transformation matrix between
         the two.
     ind_ord : int
         The ordering of the array. see util.array_index_order_codes.  Default

nibabel/gifti/tests/test_gifti.py

@@ -146,7 +146,7 @@ def test_dataarray_init():
     pytest.raises(KeyError, gda, datatype='not_datatype')
     # Float32 datatype comes from array if datatype not set
     assert gda(arr).datatype == 16
-    # Can be overriden by init
+    # Can be overridden by init
     assert gda(arr, datatype='uint8').datatype == 2
     # Encoding
     assert gda(encoding=1).encoding == 1

nibabel/imageclasses.py

@@ -125,7 +125,7 @@ def __getitem__(self, *args, **kwargs):
 
 
 def spatial_axes_first(img):
-    """ True if spatial image axes for `img` always preceed other axes
+    """ True if spatial image axes for `img` always precede other axes
 
     Parameters
     ----------
@@ -136,7 +136,7 @@ def spatial_axes_first(img):
     -------
     spatial_axes_first : bool
         True if image only has spatial axes (number of axes < 4) or image type
-        known to have spatial axes preceeding other axes.
+        known to have spatial axes preceding other axes.
     """
     if len(img.shape) < 4:
         return True

nibabel/loadsave.py

@@ -243,7 +243,7 @@ def read_img_data(img, prefer='scaled'):
     array is given by the raw data on disk, multiplied by a scalefactor
     and maybe with the addition of a constant.  This function, with
     ``unscaled`` returns the data on the disk, without these
-    format-specific scalings applied.  Please use this funciton only if
+    format-specific scalings applied.  Please use this function only if
     you absolutely need the unscaled data, and the magnitude of the
     data, as given by the scalefactor, is not relevant to your
     application.  The Analyze-type formats have a single scalefactor +/-