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Merge pull request #1244 from DimitriPapadopoulos/codespell
DOC: Fix typos found by codespell
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doc/source/dicom/dicom_intro.rst

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@@ -228,22 +228,22 @@ Here is the start of the relevant section from PS 3.5:
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7.8.1 PRIVATE DATA ELEMENT TAGS
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It is possible that multiple implementors may define Private Elements with the
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It is possible that multiple implementers may define Private Elements with the
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same (odd) group number. To avoid conflicts, Private Elements shall be
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assigned Private Data Element Tags according to the following rules.
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a) Private Creator Data Elements numbered (gggg,0010-00FF) (gggg is odd) shall
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be used to reserve a block of Elements with Group Number gggg for use by an
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individual implementor. The implementor shall insert an identification code
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individual implementer. The implementer shall insert an identification code
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in the first unused (unassigned) Element in this series to reserve a block of
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Private Elements. The VR of the private identification code shall be LO (Long
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String) and the VM shall be equal to 1.
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b) Private Creator Data Element (gggg,0010), is a Type 1 Data Element that
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identifies the implementor reserving element (gggg,1000-10FF), Private Creator
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Data Element (gggg,0011) identifies the implementor reserving elements
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identifies the implementer reserving element (gggg,1000-10FF), Private Creator
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Data Element (gggg,0011) identifies the implementer reserving elements
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(gggg,1100-11FF), and so on, until Private Creator Data Element (gggg,00FF)
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identifies the implementor reserving elements (gggg,FF00- FFFF).
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identifies the implementer reserving elements (gggg,FF00- FFFF).
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c) Encoders of Private Data Elements shall be able to dynamically assign
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private data to any available (unreserved) block(s) within the Private group,

doc/source/external/nifti1.h

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@@ -869,7 +869,7 @@ typedef struct { unsigned char r,g,b; } rgb_byte ;
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as a displacement field or vector:
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- dataset must have a 5th dimension
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- intent_code must be NIFTI_INTENT_DISPVECT
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- dim[5] must be the dimensionality of the displacment
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- dim[5] must be the dimensionality of the displacement
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vector (e.g., 3 for spatial displacement, 2 for in-plane) */
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#define NIFTI_INTENT_DISPVECT 1006 /* specifically for displacements */

doc/source/old/format_design.txt

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@@ -13,7 +13,7 @@ The Image and the Format objects form a `bridge pattern
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diagram
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<https://en.wikipedia.org/wiki/File:Bridge_UML_class_diagram.svg>`_ the
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Image class plays the role of the Abstraction, and the Format plays the
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role of the implementor.
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role of the implementer.
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The Format object provides an interface to the underlying file format.
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nibabel/cifti2/cifti2_axes.py

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@@ -59,7 +59,7 @@
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In this very simple case ``bm_cortex`` describes a left cortical surface skipping the second
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out of four vertices. ``bm_thal`` contains all voxels in a 2x2x2 volume.
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Brain structure names automatically get converted to valid CIFTI-2 indentifiers using
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Brain structure names automatically get converted to valid CIFTI-2 identifiers using
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:meth:`BrainModelAxis.to_cifti_brain_structure_name`.
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A 1-dimensional mask will be automatically interpreted as a surface element and a 3-dimensional
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mask as a volume element.

nibabel/tests/test_loadsave.py

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@@ -192,7 +192,7 @@ def test_read_img_data_nifti():
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assert_array_equal(actual_unscaled, read_img_data(img_back, prefer='unscaled'))
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# Check the offset too
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img.header.set_data_offset(1024)
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# Delete arrays still pointing to file, so Windows can re-use
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# Delete arrays still pointing to file, so Windows can reuse
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del actual_unscaled, unscaled_back
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img.to_file_map()
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# Write an integer of zeros after

nibabel/tests/test_nifti1.py

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@@ -1169,7 +1169,7 @@ def test_dynamic_dtype_aliases(self):
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assert img.get_data_dtype() == alias
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img_rt = bytesio_round_trip(img)
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assert img_rt.get_data_dtype() == effective_dt
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# Seralizing does not finalize the source image
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# Serializing does not finalize the source image
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assert img.get_data_dtype() == alias
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def test_static_dtype_aliases(self):

nibabel/tests/test_processing.py

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@@ -222,7 +222,7 @@ def test_resample_from_to(caplog):
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@needs_scipy
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def test_resample_to_output(caplog):
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# Test routine to sample iamges to output space
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# Test routine to sample images to output space
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# Image aligned to output axes - no-op
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data = np.arange(24, dtype='int32').reshape((2, 3, 4))
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img = Nifti1Image(data, np.eye(4))

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