From f80fd3213c164b251e1cbe07c3284ac569ed9a5b Mon Sep 17 00:00:00 2001 From: Chris Bridge Date: Wed, 21 Jun 2023 06:41:05 -0400 Subject: [PATCH] Fix codespell errors --- docs/generalsr.rst | 4 ++-- docs/quickstart.rst | 2 +- docs/seg.rst | 10 +++++----- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/generalsr.rst b/docs/generalsr.rst index 1b527af6..c5de763c 100644 --- a/docs/generalsr.rst +++ b/docs/generalsr.rst @@ -107,7 +107,7 @@ image in the report. Scoord (spatial coordinate) Content Items describe locations in 2D image coordinates. Image coordinates are decimal numbers with sub-pixel accuracy that are defined in a coordinate system from (0.0, 0.0) at the top left corner of -the top left pixel of the image and (rows, colums) at the bottom right corner +the top left pixel of the image and (rows, columns) at the bottom right corner of the bottom right pixel of the image. I.e. the center of the top left pixel is at location (0.5, 0.5). @@ -249,7 +249,7 @@ Content Items like this: relationship_type=hd.sr.RelationshipTypeValues.INFERRED_FROM, ) - # A tracking identifier identifying the measurment + # A tracking identifier identifying the measurement tracking_item = hd.sr.UIDRefContentItem( name=codes.DCM.TrackingIdentifier, value=hd.UID(), # a newly generated UID diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 57e4442f..ca4732a9 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -334,7 +334,7 @@ Parsing Structured Report (SR) documents ---------------------------------------- Highdicom has special support for parsing structured reports conforming to the -TID1500 "Measurment Report" template using specialized Python classes for +TID1500 "Measurement Report" template using specialized Python classes for templates. .. code-block:: python diff --git a/docs/seg.rst b/docs/seg.rst index 86b0c724..ebe73871 100644 --- a/docs/seg.rst +++ b/docs/seg.rst @@ -146,7 +146,7 @@ to 1. A second attribute, "Segmentation Fractional Type" (0062,0010) specifies how these values should be interpreted. There are two options, represented by the enumerated type :class:`highdicom.seg.SegmentationFractionalTypeValues`: -- ``"PROBABILITY"``, i.e. the number between 0 and 1 respresents a probability +- ``"PROBABILITY"``, i.e. the number between 0 and 1 represents a probability that a pixel belongs to the segment - ``"OCCUPANCY"`` i.e. the number represents the fraction of the volume of the pixel's (or voxel's) area (or volume) that belongs to the segment @@ -292,8 +292,8 @@ a convenient shorthand for the special case where there is only a single source frame and a single segment. It is equivalent in every way to passing a 3D array with a single frame down axis 0. -Constructing Binary SEG Images of Multiframe Souce Images ---------------------------------------------------------- +Constructing Binary SEG Images of Multiframe Source Images +---------------------------------------------------------- Alternatively, we could create a segmentation of a source image that is itself a multiframe image (such as an Enhanced CT, Enhanced MR image, or a Whole Slide @@ -714,12 +714,12 @@ frames are stored within the SEG as an array indexed by a frame number SEG a lot of freedom about how to organize the resulting frames within the 1D list within the SEG. To complicate matters further, frames in the segmentation image that would otherwise be "empty" (contain only 0s) may be omitted from the -SEG image entirely (this is `highdicom`'s default behavior but cant be turned +SEG image entirely (this is `highdicom`'s default behavior but can be turned off if you prefer by specifying ``omit_empty_frames=False`` in the constructor). Every ``pydicom.Dataset`` has the ``.pixel_array`` property, which, in the case of a multiframe image, returns the full list of frames in the image as an array -of shape (frames x rows x colums), with frames organized in whatever manner +of shape (frames x rows x columns), with frames organized in whatever manner they were organized in by the creator of the object. A :class:`highdicom.seg.Segmentation` is a sub-class of ``pydicom.Dataset``, and therefore also has the ``.pixel_array`` property. However, given the