examples updated to pass doctests, might need change ImagingDataCommons#126

Author: shishirpy

src/highdicom/seg/sop.py

@@ -1478,28 +1478,33 @@ def get_segment_numbers(
         generated by an automatic algorithm from a segmentation object ``seg``:
 
         >>> from pydicom.sr.codedict import codes
-        >>> from highdicom.seg import SegmentAlgorithmTypeValues
-        >>>
+        >>> from highdicom.seg import SegmentAlgorithmTypeValues, Segmentation
+        >>> from pydicom import dcmread
+        >>> ds = dcmread('data/test_files/seg_image_ct_true_fractional.dcm')
+        >>> seg = Segmentation.from_dataset(ds)
         >>> segment_numbers = seg.get_segment_numbers(
-        >>>     segmented_property_type=codes.SCT.Tumor,
-        >>>     algorithm_type=SegmentAlgorithmTypeValues.AUTOMATIC
-        >>> )
-        [1, 2, 3]
+        ...     segmented_property_type=codes.SCT.Tumor,
+        ...     algorithm_type=SegmentAlgorithmTypeValues.AUTOMATIC
+        ... )
+        >>> segment_numbers
+        []
 
         Get segment numbers of all segments identified by a given
         institution-specific tracking ID:
 
         >>> segment_numbers = seg.get_segment_numbers(
-        >>>     tracking_id='Tumor #1234567'
-        >>> )
-        [13]
+        ...     tracking_id='Tumor #1234567'
+        ... )
+        >>> segment_numbers
+        []
 
         Get segment numbers of all segments identified a globally unique
         tracking UID:
 
         >>> uid = '1.2.826.0.1.3680043.10.511.3.73025483745501512180439199223117347'
         >>> segment_numbers = seg.get_segment_numbers(tracking_uid=uid)
-        [5]
+        >>> segment_numbers
+        []
 
         """  # noqa: E501
         filter_funcs = []
@@ -1583,13 +1588,12 @@ def get_tracking_ids(
 
         List the tracking IDs and UIDs present in the segmentation image:
 
-        >>> seg.get_tracking_ids()
-        [('Spine', '1.2.826.0.1.3680043.10.511.3.10042414969629429693880339016394772'),
-         ('Bone', '1.2.826.0.1.3680043.10.511.3.83271046815894549094043330632275067')]
+        >>> sorted(seg.get_tracking_ids(), reverse=True)  # otherwise its a random order
+        [('Spine', '1.2.826.0.1.3680043.10.511.3.10042414969629429693880339016394772'), ('Bone', '1.2.826.0.1.3680043.10.511.3.83271046815894549094043330632275067')]
 
         >>> for seg_num in seg.segment_numbers:
-        >>>     desc = seg.get_segment_description(seg_num)
-        >>>     print(desc.segmented_property_type.meaning)
+        ...     desc = seg.get_segment_description(seg_num)
+        ...     print(desc.segmented_property_type.meaning)
         Bone
         Spine
 
@@ -2086,7 +2090,7 @@ def get_pixels_by_source_instance(
         List the source images for this segmentation:
 
         >>> for study_uid, series_uid, sop_uid in seg.get_source_image_uids():
-        >>>     print(sop_uid)
+        ...     print(sop_uid)
         1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.93
         1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.94
         1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.95
@@ -2095,11 +2099,11 @@ def get_pixels_by_source_instance(
         Get the segmentation array for a subset of these images:
 
         >>> pixels = seg.get_pixels_by_source_instance(
-        >>>     source_sop_instance_uids=[
-        >>>         '1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.93',
-        >>>         '1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.94'
-        >>>     ]
-        >>> )
+        ...     source_sop_instance_uids=[
+        ...         '1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.93',
+        ...         '1.3.6.1.4.1.5962.1.1.0.0.0.1196530851.28319.0.94'
+        ...     ]
+        ... )
         >>> pixels.shape
         (2, 16, 16, 1)
 
@@ -2315,53 +2319,54 @@ def get_pixels_by_source_frame(
         List the source image SOP instance UID for this segmentation:
 
         >>> sop_uid = seg.get_source_image_uids()[0][2]
-        '1.2.826.0.1.3680043.9.7433.3.12857516184849951143044513877282227'
+        
+        # '1.2.826.0.1.3680043.9.7433.3.12857516184849951143044513877282227'
 
         Get the segmentation array for 3 of the frames in the multiframe source
         image.  The resulting segmentation array has 3 10 x 10 frames, one for
         each source frame. The final dimension contains the 20 different
         segments present in this segmentation.
 
         >>> pixels = seg.get_pixels_by_source_frame(
-        >>>     source_sop_instance_uid=sop_uid,
-        >>>     source_frame_numbers=[4, 5, 6]
-        >>> )
+        ...     source_sop_instance_uid=sop_uid,
+        ...     source_frame_numbers=[4, 5, 6]
+        ... )
         >>> pixels.shape
         (3, 10, 10, 20)
 
         This time, select only 4 of the 20 segments:
 
         >>> pixels = seg.get_pixels_by_source_frame(
-        >>>     source_sop_instance_uid=sop_uid,
-        >>>     source_frame_numbers=[4, 5, 6],
-        >>>     segment_numbers=[10, 11, 12, 13]
-        >>> )
+        ...     source_sop_instance_uid=sop_uid,
+        ...     source_frame_numbers=[4, 5, 6],
+        ...     segment_numbers=[10, 11, 12, 13]
+        ... )
         >>> pixels.shape
         (3, 10, 10, 4)
 
         Instead create a multiclass label map for each source frame. Note
         that segments 6, 8, and 10 are present in the three chosen frames.
 
         >>> pixels = seg.get_pixels_by_source_frame(
-        >>>     source_sop_instance_uid=sop_uid,
-        >>>     source_frame_numbers=[4, 5, 6],
-        >>>     combine_segments=True
-        >>> )
+        ...     source_sop_instance_uid=sop_uid,
+        ...     source_frame_numbers=[4, 5, 6],
+        ...     combine_segments=True
+        ... )
         >>> pixels.shape, np.unique(pixels)
-        (3, 10, 10), array([0, 6, 8, 10])
+        ((3, 10, 10), array([ 0,  6,  8, 10]))
 
         Now relabel the segments to give a pixel map with values between 0
         and 3 (inclusive):
 
         >>> pixels = seg.get_pixels_by_source_frame(
-        >>>     source_sop_instance_uid=sop_uid,
-        >>>     source_frame_numbers=[4, 5, 6],
-        >>>     segment_numbers=[6, 8, 10]
-        >>>     combine_segments=True,
-        >>>     relabel=True
-        >>> )
+        ...     source_sop_instance_uid=sop_uid,
+        ...     source_frame_numbers=[4, 5, 6],
+        ...     segment_numbers=[6, 8, 10],
+        ...     combine_segments=True,
+        ...     relabel=True
+        ... )
         >>> pixels.shape, np.unique(pixels)
-        (3, 10, 10), array([0, 1, 2, 3])
+        ((3, 10, 10), array([0, 1, 2, 3]))
 
         """
         # Check that indexing in this way is possible
@@ -2568,14 +2573,14 @@ def get_pixels_by_dimension_index_values(
         >>> import highdicom as hd
         >>> from pydicom.datadict import keyword_for_tag, tag_for_keyword
         >>> from pydicom import dcmread
-        >>>
+        ...
         >>> ds = dcmread('data/test_files/seg_image_sm_control.dcm')
         >>> seg = hd.seg.Segmentation.from_dataset(ds)
 
         Get the default list of dimension index values
 
         >>> for tag in seg.get_default_dimension_index_pointers():
-        >>>     print(keyword_for_tag(tag))
+        ...     print(keyword_for_tag(tag))
         ColumnPositionInTotalImagePixelMatrix
         RowPositionInTotalImagePixelMatrix
         XOffsetInSlideCoordinateSystem
@@ -2586,18 +2591,18 @@ def get_pixels_by_dimension_index_values(
         Use a subset of these index pointers to index the image
 
         >>> tags = [
-        >>>     tag_for_keyword('ColumnPositionInTotalImagePixelMatrix'),
-        >>>     tag_for_keyword('RowPositionInTotalImagePixelMatrix')
-        >>> ]
+        ...     tag_for_keyword('ColumnPositionInTotalImagePixelMatrix'),
+        ...     tag_for_keyword('RowPositionInTotalImagePixelMatrix')
+        ... ]
         >>> assert seg.are_dimension_indices_unique(tags)  # True
 
         It is therefore possible to index using just this subset of
         dimension indices
 
         >>> pixels = seg.get_pixels_by_dimension_index_values(
-        >>>     dimension_index_pointers=tags,
-        >>>     dimension_index_values=[[1, 1], [1, 2]]
-        >>> )
+        ...     dimension_index_pointers=tags,
+        ...     dimension_index_values=[[1, 1], [1, 2]]
+        ... )
         >>> pixels.shape
         (2, 10, 10, 20)