BUG: skip doctests in example code

Author: MichielCottaar

nibabel/cifti2/cifti2_axes.py

@@ -49,9 +49,10 @@
 We can create brain models covering the left cortex and left thalamus using:
 
 >>> from nibabel import cifti2
->>> bm_cortex = cifti2.BrainModel.from_mask(cortex_mask, brain_structure='cortex_left')
+>>> bm_cortex = cifti2.BrainModel.from_mask(cortex_mask,
+...                                         brain_structure='cortex_left') # doctest: +SKIP
 >>> bm_thal = cifti2.BrainModel.from_mask(thalamus_mask, affine=affine,
-                                          brain_structure='thalamus_left')
+...                                       brain_structure='thalamus_left') # doctest: +SKIP
 
 Brain structure names automatically get converted to valid CIfTI2 indentifiers using
 :meth:`BrainModel.to_cifti_brain_structure_name`.
@@ -61,20 +62,20 @@
 These can be concatenated in a single brain model covering the left cortex and thalamus by
 simply adding them together
 
->>> bm_full = bm_cortex + bm_thal
+>>> bm_full = bm_cortex + bm_thal  # doctest: +SKIP
 
 Brain models covering the full HCP grayordinate space can be constructed by adding all the
 volumetric and surface brain models together like this (or by reading one from an already
 existing HCP file).
 
 Getting a specific brain region from the full brain model is as simple as:
 
->>> assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_CORTEX_LEFT'] == bm_cortex
->>> assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_THALAMUS_LEFT'] == bm_thal
+>>> assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_CORTEX_LEFT'] == bm_cortex  # doctest: +SKIP
+>>> assert bm_full[bm_full.name == 'CIFTI_STRUCTURE_THALAMUS_LEFT'] == bm_thal  # doctest: +SKIP
 
 You can also iterate over all brain structures in a brain model:
 
->>> for name, slc, bm in bm_full.iter_structures(): ...
+>>> for name, slc, bm in bm_full.iter_structures(): ...  # doctest: +SKIP
 
 In this case there will be two iterations, namely:
 ('CIFTI_STRUCTURE_CORTEX_LEFT', slice(0, <size of cortex mask>), bm_cortex)
@@ -84,25 +85,26 @@
 Parcels can be constructed from selections of these brain models:
 
 >>> parcel = cifti2.Parcels.from_brain_models([
-        ('surface_parcel', bm_cortex[:100]),  # contains first 100 cortical vertices
-        ('volume_parcel', bm_thal),  # contains thalamus
-        ('combined_parcel', bm_full[[1, 8, 10, 50, 120, 127])  # contains selected voxels/vertices
-    ])
+...        ('surface_parcel', bm_cortex[:100]),  # contains first 100 cortical vertices
+...        ('volume_parcel', bm_thal),  # contains thalamus
+...        ('combined_parcel', bm_full[[1, 8, 10, 50, 120, 127])  # contains selected voxels/vertices
+...    ])  # doctest: +SKIP
 
 Time series are represented by their starting time (typically 0), step size
 (i.e. sampling time or TR), and number of elements:
 
->>> series = cifti2.Series(start=0, step=100, size=5000)
+>>> series = cifti2.Series(start=0, step=100, size=5000)  # doctest: +SKIP
 
 So a header for fMRI data with a TR of 100 ms covering the left cortex and thalamus with
 5000 timepoints could be created with
 
->>> cifti2.Cifti2Header.from_axes((series, bm_cortex + bm_thal))
+>>> cifti2.Cifti2Header.from_axes((series, bm_cortex + bm_thal))  # doctest: +SKIP
 
 Similarly the curvature and cortical thickness on the left cortex could be stored using a header
 like:
 
->>> cifti2.Cifti2Header.from_axes((cifti.Scalar(['curvature', 'thickness'], bm_cortex))
+>>> cifti2.Cifti2Header.from_axes((cifti.Scalar(['curvature', 'thickness'],
+...                                             bm_cortex))  # doctest: +SKIP
 """
 import numpy as np
 from . import cifti2