More work.

src/smripost_linc/utils/boilerplate.py

@@ -0,0 +1,45 @@
+"""Functions for generating boilerplate text."""
+
+
+def describe_atlases(atlases):
+    """Build a text description of the atlases that will be used."""
+    from smripost_linc.utils.utils import list_to_str
+
+    atlas_descriptions = {
+        'Glasser': 'the Glasser atlas [@Glasser_2016]',
+        'Gordon': 'the Gordon atlas [@Gordon_2014]',
+        'Tian': 'the Tian subcortical atlas [@tian2020topographic]',
+        'HCP': 'the HCP CIFTI subcortical atlas [@glasser2013minimal]',
+        'MIDB': (
+            'the MIDB precision brain atlas derived from ABCD data and thresholded at 75% '
+            'probability [@hermosillo2022precision]'
+        ),
+        'MyersLabonte': (
+            'the Myers-Labonte infant atlas thresholded at 50% probability [@myers2023functional]'
+        ),
+    }
+
+    atlas_strings = []
+    described_atlases = []
+    atlases_4s = [atlas for atlas in atlases if str(atlas).startswith('4S')]
+    described_atlases += atlases_4s
+    if atlases_4s:
+        parcels = [int(str(atlas[2:-7])) for atlas in atlases_4s]
+        s = (
+            'the Schaefer Supplemented with Subcortical Structures (4S) atlas '
+            '[@Schaefer_2017;@pauli2018high;@king2019functional;@najdenovska2018vivo;'
+            '@glasser2013minimal] '
+            f'at {len(atlases_4s)} different resolutions ({list_to_str(parcels)} parcels)'
+        )
+        atlas_strings.append(s)
+
+    for k, v in atlas_descriptions.items():
+        if k in atlases:
+            atlas_strings.append(v)
+            described_atlases.append(k)
+
+    undescribed_atlases = [atlas for atlas in atlases if atlas not in described_atlases]
+    for atlas in undescribed_atlases:
+        atlas_strings.append(f'the {atlas} atlas')
+
+    return list_to_str(atlas_strings)

src/smripost_linc/utils/utils.py

@@ -227,3 +227,17 @@ def _convert_to_tsv(in_file):
     arr = np.loadtxt(in_file)
     np.savetxt(out_file, arr, delimiter='\t')
     return out_file
+
+
+def list_to_str(lst):
+    """Convert a list to a pretty string."""
+    if not lst:
+        raise ValueError('Zero-length list provided.')
+
+    lst_str = [str(item) for item in lst]
+    if len(lst_str) == 1:
+        return lst_str[0]
+    elif len(lst_str) == 2:
+        return ' and '.join(lst_str)
+    else:
+        return f"{', '.join(lst_str[:-1])}, and {lst_str[-1]}"

src/smripost_linc/workflows/parcellation.py

@@ -11,107 +11,6 @@
 from smripost_linc.interfaces.bids import BIDSURI
 
 
-def remove_non_alphabetic(input_string):
-    """Use regular expression to remove non-alphabetic characters."""
-    import re
-
-    clean_string = re.sub(r'[^a-zA-Z_0-9]', '', input_string.replace(' ', '_'))
-    return clean_string
-
-
-def fake_neuroparc_from_nifti(nifti_file):
-    """Create a fake neuroparc JSON from a nifti file."""
-    import nibabel as nb
-    import numpy as np
-
-    img = nb.load(nifti_file)
-    unique_labels = np.unique(img.get_fdata().astype(np.int32))
-    return {str(label): {} for label in unique_labels}
-
-
-def fill_missing_parc(spec):
-    maxval = max(map(int, spec.keys()))
-    for key in range(maxval):
-        strkey = str(key)
-        if strkey not in spec:
-            spec[strkey] = {'label': 'Unknown'}
-    return spec
-
-
-def ctab_from_neuroparc_json(neuroparc_json_file=None, atlas_nifti_file=None):
-    """Extract colors and labels from a neuroparc JSON file."""
-    import json
-
-    if neuroparc_json_file is not None:
-        with open(neuroparc_json_file) as jsonf:
-            initial_specs = json.load(jsonf)
-            if 'MetaData' in initial_specs:
-                del initial_specs['MetaData']
-    else:
-        initial_specs = fake_neuroparc_from_nifti(atlas_nifti_file)
-
-    parc_specs = fill_missing_parc(initial_specs)
-    # Get a mapping from ints to label names from neuroparc
-    int_to_label_map = {}
-    for key, value in parc_specs.items():
-        if not key.isnumeric():
-            continue
-        key = int(key)
-        label = value.get('label') or f'region{key:05d}'
-        int_to_label_map[key] = label
-
-    int_to_label_map[0] = 'Unknown'
-    labels = [
-        remove_non_alphabetic(int_to_label_map[key]) for key in sorted(int_to_label_map.keys())
-    ]
-    colors = _create_colors(len(labels))
-    return colors, labels
-
-
-def create_annots(nifti_file, atlas, json_file=None):
-    """Create .annot files from a nifti file and a json file."""
-    import nibabel as nb
-    import numpy as np
-    from neuromaps import transforms
-
-    lh_gii, rh_gii = transforms.mni152_to_fsaverage(
-        nifti_file,
-        fsavg_density='164k',
-        method='nearest',
-    )
-    colors, names = ctab_from_neuroparc_json(
-        neuroparc_json_file=json_file,
-        atlas_nifti_file=nifti_file,
-    )
-    lh_annot = f'annots/lh.{atlas}.annot'
-    nb.freesurfer.write_annot(
-        lh_annot,
-        labels=lh_gii.agg_data().astype(np.int32),
-        ctab=colors,
-        names=names,
-        fill_ctab=True,
-    )
-    rh_annot = f'annots/rh.{atlas}.annot'
-    nb.freesurfer.write_annot(
-        rh_annot,
-        labels=rh_gii.agg_data().astype(np.int32),
-        ctab=colors,
-        names=names,
-        fill_ctab=True,
-    )
-    return lh_annot, rh_annot
-
-
-def select_first(inlist):
-    """Select the first element of a list."""
-    return inlist[0]
-
-
-def select_second(inlist):
-    """Select the second element of a list."""
-    return inlist[1]
-
-
 def init_load_atlases_wf(name='load_atlases_wf'):
     """Load atlases, warp them to fsnative, and convert them to annot files.