Merge pull request #209 from MICA-MNI/development

Version 0.2.3

.github/labeler.yml

@@ -3,10 +3,14 @@ Documentation:
   - docs/**/*
   - ./*.rst
   - ./*.md
+  - LICENSE
+  - .readthedocs.yml
 
 Python:
   - brainstat/*
   - brainstat/**/*
+  - setup.py
+  - requirements.txt
 
 MATLAB:
   - brainstat_matlab/*

.github/workflows/python_unittests.yml

@@ -4,7 +4,6 @@ on:
   push:
     branches:
       - master
-      - development
   pull_request:
 
 jobs:
@@ -13,7 +12,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
         os: [ubuntu-latest, windows-latest, macos-latest]
 
     runs-on: ${{ matrix.os }}
@@ -29,9 +28,7 @@ jobs:
       run: | 
         [[ -z $(git remote show origin | grep "Fetch URL:" | grep [email protected]:MICA-MNI/BrainStat.git) ]] && git config remote.upstream.fetch refs/heads/*:refs/remotes/upstream/* || git config remote.origin.fetch refs/heads/*:refs/origin/upstream/*
         git fetch origin test-data-2.0
-        python -m pip install --upgrade pip
-        pip install pytest mypy gitpython
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        python -m pip install -e .[dev]
 
     - name: Test with pytest
       shell: bash

.github/workflows/tagged_release.yml

@@ -32,7 +32,7 @@ jobs:
 
     - name: Modify MATLAB version and path
       run: |
-        current_release=$(cat setup.py | grep "version=" | grep -Eo "[0-9]\.[0-9]\.[0-9]")
+        current_release=$(cat brainstat/__init__.py | grep "__version__ =" | grep -Eo "[0-9]\.[0-9]\.[0-9]")
         brainstat_dir=$(pwd)
         sed -i "s:<param\.version>.*</param\.version>:<param\.version>${current_release}</param\.version>:" .github/matlab_toolbox/BrainStat.prj
         sed -i "s:BRAINSTAT_DIR/:${brainstat_dir}/:g" .github/matlab_toolbox/BrainStat.prj

CONTRIBUTING.rst

@@ -1,5 +1,5 @@
 Contributing to BrainStat
-====================
+=========================
 
 .. start-marker-cont
 

brainstat/__init__.py

@@ -1 +1,12 @@
 """Neuroimaging statistics toolbox."""
+import sys
+import warnings
+
+__version__ = "0.2.3"
+
+if sys.version_info[1] == 6:
+    warnings.simplefilter("always", DeprecationWarning)
+    warnings.warn(
+        "Support for Python3.6 has been dropped. Future versions may not install on Python3.6.",
+        DeprecationWarning,
+    )

brainstat/context/genetics.py

@@ -1,14 +1,14 @@
 """Genetic decoding using abagen."""
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Sequence, Union
 
 import numpy as np
 import pandas as pd
 from abagen import check_atlas, get_expression_data
 
 
 def surface_genetic_expression(
-    labels: Union[List[str], np.ndarray],
-    surfaces: Union[List[str], Tuple[str, ...]] = None,
+    labels: Union[Sequence[str], np.ndarray],
+    surfaces: Optional[Union[str, Sequence[str]]] = None,
     space: Optional[str] = None,
     *,
     atlas_info: str = None,

brainstat/context/meta_analysis.py

@@ -3,38 +3,44 @@
 import os
 import tempfile
 from pathlib import Path
+from typing import Optional, Sequence, Tuple, Union
 
 import nimare
+import numpy as np
+import pandas as pd
+from brainspace.vtk_interface.wrappers.data_object import BSPolyData
 from neurosynth.base.dataset import Dataset, download
+from nibabel.nifti1 import NiftiImage
 from nilearn.datasets import load_mni152_brain_mask
+from nilearn.input_data import NiftiMasker
 
 from .utils import multi_surface_to_volume
 
 
 def surface_decode_nimare(
-    pial,
-    white,
-    stat_labels,
-    mask_labels,
-    interpolation="linear",
-    data_dir=None,
-    feature_group=None,
-    features=None,
-):
+    pial: Union[str, BSPolyData, Sequence[Union[str, BSPolyData]]],
+    white: Union[str, BSPolyData, Sequence[Union[str, BSPolyData]]],
+    stat_labels: Union[str, np.ndarray, Sequence[Union[str, np.ndarray]]],
+    mask_labels: Union[str, np.ndarray, Sequence[Union[str, np.ndarray]]],
+    interpolation: str = "linear",
+    data_dir: str = None,
+    feature_group: str = None,
+    features: Sequence[str] = None,
+) -> pd.DataFrame:
     """Meta-analytic decoding of surface maps using NeuroSynth or Brainmap.
 
     Parameters
     ----------
-    pial : str, BSPolyData, list
+    pial : str, BSPolyData, sequence of str or BSPolyData
         Path of a pial surface file, BSPolyData of a pial surface or a list
         containing multiple of the aforementioned.
-    white : str, BSPolyData, list
+    white : str, BSPolyData, sequence of str or BSPolyData
         Path of a white matter surface file, BSPolyData of a pial surface or a
         list containing multiple of the aforementioned.
-    stat_labels : str, numpy.ndarray, list
+    stat_labels : str, numpy.ndarray, sequence of str or numpy.ndarray
         Path to a label file for the surfaces, numpy array containing the
         labels, or a list containing multiple of the aforementioned.
-    mask_labels : str, numpy.ndarray, list
+    mask_labels : str, numpy.ndarray, sequence of str of or numpy.ndarray
         Path to a mask file for the surfaces, numpy array containing the
         mask, or a list containing multiple of the aforementioned. If None
         all vertices are included in the mask. Defaults to None.
@@ -66,20 +72,20 @@ def surface_decode_nimare(
     mask_image = tempfile.NamedTemporaryFile(suffix=".nii.gz")
 
     multi_surface_to_volume(
-        pial,
-        white,
-        mni152,
-        stat_labels,
-        stat_image.name,
+        pial=pial,
+        white=white,
+        volume_template=mni152,
+        output_file=stat_image.name,
+        labels=stat_labels,
         interpolation=interpolation,
     )
     multi_surface_to_volume(
-        pial,
-        white,
-        mni152,
-        mask_labels,
-        mask_image.name,
-        interpolation="nearest",
+        pial=pial,
+        white=white,
+        volume_template=mni152,
+        output_file=mask_image.name,
+        labels=mask_labels,
+        interpolation=interpolation,
     )
 
     dataset = fetch_nimare_dataset(data_dir, mask=mask_image.name, keep_neurosynth=True)
@@ -96,7 +102,11 @@ def surface_decode_nimare(
     return decoder.transform(stat_image.name)
 
 
-def fetch_nimare_dataset(data_dir, mask=None, keep_neurosynth=True):
+def fetch_nimare_dataset(
+    data_dir: str,
+    mask: Optional[Union[str, NiftiImage, NiftiMasker]] = None,
+    keep_neurosynth: bool = True,
+) -> str:
     """Downloads the nimare dataset and fetches its path.
 
     Parameters
@@ -126,7 +136,7 @@ def fetch_nimare_dataset(data_dir, mask=None, keep_neurosynth=True):
         D = tempfile.TemporaryDirectory()
         ns_dir = D.name
 
-    ns_data_file, ns_feature_file = fetch_neurosynth_dataset(ns_dir, return_pkl=False)
+    ns_data_file, ns_feature_file = fetch_neurosynth_dataset(ns_dir, return_pkl=False)  # type: ignore
 
     ns_dict = nimare.io.convert_neurosynth_to_dict(
         ns_data_file, annotations_file=ns_feature_file
@@ -138,7 +148,9 @@ def fetch_nimare_dataset(data_dir, mask=None, keep_neurosynth=True):
     return dset
 
 
-def fetch_neurosynth_dataset(data_dir, return_pkl=True):
+def fetch_neurosynth_dataset(
+    data_dir: str, return_pkl: bool = True
+) -> Union[Tuple[str, str], str]:
     """Downloads the Neurosynth dataset
 
     Parameters

brainstat/context/utils.py

@@ -4,7 +4,7 @@
 import os
 import shutil
 import tempfile
-from typing import List, Tuple, Union
+from typing import List, Sequence, Tuple, Union
 
 import nibabel as nib
 import numpy as np
@@ -26,7 +26,7 @@ def multi_surface_to_volume(
     white: valid_surfaces,
     volume_template: Union[str, nib.nifti1.Nifti1Image],
     output_file: str,
-    labels: Union[str, np.ndarray, List[Union[np.ndarray, str]]],
+    labels: Union[str, np.ndarray, Sequence[Union[np.ndarray, str]]],
     interpolation: str = "nearest",
 ) -> None:
     """Interpolates multiple surfaces to the volume.
@@ -39,14 +39,14 @@ def multi_surface_to_volume(
     white : str, BSPolyData, list, tuple
         Path of a white matter surface file, BSPolyData of a pial surface or a
         list containing multiple of the aforementioned.
-    labels : str, numpy.ndarray, list, tuple
-        Path to a label file for the surfaces, numpy array containing the
-        labels, or a list containing multiple of the aforementioned.
-    output_file: str
-        Path to the output file, must end in .nii or .nii.gz.
     volume_template : str, nibabel.nifti1.Nifti1Image
         Path to a nifti file to use as a template for the surface to volume
         procedure, or a loaded NIfTI image.
+    output_file: str
+        Path to the output file, must end in .nii or .nii.gz.
+    labels : str, numpy.ndarray, list, tuple
+        Path to a label file for the surfaces, numpy array containing the
+        labels, or a list containing multiple of the aforementioned.
     interpolation : str
         Either 'nearest' for nearest neighbor interpolation, or 'linear'
         for trilinear interpolation, defaults to 'nearest'.
@@ -60,14 +60,14 @@ def multi_surface_to_volume(
 
     # Deal with variety of ways to provide input.
     if type(pial) is not type(white):
-        ValueError("Pial and white must be of the same type.")
+        raise ValueError("Pial and white must be of the same type.")
 
     pial_list = _input_to_list(pial)
     white_list = _input_to_list(white)
     labels_list = _input_to_list(labels)
 
     if len(pial_list) is not len(white):
-        ValueError("The same number of pial and white surfces must be provided.")
+        raise ValueError("The same number of pial and white surfces must be provided.")
 
     for i in range(len(pial_list)):
         if not isinstance(pial_list[i], BSPolyData):
@@ -154,7 +154,7 @@ def load_mesh_labels(label_file: str, as_int: bool = True) -> np.ndarray:
     elif label_file.endswith(".csv"):
         labels = np.loadtxt(label_file)
     else:
-        ValueError("Unrecognized label file type.")
+        raise ValueError("Unrecognized label file type.")
 
     if as_int:
         labels = np.round(labels).astype(int)

brainstat/datasets/base.py

@@ -12,9 +12,9 @@
 
 
 def fetch_parcellation(
+    template: str,
     atlas: str,
     n_regions: int,
-    template: str = "fsaverage5",
     join: bool = True,
     seven_networks: bool = True,
     data_dir: Optional[str] = None,
@@ -23,15 +23,15 @@ def fetch_parcellation(
 
     Parameters
     ----------
+    template : str,
+        The surface template. Valid values are "fsaverage", "fsaverage5",
+        "fsaverage6", "fslr32k", by default "fsaverage5".
     atlas : str
         Name of the atlas. Valid names are "schaefer", "cammoun".
     n_regions : int
         Number of regions of the requested atlas. Valid values for the "schaefer " atlas are
         100, 200, 300, 400, 500, 600, 800, 1000. Valid values for the cammoun atlas are 33,
         60, 125, 250, 500.
-    template : str, optional
-        The surface template. Valid values are "fsaverage", "fsaverage5",
-        "fsaverage6", "fslr32k", by default "fsaverage5".
     join : bool, optional
         If true, returns parcellation as a single array, if false, returns an
         array per hemisphere, by default True.