Merge pull request #164 from netneurolab/update_register_cmap

[FIX] Fix import error due to deprecated matplotlib function

.github/workflows/tests.yml

@@ -23,7 +23,7 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install ruff
       - name: Run style checks
-        run: ruff .
+        run: ruff check .
   codespell:
     runs-on: ubuntu-latest
     steps:

neuromaps/datasets/atlases.py

@@ -127,7 +127,7 @@ def _fetch_atlas(atlas, density, keys, url=None, data_dir=None, verbose=1):
     return _bunch_outputs(keys, data, atlas != 'MNI152')
 
 
-def fetch_civet(density='41k', url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_civet(density='41k', url=None, data_dir=None, verbose=1):  # noqa: D103
     keys = ['white', 'midthickness', 'inflated', 'veryinflated', 'sphere']
     return _fetch_atlas(
         'civet', density, keys, url=url, data_dir=data_dir, verbose=verbose
@@ -151,7 +151,7 @@ def fetch_civet(density='41k', url=None, data_dir=None, verbose=1): # noqa: D103
 """.format(**_atlas_docs, densities="', '".join(DENSITIES['civet']))
 
 
-def fetch_fsaverage(density='41k', url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_fsaverage(density='41k', url=None, data_dir=None, verbose=1):  # noqa: D103
     keys = ['white', 'pial', 'inflated', 'sphere']
     return _fetch_atlas(
         'fsaverage', density, keys, url=url, data_dir=data_dir, verbose=verbose
@@ -175,7 +175,7 @@ def fetch_fsaverage(density='41k', url=None, data_dir=None, verbose=1): # noqa:
 """.format(**_atlas_docs, densities="', '".join(DENSITIES['fsaverage']))
 
 
-def fetch_fslr(density='32k', url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_fslr(density='32k', url=None, data_dir=None, verbose=1):  # noqa: D103
     keys = ['midthickness', 'inflated', 'veryinflated', 'sphere']
     if density in ('4k', '8k'):
         keys.remove('veryinflated')
@@ -201,7 +201,7 @@ def fetch_fslr(density='32k', url=None, data_dir=None, verbose=1): # noqa: D103
 """.format(**_atlas_docs, densities="', '".join(DENSITIES['fsLR']))
 
 
-def fetch_mni152(density='1mm', url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_mni152(density='1mm', url=None, data_dir=None, verbose=1):  # noqa: D103
     keys = ['2009cAsym_T1w', '2009cAsym_T2w', '2009cAsym_PD',
             '2009cAsym_brainmask', '2009cAsym_CSF', '2009cAsym_GM',
             '2009cAsym_WM']
@@ -229,7 +229,7 @@ def fetch_mni152(density='1mm', url=None, data_dir=None, verbose=1): # noqa: D10
 """.format(**_atlas_docs, densities="', '".join(DENSITIES['MNI152']))
 
 
-def fetch_regfusion(atlas, url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_regfusion(atlas, url=None, data_dir=None, verbose=1):  # noqa: D103
     atlas = _sanitize_atlas(atlas)
     densities = DENSITIES[atlas].copy()
     invalid = dict(civet=('164k',), fsLR=('4k', '8k'))
@@ -280,7 +280,7 @@ def fetch_regfusion(atlas, url=None, data_dir=None, verbose=1): # noqa: D103
 """.format(**_atlas_docs)
 
 
-def fetch_atlas(atlas, density, url=None, data_dir=None, verbose=1): # noqa: D103
+def fetch_atlas(atlas, density, url=None, data_dir=None, verbose=1):  # noqa: D103
     atlas = _sanitize_atlas(atlas)
     fetcher = globals()[f'fetch_{atlas.lower()}']
     return fetcher(density, url=url, data_dir=data_dir, verbose=verbose)
@@ -305,7 +305,7 @@ def fetch_atlas(atlas, density, url=None, data_dir=None, verbose=1): # noqa: D10
 """.format(**_atlas_docs, atlases="', '".join(DENSITIES.keys()))
 
 
-def fetch_all_atlases(data_dir=None, verbose=1): # noqa: D103
+def fetch_all_atlases(data_dir=None, verbose=1):  # noqa: D103
     atlases = {'regfusion': {}}
     for key, resolutions in DENSITIES.items():
         atlases[key] = {}
@@ -334,7 +334,7 @@ def fetch_all_atlases(data_dir=None, verbose=1): # noqa: D103
 """
 
 
-def get_atlas_dir(atlas, data_dir=None): # noqa: D103
+def get_atlas_dir(atlas, data_dir=None):  # noqa: D103
     try:
         atlas = _sanitize_atlas(atlas)
     except ValueError as err:

neuromaps/nulls/nulls.py

@@ -130,7 +130,7 @@
 )
 
 
-def alexander_bloch(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def alexander_bloch(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
                     n_perm=1000, seed=None, spins=None, surfaces=None):
     if spins is None:
         if surfaces is None:
@@ -178,7 +178,7 @@ def alexander_bloch(data, atlas='fsaverage', density='10k', parcellation=None, #
 vazquez_rodriguez = alexander_bloch
 
 
-def vasa(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def vasa(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
          n_perm=1000, seed=None, spins=None, surfaces=None):
     if parcellation is None:
         raise ValueError('Cannot use `vasa()` null method without specifying '
@@ -229,7 +229,7 @@ def vasa(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
 """.format(**_nulls_input_docs)
 
 
-def hungarian(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def hungarian(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
               n_perm=1000, seed=None, spins=None, surfaces=None):
     if parcellation is None:
         raise ValueError('Cannot use `hungarian()` null method without '
@@ -278,7 +278,7 @@ def hungarian(data, atlas='fsaverage', density='10k', parcellation=None, # noqa:
 """.format(**_nulls_input_docs)
 
 
-def baum(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def baum(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
          n_perm=1000, seed=None, spins=None, surfaces=None):
     if parcellation is None:
         raise ValueError('Cannot use `baum()` null method without specifying '
@@ -326,7 +326,7 @@ def baum(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
 """.format(**_nulls_input_docs)
 
 
-def cornblath(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def cornblath(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
               n_perm=1000, seed=None, spins=None, surfaces=None):
     if parcellation is None:
         raise ValueError('Cannot use `cornblath()` null method without '
@@ -370,7 +370,7 @@ def cornblath(data, atlas='fsaverage', density='10k', parcellation=None, # noqa:
 """.format(**_nulls_input_docs)
 
 
-def _get_distmat(hemisphere, atlas='fsaverage', density='10k', # noqa: D103
+def _get_distmat(hemisphere, atlas='fsaverage', density='10k',  # noqa: D103
                  parcellation=None, drop=None, n_proc=1):
     hemi = HEMI.get(hemisphere, hemisphere)
     if hemi not in ('L', 'R'):
@@ -602,7 +602,7 @@ def _make_surrogates(data, method, atlas='fsaverage', density='10k',
 """.format(**_nulls_input_docs)
 
 
-def burt2018(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def burt2018(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
              n_perm=1000, seed=None, distmat=None, tempdir=None, n_proc=1,
              **kwargs):
     return _make_surrogates(data, 'burt2018', atlas=atlas, density=density,
@@ -641,7 +641,7 @@ def burt2018(data, atlas='fsaverage', density='10k', parcellation=None, # noqa:
 """.format(**_nulls_input_docs)
 
 
-def burt2020(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def burt2020(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
              n_perm=1000, seed=None, distmat=None, n_proc=1, tempdir=None,
              **kwargs):
     if not _brainsmash_avail:
@@ -689,7 +689,7 @@ def burt2020(data, atlas='fsaverage', density='10k', parcellation=None, # noqa:
 """.format(**_nulls_input_docs)
 
 
-def moran(data, atlas='fsaverage', density='10k', parcellation=None, # noqa: D103
+def moran(data, atlas='fsaverage', density='10k', parcellation=None,  # noqa: D103
           n_perm=1000, seed=None, distmat=None, tempdir=None, n_proc=1,
           **kwargs):
     if not _brainspace_avail:

neuromaps/nulls/spins.py

@@ -144,7 +144,7 @@ def get_parcel_centroids(surfaces, parcellation=None, method='surface',
             centroids.append(vertices)
             hemiid.extend([n] * len(vertices))
 
-    return np.row_stack(centroids), np.asarray(hemiid)
+    return np.vstack(centroids), np.asarray(hemiid)
 
 
 def _gen_rotation(seed=None):

neuromaps/plotting.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 """Functionality for plotting."""
 
+import matplotlib
 from matplotlib import colors as mcolors, pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D  # noqa
 from nilearn.plotting import plot_surf
@@ -11,11 +12,14 @@
 from neuromaps.transforms import _check_hemi
 
 HEMI = dict(L='left', R='right')
-plt.cm.register_cmap(
-    'caret_blueorange', mcolors.LinearSegmentedColormap.from_list('blend', [
-        '#00d2ff', '#009eff', '#006cfe', '#0043fe',
-        '#fd4604', '#fe6b01', '#ffd100', '#ffff04'
-    ])
+
+matplotlib.colormaps.register(
+    mcolors.LinearSegmentedColormap.from_list(
+        'caret_blueorange', [
+            '#00d2ff', '#009eff', '#006cfe', '#0043fe',
+            '#fd4604', '#fe6b01', '#ffd100', '#ffff04'
+            ]),
+    name="caret_blueorange"
 )
 
 

neuromaps/points.py

@@ -269,7 +269,7 @@ def make_surf_graph(vertices, faces, mask=None):
     # get all (direct + indirect) edges from surface
     direct_edges, direct_weights = get_direct_edges(vertices, faces)
     indirect_edges, indirect_weights = get_indirect_edges(vertices, faces)
-    edges = np.row_stack((direct_edges, indirect_edges))
+    edges = np.vstack((direct_edges, indirect_edges))
     weights = np.hstack((direct_weights, indirect_weights))
 
     # remove edges that include a vertex in `mask`
@@ -374,13 +374,13 @@ def get_surface_distance(surface, parcellation=None, medial=None,
 
     # calculate distance from each vertex to all other vertices
     graph = make_surf_graph(vert, faces, mask=mask)
-    dist = np.row_stack(Parallel(n_jobs=n_proc, max_nbytes=None)(
+    dist = np.vstack(Parallel(n_jobs=n_proc, max_nbytes=None)(
         delayed(_get_graph_distance)(n, graph, labels) for n in range(n_vert)
     ))
 
     # average distance for all vertices within a parcel + set diagonal to 0
     if labels is not None:
-        dist = np.row_stack([
+        dist = np.vstack([
             dist[labels == lab].mean(axis=0) for lab in np.unique(labels)
         ])
         dist[np.diag_indices_from(dist)] = 0

neuromaps/resampling.py

@@ -31,7 +31,7 @@
 )
 
 
-def downsample_only(src, trg, src_space, trg_space, method='linear', hemi=None): # noqa: D103
+def downsample_only(src, trg, src_space, trg_space, method='linear', hemi=None):  # noqa: D103
     src_den, trg_den = transforms._estimate_density((src, trg), hemi)
     src_num, trg_num = int(src_den[:-1]), int(trg_den[:-1])
     src_space, trg_space = src_space.lower(), trg_space.lower()
@@ -63,8 +63,8 @@ def downsample_only(src, trg, src_space, trg_space, method='linear', hemi=None):
 """.format(**_resampling_docs)
 
 
-def transform_to_src(src, trg, src_space, trg_space, method='linear', hemi=None): # noqa: D103
-    src_den, trg_den = transforms._estimate_density((src, trg), hemi)
+def transform_to_src(src, trg, src_space, trg_space, method='linear', hemi=None):  # noqa: D103
+    src_den, _ = transforms._estimate_density((src, trg), hemi)
 
     func = getattr(transforms, f'{trg_space.lower()}_to_{src_space.lower()}')
     trg = func(trg, src_den, hemi=hemi, method=method)
@@ -86,8 +86,8 @@ def transform_to_src(src, trg, src_space, trg_space, method='linear', hemi=None)
 """.format(**_resampling_docs)
 
 
-def transform_to_trg(src, trg, src_space, trg_space, hemi=None, method='linear'): # noqa: D103
-    src_den, trg_den = transforms._estimate_density((src, trg), hemi)
+def transform_to_trg(src, trg, src_space, trg_space, hemi=None, method='linear'):  # noqa: D103
+    _, trg_den = transforms._estimate_density((src, trg), hemi)
 
     func = getattr(transforms, f'{src_space.lower()}_to_{trg_space.lower()}')
     src = func(src, trg_den, hemi=hemi, method=method)
@@ -138,7 +138,7 @@ def transform_to_alt(src, trg, src_space, trg_space, method='linear',  # noqa: D
 """.format(**_resampling_docs)
 
 
-def mni_transform(src, trg, src_space, trg_space, method='linear', hemi=None): # noqa: D103
+def mni_transform(src, trg, src_space, trg_space, method='linear', hemi=None):  # noqa: D103
     if src_space != 'MNI152':
         raise ValueError('Cannot perform MNI transformation when src_space is '
                          f'not "MNI152." Received: {src_space}.')

neuromaps/stats.py

@@ -233,7 +233,7 @@ def efficient_pearsonr(a, b, ddof=1, nan_policy='propagate', return_pval=True):
     If either input contains nan and nan_policy is set to 'omit', both arrays
     will be masked to omit the nan entries.
     """
-    a, b, axis = _chk2_asarray(a, b, 0)
+    a, b, _ = _chk2_asarray(a, b, 0)
     if len(a) != len(b):
         raise ValueError('Provided arrays do not have same length')
 

neuromaps/tests/test_plotting.py

@@ -8,3 +8,13 @@
 def test_plot_surf_template():
     """Test plotting a surface template."""
     assert False
+
+
+def test_register_cmap():
+    """Test registering a colormap."""
+    import matplotlib
+    from neuromaps import plotting  # noqa: F401
+    if "caret_blueorange" in matplotlib.colormaps:
+        assert True
+    else:
+        assert False

pyproject.toml

@@ -22,7 +22,7 @@ classifiers = [
 dependencies = [
   "numpy >=1.14.0",
   "scipy >=0.17",
-  "matplotlib",
+  "matplotlib >=3.5.0",
   "scikit-learn",
   "nibabel >=3.0.0",
   "nilearn"
@@ -73,8 +73,6 @@ tag_prefix = ""
 parentdir_prefix = ""
 
 [tool.ruff]
-select = ["E", "F", "B", "D", "NPY"]
-ignore = ["E402"]
 line-length = 88
 exclude = [
   "setup.py",
@@ -85,10 +83,18 @@ exclude = [
 ]
 target-version = "py38"
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint]
+select = ["E", "F", "B", "D", "NPY"]
+ignore = [
+    "E402"
+]
+extend-select = ["E302"]
+preview = true
+
+[tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["D104"]
 "neuromaps/tests/*" = ["B011"]
 "neuromaps/nulls/tests/*" = ["B011"]

requirements.txt

@@ -1,4 +1,4 @@
-matplotlib
+matplotlib>=3.5.0
 nibabel>=3.0.0
 nilearn
 numpy>=1.14.0