[MAINT] use updated black (#876)

use updated black

nimare/__init__.py

@@ -1,4 +1,5 @@
 """NiMARE: Neuroimaging Meta-Analysis Research Environment."""
+
 import logging
 import warnings
 

nimare/annotate/cogat.py

@@ -1,4 +1,5 @@
 """Automated annotation of Cognitive Atlas labels."""
+
 import logging
 import re
 

nimare/annotate/gclda.py

@@ -1,4 +1,5 @@
 """Topic modeling with generalized correspondence latent Dirichlet allocation."""
+
 import logging
 import os.path as op
 

nimare/annotate/lda.py

@@ -1,4 +1,5 @@
 """Topic modeling with latent Dirichlet allocation."""
+
 import numpy as np
 import pandas as pd
 from sklearn.decomposition import LatentDirichletAllocation

nimare/annotate/text.py

@@ -1,4 +1,5 @@
 """Text extraction tools."""
+
 import logging
 import os.path as op
 

nimare/annotate/utils.py

@@ -1,4 +1,5 @@
 """Utility functions for ontology tools."""
+
 import numpy as np
 import pandas as pd
 

nimare/base.py

@@ -1,4 +1,5 @@
 """Base classes for NiMARE."""
+
 import gzip
 import inspect
 import logging

nimare/cli.py

@@ -1,4 +1,5 @@
 """Command-line interfaces for common workflows."""
+
 import argparse
 import os.path as op
 

nimare/correct.py

@@ -1,4 +1,5 @@
 """Multiple comparisons correction methods."""
+
 import inspect
 import logging
 from abc import abstractproperty
@@ -142,9 +143,9 @@ def inspect(cls, result):
         duplicate_methods = list(set(corr_methods) & set(est_methods))
         for duplicate_method in duplicate_methods:
             if duplicate_method in corr_methods:
-                corr_methods[
-                    corr_methods.index(duplicate_method)
-                ] = f"{duplicate_method} (overridden)"
+                corr_methods[corr_methods.index(duplicate_method)] = (
+                    f"{duplicate_method} (overridden)"
+                )
 
         LGR.info(
             f"Available non-specific methods: {', '.join(corr_methods)}\n"

nimare/dataset.py

@@ -1,4 +1,5 @@
 """Classes for representing datasets of images and/or coordinates."""
+
 import copy
 import inspect
 import json

nimare/decode/base.py

@@ -1,4 +1,5 @@
 """Base classes for the decode module."""
+
 import logging
 from abc import abstractmethod
 

nimare/decode/continuous.py

@@ -1,4 +1,5 @@
 """Methods for decoding unthresholded brain maps into text."""
+
 import inspect
 import logging
 import os

nimare/decode/discrete.py

@@ -1,4 +1,5 @@
 """Methods for decoding subsets of voxels or experiments into text."""
+
 import numpy as np
 import pandas as pd
 from nilearn._utils import load_niimg

nimare/decode/encode.py

@@ -1,4 +1,5 @@
 """Methods for encoding text into brain maps."""
+
 import numpy as np
 from nilearn.masking import unmask
 from sklearn.feature_extraction.text import CountVectorizer

nimare/decode/utils.py

@@ -1,4 +1,5 @@
 """Utility functions for decoding/encoding."""
+
 import numpy as np
 
 

nimare/diagnostics.py

@@ -1,4 +1,5 @@
 """Methods for diagnosing problems in meta-analytic datasets or analyses."""
+
 import copy
 import logging
 from abc import abstractmethod
@@ -154,9 +155,11 @@ def transform(self, result):
             clusters_table = clusters_table.astype({"Cluster ID": "str"})
             # Rename the clusters_table cluster IDs to match the contribution table columns
             clusters_table["Cluster ID"] = [
-                f"{POSTAIL_LBL} {row['Cluster ID']}"
-                if row["Peak Stat"] > 0
-                else f"{NEGTAIL_LBL} {row['Cluster ID']}"
+                (
+                    f"{POSTAIL_LBL} {row['Cluster ID']}"
+                    if row["Peak Stat"] > 0
+                    else f"{NEGTAIL_LBL} {row['Cluster ID']}"
+                )
                 for _, row in clusters_table.iterrows()
             ]
 

nimare/estimator.py

@@ -1,4 +1,5 @@
 """Base class for estimators."""
+
 from abc import abstractmethod
 
 from joblib import Memory

nimare/extract/__init__.py

@@ -1,4 +1,5 @@
 """Dataset and trained model downloading functions."""
+
 from . import utils
 from .extract import (
     download_abstracts,

nimare/extract/extract.py

@@ -1,4 +1,5 @@
 """Tools for downloading datasets."""
+
 import itertools
 import json
 import logging

nimare/extract/utils.py

@@ -1,4 +1,5 @@
 """Utility functions for the extract module."""
+
 from __future__ import division
 
 import logging

nimare/generate.py

@@ -1,4 +1,5 @@
 """Utilities for generating data for testing."""
+
 from itertools import zip_longest
 
 import numpy as np

nimare/io.py

@@ -1,4 +1,5 @@
 """Input/Output operations."""
+
 import json
 import logging
 import re

nimare/meta/cbma/__init__.py

@@ -1,4 +1,5 @@
 """Coordinate-based meta-analytic estimators."""
+
 from .ale import ALE, SCALE, ALESubtraction
 from .mkda import KDA, MKDAChi2, MKDADensity
 

nimare/meta/cbma/ale.py

@@ -1,4 +1,5 @@
 """CBMA methods from the activation likelihood estimation (ALE) family."""
+
 import logging
 
 import numpy as np

nimare/meta/cbma/base.py

@@ -1,4 +1,5 @@
 """CBMA methods from the ALE and MKDA families."""
+
 import logging
 from abc import abstractmethod
 
@@ -523,9 +524,9 @@ def _compute_null_montecarlo(self, n_iters, n_cores):
         for perm in fwe_voxel_max:
             histweights[perm] += 1
 
-        self.null_distributions_[
-            "histweights_level-voxel_corr-fwe_method-montecarlo"
-        ] = histweights
+        self.null_distributions_["histweights_level-voxel_corr-fwe_method-montecarlo"] = (
+            histweights
+        )
 
     def _correct_fwe_montecarlo_permutation(
         self,
@@ -793,9 +794,9 @@ def correct_fwe_montecarlo(
             # Voxel-level FWE
             LGR.info("Using null distribution for voxel-level FWE correction.")
             p_vfwe_values = null_to_p(stat_values, fwe_voxel_max, tail="upper")
-            self.null_distributions_[
-                "values_level-voxel_corr-fwe_method-montecarlo"
-            ] = fwe_voxel_max
+            self.null_distributions_["values_level-voxel_corr-fwe_method-montecarlo"] = (
+                fwe_voxel_max
+            )
 
         z_vfwe_values = p_to_z(p_vfwe_values, tail="one")
         logp_vfwe_values = -np.log10(p_vfwe_values)

nimare/meta/cbma/mkda.py

@@ -1,4 +1,5 @@
 """CBMA methods from the multilevel kernel density analysis (MKDA) family."""
+
 import logging
 
 import nibabel as nib
@@ -884,9 +885,9 @@ def correct_fwe_montecarlo(self, result, voxel_thresh=0.001, n_iters=1000, n_cor
             cmfwe_null=pAgF_cmfwe_null,
         )
 
-        self.null_distributions_[
-            "values_desc-pAgF_level-voxel_corr-fwe_method-montecarlo"
-        ] = pAgF_vfwe_null
+        self.null_distributions_["values_desc-pAgF_level-voxel_corr-fwe_method-montecarlo"] = (
+            pAgF_vfwe_null
+        )
         self.null_distributions_[
             "values_desc-pAgFsize_level-cluster_corr-fwe_method-montecarlo"
         ] = pAgF_csfwe_null
@@ -905,9 +906,9 @@ def correct_fwe_montecarlo(self, result, voxel_thresh=0.001, n_iters=1000, n_cor
             cmfwe_null=pFgA_cmfwe_null,
         )
 
-        self.null_distributions_[
-            "values_desc-pFgA_level-voxel_corr-fwe_method-montecarlo"
-        ] = pFgA_vfwe_null
+        self.null_distributions_["values_desc-pFgA_level-voxel_corr-fwe_method-montecarlo"] = (
+            pFgA_vfwe_null
+        )
         self.null_distributions_[
             "values_desc-pFgAsize_level-cluster_corr-fwe_method-montecarlo"
         ] = pFgA_csfwe_null

nimare/meta/cbmr.py

@@ -835,12 +835,12 @@ def _glh_con_group(self):
                     self.result.maps[
                         f"chiSquare_group-{self.t_con_groups_name[con_group_count]}"
                     ] = chi_sq_spatial
-                self.result.maps[
-                    f"p_group-{self.t_con_groups_name[con_group_count]}"
-                ] = p_vals_spatial
-                self.result.maps[
-                    f"z_group-{self.t_con_groups_name[con_group_count]}"
-                ] = z_stats_spatial
+                self.result.maps[f"p_group-{self.t_con_groups_name[con_group_count]}"] = (
+                    p_vals_spatial
+                )
+                self.result.maps[f"z_group-{self.t_con_groups_name[con_group_count]}"] = (
+                    z_stats_spatial
+                )
             else:
                 if m > 1:  # GLH tests (with multiple contrasts)
                     self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial
@@ -950,17 +950,17 @@ def _glh_con_moderator(self):
                     self.result.tables[
                         f"chi_square_{self.t_con_moderators_name[con_moderator_count]}"
                     ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"])
-                self.result.tables[
-                    f"p_{self.t_con_moderators_name[con_moderator_count]}"
-                ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p"])
-                self.result.tables[
-                    f"z_{self.t_con_moderators_name[con_moderator_count]}"
-                ] = pd.DataFrame(data=np.array(z_stats_moderator), columns=["z"])
+                self.result.tables[f"p_{self.t_con_moderators_name[con_moderator_count]}"] = (
+                    pd.DataFrame(data=np.array(p_vals_moderator), columns=["p"])
+                )
+                self.result.tables[f"z_{self.t_con_moderators_name[con_moderator_count]}"] = (
+                    pd.DataFrame(data=np.array(z_stats_moderator), columns=["z"])
+                )
             else:
                 if m_con_moderator > 1:
-                    self.result.tables[
-                        f"chi_square_GLH_moderators_{con_moderator_count}"
-                    ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"])
+                    self.result.tables[f"chi_square_GLH_moderators_{con_moderator_count}"] = (
+                        pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"])
+                    )
                 self.result.tables[f"p_GLH_moderators_{con_moderator_count}"] = pd.DataFrame(
                     data=np.array(p_vals_moderator), columns=["p"]
                 )

nimare/meta/ibma.py

@@ -1,4 +1,5 @@
 """Image-based meta-analysis estimators."""
+
 from __future__ import division
 
 import logging
@@ -102,9 +103,11 @@ def _preprocess_input(self, dataset):
             if type_ == "image":
                 # Resampling will only occur if shape/affines are different
                 imgs = [
-                    nib.load(img)
-                    if _check_same_fov(nib.load(img), reference_masker=mask_img)
-                    else resample_to_img(nib.load(img), mask_img, **self._resample_kwargs)
+                    (
+                        nib.load(img)
+                        if _check_same_fov(nib.load(img), reference_masker=mask_img)
+                        else resample_to_img(nib.load(img), mask_img, **self._resample_kwargs)
+                    )
                     for img in self.inputs_[name]
                 ]
 

nimare/meta/kernel.py

@@ -4,6 +4,7 @@
 (Contrasts) from sets of foci and optional additional information (e.g., sample
 size and test statistic values).
 """
+
 from __future__ import division
 
 import logging

nimare/meta/models.py

@@ -1,4 +1,5 @@
 """CBMR Models."""
+
 import abc
 import logging
 
@@ -290,9 +291,9 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group):
             group_spatial_intensity_estimation = np.exp(
                 np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)
             )
-            spatial_intensity_estimation[
-                "spatialIntensity_group-" + group
-            ] = group_spatial_intensity_estimation
+            spatial_intensity_estimation["spatialIntensity_group-" + group] = (
+                group_spatial_intensity_estimation
+            )
 
         # Extract optimized regression coefficient of study-level moderators from the model
         if self.moderators_coef_dim:
@@ -913,12 +914,7 @@ def _log_likelihood_single_group(
             ).reshape((-1, 1))
             mu_moderators = torch.exp(log_mu_moderators)
         # parameter of a NB variable to approximate a sum of NB variables
-        r = (
-            1
-            / group_overdispersion
-            * torch.sum(mu_moderators) ** 2
-            / torch.sum(mu_moderators**2)
-        )
+        r = 1 / group_overdispersion * torch.sum(mu_moderators) ** 2 / torch.sum(mu_moderators**2)
         p = 1 / (
             1
             + torch.sum(mu_moderators)

nimare/meta/utils.py

@@ -1,4 +1,5 @@
 """Utilities for coordinate-based meta-analysis estimators."""
+
 import warnings
 
 import numpy as np

nimare/reports/figures.py

@@ -1,4 +1,5 @@
 """Plot figures for report."""
+
 import matplotlib.colors as mcolors
 import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt

nimare/results.py

@@ -1,4 +1,5 @@
 """Tools for managing meta-analytic results."""
+
 import copy
 import logging
 import os

nimare/stats.py

@@ -1,4 +1,5 @@
 """Various statistical helper functions."""
+
 import logging
 import warnings
 

nimare/tests/conftest.py

@@ -1,4 +1,5 @@
 """Generate fixtures for tests."""
+
 import json
 import os
 from shutil import copyfile

nimare/tests/test_annotate_cogat.py

@@ -1,4 +1,5 @@
 """Test nimare.annotate.cogat (Cognitive Atlas extraction methods)."""
+
 import pandas as pd
 
 from nimare import annotate, extract, utils

nimare/tests/test_annotate_gclda.py

@@ -1,4 +1,5 @@
 """Test nimare.annotate.gclda (GCLDA)."""
+
 import nibabel as nib
 import numpy as np
 import pandas as pd

nimare/tests/test_annotate_lda.py

@@ -1,4 +1,5 @@
 """Test nimare.annotate.lda (LDA)."""
+
 import numpy as np
 import pandas as pd
 

nimare/tests/test_dataset.py

@@ -1,4 +1,5 @@
 """Test nimare.dataset (Dataset IO/transformations)."""
+
 import copy
 import json
 import os.path as op