Merge remote-tracking branch 'upstream/master' into loadbidslayout

Author: effigies

.github/workflows/tests.yml

@@ -26,8 +26,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: ['ubuntu-18.04']
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        os: ['ubuntu-latest']
+        python-version: [3.6, 3.7, 3.8, 3.9, "3.10"]
         check: ['test']
         pip-flags: ['']
         depends: ['REQUIREMENTS']

.mailmap

@@ -121,6 +121,7 @@ Kshitij Chawla <[email protected]>
 Leonie Lampe <[email protected]>
 Lukas Snoek <[email protected]>
 Marcel Falkiewicz <[email protected]> <[email protected]>
+Maria de Fatima Dias <[email protected]> <[email protected]>
 Martin Perez-Guevara <[email protected]>
 Mathias Goncalves <[email protected]> <[email protected]>
 Mathias Goncalves <[email protected]> <[email protected]>

.zenodo.json

@@ -263,7 +263,7 @@
     },
     {
       "affiliation": "CIBIT, UC",
-      "name": "Machado, F\u00e1tima",
+      "name": "Dias, Maria de Fatima",
       "orcid": "0000-0001-8878-1750"
     },
     {
@@ -439,7 +439,8 @@
     },
     {
       "affiliation": "Sagol School of Neuroscience, Tel Aviv University",
-      "name": "Baratz, Zvi"
+      "name": "Baratz, Zvi",
+      "orcid": "0000-0001-7159-1387"
     },
     {
       "affiliation": "University of Iowa",

nipype/algorithms/confounds.py

@@ -1435,7 +1435,7 @@ def compute_noise_components(
             else:
                 u = np.full((M.shape[0], 1), np.nan, dtype=np.float32)
 
-        variance_explained = (s ** 2) / np.sum(s ** 2)
+        variance_explained = (s**2) / np.sum(s**2)
         cumulative_variance_explained = np.cumsum(variance_explained)
 
         num_components = int(components_criterion)

nipype/algorithms/icc.py

@@ -116,7 +116,7 @@ def ICC_rep_anova(Y):
     # Sum Square Error
     predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten("F"))
     residuals = Y.flatten("F") - predicted_Y
-    SSE = (residuals ** 2).sum()
+    SSE = (residuals**2).sum()
 
     residuals.shape = Y.shape
 

nipype/algorithms/metrics.py

@@ -337,7 +337,7 @@ def _run_interface(self, runtime):
         if self.inputs.weighting != "none":
             weights = weights / np.array(volumes1)
             if self.inputs.weighting == "squared_vol":
-                weights = weights ** 2
+                weights = weights**2
         weights = weights / np.sum(weights)
 
         both_data = np.zeros(data1.shape)
@@ -500,7 +500,7 @@ def _run_interface(self, runtime):
             volumes = np.sum((refdata + tstdata) > 0, axis=1).reshape((-1, ncomp))
             weights = 1.0 / volumes
             if self.inputs.weighting == "squared_vol":
-                weights = weights ** 2
+                weights = weights**2
 
         weights = weights / np.sum(weights)
         dices = 2.0 * jaccards / (jaccards + 1.0)
@@ -591,7 +591,7 @@ def _run_interface(self, runtime):
 
         # Scale the difference
         if self.inputs.metric == "sqeuclidean":
-            errvector = diffvector ** 2
+            errvector = diffvector**2
             if comps > 1:
                 errvector = np.sum(errvector, axis=1)
             else:

nipype/algorithms/tests/test_ErrorMap.py

@@ -75,4 +75,4 @@ def test_errormap(tmpdir):
 
     errmap.inputs.metric = "euclidean"
     result = errmap.run()
-    assert result.outputs.distance == np.float32(1.25 * (2 ** 0.5))
+    assert result.outputs.distance == np.float32(1.25 * (2**0.5))

nipype/info.py

@@ -57,6 +57,8 @@ def get_nipype_gitversion():
     "Programming Language :: Python :: 3.6",
     "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
     "Topic :: Scientific/Engineering",
 ]
 PYTHON_REQUIRES = ">= 3.6"
@@ -169,7 +171,6 @@ def get_nipype_gitversion():
         "sphinx-argparse",
         "sphinx>=2.1.2",
         "sphinxcontrib-apidoc",
-        "sphinxcontrib-napoleon",
     ],
     "duecredit": ["duecredit"],
     "nipy": ["nitime", "nilearn", "dipy", "nipy", "matplotlib"],

nipype/interfaces/ants/registration.py

@@ -418,6 +418,7 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         "BSpline",
         "MultiLabel",
         "Gaussian",
+        "GenericLabel",
         argstr="%s",
         usedefault=True,
     )
@@ -426,6 +427,7 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         traits.Tuple(
             traits.Float(), traits.Float()  # Gaussian/MultiLabel (sigma, alpha)
         ),
+        traits.Tuple(traits.Str()),  # GenericLabel (interpolator)
     )
 
     write_composite_transform = traits.Bool(
@@ -1291,15 +1293,12 @@ def _format_arg(self, opt, spec, val):
                 do_center_of_mass_init,
             )
         elif opt == "interpolation":
-            if (
-                self.inputs.interpolation
-                in [
-                    "BSpline",
-                    "MultiLabel",
-                    "Gaussian",
-                ]
-                and isdefined(self.inputs.interpolation_parameters)
-            ):
+            if self.inputs.interpolation in [
+                "BSpline",
+                "MultiLabel",
+                "Gaussian",
+                "GenericLabel",
+            ] and isdefined(self.inputs.interpolation_parameters):
                 return "--interpolation %s[ %s ]" % (
                     self.inputs.interpolation,
                     ", ".join(
@@ -1592,14 +1591,17 @@ class MeasureImageSimilarity(ANTSCommand):
     output_spec = MeasureImageSimilarityOutputSpec
 
     def _metric_constructor(self):
-        retval = '--metric {metric}["{fixed_image}","{moving_image}",{metric_weight},' "{radius_or_number_of_bins},{sampling_strategy},{sampling_percentage}]".format(
-            metric=self.inputs.metric,
-            fixed_image=self.inputs.fixed_image,
-            moving_image=self.inputs.moving_image,
-            metric_weight=self.inputs.metric_weight,
-            radius_or_number_of_bins=self.inputs.radius_or_number_of_bins,
-            sampling_strategy=self.inputs.sampling_strategy,
-            sampling_percentage=self.inputs.sampling_percentage,
+        retval = (
+            '--metric {metric}["{fixed_image}","{moving_image}",{metric_weight},'
+            "{radius_or_number_of_bins},{sampling_strategy},{sampling_percentage}]".format(
+                metric=self.inputs.metric,
+                fixed_image=self.inputs.fixed_image,
+                moving_image=self.inputs.moving_image,
+                metric_weight=self.inputs.metric_weight,
+                radius_or_number_of_bins=self.inputs.radius_or_number_of_bins,
+                sampling_strategy=self.inputs.sampling_strategy,
+                sampling_percentage=self.inputs.sampling_percentage,
+            )
         )
         return retval
 

nipype/interfaces/ants/resampling.py

@@ -504,15 +504,11 @@ def _format_arg(self, opt, spec, val):
         elif opt == "transforms":
             return self._get_transform_filenames()
         elif opt == "interpolation":
-            if (
-                self.inputs.interpolation
-                in [
-                    "BSpline",
-                    "MultiLabel",
-                    "Gaussian",
-                ]
-                and isdefined(self.inputs.interpolation_parameters)
-            ):
+            if self.inputs.interpolation in [
+                "BSpline",
+                "MultiLabel",
+                "Gaussian",
+            ] and isdefined(self.inputs.interpolation_parameters):
                 return "--interpolation %s[ %s ]" % (
                     self.inputs.interpolation,
                     ", ".join(