STY: Enforce ruff rules

.pre-commit-config.yaml

@@ -12,6 +12,11 @@ repos:
     rev: 24.2.0
     hooks:
       - id: black
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.0
+    hooks:
+      - id: ruff
+        args: ["--fix", "--show-source"]
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:

doc/devel/matlab_example1.py

@@ -11,7 +11,7 @@
 
 class ConmapTxt2MatInputSpec(BaseInterfaceInputSpec):
     in_file = File(exists=True, mandatory=True)
-    out_file = File('cmatrix.mat', usedefault=True)
+    out_file = File("cmatrix.mat", usedefault=True)
 
 
 class ConmapTxt2MatOutputSpec(TraitedSpec):
@@ -48,5 +48,5 @@ def _run_interface(self, runtime):
 
     def _list_outputs(self):
         outputs = self._outputs().get()
-        outputs['out_file'] = os.path.abspath(self.inputs.out_file)
+        outputs["out_file"] = os.path.abspath(self.inputs.out_file)
         return outputs

doc/devel/matlab_example2.py

@@ -4,7 +4,7 @@
 
 
 class HelloWorldInputSpec(MatlabInputSpec):
-    name = traits.Str(mandatory=True, desc='Name of person to say hello to')
+    name = traits.Str(mandatory=True, desc="Name of person to say hello to")
 
 
 class HelloWorldOutputSpec(TraitedSpec):

nipype/algorithms/confounds.py

@@ -53,7 +53,7 @@ class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
     variance_tol = traits.Float(
         1e-7,
         usedefault=True,
-        desc="maximum variance to consider \"close to\" zero for the purposes of removal",
+        desc='maximum variance to consider "close to" zero for the purposes of removal',
     )
     save_std = traits.Bool(True, usedefault=True, desc="save standardized DVARS")
     save_nstd = traits.Bool(False, usedefault=True, desc="save non-standardized DVARS")
@@ -608,8 +608,7 @@ def _run_interface(self, runtime):
 
         if len(imgseries.shape) != 4:
             raise ValueError(
-                "{} expected a 4-D nifti file. Input {} has "
-                "{} dimensions (shape {})".format(
+                "{} expected a 4-D nifti file. Input {} has {} dimensions (shape {})".format(
                     self._header,
                     self.inputs.realigned_file,
                     len(imgseries.shape),
@@ -648,8 +647,7 @@ def _run_interface(self, runtime):
 
                 if TR == 0:
                     raise ValueError(
-                        "{} cannot detect repetition time from image - "
-                        "Set the repetition_time input".format(self._header)
+                        f"{self._header} cannot detect repetition time from image - Set the repetition_time input"
                     )
 
         if isdefined(self.inputs.variance_threshold):
@@ -753,8 +751,7 @@ def _run_interface(self, runtime):
                 f.write("\t".join(["component"] + list(metadata.keys())) + "\n")
                 for i in zip(components_names, *metadata.values()):
                     f.write(
-                        "{0[0]}\t{0[1]}\t{0[2]:.10f}\t"
-                        "{0[3]:.10f}\t{0[4]:.10f}\t{0[5]}\n".format(i)
+                        f"{i[0]}\t{i[1]}\t{i[2]:.10f}\t{i[3]:.10f}\t{i[4]:.10f}\t{i[5]}\n"
                     )
 
         return runtime
@@ -1398,9 +1395,7 @@ def compute_noise_components(
         if imgseries.shape[:3] != mask.shape:
             raise ValueError(
                 "Inputs for CompCor, timeseries and mask, do not have "
-                "matching spatial dimensions ({} and {}, respectively)".format(
-                    imgseries.shape[:3], mask.shape
-                )
+                f"matching spatial dimensions ({imgseries.shape[:3]} and {mask.shape}, respectively)"
             )
 
         voxel_timecourses = imgseries[mask, :]

nipype/algorithms/mesh.py

@@ -244,7 +244,7 @@
         if self.inputs.weighting == "area":
             faces = vtk1.polys.to_array().reshape(-1, 4).astype(int)[:, 1:]
 
-            for i, p1 in enumerate(points2):
+            for i, _ in enumerate(points2):
                 # compute surfaces, set in weight
                 w = 0.0
                 point_faces = faces[(faces[:, :] == i).any(axis=1)]

nipype/algorithms/modelgen.py

@@ -389,7 +389,7 @@
             if hasattr(info, "conditions") and info.conditions is not None:
                 for cid, cond in enumerate(info.conditions):
                     sessinfo[i]["cond"].insert(cid, dict())
-                    sessinfo[i]["cond"][cid]["name"] = info.conditions[cid]
+                    sessinfo[i]["cond"][cid]["name"] = cond
                     scaled_onset = scale_timings(
                         info.onsets[cid],
                         self.inputs.input_units,
@@ -434,12 +434,11 @@
                         sessinfo[i]["regress"][j]["name"] = info.regressor_names[j]
                     else:
                         sessinfo[i]["regress"][j]["name"] = "UR%d" % (j + 1)
-                    sessinfo[i]["regress"][j]["val"] = info.regressors[j]
+                    sessinfo[i]["regress"][j]["val"] = r
             sessinfo[i]["scans"] = functional_runs[i]
 
         if realignment_parameters is not None:
-            for i, rp in enumerate(realignment_parameters):
-                mc = realignment_parameters[i]
+            for i, mc in enumerate(realignment_parameters):
                 for col in range(mc.shape[1]):
                     colidx = len(sessinfo[i]["regress"])
                     sessinfo[i]["regress"].insert(colidx, dict(name="", val=[]))
@@ -597,14 +596,14 @@
         for i, info in enumerate(infolist[1:]):
             # info.[conditions, tmod] remain the same
             if info.onsets:
-                for j, val in enumerate(info.onsets):
+                for j, onsets in enumerate(info.onsets):
                     if self.inputs.input_units == "secs":
-                        onsets = np.array(
-                            info.onsets[j]
-                        ) + self.inputs.time_repetition * sum(nscans[0 : (i + 1)])
+                        onsets = np.array(onsets) + self.inputs.time_repetition * sum(
+                            nscans[0 : (i + 1)]
+                        )
                         infoout.onsets[j].extend(onsets.tolist())
                     else:
-                        onsets = np.array(info.onsets[j]) + sum(nscans[0 : (i + 1)])
+                        onsets = np.array(onsets) + sum(nscans[0 : (i + 1)])
                         infoout.onsets[j].extend(onsets.tolist())
 
                 for j, val in enumerate(info.durations):
@@ -621,8 +620,8 @@
                         )
 
                 if hasattr(info, "amplitudes") and info.amplitudes:
-                    for j, val in enumerate(info.amplitudes):
-                        infoout.amplitudes[j].extend(info.amplitudes[j])
+                    for j, amplitudes in enumerate(info.amplitudes):
+                        infoout.amplitudes[j].extend(amplitudes)
 
                 if hasattr(info, "pmod") and info.pmod:
                     for j, val in enumerate(info.pmod):
@@ -633,8 +632,8 @@
             if hasattr(info, "regressors") and info.regressors:
                 # assumes same ordering of regressors across different
                 # runs and the same names for the regressors
-                for j, v in enumerate(info.regressors):
-                    infoout.regressors[j].extend(info.regressors[j])
+                for j, regressors in enumerate(info.regressors):
+                    infoout.regressors[j].extend(regressors)
 
             # insert session regressors
             if not hasattr(infoout, "regressors") or not infoout.regressors:

nipype/algorithms/tests/test_CompCor.py

@@ -28,7 +28,7 @@ def close_up_to_column_sign(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
 
 
 @pytest.mark.parametrize(
-    "a, b, close",
+    ("a", "b", "close"),
     [
         ([[0.1, 0.2], [0.3, 0.4]], [[-0.1, 0.2], [-0.3, 0.4]], True),
         ([[0.1, 0.2], [0.3, 0.4]], [[-0.1, 0.2], [0.3, -0.4]], False),

nipype/algorithms/tests/test_stats.py

@@ -22,7 +22,7 @@ def test_ActivationCount(tmpdir):
 
 
 @pytest.mark.parametrize(
-    "threshold, above_thresh",
+    ("threshold", "above_thresh"),
     [
         (1, 15.865),  # above one standard deviation (one side)
         (2, 2.275),  # above two standard deviations (one side)

nipype/interfaces/ants/registration.py

@@ -1273,7 +1273,7 @@
                     self.inputs.moving_image_mask,
                 )
             else:
-                return "--masks %s" % self.inputs.fixed_image_mask
+                return "--masks {}".format(self.inputs.fixed_image_mask)
         elif opt == "transforms":
             return self._format_registration()
         elif opt == "initial_moving_transform":
@@ -1319,7 +1319,9 @@
                     out_filename,
                 )
             else:
-                return "--output %s" % self.inputs.output_transform_prefix
+                return "--output {}".format(
+                    self.inputs.output_transform_prefix,
+                )
         elif opt == "winsorize_upper_quantile" or opt == "winsorize_lower_quantile":
             if not self._quantilesDone:
                 return self._format_winsorize_image_intensities()
@@ -1610,7 +1612,7 @@
             )
         else:
             retval = '--masks "{fixed_image_mask}"'.format(
-                fixed_image_mask=self.inputs.fixed_image_mask
+                fixed_image_mask=self.inputs.fixed_image_mask,
             )
         return retval
 
@@ -1870,9 +1872,7 @@
                 f"00_{self.inputs.output_prefix}_AffineTransform.mat"
             )
             outputs["displacement_field"] = os.path.abspath(
-                "01_{}_DisplacementFieldTransform.nii.gz".format(
-                    self.inputs.output_prefix
-                )
+                f"01_{self.inputs.output_prefix}_DisplacementFieldTransform.nii.gz"
             )
         if self.inputs.process == "assemble":
             outputs["out_file"] = os.path.abspath(self.inputs.out_file)

nipype/interfaces/ants/tests/test_base.py

@@ -20,6 +20,6 @@
 # fmt: on
 
 
-@pytest.mark.parametrize("raw_info, version", ANTS_VERSIONS)
+@pytest.mark.parametrize(("raw_info", "version"), ANTS_VERSIONS)
 def test_version_parser(raw_info, version):
     assert Info.parse_version(raw_info) == version

nipype/interfaces/ants/tests/test_resampling.py

@@ -103,4 +103,4 @@ def test_WarpTimeSeriesImageMultiTransform_invaffine_wrong(change_dir, create_wt
     wtsimt = create_wtsimt
     wtsimt.inputs.invert_affine = [0]
     with pytest.raises(Exception):
-        wtsimt.cmdline
+        wtsimt.cmdline  # noqa: B018

nipype/interfaces/ants/tests/test_segmentation.py

@@ -46,20 +46,20 @@ def test_LaplacianThickness_wrongargs(change_dir, create_lt):
     with pytest.raises(
         ValueError, match=r".* requires a value for input 'sulcus_prior' .*"
     ):
-        lt.cmdline
+        lt.cmdline  # noqa: B018
     lt.inputs.sulcus_prior = 0.15
     with pytest.raises(ValueError, match=r".* requires a value for input 'dT' .*"):
-        lt.cmdline
+        lt.cmdline  # noqa: B018
     lt.inputs.dT = 0.01
     with pytest.raises(
         ValueError, match=r".* requires a value for input 'prior_thickness' .*"
     ):
-        lt.cmdline
+        lt.cmdline  # noqa: B018
     lt.inputs.prior_thickness = 5.9
     with pytest.raises(
         ValueError, match=r".* requires a value for input 'smooth_param' .*"
     ):
-        lt.cmdline
+        lt.cmdline  # noqa: B018
     lt.inputs.smooth_param = 4.5
     assert (
         lt.cmdline == "LaplacianThickness functional.nii diffusion_weighted.nii "

nipype/interfaces/base/core.py

@@ -849,8 +849,8 @@ def _filename_from_source(self, name, chain=None):
 
             if not isinstance(ns, (str, bytes)):
                 raise ValueError(
-                    "name_source of '{}' trait should be an input trait "
-                    "name, but a type {} object was found".format(name, type(ns))
+                    f"name_source of '{name}' trait should be an input trait "
+                    f"name, but a type {type(ns)} object was found"
                 )
 
             if isdefined(getattr(self.inputs, ns)):

nipype/interfaces/base/specs.py

@@ -142,10 +142,7 @@ def _deprecated_warn(self, obj, name, old, new):
                 raise TraitError(msg)
             else:
                 if trait_spec.new_name:
-                    msg += "Unsetting old value {}; setting new value {}.".format(
-                        name,
-                        trait_spec.new_name,
-                    )
+                    msg += f"Unsetting old value {name}; setting new value {trait_spec.new_name}."
                 warn(msg)
                 if trait_spec.new_name:
                     self.trait_set(

nipype/interfaces/base/tests/test_resource_monitor.py

@@ -55,7 +55,9 @@ class UseResources(CommandLine):
 
 @pytest.mark.skip(reason="inconsistent readings")
 @pytest.mark.skipif(os.getenv("CI_SKIP_TEST", False), reason="disabled in CI tests")
-@pytest.mark.parametrize("mem_gb,n_procs", [(0.5, 3), (2.2, 8), (0.8, 4), (1.5, 1)])
+@pytest.mark.parametrize(
+    ("mem_gb", "n_procs"), [(0.5, 3), (2.2, 8), (0.8, 4), (1.5, 1)]
+)
 def test_cmdline_profiling(tmpdir, mem_gb, n_procs, use_resource_monitor):
     """
     Test runtime profiler correctly records workflow RAM/CPUs consumption
@@ -80,7 +82,9 @@ def test_cmdline_profiling(tmpdir, mem_gb, n_procs, use_resource_monitor):
 @pytest.mark.skipif(
     True, reason="test disabled temporarily, until function profiling works"
 )
-@pytest.mark.parametrize("mem_gb,n_procs", [(0.5, 3), (2.2, 8), (0.8, 4), (1.5, 1)])
+@pytest.mark.parametrize(
+    ("mem_gb", "n_procs"), [(0.5, 3), (2.2, 8), (0.8, 4), (1.5, 1)]
+)
 def test_function_profiling(tmpdir, mem_gb, n_procs, use_resource_monitor):
     """
     Test runtime profiler correctly records workflow RAM/CPUs consumption

nipype/interfaces/base/tests/test_specs.py

@@ -71,12 +71,12 @@
     a.foo = 1
     assign_a = lambda: setattr(a, "foo", "a")
     with pytest.raises(Exception):
-        assign_a
+        assign_a()
     pkld_a = dumps(a)
     unpkld_a = loads(pkld_a)
     assign_a_again = lambda: setattr(unpkld_a, "foo", "a")
     with pytest.raises(Exception):
-        assign_a_again
+        assign_a_again()
 
 
 def test_DynamicTraitedSpec_tab_completion():
@@ -276,7 +276,7 @@
     to0 = TestCycle()
     not_raised = True
     try:
-        to0.cmdline
+        to0.cmdline  # noqa: B018
     except nib.NipypeInterfaceError:
         not_raised = False
     assert not not_raised

nipype/interfaces/camino/dti.py

@@ -936,7 +936,7 @@ class TrackDT(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "dt"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackPICoInputSpec(TrackInputSpec):
@@ -973,7 +973,7 @@ class TrackPICo(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "pico"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackBedpostxDeterInputSpec(TrackInputSpec):
@@ -1023,7 +1023,7 @@ class TrackBedpostxDeter(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "bedpostx_dyad"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackBedpostxProbaInputSpec(TrackInputSpec):
@@ -1084,7 +1084,7 @@ class TrackBedpostxProba(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "bedpostx"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackBayesDiracInputSpec(TrackInputSpec):
@@ -1185,7 +1185,7 @@ class TrackBayesDirac(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "bayesdirac"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackBallStick(Track):
@@ -1204,7 +1204,7 @@ class TrackBallStick(Track):
 
     def __init__(self, command=None, **inputs):
         inputs["inputmodel"] = "ballstick"
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class TrackBootstrapInputSpec(TrackInputSpec):
@@ -1266,7 +1266,7 @@ class TrackBootstrap(Track):
     input_spec = TrackBootstrapInputSpec
 
     def __init__(self, command=None, **inputs):
-        return super().__init__(command, **inputs)
+        super().__init__(command, **inputs)
 
 
 class ComputeMeanDiffusivityInputSpec(CommandLineInputSpec):