Merge pull request #579 from DimitriPapadopoulos/SIM

effigies · web-flow · commit 09ed35eda68c · 2024-10-06T08:14:54.000-04:00
Apply ruff/flake8-simplify rules (SIM)
diff --git a/nipy/algorithms/registration/optimizer.py b/nipy/algorithms/registration/optimizer.py
@@ -46,7 +46,4 @@ def configure_optimizer(optimizer, fprime=None, fhess=None, **kwargs):
 
 
 def use_derivatives(optimizer):
-    if optimizer in ('simplex', 'powell'):
-        return False
-    else:
-        return True
+    return optimizer not in ('simplex', 'powell')
diff --git a/nipy/algorithms/statistics/models/regression.py b/nipy/algorithms/statistics/models/regression.py
@@ -264,9 +264,7 @@ def has_intercept(self):
         o = np.ones(self.design.shape[0])
         obeta = np.dot(self.calc_beta, o)
         ohat = np.dot(self.wdesign, obeta)
-        if np.allclose(ohat, o):
-            return True
-        return False
+        return np.allclose(ohat, o)
 
     @cached_property
     def rank(self):
@@ -872,6 +870,4 @@ def isestimable(C, D):
     if C.shape[1] != D.shape[1]:
         raise ValueError('Contrast should have %d columns' % D.shape[1])
     new = np.vstack([C, D])
-    if matrix_rank(new) != matrix_rank(D):
-        return False
-    return True
+    return matrix_rank(new) == matrix_rank(D)
diff --git a/nipy/io/nifti_ref.py b/nipy/io/nifti_ref.py
@@ -580,7 +580,7 @@ def nifti2nipy(ni_img):
     if ndim == 3:
         return Image(data, cmap3, {'header': hdr})
     space_units, time_like_units = hdr.get_xyzt_units()
-    units_info = TIME_LIKE_UNITS.get(time_like_units, None)
+    units_info = TIME_LIKE_UNITS.get(time_like_units)
     n_ns = ndim - 3
     ns_zooms = list(hdr.get_zooms()[3:])
     ns_trans = [0] * n_ns
diff --git a/nipy/io/tests/test_nibcompat.py b/nipy/io/tests/test_nibcompat.py
@@ -42,7 +42,7 @@ def test_unscaled_data(in_tmp_path):
         header = get_header(img_back)
         dao = get_dataobj(img_back)
         slope = header['scl_slope']
-        inter = (0. if 'scl_inter' not in header else header['scl_inter'])
+        inter = (header.get('scl_inter', 0.0))
         if np.isnan(slope):
             slope, inter = dao.slope, dao.inter
         data_back = np.array(dao)
