Merge pull request #582 from DimitriPapadopoulos/PERF

effigies · web-flow · commit f4aa8e42ea10 · 2024-10-06T08:16:49.000-04:00
Apply ruff/Perflint rules (PERF)
diff --git a/examples/interfaces/process_ds105.py b/examples/interfaces/process_ds105.py
@@ -308,10 +308,7 @@ def process_subject(ddef, study_def, ana_def):
 
 
 def get_subjects(data_path, subj_ids, study_def, ana_def):
-    ddefs = []
-    for subj_id in subj_ids:
-        ddefs.append(get_fdata(data_path, subj_id))
-    return ddefs
+    return [get_fdata(data_path, subj_id) for subj_id in subj_ids]
 
 
 def main():
diff --git a/nipy/algorithms/clustering/gmm.py b/nipy/algorithms/clustering/gmm.py
@@ -848,8 +848,7 @@ def show_components(self, x, gd, density=None, mpaxes=None):
                      fontsize=12)
 
         legend = ['data']
-        for k in range(self.k):
-            legend.append('component %d' % (k + 1))
+        legend.extend(f'component {k}' for k in range(1, self.k + 1))
         l = ax.legend(tuple(legend))
         for t in l.get_texts():
             t.set_fontsize(12)
diff --git a/nipy/algorithms/clustering/hierarchical_clustering.py b/nipy/algorithms/clustering/hierarchical_clustering.py
@@ -221,10 +221,8 @@ def list_of_subtrees(self):
         way such that parent[i]>i for all i
         Only the leaves are listeed, not the subtrees themselves
         """
-        lst = []
+        lst = [np.array([], np.int_) for i in range(self.V)]
         n = np.sum(self.isleaf())
-        for i in range(self.V):
-            lst.append(np.array([], np.int_))
         for i in range(n):
             lst[i] = np.array([i], np.int_)
         for i in range(self.V - 1):
diff --git a/nipy/algorithms/graph/forest.py b/nipy/algorithms/graph/forest.py
@@ -155,9 +155,7 @@ def get_descendants(self, v, exclude_self=False):
         else:
             desc = [v]
             for w in self.children[v]:
-                temp = self.get_descendants(w)
-                for q in temp:
-                    desc.append(q)
+                desc.extend(self.get_descendants(w))
         desc.sort()
         if exclude_self and v in desc:
             desc = [i for i in desc if i != v]
diff --git a/nipy/algorithms/graph/graph.py b/nipy/algorithms/graph/graph.py
@@ -1221,9 +1221,7 @@ def left_incidence(self):
             list[[e.0.0, .., e.0.i(0)], .., [e.V.0, E.V.i(V)]] where e.i.j is
             the set of edge indexes so that e.i.j[0] = i
         """
-        linc = []
-        for i in range(self.V):
-            linc.append([])
+        linc = [[] for i in range(self.V)]
         for e in range(self.E):
             i = self.edges[e, 0]
             a = linc[i]
@@ -1240,9 +1238,7 @@ def right_incidence(self):
             list[[e.0.0, .., e.0.i(0)], .., [e.V.0, E.V.i(V)]] where e.i.j is
             the set of edge indexes so that e.i.j[1] = i
         """
-        rinc = []
-        for i in range(self.V):
-            rinc.append([])
+        rinc = [[] for i in range(self.V)]
         for e in range(self.E):
             i = self.edges[e, 1]
             a = rinc[i]
diff --git a/nipy/algorithms/statistics/formula/formulae.py b/nipy/algorithms/statistics/formula/formulae.py
@@ -302,11 +302,10 @@ def getparams(expression):
         expression = expression.reshape((np.prod(expression.shape),))
     for term in expression:
         atoms = atoms.union(sympy.sympify(term).atoms())
-    params = []
-    for atom in atoms:
-        if isinstance(atom, sympy.Symbol) and not is_term(atom):
-            params.append(atom)
-    params.sort(key=default_sort_key)
+    params = sorted((atom
+                     for atom in atoms
+                     if isinstance(atom, sympy.Symbol) and not is_term(atom)),
+                    key=default_sort_key)
     return params
 
 
@@ -330,11 +329,8 @@ def getterms(expression):
         expression = expression.reshape((np.prod(expression.shape),))
     for e in expression:
         atoms = atoms.union(e.atoms())
-    terms = []
-    for atom in atoms:
-        if is_term(atom):
-            terms.append(atom)
-    terms.sort(key=default_sort_key)
+    terms = sorted((atom for atom in atoms if is_term(atom)),
+                   key=default_sort_key)
     return terms
 
 
diff --git a/nipy/algorithms/statistics/models/tests/test_anova.py b/nipy/algorithms/statistics/models/tests/test_anova.py
@@ -85,9 +85,7 @@
 #
 # http://www-stat.stanford.edu/~jtaylo/courses/stats191/data/kidney.table
 
-D = []
-for row in StringIO(data):
-    D.append([float(val) for val in row.split()])
+D = [[float(val) for val in row.split()] for row in StringIO(data)]
 D = make_recarray(D, ['Days', 'Duration', 'Weight', 'ID'])
 
 # Create the categorical regressors, known as Factors
diff --git a/nipy/algorithms/statistics/tests/test_intrinsic_volumes.py b/nipy/algorithms/statistics/tests/test_intrinsic_volumes.py
@@ -28,9 +28,7 @@ def randorth(p=10):
 
 def box(shape, edges):
     data = np.zeros(shape)
-    sl = []
-    for i in range(len(shape)):
-        sl.append(slice(edges[i][0], edges[i][1],1))
+    sl = [slice(edges[i][0], edges[i][1],1) for i in range(len(shape))]
     data[tuple(sl)] = 1
     return data.astype(np.int_)
 
diff --git a/nipy/algorithms/utils/matrices.py b/nipy/algorithms/utils/matrices.py
@@ -105,9 +105,7 @@ def full_rank(X, r=None):
     V, D, U = spl.svd(X, full_matrices=0)
     order = np.argsort(D)
     order = order[::-1]
-    value = []
-    for i in range(r):
-        value.append(V[:,order[i]])
+    value = [V[:,order[i]] for i in range(r)]
     return np.asarray(np.transpose(value)).astype(np.float64)
 
 
diff --git a/nipy/interfaces/spm.py b/nipy/interfaces/spm.py
@@ -97,7 +97,4 @@ def fname_presuffix(fname, prefix='', suffix='', use_ext=True):
 
 
 def fnames_presuffix(fnames, prefix='', suffix=''):
-    f2 = []
-    for fname in fnames:
-        f2.append(fname_presuffix(fname, prefix, suffix))
-    return f2
+    return [fname_presuffix(fname, prefix, suffix) for fname in fnames]
diff --git a/nipy/io/nifti_ref.py b/nipy/io/nifti_ref.py
@@ -345,10 +345,10 @@ def nipy2nifti(img, data_dtype=None, strict=None, fix0=True):
     # Use list() to get .index method for python < 2.6
     input_names = list(coordmap.function_domain.coord_names)
     spatial_names = input_names[:3]
-    dim_infos = []
-    for fps in 'freq', 'phase', 'slice':
-        dim_infos.append(
-            spatial_names.index(fps) if fps in spatial_names else None)
+    dim_infos = [
+        spatial_names.index(fps) if fps in spatial_names else None
+        for fps in ('freq', 'phase', 'slice')
+    ]
     hdr.set_dim_info(*dim_infos)
     # Set units without knowing time
     hdr.set_xyzt_units(xyz='mm')
diff --git a/nipy/labs/utils/reproducibility_measures.py b/nipy/labs/utils/reproducibility_measures.py
@@ -128,10 +128,11 @@ def get_cluster_position_from_thresholded_map(stat_map, domain, thr=3.0,
     coord = thresholded_domain.get_coord()
 
     # get the barycenters
-    baryc = []
-    for i in range(label.max() + 1):
-        if np.sum(label == i) >= csize:
-            baryc.append(np.mean(coord[label == i], 0))
+    baryc = [
+        np.mean(coord[label == i], 0)
+        for i in range(label.max() + 1)
+        if np.sum(label == i) >= csize
+    ]
 
     if len(baryc) == 0:
         return None
diff --git a/nipy/modalities/fmri/design_matrix.py b/nipy/modalities/fmri/design_matrix.py
@@ -131,9 +131,7 @@ def _make_drift(drift_model, frametimes, order=1, hfcut=128.):
         drift = _blank_drift(frametimes)
     else:
         raise NotImplementedError(f"Unknown drift model {drift_model!r}")
-    names = []
-    for k in range(drift.shape[1] - 1):
-        names.append('drift_%d' % (k + 1))
+    names = [f'drift_{k}' for k in range(1, drift.shape[1])]
     names.append('constant')
     return drift, names
 

Original file line number	Diff line number	Diff line change
`@@ -85,9 +85,7 @@`
`85`	`85`	`#`
`86`	`86`	`# http://www-stat.stanford.edu/~jtaylo/courses/stats191/data/kidney.table`
`87`	`87`
`88`		`-D = []`
`89`		`-for row in StringIO(data):`
`90`		`- D.append([float(val) for val in row.split()])`
	`88`	`+D = [[float(val) for val in row.split()] for row in StringIO(data)]`
`91`	`89`	`D = make_recarray(D, ['Days', 'Duration', 'Weight', 'ID'])`
`92`	`90`
`93`	`91`	`# Create the categorical regressors, known as Factors`