WHT: break long lines + pep8

4 files changed, 21 insertions, 14 deletions

diff --git a/benchmarks/benchmarks/bench_app.py b/benchmarks/benchmarks/bench_app.py
index 85aefe9d3..0e2aca64b 100644
--- a/benchmarks/benchmarks/bench_app.py
+++ b/benchmarks/benchmarks/bench_app.py
@@ -18,7 +18,9 @@ class LaplaceInplace(Benchmark):
         dy2 = (dy * dy)
 
         def num_update(u, dx2, dy2):
-            u[1:(-1), 1:(-1)] = ((((u[2:, 1:(-1)] + u[:(-2), 1:(-1)]) * dy2) + ((u[1:(-1), 2:] + u[1:(-1), :(-2)]) * dx2)) / (2 * (dx2 + dy2)))
+            u[1:(-1), 1:(-1)] = ((((u[2:, 1:(-1)] + u[:(-2), 1:(-1)]) * dy2) +
+                                  ((u[1:(-1), 2:] + u[1:(-1), :(-2)]) * dx2))
+                                 / (2 * (dx2 + dy2)))
 
         def num_inplace(u, dx2, dy2):
             tmp = u[:(-2), 1:(-1)].copy()
@@ -28,7 +30,8 @@ class LaplaceInplace(Benchmark):
             np.add(tmp2, u[1:(-1), :(-2)], out=tmp2)
             np.multiply(tmp2, dx2, out=tmp2)
             np.add(tmp, tmp2, out=tmp)
-            np.multiply(tmp, (1.0 / (2.0 * (dx2 + dy2))), out=u[1:(-1), 1:(-1)])
+            np.multiply(tmp, (1.0 / (2.0 * (dx2 + dy2))),
+                        out=u[1:(-1), 1:(-1)])
 
         def laplace(N, Niter=100, func=num_update, args=()):
             u = np.zeros([N, N], order='C')
@@ -55,7 +58,8 @@ class MaxesOfDots(Benchmark):
         nfeat = 100
         ntime = 200
 
-        self.arrays = [np.random.normal(size=(ntime, nfeat)) for i in xrange(nsubj)]
+        self.arrays = [np.random.normal(size=(ntime, nfeat))
+                       for i in xrange(nsubj)]
 
     def maxes_of_dots(self, arrays):
         """
diff --git a/benchmarks/benchmarks/bench_indexing.py b/benchmarks/benchmarks/bench_indexing.py
index 98024e991..4f2482ef8 100644
--- a/benchmarks/benchmarks/bench_indexing.py
+++ b/benchmarks/benchmarks/bench_indexing.py
@@ -23,9 +23,10 @@ class Indexing(Benchmark):
               'indexes_rand_': indexes_rand_}
 
         if sys.version_info[0] >= 3:
-            code = "def run():\n    for a in squares_.values(): a[%s]%s" % (sel, op)
+            code = "def run():\n    for a in squares_.values(): a[%s]%s"
         else:
-            code = "def run():\n    for a in squares_.itervalues(): a[%s]%s" % (sel, op)
+            code = "def run():\n    for a in squares_.itervalues(): a[%s]%s"
+        code = code % (sel, op)
 
         six.exec_(code, ns)
         self.func = ns['run']
diff --git a/benchmarks/benchmarks/bench_linalg.py b/benchmarks/benchmarks/bench_linalg.py
index eecea632d..c844cc79e 100644
--- a/benchmarks/benchmarks/bench_linalg.py
+++ b/benchmarks/benchmarks/bench_linalg.py
@@ -4,6 +4,7 @@ from .common import Benchmark, squares_, indexes_rand
 
 import numpy as np
 
+
 class Eindot(Benchmark):
     def setup(self):
         self.a = np.arange(60000.0).reshape(150, 400)
diff --git a/benchmarks/benchmarks/common.py b/benchmarks/benchmarks/common.py
index 9c5073c6c..c99b0afb8 100644
--- a/benchmarks/benchmarks/common.py
+++ b/benchmarks/benchmarks/common.py
@@ -16,19 +16,20 @@ nxs, nys = 100, 100
 
 # a set of interesting types to test
 TYPES1 = [
-        'int16', 'float16',
-        'int32', 'float32',
-        'int64', 'float64',  'complex64',
-                 'longfloat', 'complex128',
-                             'complex256',
-        ]
+    'int16', 'float16',
+    'int32', 'float32',
+    'int64', 'float64',  'complex64',
+    'longfloat', 'complex128',
+    'complex256',
+]
 
 # values which will be used to construct our sample data matrices
 # replicate 10 times to speed up initial imports of this helper
 # and generate some redundancy
 values = [random.uniform(0, 100) for x in range(nx*ny/10)]*10
 
-squares = {t: numpy.array(values, dtype=getattr(numpy, t)).reshape((nx, ny))
+squares = {t: numpy.array(values,
+                          dtype=getattr(numpy, t)).reshape((nx, ny))
            for t in TYPES1}
 
 # adjust complex ones to have non-degenerated imagery part -- use
@@ -38,9 +39,9 @@ for t, v in squares.iteritems():
         v += v.T*1j
 
 # smaller squares
-squares_ = {t: s[:nxs, :nys] for t, s  in squares.iteritems()}
+squares_ = {t: s[:nxs, :nys] for t, s in squares.iteritems()}
 # vectors
-vectors = {t: s[0] for t, s  in squares.iteritems()}
+vectors = {t: s[0] for t, s in squares.iteritems()}
 
 indexes = range(nx)
 # so we do not have all items