Autopep8 on all benchmarks

Makefile

@@ -118,7 +118,8 @@ test: all
 
 
 lint:
-	flake8 src test tools pyodide_build benchmark/benchmark.py benchmark/plot_benchmark.py
+	flake8 src test tools pyodide_build benchmark/benchmark.py benchmark/plot_benchmark.py \
+		benchmark/benchmarks/
 	clang-format -output-replacements-xml src/*.c src/*.h src/*.js | (! grep '<replacement ')
 
 

benchmark/benchmark.py

@@ -66,7 +66,7 @@ def parse_numpy_benchmark(filename):
     lines = []
     with open(filename) as fp:
         for line in fp:
-            m = re.match('^#(setup|run): (.*)$', line)
+            m = re.match('^#\s*(setup|run): (.*)$', line)
             if m:
                 line = '{} = {!r}\n'.format(m.group(1), m.group(2))
             lines.append(line)

benchmark/benchmarks/README.md

-From https://github.com/serge-sans-paille/numpy-benchmarks
+From https: // github.com / serge - sans - paille / numpy - benchmarks

benchmark/benchmarks/allpairs_distances.py

-#setup: import numpy as np ; N = 50 ; X, Y = np.random.randn(100,N), np.random.randn(40,N)
-#run: allpairs_distances(X, Y)
+# setup: import numpy as np ; N = 50 ; X, Y = np.random.randn(100,N), np.random.randn(40,N)  # noqa
+# run: allpairs_distances(X, Y)
 
-#pythran export allpairs_distances(float64[][], float64[][])
+# pythran export allpairs_distances(float64[][], float64[][])
 import numpy as np
 
 
@@ -11,4 +11,4 @@ def allpairs_distances(A, B):
     """
     A2 = np.einsum('ij,ij->i', A, A)
     B2 = np.einsum('ij,ij->i', B, B)
-    return A2[:, None] + B2[None, :] - 2*np.dot(A, B.T)
+    return A2[:, None] + B2[None, :] - 2 * np.dot(A, B.T)

benchmark/benchmarks/allpairs_distances_loops.py

-#setup: import numpy as np ; N = 50 ; X, Y = np.random.randn(30,N), np.random.randn(20,N)
-#run: allpairs_distances_loops(X, Y)
+# setup: import numpy as np ; N = 50 ; X, Y = np.random.randn(30,N), np.random.randn(20,N)  # noqa
+# run: allpairs_distances_loops(X, Y)
 
-#pythran export allpairs_distances_loops(float64[][], float64[][])
+# pythran export allpairs_distances_loops(float64[][], float64[][])
 import numpy as np
 
-def allpairs_distances_loops(X,Y):
-  result = np.zeros( (X.shape[0], Y.shape[0]), X.dtype)
-  for i in range(X.shape[0]):
-    for j in range(Y.shape[0]):
-      result[i,j] = np.sum( (X[i,:] - Y[j,:]) ** 2)
-  return result
+
+def allpairs_distances_loops(X, Y):
+    result = np.zeros((X.shape[0], Y.shape[0]), X.dtype)
+    for i in range(X.shape[0]):
+        for j in range(Y.shape[0]):
+            result[i, j] = np.sum((X[i, :] - Y[j, :]) ** 2)
+    return result

benchmark/benchmarks/arc_distance.py

-#setup: N = 5000 ; import numpy as np ; t0, p0, t1, p1 = np.random.randn(N), np.random.randn(N), np.random.randn(N), np.random.randn(N)
-#run: arc_distance(t0, p0, t1, p1)
+# setup: N = 5000 ; import numpy as np ; t0, p0, t1, p1 = np.random.randn(N), np.random.randn(N), np.random.randn(N), np.random.randn(N)  # noqa
+# run: arc_distance(t0, p0, t1, p1)
 
-#pythran export arc_distance(float64 [], float64[], float64[], float64[])
+# pythran export arc_distance(float64 [], float64[], float64[], float64[])
 
 import numpy as np
 
@@ -10,7 +10,7 @@ def arc_distance(theta_1, phi_1, theta_2, phi_2):
     """
     Calculates the pairwise arc distance between all points in vector a and b.
     """
-    temp = (np.sin((theta_2-theta_1)/2)**2
-            + np.cos(theta_1) * np.cos(theta_2) * np.sin((phi_2-phi_1)/2)**2)
-    distance_matrix = 2 * (np.arctan2(np.sqrt(temp), np.sqrt(1-temp)))
+    temp = (np.sin((theta_2 - theta_1) / 2)**2 + np.cos(theta_1)
+            * np.cos(theta_2) * np.sin((phi_2 - phi_1) / 2)**2)
+    distance_matrix = 2 * (np.arctan2(np.sqrt(temp), np.sqrt(1 - temp)))
     return distance_matrix

benchmark/benchmarks/check_mask.py

-#setup: n=100 ; import numpy as np; db = np.array(np.random.randint(2, size=(n, 4)), dtype=bool)
-#run: check_mask(db)
-#from: http://stackoverflow.com/questions/34500913/numba-slower-for-numpy-bitwise-and-on-boolean-arrays
+# setup: n=100 ; import numpy; db = numpy.random.randint(2, size=(n, 4), dtype='bool')  # noqa
+# run: check_mask(db)
+# from:
+# http://stackoverflow.com/questions/34500913/numba-slower-for-numpy-bitwise-and-on-boolean-arrays
 
-#pythran export check_mask(bool[][])
+# pythran export check_mask(bool[][])
 import numpy as np
+
+
 def check_mask(db, mask=[1, 0, 1]):
-    out = np.zeros(db.shape[0],dtype=bool)
+    out = np.zeros(db.shape[0], dtype=bool)
     for idx, line in enumerate(db):
         target, vector = line[0], line[1:]
         if (mask == np.bitwise_and(mask, vector)).all():

benchmark/benchmarks/create_grid.py

-#from: http://stackoverflow.com/questions/13815719/creating-grid-with-numpy-performance
-#pythran export create_grid(float [])
-#setup: import numpy as np ; N = 800 ; x = np.arange(0,1,1./N)
-#run: create_grid(x)
+# http://stackoverflow.com/questions/13815719/creating-grid-with-numpy-performance
+# pythran export create_grid(float [])
+# setup: import numpy as np ; N = 800 ; x = np.arange(0,1,1./N)
+# run: create_grid(x)
 import numpy as np
 
 
 def create_grid(x):
     N = x.shape[0]
     z = np.zeros((N, N, 3))
-    z[:,:,0] = x.reshape(-1,1)
-    z[:,:,1] = x
-    fast_grid = z.reshape(N*N, 3)
+    z[:, :, 0] = x.reshape(-1, 1)
+    z[:, :, 1] = x
+    fast_grid = z.reshape(N * N, 3)
     return fast_grid

benchmark/benchmarks/cronbach.py

-#from: http://stackoverflow.com/questions/20799403/improving-performance-of-cronbach-alpha-code-python-numpy
-#pythran export cronbach(float [][])
-#setup: import numpy as np ; N = 600 ; items = np.random.rand(N,N)
-#run: cronbach(items)
+# http://stackoverflow.com/questions/20799403/improving-performance-of-cronbach-alpha-code-python-numpy
+# pythran export cronbach(float [][])
+# setup: import numpy as np ; N = 600 ; items = np.random.rand(N,N)
+# run: cronbach(items)
 
 
 def cronbach(itemscores):
     itemvars = itemscores.var(axis=1, ddof=1)
     tscores = itemscores.sum(axis=0)
     nitems = len(itemscores)
-    return nitems / (nitems-1) * (1 - itemvars.sum() / tscores.var(ddof=1))
+    return nitems / (nitems - 1) * (1 - itemvars.sum() / tscores.var(ddof=1))

benchmark/benchmarks/diffusion.py

-#setup: import numpy as np;lx,ly=(2**6,2**6);u=np.zeros([lx,ly],dtype=np.double);u[lx//2,ly//2]=1000.0;tempU=np.zeros([lx,ly],dtype=np.double)
-#run: diffusion(u,tempU,100)
+# setup: import numpy as np;lx,ly=(2**6,2**6);u=np.zeros([lx,ly],dtype=np.double);u[lx//2,ly//2]=1000.0;tempU=np.zeros([lx,ly],dtype=np.double)  # noqa
+# run: diffusion(u,tempU,100)
 
-#pythran export diffusion(float [][], float [][], int)
-import numpy as np
+# pythran export diffusion(float [][], float [][], int)
 
 
 def diffusion(u, tempU, iterNum):

benchmark/benchmarks/evolve.py

-#setup: import numpy as np ; grid_shape = (512, 512) ; grid = np.zeros(grid_shape) ; block_low = int(grid_shape[0] * .4) ; block_high = int(grid_shape[0] * .5) ; grid[block_low:block_high, block_low:block_high] = 0.005
-#run: evolve(grid, 0.1)
-#from: High Performance Python by Micha Gorelick and Ian Ozsvald, http://shop.oreilly.com/product/0636920028963.do
+# setup: import numpy as np ; grid_shape = (512, 512) ; grid = np.zeros(grid_shape) ; block_low = int(grid_shape[0] * .4) ; block_high = int(grid_shape[0] * .5) ; grid[block_low:block_high, block_low:block_high] = 0.005  # noqa
+# run: evolve(grid, 0.1)
+# from: High Performance Python by Micha Gorelick and Ian Ozsvald,
+# http://shop.oreilly.com/product/0636920028963.do
 
-#pythran export evolve(float64[][], float)
+# pythran export evolve(float64[][], float)
 
 import numpy as np
 

benchmark/benchmarks/fdtd.py

-#from: http://stackoverflow.com/questions/19367488/converting-function-to-numbapro-cuda
-#setup: N = 10 ; import numpy ; a = numpy.random.rand(N,N)
-#run: fdtd(a,10)
+# http://stackoverflow.com/questions/19367488/converting-function-to-numbapro-cuda
+# setup: N = 10 ; import numpy ; a = numpy.random.rand(N,N)
+# run: fdtd(a,10)
 
-#pythran export fdtd(float[][], int)
+# pythran export fdtd(float[][], int)
 import numpy as np
 
+
 def fdtd(input_grid, steps):
     grid = input_grid.copy()
     old_grid = np.zeros_like(input_grid)
@@ -19,17 +20,17 @@ def fdtd(input_grid, steps):
 
         for x in range(l_x):
             for y in range(l_y):
-                grid[x,y] = 0.0
-                if 0 < x+1 < l_x:
-                    grid[x,y] += old_grid[x+1,y]
-                if 0 < x-1 < l_x:
-                    grid[x,y] += old_grid[x-1,y]
-                if 0 < y+1 < l_y:
-                    grid[x,y] += old_grid[x,y+1]
-                if 0 < y-1 < l_y:
-                    grid[x,y] += old_grid[x,y-1]
-
-                grid[x,y] /= 2.0
-                grid[x,y] -= previous_grid[x,y]
+                grid[x, y] = 0.0
+                if 0 < x + 1 < l_x:
+                    grid[x, y] += old_grid[x + 1, y]
+                if 0 < x - 1 < l_x:
+                    grid[x, y] += old_grid[x - 1, y]
+                if 0 < y + 1 < l_y:
+                    grid[x, y] += old_grid[x, y + 1]
+                if 0 < y - 1 < l_y:
+                    grid[x, y] += old_grid[x, y - 1]
+
+                grid[x, y] /= 2.0
+                grid[x, y] -= previous_grid[x, y]
 
     return grid

benchmark/benchmarks/fft.py

-#setup: N = 2**11 ; import numpy ; a = numpy.array(numpy.random.rand(N), dtype=complex)
-#run: fft(a)
+# setup: N = 2**11 ; import numpy ; a = numpy.array(numpy.random.rand(N), dtype=complex)  # noqa
+# run: fft(a)
 
-#pythran export fft(complex [])
+# pythran export fft(complex [])
 
 import numpy as np
 
+
 def fft(x):
     return np.fft(x)

benchmark/benchmarks/grayscott.py

-#from http://stackoverflow.com/questions/26823312/numba-or-cython-acceleration-in-reaction-diffusion-algorithm
-#setup: pass
-#run: grayscott(40, 0.16, 0.08, 0.04, 0.06)
+# http://stackoverflow.com/questions/26823312/numba-or-cython-acceleration-in-reaction-diffusion-algorithm
+# setup: pass
+# run: grayscott(40, 0.16, 0.08, 0.04, 0.06)
 
-#pythran export grayscott(int, float, float, float, float)
+# pythran export grayscott(int, float, float, float, float)
 import numpy as np
 
+
 def grayscott(counts, Du, Dv, F, k):
     n = 100
-    U = np.zeros((n+2,n+2), dtype=np.float32)
-    V = np.zeros((n+2,n+2), dtype=np.float32)
-    u, v = U[1:-1,1:-1], V[1:-1,1:-1]
+    U = np.zeros((n + 2, n + 2), dtype=np.float32)
+    V = np.zeros((n + 2, n + 2), dtype=np.float32)
+    u, v = U[1:-1, 1:-1], V[1:-1, 1:-1]
 
     r = 20
     u[:] = 1.0
-    U[n//2-r:n//2+r,n//2-r:n//2+r] = 0.50
-    V[n//2-r:n//2+r,n//2-r:n//2+r] = 0.25
-    u += 0.15*np.random.random((n,n))
-    v += 0.15*np.random.random((n,n))
+    U[n // 2 - r:n // 2 + r, n // 2 - r:n // 2 + r] = 0.50
+    V[n // 2 - r:n // 2 + r, n // 2 - r:n // 2 + r] = 0.25
+    u += 0.15 * np.random.random((n, n))
+    v += 0.15 * np.random.random((n, n))
 
     for i in range(counts):
-        Lu = (                 U[0:-2,1:-1] +
-              U[1:-1,0:-2] - 4*U[1:-1,1:-1] + U[1:-1,2:] +
-                               U[2:  ,1:-1] )
-        Lv = (                 V[0:-2,1:-1] +
-              V[1:-1,0:-2] - 4*V[1:-1,1:-1] + V[1:-1,2:] +
-                               V[2:  ,1:-1] )
-        uvv = u*v*v
-        u += Du*Lu - uvv + F*(1 - u)
-        v += Dv*Lv + uvv - (F + k)*v
+        Lu = (U[0:-2, 1:-1] +
+              U[1:-1, 0:-2] - 4 * U[1:-1, 1:-1] + U[1:-1, 2:] +
+              U[2:, 1:-1])
+        Lv = (V[0:-2, 1:-1] +
+              V[1:-1, 0:-2] - 4 * V[1:-1, 1:-1] + V[1:-1, 2:] +
+              V[2:, 1:-1])
+        uvv = u * v * v
+        u += Du * Lu - uvv + F * (1 - u)
+        v += Dv * Lv + uvv - (F + k) * v
 
     return V

benchmark/benchmarks/grouping.py

-#from: http://stackoverflow.com/questions/4651683/numpy-grouping-using-itertools-groupby-performance
-#setup: import numpy as np ; N = 350000 ; values = np.array(np.random.randint(0,3298,size=N),dtype='u4') ; values.sort()
-#run: grouping(values)
+# http://stackoverflow.com/questions/4651683/numpy-grouping-using-itertools-groupby-performance
+# setup: import numpy as np ; N = 350000 ; values = np.array(np.random.randint(0,3298,size=N),dtype='u4') ; values.sort()  # noqa
+# run: grouping(values)
+
+# pythran export grouping(uint32 [])
 
-#pythran export grouping(uint32 [])
 
 def grouping(values):
     import numpy as np

benchmark/benchmarks/growcut.py

-#from: http://continuum.io/blog/numba_performance
-#setup: N = 10 ; import numpy as np ; image = np.random.rand(N, N, 3) ; state = np.zeros((N, N, 2)) ; state_next = np.empty_like(state) ; state[0, 0, 0] = state[0, 0, 1] = 1
-#run: growcut(image, state, state_next, 2)
+# from: http://continuum.io/blog/numba_performance
+# setup: N = 10 ; import numpy as np ; image = np.random.rand(N, N, 3) ; state = np.zeros((N, N, 2)) ; state_next = np.empty_like(state) ; state[0, 0, 0] = state[0, 0, 1] = 1   # noqa
+# run: growcut(image, state, state_next, 2)
 
-#pythran export growcut(float[][][], float[][][], float[][][], int)
+# pythran export growcut(float[][][], float[][][], float[][][], int)
 import math
-import numpy as np
+
+
 def window_floor(idx, radius):
     if radius > idx:
         return 0
@@ -18,6 +19,7 @@ def window_ceil(idx, ceil, radius):
     else:
         return idx + radius
 
+
 def growcut(image, state, state_next, window_radius):
     changes = 0
     sqrt_3 = math.sqrt(3.0)
@@ -32,16 +34,16 @@ def growcut(image, state, state_next, window_radius):
             defense_strength = state[i, j, 1]
 
             for jj in range(window_floor(j, window_radius),
-                             window_ceil(j+1, width, window_radius)):
+                            window_ceil(j + 1, width, window_radius)):
                 for ii in range(window_floor(i, window_radius),
-                                 window_ceil(i+1, height, window_radius)):
+                                window_ceil(i + 1, height, window_radius)):
                     if (ii != i and jj != j):
                         d = image[i, j, 0] - image[ii, jj, 0]
                         s = d * d
                         for k in range(1, 3):
                             d = image[i, j, k] - image[ii, jj, k]
                             s += d * d
-                        gval = 1.0 - math.sqrt(s)/sqrt_3
+                        gval = 1.0 - math.sqrt(s) / sqrt_3
 
                         attack_strength = gval * state[ii, jj, 1]
 

benchmark/benchmarks/harris.py

-#from: parakeet testbed
-#setup: import numpy as np ; M, N = 512, 512 ; I = np.random.randn(M,N)
-#run: harris(I)
-
-#pythran export harris(float64[][])
-import numpy as np
+# from: parakeet testbed
+# setup: import numpy as np ; M, N = 512, 512 ; I = np.random.randn(M,N)
+# run: harris(I)
 
+# pythran export harris(float64[][])
 
 
 def harris(I):
-  m,n = I.shape
-  dx = (I[1:, :] - I[:m-1, :])[:, 1:]
-  dy = (I[:, 1:] - I[:, :n-1])[1:, :]
+    m, n = I.shape
+    dx = (I[1:, :] - I[:m - 1, :])[:, 1:]
+    dy = (I[:, 1:] - I[:, :n - 1])[1:, :]
 
-  #
-  #   At each point we build a matrix
-  #   of derivative products
-  #   M =
-  #   | A = dx^2     C = dx * dy |
-  #   | C = dy * dx  B = dy * dy |
-  #
-  #   and the score at that point is:
-  #      det(M) - k*trace(M)^2
-  #
-  A = dx * dx
-  B = dy * dy
-  C = dx * dy
-  tr = A + B
-  det = A * B - C * C
-  k = 0.05
-  return det - k * tr * tr
+    #
+    #   At each point we build a matrix
+    #   of derivative products
+    #   M =
+    #   | A = dx^2     C = dx * dy |
+    #   | C = dy * dx  B = dy * dy |
+    #
+    #   and the score at that point is:
+    #      det(M) - k*trace(M)^2
+    #
+    A = dx * dx
+    B = dy * dy
+    C = dx * dy
+    tr = A + B
+    det = A * B - C * C
+    k = 0.05
+    return det - k * tr * tr

benchmark/benchmarks/hasting.py

-#from: http://wiki.scipy.org/Cookbook/Theoretical_Ecology/Hastings_and_Powell
-#setup: import numpy as np ; y = np.random.rand(3) ; args = np.random.rand(7)
-#run: hasting(y, *args)
+# from: http://wiki.scipy.org/Cookbook/Theoretical_Ecology/Hastings_and_Powell
+# setup: import numpy as np ; y = np.random.rand(3) ; args = np.random.rand(7)
+# run: hasting(y, *args)
 
-#pythran export hasting(float [], float, float, float, float, float, float, float)
+# pythran export hasting(float [], float, float, float, float, float,
+# float, float)
 import numpy as np
+
+
 def hasting(y, t, a1, a2, b1, b2, d1, d2):
     yprime = np.empty((3,))
-    yprime[0] = y[0] * (1. - y[0]) - a1*y[0]*y[1]/(1. + b1 * y[0])
-    yprime[1] = a1*y[0]*y[1] / (1. + b1 * y[0]) - a2 * y[1]*y[2] / (1. + b2 * y[1]) - d1 * y[1]
-    yprime[2] = a2*y[1]*y[2]/(1. + b2*y[1]) - d2*y[2]
+    yprime[0] = y[0] * (1. - y[0]) - a1 * y[0] * y[1] / (1. + b1 * y[0])
+    yprime[1] = a1 * y[0] * y[1] / (1. + b1 * y[0]) - \
+        a2 * y[1] * y[2] / (1. + b2 * y[1]) - d1 * y[1]
+    yprime[2] = a2 * y[1] * y[2] / (1. + b2 * y[1]) - d2 * y[2]
     return yprime

benchmark/benchmarks/hyantes.py

-#setup: import numpy ; a = numpy.array([ [i/10., i/10., i/20.] for i in range(44440)], dtype=numpy.double)
-#run: hyantes(0, 0, 90, 90, 1, 100, 80, 80, a)
+# setup: import numpy ; a = numpy.array([ [i/10., i/10., i/20.] for i in range(44440)], dtype=numpy.double)  # noqa
+# run: hyantes(0, 0, 90, 90, 1, 100, 80, 80, a)
 
-#pythran export hyantes(float, float, float, float, float, float, int, int, float[][])
+# pythran export hyantes(float, float, float, float, float, float, int,
+# int, float[][])
 import numpy as np
+
+
 def hyantes(xmin, ymin, xmax, ymax, step, range_, range_x, range_y, t):
-    X,Y = t.shape
-    pt = np.zeros((X,Y))
+    X, Y = t.shape
+    pt = np.zeros((X, Y))
     for i in range(X):
         for j in range(Y):
             for k in t:
-                tmp = 6368.* np.arccos( np.cos(xmin+step*i)*np.cos( k[0] ) * np.cos((ymin+step*j)-k[1])+  np.sin(xmin+step*i)*np.sin(k[0]))
+                tmp = (6368. * np.arccos(np.cos(xmin + step * i)
+                       * np.cos(k[0]) * np.cos((ymin + step * j) - k[1])
+                       + np.sin(xmin + step * i) * np.sin(k[0])))
                 if tmp < range_:
-                    pt[i,j]+=k[2] / (1+tmp)
+                    pt[i, j] += k[2] / (1 + tmp)
     return pt

benchmark/benchmarks/julia.py

-#setup: N=10
-#run: julia(1., 1., N, 1.5, 10., 1e4)
+# setup: N=10
+# run: julia(1., 1., N, 1.5, 10., 1e4)
 
-#pythran export julia(float, float, int, float, float, float)
+# pythran export julia(float, float, int, float, float, float)
 import numpy as np
-from time import time
+
 
 def kernel(zr, zi, cr, ci, lim, cutoff):
     ''' Computes the number of iterations `n` such that
         |z_n| > `lim`, where `z_n = z_{n-1}**2 + c`.
     '''
     count = 0
-    while ((zr*zr + zi*zi) < (lim*lim)) and count < cutoff:
+    while ((zr * zr + zi * zi) < (lim * lim)) and count < cutoff:
         zr, zi = zr * zr - zi * zi + cr, 2 * zr * zi + ci
         count += 1
     return count
 
+
 def julia(cr, ci, N, bound=1.5, lim=1000., cutoff=1e6):
     ''' Pure Python calculation of the Julia set for a given `c`.  No NumPy
         array operations are used.
@@ -23,5 +24,5 @@ def julia(cr, ci, N, bound=1.5, lim=1000., cutoff=1e6):
     grid_x = np.linspace(-bound, bound, N)
     for i, x in enumerate(grid_x):
         for j, y in enumerate(grid_x):
-            julia[i,j] = kernel(x, y, cr, ci, lim, cutoff)
+            julia[i, j] = kernel(x, y, cr, ci, lim, cutoff)
     return julia

benchmark/benchmarks/l2norm.py

-#from: http://stackoverflow.com/questions/7741878/how-to-apply-numpy-linalg-norm-to-each-row-of-a-matrix/7741976#7741976
-#setup: import numpy as np ; N = 1000; x = np.random.rand(N,N)
-#run: l2norm(x)
+# http://stackoverflow.com/questions/7741878/how-to-apply-numpy-linalg-norm-to-each-row-of-a-matrix/7741976#7741976
+# setup: import numpy as np ; N = 1000; x = np.random.rand(N,N)
+# run: l2norm(x)
 
-#pythran export l2norm(float64[][])
+# pythran export l2norm(float64[][])
 import numpy as np
 
 

benchmark/benchmarks/local_maxima.py

-#from: https://github.com/iskandr/parakeet/blob/master/benchmarks/nd_local_maxima.py
-#setup: import numpy as np ; shape = (3,2,3,2) ; x = np.arange(36, dtype=np.float64).reshape(*shape)
-#run: local_maxima(x)
+# https://github.com/iskandr/parakeet/blob/master/benchmarks/nd_local_maxima.py
+# setup: import numpy as np ; shape = (3,2,3,2) ; x = np.arange(36, dtype=np.float64).reshape(*shape)  # noqa
+# run: local_maxima(x)
 
-#pythran export local_maxima(float [][][][])
+# pythran export local_maxima(float [][][][])
 import numpy as np
 
 
@@ -11,12 +11,12 @@ def wrap(pos, offset, bound):
 
 
 def clamp(pos, offset, bound):
-    return min(bound-1, max(0, pos+offset))
+    return min(bound - 1, max(0, pos + offset))
 
 
 def reflect(pos, offset, bound):
-    idx = pos+offset
-    return min(2*(bound-1)-idx, max(idx, -idx))
+    idx = pos + offset
+    return min(2 * (bound - 1) - idx, max(idx, -idx))
 
 
 def local_maxima(data, mode=wrap):
@@ -25,7 +25,7 @@ def local_maxima(data, mode=wrap):
     for pos in np.ndindex(data.shape):
         myval = data[pos]
         for offset in np.ndindex(wsize):
-            neighbor_idx = tuple(mode(p, o-w//2, w)
+            neighbor_idx = tuple(mode(p, o - w // 2, w)
                                  for (p, o, w) in zip(pos, offset, wsize))
             result[pos] &= (data[neighbor_idx] <= myval)
     return result

benchmark/benchmarks/log_likelihood.py

-#setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1
-#run: log_likelihood(a, b, c)
-#from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
+# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1  # noqa
+# run: log_likelihood(a, b, c)
+# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
 import numpy
 
-#pythran export log_likelihood(float64[], float64, float64)
+# pythran export log_likelihood(float64[], float64, float64)
+
 
 def log_likelihood(data, mean, sigma):
     s = (data - mean) ** 2 / (2 * (sigma ** 2))

benchmark/benchmarks/lstsqr.py

-#setup: import numpy as np ; N = 500000 ; X, Y = np.random.rand(N), np.random.rand(N)
-#run: lstsqr(X, Y)
-#from: http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb
+# setup: import numpy as np ; N = 500000 ; X, Y = np.random.rand(N), np.random.rand(N)  # noqa
+# run: lstsqr(X, Y)
+# from:
+# http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb
 
-#pythran export lstsqr(float[], float[])
+# pythran export lstsqr(float[], float[])
 import numpy as np
 
 
@@ -11,9 +12,8 @@ def lstsqr(x, y):
     x_avg = np.average(x)
     y_avg = np.average(y)
     dx = x - x_avg
-    dy = y - y_avg
     var_x = np.sum(dx**2)
     cov_xy = np.sum(dx * (y - y_avg))
     slope = cov_xy / var_x
-    y_interc = y_avg - slope*x_avg
+    y_interc = y_avg - slope * x_avg
     return (slope, y_interc)

benchmark/benchmarks/mandel.py

-#setup: import numpy as np; image = np.zeros((64, 32), dtype = np.uint8)
-#run: mandel(-2.0, 1.0, -1.0, 1.0, image, 20)
+# setup: import numpy as np; image = np.zeros((64, 32), dtype = np.uint8)
+# run: mandel(-2.0, 1.0, -1.0, 1.0, image, 20)
 
-#pythran export mandel(float, float, float, float, uint8[][], int)
+# pythran export mandel(float, float, float, float, uint8[][], int)
 
 
 def kernel(x, y, max_iters):
@@ -13,8 +13,8 @@ def kernel(x, y, max_iters):
     c = complex(x, y)
     z = 0.0j
     for i in range(max_iters):
-        z = z*z + c
-        if (z.real*z.real + z.imag*z.imag) >= 4:
+        z = z * z + c
+        if (z.real * z.real + z.imag * z.imag) >= 4:
             return i
 
     return max_iters

benchmark/benchmarks/multiple_sum.py

-#from http://stackoverflow.com/questions/77999777799977/numpy-vs-cython-speed
-#pythran export multiple_sum(float[][])
-#setup: import numpy as np ; r = np.random.rand(100,100)
-#run: multiple_sum(r)
+# from http://stackoverflow.com/questions/77999777799977/numpy-vs-cython-speed
+# pythran export multiple_sum(float[][])
+# setup: import numpy as np ; r = np.random.rand(100,100)
+# run: multiple_sum(r)
 import numpy as np
 
 

benchmark/benchmarks/pairwise_loop.py

-#from: http://jakevdp.github.com/blog/2012/08/24/numba-vs-cython/
-#setup: import numpy as np ; X = np.linspace(0,10,200).reshape(20,10)
-#run: pairwise_loop(X)
+# from: http://jakevdp.github.com/blog/2012/08/24/numba-vs-cython/
+# setup: import numpy as np ; X = np.linspace(0,10,200).reshape(20,10)
+# run: pairwise_loop(X)
 
-#pythran export pairwise_loop(float [][])
+# pythran export pairwise_loop(float [][])
 
 import numpy as np
+
+
 def pairwise_loop(X):
     M, N = X.shape
-    D = np.empty((M,M))
+    D = np.empty((M, M))
     for i in range(M):
         for j in range(M):
             d = 0.0
             for k in range(N):
-                tmp = X[i,k] - X[j,k]
+                tmp = X[i, k] - X[j, k]
                 d += tmp * tmp
-            D[i,j] = np.sqrt(d)
+            D[i, j] = np.sqrt(d)
     return D

benchmark/benchmarks/periodic_dist.py

-#setup: import numpy as np ; N = 20 ; x = y = z = np.arange(0., N, 0.1) ; L = 4 ; periodic = True
-#run: periodic_dist(x, x, x, L,periodic, periodic, periodic)
+# setup: import numpy as np ; N = 20 ; x = y = z = np.arange(0., N, 0.1) ; L = 4 ; periodic = True  # noqa
+# run: periodic_dist(x, x, x, L,periodic, periodic, periodic)
+
+# pythran export periodic_dist(float [], float[], float[], int, bool,
+# bool, bool)
+import numpy as np
 
-#pythran export periodic_dist(float [], float[], float[], int, bool, bool, bool)
-import numpy as np 
 
 def periodic_dist(x, y, z, L, periodicX, periodicY, periodicZ):
-    " ""Computes distances between all particles and places the result in a matrix such that the ij th matrix entry corresponds to the distance between particle i and j"" "
+    """Computes distances between all particles and places the result
+    in a matrix such that the ij th matrix entry corresponds to the
+    distance between particle i and j"""
     N = len(x)
-    xtemp = np.tile(x,(N,1))
+    xtemp = np.tile(x, (N, 1))
     dx = xtemp - xtemp.T
-    ytemp = np.tile(y,(N,1))
+    ytemp = np.tile(y, (N, 1))
     dy = ytemp - ytemp.T
-    ztemp = np.tile(z,(N,1))
+    ztemp = np.tile(z, (N, 1))
     dz = ztemp - ztemp.T
 
     # Particles 'feel' each other across the periodic boundaries
     if periodicX:
-        dx[dx>L/2]=dx[dx > L/2]-L
-        dx[dx<-L/2]=dx[dx < -L/2]+L
+        dx[dx > L / 2] = dx[dx > L / 2] - L
+        dx[dx < -L / 2] = dx[dx < -L / 2] + L
 
     if periodicY:
-        dy[dy>L/2]=dy[dy>L/2]-L
-        dy[dy<-L/2]=dy[dy<-L/2]+L
+        dy[dy > L / 2] = dy[dy > L / 2] - L
+        dy[dy < -L / 2] = dy[dy < -L / 2] + L
 
     if periodicZ:
-        dz[dz>L/2]=dz[dz>L/2]-L
-        dz[dz<-L/2]=dz[dz<-L/2]+L
+        dz[dz > L / 2] = dz[dz > L / 2] - L
+        dz[dz < -L / 2] = dz[dz < -L / 2] + L
 
     # Total Distances
-    d = np.sqrt(dx**2+dy**2+dz**2)
+    d = np.sqrt(dx**2 + dy**2 + dz**2)
 
     # Mark zero entries with negative 1 to avoid divergences
-    d[d==0] = -1
+    d[d == 0] = -1
 
     return d, dx, dy, dz

benchmark/benchmarks/repeating.py

-#from: http://stackoverflow.com/questions/14553331/how-to-improve-numpy-performance-in-this-short-code
-#pythran export repeating(float[], int)
-#setup: import numpy as np ; a = np.random.rand(10000)
-#run: repeating(a, 20)
+# http://stackoverflow.com/questions/14553331/how-to-improve-numpy-performance-in-this-short-code
+# pythran export repeating(float[], int)
+# setup: import numpy as np ; a = np.random.rand(10000)
+# run: repeating(a, 20)
 
 import numpy as np
 
+
 def repeating(x, nvar_y):
     nvar_x = x.shape[0]
-    y = np.empty(nvar_x*(1+nvar_y))
+    y = np.empty(nvar_x * (1 + nvar_y))
     y[0:nvar_x] = x[0:nvar_x]
-    y[nvar_x:] = np.repeat(x,nvar_y)
+    y[nvar_x:] = np.repeat(x, nvar_y)
     return y

benchmark/benchmarks/reverse_cumsum.py

-#from: http://stackoverflow.com/questions/16541618/perform-a-reverse-cumulative-sum-on-a-numpy-array
-#pythran export reverse_cumsum(float[])
-#setup: import numpy as np ; r = np.random.rand(1000000)
-#run: reverse_cumsum(r)
+# http://stackoverflow.com/questions/16541618/perform-a-reverse-cumulative-sum-on-a-numpy-array
+# pythran export reverse_cumsum(float[])
+# setup: import numpy as np ; r = np.random.rand(1000000)
+# run: reverse_cumsum(r)
 import numpy as np
 
+
 def reverse_cumsum(x):
     return np.cumsum(x[::-1])[::-1]

benchmark/benchmarks/rosen.py

-#setup: import numpy as np; r = np.arange(1000000, dtype=float)
-#run: rosen(r)
+# setup: import numpy as np; r = np.arange(1000000, dtype=float)
+# run: rosen(r)
 import numpy as np
 
-#pythran export rosen(float[])
+# pythran export rosen(float[])
 
 
 def rosen(x):

benchmark/benchmarks/slowparts.py

-#from: https://groups.google.com/forum/#!topic/parakeet-python/p-flp2kdE4U
-#setup: import numpy as np ;d = 10 ;re = 5 ;params = (d, re, np.ones((2*d, d+1, re)), np.ones((d, d+1, re)),  np.ones((d, 2*d)), np.ones((d, 2*d)), np.ones((d+1, re, d)), np.ones((d+1, re, d)), 1)
-#run: slowparts(*params)
+# from: https://groups.google.com/forum/#!topic/parakeet-python/p-flp2kdE4U
+# setup: import numpy as np ;d = 10 ;re = 5 ;params = (d, re, np.ones((2*d, d+1, re)), np.ones((d, d+1, re)),  np.ones((d, 2*d)), np.ones((d, 2*d)), np.ones((d+1, re, d)), np.ones((d+1, re, d)), 1) # noqa
+# run: slowparts(*params)
 
-#pythran export slowparts(int, int, float [][][], float [][][], float [][], float [][], float [][][], float [][][], int)
+# pythran export slowparts(int, int, float [][][], float [][][], float
+# [][], float [][], float [][][], float [][][], int)
 from numpy import zeros, power, tanh
 
 
 def slowparts(d, re, preDz, preWz, SRW, RSW, yxV, xyU, resid):
     """ computes the linear algebra intensive part of the gradients of the grae
     """
-    fprime = lambda x: 1 - power(tanh(x), 2)
+    def fprime(x): return 1 - power(tanh(x), 2)
 
-    partialDU = zeros((d+1, re, 2*d, d))
-    for k in range(2*d):
+    partialDU = zeros((d + 1, re, 2 * d, d))
+    for k in range(2 * d):
         for i in range(d):
-            partialDU[:,:,k,i] = fprime(preDz[k]) * fprime(preWz[i]) * (SRW[i,k] + RSW[i,k]) * yxV[:,:,i]
+            partialDU[:, :, k, i] = (
+                fprime(preDz[k]) * fprime(preWz[i])
+                * (SRW[i, k] + RSW[i, k]) * yxV[:, :, i])
 
     return partialDU

benchmark/benchmarks/smoothing.py

-#setup: import numpy as np ; N = 1000 ; a = np.arange(0,1,N)
-#run: smoothing(a, .4)
-#from: http://www.parakeetpython.com/
+# setup: import numpy as np ; N = 1000 ; a = np.arange(0,1,N)
+# run: smoothing(a, .4)
+# from: http://www.parakeetpython.com/
+
+# pythran export smoothing(float[], float)
 
-#pythran export smoothing(float[], float)
 
 def smoothing(x, alpha):
-  """
-  Exponential smoothing of a time series
-  For x = 10**6 floats
-  - Python runtime: 9 seconds
-  - Parakeet runtime: .01 seconds
-  """
-  s = x.copy()
-  for i in range(1, len(x)):
-    s[i] = alpha * x[i] + (1 - alpha) * s[i-1]
-  return s
+    """
+    Exponential smoothing of a time series
+    For x = 10**6 floats
+    - Python runtime: 9 seconds
+    - Parakeet runtime: .01 seconds
+    """
+    s = x.copy()
+    for i in range(1, len(x)):
+        s[i] = alpha * x[i] + (1 - alpha) * s[i - 1]
+    return s

benchmark/benchmarks/specialconvolve.py

-#from: http://stackoverflow.com/questions/2196693/improving-numpy-performance
-#pythran export specialconvolve(uint32 [][])
-#setup: import numpy as np ; r = np.arange(100*10000, dtype=np.uint32).reshape(1000,1000)
-#run: specialconvolve(r)
+# from: http://stackoverflow.com/questions/2196693/improving-numpy-performance
+# pythran export specialconvolve(uint32 [][])
+# setup: import numpy as np ; r = np.arange(100*10000, dtype=np.uint32).reshape(1000,1000)  # noqa
+# run: specialconvolve(r)
+
 
 def specialconvolve(a):
     # sorry, you must pad the input yourself
-    rowconvol = a[1:-1,:] + a[:-2,:] + a[2:,:]
-    colconvol = rowconvol[:,1:-1] + rowconvol[:,:-2] + rowconvol[:,2:] - 9*a[1:-1,1:-1]
+    rowconvol = a[1:-1, :] + a[:-2, :] + a[2:, :]
+    colconvol = rowconvol[:, 1:-1] + rowconvol[:, :-2] + \
+        rowconvol[:, 2:] - 9 * a[1:-1, 1:-1]
     return colconvol

benchmark/benchmarks/vibr_energy.py

-#from: http://stackoverflow.com/questions/17112550/python-and-numba-for-vectorized-functions
-#setup: import numpy as np ; N = 100000 ; a, b, c = np.random.rand(N), np.random.rand(N), np.random.rand(N)
-#run: vibr_energy(a, b, c)
+# from: http://stackoverflow.com/questions/17112550/python-and-numba-for-vectorized-functions # noqa
+# setup: import numpy as np ; N = 100000 ; a, b, c = np.random.rand(N), np.random.rand(N), np.random.rand(N)  # noqa
+# run: vibr_energy(a, b, c)
 
-#pythran export vibr_energy(float64[], float64[], float64[])
+# pythran export vibr_energy(float64[], float64[], float64[])
 import numpy
 
 

benchmark/benchmarks/wave.py

-#from https://github.com/sklam/numba-example-wavephysics
-#setup: N=100
-#run: wave(N)
+# from https://github.com/sklam/numba-example-wavephysics
+# setup: N=100
+# run: wave(N)
 import numpy as np
-from math import ceil
+
 
 def physics(masspoints, dt, plunk, which):
-  ppos = masspoints[1]
-  cpos = masspoints[0]
-  N = cpos.shape[0]
-  # apply hooke's law
-  HOOKE_K = 2100000.
-  DAMPING = 0.0001
-  MASS = .01
-
-  force = np.zeros((N, 2))
-  for i in range(1, N):
-    dx, dy = cpos[i] - cpos[i - 1]
-    dist = np.sqrt(dx**2 + dy**2)
-    assert dist != 0
-    fmag = -HOOKE_K * dist
-    cosine = dx / dist
-    sine = dy / dist
-    fvec = np.array([fmag * cosine, fmag * sine])
-    force[i - 1] -= fvec
-    force[i] += fvec
-
-  force[0] = force[-1] = 0, 0
-  force[which][1] += plunk
-  accel = force / MASS
-
-  # verlet integration
-  npos = (2 - DAMPING) * cpos - (1 - DAMPING) * ppos + accel * (dt**2)
-
-  masspoints[1] = cpos
-  masspoints[0] = npos
-
-#pythran export wave(int)
+    ppos = masspoints[1]
+    cpos = masspoints[0]
+    N = cpos.shape[0]
+    # apply hooke's law
+    HOOKE_K = 2100000.
+    DAMPING = 0.0001
+    MASS = .01
+
+    force = np.zeros((N, 2))
+    for i in range(1, N):
+        dx, dy = cpos[i] - cpos[i - 1]
+        dist = np.sqrt(dx**2 + dy**2)
+        assert dist != 0
+        fmag = -HOOKE_K * dist
+        cosine = dx / dist
+        sine = dy / dist
+        fvec = np.array([fmag * cosine, fmag * sine])
+        force[i - 1] -= fvec
+        force[i] += fvec
+
+    force[0] = force[-1] = 0, 0
+    force[which][1] += plunk
+    accel = force / MASS
+
+    # verlet integration
+    npos = (2 - DAMPING) * cpos - (1 - DAMPING) * ppos + accel * (dt**2)
+
+    masspoints[1] = cpos
+    masspoints[0] = npos
+
+# pythran export wave(int)
+
+
 def wave(PARTICLE_COUNT):
     SUBDIVISION = 300
     FRAMERATE = 60
@@ -50,5 +52,5 @@ def wave(PARTICLE_COUNT):
     masspoints[:, :, 1] = height / 2
     f = 15
     plunk_pos = count // 2
-    physics( masspoints, 1./ (SUBDIVISION * FRAMERATE), f, plunk_pos)
+    physics(masspoints, 1. / (SUBDIVISION * FRAMERATE), f, plunk_pos)
     return masspoints[0, count // 2]