PEP8: Fix E228 missing whitespace around modulo operator

scipy/fftpack/benchmarks/bench_basic.py

@@ -166,7 +166,7 @@ def bench_random(self):
             x1[0] = x[0]
             for i in range(1,size/2):
                 x1[i] = x[2*i-1] + 1j * x[2*i]
-            if not size%2:
+            if not size % 2:
                 x1[-1] = x[-1]
             y = irfft(x)
 
@@ -196,7 +196,7 @@ def bench_random(self):
                             ((256,256),10),
                             ((512,512),3),
                             ]:
-            print('%9s' % ('%sx%s'%size), end=' ')
+            print('%9s' % ('%sx%s' % size), end=' ')
             sys.stdout.flush()
 
             for x in [random(size).astype(double),

scipy/fftpack/pseudo_diffs.py

@@ -72,7 +72,7 @@ def kernel(k,order=order,c=c):
                                                  zero_nyquist=1)
         _cache[(n,order,c)] = omega
     overwrite_x = _datacopied(tmp, x)
-    return convolve.convolve(tmp,omega,swap_real_imag=order%2,
+    return convolve.convolve(tmp,omega,swap_real_imag=order % 2,
                              overwrite_x=overwrite_x)
 del _cache
 

scipy/integrate/quadrature.py

@@ -780,7 +780,7 @@ def newton_cotes(rn, equal=0):
     vec = 2.0/ (nvec[::2]+1)
     ai = np.dot(Cinv[:,::2],vec) * N/2
 
-    if (N%2 == 0) and equal:
+    if (N % 2 == 0) and equal:
         BN = N/(N+3.)
         power = N+2
     else:

scipy/interpolate/fitpack.py

@@ -214,7 +214,7 @@ def splprep(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None,
     if per:
         for i in range(idim):
             if x[i][0]!=x[i][-1]:
-                if quiet<2:print('Warning: Setting x[%d][%d]=x[%d][0]'%(i,m,i))
+                if quiet<2:print('Warning: Setting x[%d][%d]=x[%d][0]' % (i,m,i))
                 x[i][-1]=x[i][0]
     if not 0 < idim < 11:
         raise TypeError('0 < idim < 11 must hold')
@@ -272,7 +272,7 @@ def splprep(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None,
     tcku = [t,list(c),k],u
     if ier<=0 and not quiet:
         print(_iermess[ier][0])
-        print("\tk=%d n=%d m=%d fp=%f s=%f"%(k,len(t),m,fp,s))
+        print("\tk=%d n=%d m=%d fp=%f s=%f" % (k,len(t),m,fp,s))
     if ier>0 and not full_output:
         if ier in [1,2,3]:
             print("Warning: "+_iermess[ier][0])
@@ -456,7 +456,7 @@ def splrep(x,y,w=None,xb=None,xe=None,k=3,task=0,s=None,t=None,
     tck = (t[:n],c[:n],k)
     if ier<=0 and not quiet:
         print(_iermess[ier][0])
-        print("\tk=%d n=%d m=%d fp=%f s=%f"%(k,len(t),m,fp,s))
+        print("\tk=%d n=%d m=%d fp=%f s=%f" % (k,len(t),m,fp,s))
     if ier>0 and not full_output:
         if ier in [1,2,3]:
             print("Warning: "+_iermess[ier][0])
@@ -540,7 +540,7 @@ def splev(x, tck, der=0, ext=0):
         return list(map(lambda c, x=x, t=t, k=k, der=der : splev(x, [t,c,k], der, ext), c))
     else:
         if not (0 <= der <= k):
-            raise ValueError("0<=der=%d<=k=%d must hold"%(der,k))
+            raise ValueError("0<=der=%d<=k=%d must hold" % (der,k))
         if not ext in (0,1,2):
             raise ValueError("ext not in (0, 1, 2)")
 
@@ -882,12 +882,12 @@ def bisplrep(x,y,z,w=None,xb=None,xe=None,yb=None,ye=None,kx=3,ky=3,task=0,
     ierm=min(11,max(-3,ier))
     if ierm<=0 and not quiet:
         print(_iermess2[ierm][0])
-        print("\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f"%(kx,ky,len(tx),
+        print("\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f" % (kx,ky,len(tx),
                                                            len(ty),m,fp,s))
     if ierm>0 and not full_output:
         if ier in [1,2,3,4,5]:
             print("Warning: "+_iermess2[ierm][0])
-            print("\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f"%(kx,ky,len(tx),
+            print("\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f" % (kx,ky,len(tx),
                                                            len(ty),m,fp,s))
         else:
             try:

scipy/interpolate/polyint.py

@@ -415,7 +415,7 @@ def approximate_taylor_polynomial(f,x,degree,scale,order=None):
     # a way that avoids the Runge phenomenon. Ensure, by including the
     # endpoint or not as appropriate, that one point always falls at x
     # exactly.
-    xs = scale*np.cos(np.linspace(0,np.pi,n,endpoint=n%1)) + x
+    xs = scale*np.cos(np.linspace(0,np.pi,n,endpoint=n % 1)) + x
 
     P = KroghInterpolator(xs, f(xs))
     d = P.derivatives(x,der=degree+1)

scipy/interpolate/tests/test_fitpack.py

@@ -14,18 +14,18 @@ def norm2(x):
 def f1(x,d=0):
     if d is None: return "sin"
     if x is None: return "sin(x)"
-    if d%4 == 0: return sin(x)
-    if d%4 == 1: return cos(x)
-    if d%4 == 2: return -sin(x)
-    if d%4 == 3: return -cos(x)
+    if d % 4 == 0: return sin(x)
+    if d % 4 == 1: return cos(x)
+    if d % 4 == 2: return -sin(x)
+    if d % 4 == 3: return -cos(x)
 
 def f2(x,y=0,dx=0,dy=0):
     if x is None: return "sin(x+y)"
     d=dx+dy
-    if d%4 == 0: return sin(x+y)
-    if d%4 == 1: return cos(x+y)
-    if d%4 == 2: return -sin(x+y)
-    if d%4 == 3: return -cos(x+y)
+    if d % 4 == 0: return sin(x+y)
+    if d % 4 == 1: return cos(x+y)
+    if d % 4 == 2: return -sin(x+y)
+    if d % 4 == 3: return -cos(x+y)
 
 def makepairs(x, y):
     """Helper function to create an array of pairs of x and y."""
@@ -72,20 +72,20 @@ def err_est(k, d):
                 assert_(err < tol, (k, d, err, tol))
                 nd.append((err, tol))
             nk.append(nd)
-        put("\nf = %s  s=S_k(x;t,c)  x in [%s, %s] > [%s, %s]"%(f(None),
+        put("\nf = %s  s=S_k(x;t,c)  x in [%s, %s] > [%s, %s]" % (f(None),
                                                         repr(round(xb,3)),repr(round(xe,3)),
                                                           repr(round(a,3)),repr(round(b,3))))
         if at:
             str="at knots"
         else:
             str="at the middle of nodes"
-        put(" per=%d s=%s Evaluation %s"%(per,repr(s),str))
+        put(" per=%d s=%s Evaluation %s" % (per,repr(s),str))
         put(" k :  |f-s|^2  |f'-s'| |f''-.. |f'''-. |f''''- |f'''''")
         k=1
         for l in nk:
-            put(' %d : '%k)
+            put(' %d : ' % k)
             for r in l:
-                put(' %.1e  %.1e'%r)
+                put(' %.1e  %.1e' % r)
             put('\n')
             k=k+1
 
@@ -108,23 +108,23 @@ def err_est(k, d):
         for k in range(1,6):
             tck=splrep(x,v,s=s,per=per,k=k,xe=xe)
             nk.append([splint(ia,ib,tck),spalde(dx,tck)])
-        put("\nf = %s  s=S_k(x;t,c)  x in [%s, %s] > [%s, %s]"%(f(None),
+        put("\nf = %s  s=S_k(x;t,c)  x in [%s, %s] > [%s, %s]" % (f(None),
                                                    repr(round(xb,3)),repr(round(xe,3)),
                                                     repr(round(a,3)),repr(round(b,3))))
-        put(" per=%d s=%s N=%d [a, b] = [%s, %s]  dx=%s"%(per,repr(s),N,repr(round(ia,3)),repr(round(ib,3)),repr(round(dx,3))))
+        put(" per=%d s=%s N=%d [a, b] = [%s, %s]  dx=%s" % (per,repr(s),N,repr(round(ia,3)),repr(round(ib,3)),repr(round(dx,3))))
         put(" k :  int(s,[a,b]) Int.Error   Rel. error of s^(d)(dx) d = 0, .., k")
         k=1
         for r in nk:
             if r[0]<0: sr='-'
             else: sr=' '
-            put(" %d   %s%.8f   %.1e "%(k,sr,abs(r[0]),
+            put(" %d   %s%.8f   %.1e " % (k,sr,abs(r[0]),
                                          abs(r[0]-(f(ib,-1)-f(ia,-1)))))
             d=0
             for dr in r[1]:
                 err = abs(1-dr/f(dx,d))
                 tol = err_est(k, d)
                 assert_(err < tol, (k, d))
-                put(" %.1e %.1e"%(err, tol))
+                put(" %.1e %.1e" % (err, tol))
                 d=d+1
             put("\n")
             k=k+1
@@ -144,7 +144,7 @@ def check_3(self,f=f1,per=0,s=0,a=0,b=2*pi,N=20,xb=None,xe=None,
             if k == 3:
                 assert_allclose(roots, pi*array([1, 2, 3, 4]),
                                 rtol=1e-3)
-            put('  %d  : %s'%(k,repr(roots.tolist())))
+            put('  %d  : %s' % (k,repr(roots.tolist())))
 
     def check_4(self,f=f1,per=0,s=0,a=0,b=2*pi,N=20,xb=None,xe=None,
               ia=0,ib=2*pi,dx=0.2*pi):
@@ -154,8 +154,8 @@ def check_4(self,f=f1,per=0,s=0,a=0,b=2*pi,N=20,xb=None,xe=None,
         x1=a+(b-a)*arange(1,N,dtype=float)/float(N-1) # middle points of the nodes
         v,v1=f(x),f(x1)
         nk=[]
-        put(" u = %s   N = %d"%(repr(round(dx,3)),N))
-        put("  k  :  [x(u), %s(x(u))]  Error of splprep  Error of splrep "%(f(0,None)))
+        put(" u = %s   N = %d" % (repr(round(dx,3)),N))
+        put("  k  :  [x(u), %s(x(u))]  Error of splprep  Error of splrep " % (f(0,None)))
         for k in range(1,6):
             tckp,u=splprep([x,v],s=s,per=per,k=k,nest=-1)
             tck=splrep(x,v,s=s,per=per,k=k)
@@ -173,7 +173,7 @@ def check_4(self,f=f1,per=0,s=0,a=0,b=2*pi,N=20,xb=None,xe=None,
         tckp,u=splprep([x,v],s=s,per=per,k=k,nest=-1)
         for d in range(1,k+1):
             uv=splev(dx,tckp,d)
-            put(" %s "%(repr(uv[0])))
+            put(" %s " % (repr(uv[0])))
 
     def check_5(self,f=f2,kx=3,ky=3,xb=0,xe=2*pi,yb=0,ye=2*pi,Nx=20,Ny=20,s=0):
         x=xb+(xe-xb)*arange(Nx+1,dtype=float)/float(Nx)

scipy/interpolate/tests/test_polyint.py

@@ -33,7 +33,7 @@ def check_shape(interpolator_cls, x_shape, y_shape, deriv_shape=None, axis=0):
     # check also values
     if xi.size > 0 and deriv_shape is None:
         bs_shape = (y.shape[:axis] + ((1,)*len(x_shape)) + y.shape[axis:][1:])
-        yv = y[((slice(None,None,None),)*(axis%y.ndim))+(1,)].reshape(bs_shape)
+        yv = y[((slice(None,None,None),)*(axis % y.ndim))+(1,)].reshape(bs_shape)
 
         yi, y = np.broadcast_arrays(yi, yv)
         assert_allclose(yi, y)

scipy/io/matlab/mio5.py

@@ -765,7 +765,7 @@ def _write_items(self, arr):
                  % (max_length-1))
         self.write_element(np.array([length], dtype='i4'))
         self.write_element(
-            np.array(fieldnames, dtype='S%d'%(length)),
+            np.array(fieldnames, dtype='S%d' % (length)),
             mdtype=miINT8)
         A = np.atleast_2d(arr).flatten('F')
         for el in A:

scipy/optimize/benchmarks/bench_zeros.py

@@ -19,18 +19,18 @@ def bench_run(self):
         print(description)
 
         print('TESTING SPEED\n')
-        print('times in seconds for %d iterations \n'%repeat)
+        print('times in seconds for %d iterations \n' % repeat)
         for i in range(len(functions)) :
-            print('function %s\n'%fstrings[i])
+            print('function %s\n' % fstrings[i])
             func = functions[i]
             for j in range(len(methods)) :
                 meth = methods[j]
                 try:
                     t = measure("meth(func,a,b)",repeat)
                 except:
-                    print('%s : failed'%mstrings[j])
+                    print('%s : failed' % mstrings[j])
                 else:
-                    print('%s : %5.3f'%(mstrings[j],t))
+                    print('%s : %5.3f' % (mstrings[j],t))
             print('\n\n')
 
 if __name__ == '__main__' :

scipy/optimize/tests/test_minpack.py

@@ -201,7 +201,7 @@ def test_basic(self):
         p0 = array([0,0,0])
         params_fit, ier = leastsq(self.residuals, p0,
                                   args=(self.y_meas, self.x))
-        assert_(ier in (1,2,3,4), 'solution not found (ier=%d)'%ier)
+        assert_(ier in (1,2,3,4), 'solution not found (ier=%d)' % ier)
         # low precision due to random
         assert_array_almost_equal(params_fit, self.abc, decimal=2)
 
@@ -211,7 +211,7 @@ def test_full_output(self):
                               args=(self.y_meas, self.x),
                               full_output=True)
         params_fit, cov_x, infodict, mesg, ier = full_output
-        assert_(ier in (1,2,3,4), 'solution not found: %s'%mesg)
+        assert_(ier in (1,2,3,4), 'solution not found: %s' % mesg)
 
     def test_input_untouched(self):
         p0 = array([0,0,0],dtype=float64)
@@ -220,7 +220,7 @@ def test_input_untouched(self):
                               args=(self.y_meas, self.x),
                               full_output=True)
         params_fit, cov_x, infodict, mesg, ier = full_output
-        assert_(ier in (1,2,3,4), 'solution not found: %s'%mesg)
+        assert_(ier in (1,2,3,4), 'solution not found: %s' % mesg)
         assert_array_equal(p0, p0_copy)
 
     def test_wrong_shape_func_callable(self):

scipy/sparse/linalg/isolve/lsqr.py

@@ -265,8 +265,8 @@ def lsqr(A, b, damp=0.0, atol=1e-8, btol=1e-8, conlim=1e8,
         print('LSQR            Least-squares solution of  Ax = b')
         str1 = 'The matrix A has %8g rows  and %8g cols' % (m, n)
         str2 = 'damp = %20.14e   calc_var = %8g' % (damp, calc_var)
-        str3 = 'atol = %8.2e                 conlim = %8.2e'%( atol, conlim)
-        str4 = 'btol = %8.2e               iter_lim = %8g'  %( btol, iter_lim)
+        str3 = 'atol = %8.2e                 conlim = %8.2e' % ( atol, conlim)
+        str4 = 'btol = %8.2e               iter_lim = %8g'   % ( btol, iter_lim)
         print(str1)
         print(str2)
         print(str3)
@@ -333,7 +333,7 @@ def lsqr(A, b, damp=0.0, atol=1e-8, btol=1e-8, conlim=1e8,
         test1  = 1
         test2  = alfa / beta
         str1   = '%6g %12.5e'    %(    itn,   x[0] )
-        str2   = ' %10.3e %10.3e'%( r1norm, r2norm )
+        str2   = ' %10.3e %10.3e' % ( r1norm, r2norm )
         str3   = '  %8.1e %8.1e' %(  test1,  test2 )
         print(str1, str2, str3)
 

scipy/stats/distributions.py

@@ -1064,7 +1064,7 @@ def _construct_default_doc(self, longname=None, extradoc=None):
             extradoc = ''
         if extradoc.startswith('\n\n'):
             extradoc = extradoc[2:]
-        self.__doc__ = ''.join(['%s continuous random variable.'%longname,
+        self.__doc__ = ''.join(['%s continuous random variable.' % longname,
                                 '\n\n%(before_notes)s\n', docheaders['notes'],
                                 extradoc, '\n%(example)s'])
         self._construct_doc()
@@ -5842,7 +5842,7 @@ def _construct_default_doc(self, longname=None, extradoc=None):
             extradoc = ''
         if extradoc.startswith('\n\n'):
             extradoc = extradoc[2:]
-        self.__doc__ = ''.join(['%s discrete random variable.'%longname,
+        self.__doc__ = ''.join(['%s discrete random variable.' % longname,
                                 '\n\n%(before_notes)s\n', docheaders['notes'],
                                 extradoc, '\n%(example)s'])
         self._construct_doc()

scipy/stats/morestats.py

@@ -380,7 +380,7 @@ def ppcc_max(x, brack=(0.0,1.0), dist='tukeylambda'):
     See also ppcc_plot
     """
     try:
-        ppf_func = eval('distributions.%s.ppf'%dist)
+        ppf_func = eval('distributions.%s.ppf' % dist)
     except AttributeError:
         raise ValueError("%s is not a valid distribution with a ppf." % dist)
     """

scipy/stats/tests/test_distributions.py

@@ -82,10 +82,10 @@ def test_all_distributions():
 
 def check_vonmises_pdf_periodic(k,l,s,x):
     vm = stats.vonmises(k,loc=l,scale=s)
-    assert_almost_equal(vm.pdf(x),vm.pdf(x%(2*numpy.pi*s)))
+    assert_almost_equal(vm.pdf(x),vm.pdf(x % (2*numpy.pi*s)))
 def check_vonmises_cdf_periodic(k,l,s,x):
     vm = stats.vonmises(k,loc=l,scale=s)
-    assert_almost_equal(vm.cdf(x)%1,vm.cdf(x%(2*numpy.pi*s))%1)
+    assert_almost_equal(vm.cdf(x) % 1,vm.cdf(x % (2*numpy.pi*s)) % 1)
 
 def test_vonmises_pdf_periodic():
     for k in [0.1, 1, 101]:

scipy/stats/tests/test_stats.py

@@ -2059,10 +2059,10 @@ def test_binomtest():
 
     for p, res in zip(pp,results):
         assert_approx_equal(stats.binom_test(x, n, p), res,
-                            significant=12, err_msg='fail forp=%f'%p)
+                            significant=12, err_msg='fail forp=%f' % p)
 
     assert_approx_equal(stats.binom_test(50,100,0.1), 5.8320387857343647e-024,
-                            significant=12, err_msg='fail forp=%f'%p)
+                            significant=12, err_msg='fail forp=%f' % p)
 
 class Test_Trim(object):
     # test trim functions