PEP8: Fix E222 multiple spaces after operator

scipy/io/harwell_boeing/hb.py

@@ -309,7 +309,7 @@ def _expect_int(value, msg=None):
 
 def _read_hb_data(content, header):
     # XXX: look at a way to reduce memory here (big string creation)
-    ptr_string =  "".join([content.read(header.pointer_nbytes_full),
+    ptr_string = "".join([content.read(header.pointer_nbytes_full),
                            content.readline()])
     ptr = np.fromstring(ptr_string,
             dtype=np.int, sep=' ')

scipy/io/matlab/mio5.py

@@ -233,7 +233,7 @@ def read_var_header(self):
         else:
             self._matrix_reader.set_stream(self.mat_stream)
         if not mdtype == miMATRIX:
-            raise TypeError('Expecting miMATRIX type here, got %d' %  mdtype)
+            raise TypeError('Expecting miMATRIX type here, got %d' % mdtype)
         header = self._matrix_reader.read_header()
         return header, next_pos
 
@@ -823,7 +823,7 @@ def __init__(self, file_stream,
 
     def write_file_header(self):
         # write header
-        hdr =  np.zeros((), NDT_FILE_HDR)
+        hdr = np.zeros((), NDT_FILE_HDR)
         hdr['description']='MATLAB 5.0 MAT-file Platform: %s, Created on: %s' \
             % (os.name,time.asctime())
         hdr['version']= 0x0100

scipy/linalg/matfuncs.py

@@ -439,9 +439,9 @@ def signm(a, disp=True):
     def rounded_sign(x):
         rx = real(x)
         if rx.dtype.char=='f':
-            c =  1e3*feps*amax(x)
+            c = 1e3*feps*amax(x)
         else:
-            c =  1e3*eps*amax(x)
+            c = 1e3*eps*amax(x)
         return sign( (absolute(rx) > c) * rx )
     result,errest = funm(a, rounded_sign, disp=0)
     errtol = {0:1e3*feps, 1:1e3*eps}[_array_precision[result.dtype.char]]

scipy/linalg/tests/test_decomp.py

@@ -298,7 +298,7 @@ def create_bandmat(self):
 
         # symmetric band matrix
         self.sym_mat = ( diag(1.0*ones(N))
-                     +  diag(-1.0*ones(N-1), -1) + diag(-1.0*ones(N-1), 1)
+                     + diag(-1.0*ones(N-1), -1) + diag(-1.0*ones(N-1), 1)
                      + diag(-2.0*ones(N-2), -2) + diag(-2.0*ones(N-2), 2) )
 
         # hermitian band matrix
@@ -308,12 +308,12 @@ def create_bandmat(self):
 
         # general real band matrix
         self.real_mat = ( diag(1.0*ones(N))
-                     +  diag(-1.0*ones(N-1), -1) + diag(-3.0*ones(N-1), 1)
+                     + diag(-1.0*ones(N-1), -1) + diag(-3.0*ones(N-1), 1)
                      + diag(2.0*ones(N-2), -2) + diag(-2.0*ones(N-2), 2) )
 
         # general complex band matrix
         self.comp_mat = ( 1j*diag(1.0*ones(N))
-                     +  diag(-1.0*ones(N-1), -1) + 1j*diag(-3.0*ones(N-1), 1)
+                     + diag(-1.0*ones(N-1), -1) + 1j*diag(-3.0*ones(N-1), 1)
                      + diag(2.0*ones(N-2), -2) + diag(-2.0*ones(N-2), 2) )
 
         # Eigenvalues and -vectors from linalg.eig
@@ -1778,7 +1778,7 @@ def test_qz_double_sort(self):
         #              [1.0,   3.0,  -5.0,   4.0],
         #              [1.0,   3.0,  -4.0,   3.0],
         #              [1.0,   3.0,  -4.0,   4.0]])
-        A =   np.array([[3.9,  12.5, -34.5,  2.5],
+        A = np.array([[3.9,  12.5, -34.5,  2.5],
                  [ 4.3,  21.5, -47.5,   7.5],
                  [ 4.3,  1.5, -43.5,   3.5],
                  [ 4.4,  6.0, -46.0,   6.0 ]])

scipy/optimize/tests/test_anneal.py

@@ -19,7 +19,7 @@ def setUp(self):
         first one is used since the second fails for the 'fast' schedule at
         least.
         """
-        self.fun = (lambda x: np.cos(14.5 * x - 0.3)  +  (x + 0.2) * x,
+        self.fun = (lambda x: np.cos(14.5 * x - 0.3)  + (x + 0.2) * x,
                     lambda x: np.cos(14.5 * x[0] - 0.3)  +
                              (x[1] + 0.2) * x[1] + (x[0] + 0.2) * x[0])
         self.x0 = (1.0, [1.0, 1.0])

scipy/optimize/tests/test_tnc.py

@@ -102,11 +102,11 @@ def f45(self, x):
 
     def g45(self, x):
         dif = [0] * 5
-        dif[0] =  - x[1] * x[2] * x[3] * x[4] / 120.0
-        dif[1] =  - x[0] * x[2] * x[3] * x[4] / 120.0
-        dif[2] =  - x[0] * x[1] * x[3] * x[4] / 120.0
-        dif[3] =  - x[0] * x[1] * x[2] * x[4] / 120.0
-        dif[4] =  - x[0] * x[1] * x[2] * x[3] / 120.0
+        dif[0] = - x[1] * x[2] * x[3] * x[4] / 120.0
+        dif[1] = - x[0] * x[2] * x[3] * x[4] / 120.0
+        dif[2] = - x[0] * x[1] * x[3] * x[4] / 120.0
+        dif[3] = - x[0] * x[1] * x[2] * x[4] / 120.0
+        dif[4] = - x[0] * x[1] * x[2] * x[3] / 120.0
         return dif
 
     def fg45(self, x):

scipy/optimize/tnc.py

@@ -58,14 +58,14 @@
 }
 
 INFEASIBLE   = -1 # Infeasible (low > up)
-LOCALMINIMUM =  0 # Local minima reach (|pg| ~= 0)
-FCONVERGED   =  1 # Converged (|f_n-f_(n-1)| ~= 0)
-XCONVERGED   =  2 # Converged (|x_n-x_(n-1)| ~= 0)
-MAXFUN       =  3 # Max. number of function evaluations reach
-LSFAIL       =  4 # Linear search failed
-CONSTANT     =  5 # All lower bounds are equal to the upper bounds
-NOPROGRESS   =  6 # Unable to progress
-USERABORT    =  7 # User requested end of minimization
+LOCALMINIMUM = 0  # Local minima reach (|pg| ~= 0)
+FCONVERGED   = 1  # Converged (|f_n-f_(n-1)| ~= 0)
+XCONVERGED   = 2  # Converged (|x_n-x_(n-1)| ~= 0)
+MAXFUN       = 3  # Max. number of function evaluations reach
+LSFAIL       = 4  # Linear search failed
+CONSTANT     = 5  # All lower bounds are equal to the upper bounds
+NOPROGRESS   = 6  # Unable to progress
+USERABORT    = 7  # User requested end of minimization
 
 RCSTRINGS = {
         INFEASIBLE   : "Infeasible (low > up)",

scipy/sparse/benchmarks/bench_sparse.py

@@ -36,7 +36,7 @@ def poisson2d(N,dtype='d',format=None):
 
     diags = empty((5,N**2),dtype=dtype)
 
-    diags[0]  =  4 # main diagonal
+    diags[0]  = 4 # main diagonal
     diags[1:] = -1 # all offdiagonals
 
     diags[3,N-1::N] = 0  # first lower diagonal

scipy/sparse/csr.py

@@ -411,7 +411,7 @@ def check_bounds( i0, i1, num ):
                 self.indptr, self.indices, self.data,
                 int(i0), int(i1), int(j0), int(j1) )
 
-        shape =  (i1 - i0, j1 - j0)
+        shape = (i1 - i0, j1 - j0)
 
         return self.__class__( (data,indices,indptr), shape=shape )
 

scipy/sparse/dia.py

@@ -144,7 +144,7 @@ def __repr__(self):
         format = self.getformat()
         return "<%dx%d sparse matrix of type '%s'\n" \
                "\twith %d stored elements (%d diagonals) in %s format>" % \
-               ( self.shape +  (self.dtype.type, nnz, self.data.shape[0],
+               ( self.shape + (self.dtype.type, nnz, self.data.shape[0],
                  _formats[format][1],) )
 
     def getnnz(self):

scipy/sparse/linalg/dsolve/tests/test_linsolve.py

@@ -45,7 +45,7 @@ def test_bvector_smoketest(self):
         Adense = matrix([[ 0.,  1.,  1.],
                          [ 1.,  0.,  1.],
                          [ 0.,  0.,  1.]])
-        As =  csc_matrix(Adense)
+        As = csc_matrix(Adense)
         random.seed(1234)
         x = random.randn(3)
         b = As*x
@@ -57,7 +57,7 @@ def test_bmatrix_smoketest(self):
         Adense = matrix([[ 0.,  1.,  1.],
                          [ 1.,  0.,  1.],
                          [ 0.,  0.,  1.]])
-        As =  csc_matrix(Adense)
+        As = csc_matrix(Adense)
         random.seed(1234)
         x = random.randn(3, 4)
         Bdense = As.dot(x)
@@ -180,7 +180,7 @@ def test_lu_refcount(self):
         import sys
         rc = sys.getrefcount(lu)
         for attr in ('perm_r', 'perm_c'):
-            perm =  getattr(lu, attr)
+            perm = getattr(lu, attr)
             assert_equal(sys.getrefcount(lu), rc + 1)
             del perm
             assert_equal(sys.getrefcount(lu), rc)

scipy/sparse/linalg/eigen/lobpcg/lobpcg.py

@@ -342,7 +342,7 @@ def lobpcg( A, X,
     # Main iteration loop.
     for iterationNumber in xrange( maxIterations ):
         if verbosityLevel > 0:
-            print('iteration %d' %  iterationNumber)
+            print('iteration %d' % iterationNumber)
 
         aux = blockVectorBX * _lambda[np.newaxis,:]
         blockVectorR = blockVectorAX - aux

scipy/sparse/linalg/eigen/lobpcg/tests/large_scale.py

@@ -17,7 +17,7 @@ def sakurai(n):
     A = sparse.eye( n, n )
     d0 = array(r_[5,6*ones(n-2),5])
     d1 = -4*ones(n)
-    d2 =  ones(n)
+    d2 = ones(n)
     B = sparse.spdiags([d2,d1,d0,d1,d2],[-2,-1,0,1,2],n,n)
 
     k = arange(1,n+1)

scipy/sparse/linalg/isolve/lsqr.py

@@ -399,7 +399,7 @@ def lsqr(A, b, damp=0.0, atol=1e-8, btol=1e-8, conlim=1e8,
         cs2 = gambar / gamma
         sn2 = theta  / gamma
         z = rhs / gamma
-        xxnorm = xxnorm  +  z**2
+        xxnorm = xxnorm  + z**2
 
         # Test for convergence.
         # First, estimate the condition of the matrix  Abar,
@@ -429,7 +429,7 @@ def lsqr(A, b, damp=0.0, atol=1e-8, btol=1e-8, conlim=1e8,
         test2 = arnorm / (anorm * rnorm)
         test3 = 1 / acond
         t1 = test1 / (1 + anorm * xnorm / bnorm)
-        rtol = btol + atol *  anorm * xnorm / bnorm
+        rtol = btol + atol * anorm * xnorm / bnorm
 
         # The following tests guard against extremely small values of
         # atol, btol  or  ctol.  (The user may have set any or all of

scipy/sparse/linalg/isolve/minres.py

@@ -184,10 +184,10 @@ def minres(A, b, x0=None, shift=0.0, tol=1e-5, maxiter=None, xtype=None,
         #   [gbar k dbar k+1]   [sn -cs][alfak betak+1].
 
         oldeps = epsln
-        delta  = cs * dbar  +  sn * alfa   # delta1 = 0         deltak
-        gbar   = sn * dbar  -  cs * alfa   # gbar 1 = alfa1     gbar k
-        epsln  =               sn * beta   # epsln2 = 0         epslnk+1
-        dbar   =            -  cs * beta   # dbar 2 = beta2     dbar k+1
+        delta  = cs * dbar + sn * alfa   # delta1 = 0         deltak
+        gbar   = sn * dbar - cs * alfa   # gbar 1 = alfa1     gbar k
+        epsln  = sn * beta     # epsln2 = 0         epslnk+1
+        dbar   = - cs * beta   # dbar 2 = beta2     dbar k+1
         root   = norm([gbar, dbar])
         Arnorm = phibar * root
 
@@ -213,9 +213,9 @@ def minres(A, b, x0=None, shift=0.0, tol=1e-5, maxiter=None, xtype=None,
         gmax   = max(gmax, gamma)
         gmin   = min(gmin, gamma)
         z      = rhs1 / gamma
-        ynorm2 = z**2  +  ynorm2
-        rhs1   = rhs2 -  delta*z
-        rhs2   =      -  epsln*z
+        ynorm2 = z**2  + ynorm2
+        rhs1   = rhs2 - delta*z
+        rhs2   = - epsln*z
 
         # Estimate various norms and test for convergence.
 
@@ -231,7 +231,7 @@ def minres(A, b, x0=None, shift=0.0, tol=1e-5, maxiter=None, xtype=None,
         qrnorm = phibar
         rnorm  = qrnorm
         test1  = rnorm / (Anorm*ynorm)    # ||r||  / (||A|| ||x||)
-        test2  = root  /  Anorm           # ||Ar|| / (||A|| ||r||)
+        test2  = root  / Anorm            # ||Ar|| / (||A|| ||r||)
 
         # Estimate  cond(A).
         # In this version we look at the diagonals of  R  in the
@@ -268,7 +268,7 @@ def minres(A, b, x0=None, shift=0.0, tol=1e-5, maxiter=None, xtype=None,
         if qrnorm   <= 10*epsx    : prnt = True
         if qrnorm   <= 10*epsr    : prnt = True
         if Acond    <= 1e-2/eps   : prnt = True
-        if istop  !=  0           : prnt = True
+        if istop  != 0            : prnt = True
 
         if show and prnt:
             str1 = '%6g %12.5e %10.3e'  % (itn, x[0], test1)
@@ -289,7 +289,7 @@ def minres(A, b, x0=None, shift=0.0, tol=1e-5, maxiter=None, xtype=None,
         print(last + ' istop   =  %3g               itn   =%5g' % (istop,itn))
         print(last + ' Anorm   =  %12.4e      Acond =  %12.4e'  % (Anorm,Acond))
         print(last + ' rnorm   =  %12.4e      ynorm =  %12.4e'  % (rnorm,ynorm))
-        print(last + ' Arnorm  =  %12.4e'                       %  (Arnorm,))
+        print(last + ' Arnorm  =  %12.4e'                       % (Arnorm,))
         print(last + msg[istop+1])
 
     if istop == 6:

scipy/sparse/linalg/isolve/tests/test_iterative.py

@@ -47,7 +47,7 @@ def __init__(self):
         # Symmetric and Positive Definite
         N = 40
         data = ones((3,N))
-        data[0,:] =  2
+        data[0,:] = 2
         data[1,:] = -1
         data[2,:] = -1
         Poisson1D = spdiags(data, [0,-1,1], N, N, format='csr')
@@ -104,7 +104,7 @@ def __init__(self):
         # cgs, qmr, and bicg fail to converge on this one
         #   -- algorithmic limitation apparently
         data = ones((2,10))
-        data[0,:] =  2
+        data[0,:] = 2
         data[1,:] = -1
         A = spdiags( data, [0,-1], 10, 10, format='csr')
         self.cases.append(Case("nonsymposdef", A,

scipy/sparse/linalg/isolve/tests/test_lsmr.py

@@ -141,8 +141,8 @@ def lsmrtest(m, n, damp):
     r    = b - Afun.matvec(x)
     r2   = sqrt(norm(r)**2 + (damp*norm(x))**2)
     print(' ')
-    str =  'normr (est.)  %17.10e' %(normr )
-    str2 =  'normr (true)  %17.10e' %(r2 )
+    str = 'normr (est.)  %17.10e' %(normr )
+    str2 = 'normr (true)  %17.10e' %(r2 )
     print(str)
     print(str2)
     print(' ')

scipy/sparse/linalg/isolve/tests/test_lsqr.py

@@ -53,11 +53,11 @@ def test_basic():
     print("Norms computed in %.2fs by LSQR" % (time() - tic))
     print(" ||x||  %9.4e  ||r|| %9.4e  ||Ar||  %9.4e " %( chio, phio, psio))
     print("Residual norms computed directly:")
-    print(" ||x||  %9.4e  ||r|| %9.4e  ||Ar||  %9.4e" %  (norm(xo),
+    print(" ||x||  %9.4e  ||r|| %9.4e  ||Ar||  %9.4e" % (norm(xo),
                                                           norm(G*xo - b),
                                                           norm(G.T*(G*xo-b))))
     print("Direct solution norms:")
-    print(" ||x||  %9.4e  ||r|| %9.4e " %  (norm(svx), norm(G*svx -b)))
+    print(" ||x||  %9.4e  ||r|| %9.4e " % (norm(svx), norm(G*svx -b)))
     print("")
     print(" || x_{direct} - x_{LSQR}|| %9.4e " % norm(svx-xo))
     print("")

scipy/spatial/tests/test_qhull.py

@@ -225,7 +225,7 @@ def barycentric_transform(tr, x):
 
         if unit_cube:
             # If in unit cube, no interior point should be marked out of hull
-            at_boundary =  (centroids <= unit_cube_tol).any(axis=1)
+            at_boundary = (centroids <= unit_cube_tol).any(axis=1)
             at_boundary |= (centroids >= 1 - unit_cube_tol).any(axis=1)
 
             ok = (j != -1) | at_boundary

scipy/special/orthogonal.py

@@ -252,7 +252,7 @@ def js_roots(n, p1, q1, mu=0):
     #  so roots of Gn(p,q,x) are (roots of P^(p-q,q-1)_n + 1) / 2.0
     g = _gam
     # integral of weight over interval
-    mu0 =  g(q)*g(p-q+1)/g(p+1)
+    mu0 = g(q)*g(p-q+1)/g(p+1)
     val = gen_roots_and_weights(n,an_Js,sbn_Js,mu0)
     if mu:
         return val + [mu0]

scipy/special/tests/test_basic.py

@@ -2259,7 +2259,7 @@ def test_mathieu_a(self):
         pass
 
     def test_mathieu_even_coef(self):
-        mc =  special.mathieu_even_coef(2,5)
+        mc = special.mathieu_even_coef(2,5)
         #Q not defined broken and cannot figure out proper reporting order
 
     def test_mathieu_odd_coef(self):

scipy/special/tests/test_mpmath.py

@@ -382,7 +382,7 @@ def __init__(self, scipy_func, mpmath_func, arg_spec, name=None,
         if not name or name == '<lambda>':
             name = getattr(scipy_func, '__name__', None)
         if not name or name == '<lambda>':
-            name =  getattr(mpmath_func, '__name__', None)
+            name = getattr(mpmath_func, '__name__', None)
         self.name = name
 
     def check(self):

scipy/stats/_tukeylambda_stats.py

@@ -95,7 +95,7 @@ def tukeylambda_variance(lam):
     v[neghalf_mask] = np.inf
     if small.size > 0:
         # Use the Pade approximation near lambda = 0.
-        v[small_mask] =  _tukeylambda_var_p(small) / _tukeylambda_var_q(small)
+        v[small_mask] = _tukeylambda_var_p(small) / _tukeylambda_var_q(small)
     if reg.size > 0:
         v[reg_mask] = (2.0 / reg**2) * (1.0 / (1.0 + 2 * reg) -
                                       beta(reg + 1, reg + 1))

scipy/stats/distributions.py

@@ -4560,7 +4560,7 @@ class pareto_gen(rv_continuous):
     def _pdf(self, x, b):
         return b * x**(-b-1)
     def _cdf(self, x, b):
-        return 1 -  x**(-b)
+        return 1 - x**(-b)
     def _ppf(self, q, b):
         return pow(1-q, -1.0/b)
     def _stats(self, b, moments='mv'):

scipy/stats/mstats_basic.py

@@ -553,7 +553,7 @@ def kendalltau_seasonal(x):
     #
     n_tot = x.count()
     ties = count_tied_groups(x.compressed())
-    corr_ties =  np.sum(v*k*(k-1) for (k,v) in iteritems(ties))
+    corr_ties = np.sum(v*k*(k-1) for (k,v) in iteritems(ties))
     denom_tot = ma.sqrt(1.*n_tot*(n_tot-1)*(n_tot*(n_tot-1)-corr_ties))/2.
     #
     R = rankdata(x, axis=0, use_missing=True)

scipy/stats/tests/test_mstats_basic.py

@@ -78,7 +78,7 @@ class TestHMean(TestCase):
     def test_1D(self):
         a = (1,2,3,4)
         actual= mstats.hmean(a)
-        desired =  4. / (1./1 + 1./2 + 1./3 + 1./4)
+        desired = 4. / (1./1 + 1./2 + 1./3 + 1./4)
         assert_almost_equal(actual, desired, decimal=14)
         desired1 = mstats.hmean(ma.array(a),axis=-1)
         assert_almost_equal(actual, desired1, decimal=14)