made code python 3 compatible:

Replaced xrange with range
Replaced map with list comprehension

wafo/fig.py

@@ -538,7 +538,7 @@ def stack(*figs):
 #% Location (1,1) is at bottom left corner
 #
         #print('Screensz = ',screenpos)
-        for iy in xrange(numfigs):
+        for iy in range(numfigs):
             pos = list(GetWindowRect(wnds[iy]))
             pos[3] -= pos[1]
             pos[2] -= pos[0]
@@ -639,9 +639,9 @@ def tile(*figs, **kwds):
 #% Put the figures where they belong.
 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
         idx = 0
-        for unused_ix in xrange(nlayers):
-            for row in xrange(nv):
-                for col in xrange(nh):
+        for unused_ix in range(nlayers):
+            for row in range(nv):
+                for col in range(nh):
                     if  (row) * nh + col < nfigspertile :
                         if idx < nfigs:
                             figlft = int(screenpos[0] + (col + 1) * hspc + col * figwid)
@@ -654,6 +654,7 @@ def tile(*figs, **kwds):
                             #figure(figs[idx]);                              # Raise figure.
                         idx += 1
 
+
 def cycle(*figs, **kwds):
     ''' Cycle through figure windows.
 

wafo/integrate.py

@@ -255,7 +255,7 @@ def romberg(fun, a, b, releps=1e-3, abseps=1e-3):
     # [res,abserr,epstab,newflg] = dea(newflg,Ih4,abserr,epstab)
     two = 1
     one = 0
-    for i in xrange(1, tableLimit):
+    for i in range(1, tableLimit):
         h *= 0.5
         Un5 = np.sum(fun(a + np.arange(1, 2 * ipower, 2) * h)) * h
 
@@ -265,7 +265,7 @@ def romberg(fun, a, b, releps=1e-3, abseps=1e-3):
 
         fp[i] = 4 * fp[i - 1]
         #   Richardson extrapolation
-        for k in xrange(i):
+        for k in range(i):
             rom[two, k + 1] = rom[two, k] + \
                 (rom[two, k] - rom[one, k]) / (fp[k] - 1)
 
@@ -354,12 +354,12 @@ def h_roots(n, method='newton'):
         L = zeros((3, len(z)))
         k0 = 0
         kp1 = 1
-        for _its in xrange(max_iter):
+        for _its in range(max_iter):
             # Newtons method carried out simultaneously on the roots.
             L[k0, :] = 0
             L[kp1, :] = PIM4
 
-            for j in xrange(1, n + 1):
+            for j in range(1, n + 1):
                 # Loop up the recurrence relation to get the Hermite
                 # polynomials evaluated at z.
                 km1 = k0
@@ -454,13 +454,13 @@ def j_roots(n, alpha, beta, method='newton'):
         L = zeros((3, len(z)))
         k0 = 0
         kp1 = 1
-        for _its in xrange(max_iter):
+        for _its in range(max_iter):
             # Newton's method carried out simultaneously on the roots.
             tmp = 2 + alfbet
             L[k0, :] = 1
             L[kp1, :] = (alpha - beta + tmp * z) / 2
 
-            for j in xrange(2, n + 1):
+            for j in range(2, n + 1):
                 # Loop up the recurrence relation to get the Jacobi
                 # polynomials evaluated at z.
                 km1 = k0
@@ -565,12 +565,12 @@ def la_roots(n, alpha=0, method='newton'):
         k0 = 0
         kp1 = 1
         k = slice(len(z))
-        for _its in xrange(max_iter):
+        for _its in range(max_iter):
             # Newton's method carried out simultaneously on the roots.
             L[k0, k] = 0.
             L[kp1, k] = 1.
 
-            for jj in xrange(1, n + 1):
+            for jj in range(1, n + 1):
                 # Loop up the recurrence relation to get the Laguerre
                 # polynomials evaluated at z.
                 km1 = k0
@@ -683,11 +683,11 @@ def p_roots(n, method='newton', a=-1, b=1):
             k = slice(m)
             k0 = 0
             kp1 = 1
-            for _ix in xrange(max_iter):
+            for _ix in range(max_iter):
                 L[k0, k] = 1
                 L[kp1, k] = xo[k]
 
-                for jj in xrange(2, n + 1):
+                for jj in range(2, n + 1):
                     km1 = k0
                     k0 = kp1
                     kp1 = np.mod(k0 + 1, 3)
@@ -712,10 +712,10 @@ def p_roots(n, method='newton', a=-1, b=1):
 
             e1 = n * (n + 1)
 
-            for _j in xrange(2):
+            for _j in range(2):
                 pkm1 = 1
                 pk = xo
-                for k in xrange(2, n + 1):
+                for k in range(2, n + 1):
                     t1 = xo * pk
                     pkp1 = t1 - pkm1 - (t1 - pkm1) / k + t1
                     pkm1 = pk
@@ -1008,7 +1008,7 @@ class _Gaussq(object):
     def _initialize(self, wfun, a, b, args):
         args = np.broadcast_arrays(*np.atleast_1d(a, b, *args))
         a_shape = args[0].shape
-        args = map(lambda x: np.reshape(x, (-1, 1)), args)
+        args = [np.reshape(x, (-1, 1)) for x in args]
         A, B = args[:2]
         args = args[2:]
         if wfun in [2, 3]:
@@ -1036,7 +1036,7 @@ class _Gaussq(object):
         k = np.arange(nk)
         opts = (nk, dtype)
         val, val_old, abserr = zeros(*opts), ones(*opts), zeros(*opts)
-        for i in xrange(max_iter):
+        for i in range(max_iter):
             xn, w = self._points_and_weights(gn, wfun, alpha, beta)
             x = (xn + shift) * jacob[k, :] + A[k, :]
 
@@ -1181,7 +1181,7 @@ def quadgr(fun, a, b, abseps=1e-5, max_iter=17):
     Q0[0] = hh * np.sum(wq * fun(x), axis=0)
 
     # Successive bisection of intervals
-    for k in xrange(1, max_iter):
+    for k in range(1, max_iter):
 
         # Interval bisection
         hh = hh / 2
@@ -1312,7 +1312,7 @@ def qdemo(f, a, b, kmax=9, plot_error=False):
     # try various approximations
     methods = [trapz, simps, boole, ]
 
-    for k in xrange(kmax):
+    for k in range(kmax):
         n = 2 ** (k + 1) + 1
         neval[k] = n
         x = np.linspace(a, b, n)
@@ -1365,7 +1365,7 @@ def qdemo(f, a, b, kmax=9, plot_error=False):
             data.append(vals_dic[name])
             data.append(err_dic[name])
         data = np.vstack(tuple(data)).T
-        for k in xrange(kmax):
+        for k in range(kmax):
             tmp = data[k].tolist()
             print(''.join(fi % t for fi, t in zip(formats, tmp)))
         if plot_error:

wafo/interpolate.py

@@ -322,7 +322,7 @@ class PPform(object):
         dx = xx - self.breaks.take(indxs)
 
         v = pp[0, indxs]
-        for i in xrange(1, self.order):
+        for i in range(1, self.order):
             v = dx * v + pp[i, indxs]
         values = v
 
@@ -409,7 +409,7 @@ class PPform(object):
 
             vv = xs * cof[0, index]
             k = self.order
-            for i in xrange(1, k):
+            for i in range(1, k):
                 vv = xs * (vv + cof[i, index])
 
             cof[-1] = np.hstack((0, vv)).cumsum()
@@ -419,7 +419,7 @@ class PPform(object):
 #     def fromspline(self, xk, cvals, order, fill=0.0):
 #         N = len(xk) - 1
 #         sivals = np.empty((order + 1, N), dtype=float)
-#         for m in xrange(order, -1, -1):
+#         for m in range(order, -1, -1):
 #             fact = spec.gamma(m + 1)
 #             res = _fitpack._bspleval(xk[:-1], xk, cvals, order, m)
 #             res /= fact

wafo/kdetools.py

@@ -3172,10 +3172,10 @@ def gridcount(data, X, y=1):
         # fact1 = fact1(ones(n,1),:);
         bt0 = [0, 0]
         X1 = X.ravel()
-        for ir in xrange(2 ** (d - 1)):
+        for ir in range(2 ** (d - 1)):
             bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1))
             bt0[1] = 1 - bt0[0]
-            for ix in xrange(2):
+            for ix in range(2):
                 one = np.mod(ix, 2)
                 two = np.mod(ix + 1, 2)
                 # Convert to linear index

wafo/misc.py

@@ -1069,7 +1069,7 @@ def findrfc(tp, h=0.0, method='clib'):
     if clib is None or method not in ('clib'):
         ind = zeros(n, dtype=np.int)
         NC = np.int(NC)
-        for i in xrange(NC):
+        for i in range(NC):
             Tmi = Tstart + 2 * i
             Tpl = Tstart + 2 * i + 2
             xminus = y[2 * i]
@@ -1557,7 +1557,7 @@ def findtc(x_in, v=None, kind=None):
 
     first_is_down_crossing = (x[v_ind[0]] > x[v_ind[0] + 1])
     if first_is_down_crossing:
-        for i in xrange(n_tc):
+        for i in range(n_tc):
             # trough
             j = 2 * i
             ind[j] = x[v_ind[j] + 1:v_ind[j + 1] + 1].argmin()
@@ -1569,7 +1569,7 @@ def findtc(x_in, v=None, kind=None):
             ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]].argmin()
 
     else:  # the first is a up-crossing
-        for i in xrange(n_tc):
+        for i in range(n_tc):
             # crest
             j = 2 * i
             ind[j] = x[v_ind[j] + 1:v_ind[j + 1] + 1].argmax()

wafo/objects.py

@@ -318,7 +318,7 @@ class LevelCrossings(PlotData):
         ff = [f[0], ]
         tt = [t[0], ]
 
-        for i in xrange(1, n):
+        for i in range(1, n):
             if f[i] > ff[-1]:
                 ff.append(f[i])
                 tt.append(t[i])
@@ -741,7 +741,7 @@ class CyclePairs(PlotData):
         n = extremes.shape[1]
         extr = zeros((4, n))
         extr[:, 0] = extremes[:, 0]
-        for i in xrange(1, n):
+        for i in range(1, n):
             if extremes[0, i] == extr[0, ii]:
                 extr[1:4, ii] = extr[1:4, ii] + extremes[1:4, i]
             else:
@@ -1563,7 +1563,7 @@ class TimeSeries(PlotData):
         >>> import wafo.objects as wo
         >>> x = wd.sea()
         >>> ts = wo.mat2timeseries(x)
-        >>> for i in xrange(-3,4):
+        >>> for i in range(-3,4):
         ...     S, H = ts.wave_height_steepness(method=i)
         ...     print(S[:2],H[:2])
         (array([ 0.01186982,  0.04852534]), array([ 0.69,  0.86]))
@@ -2207,10 +2207,10 @@ class TimeSeries(PlotData):
         plot = plotbackend.plot
         subplot = plotbackend.subplot
         figs = []
-        for unused_iz in xrange(nfig):
+        for unused_iz in range(nfig):
             figs.append(plotbackend.figure())
             plotbackend.title('Surface elevation from mean water level (MWL).')
-            for ix in xrange(nsub):
+            for ix in range(nsub):
                 if nsub > 1:
                     subplot(nsub, 1, ix)
                 h_scale = array([tn[ind[0]], tn[ind[-1]]])

wafo/polynomial.py

@@ -961,7 +961,7 @@ def cheb2poly(ck, a=-1, b=1):
     y2 = 2. * y
 
     # Clenshaw recurence
-    for ix in xrange(n - 1):
+    for ix in range(n - 1):
         tmp = b_Nmi
         b_Nmi = polymul(y2, b_Nmi)  # polynomial multiplication
         nb = len(b_Nmip1)
@@ -1327,7 +1327,7 @@ def _chebval(x, ck, kind=1):
     b_Nmip1 = b_Nmi.copy()    # b_(N-i+1)
     x2 = 2 * x
     # Clenshaw reccurence
-    for ix in xrange(n - 1):
+    for ix in range(n - 1):
         tmp = b_Nmi
         b_Nmi = x2 * b_Nmi - b_Nmip1 + ck[ix]
         b_Nmip1 = tmp
@@ -1444,7 +1444,7 @@ def chebder(ck, a=-1, b=1):
     cder = zeros(n, dtype=asarray(ck).dtype)
     cder[0] = 2 * n * ck[0]
     cder[1] = 2 * (n - 1) * ck[1]
-    for j in xrange(2, n):
+    for j in range(2, n):
         cder[j] = cder[j - 2] + 2 * (n - j) * ck[j]
 
     return cder * 2. / (b - a)  # Normalize to the interval b-a.
@@ -1837,17 +1837,17 @@ def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True):
     mad = 0
 
     u = zeros((npt, ncof))
-    for ix in xrange(MAXIT):
+    for ix in range(MAXIT):
         # Set up design matrix for least squares fit.
         pow1 = wt
         bb = pow1 * (fs + abs(mad) * sign(ee))
 
-        for jx in xrange(m + 1):
+        for jx in range(m + 1):
             u[:, jx] = pow1
             pow1 = pow1 * x
 
         pow1 = -bb
-        for jx in xrange(m + 1, ncof):
+        for jx in range(m + 1, ncof):
             pow1 = pow1 * x
             u[:, jx] = pow1
 

wafo/stats/estimation.py

@@ -253,7 +253,7 @@ class Profile(object):
 
         for size, step in ((-1, -1), (pvec.size, 1)):
             phatfree = phatfree0.copy()
-            for ix in xrange(k1, size, step):
+            for ix in range(k1, size, step):
                 Lmax, phatfree = self._profile_optimum(phatfree, pvec[ix])
                 self.data[ix] = Lmax
                 if self.data[ix] < self.alpha_cross_level:
@@ -728,7 +728,7 @@ class FitDistribution(rv_frozen):
         LL = nnlf(theta, data)
         H = zeros((num_par, num_par))   # Hessian matrix
         theta = tuple(theta)
-        for ix in xrange(num_par):
+        for ix in range(num_par):
             sparam = list(theta)
             sparam[ix] = theta[ix] + delta
             fp = nnlf(sparam, data)

wafo/transform/estimation.py

@@ -104,7 +104,7 @@ class TransformEstimator(object):
         x = tr.args
         mean = tr.mean
         sigma = tr.sigma
-        for ix in xrange(5):
+        for ix in range(5):
             dy = np.diff(tr.data)
             if (dy <= 0).any():
                 dy[dy > 0] = eps

wafo/win32_utils.py

@@ -250,7 +250,7 @@ if __name__ == '__main__':
     ErrorDlg('This is an example of an error message')
     wb = Waitbar('Waitbar example')
 #    wb2 = Waitbar2('Waitbar example')
-    for i in xrange(20):
+    for i in range(20):
         print(wb.update(i * 5))
 #        wb2.update(i)
         sleep(0.1)