Simplified code.

wafo/containers.py

@@ -62,12 +62,11 @@ class PlotData(object):
     >>> h1 = d2()
 
     # Plot with confidence interval
-    >>> d3 = PlotData(np.sin(x), x)
-    >>> d3.children = [PlotData(np.vstack([np.sin(x)*0.9,
-    ...                                    np.sin(x)*1.2]).T, x)]
-    >>> d3.plot_args_children = [':r']
-
-    >>> h = d3.plot()
+    >>> ci = PlotData(np.vstack([np.sin(x)*0.9, np.sin(x)*1.2]).T, x,
+    ...               plot_args=[':r'])
+    >>> d3 = PlotData(np.sin(x), x, children=[ci])
+    >>> h = d3.plot() # plot data, CI red dotted line
+    >>> h = d3.plot(plot_args_children=['b--']) # CI with blue dashed line
 
     '''
 
@@ -165,9 +164,10 @@ class PlotData(object):
     def integrate(self, a=None, b=None, **kwds):
         '''
         >>> x = np.linspace(0,5,60)
-        >>> d = PlotData(np.sin(x), x)
-        >>> d.dataCI = np.vstack((d.data*.9,d.data*1.1)).T
-        >>> d.integrate(0,np.pi/2, return_ci=True)
+        >>> y = np.sin(x)
+        >>> ci = PlotData(np.vstack((y*.9, y*1.1)).T, x)
+        >>> d = PlotData(y, x, children=[ci])
+        >>> d.integrate(0, np.pi/2, return_ci=True)
         array([ 0.99940055,  0.85543644,  1.04553343])
         >>> np.allclose(d.integrate(0, 5, return_ci=True),
         ...    d.integrate(return_ci=True))
@@ -187,12 +187,18 @@ class PlotData(object):
             b = x[-1]
         ix = np.flatnonzero((a < x) & (x < b))
         xi = np.hstack((a, x.take(ix), b))
+        if self.data.ndim > 1:
+            fi = np.vstack((self.eval_points(a),
+                            self.data[ix, :],
+                            self.eval_points(b))).T
+        else:
             fi = np.hstack((self.eval_points(a), self.data.take(ix),
                             self.eval_points(b)))
         res = fun(fi, xi, **kwds)
         if return_ci:
-            return np.hstack(
-                (res, fun(self.dataCI[ix, :].T, xi[1:-1], **kwds)))
+            res_ci = [child.integrate(a, b, method=method)
+                      for child in self.children]
+            return np.hstack((res, res_ci))
         return res
 
     def plot(self, *args, **kwds):
@@ -205,15 +211,13 @@ class PlotData(object):
         if not plotflag and self.children is not None:
             axis.hold('on')
             tmp = []
-            child_args = kwds.pop(
-                'plot_args_children',
-                tuple(
-                    self.plot_args_children))
+            child_args = kwds.pop('plot_args_children',
+                                  tuple(self.plot_args_children))
             child_kwds = dict(self.plot_kwds_children).copy()
             child_kwds.update(kwds.pop('plot_kwds_children', {}))
             child_kwds['axis'] = axis
             for child in self.children:
-                tmp1 = child(*child_args, **child_kwds)
+                tmp1 = child.plot(*child_args, **child_kwds)
                 if tmp1 is not None:
                     tmp.append(tmp1)
             if len(tmp) == 0:
@@ -267,8 +271,8 @@ class AxisLabels:
         return self.__str__()
 
     def __str__(self):
-        return '%s\n%s\n%s\n%s\n' % (
-            self.title, self.xlab, self.ylab, self.zlab)
+        txt = 'AxisLabels(title={}, xlab={}, ylab={}, zlab={})'
+        return txt.format(self.title, self.xlab, self.ylab, self.zlab)
 
     def copy(self):
         newcopy = empty_copy(self)
@@ -318,11 +322,6 @@ class Plotter_1d(object):
         if plotmethod is None:
             plotmethod = 'plot'
         self.plotmethod = plotmethod
-        # self.plotbackend = plotbackend
-#        try:
-#            self.plotfun = getattr(plotbackend, plotmethod)
-#        except:
-#            pass
 
     def show(self, *args, **kwds):
         plt.show(*args, **kwds)
@@ -350,72 +349,65 @@ class Plotter_1d(object):
 
     def _plot(self, axis, plotflag, wdata, *args, **kwds):
         x = wdata.args
-        data = transformdata(x, wdata.data, plotflag)
+        data = transformdata_1d(x, wdata.data, plotflag)
         dataCI = getattr(wdata, 'dataCI', ())
+        if dataCI:
+            dataCI = transformdata_1d(x, dataCI, plotflag)
         h1 = plot1d(axis, x, data, dataCI, plotflag, *args, **kwds)
         return h1
     __call__ = plot
 
 
-def plot1d(axis, args, data, dataCI, plotflag, *varargin, **kwds):
-
-    plottype = np.mod(plotflag, 10)
-    if plottype == 0:  # No plotting
-        return []
-    elif plottype == 1:
-        H = axis.plot(args, data, *varargin, **kwds)
-    elif plottype == 2:
-        H = axis.step(args, data, *varargin, **kwds)
-    elif plottype == 3:
-        H = axis.stem(args, data, *varargin, **kwds)
-    elif plottype == 4:
-        H = axis.errorbar(args, data,
-                          yerr=[dataCI[:, 0] - data,
-                                dataCI[:, 1] - data],
-                          *varargin, **kwds)
-    elif plottype == 5:
-        H = axis.bar(args, data, *varargin, **kwds)
-    elif plottype == 6:
-        level = 0
-        if np.isfinite(level):
-            H = axis.fill_between(args, data, level, *varargin, **kwds)
-        else:
-            H = axis.fill_between(args, data, *varargin, **kwds)
-    elif plottype == 7:
-        H = axis.plot(args, data, *varargin, **kwds)
-        H = axis.fill_between(args, dataCI[:, 0], dataCI[:, 1],
-                              alpha=0.2, color='r')
-
+def set_plot_scale(axis, f_max, plotflag):
     scale = plotscale(plotflag)
-    logXscale = 'x' in scale
-    logYscale = 'y' in scale
-    logZscale = 'z' in scale
-
-    if logXscale:
+    log_x_scale = 'x' in scale
+    log_y_scale = 'y' in scale
+    log_z_scale = 'z' in scale
+    if log_x_scale:
         axis.set(xscale='log')
-    if logYscale:
+    if log_y_scale:
         axis.set(yscale='log')
-    if logZscale:
+    if log_z_scale:
         axis.set(zscale='log')
-
-    transFlag = np.mod(plotflag // 10, 10)
-    logScale = logXscale or logYscale or logZscale
-    if logScale or (transFlag == 5 and not logScale):
+    trans_flag = np.mod(plotflag // 10, 10)
+    log_scale = log_x_scale or log_y_scale or log_z_scale
+    if log_scale or (trans_flag == 5 and not log_scale):
         ax = list(axis.axis())
-        fmax1 = data.max()
-        if transFlag == 5 and not logScale:
-            ax[3] = 11 * np.log10(fmax1)
+        if trans_flag == 8 and not log_scale:
+            ax[3] = 11 * np.log10(f_max)
             ax[2] = ax[3] - 40
         else:
-            ax[3] = 1.15 * fmax1
+            ax[3] = 1.15 * f_max
             ax[2] = ax[3] * 1e-4
-
         axis.axis(ax)
 
+
+def plot1d(axis, args, data, dataCI, plotflag, *varargin, **kwds):
+    h = []
+    plottype = np.mod(plotflag, 10)
+    if plottype == 0:  # No plotting
+        return h
+    fun = {1: 'plot', 2: 'step', 3: 'stem', 5: 'bar'}.get(plottype)
+    if fun is not None:
+        plotfun = getattr(axis, fun)
+        h.extend(plotfun(args, data, *varargin, **kwds))
         if np.any(dataCI) and plottype < 3:
             axis.hold(True)
-        plot1d(axis, args, dataCI, (), plotflag, 'r--')
-    return H
+            h.extend(plotfun(args, dataCI, 'r--'))
+    elif plottype == 4:
+        h = axis.errorbar(args, data,
+                          yerr=[dataCI[:, 0] - data,
+                                dataCI[:, 1] - data],
+                          *varargin, **kwds)
+    elif plottype == 6:
+        h = axis.fill_between(args, data, 0, *varargin, **kwds)
+    elif plottype == 7:
+        h = axis.plot(args, data, *varargin, **kwds)
+        h.extend(axis.fill_between(args, dataCI[:, 0], dataCI[:, 1],
+                                   alpha=0.2, color='r'))
+    fmax1 = data.max()
+    set_plot_scale(axis, fmax1, plotflag)
+    return h
 
 
 def plotscale(plotflag):
@@ -462,6 +454,10 @@ def plotscale(plotflag):
     'ylog'
     >>> plotscale(10000)
     'zlog'
+    >>> plotscale(1100)
+    'xylog'
+    >>> plotscale(11100)
+    'xyzlog'
 
      See also
      ---------
@@ -480,25 +476,41 @@ def plotscale(plotflag):
     return scales[scaleId]
 
 
-def transformdata(x, f, plotflag):
-    transFlag = np.mod(plotflag // 10, 10)
-    if transFlag == 0:
-        data = f
-    elif transFlag == 1:
-        data = 1 - f
-    elif transFlag == 2:
-        data = cumtrapz(f, x)
-    elif transFlag == 3:
-        data = 1 - cumtrapz(f, x)
-    if transFlag in (4, 5):
-        if transFlag == 4:
-            data = -np.log1p(-cumtrapz(f, x))
-        else:
-            if any(f < 0):
-                raise ValueError('Invalid plotflag: Data or dataCI is ' +
-                                 'negative, but must be positive')
-            data = 10 * np.log10(f)
-    return data
+def plotflag2plottype_1d(plotflag):
+    plottype = np.mod(plotflag, 10)
+    return ['', 'plot', 'step', 'stem', 'errorbar', 'bar'][plottype]
+
+
+def plotflag2transform_id(plotflag):
+    transform_id = np.mod(plotflag // 10, 10)
+    return ['f', '1-f',
+            'cumtrapz(f)', '1-cumtrapz(f)',
+            'log(f)', 'log(1-f)'
+            'log(cumtrapz(f))', 'log(cumtrapz(f))',
+            'log10(f)'][transform_id]
+
+
+def transform_id2plotflag2(transform_id):
+    return {'': 0, 'None': 0, 'f': 0, '1-f': 1,
+            'cumtrapz(f)': 2, '1-cumtrapz(f)': 3,
+            'log(f)': 4, 'log(1-f)': 5,
+            'log(cumtrapz(f))': 6, 'log(1-cumtrapz(f))': 7,
+            '10log10(f)': 8}[transform_id] * 10
+
+
+def transformdata_1d(x, f, plotflag):
+    transform_id = np.mod(plotflag // 10, 10)
+    transform = [lambda f, x: f,
+                 lambda f, x: 1 - f,
+                 lambda f, x: cumtrapz(f, x),
+                 lambda f, x: 1 - cumtrapz(f, x),
+                 lambda f, x: np.log(f),
+                 lambda f, x: np.log1p(-f),
+                 lambda f, x: np.log(cumtrapz(f, x)),
+                 lambda f, x: np.log1p(-cumtrapz(f, x)),
+                 lambda f, x: 10*np.log10(f)
+                 ][transform_id]
+    return transform(f, x)
 
 
 class Plotter_2d(Plotter_1d):
@@ -589,14 +601,15 @@ def test_plotdata():
     x = np.linspace(0, np.pi, 9)
     xi = np.linspace(0, np.pi, 4*9)
 
-    d = PlotData(np.sin(x)/2, x, xlab='x', ylab='sin', title='sinus',
+    d = PlotData(np.sin(x)/2, x, dataCI=[], xlab='x', ylab='sin', title='sinus',
                  plot_args=['r.'])
     di = PlotData(d.eval_points(xi, method='cubic'), xi)
     unused_hi = di.plot()
     unused_h = d.plot()
     f = di.to_cdf()
+
     for i in range(4):
-        _ = f.plot(plotflag=i)
+        _ = di.plot(plotflag=i)
     d.show('hold')
 
 
@@ -607,5 +620,5 @@ def test_docstrings():
 
 
 if __name__ == '__main__':
-    test_docstrings()
-    # test_plotdata()
+    # test_docstrings()
+    test_plotdata()

wafo/misc.py

@@ -7,7 +7,7 @@ import fractions
 import numpy as np
 from numpy import (
     meshgrid,
-    abs, amax, any, logical_and, arange, linspace, atleast_1d,
+    amax, logical_and, arange, linspace, atleast_1d,
     asarray, ceil, floor, frexp, hypot,
     sqrt, arctan2, sin, cos, exp, log, log1p, mod, diff,
     finfo, inf, pi, interp, isscalar, zeros, ones, linalg,
@@ -39,7 +39,7 @@ __all__ = ['now', 'spaceline', 'narg_smallest', 'args_flat', 'is_numlike',
            'betaloge', 'gravity', 'nextpow2', 'discretize', 'polar2cart',
            'cart2polar', 'meshgrid', 'ndgrid', 'trangood', 'tranproc',
            'plot_histgrm', 'num2pistr', 'test_docstrings', 'lazywhere',
-           'lazyselect'
+           'lazyselect',
            'piecewise',
            'valarray', 'check_random_state']
 
@@ -269,8 +269,8 @@ def lazyselect(condlist, choicelist, arrays, default=0):
     arrays = np.broadcast_arrays(*arrays)
     tcode = np.mintypecode([a.dtype.char for a in arrays])
     out = valarray(np.shape(arrays[0]), value=default, typecode=tcode)
-    for index in range(len(condlist)):
-        func, cond = choicelist[index], condlist[index]
+    for index, cond in enumerate(condlist):
+        func = choicelist[index]
         if np.all(cond is False):
             continue
         cond, _ = np.broadcast_arrays(cond, arrays[0])
@@ -669,8 +669,8 @@ class Bunch(object):
         self.__dict__.update(kwargs)
 
 
-def printf(format, *args):  # @ReservedAssignment
-    sys.stdout.write(format % args)
+def printf(format_, *args):
+    sys.stdout.write(format_ % args)
 
 
 def sub_dict_select(somedict, somekeys):
@@ -1612,8 +1612,8 @@ def mctp2rfc(fmM, fMm=None):
                     # end
                 # end
                 fx = 0.0
-                if (max(abs(e)) > 1e-6 and
-                        max(abs(NN)) > 1e-6 * max(MA[0], mA[0])):
+                if (max(np.abs(e)) > 1e-6 and
+                        max(np.abs(NN)) > 1e-6 * max(MA[0], mA[0])):
                     PMm = _get_PMm(AA1, MA, nA)
 
                     A = PMm
@@ -1722,7 +1722,7 @@ def _findrfc2(y, h, method=0):
                 t1, y1 = (t0, y0) if y0 > yi else (ti, yi)
 
         # Update y if y0 is a turning point
-        if abs(z0 - z1) == 2:
+        if np.abs(z0 - z1) == 2:
             j += 1
             t[j] = t0
 
@@ -1731,7 +1731,7 @@ def _findrfc2(y, h, method=0):
     # end
 
     # Update y if last y0 is greater than (or equal) threshold
-    if cmpfun1(h, abs(y0 - y[t[j]])):
+    if cmpfun1(h, np.abs(y0 - y[t[j]])):
         j += 1
         t[j] = t0
     return t[:j + 1]
@@ -1965,28 +1965,20 @@ 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 range(n_tc):
-            # trough
-            j = 2 * i
-            ind[j] = x[v_ind[j] + 1:v_ind[j + 1] + 1].argmin()
-            # crest
-            ind[j + 1] = x[v_ind[j + 1] + 1:v_ind[j + 2] + 1].argmax()
-
-        if (2 * n_tc + 1 < n_c) and (kind in (None, 'tw')):
-            # trough
-            ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1].argmin()
+        f1, f2 = np.argmin, np.argmax
+    else:
+        f1, f2 = np.argmax, np.argmin
 
-    else:  # the first is a up-crossing
     for i in range(n_tc):
-            # crest
+        # trough or crest
         j = 2 * i
-            ind[j] = x[v_ind[j] + 1:v_ind[j + 1] + 1].argmax()
-            # trough
-            ind[j + 1] = x[v_ind[j + 1] + 1:v_ind[j + 2] + 1].argmin()
+        ind[j] = f1(x[v_ind[j] + 1:v_ind[j + 1] + 1])
+        # crest or trough
+        ind[j + 1] = f2(x[v_ind[j + 1] + 1:v_ind[j + 2] + 1])
 
-        if (2 * n_tc + 1 < n_c) and (kind in (None, 'cw')):
-            # crest
-            ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1].argmax()
+    if (2 * n_tc + 1 < n_c) and (kind in (None, 'tw', 'cw')):
+        # trough or crest
+        ind[n_c - 2] = f1(x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1])
 
     return v_ind[:n_c - 1] + ind + 1, v_ind
 
@@ -2081,7 +2073,7 @@ def findoutliers(x, zcrit=0.0, dcrit=None, ddcrit=None, verbose=False):
 
     def _find_consecutive_equal_values(dxn, zcrit):
 
-        mask_small = (abs(dxn) <= zcrit)
+        mask_small = (np.abs(dxn) <= zcrit)
         i_small = np.flatnonzero(mask_small)
         if verbose:
             if zcrit == 0.:
@@ -2130,7 +2122,7 @@ def findoutliers(x, zcrit=0.0, dcrit=None, ddcrit=None, verbose=False):
     indg, = nonzero(indg)
 
     if verbose:
-        print('Found the total of %d spurious points' % ind.size)
+        print('Found the total of %d spurious points' % np.size(ind))
 
     return ind, indg
 
@@ -2174,8 +2166,7 @@ def common_shape(*args, ** kwds):
     '''
     shape = kwds.get('shape')
     x0 = 1 if shape is None else np.ones(shape)
-    x1 = np.broadcast(x0, *args)
-    return tuple(x1.shape)
+    return tuple(np.broadcast(x0, *args).shape)
 
 
 def argsreduce(condition, * args):
@@ -2275,15 +2266,15 @@ def stirlerr(n):
     n500 = 500 < n1
     y[n500] = (S0 - S1 / nn[n500]) / n1[n500]
     n80 = logical_and(80 < n1, n1 <= 500)
-    if any(n80):
+    if np.any(n80):
         y[n80] = (S0 - (S1 - S2 / nn[n80]) / nn[n80]) / n1[n80]
     n35 = logical_and(35 < n1, n1 <= 80)
-    if any(n35):
+    if np.any(n35):
         nn35 = nn[n35]
         y[n35] = (S0 - (S1 - (S2 - S3 / nn35) / nn35) / nn35) / n1[n35]
 
     n15 = logical_and(15 < n1, n1 <= 35)
-    if any(n15):
+    if np.any(n15):
         nn15 = nn[n15]
         y[n15] = (
             S0 - (S1 - (S2 - (S3 - S4 / nn15) / nn15) / nn15) / nn15) / n1[n15]
@@ -2426,7 +2417,7 @@ def binomln(z, w):
     Example
     -------
 
-    >>> abs(binomln(3,2)- 1.09861229)<1e-7
+    >>> np.abs(binomln(3,2)- 1.09861229)<1e-7
     array([ True], dtype=bool)
 
     See also
@@ -2463,7 +2454,7 @@ def betaloge(z, w):
     Example
     -------
     >>> import wafo.misc as wm
-    >>> abs(wm.betaloge(3,2)+2.48490665)<1e-7
+    >>> np.abs(wm.betaloge(3,2)+2.48490665)<1e-7
     array([ True], dtype=bool)
 
     See also
@@ -2545,7 +2536,7 @@ def nextpow2(x):
     if (t > 1):
         f, n = frexp(t)
     else:
-        f, n = frexp(abs(x))
+        f, n = frexp(np.abs(x))
 
     if (f == 0.5):
         n = n - 1
@@ -2615,7 +2606,7 @@ def _discretize_linear(fun, a, b, tol=0.005, n=5):
         x = linspace(a, b, n)
         y = fun(x)
         y00 = interp(x, x0, y0)
-        err = 0.5 * amax(abs((y00 - y) / (abs(y00 + y) + _TINY)))
+        err = 0.5 * amax(np.abs((y00 - y) / (np.abs(y00 + y) + _TINY)))
     return x, y
 
 
@@ -2644,7 +2635,7 @@ def _discretize_adaptive(fun, a, b, tol=0.005, n=5):
             fy = fun(y)
 
             fy0 = interp(y, x, fx)
-            erri = 0.5 * (abs((fy0 - fy) / (abs(fy0 + fy) + _TINY)))
+            erri = 0.5 * (np.abs((fy0 - fy) / (np.abs(fy0 + fy) + _TINY)))
 
             err = erri.max()
 
@@ -2794,7 +2785,7 @@ def trangood(x, f, min_n=None, min_x=None, max_x=None, max_n=inf):
     xn = xo[-1]
     x0 = xo[0]
     L = float(xn - x0)
-    if ((nf < min_n) or (max_n < nf) or any(abs(ddx) > 10 * _EPS * (L))):
+    if ((nf < min_n) or (max_n < nf) or np.any(np.abs(ddx) > 10 * _EPS * (L))):
         # pab 07.01.2001: Always choose the stepsize df so that
         # it is an exactly representable number.
         # This is important when calculating numerical derivatives and is
@@ -3145,10 +3136,10 @@ def num2pistr(x, n=3, numerator_max=10, denominator_max=10):
     True
     '''
     def _denominator_text(den):
-        return '' if abs(den) == 1 else '/%d' % den
+        return '' if np.abs(den) == 1 else '/%d' % den
 
     def _numerator_text(num):
-        if abs(num) == 1:
+        if np.abs(num) == 1:
             return '-' if num == -1 else ''
         return '{:d}'.format(num)
     frac = fractions.Fraction.from_float(x / pi).limit_denominator(int(1e+13))

wafo/sg_filter.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, division
 import numpy as np
 # from math import pow
 # from numpy import zeros,dot
-from numpy import (pi, abs, size, convolve, linalg, concatenate, sqrt)
+from numpy import (pi, convolve, linalg, concatenate, sqrt)
 from scipy.sparse import spdiags
 from scipy.sparse.linalg import spsolve, expm
 from scipy.signal import medfilt
@@ -132,14 +132,14 @@ class SavitzkyGolay(object):
 
     def smooth_last(self, signal, k=0):
         coeff = self._coeff
-        n = size(coeff - 1) // 2
+        n = np.size((coeff - 1) // 2
         y = np.squeeze(signal)
         if n == 0:
             return y
         if y.ndim > 1:
             coeff.shape = (-1, 1)
-        first_vals = y[0] - abs(y[n:0:-1] - y[0])
-        last_vals = y[-1] + abs(y[-2:-n - 2:-1] - y[-1])
+        first_vals = y[0] - np.abs(y[n:0:-1] - y[0])
+        last_vals = y[-1] + np.abs(y[-2:-n - 2:-1] - y[-1])
         y = concatenate((first_vals, y, last_vals))
         return (y[-2 * n - 1 - k:-k] * coeff).sum(axis=0)
 
@@ -147,9 +147,8 @@ class SavitzkyGolay(object):
         return self.smooth(signal)
 
     def smooth(self, signal):
-        x = np.asarray(signal)
-        if x.dtype != np.float64 and x.dtype != np.float32:
-            x = x.astype(np.float64)
+        dtype = np.result_type(signal, np.float)
+        x = np.asarray(signal, dtype=dtype)
 
         coeffs = self._coeff
         mode, axis = self.mode, self.axis
@@ -180,13 +179,13 @@ class SavitzkyGolay(object):
             the smoothed signal (or it's n-th derivative).
         """
         coeff = self._coeff
-        n = size(coeff - 1) // 2
+        n = np.size(coeff - 1) // 2
         y = np.squeeze(signal)
         if n == 0:
             return y
         if pad:
-            first_vals = y[0] - abs(y[n:0:-1] - y[0])
-            last_vals = y[-1] + abs(y[-2:-n - 2:-1] - y[-1])
+            first_vals = y[0] - np.abs(y[n:0:-1] - y[0])
+            last_vals = y[-1] + np.abs(y[-2:-n - 2:-1] - y[-1])
             y = concatenate((first_vals, y, last_vals))
             n *= 2
         d = y.ndim
@@ -424,8 +423,8 @@ class _Filter(object):
 
     @staticmethod
     def _studentized_residuals(r, I, h):
-        median_abs_deviation = np.median(abs(r[I] - np.median(r[I])))
-        return abs(r / (1.4826 * median_abs_deviation) / sqrt(1 - h))
+        median_abs_deviation = np.median(np.abs(r[I] - np.median(r[I])))
+        return np.abs(r / (1.4826 * median_abs_deviation) / sqrt(1 - h))
 
     def robust_weights(self, r, I, h):
         """Return weights for robust smoothing."""
@@ -458,10 +457,10 @@ class _Filter(object):
 
     def check_smooth_parameter(self, s):
         if self.auto_smooth:
-            if abs(np.log10(s) - np.log10(self.s_min)) < self.errp:
+            if np.abs(np.log10(s) - np.log10(self.s_min)) < self.errp:
                 warnings.warn('''s = %g: the lower bound for s has been reached.
             Put s as an input variable if required.''' % s)
-            elif abs(np.log10(s) - np.log10(self.s_max)) < self.errp:
+            elif np.abs(np.log10(s) - np.log10(self.s_max)) < self.errp:
                 warnings.warn('''s = %g: the Upper bound for s has been reached.
             Put s as an input variable if required.''' % s)
 
@@ -723,8 +722,8 @@ def test_smoothn_1d():
     y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
     z = smoothn(y)  # Regular smoothing
     zr = smoothn(y, robust=True)  # Robust smoothing
-    plt.subplot(121),
-    unused_h = plt.plot(x, y, 'r.', x, z, 'k', linewidth=2)
+    _h0 = plt.subplot(121),
+    _h = plt.plot(x, y, 'r.', x, z, 'k', linewidth=2)
     plt.title('Regular smoothing')
     plt.subplot(122)
     plt.plot(x, y, 'r.', x, zr, 'k', linewidth=2)
@@ -735,18 +734,18 @@ def test_smoothn_1d():
 def test_smoothn_2d():
 
     # import mayavi.mlab as plt
-    xp = np.r_[0:1:.02]
+    xp = np.r_[0:1:0.02]
     [x, y] = np.meshgrid(xp, xp)
     f = np.exp(x + y) + np.sin((x - 2 * y) * 3)
     fn = f + np.random.randn(*f.shape) * 0.5
-    fs, s = smoothn(fn, fulloutput=True)  # @UnusedVariable
+    _fs, s = smoothn(fn, fulloutput=True)
     fs2 = smoothn(fn, s=2 * s)
-    plt.subplot(131),
-    plt.contourf(xp, xp, fn)
-    plt.subplot(132),
-    plt.contourf(xp, xp, fs2)
-    plt.subplot(133),
-    plt.contourf(xp, xp, f)
+    _h = plt.subplot(131),
+    _h = plt.contourf(xp, xp, fn)
+    _h = plt.subplot(132),
+    _h = plt.contourf(xp, xp, fs2)
+    _h = plt.subplot(133),
+    _h = plt.contourf(xp, xp, f)
     plt.show('hold')
 
 
@@ -1048,7 +1047,7 @@ class Kalman(object):
 
     def _update_covariance(self, P, K):
         return P - np.dot(K, np.dot(self.H, P))
-        return np.dot(np.eye(len(P)) - K * self.H, P)
+        # return np.dot(np.eye(len(P)) - K * self.H, P)
 
     def _filter_main(self, z, u):
         ''' This is the code which implements the discrete Kalman filter:
@@ -1187,7 +1186,7 @@ def test_kalman_sine():
     x = np.zeros((n, m))
 
     for i, zi in enumerate(z):
-        x[i] = filt(zi, u=0).ravel()
+        x[i] = np.ravel(filt(zi, u=0))
 
     _hz = plt.plot(z, 'r.', label='observations')
     # a-posteriori state estimates:
@@ -1321,7 +1320,8 @@ class HampelFilter(object):
         self.adaptive = adaptive
         self.fulloutput = fulloutput
 
-    def _check(self, dx):
+    @staticmethod
+    def _check(dx):
         if not np.isscalar(dx):
             raise ValueError('DX must be a scalar.')
         if dx < 0:
@@ -1357,7 +1357,7 @@ class HampelFilter(object):
             while S0Rel > self.adaptive:
                 Y0Tmp[j], S0Tmp[j] = localwindow(X, Y, DXTmp[j], i)
                 if j > 0:
-                    S0Rel = abs((S0Tmp[j - 1] - S0Tmp[j]) /
+                    S0Rel = np.abs((S0Tmp[j - 1] - S0Tmp[j]) /
                                    (S0Tmp[j - 1] + S0Tmp[j]) / 2)
                 j += 1