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diff --git a/pyspike/PieceWiseLinFunc.py b/pyspike/PieceWiseLinFunc.py
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+"""
+Class representing piece-wise linear functions.
+
+Copyright 2014-2015, Mario Mulansky <mario.mulansky@gmx.net>
+
+Distributed under the BSD License
+
+"""
+from __future__ import print_function
+
+import numpy as np
+import collections
+
+
+##############################################################
+# PieceWiseLinFunc
+##############################################################
+class PieceWiseLinFunc:
+    """ A class representing a piece-wise linear function. """
+
+    def __init__(self, x, y1, y2):
+        """ Constructs the piece-wise linear function.
+
+        :param x: array of length N+1 defining the edges of the intervals of
+                  the pwc function.
+        :param y1: array of length N defining the function values at the left
+                  of the intervals.
+        :param y2: array of length N defining the function values at the right
+                  of the intervals.
+        """
+        # convert to array, which also ensures copying
+        self.x = np.array(x)
+        self.y1 = np.array(y1)
+        self.y2 = np.array(y2)
+
+    def copy(self):
+        """ Returns a copy of itself
+
+        :rtype: :class:`PieceWiseLinFunc`
+        """
+        return PieceWiseLinFunc(self.x, self.y1, self.y2)
+
+    def almost_equal(self, other, decimal=14):
+        """ Checks if the function is equal to another function up to `decimal`
+        precision.
+
+        :param other: another :class:`PieceWiseLinFunc`
+        :returns: True if the two functions are equal up to `decimal` decimals,
+                  False otherwise
+        :rtype: bool
+        """
+        eps = 10.0**(-decimal)
+        return np.allclose(self.x, other.x, atol=eps, rtol=0.0) and \
+            np.allclose(self.y1, other.y1, atol=eps, rtol=0.0) and \
+            np.allclose(self.y2, other.y2, atol=eps, rtol=0.0)
+
+    def get_plottable_data(self):
+        """ Returns two arrays containing x- and y-coordinates for immeditate
+        plotting of the piece-wise function.
+
+        :returns: (x_plot, y_plot) containing plottable data
+        :rtype: pair of np.array
+
+        Example::
+
+            x, y = f.get_plottable_data()
+            plt.plot(x, y, '-o', label="Piece-wise const function")
+        """
+        x_plot = np.empty(2*len(self.x)-2)
+        x_plot[0] = self.x[0]
+        x_plot[1::2] = self.x[1:]
+        x_plot[2::2] = self.x[1:-1]
+        y_plot = np.empty_like(x_plot)
+        y_plot[0::2] = self.y1
+        y_plot[1::2] = self.y2
+        return x_plot, y_plot
+
+    def integral(self, interval=None):
+        """ Returns the integral over the given interval.
+
+        :param interval: integration interval given as a pair of floats, if
+                         None the integral over the whole function is computed.
+        :type interval: Pair of floats or None.
+        :returns: the integral
+        :rtype: float
+        """
+
+        def intermediate_value(x0, x1, y0, y1, x):
+            """ computes the intermediate value of a linear function """
+            return y0 + (y1-y0)*(x-x0)/(x1-x0)
+
+        if interval is None:
+            # no interval given, integrate over the whole spike train
+            integral = np.sum((self.x[1:]-self.x[:-1]) * 0.5*(self.y1+self.y2))
+        else:
+            # find the indices corresponding to the interval
+            start_ind = np.searchsorted(self.x, interval[0], side='right')
+            end_ind = np.searchsorted(self.x, interval[1], side='left')-1
+            assert start_ind > 0 and end_ind < len(self.x), \
+                "Invalid averaging interval"
+            # first the contribution from between the indices
+            integral = np.sum((self.x[start_ind+1:end_ind+1] -
+                               self.x[start_ind:end_ind]) *
+                              0.5*(self.y1[start_ind:end_ind] +
+                                   self.y2[start_ind:end_ind]))
+            # correction from start to first index
+            integral += (self.x[start_ind]-interval[0]) * 0.5 * \
+                        (self.y2[start_ind-1] +
+                         intermediate_value(self.x[start_ind-1],
+                                            self.x[start_ind],
+                                            self.y1[start_ind-1],
+                                            self.y2[start_ind-1],
+                                            interval[0]
+                                            ))
+            # correction from last index to end
+            integral += (interval[1]-self.x[end_ind]) * 0.5 * \
+                        (self.y1[end_ind] +
+                         intermediate_value(self.x[end_ind], self.x[end_ind+1],
+                                            self.y1[end_ind], self.y2[end_ind],
+                                            interval[1]
+                                            ))
+        return integral
+
+    def avrg(self, interval=None):
+        """ Computes the average of the piece-wise linear function:
+        :math:`a = 1/T int_0^T f(x) dx` where T is the length of the interval.
+
+        :param interval: averaging interval given as a pair of floats, a
+                         sequence of pairs for averaging multiple intervals, or
+                         None, if None the average over the whole function is
+                         computed.
+        :type interval: Pair, sequence of pairs, or None.
+        :returns: the average a.
+        :rtype: float
+
+        """
+
+        if interval is None:
+            # no interval given, average over the whole spike train
+            return self.integral() / (self.x[-1]-self.x[0])
+
+        # check if interval is as sequence
+        assert isinstance(interval, collections.Sequence), \
+            "Invalid value for `interval`. None, Sequence or Tuple expected."
+        # check if interval is a sequence of intervals
+        if not isinstance(interval[0], collections.Sequence):
+            # just one interval
+            a = self.integral(interval) / (interval[1]-interval[0])
+        else:
+            # several intervals
+            a = 0.0
+            int_length = 0.0
+            for ival in interval:
+                a += self.integral(ival)
+                int_length += ival[1] - ival[0]
+            a /= int_length
+        return a
+
+    def add(self, f):
+        """ Adds another PieceWiseLin function to this function.
+        Note: only functions defined on the same interval can be summed.
+
+        :param f: :class:`PieceWiseLinFunc` function to be added.
+        :rtype: None
+        """
+        assert self.x[0] == f.x[0], "The functions have different intervals"
+        assert self.x[-1] == f.x[-1], "The functions have different intervals"
+
+        # python implementation
+        # from python_backend import add_piece_wise_lin_python
+        # self.x, self.y1, self.y2 = add_piece_wise_lin_python(
+        #     self.x, self.y1, self.y2, f.x, f.y1, f.y2)
+
+        # cython version
+        try:
+            from cython_add import add_piece_wise_lin_cython as \
+                add_piece_wise_lin_impl
+        except ImportError:
+            print("Warning: add_piece_wise_lin_cython not found. Make sure \
+that PySpike is installed by running\n 'python setup.py build_ext --inplace'! \
+\n Falling back to slow python backend.")
+            # use python backend
+            from python_backend import add_piece_wise_lin_python as \
+                add_piece_wise_lin_impl
+
+        self.x, self.y1, self.y2 = add_piece_wise_lin_impl(
+            self.x, self.y1, self.y2, f.x, f.y1, f.y2)
+
+    def mul_scalar(self, fac):
+        """ Multiplies the function with a scalar value
+
+        :param fac: Value to multiply
+        :type fac: double
+        :rtype: None
+        """
+        self.y1 *= fac
+        self.y2 *= fac