added sphinx doc generation

3 files changed, 181 insertions, 150 deletions

diff --git a/pyspike/distances.py b/pyspike/distances.py
index 3e97b77..b0af24c 100644
--- a/pyspike/distances.py
+++ b/pyspike/distances.py
@@ -17,15 +17,17 @@ from pyspike import PieceWiseConstFunc, PieceWiseLinFunc
 # isi_profile
 ############################################################
 def isi_profile(spikes1, spikes2):
-    """ Computes the isi-distance profile S_isi(t) of the two given spike
-    trains. Retruns the profile as a PieceWiseConstFunc object. The S_isi
+    """ Computes the isi-distance profile :math:`S_{isi}(t)` of the two given
+    spike trains. Retruns the profile as a PieceWiseConstFunc object. The S_isi
     values are defined positive S_isi(t)>=0.  The spike trains are expected
     to have auxiliary spikes at the beginning and end of the interval. Use the
     function add_auxiliary_spikes to add those spikes to the spike train.
-    Args:
-    - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes.
-    Returns:
-    - PieceWiseConstFunc describing the isi-distance.
+
+    :param spikes1: ordered array of spike times with auxiliary spikes.
+    :param spikes2: ordered array of spike times with auxiliary spikes.
+    :returns: The isi-distance profile :math:`S_{isi}(t)`
+    :rtype: :class:`pyspike.function.PieceWiseConstFunc`
+
     """
     # check for auxiliary spikes - first and last spikes should be identical
     assert spikes1[0] == spikes2[0], \
@@ -52,12 +54,15 @@ Falling back to slow python backend.")
 ############################################################
 def isi_distance(spikes1, spikes2):
     """ Computes the isi-distance I of the given spike trains. The
-    isi-distance is the integral over the isi distance profile S_isi(t):
-     I = \int_^T S_isi(t) dt.
-    Args:
-    - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes.
-    Returns:
-    - double value: The isi-distance I.
+    isi-distance is the integral over the isi distance profile
+    :math:`S_{isi}(t)`:
+
+    .. math:: I = \int_0^T S_{isi}(t) dt.
+
+    :param spikes1: ordered array of spike times with auxiliary spikes.
+    :param spikes2: ordered array of spike times with auxiliary spikes.
+    :returns: The isi-distance I.
+    :rtype: double
     """
     return isi_profile(spikes1, spikes2).avrg()
 
@@ -71,10 +76,12 @@ def spike_profile(spikes1, spikes2):
     values are defined positive S_spike(t)>=0. The spike trains are expected to
     have auxiliary spikes at the beginning and end of the interval. Use the
     function add_auxiliary_spikes to add those spikes to the spike train.
-    Args:
-    - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes.
-    Returns:
-    - PieceWiseLinFunc describing the spike-distance.
+
+    :param spikes1: ordered array of spike times with auxiliary spikes.
+    :param spikes2: ordered array of spike times with auxiliary spikes.
+    :returns: The spike-distance profile :math:`S_{spike}(t).
+    :rtype: :class:`pyspike.function.PieceWiseLinFunc`
+
     """
     # check for auxiliary spikes - first and last spikes should be identical
     assert spikes1[0] == spikes2[0], \
@@ -104,18 +111,20 @@ def spike_distance(spikes1, spikes2):
     """ Computes the spike-distance S of the given spike trains. The
     spike-distance is the integral over the isi distance profile S_spike(t):
      S = \int_^T S_spike(t) dt.
-    Args:
-    - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes.
-    Returns:
-    - double value: The spike-distance S.
+
+    :param spikes1: ordered array of spike times with auxiliary spikes.
+    :param spikes2: ordered array of spike times with auxiliary spikes.
+    :returns: The spike-distance.
+    :rtype: double
+
     """
     return spike_profile(spikes1, spikes2).avrg()
 
 
 ############################################################
-# generic_profile_multi
+# _generic_profile_multi
 ############################################################
-def generic_profile_multi(spike_trains, pair_distance_func, indices=None):
+def _generic_profile_multi(spike_trains, pair_distance_func, indices=None):
     """ Internal implementation detail, don't call this function directly,
     use isi_profile_multi or spike_profile_multi instead.
 
@@ -153,7 +162,7 @@ def generic_profile_multi(spike_trains, pair_distance_func, indices=None):
 ############################################################
 # multi_distance_par
 ############################################################
-def multi_distance_par(spike_trains, pair_distance_func, indices=None):
+def _multi_distance_par(spike_trains, pair_distance_func, indices=None):
     """ parallel implementation of the multi-distance. Not currently used as
     it does not improve the performance.
     """
@@ -210,14 +219,15 @@ def isi_profile_multi(spike_trains, indices=None):
     trains. That is the average isi-distance of all pairs of spike-trains:
     S_isi(t) = 2/((N(N-1)) sum_{<i,j>} S_{isi}^{i,j},
     where the sum goes over all pairs <i,j>
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike trains to use,
-    if None all given spike trains are used (default=None)
-    Returns:
-    - A PieceWiseConstFunc representing the averaged isi distance S_isi(t)
+
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type state: list or None
+    :returns: The averaged isi profile :math:`<S_{isi}>(t)`
+    :rtype: :class:`pyspike.function.PieceWiseConstFunc`
     """
-    return generic_profile_multi(spike_trains, isi_profile, indices)
+    return _generic_profile_multi(spike_trains, isi_profile, indices)
 
 
 ############################################################
@@ -226,14 +236,14 @@ def isi_profile_multi(spike_trains, indices=None):
 def isi_distance_multi(spike_trains, indices=None):
     """ computes the multi-variate isi-distance for a set of spike-trains.
     That is the time average of the multi-variate spike profile:
-    S_isi = \int_0^T 2/((N(N-1)) sum_{<i,j>} S_{isi}^{i,j},
+    I = \int_0^T 2/((N(N-1)) sum_{<i,j>} S_{isi}^{i,j},
     where the sum goes over all pairs <i,j>
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike trains to use,
-    if None all given spike trains are used (default=None)
-    Returns:
-    - A double value representing the averaged isi distance S_isi
+
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :returns: The time-averaged isi distance :math:`I`
+    :rtype: double
     """
     return isi_profile_multi(spike_trains, indices).avrg()
 
@@ -246,14 +256,14 @@ def spike_profile_multi(spike_trains, indices=None):
     trains. That is the average spike-distance of all pairs of spike-trains:
     S_spike(t) = 2/((N(N-1)) sum_{<i,j>} S_{spike}^{i, j},
     where the sum goes over all pairs <i,j>
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike-trains to use,
-    if None all given spike trains are used (default=None)
-    Returns:
-    - A PieceWiseLinFunc representing the averaged spike distance S(t)
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type indices: list or None
+    :returns: The averaged spike profile :math:`<S_{spike}>(t)`
+    :rtype: :class:`pyspike.function.PieceWiseLinFunc`
     """
-    return generic_profile_multi(spike_trains, spike_profile, indices)
+    return _generic_profile_multi(spike_trains, spike_profile, indices)
 
 
 ############################################################
@@ -264,12 +274,13 @@ def spike_distance_multi(spike_trains, indices=None):
     That is the time average of the multi-variate spike profile:
     S_{spike} = \int_0^T 2/((N(N-1)) sum_{<i,j>} S_{spike}^{i, j} dt
     where the sum goes over all pairs <i,j>
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike-trains to use,
-    if None all given spike trains are used (default=None)
-    Returns:
-    - A double value representing the averaged spike distance S
+
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type indices: list or None
+    :returns: The averaged spike distance S.
+    :rtype: double
     """
     return spike_profile_multi(spike_trains, indices).avrg()
 
@@ -277,7 +288,7 @@ def spike_distance_multi(spike_trains, indices=None):
 ############################################################
 # generic_distance_matrix
 ############################################################
-def generic_distance_matrix(spike_trains, dist_function, indices=None):
+def _generic_distance_matrix(spike_trains, dist_function, indices=None):
     """ Internal implementation detail. Don't use this function directly.
     Instead use isi_distance_matrix or spike_distance_matrix.
     Computes the time averaged distance of all pairs of spike-trains.
@@ -311,15 +322,16 @@ def generic_distance_matrix(spike_trains, dist_function, indices=None):
 ############################################################
 def isi_distance_matrix(spike_trains, indices=None):
     """ Computes the time averaged isi-distance of all pairs of spike-trains.
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike-trains to use
-    if None all given spike-trains are used (default=None)
-    Return:
-    - a 2D array of size len(indices)*len(indices) containing the average
-    pair-wise isi-distance
+
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type indices: list or None
+    :returns: 2D array with the pair wise time average isi distances
+              :math:`I_{ij}`
+    :rtype: np.array
     """
-    return generic_distance_matrix(spike_trains, isi_distance, indices)
+    return _generic_distance_matrix(spike_trains, isi_distance, indices)
 
 
 ############################################################
@@ -327,12 +339,13 @@ def isi_distance_matrix(spike_trains, indices=None):
 ############################################################
 def spike_distance_matrix(spike_trains, indices=None):
     """ Computes the time averaged spike-distance of all pairs of spike-trains.
-    Args:
-    - spike_trains: list of spike trains
-    - indices: list of indices defining which spike-trains to use
-    if None all given spike-trains are used (default=None)
-    Return:
-    - a 2D array of size len(indices)*len(indices) containing the average
-    pair-wise spike-distance
+
+    :param spike_trains: list of spike trains
+    :param indices: list of indices defining which spike trains to use,
+                    if None all given spike trains are used (default=None)
+    :type indices: list or None
+    :returns: 2D array with the pair wise time average spike distances
+              :math:`S_{ij}`
+    :rtype: np.array
     """
-    return generic_distance_matrix(spike_trains, spike_distance, indices)
+    return _generic_distance_matrix(spike_trains, spike_distance, indices)
diff --git a/pyspike/function.py b/pyspike/function.py
index b161034..ed47f27 100644
--- a/pyspike/function.py
+++ b/pyspike/function.py
@@ -16,15 +16,16 @@ import numpy as np
 ##############################################################
 # PieceWiseConstFunc
 ##############################################################
-class PieceWiseConstFunc:
+class PieceWiseConstFunc(object):
     """ A class representing a piece-wise constant function. """
 
     def __init__(self, x, y):
         """ Constructs the piece-wise const function.
-        Args:
-        - x: array of length N+1 defining the edges of the intervals of the pwc
-        function.
-        - y: array of length N defining the function values at the intervals.
+
+        :param x: array of length N+1 defining the edges of the intervals of
+                  the pwc function.
+        :param y: array of length N defining the function values at the
+                  intervals.
         """
         # convert parameters to arrays, also ensures copying
         self.x = np.array(x)
@@ -32,19 +33,19 @@ class PieceWiseConstFunc:
 
     def copy(self):
         """ Returns a copy of itself
-        Returns:
-        - PieceWiseConstFunc copy
+
+        :rtype: :class:`PieceWiseConstFunc`
         """
         return PieceWiseConstFunc(self.x, self.y)
 
     def almost_equal(self, other, decimal=14):
         """ Checks if the function is equal to another function up to `decimal`
         precision.
-        Args:
-        - other: another PieceWiseConstFunc object
-        Returns:
-        True if the two functions are equal up to `decimal` decimals,
-        False otherwise
+
+        :param: other: another :class:`PieceWiseConstFunc`
+        :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 \
@@ -53,6 +54,14 @@ class PieceWiseConstFunc:
     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)
@@ -67,9 +76,10 @@ class PieceWiseConstFunc:
 
     def avrg(self):
         """ Computes the average of the piece-wise const function:
-        a = 1/T int f(x) dx where T is the length of the interval.
-        Returns:
-        - the average a.
+        :math:`a = 1/T int_0^T f(x) dx` where T is the length of the interval.
+
+        :returns: the average a.
+        :rtype: double
         """
         return np.sum((self.x[1:]-self.x[:-1]) * self.y) / \
             (self.x[-1]-self.x[0])
@@ -77,8 +87,9 @@ class PieceWiseConstFunc:
     def add(self, f):
         """ Adds another PieceWiseConst function to this function.
         Note: only functions defined on the same interval can be summed.
-        Args:
-        - f: PieceWiseConst function to be added.
+
+        :param f: :class:`PieceWiseConstFunc` 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"
@@ -99,8 +110,10 @@ that PySpike is installed by running\n 'python setup.py build_ext --inplace'! \
 
     def mul_scalar(self, fac):
         """ Multiplies the function with a scalar value
-        Args:
-        - fac: Value to multiply
+
+        :param fac: Value to multiply
+        :type fac: double
+        :rtype: None
         """
         self.y *= fac
 
@@ -113,13 +126,13 @@ class PieceWiseLinFunc:
 
     def __init__(self, x, y1, y2):
         """ Constructs the piece-wise linear function.
-        Args:
-        - x: array of length N+1 defining the edges of the intervals of the pwc
-        function.
-        - y1: array of length N defining the function values at the left of the
-        intervals.
-        - y2: array of length N defining the function values at the right of
-        the intervals.
+
+        :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)
@@ -128,19 +141,19 @@ class PieceWiseLinFunc:
 
     def copy(self):
         """ Returns a copy of itself
-        Returns:
-        - PieceWiseLinFunc copy
+
+        :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.
-        Args:
-        - other: another PieceWiseLinFunc object
-        Returns:
-        True if the two functions are equal up to `decimal` decimals,
-        False otherwise
+
+        :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 \
@@ -150,6 +163,14 @@ class PieceWiseLinFunc:
     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]
@@ -162,27 +183,20 @@ class PieceWiseLinFunc:
 
     def avrg(self):
         """ Computes the average of the piece-wise linear function:
-        a = 1/T int f(x) dx where T is the length of the interval.
-        Returns:
-        - the average a.
+        :math:`a = 1/T int_0^T f(x) dx` where T is the length of the interval.
+
+        :returns: the average a.
+        :rtype: double
         """
         return np.sum((self.x[1:]-self.x[:-1]) * 0.5*(self.y1+self.y2)) / \
             (self.x[-1]-self.x[0])
 
-    def abs_avrg(self):
-        """ Computes the absolute average of the piece-wise linear function:
-        a = 1/T int |f(x)| dx where T is the length of the interval.
-        Returns:
-        - the average a.
-        """
-        return np.sum((self.x[1:]-self.x[:-1]) * 0.5 *
-                      (np.abs(self.y1)+np.abs(self.y2)))/(self.x[-1]-self.x[0])
-
     def add(self, f):
         """ Adds another PieceWiseLin function to this function.
         Note: only functions defined on the same interval can be summed.
-        Args:
-        - f: PieceWiseLin function to be added.
+
+        :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"
@@ -209,8 +223,10 @@ that PySpike is installed by running\n 'python setup.py build_ext --inplace'! \
 
     def mul_scalar(self, fac):
         """ Multiplies the function with a scalar value
-        Args:
-        - fac: Value to multiply
+
+        :param fac: Value to multiply
+        :type fac: double
+        :rtype: None
         """
         self.y1 *= fac
         self.y2 *= fac
@@ -218,12 +234,12 @@ that PySpike is installed by running\n 'python setup.py build_ext --inplace'! \
 
 def average_profile(profiles):
     """ Computes the average profile from the given ISI- or SPIKE-profiles.
-    Args:
-    - profiles: list of PieceWiseConstFunc or PieceWiseLinFunc representing
-    ISI- or SPIKE-profiles to be averaged
-    Returns:
-    - avrg_profile: PieceWiseConstFunc or PieceWiseLinFunc containing the
-    average profile.
+
+    :param profiles: list of :class:`PieceWiseConstFunc` or
+                     :class:`PieceWiseLinFunc` representing ISI- or
+                     SPIKE-profiles to be averaged.
+    :returns: the averages profile :math:`<S_{isi}>` or :math:`<S_{spike}>`.
+    :rtype: :class:`PieceWiseConstFunc` or :class:`PieceWiseLinFunc`
     """
     assert len(profiles) > 1
 
diff --git a/pyspike/spikes.py b/pyspike/spikes.py
index 68c8bc1..6b6e2e7 100644
--- a/pyspike/spikes.py
+++ b/pyspike/spikes.py
@@ -15,15 +15,16 @@ import numpy as np
 ############################################################
 def add_auxiliary_spikes(spike_train, time_interval):
     """ Adds spikes at the beginning and end of the given time interval.
-    Args:
-    - spike_train: ordered array of spike times
-    - time_interval: A pair (T_start, T_end) of values representing the start
-    and end time of the spike train measurement or a single value representing
-    the end time, the T_start is then assuemd as 0. Auxiliary spikes will be
-    added to the spike train at the beginning and end of this interval, if they
-    are not yet present.
-    Returns:
-    - spike train with additional spikes at T_start and T_end.
+
+    :param spike_train: ordered array of spike times
+    :param time_interval: A pair (T_start, T_end) of values representing the
+                          start and end time of the spike train measurement or
+                          a single value representing the end time, the T_start
+                          is then assuemd as 0. Auxiliary spikes will be added
+                          to the spike train at the beginning and end of this
+                          interval, if they are not yet present.
+    :type time_interval: pair of doubles or double
+    :returns: spike train with additional spikes at T_start and T_end.
 
     """
     try:
@@ -49,12 +50,11 @@ def add_auxiliary_spikes(spike_train, time_interval):
 ############################################################
 def spike_train_from_string(s, sep=' ', sort=True):
     """ Converts a string of times into an array of spike times.
-    Args:
-    - s: the string with (ordered) spike times
-    - sep: The separator between the time numbers, default=' '.
-    - sort: If True, the spike times are order via `np.sort`, default=True.
-    Returns:
-    - array of spike times
+
+    :param s: the string with (ordered) spike times
+    :param sep: The separator between the time numbers, default=' '.
+    :param sort: If True, the spike times are order via `np.sort`, default=True
+    :returns: array of spike times
     """
     if sort:
         return np.sort(np.fromstring(s, sep=sep))
@@ -75,15 +75,18 @@ def load_spike_trains_from_txt(file_name, time_interval=None,
     end of each spike train. However, if `time_interval == None`, no auxiliary
     spikes are added, but note that the Spike and ISI distance both require
     auxiliary spikes.
-    Args:
-    - file_name: The name of the text file.
-    - time_interval: A pair (T_start, T_end) of values representing the start
-    and end time of the spike train measurement or a single value representing
-    the end time, the T_start is then assuemd as 0. Auxiliary spikes will be
-    added to the spike train at the beginning and end of this interval.
-    - separator: The character used to seprate the values in the text file.
-    - comment: Lines starting with this character are ignored.
-    - sort: If true, the spike times are order via `np.sort`, default=True.
+
+    :param file_name: The name of the text file.
+    :param time_interval: A pair (T_start, T_end) of values representing the
+                          start and end time of the spike train measurement
+                          or a single value representing the end time, the
+                          T_start is then assuemd as 0. Auxiliary spikes will
+                          be added to the spike train at the beginning and end
+                          of this interval.
+    :param separator: The character used to seprate the values in the text file
+    :param comment: Lines starting with this character are ignored.
+    :param sort: If true, the spike times are order via `np.sort`, default=True
+    :returns: list of spike trains
     """
     spike_trains = []
     spike_file = open(file_name, 'r')
@@ -102,10 +105,9 @@ def load_spike_trains_from_txt(file_name, time_interval=None,
 ############################################################
 def merge_spike_trains(spike_trains):
     """ Merges a number of spike trains into a single spike train.
-    Args:
-    - spike_trains: list of arrays of spike times
-    Returns:
-    - array with the merged spike times
+
+    :param spike_trains: list of arrays of spike times
+    :returns: spike train with the merged spike times
     """
     # get the lengths of the spike trains
     lens = np.array([len(st) for st in spike_trains])