(fix) comment for docstring

docs/modules/step_pattern.rst

@@ -1,3 +1,5 @@
+.. module:: dtwalign.step_pattern
+
 =============
 Step patterns
 =============

docs/tutorial.rst

@@ -39,7 +39,7 @@ Then run :func:`~dtwalign.dtw` method which returns :class:`~dtwalign.result.Dtw
 DTW distance
 ------------
 
-DTW distance can be refered via ``DtwResult`` object.
+DTW distance can be refered via :class:`~dtwalign.result.DtwResult` object.
 
 .. code-block:: python
 
@@ -55,7 +55,7 @@ DTW distance can be refered via ``DtwResult`` object.
 Alignment path
 --------------
 
-``DtwResult`` object offers a method which visualize alignment path with cumsum cost matrix.
+:class:`~dtwalign.result.DtwResult` object offers a method which visualize alignment path with cumsum cost matrix.
 
 .. code-block:: python
 
@@ -103,7 +103,7 @@ Advanced Usage
 Global constraint
 -----------------
 
-``dtw`` method can take ``window_type`` parameter to constrain
+:func:`~dtwalign.dtw` method can take ``window_type`` parameter to constrain
 the warping path globally which is also known as 'windowing'.
 
 .. code-block:: python
@@ -168,7 +168,7 @@ Following step patterns are supported:
 Partial alignment
 -----------------
 
-``dtw`` method also be able to perform partial matching algorithm
+:func:`~dtwalign.dtw` method also be able to perform partial matching algorithm
 by setting ``open_begin`` and ``open_end``.
 Before see example code, let's make toy data via following:
 

dtwalign/dtw.py

@@ -45,16 +45,16 @@ def dtw(x, y, dist="euclidean", window_type="none", window_size=None,
     y : 1D or 2D array (sample * feature)
         Reference time series.
 
-    dist : string or function
-        Define how to calclulate pair-wise distance between x and y.
-        string - metric argument of scipy.spatial.distance
-        function - user function that defines metric between two samples.
-            ex) euclidean distance: user_func = lambda x,y : np.sqrt((x-y)**2)
+    dist : string or callable
+        Define how to calclulate pair-wise distance between x and y.  
+        * string: metric argument of ``scipy.spatial.distance``  
+        * callable: user function that defines metric between two samples.  
+          ex) lambda x, y : np.sqrt((x - y)**2)
 
     window_type : string
-        Window type to use.
-        "sakoechiba" - Sakoechiba window
-        "itakura" - Itakura window
+        Window type to use.  
+        * "sakoechiba": Sakoechiba window
+        * "itakura": Itakura window
 
     window_size : int
         Window size to use for Sakoechiba window.
@@ -63,15 +63,15 @@ def dtw(x, y, dist="euclidean", window_type="none", window_size=None,
         Step pattern to use.
 
     dist_only : bool
-        Whether or not to obtain warping path. If true,
+        Whether or not to obtain warping path. If true,  
         only alignment distance will be calculated.
 
     open_begin : bool
-        Whether or not perform open-ended alignment at the starting point of
+        Whether or not perform open-ended alignment at the starting point of  
         query time series. If true, partial alignment will be performed.
 
     open_end : bool
-        Whether or not perform open-ended alignment at the end point of
+        Whether or not perform open-ended alignment at the end point of  
         query time series. If true, partial alignment will be performed.
 
     Returns
@@ -109,7 +109,8 @@ def dtw_from_distance_matrix(X, window_type="none", window_size=None,
     X : 2D array
         Pre-computed pair-wise distance matrix.
 
-    others : see `dtw` function.
+    others : 
+        see :func:`~dtwalign.dtw` function.
 
     Returns
     -------
@@ -138,7 +139,8 @@ def dtw_low(X, window, pattern, dist_only=False,
     pattern : dtwalign.step_pattern.BasePattern object
         step pattern object.
 
-    others : see `dtw` function.
+    others : 
+        see :func:`~dtwalign.dtw` function.
 
     Returns
     -------

dtwalign/result.py

@@ -5,7 +5,20 @@
 from scipy.interpolate import interp1d
 
 class DtwResult():
-    """Result of DTW."""
+    """Result of DTW.
+    
+    Attributes
+    ----------
+    path : 2d array
+        Alignment path.  
+        * First column: query path array
+        * Second column: reference path array
+    distance : float
+        Alignment distance.
+    normalized_distance : float
+        Normalized alignment distance.
+
+    """
     def __init__(self, cumsum_matrix, path, window, pattern):
         self.cumsum_matrix = cumsum_matrix
 
@@ -23,7 +36,7 @@ def get_warping_path(self, target="query"):
 
         Parameters
         ----------
-        target : "query" or "reference"
+        target : string, "query" or "reference"
             Specify the target to be warped.
 
         Returns
@@ -74,7 +87,7 @@ def plot_path(self, with_="cum"):
 
         Parameters
         ----------
-        with_ : "win", "cum" or None
+        with_ : string, "win" or "cum" or None
             If given, following will be plotted with alignment path:  
             * "win" : window matrix
             * "cum" : cumsum matrix

dtwalign/step_pattern.py

@@ -782,8 +782,9 @@ def __init__(self, pattern_info, normalize_guide):
         Parameters
         ----------
         pattern_info : list
-            list contains pattern information.
-            ex) the case of symmetric2 pattern:
+            list contains pattern information.  
+            ex) the case of symmetric2 pattern:  
+                ```
                 pattern_info = [
                     dict(
                         indices=[(-1,0),(0,0)],
@@ -798,9 +799,10 @@ def __init__(self, pattern_info, normalize_guide):
                         weights=[1]
                     )
                 ]
+                ```
 
         normalize_guide : string ('N','M','N+M','none')
-            guide to compute normalized distance.
+            Guide to compute normalized distance.
 
         """
         # validation

dtwalign/window.py

@@ -13,6 +13,8 @@ def __init__(self):
         pass
 
     def plot(self):
+        """Show window.
+        """
         _, ax = plt.subplots(1)
         sns.heatmap(self.matrix.T, vmin=0, vmax=1,
             xticklabels=self.matrix.shape[0]//10,
@@ -28,6 +30,13 @@ def plot(self):
 class NoWindow(BaseWindow):
     label = "no window"
     def __init__(self, len_x, len_y):
+        """No window class which will be used for no constraint.
+        len_x : int
+            Length of query.
+        len_y : int
+            Length of reference.
+
+        """
         self._gen_window(len_x, len_y)
 
     def _gen_window(self, len_x, len_y):
@@ -38,6 +47,15 @@ def _gen_window(self, len_x, len_y):
 class SakoechibaWindow(BaseWindow):
     label = "sakoechiba window"
     def __init__(self, len_x, len_y, size):
+        """Sakoechiba window.
+        len_x : int
+            Length of query.
+        len_y : int
+            Length of reference.
+        size : int
+            Size of window width.
+            
+        """
         self._gen_window(len_x, len_y, size)
 
     def _gen_window(self, len_x, len_y, size):
@@ -50,6 +68,13 @@ def _gen_window(self, len_x, len_y, size):
 class ItakuraWindow(BaseWindow):
     label = "itakura window"
     def __init__(self, len_x, len_y):
+        """Itakura window.
+        len_x : int
+            Length of query.
+        len_y : int
+            Length of reference.
+            
+        """
         self._gen_window(len_x, len_y)
 
     def _gen_window(self, len_x, len_y):
@@ -77,13 +102,13 @@ def __init__(self, len_x, len_y, win_func, *args, **kwargs):
         Parameters
         ----------
         len_x : int
-            length of query.
+            Length of query.
         len_y : int
-            length of reference.
+            Length of reference.
         win_func : callable
-            any function which returns bool.
+            Any function which returns bool.
         *args, **kwargs : 
-            arguments for win_func
+            Arguments for win_func
 
         """
         self._gen_window(len_x, len_y, win_func, *args, **kwargs)