Use standard Sphinx extensions for trait documenting

chaco/abstract_data_range.py

@@ -15,30 +15,30 @@ class AbstractDataRange(HasTraits):
     They support "autoscaling" by querying their associated data sources.
     """
 
-    # The list of data sources to which this range responds.
+    #: The list of data sources to which this range responds.
     sources = List(Instance(AbstractDataSource))
 
-    # The actual value of the lower bound of this range. To set it, use
-    # low_setting. (Setting this attribute directly just calls the setter for
-    # low_setting.) Although the default value is specified as 0.0, subclasses
-    # can redefine the default. Also, subclasses can redefined the type to
-    # correspond to their dimensionality.
+    #: The actual value of the lower bound of this range. To set it, use
+    #: low_setting. (Setting this attribute directly just calls the setter for
+    #: low_setting.) Although the default value is specified as 0.0, subclasses
+    #: can redefine the default. Also, subclasses can redefined the type to
+    #: correspond to their dimensionality.
     low = Float(0.0)
 
-    # The actual value of the upper bound of this range. To set it, use
-    # high_setting. (Setting this attribute directly just calls the setter for
-    # high_setting.) Although the default value is specified as 1.0, subclasses
-    # can redefine the default. Also, subclasses can redefined the type to
-    # correspond to their dimensionality.
+    #: The actual value of the upper bound of this range. To set it, use
+    #: high_setting. (Setting this attribute directly just calls the setter for
+    #: high_setting.) Although the default value is specified as 1.0, subclasses
+    #: can redefine the default. Also, subclasses can redefined the type to
+    #: correspond to their dimensionality.
     high = Float(1.0)
 
-    # Setting for the lower bound of this range.
+    #: Setting for the lower bound of this range.
     low_setting = Trait('auto', 'auto', Float)
-    # Setting for the upper bound of this range.
+    #: Setting for the upper bound of this range.
     high_setting = Trait('auto', 'auto', Float)
 
-    # Event that is fired when the actual bounds values change; the value
-    # of the event is a tuple (low_bound, high_bound)
+    #: Event that is fired when the actual bounds values change; the value
+    #: of the event is a tuple (low_bound, high_bound)
     updated = Event
 
     #------------------------------------------------------------------------

chaco/abstract_data_source.py

@@ -22,34 +22,34 @@ class AbstractDataSource(HasTraits):
     possible, domain classes (or an adapter) must implement AbstractDataSource.
     """
 
-    # The dimensionality of the value at each index point.
-    # Subclasses re-declare this trait as a read-only trait with
-    # the right default value.
+    #: The dimensionality of the value at each index point.
+    #: Subclasses re-declare this trait as a read-only trait with
+    #: the right default value.
     value_dimension = DimensionTrait
 
-    # The dimensionality of the indices into this data source.
-    # Subclasses re-declare this trait as a read-only trait with
-    # the right default value.
+    #: The dimensionality of the indices into this data source.
+    #: Subclasses re-declare this trait as a read-only trait with
+    #: the right default value.
     index_dimension = DimensionTrait
 
-    # A dictionary keyed on strings.  In general, it maps to indices (or tuples
-    # of indices, depending on **value_dimension**), as in the case of
-    # selections and annotations.  Applications and renderers can add their own
-    # custom metadata, but must avoid using keys that might result in name
-    # collision.
+    #: A dictionary keyed on strings.  In general, it maps to indices (or tuples
+    #: of indices, depending on **value_dimension**), as in the case of
+    #: selections and annotations.  Applications and renderers can add their own
+    #: custom metadata, but must avoid using keys that might result in name
+    #: collision.
     metadata = Dict
 
-    # Event that fires when the data values change.
+    #: Event that fires when the data values change.
     data_changed = Event
 
-    # Event that fires when just the bounds change.
+    #: Event that fires when just the bounds change.
     bounds_changed = Event
 
-    # Event that fires when metadata structure is changed.
+    #: Event that fires when metadata structure is changed.
     metadata_changed = Event
 
-    # Should the data that this datasource refers to be serialized when
-    # the datasource is serialized?
+    #: Should the data that this datasource refers to be serialized when
+    #: the datasource is serialized?
     persist_data = Bool(True)
 
     #------------------------------------------------------------------------

chaco/abstract_mapper.py

@@ -12,16 +12,16 @@ class AbstractMapper(HasTraits):
     region in output space.
     """
 
-    # A generic "update" event that generally means that anything that relies
-    # on this mapper for visual output should do a redraw or repaint.
+    #: A generic "update" event that generally means that anything that relies
+    #: on this mapper for visual output should do a redraw or repaint.
     updated = Event
 
     # FIXME: domain_limits is never used
 
-    # A tuple representing the minimum and maximum values of the domain (data
-    # space).  The dimensionality of each value varies depending on the
-    # dimensions of the mapper, so for 1D mappers these will be scalars, for
-    # image and 2D mappers these will be tuples.
+    #: A tuple representing the minimum and maximum values of the domain (data
+    #: space).  The dimensionality of each value varies depending on the
+    #: dimensions of the mapper, so for 1D mappers these will be scalars, for
+    #: image and 2D mappers these will be tuples.
     domain_limits = Tuple(None, None)
 
     def map_screen(self, data_array):

chaco/abstract_overlay.py

@@ -19,15 +19,15 @@ class AbstractOverlay(PlotComponent):
     containment-ownership relationship.
     """
 
-    # The component that this object overlays. This can be None. By default, if
-    # this object is called to draw(), it tries to render onto this component.
+    #: The component that this object overlays. This can be None. By default, if
+    #: this object is called to draw(), it tries to render onto this component.
     component = Instance(Component)
 
-    # The default layer that this component draws into.
+    #: The default layer that this component draws into.
     draw_layer = "overlay"
 
-    # The background color (overrides PlotComponent).
-    # Typically, an overlay does not render a background.
+    #: The background color (overrides PlotComponent).
+    #: Typically, an overlay does not render a background.
     bgcolor = "transparent"
 
     def __init__(self, component=None, *args, **kw):

chaco/abstract_plot_data.py

@@ -12,9 +12,9 @@ class AbstractPlotData(HasTraits):
     # Events that consumers of this data should use
     #-------------------------------------------------------------------------
 
-    # Indicates that some of the data has changed.  The event object must
-    # be a dict with keys "added", "removed", "changed" and values that are
-    # lists of strings. This event is used by consumers of this data.
+    #: Indicates that some of the data has changed.  The event object must
+    #: be a dict with keys "added", "removed", "changed" and values that are
+    #: lists of strings. This event is used by consumers of this data.
     data_changed = Event
 
 
@@ -23,11 +23,11 @@ class AbstractPlotData(HasTraits):
     # interact with it.  (Typically "consumers" just refers to Plots.)
     #-------------------------------------------------------------------------
 
-    # Can consumers (Plots) write data back through this interface using
-    # set_data()?
+    #: Can consumers (Plots) write data back through this interface using
+    #: set_data()?
     writable = Bool(True)
 
-    # Can consumers (Plots) set selections?
+    #: Can consumers (Plots) set selections?
     selectable = Bool(True)
 
 

chaco/abstract_plot_renderer.py

@@ -22,10 +22,10 @@ class AbstractPlotRenderer(PlotComponent):
     # Override default values of inherited traits PlotComponent
     #------------------------------------------------------------------------
 
-    # Overrides the default value inherited from PlotComponent.
+    #: Overrides the default value inherited from PlotComponent.
     bgcolor = "transparent"
 
-    # Overrides the default value inherited from PlotComponent.
+    #: Overrides the default value inherited from PlotComponent.
     resizable = "hv"
 
 
@@ -65,13 +65,14 @@ def map_index(self, screen_pt, threshold=0.0, outside_returns_none=True, \
 
         Returns
         -------
-        An index into the plot's index array(s). Typically this index is just
-        an integer, but if the plot has a 2-D index dimension, then this method
-        returns a tuple of integers. If the input point cannot be mapped to an
-        index, then None is returned.
-
-        If *screen_pt* corresponds to multiple indices, then only the first
-        index is returned.
+        index : int
+            An index into the plot's index array(s). Typically this index is just
+            an integer, but if the plot has a 2-D index dimension, then this method
+            returns a tuple of integers. If the input point cannot be mapped to an
+            index, then None is returned.
+
+            If *screen_pt* corresponds to multiple indices, then only the first
+            index is returned.
         """
         raise NotImplementedError
 

chaco/array_data_source.py

@@ -63,17 +63,17 @@ class ArrayDataSource(AbstractDataSource):
     # AbstractDataSource traits
     #------------------------------------------------------------------------
 
-    # The dimensionality of the indices into this data source (overrides
-    # AbstractDataSource).
+    #: The dimensionality of the indices into this data source (overrides
+    #: AbstractDataSource).
     index_dimension = Constant('scalar')
 
-    # The dimensionality of the value at each index point (overrides
-    # AbstractDataSource).
+    #: The dimensionality of the value at each index point (overrides
+    #: AbstractDataSource).
     value_dimension = Constant('scalar')
 
-    # The sort order of the data.
-    # This is a specialized optimization for 1-D arrays, but it's an important
-    # one that's used everywhere.
+    #: The sort order of the data.
+    #: This is a specialized optimization for 1-D arrays, but it's an important
+    #: one that's used everywhere.
     sort_order = SortOrderTrait
 
 

chaco/array_plot_data.py

@@ -24,14 +24,14 @@ class ArrayPlotData(AbstractPlotData):
     # Public traits
     #-------------------------------------------------------------------------
 
-    # Map of names to arrays.  Although there is no restriction on the array
-    # dimensions, each array must correspond to a single plot item; that
-    # is, a single name must not map to a multi-dimensional array unless
-    # the array is being used for an image plot or for something that can handle
-    # multi-dimensional input data.
+    #: Map of names to arrays.  Although there is no restriction on the array
+    #: dimensions, each array must correspond to a single plot item; that
+    #: is, a single name must not map to a multi-dimensional array unless
+    #: the array is being used for an image plot or for something that can handle
+    #: multi-dimensional input data.
     arrays = Dict
 
-    # Consumers can write data to this object (overrides AbstractPlotData).
+    #: Consumers can write data to this object (overrides AbstractPlotData).
     writable = True
 
     def __init__(self, *data, **kw):