Reformatted .rst files for 80 characters

Author: jbednar

docs/source/core.rst

@@ -15,7 +15,9 @@ Map, emit, and sink
    map
    sink
 
-You can create a basic pipeline by instantiating the ``Streamz`` object and then using methods like ``map``, ``accumulate``, and ``sink``.
+You can create a basic pipeline by instantiating the ``Streamz``
+object and then using methods like ``map``, ``accumulate``, and
+``sink``.
 
 .. code-block:: python
 
@@ -27,7 +29,10 @@ You can create a basic pipeline by instantiating the ``Streamz`` object and then
    source = Stream()
    source.map(increment).sink(print)
 
-The ``map`` and ``sink`` methods both take a function and apply that function to every element in the stream.  The ``map`` method returns a new stream with the modified elements while ``sink`` is typically used at the end of a stream for final actions.
+The ``map`` and ``sink`` methods both take a function and apply that
+function to every element in the stream.  The ``map`` method returns a
+new stream with the modified elements while ``sink`` is typically used
+at the end of a stream for final actions.
 
 To push data through our pipeline we call ``emit``
 
@@ -383,14 +388,33 @@ want to read further about :doc:`collections <collections>`
 Metadata
 --------
 
-Metadata can be emitted into the pipeline to accompany the data as a list of dictionaries. Most functions will pass the metadata to the downstream function without making any changes. However, functions that make the pipeline asynchronous require logic that dictates how and when the metadata will be passed downstream. Synchronous functions and asynchronous functions that have a 1:1 ratio of the number of values on the input to the number of values on the output will emit the metadata collection without any modification. However, functions that have multiple input streams or emit collections of data will emit the metadata associated with the emitted data as a collection.
+Metadata can be emitted into the pipeline to accompany the data as a
+list of dictionaries. Most functions will pass the metadata to the
+downstream function without making any changes. However, functions
+that make the pipeline asynchronous require logic that dictates how
+and when the metadata will be passed downstream. Synchronous functions
+and asynchronous functions that have a 1:1 ratio of the number of
+values on the input to the number of values on the output will emit
+the metadata collection without any modification. However, functions
+that have multiple input streams or emit collections of data will emit
+the metadata associated with the emitted data as a collection.
 
 
 Reference Counting and Checkpointing
 ------------------------------------
 
-Checkpointing is achieved in Streamz through the use of reference counting. With this method, a checkpoint can be saved when and only when data has progressed through all of the the pipeline without any issues. This prevents data loss and guarantees at-least-once semantics.
-
-Any node that caches or holds data after it returns increments the reference counter associated with the given data by one. When a node is no longer holding the data, it will release it by decrementing the counter by one. When the counter changes to zero, a callback associated with the data is triggered.
-
-References are passed in the metadata as a value of the `ref` keyword. Each metadata object contains only one reference counter object.
+Checkpointing is achieved in Streamz through the use of reference
+counting. With this method, a checkpoint can be saved when and only
+when data has progressed through all of the the pipeline without any
+issues. This prevents data loss and guarantees at-least-once
+semantics.
+
+Any node that caches or holds data after it returns increments the
+reference counter associated with the given data by one. When a node
+is no longer holding the data, it will release it by decrementing the
+counter by one. When the counter changes to zero, a callback
+associated with the data is triggered.
+
+References are passed in the metadata as a value of the `ref`
+keyword. Each metadata object contains only one reference counter
+object.

docs/source/dask.rst

@@ -123,17 +123,20 @@ Gotchas
 +++++++
 
 
-An important gotcha with ``DaskStream`` is that it is a subclass of ``Stream``, and so can be used as an input 
-to any function expecting a ``Stream``. If there is no intervening ``.gather()``, then the downstream node will
-receive Dask futures instead of the data they represent::
+An important gotcha with ``DaskStream`` is that it is a subclass of
+``Stream``, and so can be used as an input to any function expecting a
+``Stream``. If there is no intervening ``.gather()``, then the
+downstream node will receive Dask futures instead of the data they
+represent::
 
     source = Stream()
     source2 = Stream()
     a = source.scatter().map(inc)
     b = source2.combine_latest(a)
 
-In this case, the combine operation will get real values from ``source2``, and Dask futures. 
-Downstream nodes would be free to operate on the futures, but more likely, the line should be::
+In this case, the combine operation will get real values from
+``source2``, and Dask futures.  Downstream nodes would be free to
+operate on the futures, but more likely, the line should be::
 
     b = source2.combine_latest(a.gather())
 

docs/source/plotting.rst

@@ -1,19 +1,29 @@
 Visualizing streamz
 ===================
 
-A variety of tools are available to help you understand, debug, visualize your streaming objects:
+A variety of tools are available to help you understand, debug,
+visualize your streaming objects:
 
-- Most Streamz objects automatically display themselves in Jupyter notebooks, periodically updating their visual representation as text or tables by registering events with the Tornado IOLoop used by Jupyter
-- The network graph underlying a stream can be visualized using `dot` to render a PNG using `Stream.visualize(filename)`
-- Streaming data can be visualized using the optional separate packages hvPlot, HoloViews, and Panel (see below)
+- Most Streamz objects automatically display themselves in Jupyter
+  notebooks, periodically updating their visual representation as text
+  or tables by registering events with the Tornado IOLoop used by Jupyter
+- The network graph underlying a stream can be visualized using `dot` to
+  render a PNG using `Stream.visualize(filename)`
+- Streaming data can be visualized using the optional separate packages
+  hvPlot, HoloViews, and Panel (see below)
 
 
 hvplot.streamz
 --------------
 
-hvPlot is a separate plotting library providing Bokeh-based plots for Pandas dataframes and a variety of other object types, including streamz DataFrame and Series objects. 
+hvPlot is a separate plotting library providing Bokeh-based plots for
+Pandas dataframes and a variety of other object types, including
+streamz DataFrame and Series objects.
 
-See `hvplot.holoviz.org <https://hvplot.holoviz.org>`_ for instructions on how to install hvplot.  Once it is installed, you can use the Pandas .plot() API to get a dynamically updating plot in Jupyter or in Bokeh/Panel Server:
+See `hvplot.holoviz.org <https://hvplot.holoviz.org>`_ for
+instructions on how to install hvplot.  Once it is installed, you can
+use the Pandas .plot() API to get a dynamically updating plot in
+Jupyter or in Bokeh/Panel Server:
 
 .. code-block:: python
 
@@ -23,15 +33,29 @@ See `hvplot.holoviz.org <https://hvplot.holoviz.org>`_ for instructions on how t
    df = Random()
    df.hvplot(backlog=100)
 
-See the `streaming section <https://hvplot.holoviz.org/user_guide/Streaming.html>`_ of the hvPlot user guide for more details, and the
-`dataframes.ipynb` example that comes with streamz for a simple runnable example.
+See the `streaming section
+<https://hvplot.holoviz.org/user_guide/Streaming.html>`_ of the hvPlot
+user guide for more details, and the `dataframes.ipynb` example that
+comes with streamz for a simple runnable example.
 
 
 HoloViews
 ---------
-hvPlot is built on HoloViews, and you can also use HoloViews directly if you want more control over events and how they are processed.  See the `HoloViews user guide <http://holoviews.org/user_guide/Streaming_Data.html>`_ for more details.
+
+hvPlot is built on HoloViews, and you can also use HoloViews directly
+if you want more control over events and how they are processed.  See
+the `HoloViews user guide
+<http://holoviews.org/user_guide/Streaming_Data.html>`_ for more
+details.
 
 
 Panel
 -----
-Panel is a general purpose dashboard and app framework, supporting a wide variety of displayable objects as "Panes". Panel provides a `streamz Pane <https://panel.holoviz.org/reference/panes/Streamz.html>`_ for rendering arbitrary streamz objects, and streamz DataFrames are handled by the Panel `DataFrame Pane <https://panel.holoviz.org/reference/panes/DataFrame.html>`_.
+
+Panel is a general purpose dashboard and app framework, supporting a
+wide variety of displayable objects as "Panes". Panel provides a
+`streamz Pane
+<https://panel.holoviz.org/reference/panes/Streamz.html>`_ for
+rendering arbitrary streamz objects, and streamz DataFrames are
+handled by the Panel `DataFrame Pane
+<https://panel.holoviz.org/reference/panes/DataFrame.html>`_.