edited README

Author: davidthaler

README.rst

@@ -7,39 +7,18 @@ This project uses rpy2 to expose most of the functionality of R's Forecast packa
 Some related functions in the stats and base packages are also exposed (e.g. seasonal decompositions).
 A few less-commnonly used functions and arguments are not exposed.
 
-An example of generating a forecast:
-
-.. code-block:: 
-
-   from rforecast import wrappers  
-   from rforecast import ts_io  
-  
-   stock = ts_io.read_series('data/livestock.csv')  
-   fc = wrappers.forecast(stock)  
-   print fc  
-
-This example uses the ``livestock`` series in ``data/`` under the installation directory.
-
-An example of generating an STL decomposition:
-
-.. code-block:: 
-
-   aus = ts_io.read_series('aus.csv')  
-   dc = wrappers.stl(aus, s_window=5)  
-   print dc  
-
-
 The rforecast.py package uses Pandas Series objects to represent time series.
 For seasonal series, it uses a multindex with the second level of the index
 denoting the season. The ``read_series`` function in ``ts_io`` will return a 
-series with the index constructed correctly. 
+series with the index constructed correctly, given .csv files like those in the
+``/data`` directory. 
 If the data are already in a Python sequence, such as a list or numpy array,
 you can convert it to a series of the right form like this:
 
 .. code-block:: python
 
    from rforecast import converters  
-   # A slice of the 'oil' data from R package fpp, available in data/  
+   # A slice of the 'oil' data from R package fpp, also available in data/  
    data =  [509, 506, 340, 240, 219, 172, 252, 221, 276, 271, 342, 428, 442, 432, 437]  
    ts = converters.sequence_as_series(data, start=1980)  
    print ts
@@ -50,6 +29,26 @@ you can convert it to a series of the right form like this:
    ts = converters.sequence_as_series(data, start=(1991, 1), freq=4)
    print ts
 
+Forecasting and decomposition methods are in the wrappers module:
+
+.. code-block:: 
+
+   from rforecast import wrappers  
+
+An example of generating a forecast (using ts above):
+
+.. code-block::
+ 
+   fc = wrappers.forecast(ts)  
+   print fc  
+
+An example of generating an STL decomposition :
+
+.. code-block:: 
+ 
+   dc = wrappers.stl(ts, s_window=5)  
+   print dc 
+
 There is more information in the `.rst` files under ``doc/``. 
 The documentation is built with Sphinx. 
 If you have Sphinx installed, you can build the documentation using the Makefile