README.markdown in datashift-0.0.2 vs README.markdown in datashift-0.1.0

@@ -52,32 +52,36 @@
 
 Provides high level rake tasks for importing data via ActiveRecord models into a DB,
  from various sources, currently csv or .xls files (Excel/Open Office)
 
 
-    bundle exec rake datashift:import:excel model=BlogPost input=BlogPostImport.xls verbose=true 
+    jruby -S rake datashift:import:excel model=BlogPost input=BlogPostImport.xls verbose=true 
 
+Provides high level rake tasks for exporting data to various sources, currently .xls files (Excel/Open Office)
 
+    jruby -S rake datashift:export:excel model=BlogPost result=BlogExport.xls 
+
+
 The library can be easily extended with Loaders to deal with non trivial cases required to exchange data between
  such files and any active record supported database.
 
 Spree loaders are an example, these illustrate over riding processing for specific columns with
 complicated lookup requirements.
 
-A core feature of DataShift is the MethodMapper, which provides features for collecting
+A core feature of DataShift is the MethodDictionary and MethodMapper, which provides features for collecting
 reflection information from ActiveRecord models (all different associations, including join tables with many-to-many relationships).
 
 A full picture of all possible operations on a class can be created very easily, for example ona  Blog model :
 
-    MethodMapper.find_operators( Blog )
+    MethodDictionary.find_operators( Blog )
 
-This then allows Import/Export to be achieved, by mapping the file's header and column data to MethodMapper's operators
-(i.e. the model's attributes and associations).
+This then allows Import/Export to be achieved, by mapping the file's header and column data to the found operators
+i.e. the methods to set data on model's attributes and associations.
 
 Here we retrieve the method details for a column name from a file, "Blog Date"
 
-    MethodMapper.find_method_detail( Blog, "Blog Date" )
+    MethodDictionary.find_method_detail( Blog, "Blog Date" )
 
 Loaders can use this method lookup functionality, to find the correct association for a column heading,
 and populate AR object with row data.
 
 This means data can be mapped to any model without any further coding. Generators are also supplied to export
@@ -86,11 +90,11 @@
 MethodMapper also stores column type information so the raw file data can be provided as is,
 and whenever possible, under the bonnet the data will be cast to correct DB type.
 
 Here we show how a column name from a file, "Blog Date", can be mapped to Assign a stringified date, to the blog_date column, on a new Blog object :
 
-    MethodMapper.find_method_detail( Blog, "Blog Date" ).assign( Blog.new, "Sat Jul 23 2011" )
+    MethodDictionary.find_method_detail( Blog, "Blog Date" ).assign( Blog.new, "Sat Jul 23 2011" )
 
 Because it's based on reflection against the model, can build complex relationships between tables during import/export, 
 and extending data files with new columns need not require any additional Ruby coding.
 
 New columns can simply be added to the Excel/Open Office spreadsheet, or CSV file, setting the new
@@ -171,16 +175,13 @@
     jruby -S rake datashift:import:excel model=MusicTrack input=MyTrackListing.xls
 
 
 - *Spree Rake Tasks*
 
-  Specific Rake tasks are also provided for Spree loading - currently supports Product with associations,
-  and Image loading.
+  Spree's product associations are non trivial so specific Rake tasks are also provided for loading Spree Producta
+  with all associations and Image loading.
 
     jruby -S rake datashift:spree:products input=C:\MyProducts.xls
-
-
-  **Product loading from Excel files specifically requires JRuby (But not Excel or OLE)**.
 
 
 - *Seamless Spree Image loading can be achieved by ensuring SKU or class Name features in Image filename.
 
   Lookup is performed either via the SKU being prepended to the image name, or by the image name being equal to the **name attribute** of the klass in question.
\ No newline at end of file