X-Git-Url: http://git.rot13.org/?a=blobdiff_plain;f=README;h=1c9f8aa8d250c38d5114fbaa35ccfc4d6269ce47;hb=93c61772e6ec92b31c27d0b5b62220f092dad60f;hp=60f9b7a28fca9a230a6daf8e7300dc42b1ec5208;hpb=7e138ee6bb2909b6cf4930dbbafb732d159d8483;p=MojoFacets.git

diff --git a/README b/README
index 60f9b7a..1c9f8aa 100644
--- a/README
+++ b/README
@@ -12,60 +12,81 @@ Multi-line values should be wrapped in ^multi-line-text^
 If you save bounch of html files with table in directory with .html extension, they will
 we all read as single data set, allowing easy analysys of search results, for example.
 
+CSV files with .csv extension are parsed using , as delimiter. Encoding is utf8
+and can be specified in filename, before extension like this: data.encoding.csv
 
-Start with:
 
-  LANG=hr_HR.utf8 ./script/mojo_facets daemon --reload
+Start with:
 
+    LANG=hr_HR.utf8 ./script/mojo_facets daemon --reload
 
-Data replication:
 
-  # master
-  ./script/mojo_facets daemon --listen 'http://*:4444' --reload
+Changing tabular data:
 
-  # slave
-  MASTER=http://localhost:4444 ./script/mojo_facets daemon --reload
+Just double click on any table cell and click outside or focus out to save change.
 
 
-Turning actions into changes:
+Data action and changes:
 
 There are two kinds of audit log in MojoFacets:
 
-1. actions stored in /tmp/actions are clicks on user interface with parameters
+1. actions stored in /tmp/actions are clicks on user interface with parameters,
+   they will probably be erased on next reboot since they are in /tmp
 
-2. changes are more structured, including old value and unique values from
-   edit of one value in dataset
+2. changes in data/database.changes are more structured, including old value
+   and unique identifiers for that row
 
-If you want to create changes which can be applied again on original dataset or
-any other dataset which has same unique values you can use helper script:
+Changes can be applied on any dataset currently in memory.
 
-  $ ./script/actions-to-changes /data/mojo_facets/*
+Whole idea of changes is to create audit log which is detailed enough to recostruct
+current state of dataset from source file and list of changes. However, to speed
+up operations, you can periodically save your in-memory data to /tmp/ in perl
+storeable format using save link in interface.
 
 
-Modify your data using perl snippets
+Data replication:
 
-Experimental REPL console supports perl snippets which get $row hash which is one
-element from your dataset. All values are repetable, so you always have to create
-array of values if you are creating new columns, even for single value.
+Actions can be replicated to other hosts using MASTER enviroment variable
+or config menu
 
-* generate new columns with number of elements in some other column
+    # slave
+    MASTER=http://localhost:4444 ./script/mojo_facets daemon --reload
 
-  $row->{author_count} = [ $#{ $row->{autor} } + 1 ];
 
-* generate century column from year
+Code console to modify data using perl snippets:
 
-  foreach my $year ( @{ $row->{Year} } ) {
-    push @{ $row->{century} }, int($year/100)+1;
-  }
+Experimental REPL console supports perl snippets which get $row hash which is one
+element from your dataset.
+
+If you want to create or update values, you will have to use $update hash to set
+new values.
 
-* modify existing column
+If you want to report something from your dataset (also called reduce in map/reduce
+terminology) you can use $out hash to store values which will be used to generate
+new dataset using $key and $value for column names.
 
-  foreach ( @{ $rec->{'Cited Author'} } ) {
-    s/^\.+//;
-    $_ = uc $_; # assigment is needed to modify value!
-  }
+All values are repetable, but if you create just a scalar, magic(tm) inside MojoFacets
+will try to upgrade it to [ $scalar ] so you don't have to do it explicitly.
 
 Code examples are stored in public/code
 
-They use Column name.description.pl notatition so only snippets which have applicable
+They use column1,column2.description.pl notatition so only snippets which have applicable
 column will be shown.
+
+
+Facet code eval:
+
+Code snippet will be executed for each facet $value and will report $count and $checked state.
+You can also update $checked to programatically select part of facet values.
+
+
+Export data:
+
+All exported data is stored in public/export/database/
+
+There you can find saved filters and items generated with export checkbox
+
+    filter.column_name.optional_description
+	items.column1.column2.column3
+
+