rebaseline_server JSON: pass category values as values, not keys

gm/rebaseline_server/column.py

 #!/usr/bin/python
 

[epoger, 2014/05/07 21:26:17]

Reason for the CL: the fact that the rebaseline_server JSON included arbitrary
field values as dictionary keys limited which fields we could search and sort
on.  See http://skbug.com/1758 for more details.

This is a different approach than I had originally envisioned in the bug , but
as I started on the implementation I decided this approach was better, and
should resolve the same root problem.

[borenet, 2014/05/08 12:40:18]

Top-level comment about parsing JSON: I really like the way it's done in Go. 
You provide a struct which gets filled in by the decoder:

myData := struct {
  MyField:      string
  MyOtherField: int
}{}
json.Unmarshal(jsonData, &myData)

You can even specify how to convert the names of the fields from JSON to Go:

myData := struct {
  MyField:      string `json:"my_field"`
  MyOtherField: int    `json:"another_field"`
}{}

There's no guarantee that all of the fields of the struct get filled in, or that
everything in the JSON data was put into a field.

I'm just wondering if there might be a way to do something similar in Python and
avoid having to use all these constants.

[epoger, 2014/05/08 15:04:35]

Thanks for pointing out this other way of living.  I agree that it would be
better to access the fields using attribute names (given that the field names
are supposed to be predictable!) rather than arbitrary string keys into a dict.

Python's built-in JSON library is mainly geared towards using dicts with
arbitrary string keys, but there are some solutions that fit object attributes
on top of that.  Best reference I could find so far:
http://stackoverflow.com/questions/6578986/how-to-convert-json-data-into-a-py...

I think that may be worthwhile in the long term; I don't want to tackle it now.

Note: I think we'll need to define the string constants in both Python and
Javascript anyway, since the client and server need to agree on them.

 """
 Copyright 2014 Google Inc.
 
 Use of this source code is governed by a BSD-style license that can be
 found in the LICENSE file.
 
 ColumnHeaderFactory class (see class docstring for details)
 """
 
+import utils
+
 # Keys used within dictionary representation of each column header.
 # NOTE: Keep these in sync with static/constants.js
-KEY__HEADER_TEXT = 'headerText'
-KEY__HEADER_URL = 'headerUrl'
-KEY__IS_FILTERABLE = 'isFilterable'
-KEY__IS_SORTABLE = 'isSortable'
-KEY__VALUES_AND_COUNTS = 'valuesAndCounts'
+KEY__EXTRACOLUMNHEADERS = 'extraColumnHeaders'
+KEY__EXTRACOLUMNHEADERS__COUNTS = 'counts'

[epoger, 2014/05/07 21:26:17]

While I was at it, renamed these particular key constants to be more easily
searchable.

+KEY__EXTRACOLUMNHEADERS__HEADER_TEXT = 'headerText'
+KEY__EXTRACOLUMNHEADERS__HEADER_URL = 'headerUrl'
+KEY__EXTRACOLUMNHEADERS__IS_FILTERABLE = 'isFilterable'
+KEY__EXTRACOLUMNHEADERS__IS_SORTABLE = 'isSortable'
+KEY__EXTRACOLUMNHEADERS__VALUES = 'values'
 
 
 class ColumnHeaderFactory(object):
   """Factory which assembles the header for a single column of data."""
 
   def __init__(self, header_text, header_url=None,
                is_filterable=True, is_sortable=True,
                include_values_and_counts=True):
     """
     Args:
@@ skipping 10 matching lines @@
     self._header_text = header_text
     self._header_url = header_url
     self._is_filterable = is_filterable
     self._is_sortable = is_sortable
     self._include_values_and_counts = include_values_and_counts
 
   def create_as_dict(self, values_and_counts_dict=None):
     """Creates the header for this column, in dictionary form.
 
     Creates the header for this column in dictionary form, as needed when
-    constructing the JSON representation.  Uses the KEY__* constants as keys.
+    constructing the JSON representation.  Uses the KEY__EXTRACOLUMNHEADERS__*
+    constants as keys.
 
     Args:
       values_and_counts_dict: dictionary mapping each possible column value
           to its count (how many entries in the column have this value), or
           None if this information is not available.
     """
     asdict = {
-        KEY__HEADER_TEXT: self._header_text,
-        KEY__IS_FILTERABLE: self._is_filterable,
-        KEY__IS_SORTABLE: self._is_sortable,
+        KEY__EXTRACOLUMNHEADERS__HEADER_TEXT: self._header_text,
+        KEY__EXTRACOLUMNHEADERS__IS_FILTERABLE: self._is_filterable,
+        KEY__EXTRACOLUMNHEADERS__IS_SORTABLE: self._is_sortable,
     }
     if self._header_url:
-      asdict[KEY__HEADER_URL] = self._header_url
+      asdict[KEY__EXTRACOLUMNHEADERS__HEADER_URL] = self._header_url
     if self._include_values_and_counts and values_and_counts_dict:
-      asdict[KEY__VALUES_AND_COUNTS] = values_and_counts_dict
+      values, counts = utils.unzip_dict(values_and_counts_dict)
+      asdict[KEY__EXTRACOLUMNHEADERS__VALUES] = values
+      asdict[KEY__EXTRACOLUMNHEADERS__COUNTS] = counts
     return asdict