Allow charting of extra() and annotate() fields. Fixes #8. Fixes #12

this is a slight refactoring of PR#15 to allow extra and annotated fields be used not only in categories but other places as well.
chartit · Aug 9, 2016 · a086261 · a086261
1 parent 5e96304
commit a086261
Show file tree

Hide file tree

Showing 4 changed files with 161 additions and 10 deletions.
diff --git a/README.rst b/README.rst
@@ -41,6 +41,9 @@ Changelog
       `#5 <https://github.com/chartit/django-chartit/issues/5>`_
     * Demo project updated with Chart and PivotChart examples of
       rendering DateField values on the X axis
+    * Allow charting of extra() or annotate() fields. Fixes
+      `#8 <https://github.com/chartit/django-chartit/issues/8>`_ and
+      `#12 <https://github.com/chartit/django-chartit/issues/12>`_
 
 * 0.2.5 (August 3, 2016)
     * Workaround Python 3 vs. Python 2 list sort issue which breaks

diff --git a/chartit/validation.py b/chartit/validation.py
@@ -33,7 +33,7 @@ def get_all_field_names(meta):
     return list(names)
 
 
-def _validate_field_lookup_term(model, term):
+def _validate_field_lookup_term(model, term, query):
     """Checks whether the term is a valid field_lookup for the model.
 
     **Args**:
@@ -42,6 +42,8 @@ def _validate_field_lookup_term(model, term):
       the term is a valid field_lookup.
     - **term** (**required**) - the term to check whether it is a valid
       field lookup for the model supplied.
+    - **query** - the source query so we can check for aggregate or extra
+      fields.
 
     **Returns**:
 
@@ -52,6 +54,10 @@ def _validate_field_lookup_term(model, term):
     - APIInputError: If the term supplied is not a valid field lookup
       parameter for the model.
     """
+    # if this is an extra or annotated field then return
+    if term in query.annotations.keys() or term in query.extra.keys():
+        return term
+
     # TODO: Memoization for speed enchancements?
     terms = term.split('__')
     model_fields = get_all_field_names(model._meta)
@@ -68,7 +74,7 @@ def _validate_field_lookup_term(model, term):
         else:
             m = model
 
-        return _validate_field_lookup_term(m, '__'.join(terms[1:]))
+        return _validate_field_lookup_term(m, '__'.join(terms[1:]), query)
 
 
 def _clean_source(source):
@@ -104,11 +110,8 @@ def _clean_categories(categories, source):
                             % (categories, type(categories)))
     field_aliases = {}
     for c in categories:
-        if c in source.query.annotations.keys() or \
-           c in source.query.extra.keys():
-            field_aliases[c] = c
-        else:
-            field_aliases[c] = _validate_field_lookup_term(source.model, c)
+        field_aliases[c] = _validate_field_lookup_term(source.model, c,
+                                                       source.query)
     return categories, field_aliases
 
 
@@ -126,7 +129,8 @@ def _clean_legend_by(legend_by, source):
                             % (legend_by, type(legend_by)))
     field_aliases = {}
     for lg in legend_by:
-        field_aliases[lg] = _validate_field_lookup_term(source.model, lg)
+        field_aliases[lg] = _validate_field_lookup_term(source.model, lg,
+                                                        source.query)
     return legend_by, field_aliases
 
 
@@ -310,7 +314,8 @@ def clean_dps(series):
             except KeyError:
                 raise APIInputError("%s is missing the 'source' key." % td)
             td.setdefault('field', tk)
-            fa = _validate_field_lookup_term(td['source'].model, td['field'])
+            fa = _validate_field_lookup_term(td['source'].model, td['field'],
+                                             td['source'].query)
             # If the user supplied term is not a field name, use it as an alias
             if tk != td['field']:
                 fa = tk

diff --git a/demoproject/demoproject/chartdemo.py b/demoproject/demoproject/chartdemo.py
@@ -1,8 +1,9 @@
 from chartit import DataPool, Chart
+from django.db.models import Avg, Count
 from django.shortcuts import render_to_response
 from .decorators import add_source_code_and_doc
 from .models import MonthlyWeatherByCity, MonthlyWeatherSeattle, DailyWeather
-from .models import SalesHistory, BookStore
+from .models import SalesHistory, BookStore, Book
 
 
 @add_source_code_and_doc
@@ -928,3 +929,129 @@ def datetimefield_from_related_model(request, title, code, doc, sidebar_items):
                                 'title': title,
                                 'doc': doc,
                                 'sidebar_items': sidebar_items})
+
+
+@add_source_code_and_doc
+def extra_datefield(request, title, code, doc, sidebar_items):
+    """
+    A Basic Line Chart using QuerySet.extra() with DateField
+    --------------------------------------------------------
+    This chart plots sales quantities per day from the first book store.
+    We're using QuerySet.extra() to format the date value directly at
+    the DB level.
+    """
+
+    # start_code
+    ds = DataPool(
+            series=[{
+                'options': {
+                    # NOTE: strftime is SQLite function.
+                    # For MySQL use DATE_FORMAT
+                    'source': SalesHistory.objects.extra(
+                                    select={
+                                        'sold_at': \
+                                        "strftime('%%Y/%%m/%%d', sale_date)"
+                                    }
+                              ).filter(
+                                    bookstore=BookStore.objects.first()
+                              )[:10]
+                },
+                'terms': [
+                    'sold_at',
+                    'sale_qty',
+                ]
+            }]
+    )
+
+    cht = Chart(
+            datasource=ds,
+            series_options=[{
+                'options': {
+                    'type': 'line',
+                    'stacking': False
+                },
+                'terms': {
+                    'sold_at': [
+                        'sale_qty',
+                    ]
+                }
+            }],
+            chart_options={
+                'title': {
+                    'text': 'Sales QTY per day'
+                },
+                'xAxis': {
+                    'title': {
+                        'text': 'Sale date'
+                    }
+                }
+            }
+    )
+    # end_code
+    return render_to_response('chart_code.html',
+                              {
+                                'chart_list': cht,
+                                'code': code,
+                                'title': title,
+                                'doc': doc,
+                                'sidebar_items': sidebar_items})
+
+
+@add_source_code_and_doc
+def avg_count(request, title, code, doc, sidebar_items):
+    """
+    A Basic Line Chart using Avg() and Count()
+    ------------------------------------------
+    This chart plots the average book rating in each genre
+    together with the number of books in each genre.
+    """
+
+    # start_code
+    ds = DataPool(
+            series=[{
+                'options': {
+                    'source': Book.objects.values('genre').annotate(
+                                Avg('rating'),
+                                Count('genre')
+                              )
+                },
+                'terms': [
+                    'genre__name',
+                    'rating__avg',
+                    'genre__count'
+                ]
+            }]
+    )
+
+    cht = Chart(
+            datasource=ds,
+            series_options=[{
+                'options': {
+                    'type': 'line',
+                    'stacking': False
+                },
+                'terms': {
+                    'genre__name': [
+                        'rating__avg', 'genre__count'
+                    ]
+                }
+            }],
+            chart_options={
+                'title': {
+                    'text': 'Book rating and count per Genre'
+                },
+                'xAxis': {
+                    'title': {
+                        'text': 'Genre'
+                    }
+                }
+            }
+    )
+    # end_code
+    return render_to_response('chart_code.html',
+                              {
+                                'chart_list': cht,
+                                'code': code,
+                                'title': title,
+                                'doc': doc,
+                                'sidebar_items': sidebar_items})
diff --git a/demoproject/demoproject/urls.py b/demoproject/demoproject/urls.py
@@ -151,6 +151,22 @@
         name='line_datetime_related',
         ),
 
+    url(r'^demo/chart/extra-datefield/$', chartdemo.extra_datefield,
+        {
+            'title': 'Line chart with extra() DateField',
+            'sidebar_section': 'Charts',
+        },
+        name='line_extra_datefield',
+        ),
+
+    url(r'^demo/chart/avg-count/$', chartdemo.avg_count,
+        {
+            'title': 'Line chart with Avg() and Count()',
+            'sidebar_section': 'Charts',
+        },
+        name='line_avg_count',
+        ),
+
     # pivot chart examples
     url(r'^demo/pivot/simple/$', pivotdemo.simplepivot,
         {