Evidence graph for Explore data works

hunchworks/embeds.py

@@ -2,17 +2,20 @@
 
 import json
 import urlparse
+from hunchworks.utils.data.worldbank import indicator as wb_indicator
 from hunchworks.utils.data.worldbank import chart as wb_chart
 from django.template.loader import render_to_string
 
 
 def worldbank(url):
-  if url.startswith("http://localhost:8000/explore?"):
+  if url.startswith("http://127.0.0.1:8000/explore?"):
     qs = urlparse.parse_qs(urlparse.urlparse(url).query)
-    data = wb_chart(qs["indicator"][0], qs["country"])
-
+    data = wb_indicator(qs["indicator"][0], qs["country"][0].split(","))
+    countries, data = wb_chart(data)
+
     return render_to_string(
       "embeds/worldbank.html", {
-        "data": json.dumps(data)
+        "flat_data": json.dumps(data),
+        "flat_countries": json.dumps(countries)        
       }
     )

hunchworks/templates/base.html

@@ -64,6 +64,7 @@
 
     <script src="//ajax.googleapis.com/ajax/libs/jquery/1.7.0/jquery.js"></script>
     <script src="http://maps.googleapis.com/maps/api/js?sensor=false"></script>
+    <script type="text/javascript" src="https://www.google.com/jsapi"></script>
 
     {% compress js %}
       <!-- vendor libs -->

hunchworks/templates/embeds/worldbank.html

@@ -1 +1,3 @@
-<div class="worldbank-chart" data-x-axis="Year" data-y-axis="What" data-data="{{ data }}"></div>
+<section class="graph">
+  <div class="worldbank-chart" data-x-axis="Year" data-countries="{{ flat_countries }}" data-data="{{ flat_data }}"></div>
+</section>

hunchworks/templates/explore/explore.html

@@ -12,10 +12,6 @@
   <h1>Explore Data</h1>
 {% endblock %}
 
-{% block js %}
-  <script type="text/javascript" src="https://www.google.com/jsapi"></script>
-{% endblock %}
-
 {% block content %}
   {% if show_graph %}
     <section class="graph">

hunchworks/utils/data/worldbank.py

@@ -33,13 +33,44 @@ def countries():
 def indicator(indicator_code, country_codes):
   return _api("countries/%s/indicators/%s" % (
     ";".join(country_codes), indicator_code))
-
-
+
 def _value(value):
   if value is not None:
     return float(value)
 
-def chart(indicator_code, country_codes):
+def chart(data):
+  all_country_data = {}
+  country = ""
+
+  for item in reversed(data):
+
+    if country != item["country"]["value"]:
+      country = item["country"]["value"]
+      all_country_data[country] = {}
+
+    all_country_data[country][item["date"]] = _value(item["value"])
+
+  # This will be a unique set of years across all countries in all_country_data
+  final_year_set = set()
+
+  for country in sorted(all_country_data.keys()):
+  	final_year_set.update(all_country_data[country].keys())
+
+  # country_keys are the unique country names for all countries in the data.
+  country_keys = all_country_data.keys()
+
+  # final_country_data will be an array of arrays looking like this:
+  # [ ["1998", 13000, 14000], ["1999", 15000, 16000], ["2000", 17000, 20000] ]
+  final_country_data = []
+  for year in sorted(final_year_set):
+    temp_data = [year]
+    for country in country_keys:
+      temp_data.append( all_country_data[country][year] )
+    final_country_data.append(temp_data)
+
+  return (country_keys, final_country_data)
+
+def old_chart(indicator_code, country_codes):
   data = indicator(indicator_code, country_codes)
 
   flat_data = [

hunchworks/views/explore.py

@@ -20,10 +20,12 @@ def _render(req, template, more_context):
 def worldbank_indicators(indicator_id, country_ids):
   data = worldbank.indicator(indicator_id, country_ids)
 
-  def _extract(key):
+  # transform worldbank-style structure into tokeninput style structure
+  # { "id": 1, "value": "US" }   =>   { "id": 1, "name": "US" }
+  def _format_for_tokeninput(key):
     things = []
 
-    for item in data:
+    for item in data.copy():
       thing = item[key]
       thing["name"] = thing["value"]
       del thing["value"]
@@ -33,47 +35,12 @@ def _extract(key):
 
     return things
 
-  countries = _extract("country")
-  indicators = _extract("indicator")
-
-  def _value(value):
-    if value is not None:
-      return float(value)
-
-  # all_country_data will output a dictionary that looks like this:
-  # { Brazil: { 2003: 10000, 2004: 11000, 2005: 12000 },
-  #   China: { 2003: 12000, 2004: 14000, 2006: 16000 }
-  # }
-  all_country_data = {}
-  country = ""
-
-  for item in reversed(data):
-
-    if country != item["country"]["name"]:
-      country = item["country"]["name"]
-      all_country_data[country] = {}
-
-    all_country_data[country][item["date"]] = _value(item["value"])
-
-  # This will be a unique set of years across all countries in all_country_data
-  final_year_set = set()
-
-  for country in sorted(all_country_data.keys()):
-  	final_year_set.update(all_country_data[country].keys())
-
-  # country_keys are the unique country names for all countries in the data.
-  country_keys = all_country_data.keys()
+  countries_prepop = _format_for_tokeninput("country")
+  indicators_prepop = _format_for_tokeninput("indicator")
 
-  # final_country_data will be an array of arrays looking like this:
-  # [ ["1998", 13000, 14000], ["1999", 15000, 16000], ["2000", 17000, 20000] ]
-  final_country_data = []
-  for year in sorted(final_year_set):
-    temp_data = [year]
-    for country in country_keys:
-      temp_data.append( all_country_data[country][year] )
-    final_country_data.append(temp_data)
+  country_keys, final_country_data = worldbank.chart( data )
 
-  return (indicators, countries, country_keys, final_country_data)
+  return (indicators_prepop, countries_prepop, country_keys, final_country_data)
 
 
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