[#403] Amend publisher statistics calculations to round to the neares…

…t integer

comprehensiveness.py

@@ -147,15 +147,23 @@ def generate_row(publisher):
 
         if denominator(slug, publisher_base) != 0:
             # Populate the row with the %age
-            row[slug] = int(float(numerator_all)/denominator(slug, publisher_base)*100)
-            row[slug+'_valid'] = int(float(numerator_valid)/denominator(slug, publisher_base)*100)
+            row[slug] = int(round(
+                float(numerator_all)/denominator(slug, publisher_base)*100
+                ))
+            row[slug+'_valid'] = int(round(
+                float(numerator_valid)/denominator(slug, publisher_base)*100
+                ))
 
     # Loop for averages
     # Calculate the average for each grouping, and the overall 'summary' average
     for page in ['core', 'financials', 'valueadded', 'summary']: 
         # Note that the summary must be last, so that it can use the average calculations from the other groupings
-        row[page+'_average'] = sum((row.get(x[0]) or 0)*x[2] for x in columns[page]) / sum(x[2] for x in columns[page])
-        row[page+'_average_valid'] = sum((row.get(x[0]+'_valid') or 0)*x[2] for x in columns[page]) / sum(x[2] for x in columns[page])
+        row[page+'_average'] = int(round(
+            sum((row.get(x[0]) or 0)*x[2] for x in columns[page]) / float(sum(x[2] for x in columns[page]))
+            ))
+        row[page+'_average_valid'] = int(round(
+            sum((row.get(x[0]+'_valid') or 0)*x[2] for x in columns[page]) / float(sum(x[2] for x in columns[page]))
+            ))
 
     return row
 

coverage.py

@@ -132,7 +132,7 @@ def generate_row(publisher):
         spend_ratio_candidates.append((row['iati_spend_2015'] / row['reference_spend_2014']) if (row['reference_spend_2014'] > 0) and is_number(row['reference_spend_2014']) else 0)
 
     # Get the maximum value and convert to a percentage
-    row['spend_ratio'] = int(max(spend_ratio_candidates) * 100)
+    row['spend_ratio'] = int(round(max(spend_ratio_candidates) * 100))
 
 
     # Compute coverage score and raise to the top of its quintile

forwardlooking.py

@@ -52,6 +52,7 @@ def generate_row(publisher):
                 # Else, use the hierarchy which they are reported at
                 row['year_columns'][0][year] = by_hierarchy[hierarchies_with_nonzero_budgets[0]]['forwardlooking_activities_current'].get(year) or 0
                 row['year_columns'][1][year] = by_hierarchy[hierarchies_with_nonzero_budgets[0]]['forwardlooking_activities_with_budgets'].get(year) or 0
+
             if not int(row['year_columns'][0][year]):
                 row['year_columns'][2][year] = '-'
             else:

transparencyindicator.py

@@ -87,7 +87,7 @@ def table():
             timelag_score = 0
 
         # Compute the percentage
-        row['timeliness'] = int( (float(frequency_score + timelag_score) / 8) * 100 )
+        row['timeliness'] = int( round((float(frequency_score + timelag_score) / 8) * 100))
 
 
         # Compute forward-looking statistic
@@ -98,7 +98,7 @@ def table():
         numbers = [ int(x) for x in publisher_forwardlooking_data['year_columns'][2].itervalues() if is_number(x) ]
 
         # Compute and store the mean average for these fields
-        row['forwardlooking'] = sum(int(y) for y in numbers) / len(publisher_forwardlooking_data['year_columns'][2])
+        row['forwardlooking'] = sum(int(round(y)) for y in numbers) / len(publisher_forwardlooking_data['year_columns'][2])
 
 
         # Compute comprehensive statistic
@@ -110,7 +110,7 @@ def table():
 
 
         # Compute score
-        row['score'] = int( (row['timeliness'] + row['forwardlooking'] + row['comprehensive']) / 3 )
+        row['score'] = int( round(float(row['timeliness'] + row['forwardlooking'] + row['comprehensive']) / 3 ))
 
 
         # Get coverage statistic
@@ -122,7 +122,7 @@ def table():
 
 
         # Compute coverage-adjusted score
-        row['score_coverage_adjusted'] = int( row['score'] * (row['coverage_adjustment'] / float(100)) ) 
+        row['score_coverage_adjusted'] = int( round(row['score'] * (row['coverage_adjustment'] / float(100))) )
 
 
         # Return a generator object