Merge branch 'master' of https://github.com/martinlackner/apportionment

README.md

@@ -1,17 +1,18 @@
 # A Python implementation of common apportionment methods
 
 This is a collection of common apportionment methods. Apportionment has two main applications: 
-to assign a fixed number of parliamentary seats to parties (proportionally to their vote count), and to assign
-representatives in a senate to states (proportionally to their population count).
+to assign a fixed number of [parliamentary seats to parties](https://en.wikipedia.org/wiki/Party-list_proportional_representation) (proportionally to their vote count), and to assign
+[representatives in a senate to states](https://en.wikipedia.org/wiki/United_States_congressional_apportionment) (proportionally to their population count). 
+A recommendable overview of apportionment methods can be found in the book "Fair Representation" by Balinski and Young [2].
 
-The following methods are implemented:
+The following apportionment methods are implemented:
 * the largest remainder method (or Hamilton method)
 * the class of divisor methods including
    - D'Hondt (or Jefferson)
    - Sainte-Laguë (or Webster)
    - Huntington-Hill
    - Adams
-* the Quota method [1]
+* the quota method [1]
 
 
 ## How-to
@@ -32,3 +33,5 @@ Another example can be found in [example.py](example.py).
 ## References
 
 [1] Balinski, M. L., & Young, H. P. (1975). The quota method of apportionment. The American Mathematical Monthly, 82(7), 701-730.
+
+[2] Balinski, M. L., & Young, H. P. (1982). Fair Representation: Meeting the Ideal of One Man, One Vote. Yale University Press, 1982. (There is a second edition from 2001 by Brookings Institution Press.)

apportionment.py

@@ -177,7 +177,7 @@ def quota(distribution, seats, parties=string.ascii_uppercase, verbose=True):
                   for i in range(len(distribution))]
         # check if upper quota is violated
         for i in range(len(distribution)):
-            if representatives[i] > math.ceil(float(distribution[i])*k/sum(distribution)):  
+            if representatives[i] >= math.ceil(float(distribution[i])*k/sum(distribution)):  
                 quotas[i] = 0
         chosen = [i for i in range(len(distribution))
                   if quotas[i] == max(quotas)]