X-Git-Url: https://projects.mako.cc/source/selectricity/blobdiff_plain/1ef3925927fb35483da98af08f9fc175d5502708..e8094b780ec0f30ac06b4de4c4f3a9c2bffe209d:/app/controllers/graph_controller.rb diff --git a/app/controllers/graph_controller.rb b/app/controllers/graph_controller.rb index b019aeb..0ef4e71 100644 --- a/app/controllers/graph_controller.rb +++ b/app/controllers/graph_controller.rb @@ -2,7 +2,8 @@ require 'date' class GraphController < ApplicationController class GruffGraff - COLORS = ['#74CE00', '#005CD9', '#DC0D13', '#131313', '#990033'] + COLORS = ['#74CE00', '#005CD9', '#DC0D13', '#131313', '#A214A4', 'EFF80E', + '90E5E6', 'F58313', '437D3D', '0E026C'] BACKGROUND_COLORS = ['#74CE00', '#FFFFFF'] #for green and white background def initialize(options) @@ -45,6 +46,7 @@ class GraphController < ApplicationController end else #one dimensional array, just pass it in @graph.data( options.fetch(:data_name, "Data"), options[:data] ) + @graph.hide_legend = true end # set the labels or create an empty hash @@ -87,6 +89,8 @@ class GraphController < ApplicationController @election = Election.find(params[:id]) data, labels, scale = get_votes_per_interval_data(@election) + hide_legend = true + graph = GruffGraff.new( :graph_type => Gruff::Line, :data_name => @election.name, :data => data, @@ -108,14 +112,25 @@ class GraphController < ApplicationController @election.results unless @election.borda_result data, labels = get_borda_points(@election.borda_result) + size = "400x300" + size = "580x300" if @election.candidates.size >= 5 + + if @election.candidates.size >= 5 + marker_font_size = 17 + else + marker_font_size = 20 + end + graph = GruffGraff.new( :graph_type => Gruff::Bar, :data_name => @election.name, :data => data, :interval_labels => labels, + :size => size, :title => "Points Per Candidate", :marker_color => '#999999', + :marker_font_size => marker_font_size, :y_axis_label => "Points", - :x_axis_label => "Candidate") + :x_axis_label => "Candidates") send_data(*graph.output) end #Acording to Tufte, small, concomparitive, highly labeled data sets usually