def plotDerivedGains( runs, gainIds, costIncrement ):
sessid = str(runs[0].get_session_id())
for gainId in gainIds:
yValueLists = [
(FiguresConfig.avgPlotName, stats.get_average_derived_gains_at_total_rank_range( gainId, runs ) ),
('top50%', stats.get_average_top_derived_gains_at_total_rank_range( runs, gainId, proportion = 50 ) ),
('bottom50%', stats.get_average_top_derived_gains_at_total_rank_range( runs, gainId, proportion = 50, bottom= True ) ),
('avg +1SD', stats.get_avg_derived_gain_plusSD_at_total_rank_range( gainId, runs ) ),
('avg -1SD', stats.get_avg_derived_gain_plusSD_at_total_rank_range( gainId, runs, -1 ) )
]
defaultPlot( 'rank', gainId, stats.get_max_rank_range( runs ), yValueLists,
stats.get_amount_of_runs_at_total_rank_range(runs), get_filename(gainId + 'AtRank', runs))
yValueLists = [
(FiguresConfig.avgPlotName, stats.get_average_derived_gains_at_cost_range( gainId, runs, costIncrement ) ),
('top50%', stats.get_average_top_derived_gains_at_cost_range( runs, gainId, costIncrement, proportion = 50 ) ),
('bottom50%', stats.get_average_top_derived_gains_at_cost_range( runs, gainId, costIncrement, proportion = 50, bottom = True ) ),
('avg +1SD', stats.get_avg_derived_gain_plusSD_at_cost_range( gainId, runs, costIncrement ) ),
('avg -1SD', stats.get_avg_derived_gain_plusSD_at_cost_range( gainId, runs, costIncrement, -1 ) )
]
defaultPlot( 'cost', gainId, stats.get_max_cost_range( runs, costIncrement ), yValueLists,
stats.get_amount_of_runs_at_cost_range(runs, costIncrement),
get_filename( gainId + 'AtCost', runs ) )
def plotDerivedGains(runs, gainIds, costIncrement):
sessid = str(runs[0].get_session_id())
for gainId in gainIds:
yValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_derived_gains_at_total_rank_range(gainId,
runs)),
('top50%',
stats.get_average_top_derived_gains_at_total_rank_range(
runs, gainId, proportion=50)),
('bottom50%',
stats.get_average_top_derived_gains_at_total_rank_range(
runs, gainId, proportion=50, bottom=True)),
('avg +1SD',
stats.get_avg_derived_gain_plusSD_at_total_rank_range(
gainId, runs)),
('avg -1SD',
stats.get_avg_derived_gain_plusSD_at_total_rank_range(
gainId, runs, -1))
]
defaultPlot('rank', gainId, stats.get_max_rank_range(runs),
yValueLists,
stats.get_amount_of_runs_at_total_rank_range(runs),
get_filename(gainId + 'AtRank', runs))
yValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_derived_gains_at_cost_range(
gainId, runs, costIncrement)),
('top50%',
stats.get_average_top_derived_gains_at_cost_range(runs,
gainId,
costIncrement,
proportion=50)),
('bottom50%',
stats.get_average_top_derived_gains_at_cost_range(runs,
gainId,
costIncrement,
proportion=50,
bottom=True)),
('avg +1SD',
stats.get_avg_derived_gain_plusSD_at_cost_range(
gainId, runs, costIncrement)),
('avg -1SD',
stats.get_avg_derived_gain_plusSD_at_cost_range(
gainId, runs, costIncrement, -1))
]
defaultPlot(
'cost', gainId, stats.get_max_cost_range(runs, costIncrement),
yValueLists,
stats.get_amount_of_runs_at_cost_range(runs, costIncrement),
get_filename(gainId + 'AtCost', runs))
def plotCostsAtRank( runs ):
sessid = str(runs[0].get_session_id())
yValueLists = [
(FiguresConfig.avgPlotName, stats.get_average_cumulated_costs_at_total_rank_range( runs ) ),
('top50%', stats.get_average_top_cumulated_costs_at_total_rank_range( runs, proportion=50 ) ),
('bottom50%', stats.get_average_top_cumulated_costs_at_total_rank_range( runs, proportion=50, bottom=True ) ),
('avg +1SD', stats.get_avg_cumulated_cost_plusSD_at_total_rank_range( runs, increment = 1, factor = 1 ) ),
('avg -1SD', stats.get_avg_cumulated_cost_plusSD_at_total_rank_range( runs, increment = 1, factor = -1 ) )
]
defaultPlot( 'rank', 'cost', stats.get_max_rank_range( runs ), yValueLists,
stats.get_amount_of_runs_at_total_rank_range(runs), get_filename('costAtRank', runs))
def plotGainsAtRank(runs):
sessid = str(runs[0].get_session_id())
yValueLists = [
(FiguresConfig.avgPlotName,
stats.get_average_cumulated_gains_at_total_rank_range(runs)),
('top50%',
stats.get_average_top_cumulated_gains_at_total_rank_range(
runs, proportion=50)),
('bottom50%',
stats.get_average_top_cumulated_gains_at_total_rank_range(
runs, proportion=50, bottom=True)),
('avg +1SD',
stats.get_avg_cumulated_gain_plusSD_at_total_rank_range(runs)),
('avg -1SD',
stats.get_avg_cumulated_gain_plusSD_at_total_rank_range(runs, -1))
]
defaultPlot('rank', 'cg', stats.get_max_rank_range(runs), yValueLists,
stats.get_amount_of_runs_at_total_rank_range(runs),
get_filename('gainAtRank', runs))
def handle_stats( runs ):
output = cStringIO.StringIO()
writer = csv.writer( output )
def catWrite(listA,listB):
copy = listA[:]
copy.extend( listB )
writer.writerow( copy )
catWrite( [ 'rank' ], stats.get_max_rank_range( runs ) )
catWrite( [ 'amt runs' ], stats.get_amount_of_runs_at_total_rank_range( runs ) )
catWrite( [ 'avg gain' ], stats.get_average_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'max gain' ], stats.get_max_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'min gain' ], stats.get_min_cumulated_gains_at_total_rank_range( runs ) )
catWrite( [ 'avg cost' ], stats.get_average_cumulated_costs_at_total_rank_range( runs ) )
catWrite( [ 'max cost' ], stats.get_max_cumulated_costs_at_total_rank_range( runs ) )
catWrite( [ 'min cost' ], stats.get_min_cumulated_costs_at_total_rank_range( runs ) )
writer.writerow([])
costInterval = 10
catWrite( [ 'cost' ], stats.get_min_cost_range( runs, costInterval ) )
catWrite( [ 'amt runs' ], stats.get_amount_of_runs_at_cost_range( runs, costInterval ) )
catWrite( [ 'avg gain' ], stats.get_average_cumulated_gains_at_cost_range( runs, costInterval ) )
catWrite( [ 'max gain' ], stats.get_max_cumulated_gains_at_cost_range( runs, costInterval ) )
catWrite( [ 'min gain' ], stats.get_min_cumulated_gains_at_cost_range( runs, costInterval ) )
writer.writerow([])
writer.writerow( [ 'avgTotalRank', stats.get_average_total_rank(runs)] )
writer.writerow( [ 'avgGain', stats.get_average_cumulated_gain(runs)] )
writer.writerow( [ 'avgCost', stats.get_average_cumulated_cost(runs)] )
writer.writerow( [ 'maxTotalRank', stats.get_max_total_rank(runs)] )
writer.writerow( [ 'maxGain', stats.get_max_cumulated_gain(runs)] )
writer.writerow( [ 'maxCost', stats.get_max_cumulated_cost(runs)] )
writer.writerow( [ 'minTotalRank', stats.get_min_total_rank(runs)] )
writer.writerow( [ 'minGain', stats.get_min_cumulated_gain(runs)] )
writer.writerow( [ 'minCost', stats.get_min_cumulated_cost(runs)] )
writer.writerow( [ 'varTotalRank', stats.get_total_rank_variance(runs)] )
writer.writerow( [ 'varGain', stats.get_cumulated_gain_variance(runs)] )
writer.writerow( [ 'varCost', stats.get_cumulated_cost_variance(runs)] )
return output.getvalue()
