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()
def handle_stats( runs ):
return 'avgGainsRank = ' + repr(stats.get_average_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'maxGainsRank = ' + repr(stats.get_max_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'minGainsRank = ' + repr(stats.get_min_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'avgGainsCost = ' + repr(stats.get_average_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'maxGainsCost = ' + repr(stats.get_max_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'minGainsCost = ' + repr(stats.get_min_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'avgCostsRank = ' + repr(stats.get_average_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'maxCostsRank = ' + repr(stats.get_max_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'minCostsRank = ' + repr(stats.get_min_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'avgTotalRank = ' + repr(stats.get_average_total_rank(runs)) + '\n' \
+ 'avgGain = ' + repr(stats.get_average_cumulated_gain(runs)) + '\n' \
+ 'avgCost = ' + repr(stats.get_average_cumulated_cost(runs)) + '\n' \
+ 'maxTotalRank = ' + repr(stats.get_max_total_rank(runs)) + '\n' \
+ 'maxGain = ' + repr(stats.get_max_cumulated_gain(runs)) + '\n' \
+ 'maxCost = ' + repr(stats.get_max_cumulated_cost(runs)) + '\n' \
+ 'minTotalRank = ' + repr(stats.get_min_total_rank(runs)) + '\n' \
+ 'minGain = ' + repr(stats.get_min_cumulated_gain(runs)) + '\n' \
+ 'minCost = ' + repr(stats.get_min_cumulated_cost(runs)) + '\n' \
+ 'varTotalRank = ' + repr(stats.get_total_rank_variance(runs)) + '\n' \
+ 'varGain = ' + repr(stats.get_cumulated_gain_variance(runs)) + '\n' \
+ 'varCost = ' + repr(stats.get_cumulated_cost_variance(runs))
def handle_stats(runs):
return 'avgGainsRank = ' + repr(stats.get_average_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'maxGainsRank = ' + repr(stats.get_max_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'minGainsRank = ' + repr(stats.get_min_cumulated_gains_at_total_rank_range( runs )) + '\n' \
+ 'avgGainsCost = ' + repr(stats.get_average_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'maxGainsCost = ' + repr(stats.get_max_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'minGainsCost = ' + repr(stats.get_min_cumulated_gains_at_cost_range( runs, 10 )) + '\n' \
+ 'avgCostsRank = ' + repr(stats.get_average_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'maxCostsRank = ' + repr(stats.get_max_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'minCostsRank = ' + repr(stats.get_min_cumulated_costs_at_total_rank_range( runs )) + '\n' \
+ 'avgTotalRank = ' + repr(stats.get_average_total_rank(runs)) + '\n' \
+ 'avgGain = ' + repr(stats.get_average_cumulated_gain(runs)) + '\n' \
+ 'avgCost = ' + repr(stats.get_average_cumulated_cost(runs)) + '\n' \
+ 'maxTotalRank = ' + repr(stats.get_max_total_rank(runs)) + '\n' \
+ 'maxGain = ' + repr(stats.get_max_cumulated_gain(runs)) + '\n' \
+ 'maxCost = ' + repr(stats.get_max_cumulated_cost(runs)) + '\n' \
+ 'minTotalRank = ' + repr(stats.get_min_total_rank(runs)) + '\n' \
+ 'minGain = ' + repr(stats.get_min_cumulated_gain(runs)) + '\n' \
+ 'minCost = ' + repr(stats.get_min_cumulated_cost(runs)) + '\n' \
+ 'varTotalRank = ' + repr(stats.get_total_rank_variance(runs)) + '\n' \
+ 'varGain = ' + repr(stats.get_cumulated_gain_variance(runs)) + '\n' \
+ 'varCost = ' + repr(stats.get_cumulated_cost_variance(runs))