import argparse

import csv

import math

import tabulate as tab

parser = argparse.ArgumentParser()

parser.add_argument('-r', '--runs', type=int, required=True, help='Number of runs')

parser.add_argument('-b', '--baseline', required=True, help='Baseline CSV file')

parser.add_argument('-c', '--current', required=True, help='Current CSV file')

args = parser.parse_args()

def get_2d_list(csv_filename):

with open(csv_filename) as csv_file:

csv_reader = csv.reader(csv_file)

next(csv_reader)

return [[str(name), float(mean), float(stdev), int(count)] for name, mean, stdev, count in csv_reader]

table_baseline = get_2d_list(args.baseline)

table_current = get_2d_list(args.current)

def student(x, sx, m, y, sy, n):

s = 0.0 if m < 2 or n < 2 else math.sqrt(((m - 1) * sx**2 + (n - 1) * sy**2) / (m + n - 2))

d = x - y

t = 0.0 if s == 0.0 else math.sqrt((n * m) / (n + m)) * d / s

return d, s, t

def get_emoji(t):

quantile = 2.0 # 95% confidence interval

if t < -quantile:

return ':green_circle:'

elif t > quantile:

return ':red_circle:'

else:

return ':white_circle:'

table = []

for baseline, current in zip(table_baseline, table_current):

baseline_name, baseline_mean, baseline_stdev, count_baseline = baseline

name, mean, stdev, count = current

assert(baseline_name == name)

total_time_baseline = baseline_mean * (count_baseline // args.runs)

total_time = mean * (count // args.runs)

d, s, t = student(mean, stdev, count, baseline_mean, baseline_stdev, count_baseline)

emoji = get_emoji(t)

table.append([name, int(total_time), int(mean), f'{stdev:.2f}', count, int(total_time_baseline), int(baseline_mean), f'{baseline_stdev:.2f}', count_baseline, f'{d:.2f}', f'{s:.2f}', f'{t:.2f}', emoji])

table.sort(key = lambda row: row[1], reverse=True)

header = ['name', 'total time (\u03BCs)', 'mean (\u03BCs)', 'stdev \u03C3', 'samples', 'total time (base)', 'mean (base)', 'stdev (base)', 'samples (base)', 'diff \u0394', 'stdev (pooled)', 't', '']

print(tab.tabulate(table, header, tablefmt="github"))