#!/usr/bin/env python3
import pandas as pd
import numpy as np
import sys
from pprint import pprint
from tabulate import tabulate

def process_raw_line(line):
    fields = line.rstrip().split(' ')
    fields = [field.split(':')[-1] for field in fields]
    fields[5] = fields[5][1:-1]
    fields[0] = int(fields[0])
    fields[1:] = list(map(np.float, fields[1:]))
    return fields

with open(sys.argv[1], 'r') as f:
    data = list(map(process_raw_line, f.readlines()))

df = pd.DataFrame(data)
df.columns = ['frame', 'Y', 'U', 'V', 'All', 'dB']
df['inv'] = [1 - value for value in df['All']]

print(f'Mean overall SSIM: {df["All"].mean()}')
print(f'Median overall SSIM: {df["All"].median()}')
print(f'Frame with worst SSIM: {df.idxmin()["All"]+1}')
print(f'Frame with best SSIM: {df.idxmax()["All"]+1}')

print(tabulate(
    [(key, value * 100) for key, value in [
        ['best', df['All'].max()],
        [50, 1 - df['inv'].quantile(0.50)],
        [66.6, 1 - df['inv'].quantile(0.666)],
        [75, 1 - df['inv'].quantile(0.75)],
        [80, 1 - df['inv'].quantile(0.80)],
        [90, 1 - df['inv'].quantile(0.90)],
        [95, 1 - df['inv'].quantile(0.95)],
        [98, 1 - df['inv'].quantile(0.98)],
        [99, 1 - df['inv'].quantile(0.99)],
        [99.9, 1 - df['inv'].quantile(0.999)],
        [100, df['All'].min()]
    ]],
    headers=['% Frames', '≥ SSIM']
))