#!/usr/bin/env python
# coding: utf-8

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get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


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import os, json, glob
import pandas as pd


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nb_step = 1e6
dic_table = {}

for path in glob.glob('dump/perf-*/results.json'):
    with open(path, 'r') as f:
        dic = json.loads(f.read())
        lang, machine, nb_core, parallel = dic['name'].split('-')[1:5]
        nb_core = int(nb_core)
        if nb_core == 1: parallel = 'none'
        key = (machine, lang, nb_core, parallel)
        dic_table[key] = {
            'Runtime (s)': dic['runtime'],
            'Best Solution': min([e['dist'] for e in dic['solutions']]),
            'Nb Step/core/sec': nb_step/dic['runtime'],
            'Nb Step/sec': nb_core*nb_step/dic['runtime'],
        }

df = pd.DataFrame(dic_table).T
df.index.names = ['Machine', 'Lang', 'Nb Core', 'Parallel']
df = df.reset_index()
    

df['machine_no'] = df['Machine'].replace({'MBPro': 1, 'iMac': 2, 'LinuxVM': 3})
df['parallel_no'] = df['Parallel'].replace({'none': 0, 'concurrent': 2, 'omp': 3})
df = df.sort_values(['Lang', 'parallel_no', 'Nb Core', 'machine_no'])
df = df.reset_index(drop=True)

df = df[['Machine', 'Lang', 'Nb Core', 'Parallel', 'Runtime (s)', 
         'Best Solution', 'Nb Step/core/sec', 'Nb Step/sec']]

df['Raw Speed'] = df['Nb Step/sec']/df['Nb Step/sec'].iloc[0]

df['Speed Up/machine'] = 1.0
for i in range(1, 5):
    for j in range(3):
        k = i*3+j
        df.loc[k, 'Speed Up/machine'] = df['Raw Speed'].iloc[k]/df['Raw Speed'].iloc[j]

df['Runtime (s)'] = df['Runtime (s)'].map('{:.2f}'.format)
df['Best Solution'] = df['Best Solution'].map('{:.6f}'.format)
df['Nb Step/core/sec'] = df['Nb Step/core/sec'].map('{:,.0f}'.format)
df['Nb Step/sec'] = df['Nb Step/sec'].map('{:,.0f}'.format)
df['Raw Speed'] = df['Raw Speed'].map('{:.1f}'.format)
df['Speed Up/machine'] = df['Speed Up/machine'].map('{:.1f}'.format)


df.to_csv('dump/df_perf.csv')

df


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