#!/usr/bin/env python3
# Copyright (c) 2017-2018, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707.
# All Rights reserved. See files LICENSE and NOTICE for details.
#
# This file is part of CEED, a collection of benchmarks, miniapps, software
# libraries and APIs for efficient high-order finite element and spectral
# element discretizations for exascale applications. For more information and
# source code availability see http://github.com/ceed.
#
# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC,
# a collaborative effort of two U.S. Department of Energy organizations (Office
# of Science and the National Nuclear Security Administration) responsible for
# the planning and preparation of a capable exascale ecosystem, including
# software, applications, hardware, advanced system engineering and early
# testbed platforms, in support of the nation's exascale computing imperative.

import pandas as pd
import fileinput
import pprint

#####   Read all input files specified on the command line, or stdin and parse
#####   the content, storing it as a pandas dataframe
def read_logs(files=None):
    it=fileinput.input(files)
    state = 0
    line=''
    i=0
    data = dict(
        file='unknown',
        backend='unknown',
        test='unknown',
        num_procs=0,
        num_procs_node=0,
        degree=0,
        quadrature_pts=0,
        code='libCEED',
        )

    runs=[]
    while True:
       ##
       if state%2==0:
          ##
          try:
             line=next(it)
             i=i+1
          except StopIteration:
             break
          state=state+1
          ##
       elif state==1:
          ##
          state=0
          ## Legacy header contains number of MPI tasks
          if 'Running the tests using a total of' in line:
             data['num_procs'] = int(line.split('a total of ',1)[1].split(None,1)[0])
          ## MPI tasks per node
          elif 'tasks per node' in line:
             data['num_procs_node'] = int(line.split(' tasks per',1)[0].rsplit(None,1)[1])
          ## New Benchmark Problem
          elif "CEED Benchmark Problem" in line:
             # Starting a new block
             data = data.copy()
             runs.append(data)
             data['file'] = fileinput.filename()
             data['test'] = line.split()[-2] + " " + line.split('-- ')[1]
             data['case']='scalar' if (('Problem 1' in line) or ('Problem 3' in line)
                                      or ('Problem 5' in line)) else 'vector'
          elif "Hostname" in line:
              data['hostname'] = line.split(':')[1].strip()
          elif "Total ranks" in line:
              data['num_procs'] = int(line.split(':')[1].strip())
          elif "Ranks per node" in line:
              data['num_procs_node'] = int(line.split(':')[1].strip())
          ## Backend
          elif 'libCEED Backend MemType' in line:
             data['backend_memtype']=line.split(':')[1].strip()
          elif 'libCEED Backend' in line:
             data['backend']=line.split(':')[1].strip()
          ## P
          elif 'Basis Nodes' in line:
             data['degree']=int(line.split(':')[1]) - 1
          ## Q
          elif 'Quadrature Points' in line:
             qpts=int(line.split(':')[1])
             data['quadrature_pts']=qpts**3
          ## Total DOFs
          elif 'Global nodes' in line:
             data['num_unknowns']=int(line.split(':')[1])
             if data['case']=='vector':
                data['num_unknowns']*=3
          ## Number of elements
          elif 'Local Elements' in line:
             data['num_elem']=int(line.split(':')[1].split()[0])*data['num_procs']
          ## CG Solve Time
          elif 'Total KSP Iterations' in line:
              data['ksp_its'] = int(line.split(':')[1].split()[0])
          elif 'CG Solve Time' in line:
              data['time_per_it'] = float(line.split(':')[1].split()[0]) / data['ksp_its']
          ## CG DOFs/Sec
          elif 'DoFs/Sec in CG' in line:
             data['cg_iteration_dps']=1e6*float(line.split(':')[1].split()[0])
          ## End of output

    return pd.DataFrame(runs)

if __name__ == "__main__":
    runs = read_logs()
    print('Number of test runs read: %i'%len(runs))
    print(runs)