#!/usr/bin/python2
#
# Copyright 2015 The ANGLE Project Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
# perf_test_runner.py:
#   Helper script for running and analyzing perftest results. Runs the
#   tests in an infinite batch, printing out the mean and coefficient of
#   variation of the population continuously.
#

import glob
import subprocess
import sys
import os
import re

base_path = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))

# Might have to add lower case "release" in some configurations.
perftests_paths = glob.glob('out/*Release*')
metric = 'score'

binary_name = 'angle_perftests'
if sys.platform == 'win32':
    binary_name += '.exe'

scores = []

# Danke to http://stackoverflow.com/a/27758326
def mean(data):
    """Return the sample arithmetic mean of data."""
    n = len(data)
    if n < 1:
        raise ValueError('mean requires at least one data point')
    return float(sum(data))/float(n) # in Python 2 use sum(data)/float(n)

def sum_of_square_deviations(data, c):
    """Return sum of square deviations of sequence data."""
    ss = sum((float(x)-c)**2 for x in data)
    return ss

def coefficient_of_variation(data):
    """Calculates the population coefficient of variation."""
    n = len(data)
    if n < 2:
        raise ValueError('variance requires at least two data points')
    c = mean(data)
    ss = sum_of_square_deviations(data, c)
    pvar = ss/n # the population variance
    stddev = (pvar**0.5) # population standard deviation
    return stddev / c

def truncated_list(data, n):
    """Compute a truncated list, n is truncation size"""
    if len(data) < n * 2:
        raise ValueError('list not large enough to truncate')
    return sorted(data)[n:-n]

def truncated_mean(data, n):
    """Compute a truncated mean, n is truncation size"""
    return mean(truncated_list(data, n))

def truncated_cov(data, n):
    """Compute a truncated coefficient of variation, n is truncation size"""
    return coefficient_of_variation(truncated_list(data, n))

# Find most recent binary
newest_binary = None
newest_mtime = None

for path in perftests_paths:
    binary_path = os.path.join(base_path, path, binary_name)
    if os.path.exists(binary_path):
        binary_mtime = os.path.getmtime(binary_path)
        if (newest_binary is None) or (binary_mtime > newest_mtime):
            newest_binary = binary_path
            newest_mtime = binary_mtime

perftests_path = newest_binary

if perftests_path == None or not os.path.exists(perftests_path):
    print('Cannot find Release %s!' % binary_name)
    sys.exit(1)

if sys.platform == 'win32':
    test_name = 'DrawCallPerfBenchmark.Run/d3d11_null'
else:
    test_name = 'DrawCallPerfBenchmark.Run/gl'

if len(sys.argv) >= 2:
    test_name = sys.argv[1]

print('Using test executable: ' + perftests_path)
print('Test name: ' + test_name)

# Infinite loop of running the tests.
while True:
    process = subprocess.Popen([perftests_path, '--gtest_filter=' + test_name], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    output, err = process.communicate()

    start_index = output.find(metric + "=")
    if start_index == -1:
        print("Did not find the score of the specified test in output:")
        print(output)
        sys.exit(1)

    start_index += len(metric) + 2

    end_index = output[start_index:].find(" ")
    if end_index == -1:
        print("Error parsing output:")
        print(output)
        sys.exit(2)

    m = re.search('Running (\d+) tests', output)
    if m and int(m.group(1)) > 1:
        print("Found more than one test result in output:")
        print(output)
        sys.exit(3)

    end_index += start_index

    score = int(output[start_index:end_index])
    sys.stdout.write("score: " + str(score))

    scores.append(score)
    sys.stdout.write(", mean: %.2f" % mean(scores))

    if (len(scores) > 1):
        sys.stdout.write(", variation: %.2f%%" % (coefficient_of_variation(scores) * 100.0))

    if (len(scores) > 7):
        trucation_n = len(scores) >> 3
        sys.stdout.write(", truncated mean: %.2f" % truncated_mean(scores, trucation_n))
        sys.stdout.write(", variation: %.2f%%" % (truncated_cov(scores, trucation_n) * 100.0))

    print("")
