#!/usr/bin/env python -u

# Copyright (C) 2019 Apple Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1.  Redistributions of source code must retain the above copyright
#     notice, this list of conditions and the following disclaimer. 
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# 3.  Neither the name of Apple Inc. ("Apple") nor the names of
#     its contributors may be used to endorse or promote products derived
#     from this software without specific prior written permission. 
#
# THIS SOFTWARE IS PROVIDED BY APPLE AND ITS CONTRIBUTORS "AS IS" AND ANY
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# DISCLAIMED. IN NO EVENT SHALL APPLE OR ITS CONTRIBUTORS BE LIABLE FOR ANY
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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import sys
import argparse
import json
import itertools
from webkitpy.benchmark_runner.benchmark_results import BenchmarkResults
from webkitpy.benchmark_runner.benchmark_json_merge import mergeJSONs

try:
    from scipy import stats
except:
    print "ERROR: scipy package is not installed. Run `pip install scipy`"
    sys.exit(1)

try:
    import numpy
except:
    print "ERROR: numpy package is not installed. Run `pip install numpy`"
    sys.exit(1)

def readJSONFile(path):
    with open(path, 'r') as contents:
        result = json.loads(contents.read())
        if 'debugOutput' in result:
            del result['debugOutput']
        return result

Speedometer2 = "Speedometer2"
JetStream2 = "JetStream2"
PLT5 = "PLT5"
MotionMark = "MotionMark"
MotionMark1_1 = "MotionMark-1.1"
MotionMark1_1_1 = "MotionMark-1.1.1"

unitMarker = "__unit__"

def speedometer2Breakdown(jsonObject):
    breakdown = BenchmarkResults(jsonObject)
    result = {}
    result[unitMarker] = "ms"
    for test in breakdown._results["Speedometer-2"]["tests"].keys():
        result[test] = breakdown._results["Speedometer-2"]["tests"][test]["metrics"]["Time"]["Total"]["current"]
    return result

def jetStream2Breakdown(jsonObject):
    breakdown = BenchmarkResults(jsonObject)
    result = {}
    result[unitMarker] = "pts"
    for test in breakdown._results["JetStream2.0"]["tests"].keys():
        result[test] = breakdown._results["JetStream2.0"]["tests"][test]["metrics"]["Score"][None]["current"]
    return result

def motionMarkBreakdown(jsonObject):
    breakdown = BenchmarkResults(jsonObject)

    result = {}
    result[unitMarker] = "pts"

    if detectMotionMark(jsonObject):
        name = "MotionMark"
    elif detectMotionMark1_1(jsonObject):
        name = "MotionMark-1.1"
    else:
        name = "MotionMark-1.1.1"

    for test in breakdown._results[name]["tests"].keys():
        result[test] = breakdown._results[name]["tests"][test]["metrics"]["Score"][None]["current"]

    return result

def plt5Breakdown(jsonObject):
    nameMapping = {}

    for mappings in  jsonObject["urls"]:
        for key in mappings.keys():
            nameMapping[key] = mappings[key]

    result = {}
    result[unitMarker] = "ms"
    for test in jsonObject["iterations"][0]["warm"].keys():
        if test == "Geometric":
            continue
        result["warm--" + nameMapping[test]] = []
        result["cold--" + nameMapping[test]] = []

    for payload in jsonObject["iterations"]:
        warmTests = payload["warm"]
        coldTests = payload["cold"]
        for test in warmTests.keys():
            if test == "Geometric":
                continue
            result["warm--" + nameMapping[test]].append(warmTests[test]["Geometric"])
            result["cold--" + nameMapping[test]].append(coldTests[test]["Geometric"])

    return result

def displayStr(value):
    return "{:.6f}".format(float(value))

def computeMultipleHypothesesSignificance(a, b):
    # This is using the Benjamini-Hochberg procedure based on False Discovery Rate
    # for computing signifcance in multiple hypothesis testing 
    # Read more here:
    # - https://en.wikipedia.org/wiki/False_discovery_rate
    # - https://www.stat.berkeley.edu/~mgoldman/Section0402.pdf
    # This is best used for independent variables. We know subtests aren't
    # fully independent, this it's a reasonable approximation.
    # We use this instead of Bonferroni because we control for almost the same
    # false positive error rate (marking as signficant when it's not), but with a much
    # lower false negative error rate (not marking something as signficant when it is).

    sortedPValues = []
    reversePValueMap = {}

    for key in a.keys():
        if key == unitMarker:
            continue

        (tStatistic, pValue) = stats.ttest_ind(a[key], b[key], equal_var=False)

        sortedPValues.append(pValue)
        if pValue not in reversePValueMap:
            reversePValueMap[pValue] = []
        reversePValueMap[pValue].append(key)

    sortedPValues.sort()
    assert sortedPValues[0] <= sortedPValues[-1]

    isSignificant = False
    result = {}
    rank = float(len(sortedPValues))
    for pValue in reversed(sortedPValues):
        assert rank >= 1.0
        threshold = (rank * .05) / float(len(sortedPValues))
        if pValue <= threshold:
            isSignificant = True
        
        assert len(reversePValueMap[pValue]) > 0
        for test in reversePValueMap[pValue]:
            result[test] = isSignificant

        rank = rank - 1.0

    return result


def dumpBreakdowns(a, b):
    nameLength = len("subtest")
    aLength = len(a[unitMarker])
    bLength = len(a[unitMarker])
    ratioLength = len("b / a")

    pValueHeader = "pValue (significance using False Discovery Rate)"
    pLength = len(pValueHeader)

    isSignificant = computeMultipleHypothesesSignificance(a, b)

    for key in a.keys():
        if key == unitMarker:
            continue
        nameLength = max(nameLength, len(key))
        aLength = max(aLength, len(displayStr(numpy.mean(a[key]))))
        bLength = max(bLength, len(displayStr(numpy.mean(b[key]))))
        ratioLength = max(ratioLength, len(displayStr(numpy.mean(b[key]) / numpy.mean(a[key]))))

        (tStatistic, pValue) = stats.ttest_ind(a[key], b[key], equal_var=False)
        significantStr = ""
        if isSignificant[key]:
            significantStr = " (significant)"
        pLength = max(pLength, len(displayStr(pValue)) + len(significantStr))

    aLength += 2
    bLength += 2
    nameLength += 2
    ratioLength += 2
    pLength += 2

    strings = []
    strings.append("|{key:^{nameLength}}|{aScore:^{aLength}} |{bScore:^{bLength}} |{compare:^{ratioLength}}|{pMarker:^{pLength}}|".format(key="subtest", aScore=a[unitMarker], bScore=b[unitMarker], nameLength=nameLength, aLength=aLength, bLength=bLength , compare="b / a", ratioLength=ratioLength, pMarker=pValueHeader, pLength=pLength))
    for key in a.keys():
        if key == unitMarker:
            continue

        aScore = numpy.mean(a[key])
        bScore = numpy.mean(b[key])

        (tStatistic, pValue) = stats.ttest_ind(a[key], b[key], equal_var=False)

        significantStr = ""
        if isSignificant[key]:
            significantStr = " (significant)"

        strings.append("| {key:{nameLength}}|{aScore:{aLength}} |{bScore:{bLength}} |{compare:{ratioLength}}| {pValue:<{pLength}}|".format(key=key, aScore=displayStr(aScore), bScore=displayStr(bScore), nameLength=nameLength - 1, aLength=aLength, bLength=bLength, ratioLength=ratioLength, compare=displayStr(bScore / aScore), pValue = displayStr(pValue) + significantStr, pLength=pLength - 1))

    maxLen = 0
    for s in strings:
        maxLen = max(maxLen, len(s))

    verticalSeparator = "-" * maxLen
    strings.insert(0, verticalSeparator)
    strings.insert(2, verticalSeparator)
    strings.append(verticalSeparator)

    print "\n"
    for s in strings:
        print(s)
    print "\n"

def writeCSV(a, b, fileName):
    strings = []
    result = ""
    result += "test_name, {}, {}, b_divided_by_a, pValue, is_significant_using_False_Discovery_Rate\n".format("a_in_" + a[unitMarker], "b_in_" + b[unitMarker])

    isSignificant = computeMultipleHypothesesSignificance(a, b)

    for key in a.keys():
        if key == unitMarker:
            continue

        aScore = numpy.mean(a[key])
        bScore = numpy.mean(b[key])

        (tStatistic, pValue) = stats.ttest_ind(a[key], b[key], equal_var=False)
        significantStr = "No"
        if isSignificant[key]:
            significantStr = "Yes"
        result += "{},{},{},{},{},{}\n".format(key, displayStr(aScore), displayStr(bScore), displayStr(bScore / aScore), displayStr(pValue), significantStr)

    f = open(fileName, "w")
    f.write(result)
    f.close()


def detectJetStream2(payload):
    return "JetStream2.0" in payload

def JetStream2Results(payload):
    assert detectJetStream2(payload)

    js = payload["JetStream2.0"]
    iterations = len(js["tests"]["gaussian-blur"]["metrics"]["Score"]["current"])
    results = []
    for i in range(iterations):
        scores = []
        for test in js["tests"].keys():
            scores.append(js["tests"][test]["metrics"]["Score"]["current"][i])
        geomean = stats.gmean(scores)
        
        results.append(geomean)

    return results

def detectSpeedometer2(payload):
    return "Speedometer-2" in payload

def Speedometer2Results(payload):
    assert detectSpeedometer2(payload)
    results = []
    for arr in payload["Speedometer-2"]["metrics"]["Score"]["current"]:
        results.append(numpy.mean(arr))
    return results

def detectPLT5(payload):
    if "iterations" not in payload:
        return False
    iterations = payload["iterations"]
    if not isinstance(iterations, list):
        return False
    if not len(iterations):
        return False
    if "cold" not in iterations[0]:
        return False
    if "warm" not in iterations[0]:
        return False
    if "Geometric" not in iterations[0]:
        return False
    return True

def PLT5Results(payload):
    assert detectPLT5(payload)
    results = []
    for obj in payload["iterations"]:
        results.append(obj["Geometric"])
    return results

def detectMotionMark(payload):
    return "MotionMark" in payload

def detectMotionMark1_1(payload):
    return "MotionMark-1.1" in payload

def detectMotionMark1_1_1(payload):
    return "MotionMark-1.1.1" in payload

def motionMarkResults(payload):
    assert detectMotionMark(payload) or detectMotionMark1_1(payload) or detectMotionMark1_1_1(payload)
    if detectMotionMark(payload):
        payload = payload["MotionMark"]
    elif detectMotionMark1_1(payload):
        payload = payload["MotionMark-1.1"]
    else:
        payload = payload["MotionMark-1.1.1"]
    testNames = payload["tests"].keys()
    numTests = len(payload["tests"][testNames[0]]["metrics"]["Score"]["current"])
    results = []
    for i in range(numTests):
        scores = []
        for test in testNames:
            scores.append(payload["tests"][test]["metrics"]["Score"]["current"][i])
        results.append(stats.gmean(scores))

    return results

def detectBenchmark(payload):
    if detectJetStream2(payload):
        return JetStream2
    if detectSpeedometer2(payload):
        return Speedometer2
    if detectPLT5(payload):
        return PLT5
    if detectMotionMark(payload):
        return MotionMark
    if detectMotionMark1_1(payload):
        return MotionMark1_1
    if detectMotionMark1_1_1(payload):
        return MotionMark1_1
    return None

def biggerIsBetter(benchmarkType):
    if benchmarkType == JetStream2:
        return True
    if benchmarkType == Speedometer2:
        return True
    if benchmarkType == MotionMark:
        return True
    if benchmarkType == MotionMark1_1:
        return True
    if benchmarkType == PLT5:
        return False

    print "Should not be reached."
    assert False

def ttest(benchmarkType, a, b):
    # We use two-tailed Welch's
    (tStatistic, pValue) = stats.ttest_ind(a, b, equal_var=False)
    aMean = numpy.mean(a)
    bMean = numpy.mean(b)
    print "a mean = {:.5f}".format(aMean)
    print "b mean = {:.5f}".format(bMean)

    print "pValue = {:.10f}".format(pValue)

    if biggerIsBetter(benchmarkType):
        print "(Bigger means are better.)"
        if aMean > bMean:
            print "{:.3f} times worse".format((aMean / bMean))
        else:
            print "{:.3f} times better".format((bMean / aMean))
    else:
        print "(Smaller means are better.)"
        if aMean > bMean:
            print "{:.3f} times better".format((aMean / bMean))
        else:
            print "{:.3f} times worse".format((bMean / aMean))

    if pValue <= 0.05:
        print "Results ARE significant"
    else:
        print "Results ARE NOT significant"

def getOptions():
    parser = argparse.ArgumentParser(description="Compare two WebKit benchmark results. Pass in at least two JSON result files to compare them. This script prints the pValue along with the magnitude of the change. If more than one JSON is passed as a/b they will be merged when computing the breakdown.")

    parser.add_argument("-a",
        type=str,
        required=True,
        nargs='+',
        action="append",
        help="a JSONs of a/b. Path to JSON results file. Takes multiple values and can be passed multiple times.")

    parser.add_argument("-b",
        type=str,
        required=True,
        nargs='+',
        action="append",
        help="b JSONs of a/b. Path to JSON results file. Takes multiple values and can be passed multiple times.")

    parser.add_argument("--csv",
        type=str,
        required=False,
        help="Path to write a csv file containing subtest breakdown.")

    parser.add_argument("--breakdown", action="store_true",
        default=False, help="Print a per subtest breakdown.")

    return parser.parse_known_args()[0]


def main():
    args = getOptions()

    # Flatten the list of lists of JSON files.
    a = itertools.chain.from_iterable(args.a)
    b = itertools.chain.from_iterable(args.b)

    a = mergeJSONs(list(map(readJSONFile, a)))
    b = mergeJSONs(list(map(readJSONFile, b)))

    typeA = detectBenchmark(a)
    typeB = detectBenchmark(b)

    if typeA != typeB:
        print "-a and -b are not the same benchmark. a={} b={}".format(typeA, typeB)
        sys.exit(1)

    if not (typeA and typeB):
        print "Unknown benchmark type. a={} b={}".format(typeA, typeB)
        sys.exit(1)
    
    if typeA == JetStream2:
        if args.breakdown:
            dumpBreakdowns(jetStream2Breakdown(a), jetStream2Breakdown(b))

        ttest(typeA, JetStream2Results(a), JetStream2Results(b))

        if args.csv:
            writeCSV(jetStream2Breakdown(a), jetStream2Breakdown(b), args.csv)
    elif typeA == Speedometer2:
        if args.breakdown:
            dumpBreakdowns(speedometer2Breakdown(a), speedometer2Breakdown(b))

        ttest(typeA, Speedometer2Results(a), Speedometer2Results(b))

        if args.csv:
            writeCSV(speedometer2Breakdown(a), speedometer2Breakdown(b), args.csv)

    elif typeA == MotionMark or typeA == MotionMark1_1 or typeA == MotionMark1_1_1:
        if args.breakdown:
            dumpBreakdowns(motionMarkBreakdown(a), motionMarkBreakdown(b))

        ttest(typeA, motionMarkResults(a), motionMarkResults(b))

        if args.csv:
            writeCSV(motionMarkBreakdown(a), motionMarkBreakdown(b), args.csv)
    elif typeA == PLT5:
        if args.breakdown:
            dumpBreakdowns(plt5Breakdown(a), plt5Breakdown(b))

        ttest(typeA, PLT5Results(a), PLT5Results(b))

        if args.csv:
            writeCSV(plt5Breakdown(a), plt5Breakdown(b), args.csv)
    else:
        print "Unknown benchmark type"
        sys.exit(1)


if __name__ == "__main__":
    main()