#!/usr/bin/env python
"""Tool to filter, organize, compare and display benchmarking results. Usefull
for smaller datasets. It works great with a few dozen runs it is not designed to
deal with hundreds.
Requires the pandas library to be installed."""
from __future__ import print_function

import pandas as pd
from scipy import stats
import sys
import os.path
import re
import numbers
import argparse

GEOMEAN_ROW = 'Geomean difference'

def read_lit_json(filename):
    import json
    jsondata = json.load(open(filename))
    columns = []
    columnindexes = {}
    names = set()
    info_columns = ['hash']
    # Pass1: Figure out metrics (= the column index)
    if 'tests' not in jsondata:
        print("%s: Could not find toplevel 'tests' key")
        sys.exit(1)
    for test in jsondata['tests']:
        name = test.get("name")
        if name is None:
            sys.stderr.write("Error: Found unnamed test\n" % name)
            sys.exit(1)
        if name in names:
            sys.stderr.write("Error: Multiple tests with name '%s'\n" % name)
            sys.exit(1)
        if "metrics" not in test:
            print("Warning: '%s' has no metrics, skipping!" % test['name'])
            continue
        names.add(name)
        for name in test["metrics"].keys():
            if name not in columnindexes:
                columnindexes[name] = len(columns)
                columns.append(name)
        for name in test.keys():
            if name not in columnindexes and name in info_columns:
                columnindexes[name] = len(columns)
                columns.append(name)

    # Pass2 actual data construction
    nan = float('NaN')
    data = []
    testnames = []
    for test in jsondata['tests']:
        if "metrics" not in test:
            continue
        name = test['name']
        if 'shortname' in test:
            name = test['shortname']
        testnames.append(name)

        datarow = [nan] * len(columns)
        for (metricname, value) in test['metrics'].items():
            datarow[columnindexes[metricname]] = value
        for (name, value) in test.items():
            index = columnindexes.get(name)
            if index is not None:
                datarow[index] = test[name]
        data.append(datarow)
    index = pd.Index(testnames, name='Program')
    return pd.DataFrame(data=data, index=index, columns=columns)

def read_report_simple_csv(filename):
    return pd.read_csv(filename, na_values=['*'], index_col=0, header=0)

def read(name):
    if name.endswith(".json"):
        return read_lit_json(name)
    if name.endswith(".csv"):
        return read_report_simple_csv(name)
    raise Exception("Cannot determine file format");

def readmulti(filenames):
    # Read datasets
    datasetnames = []
    datasets = []
    prev_index = None
    for filename in filenames:
        data = read(filename)
        name = os.path.basename(filename)
        # drop .json/.csv suffix; TODO: Should we rather do this in the printing
        # logic?
        for ext in ['.csv', '.json']:
            if name.endswith(ext):
                name = name[:-len(ext)]
        datasets.append(data)
        suffix = ""
        count = 0
        while True:
            if name+suffix not in datasetnames:
                break
            suffix = str(count)
            count +=1

        datasetnames.append(name+suffix)
        # Warn if index names are different
        if prev_index is not None and prev_index.name != data.index.name:
            sys.stderr.write("Warning: Mismatched index names: '%s' vs '%s'\n"
                             % (prev_index.name, data.index.name))
        prev_index = data.index
    # Merge datasets
    d = pd.concat(datasets, axis=0, names=['run'], keys=datasetnames)
    return d

def get_values(values):
    # Create data view without diff column.
    if 'diff' in values.columns:
        values = values[[c for c in values.columns if c != 'diff']]
    has_two_runs = len(values.columns) == 2
    if has_two_runs:
        return (values.iloc[:,0], values.iloc[:,1])
    else:
        return (values.min(axis=1), values.max(axis=1))

def add_diff_column(metric, values, absolute_diff=False):
    values0, values1 = get_values(values[metric])
    values0.fillna(0.0, inplace=True)
    values1.fillna(0.0, inplace=True)
    # Quotient or absolute difference?
    if absolute_diff:
        values[(metric, 'diff')] = values1 - values0
    else:
        values[(metric, 'diff')] = (values1 / values0) - 1.0
    return values

def add_geomean_row(metrics, data, dataout):
    """
    Normalize values1 over values0, compute geomean difference and add a
    summary row to dataout.
    """
    gm = pd.DataFrame(index=[GEOMEAN_ROW], columns=dataout.columns,
                      dtype='float64')
    for metric in metrics:
        values0, values1 = get_values(data[metric])
        # Avoid infinite values in the diff and instead use NaN, as otherwise
        # the computation of the geometric mean will fail.
        values0 = values0.replace({0: float('NaN')})
        relative = values1 / values0
        gm_diff = stats.gmean(relative.dropna()) - 1.0
        gm[(metric, 'diff')] = gm_diff
    gm.Program = GEOMEAN_ROW
    return pd.concat([dataout, gm])

def filter_failed(data, key='Exec'):
    return data.loc[data[key] == "pass"]

def filter_short(data, key='Exec_Time', threshold=0.6):
    return data.loc[data[key] >= threshold]

def filter_same_hash(data, key='hash'):
    assert key in data.columns
    assert data.index.get_level_values(0).nunique() > 1

    return data.groupby(level=1).filter(lambda x: x[key].nunique() != 1)

def filter_blacklist(data, blacklist):
    return data.loc[~(data.index.get_level_values(1).isin(blacklist))]

def print_filter_stats(reason, before, after):
    n_before = len(before.groupby(level=1))
    n_after = len(after.groupby(level=1))
    n_filtered = n_before - n_after
    if n_filtered != 0:
        print("%s: %s (filtered out)" % (reason, n_filtered))

# Truncate a string to a maximum length by keeping a prefix, a suffix and ...
# in the middle
def truncate(string, prefix_len, suffix_len):
    return re.sub("^(.{%d}).*(.{%d})$" % (prefix_len, suffix_len),
                  "\g<1>...\g<2>", string)

# Search for common prefixes and suffixes in a list of names and return
# a (prefix,suffix) tuple that specifies how many characters can be dropped
# for the prefix/suffix. The numbers will be small enough that no name will
# become shorter than min_len characters.
def determine_common_prefix_suffix(names, min_len=8):
    if len(names) <= 1:
        return (0,0)
    name0 = names[0]
    prefix = name0
    prefix_len = len(name0)
    suffix = name0
    suffix_len = len(name0)
    shortest_name = len(name0)
    for name in names:
        if len(name) < shortest_name:
            shortest_name = len(name)
        while prefix_len > 0 and name[:prefix_len] != prefix:
            prefix_len -= 1
            prefix = name0[:prefix_len]
        while suffix_len > 0 and name[-suffix_len:] != suffix:
            suffix_len -= 1
            suffix = name0[-suffix_len:]

    if suffix[0] != '.' and suffix[0] != '_':
        suffix_len = 0
    suffix_len = max(0, min(shortest_name - prefix_len - min_len, suffix_len))
    prefix_len = max(0, min(shortest_name - suffix_len, prefix_len))
    return (prefix_len, suffix_len)

def format_relative_diff(value):
    if not isinstance(value, numbers.Integral):
        return "%4.1f%%" % (value * 100.)
    else:
        return "%-5d" % value

def print_result(d, limit_output=True, shorten_names=True, minimal_names=False,
                 show_diff_column=True, sortkey='diff', sort_by_abs=True,
                 absolute_diff=False):
    metrics = d.columns.levels[0]
    if sort_by_abs:
        d = d.sort_values(by=(metrics[0], sortkey), key=pd.Series.abs, ascending=False)
    else:
        d = d.sort_values(by=(metrics[0], sortkey), ascending=False)

    # Ensure that the columns are grouped by metric (rather than having the
    # diffs at the end of the line).
    d = d.reindex(columns=d.columns.levels[0], level=0)

    if not show_diff_column:
        # Remove all diff columns (using level=1 since level 0 is the metric).
        d.drop(labels='diff', level=1, axis=1, inplace=True)
    dataout = d
    if limit_output:
        # Take 15 topmost elements
        dataout = dataout.head(15)

    formatters = dict()
    if not absolute_diff:
        for m in metrics:
            formatters[(m, 'diff')] = format_relative_diff
    # Turn index into a column so we can format it...
    formatted_program = dataout.index.to_series()
    if shorten_names:
        def format_name(name, common_prefix, common_suffix):
            name = name[common_prefix:]
            if common_suffix > 0:
                name = name[:-common_suffix]
            return "%-45s" % truncate(name, 10, 30)


        def strip_name_fully(name):
            name = name.split('/')[-1]
            if name.endswith('.test'):
                name = name[:-5]
            return name

        # The to_string formatters argument appears to be ignored for
        # dtype=object, so transform the program column manually.
        if minimal_names:
            formatted_program = formatted_program.map(strip_name_fully)
        else:
            drop_prefix, drop_suffix = determine_common_prefix_suffix(formatted_program)
            formatted_program = formatted_program.map(lambda name: format_name(name, drop_prefix, drop_suffix))
    dataout.insert(0, 'Program', formatted_program)
    # Add the geometric mean row after we have formatted the program names
    # as it will otherwise interfere with common prefix/suffix computation.
    if show_diff_column and not absolute_diff:
        # geometric mean only makes sense for relative differences.
        dataout = add_geomean_row(metrics, d, dataout)

    def float_format(x):
        if x == '':
            return ''
        return "%6.2f" % (x,)

    pd.set_option("display.max_colwidth", 0)
    pd.set_option('display.width', 0)
    # Print an empty value instead of NaN (for the geomean row).
    out = dataout.to_string(index=False, justify='left', na_rep='',
                            float_format=float_format, formatters=formatters)
    print(out)
    print(d.describe())

def main():
    parser = argparse.ArgumentParser(prog='compare.py')
    parser.add_argument('-a', '--all', action='store_true')
    parser.add_argument('-f', '--full', action='store_true')
    parser.add_argument('-m', '--metric', action='append', dest='metrics',
                        default=[])
    parser.add_argument('--nodiff', action='store_false', dest='show_diff',
                        default=None)
    parser.add_argument('--diff', action='store_true', dest='show_diff')
    parser.add_argument('--absolute-diff', action='store_true',
                        help='Use an absolute instead of a relative difference')
    parser.add_argument('--filter-short', action='store_true',
                        dest='filter_short')
    parser.add_argument('--no-filter-failed', action='store_false',
                        dest='filter_failed', default=True)
    parser.add_argument('--filter-hash', action='store_true',
                        dest='filter_hash', default=False)
    parser.add_argument('--filter-blacklist',
                        dest='filter_blacklist', default=None)
    parser.add_argument('--merge-average', action='store_const',
                        dest='merge_function', const=pd.DataFrame.mean,
                        default=pd.DataFrame.min)
    parser.add_argument('--merge-min', action='store_const',
                        dest='merge_function', const=pd.DataFrame.min)
    parser.add_argument('--merge-max', action='store_const',
                        dest='merge_function', const=pd.DataFrame.max)
    parser.add_argument('--lhs-name', default="lhs",
                        help="Name used to describe left side in 'vs' mode")
    parser.add_argument('--rhs-name', default="rhs",
                        help="Name used to describe right side in 'vs' mode")
    parser.add_argument('files', metavar='FILE', nargs='+', help="To compare two groups of results, put 'vs' between them")
    parser.add_argument('--minimal-names', action='store_true',
                        dest='minimal_names', default=False)
    parser.add_argument('--no-abs-sort', action='store_true',
                        dest='no_abs_sort', default=False, help="Don't use abs() when sorting results")
    config = parser.parse_args()

    if config.show_diff is None:
        config.show_diff = len(config.files) > 1

    # Read inputs
    files = config.files
    if "vs" in files:
        split = files.index("vs")
        lhs = files[0:split]
        rhs = files[split+1:]

        # Filter minimum of lhs and rhs
        lhs_d = readmulti(lhs)
        lhs_merged = lhs_d.groupby(level=1).apply(config.merge_function)
        rhs_d = readmulti(rhs)
        rhs_merged = rhs_d.groupby(level=1).apply(config.merge_function)

        # Combine to new dataframe
        data = pd.concat([lhs_merged, rhs_merged], names=['l/r'],
                         keys=[config.lhs_name, config.rhs_name])
    else:
        data = readmulti(files)

    # Decide which metric to display / what is our "main" metric
    metrics = config.metrics
    if len(metrics) == 0:
        defaults = [ 'Exec_Time', 'exec_time', 'Value', 'Runtime' ]
        for defkey in defaults:
            if defkey in data.columns:
                metrics = [defkey]
                break
    if len(metrics) == 0:
        sys.stderr.write("No default metric found and none specified\n")
        sys.stderr.write("Available metrics:\n")
        for column in data.columns:
            sys.stderr.write("\t%s\n" % column)
        sys.exit(1)
    for metric in metrics:
        problem = False
        if metric not in data.columns:
            sys.stderr.write("Unknown metric '%s'\n" % metric)
            problem = True
        if problem:
            sys.exit(1)

    # Filter data
    proggroup = data.groupby(level=1)
    initial_size = len(proggroup.indices)
    print("Tests: %s" % (initial_size,))
    if config.filter_failed and hasattr(data, 'Exec'):
        newdata = filter_failed(data)
        print_filter_stats("Failed", data, newdata)
        newdata = newdata.drop('Exec', 1)
        data = newdata
    if config.filter_short:
        newdata = filter_short(data, metric)
        print_filter_stats("Short Running", data, newdata)
        data = newdata
    if config.filter_hash and 'hash' in data.columns and \
       data.index.get_level_values(0).nunique() > 1:
        newdata = filter_same_hash(data)
        print_filter_stats("Same hash", data, newdata)
        data = newdata
    if config.filter_blacklist:
        blacklist = open(config.filter_blacklist).readlines()
        blacklist = [line.strip() for line in blacklist]
        newdata = filter_blacklist(data, blacklist)
        print_filter_stats("In Blacklist", data, newdata)
        data = newdata
    final_size = len(data.groupby(level=1))
    if final_size != initial_size:
        print("Remaining: %d" % (final_size,))

    # Reduce / add columns
    print("Metric: %s" % (",".join(metrics),))
    if len(metrics) > 0:
        data = data[metrics]

    data = data.unstack(level=0)

    for metric in data.columns.levels[0]:
        data = add_diff_column(metric, data, absolute_diff=config.absolute_diff)

    sortkey = 'diff'
    # TODO: should we still be sorting by diff even if the diff is hidden?
    if len(config.files) == 1:
        sortkey = data.columns.levels[1][0]

    # Print data
    print("")
    shorten_names = not config.full
    limit_output = (not config.all) and (not config.full)
    print_result(data, limit_output, shorten_names, config.minimal_names,
                 config.show_diff, sortkey, config.no_abs_sort,
                 config.absolute_diff)


if __name__ == "__main__":
    main()