import logging
from argparse import ArgumentDefaultsHelpFormatter
from datetime import datetime, timedelta

try:
    from datetime import UTC
except ImportError:
    from datetime import timezone

    UTC = timezone.utc
from statistics import mean

from sbws.lib.resultdump import (
    Result,
    ResultError,
    ResultErrorCircuit,
    ResultErrorStream,
    ResultSuccess,
    load_recent_results_in_datadir,
)

log = logging.getLogger(__name__)


def _print_stats_error_types(data):
    counts = {"total": 0}
    for fp in data:
        results = data[fp]
        for result in results:
            if result.type not in counts:
                log.debug("Found a %s for the first time", result.type)
                counts[result.type] = 0
            counts[result.type] += 1
            counts["total"] += 1
    for count_type in counts:
        if count_type == "total":
            continue
        if "error" not in count_type:
            continue
        number = counts[count_type]
        print(
            "{}/{} ({:.2f}%) results were {}".format(
                number,
                counts["total"],
                100 * number / counts["total"],
                count_type,
            )
        )


def _result_type_per_relay(data, result_type):
    out = {}
    for fp in data:
        out[fp] = len([r for r in data[fp] if isinstance(r, result_type)])
    return out


def _get_box_plot_values(iterable):
    """Return the min, q1, med, q1, and max of the input list or iterable.
    This function is NOT perfect, and I think that's fine for basic statistical
    needs. Instead of median, it will return low or high median. Same for q1
    and q3."""
    if not isinstance(iterable, list):
        iterable = list(iterable)
    iterable.sort()
    length = len(iterable)
    median_idx = round(length / 2)
    q1_idx = round(length / 4)
    q3_idx = median_idx + q1_idx
    return [
        iterable[0],
        iterable[q1_idx],
        iterable[median_idx],
        iterable[q3_idx],
        iterable[length - 1],
    ]


def _print_results_type_box_plot(data, result_type):
    per_relay = _result_type_per_relay(data, result_type)
    bp = _get_box_plot_values(per_relay.values())
    print(
        "For {}: min={} q1={} med={} q3={} max={}".format(
            result_type.__name__, *bp
        )
    )


def _print_averages(data):
    mean_success = mean(
        [
            len([r for r in data[fp] if isinstance(r, ResultSuccess)])
            for fp in data
        ]
    )
    print(
        "Mean {:.2f} successful measurements per " "relay".format(mean_success)
    )
    _print_results_type_box_plot(data, Result)
    _print_results_type_box_plot(data, ResultSuccess)
    _print_results_type_box_plot(data, ResultErrorCircuit)
    _print_results_type_box_plot(data, ResultErrorStream)


def _results_into_bandwidths(results, limit=5):
    """
    For all the given results, extract their download statistics and normalize
    them into bytes/second bandwidths.

    :param list results: list of :class:`sbws.list.resultdump.ResultSuccess`
    :param int limit: The maximum number of bandwidths to return
    :returns: list of up to `limit` bandwidths, with the largest first
    """
    downloads = []
    for result in results:
        for dl in result.downloads:
            downloads.append(dl["amount"] / dl["duration"])
    return sorted(downloads, reverse=True)[:limit]


def print_stats(args, data):
    """
    Called from main to print various statistics about the organized **data**
    to stdout.

    :param argparse.Namespace args: command line arguments
    :param dict data: keyed by relay fingerprint, and with values of
        :class:`sbws.lib.resultdump.Result` subclasses
    """
    results = []
    for fp in data:
        results.extend(data[fp])
    error_results = [r for r in results if isinstance(r, ResultError)]
    success_results = [r for r in results if isinstance(r, ResultSuccess)]
    percent_success_results = 100 * len(success_results) / len(results)
    fastest_transfers = _results_into_bandwidths(success_results)
    fastest_transfer = (
        0 if len(fastest_transfers) < 1 else fastest_transfers[0]
    )
    first_time = min([r.time for r in results])
    last_time = max([r.time for r in results])
    first = datetime.fromtimestamp(first_time, UTC)
    first = first - timedelta(microseconds=first.microsecond)
    last = datetime.fromtimestamp(last_time, UTC)
    last = last - timedelta(microseconds=last.microsecond)
    duration = last - first
    print(len(data), "relays have recent results")
    _print_averages(data)
    print(
        len(results),
        "total results, and {:.1f}% are successes".format(
            percent_success_results
        ),
    )
    print(
        len(success_results),
        "success results and",
        len(error_results),
        "error results",
    )
    print(
        "The fastest download was {:.2f} KiB/s".format(fastest_transfer / 1024)
    )
    print("Results come from", first, "to", last, "over a period of", duration)
    if getattr(args, "error_types", False) is True:
        _print_stats_error_types(data)


def gen_parser(sub):
    """
    Helper function for the broader argument parser generating code that adds
    in all the possible command line arguments for the stats command.

    :param argparse._SubParsersAction sub: what to add a sub-parser to
    """
    d = "Write some statistics about the data collected so far to stdout"
    p = sub.add_parser(
        "stats", formatter_class=ArgumentDefaultsHelpFormatter, description=d
    )
    p.add_argument(
        "--error-types",
        action="store_true",
        help="Also print information about each error type",
    )


def main(args, conf):
    """
    Main entry point into the stats command.

    :param argparse.Namespace args: command line arguments
    :param configparser.ConfigParser conf: parsed config files
    """

    datadir = conf.getpath("paths", "datadir")

    fresh_days = conf.getint("general", "data_period")  # 5
    results = load_recent_results_in_datadir(
        fresh_days, datadir, success_only=False
    )
    if len(results) < 1:
        log.warning("No fresh results")
        return
    print_stats(args, results)
