from collections import defaultdict
from datetime import datetime, timedelta
import sys

import numpy
import tqdm

from labours.objects import DevDay
from labours.plotting import apply_plot_style, deploy_plot, get_plot_path, import_pyplot
from labours.utils import _format_number


def show_devs(args, name, start_date, end_date, people, days, max_people=50):
    from scipy.signal import convolve, slepian

    if len(people) > max_people:
        print("Picking top %s developers by commit count" % max_people)
        # pick top N developers by commit count
        commits = defaultdict(int)
        for devs in days.values():
            for dev, stats in devs.items():
                commits[dev] += stats.Commits
        commits = sorted(((v, k) for k, v in commits.items()), reverse=True)
        chosen_people = {people[k] for _, k in commits[:max_people]}
    else:
        chosen_people = set(people)
    dists, devseries, devstats, route = order_commits(chosen_people, days, people)
    route_map = {v: i for i, v in enumerate(route)}
    # determine clusters
    clusters = hdbscan_cluster_routed_series(dists, route)
    keys = list(devseries.keys())
    route = [keys[node] for node in route]
    print("Plotting")
    # smooth time series
    start_date = datetime.fromtimestamp(start_date)
    start_date = datetime(start_date.year, start_date.month, start_date.day)
    end_date = datetime.fromtimestamp(end_date)
    end_date = datetime(end_date.year, end_date.month, end_date.day)
    size = (end_date - start_date).days + 1
    plot_x = [start_date + timedelta(days=i) for i in range(size)]
    resolution = 64
    window = slepian(size // resolution, 0.5)
    final = numpy.zeros((len(devseries), size), dtype=numpy.float32)
    for i, s in enumerate(devseries.values()):
        arr = numpy.array(s).transpose()
        full_history = numpy.zeros(size, dtype=numpy.float32)
        mask = arr[0] < size
        full_history[arr[0][mask]] = arr[1][mask]
        final[route_map[i]] = convolve(full_history, window, "same")

    matplotlib, pyplot = import_pyplot(args.backend, args.style)
    pyplot.rcParams["figure.figsize"] = (32, 16)
    pyplot.rcParams["font.size"] = args.font_size
    prop_cycle = pyplot.rcParams["axes.prop_cycle"]
    colors = prop_cycle.by_key()["color"]
    fig, axes = pyplot.subplots(final.shape[0], 1)
    backgrounds = ("#C4FFDB", "#FFD0CD") if args.background == "white" else ("#05401C", "#40110E")
    max_cluster = numpy.max(clusters)
    for ax, series, cluster, dev_i in zip(axes, final, clusters, route):
        if cluster >= 0:
            color = colors[cluster % len(colors)]
            i = 1
            while color == "#777777":
                color = colors[(max_cluster + i) % len(colors)]
                i += 1
        else:
            # outlier
            color = "#777777"
        ax.fill_between(plot_x, series, color=color)
        ax.set_axis_off()
        author = people[dev_i]
        ax.text(0.03, 0.5, author[:36] + (author[36:] and "..."),
                horizontalalignment="right", verticalalignment="center",
                transform=ax.transAxes, fontsize=args.font_size,
                color="black" if args.background == "white" else "white")
        ds = devstats[dev_i]
        stats = "%5d %8s %8s" % (ds[0], _format_number(ds[1] - ds[2]), _format_number(ds[3]))
        ax.text(0.97, 0.5, stats,
                horizontalalignment="left", verticalalignment="center",
                transform=ax.transAxes, fontsize=args.font_size, family="monospace",
                backgroundcolor=backgrounds[ds[1] <= ds[2]],
                color="black" if args.background == "white" else "white")
    axes[0].text(0.97, 1.75, " cmts    delta  changed",
                 horizontalalignment="left", verticalalignment="center",
                 transform=axes[0].transAxes, fontsize=args.font_size, family="monospace",
                 color="black" if args.background == "white" else "white")
    axes[-1].set_axis_on()
    target_num_labels = 12
    num_months = (end_date.year - start_date.year) * 12 + end_date.month - start_date.month
    interval = int(numpy.ceil(num_months / target_num_labels))
    if interval >= 8:
        interval = int(numpy.ceil(num_months / (12 * target_num_labels)))
        axes[-1].xaxis.set_major_locator(matplotlib.dates.YearLocator(base=max(1, interval // 12)))
        axes[-1].xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
    else:
        axes[-1].xaxis.set_major_locator(matplotlib.dates.MonthLocator(interval=interval))
        axes[-1].xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m"))
    for tick in axes[-1].xaxis.get_major_ticks():
        tick.label.set_fontsize(args.font_size)
    axes[-1].spines["left"].set_visible(False)
    axes[-1].spines["right"].set_visible(False)
    axes[-1].spines["top"].set_visible(False)
    axes[-1].get_yaxis().set_visible(False)
    axes[-1].set_facecolor((1.0,) * 3 + (0.0,))

    title = ("%s commits" % name) if not args.output else ""
    if args.mode == "all" and args.output:
        output = get_plot_path(args.output, "time_series")
    else:
        output = args.output
    deploy_plot(title, output, args.background)


def order_commits(chosen_people, days, people):
    from seriate import seriate
    try:
        from fastdtw import fastdtw
    except ImportError as e:
        print("Cannot import fastdtw: %s\nInstall it from https://github.com/slaypni/fastdtw" % e)
        sys.exit(1)
    # FIXME(vmarkovtsev): remove once https://github.com/slaypni/fastdtw/pull/28 is merged&released
    try:
        sys.modules["fastdtw.fastdtw"].__norm = lambda p: lambda a, b: numpy.linalg.norm(
            numpy.atleast_1d(a) - numpy.atleast_1d(b), p)
    except KeyError:
        # the native extension does not have this bug
        pass

    devseries = defaultdict(list)
    devstats = defaultdict(lambda: DevDay(0, 0, 0, 0, {}))
    for day, devs in sorted(days.items()):
        for dev, stats in devs.items():
            if people[dev] in chosen_people:
                devseries[dev].append((day, stats.Commits))
                devstats[dev] = devstats[dev].add(stats)
    print("Calculating the distance matrix")
    # max-normalize the time series using a sliding window
    series = list(devseries.values())
    for i, s in enumerate(series):
        arr = numpy.array(s).transpose().astype(numpy.float32)
        arr[1] /= arr[1].sum()
        series[i] = arr.transpose()
    # calculate the distance matrix using dynamic time warping
    dists = numpy.full((len(series),) * 2, -100500, dtype=numpy.float32)
    # TODO: what's the total for this progress bar?
    with tqdm.tqdm() as pb:
        for x, serx in enumerate(series):
            dists[x, x] = 0
            for y, sery in enumerate(series[x + 1:], start=x + 1):
                min_day = int(min(serx[0][0], sery[0][0]))
                max_day = int(max(serx[-1][0], sery[-1][0]))
                arrx = numpy.zeros(max_day - min_day + 1, dtype=numpy.float32)
                arry = numpy.zeros_like(arrx)
                arrx[serx[:, 0].astype(int) - min_day] = serx[:, 1]
                arry[sery[:, 0].astype(int) - min_day] = sery[:, 1]
                # L1 norm
                dist, _ = fastdtw(arrx, arry, radius=5, dist=1)
                dists[x, y] = dists[y, x] = dist
                pb.update()
    print("Ordering the series")
    route = seriate(dists)
    return dists, devseries, devstats, route


def hdbscan_cluster_routed_series(dists, route):
    try:
        from hdbscan import HDBSCAN
    except ImportError as e:
        print("Cannot import hdbscan: %s" % e)
        sys.exit(1)

    opt_dist_chain = numpy.cumsum(numpy.array(
        [0] + [dists[route[i], route[i + 1]] for i in range(len(route) - 1)]))
    clusters = HDBSCAN(min_cluster_size=2).fit_predict(opt_dist_chain[:, numpy.newaxis])
    return clusters


def show_devs_efforts(args, name, start_date, end_date, people, days, max_people):
    from scipy.signal import convolve, slepian

    start_date = datetime.fromtimestamp(start_date)
    start_date = datetime(start_date.year, start_date.month, start_date.day)
    end_date = datetime.fromtimestamp(end_date)
    end_date = datetime(end_date.year, end_date.month, end_date.day)

    efforts_by_dev = defaultdict(int)
    for day, devs in days.items():
        for dev, stats in devs.items():
            efforts_by_dev[dev] += stats.Added + stats.Removed + stats.Changed
    if len(efforts_by_dev) > max_people:
        chosen = {v for k, v in sorted(
            ((v, k) for k, v in efforts_by_dev.items()), reverse=True)[:max_people]}
        print("Warning: truncated people to the most active %d" % max_people)
    else:
        chosen = set(efforts_by_dev)
    chosen_efforts = sorted(((efforts_by_dev[k], k) for k in chosen), reverse=True)
    chosen_order = {k: i for i, (_, k) in enumerate(chosen_efforts)}

    efforts = numpy.zeros((len(chosen) + 1, (end_date - start_date).days + 1), dtype=numpy.float32)
    for day, devs in days.items():
        if day < efforts.shape[1]:
            for dev, stats in devs.items():
                dev = chosen_order.get(dev, len(chosen_order))
                efforts[dev][day] += stats.Added + stats.Removed + stats.Changed
    efforts_cum = numpy.cumsum(efforts, axis=1)
    window = slepian(10, 0.5)
    window /= window.sum()
    for e in (efforts, efforts_cum):
        for i in range(e.shape[0]):
            ending = e[i][-len(window) * 2:].copy()
            e[i] = convolve(e[i], window, "same")
            e[i][-len(ending):] = ending
    matplotlib, pyplot = import_pyplot(args.backend, args.style)
    plot_x = [start_date + timedelta(days=i) for i in range(efforts.shape[1])]

    people = [people[k] for _, k in chosen_efforts] + ["others"]
    for i, name in enumerate(people):
        if len(name) > 40:
            people[i] = name[:37] + "..."

    polys = pyplot.stackplot(plot_x, efforts_cum, labels=people)
    if len(polys) == max_people + 1:
        polys[-1].set_hatch("/")
    polys = pyplot.stackplot(plot_x, -efforts * efforts_cum.max() / efforts.max())
    if len(polys) == max_people + 1:
        polys[-1].set_hatch("/")
    yticks = []
    for tick in pyplot.gca().yaxis.iter_ticks():
        if tick[1] >= 0:
            yticks.append(tick[1])
    pyplot.gca().yaxis.set_ticks(yticks)
    legend = pyplot.legend(loc=2, ncol=2, fontsize=args.font_size)
    apply_plot_style(pyplot.gcf(), pyplot.gca(), legend, args.background,
                     args.font_size, args.size or "16,10")
    if args.mode == "all" and args.output:
        output = get_plot_path(args.output, "efforts")
    else:
        output = args.output
    deploy_plot("Efforts through time (changed lines of code)", output, args.background)