#!/usr/bin/env python3

import itertools
import numpy as np
import matplotlib.pyplot as plt
import re


def is_state(aggregate, name):
    """Return true if name is a state and not UNINITIALIZED."""
    return aggregate[name]["isa"] == "state" and name != "UNINITIALIZED"


def plot_states(model, aggregate):
    keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
    data = [aggregate[key]["means"] for key in keys]
    mdata = [int(model["state"][key]["power"]["static"]) for key in keys]
    boxplot(keys, data, "Zustand", "µW", modeldata=mdata)


def plot_transitions(model, aggregate):
    keys = [
        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
    ]
    data = [aggregate[key]["rel_energies"] for key in keys]
    mdata = [int(model["transition"][key]["rel_energy"]["static"]) for key in keys]
    boxplot(keys, data, "Transition", "pJ (rel)", modeldata=mdata)
    data = [aggregate[key]["energies"] for key in keys]
    mdata = [int(model["transition"][key]["energy"]["static"]) for key in keys]
    boxplot(keys, data, "Transition", "pJ", modeldata=mdata)


def plot_states_duration(model, aggregate):
    keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
    data = [aggregate[key]["durations"] for key in keys]
    boxplot(keys, data, "Zustand", "µs")


def plot_transitions_duration(model, aggregate):
    keys = [
        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
    ]
    data = [aggregate[key]["durations"] for key in keys]
    boxplot(keys, data, "Transition", "µs")


def plot_transitions_timeout(model, aggregate):
    keys = [
        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
    ]
    data = [aggregate[key]["timeouts"] for key in keys]
    boxplot(keys, data, "Timeout", "µs")


def plot_states_clips(model, aggregate):
    keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
    data = [np.array([100]) * aggregate[key]["clip_rate"] for key in keys]
    boxplot(keys, data, "Zustand", "% Clipping")


def plot_transitions_clips(model, aggregate):
    keys = [
        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
    ]
    data = [np.array([100]) * aggregate[key]["clip_rate"] for key in keys]
    boxplot(keys, data, "Transition", "% Clipping")


def plot_substate_thresholds(model, aggregate):
    keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
    data = [aggregate[key]["sub_thresholds"] for key in keys]
    boxplot(keys, data, "Zustand", "substate threshold (mW/dmW)")


def plot_histogram(data):
    n, bins, patches = plt.hist(data, 1000, normed=1, facecolor="green", alpha=0.75)
    plt.show()


def plot_states_param(model, aggregate):
    keys = [
        key
        for key in sorted(aggregate.keys())
        if aggregate[key]["isa"] == "state" and key[0] != "UNINITIALIZED"
    ]
    data = [aggregate[key]["means"] for key in keys]
    mdata = [int(model["state"][key[0]]["power"]["static"]) for key in keys]
    boxplot(keys, data, "Transition", "µW", modeldata=mdata)


def plot_attribute(
    aggregate, attribute, attribute_unit="", key_filter=lambda x: True, **kwargs
):
    """
    Boxplot measurements of a single attribute according to the partitioning provided by aggregate.

    Plots aggregate[*][attribute] with one column per aggregate key.

    arguments:
    aggregate -- measurements. aggregate[*][attribute] must be list of numbers
    attribute -- attribute to plot, e.g. 'power' or 'duration'
    attribute_init -- attribute unit for display in X axis legend
    key_filter -- if set: Only plot keys where key_filter(key) returns True
    """
    keys = list(filter(key_filter, sorted(aggregate.keys())))
    data = [aggregate[key][attribute] for key in keys]
    boxplot(keys, data, attribute, attribute_unit, **kwargs)


def plot_substate_thresholds_p(model, aggregate):
    keys = [
        key
        for key in sorted(aggregate.keys())
        if aggregate[key]["isa"] == "state" and key[0] != "UNINITIALIZED"
    ]
    data = [aggregate[key]["sub_thresholds"] for key in keys]
    boxplot(keys, data, "Zustand", "% Clipping")


def plot_y(Y, **kwargs):
    if "family" in kwargs and kwargs["family"]:
        plot_xy(None, Y, **kwargs)
    else:
        plot_xy(np.arange(len(Y)), Y, **kwargs)


def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None, family=False):
    fig, ax1 = plt.subplots(figsize=(10, 6))
    if title is not None:
        fig.canvas.set_window_title(title)
    if xlabel is not None:
        ax1.set_xlabel(xlabel)
    if ylabel is not None:
        ax1.set_ylabel(ylabel)
    plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
    if family:
        cm = plt.get_cmap("brg", len(Y))
        for i, YY in enumerate(Y):
            if X:
                XX = X[i]
            else:
                XX = np.arange(len(YY))
            plt.plot(XX, YY, "-", markersize=2, color=cm(i))
    else:
        plt.plot(X, Y, "bo", markersize=2)
    if output:
        plt.savefig(output)
        with open("{}.txt".format(output), "w") as f:
            print("X Y", file=f)
            for i in range(len(X)):
                print("{} {}".format(X[i], Y[i]), file=f)
        print(f"plot saved to {output}")
    else:
        plt.show()


def _param_slice_eq(a, b, index):
    return (*a[1][:index], *a[1][index + 1 :]) == (
        *b[1][:index],
        *b[1][index + 1 :],
    ) and a[0] == b[0]


def plot_param(
    model,
    state_or_trans,
    attribute,
    param_idx,
    xlabel=None,
    ylabel=None,
    title=None,
    extra_function=None,
    output=None,
):
    fig, ax1 = plt.subplots(figsize=(10, 6))
    if title is not None:
        fig.canvas.set_window_title(title)
    if xlabel is not None:
        ax1.set_xlabel(xlabel)
    if ylabel is not None:
        ax1.set_ylabel(ylabel)
    plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)

    param_name = model.param_name(param_idx)

    function_filename = "plot_param_{}_{}_{}.txt".format(
        state_or_trans, attribute, param_name
    )
    data_filename_base = "measurements_{}_{}_{}".format(
        state_or_trans, attribute, param_name
    )

    param_model, param_info = model.get_fitted()

    by_other_param = {}

    XX = []

    legend_sanitizer = re.compile(r"[^0-9a-zA-Z]+")

    for k, v in model.by_param.items():
        if k[0] == state_or_trans:
            other_param_key = (*k[1][:param_idx], *k[1][param_idx + 1 :])
            if other_param_key not in by_other_param:
                by_other_param[other_param_key] = {"X": [], "Y": []}
            by_other_param[other_param_key]["X"].extend(
                [float(k[1][param_idx])] * len(v[attribute])
            )
            by_other_param[other_param_key]["Y"].extend(v[attribute])
            XX.extend(by_other_param[other_param_key]["X"])

    XX = np.array(XX)
    x_range = int((XX.max() - XX.min()) * 10)
    xsp = np.linspace(XX.min(), XX.max(), x_range)
    YY = [xsp]
    YY_legend = [param_name]
    YY2 = []
    YY2_legend = []

    cm = plt.get_cmap("brg", len(by_other_param))
    for i, k in sorted(enumerate(by_other_param), key=lambda x: x[1]):
        v = by_other_param[k]
        v["X"] = np.array(v["X"])
        v["Y"] = np.array(v["Y"])
        plt.plot(v["X"], v["Y"], "ro", color=cm(i), markersize=3)
        YY2_legend.append(legend_sanitizer.sub("_", "X_{}".format(k)))
        YY2.append(v["X"])
        YY2_legend.append(legend_sanitizer.sub("_", "Y_{}".format(k)))
        YY2.append(v["Y"])

        sanitized_k = legend_sanitizer.sub("_", str(k))
        with open("{}_{}.txt".format(data_filename_base, sanitized_k), "w") as f:
            print("X Y", file=f)
            for i in range(len(v["X"])):
                print("{} {}".format(v["X"][i], v["Y"][i]), file=f)

        # x_range = int((v['X'].max() - v['X'].min()) * 10)
        # xsp = np.linspace(v['X'].min(), v['X'].max(), x_range)
        if param_model:
            ysp = []
            for x in xsp:
                xarg = [*k[:param_idx], x, *k[param_idx:]]
                ysp.append(param_model(state_or_trans, attribute, param=xarg))
            plt.plot(xsp, ysp, "r-", color=cm(i), linewidth=0.5)
            YY.append(ysp)
            YY_legend.append(legend_sanitizer.sub("_", "regr_{}".format(k)))
        if extra_function is not None:
            ysp = []
            with np.errstate(divide="ignore", invalid="ignore"):
                for x in xsp:
                    xarg = [*k[:param_idx], x, *k[param_idx:]]
                    ysp.append(extra_function(*xarg))
            plt.plot(xsp, ysp, "r--", color=cm(i), linewidth=1, dashes=(3, 3))
            YY.append(ysp)
            YY_legend.append(legend_sanitizer.sub("_", "symb_{}".format(k)))

    with open(function_filename, "w") as f:
        print(" ".join(YY_legend), file=f)
        for elem in np.array(YY).T:
            print(" ".join(map(str, elem)), file=f)

    print(data_filename_base, function_filename)
    if output:
        plt.savefig(output)
        print(f"plot saved to {output}")
    else:
        plt.show()


def plot_param_fit(
    function,
    name,
    fitfunc,
    funp,
    parameters,
    datatype,
    index,
    X,
    Y,
    xaxis=None,
    yaxis=None,
):
    fig, ax1 = plt.subplots(figsize=(10, 6))
    fig.canvas.set_window_title("fit %s" % (function))
    plt.subplots_adjust(left=0.14, right=0.99, top=0.99, bottom=0.14)

    x_range = X[index].max() - X[index].min() + 1

    if x_range > 100 and x_range < 500:
        xsp = np.linspace(X[index].min(), X[index].max(), x_range)
    else:
        xsp = np.linspace(X[index].min(), X[index].max(), 100)
        x_range = 100

    if xaxis is not None:
        ax1.set_xlabel(xaxis)
    else:
        ax1.set_xlabel(parameters[index])
    if yaxis is not None:
        ax1.set_ylabel(yaxis)
    else:
        ax1.set_ylabel("%s %s" % (name, datatype))

    otherparams = list(set(itertools.product(*X[:index], *X[index + 1 :])))
    cm = plt.get_cmap("brg", len(otherparams))
    for i in range(len(otherparams)):
        elem = otherparams[i]
        color = cm(i)

        tt = np.full((len(X[index])), True, dtype=bool)
        for k in range(len(parameters)):
            if k < index:
                tt &= X[k] == elem[k]
            elif k > index:
                tt &= X[k] == elem[k - 1]

        plt.plot(X[index][tt], Y[tt], "rx", color=color)

        xarg = [np.array([x] * x_range) for x in elem[:index]]
        xarg.append(xsp)
        xarg.extend([np.array([x] * x_range) for x in elem[index:]])
        plt.plot(xsp, fitfunc(funp, xarg), "r-", color=color)
    plt.show()


def boxplot(ticks, measurements, xlabel="", ylabel="", modeldata=None, output=None):
    fig, ax1 = plt.subplots(figsize=(10, 6))
    fig.canvas.set_window_title("DriverEval")
    plt.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1)

    bp = plt.boxplot(measurements, notch=0, sym="+", vert=1, whis=1.5)
    plt.setp(bp["boxes"], color="black")
    plt.setp(bp["whiskers"], color="black")
    plt.setp(bp["fliers"], color="red", marker="+")

    ax1.yaxis.grid(True, linestyle="-", which="major", color="lightgrey", alpha=0.5)

    ax1.set_axisbelow(True)
    # ax1.set_title('DriverEval')
    ax1.set_xlabel(xlabel)
    ax1.set_ylabel(ylabel)

    numBoxes = len(measurements)

    xtickNames = plt.setp(ax1, xticklabels=ticks)
    plt.setp(xtickNames, rotation=0, fontsize=10)

    # boxColors = ['darkkhaki', 'royalblue']
    medians = list(range(numBoxes))
    for i in range(numBoxes):
        box = bp["boxes"][i]
        boxX = []
        boxY = []
        for j in range(5):
            boxX.append(box.get_xdata()[j])
            boxY.append(box.get_ydata()[j])
        # boxCoords = list(zip(boxX, boxY))
        # Alternate between Dark Khaki and Royal Blue
        # k = i % 2
        # boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
        # ax1.add_patch(boxPolygon)
        # Now draw the median lines back over what we just filled in
        med = bp["medians"][i]
        medianX = []
        medianY = []
        for j in range(2):
            medianX.append(med.get_xdata()[j])
            medianY.append(med.get_ydata()[j])
            plt.plot(medianX, medianY, "k")
            medians[i] = medianY[0]
        # Finally, overplot the sample averages, with horizontal alignment
        # in the center of each box
        plt.plot(
            [np.average(med.get_xdata())],
            [np.average(measurements[i])],
            color="w",
            marker="*",
            markeredgecolor="k",
        )
        if modeldata:
            plt.plot(
                [np.average(med.get_xdata())],
                [modeldata[i]],
                color="w",
                marker="o",
                markeredgecolor="k",
            )

    pos = np.arange(numBoxes) + 1
    upperLabels = [str(np.round(s, 2)) for s in medians]
    # weights = ['bold', 'semibold']
    for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
        # k = tick % 2
        y0, y1 = ax1.get_ylim()
        textpos = y0 + (y1 - y0) * 0.97
        # ypos = ax1.get_ylim()[0]
        ax1.text(
            pos[tick],
            textpos,
            upperLabels[tick],
            horizontalalignment="center",
            size="small",
            color="royalblue",
        )

    if output:
        plt.savefig(output)
        with open("{}.txt".format(output), "w") as f:
            print("X Y", file=f)
            for i, data in enumerate(measurements):
                for value in data:
                    print("{} {}".format(ticks[i], value), file=f)
        print(f"plot saved to {output}")
    else:
        plt.show()