import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats

from . import colors
from ._labels import labels


def truncate_text(text, max_len):
    if len(text) > max_len:
        return text[: int(max_len / 2) - 2] + "..." + text[-int(max_len / 2) + 1 :]
    else:
        return text


def monitoring(ind, shap_values, features, feature_names=None, show=True):
    """Create a SHAP monitoring plot.

    (Note this function is preliminary and subject to change!!)
    A SHAP monitoring plot is meant to display the behavior of a model
    over time. Often the shap_values given to this plot explain the loss
    of a model, so changes in a feature's impact on the model's loss over
    time can help in monitoring the model's performance.

    Parameters
    ----------
    ind : int
        Index of the feature to plot.

    shap_values : numpy.array
        Matrix of SHAP values (# samples x # features)

    features : numpy.array or pandas.DataFrame
        Matrix of feature values (# samples x # features)

    feature_names : list
        Names of the features (length # features)

    """
    if isinstance(features, pd.DataFrame):
        if feature_names is None:
            feature_names = features.columns
        features = features.values

    num_features = shap_values.shape[1]

    if feature_names is None:
        feature_names = np.array([labels["FEATURE"] % str(i) for i in range(num_features)])

    plt.figure(figsize=(10, 3))
    ys = shap_values[:, ind]
    xs = np.arange(len(ys))  # np.linspace(0, 12*2, len(ys))

    pvals = []
    inc = 50
    for i in range(inc, len(ys) - inc, inc):
        # stat, pval = scipy.stats.mannwhitneyu(v[:i], v[i:], alternative="two-sided")
        _, pval = scipy.stats.ttest_ind(ys[:i], ys[i:])
        pvals.append(pval)
    min_pval = np.min(pvals)
    min_pval_ind = float(np.argmin(pvals) * inc + inc)

    if min_pval < 0.05 / shap_values.shape[1]:
        plt.axvline(min_pval_ind, linestyle="dashed", color="#666666", alpha=0.2)

    plt.scatter(xs, ys, s=10, c=features[:, ind], cmap=colors.red_blue)

    plt.xlabel("Sample index")
    plt.ylabel(truncate_text(feature_names[ind], 30) + "\nSHAP value", size=13)
    plt.gca().xaxis.set_ticks_position("bottom")
    plt.gca().yaxis.set_ticks_position("left")
    plt.gca().spines["right"].set_visible(False)
    plt.gca().spines["top"].set_visible(False)
    cb = plt.colorbar()
    cb.outline.set_visible(False)  # type: ignore
    bbox = cb.ax.get_window_extent().transformed(plt.gcf().dpi_scale_trans.inverted())
    cb.ax.set_aspect((bbox.height - 0.7) * 20)
    cb.set_label(truncate_text(feature_names[ind], 30), size=13)
    if show:
        plt.show()