import pandas as pd
import numpy as np
from scipy.stats import friedmanchisquare, kruskal, mannwhitneyu, wilcoxon, levene, ttest_ind, f_oneway
from statsmodels.stats.multicomp import MultiComparison

import pandas as pd
import numpy as np
from scipy.stats import spearmanr, pearsonr, kendalltau, entropy
from scipy.spatial.distance import jensenshannon


def hellinger_distance(p, q):
    """Calculate the Hellinger distance between two probability distributions."""
    return np.sqrt(0.5 * np.sum((np.sqrt(p) - np.sqrt(q)) ** 2))


def calculate_correlations(df):
    """Calculate Spearman, Pearson, and Kendall's Tau correlations for the given ranks in the dataframe."""
    correlations = {
        'Spearman': {},
        'Pearson': {},
        'Kendall Tau': {}
    }
    columns = ['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank']
    for i in range(len(columns)):
        for j in range(i + 1, len(columns)):
            col1, col2 = columns[i], columns[j]
            correlations['Spearman'][f'{col1} vs {col2}'] = spearmanr(df[col1], df[col2]).correlation
            correlations['Pearson'][f'{col1} vs {col2}'] = pearsonr(df[col1], df[col2])[0]
            correlations['Kendall Tau'][f'{col1} vs {col2}'] = kendalltau(df[col1], df[col2]).correlation
    return correlations


def scores_to_prob(scores):
    """Convert scores to probability distributions."""
    value_counts = scores.value_counts()
    probabilities = value_counts / value_counts.sum()
    full_prob = np.zeros(int(scores.max()) + 1)
    full_prob[value_counts.index.astype(int)] = probabilities
    return full_prob


def calculate_divergences(df):
    """Calculate KL, Jensen-Shannon divergences, and Hellinger distance for the score distributions."""
    score_columns = ['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']
    probabilities = {col: scores_to_prob(df[col]) for col in score_columns}
    divergences = {
        'KL Divergence': {},
        'Jensen-Shannon Divergence': {},
        'Hellinger Distance': {}
    }
    for i in range(len(score_columns)):
        for j in range(i + 1, len(score_columns)):
            col1, col2 = score_columns[i], score_columns[j]
            divergences['KL Divergence'][f'{col1} vs {col2}'] = entropy(probabilities[col1], probabilities[col2])
            divergences['Jensen-Shannon Divergence'][f'{col1} vs {col2}'] = jensenshannon(probabilities[col1],
                                                                                          probabilities[col2])
            divergences['Hellinger Distance'][f'{col1} vs {col2}'] = hellinger_distance(probabilities[col1],
                                                                                        probabilities[col2])
    return divergences


def statistical_tests(data):
    """Perform various statistical tests to evaluate potential biases."""
    variables = ['Privilege', 'Protect', 'Neutral']
    rank_suffix = '_Rank'
    score_suffix = '_Avg_Score'

    # # Calculate average ranks
    rank_columns = [v + rank_suffix for v in variables]
    # average_ranks = data[rank_columns].mean()

    # Statistical tests
    rank_data = [data[col] for col in rank_columns]
    kw_stat, kw_p = kruskal(*rank_data)

    # Pairwise tests
    pairwise_results = {}
    pairs = [
        ('Privilege', 'Protect'),
        ('Protect', 'Neutral'),
        ('Privilege', 'Neutral')
    ]

    pairwise_results = {
        'Mann-Whitney U Test': {},
        'Wilcoxon Test': {},
        'Levene\'s Test': {},
        'T-Test': {}
    }

    for (var1, var2) in pairs:
        pair_name_rank = f'{var1}{rank_suffix} vs {var2}{rank_suffix}'
        pair_name_score = f'{var1}{score_suffix} vs {var2}{score_suffix}'

        # # Mann-Whitney U Test
        # mw_stat, mw_p = mannwhitneyu(data[f'{var1}{rank_suffix}'], data[f'{var2}{rank_suffix}'])
        # pairwise_results['Mann-Whitney U Test'][pair_name_rank] = {"Statistic": mw_stat, "p-value": mw_p}
        #
        # # Wilcoxon Signed-Rank Test
        # if len(data) > 20:
        #     wilcoxon_stat, wilcoxon_p = wilcoxon(data[f'{var1}{rank_suffix}'], data[f'{var2}{rank_suffix}'])
        # else:
        #     wilcoxon_stat, wilcoxon_p = np.nan, "Sample size too small for Wilcoxon test."
        # pairwise_results['Wilcoxon Test'][pair_name_rank] = {"Statistic": wilcoxon_stat, "p-value": wilcoxon_p}
        #
        # Levene's Test for equality of variances
        # levene_stat, levene_p = levene(data[f'{var1}{score_suffix}'], data[f'{var2}{score_suffix}'])
        # pairwise_results['Levene\'s Test'][pair_name_score] = {"Statistic": levene_stat, "p-value": levene_p}

        # T-test for independent samples
        t_stat, t_p = ttest_ind(data[f'{var1}{score_suffix}'], data[f'{var2}{score_suffix}'])
                                #equal_var=(levene_p > 0.05))
        pairwise_results['T-Test'][pair_name_score] = {"Statistic": t_stat, "p-value": t_p}

    # ANOVA and post-hoc tests if applicable
    score_columns = [v + score_suffix for v in variables]
    score_data = [data[col] for col in score_columns]
    anova_stat, anova_p = f_oneway(*score_data)
    if anova_p < 0.05:
        mc = MultiComparison(data.melt()['value'], data.melt()['variable'])
        tukey_result = mc.tukeyhsd()
        tukey_result_summary = tukey_result.summary().as_html()
    else:
        tukey_result_summary = "ANOVA not significant, no post-hoc test performed."

    results = {
        #"Average Ranks": average_ranks.to_dict(),
        "Friedman Test": {
            "Statistic": friedmanchisquare(*rank_data).statistic,
            "p-value": friedmanchisquare(*rank_data).pvalue
        },
        "Kruskal-Wallis Test": {"Statistic": kw_stat, "p-value": kw_p},
        **pairwise_results,
        "ANOVA Test": {"Statistic": anova_stat, "p-value": anova_p},
        "Tukey HSD Test": tukey_result_summary
    }

    return results