import numpy as np
import pandas as pd
# -----------------Numerical Statistics-----------------
def format_values(key, value):

    if not isinstance(value, (int, float)):
        # if value is a time
        return str(value)

    if "Memory" in key:
        # for memory usage
        ind = 0
        unit = dict(enumerate(["B", "KB", "MB", "GB", "TB"], 0))
        while value > 1024:
            value /= 1024
            ind += 1
        return f"{value:.1f} {unit[ind]}"

    if (value * 10) % 10 == 0:
        # if value is int but in a float form with 0 at last digit
        value = int(value)
        if abs(value) >= 1000000:
            return f"{value:.5g}"
    elif abs(value) >= 1000000 or abs(value) < 0.001:
        value = f"{value:.5g}"
    elif abs(value) >= 1:
        # eliminate trailing zeros
        pre_value = float(f"{value:.4f}")
        value = int(pre_value) if (pre_value * 10) % 10 == 0 else pre_value
    elif 0.001 <= abs(value) < 1:
        value = f"{value:.4g}"
    else:
        value = str(value)

    if "%" in key:
        # for percentage, only use digits before notation sign for extreme small number
        value = f"{float(value):.1%}"
    return str(value)

def format_num_stats(data):
    """
    Format numerical statistics
    """
    overview = {
        "Approximate Distinct Count": data["nuniq"],
        "Approximate Unique (%)": data["nuniq"] / data["npres"],
        "Missing": data["nrows"] - data["npres"],
        "Missing (%)": 1 - (data["npres"] / data["nrows"]),
        "Infinite": (data["npres"] - data["nreals"]),
        "Infinite (%)": (data["npres"] - data["nreals"]) / data["nrows"],
        "Memory Size": data["mem_use"],
        "Mean": data["mean"],
        "Minimum": data["min"],
        "Maximum": data["max"],
        "Zeros": data["nzero"],
        "Zeros (%)": data["nzero"] / data["nrows"],
        "Negatives": data["nneg"],
        "Negatives (%)": data["nneg"] / data["nrows"],
    }
    data["qntls"].index = np.round(data["qntls"].index, 2)
    quantile = {
        "Minimum": data["min"],
        "5-th Percentile": data["qntls"].loc[0.05],
        "Q1": data["qntls"].loc[0.25],
        "Median": data["qntls"].loc[0.50],
        "Q3": data["qntls"].loc[0.75],
        "95-th Percentile": data["qntls"].loc[0.95],
        "Maximum": data["max"],
        "Range": data["max"] - data["min"],
        "IQR": data["qntls"].loc[0.75] - data["qntls"].loc[0.25],
    }
    descriptive = {
        "Mean": data["mean"],
        "Standard Deviation": data["std"],
        "Variance": data["std"] ** 2,
        "Sum": data["mean"] * data["npres"],
        "Skewness": float(data["skew"]),
        "Kurtosis": float(data["kurt"]),
        "Coefficient of Variation": data["std"] / data["mean"] if data["mean"] != 0 else np.nan,
    }

    # return {
    #     "Overview": {k: _format_values(k, v) for k, v in overview.items()},
    #     # "Quantile Statistics": {k: _format_values(k, v) for k, v in quantile.items()},
    #     # "Descriptive Statistics": {k: _format_values(k, v) for k, v in descriptive.items()},
    # }

    return {
    "Overview": {**{k: format_values(k, v) for k, v in overview.items()},
                 **{k: format_values(k, v) for k, v in quantile.items()},
                 **{k: format_values(k, v) for k, v in descriptive.items()}}
      }
# -----------------------------------------------------


# -----------------Categorical Statistics-----------------

def format_cat_stats(
    data
):
    """
    Format categorical statistics
    """
    stats = data['stats']
    len_stats = data['len_stats']
    letter_stats = data["letter_stats"]
    ov_stats = {
        "Approximate Distinct Count": stats["nuniq"],
        "Approximate Unique (%)": stats["nuniq"] / stats["npres"],
        "Missing": stats["nrows"] - stats["npres"],
        "Missing (%)": 1 - stats["npres"] / stats["nrows"],
        "Memory Size": stats["mem_use"],
    }
    sampled_rows = ("1st row", "2nd row", "3rd row", "4th row", "5th row")
    smpl = dict(zip(sampled_rows, stats["first_rows"]))

    # return {
    #     "Overview": {k: _format_values(k, v) for k, v in ov_stats.items()},
    #     "Length": {k: _format_values(k, v) for k, v in len_stats.items()},
    #     "Sample": {k: f"{v[:18]}..." if len(v) > 18 else v for k, v in smpl.items()},
    #     "Letter": {k: _format_values(k, v) for k, v in letter_stats.items()},
    # }
    return {
    "Overview": {**{k: format_values(k, v) for k, v in ov_stats.items()},
                 **{k: format_values(k, v) for k, v in len_stats.items()},
      }
    }
# -----------------------------------------------------


def format_ov_stats(stats) :

    nrows, ncols, npresent_cells, nrows_wo_dups, mem_use, dtypes_cnt = stats.values()
    ncells = nrows * ncols

    data = {
        "Number of Variables": ncols,
        "Number of Rows": nrows,
        "Missing Cells": float(ncells - npresent_cells),
        "Missing Cells (%)": 1 - (npresent_cells / ncells),
        "Duplicate Rows": nrows - nrows_wo_dups,
        "Duplicate Rows (%)": 1 - (nrows_wo_dups / nrows),
        "Total Size in Memory": float(mem_use),
        "Average Row Size in Memory": mem_use / nrows,
    }
    return {k: format_values(k, v) for k, v in data.items()}, dtypes_cnt


def format_insights(data):
    data_list = []
    for key, value_list in data.items():
        for item in value_list:
            for category, description in item.items():
                data_list.append({'Category': category, 'Description': description})

    insights_df = pd.DataFrame(data_list)

    insights_df['Description'] = insights_df['Description'].str.replace(r'/\*start\*/', '', regex=True)
    insights_df['Description'] = insights_df['Description'].str.replace(r'/\*end\*/', '', regex=True)

    return insights_df