"""
This module provides functions for generating statistics for Maidenhead DGGS cells.
"""

import math
import pandas as pd
import numpy as np
import argparse
import geopandas as gpd
from vgrid.utils.constants import (
    AUTHALIC_AREA,
    DGGS_TYPES,
    VMIN_QUAD,
    VMAX_QUAD,
    VCENTER_QUAD,
)
from vgrid.generator.maidenheadgrid import maidenheadgrid
from vgrid.utils.geometry import check_crossing_geom, characteristic_length_scale
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import TwoSlopeNorm
from vgrid.dggs import maidenhead
from vgrid.utils.io import validate_maidenhead_resolution

min_res = DGGS_TYPES["maidenhead"]["min_res"]
max_res = DGGS_TYPES["maidenhead"]["max_res"]


def maidenhead_metrics(
    resolution, unit: str = "m"
):  # length unit is km, area unit is km2
    """
    Calculate metrics for Maidenhead DGGS cells.

    Args:
        resolution: Resolution level (0-4)
        unit: 'm' or 'km' for length; area will be 'm^2' or 'km^2'

    Returns:
        tuple: (num_cells, avg_edge_len_in_unit, avg_cell_area_in_unit_squared)
    """
    # normalize and validate unit
    unit = unit.strip().lower()
    if unit not in {"m", "km"}:
        raise ValueError("unit must be one of {'m','km'}")

    # Maidenhead grid has 324 (18x18) cells at base level
    # Each subdivision adds 10x10 = 100 cells per parent cell
    num_cells = maidenhead.num_cells(resolution)
    # Calculate area in km² first
    avg_cell_area = AUTHALIC_AREA / num_cells
    avg_edge_len = math.sqrt(avg_cell_area)
    cls = characteristic_length_scale(avg_cell_area, unit=unit)

    # Convert to requested unit
    if unit == "km":
        avg_cell_area = avg_cell_area / (10**6)  # Convert km² to m²
        avg_edge_len = avg_edge_len / (10**3)  # Convert km to m

    return num_cells, avg_edge_len, avg_cell_area, cls


def maidenheadstats(unit: str = "m"):  # length unit is km, area unit is km2
    """
    Generate statistics for Maidenhead DGGS cells.

    Args:
        unit: 'm' or 'km' for length; area will be 'm^2' or 'km^2'

    Returns:
        pandas.DataFrame: DataFrame containing Maidenhead DGGS statistics with columns:
            - resolution: Resolution level (0-4)
            - number_of_cells: Number of cells at each resolution
            - avg_edge_len_{unit}: Average edge length in the given unit
            - avg_cell_area_{unit}2: Average cell area in the squared unit
    """
    # normalize and validate unit
    unit = unit.strip().lower()
    if unit not in {"m", "km"}:
        raise ValueError("unit must be one of {'m','km'}")

    # Initialize lists to store data
    resolutions = []
    num_cells_list = []
    avg_edge_lens = []
    avg_cell_areas = []
    cls_list = []
    for res in range(min_res, max_res + 1):
        num_cells, avg_edge_len, avg_cell_area, cls = maidenhead_metrics(
            res, unit=unit
        )  # length unit is km, area unit is km2
        resolutions.append(res)
        num_cells_list.append(num_cells)
        avg_edge_lens.append(avg_edge_len)
        avg_cell_areas.append(avg_cell_area)
        cls_list.append(cls)
    # Create DataFrame
    # Build column labels with unit awareness (lower case)
    avg_edge_len = f"avg_edge_len_{unit}"
    unit_area_label = {"m": "m2", "km": "km2"}[unit]
    avg_cell_area = f"avg_cell_area_{unit_area_label}"
    cls_label = f"cls_{unit}"
    df = pd.DataFrame(
        {
            "resolution": resolutions,
            "number_of_cells": num_cells_list,
            avg_edge_len: avg_edge_lens,
            avg_cell_area: avg_cell_areas,
            cls_label: cls_list,
        }
    )

    return df


def maidenheadstats_cli():
    """
    Command-line interface for generating Maidenhead DGGS statistics.

    CLI options:
      -unit, --unit {m,km}
    """
    parser = argparse.ArgumentParser(add_help=False)
    parser.add_argument(
        "-unit", "--unit", dest="unit", choices=["m", "km"], default="m"
    )
    args, _ = parser.parse_known_args()  # type: ignore

    unit = args.unit

    # Get the DataFrame
    df = maidenheadstats(unit=unit)

    # Display the DataFrame
    print(df)


def maidenheadinspect(resolution):
    """
    Generate comprehensive inspection data for Maidenhead DGGS cells at a given resolution.

    This function creates a detailed analysis of Maidenhead cells including area variations,
    compactness measures, and dateline crossing detection.

    Args:
        resolution: Maidenhead resolution level (1-4)

    Returns:
        geopandas.GeoDataFrame: DataFrame containing Maidenhead cell inspection data with columns:
            - maidenhead: Maidenhead cell ID
            - resolution: Resolution level
            - geometry: Cell geometry
            - cell_area: Cell area in square meters
            - cell_perimeter: Cell perimeter in meters
            - crossed: Whether cell crosses the dateline
            - norm_area: Normalized area (cell_area / mean_area)
            - ipq: Isoperimetric Quotient compactness
            - zsc: Zonal Standardized Compactness
    """
    resolution = validate_maidenhead_resolution(resolution)
    maidenhead_gpd = maidenheadgrid(resolution, output_format="gpd")
    maidenhead_gpd["crossed"] = maidenhead_gpd["geometry"].apply(check_crossing_geom)
    mean_area = maidenhead_gpd["cell_area"].mean()
    # Calculate normalized area
    maidenhead_gpd["norm_area"] = maidenhead_gpd["cell_area"] / mean_area
    # Calculate IPQ compactness using the standard formula: CI = 4πA/P²
    maidenhead_gpd["ipq"] = (
        4
        * np.pi
        * maidenhead_gpd["cell_area"]
        / (maidenhead_gpd["cell_perimeter"] ** 2)
    )
    # Calculate zonal standardized compactness
    maidenhead_gpd["zsc"] = (
        np.sqrt(
            4 * np.pi * maidenhead_gpd["cell_area"]
            - np.power(maidenhead_gpd["cell_area"], 2) / np.power(6378137, 2)
        )
        / maidenhead_gpd["cell_perimeter"]
    )
    return maidenhead_gpd


def maidenhead_norm_area(maidenhead_gpd):
    """
    Plot normalized area map for Maidenhead cells.

    This function creates a visualization showing how Maidenhead cell areas vary relative
    to the mean area across the globe, highlighting areas of distortion.

    Args:
        maidenhead_gpd: GeoDataFrame from maidenheadinspect function
    """
    fig, ax = plt.subplots(figsize=(10, 5))
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("bottom", size="5%", pad=0.1)
    vmin, vmax, vcenter = (
        maidenhead_gpd["norm_area"].min(),
        maidenhead_gpd["norm_area"].max(),
        1,
    )
    norm = TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)
    maidenhead_gpd = maidenhead_gpd[
        ~maidenhead_gpd["crossed"]
    ]  # remove cells that cross the dateline
    maidenhead_gpd.to_crs("proj=moll").plot(
        column="norm_area",
        ax=ax,
        norm=norm,
        legend=True,
        cax=cax,
        cmap="RdYlBu_r",
        legend_kwds={"label": "cell area/mean cell area", "orientation": "horizontal"},
    )
    world_countries = gpd.read_file(
        "https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/world_countries.geojson"
    )
    world_countries.boundary.to_crs("proj=moll").plot(
        color=None, edgecolor="black", linewidth=0.2, ax=ax
    )
    ax.axis("off")
    cb_ax = fig.axes[1]
    cb_ax.tick_params(labelsize=14)
    cb_ax.set_xlabel(xlabel="Maidenhead Normalized Area", fontsize=14)
    ax.margins(0)
    ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
    plt.tight_layout()


def maidenhead_compactness(maidenhead_gpd):
    """
    Plot IPQ compactness map for Maidenhead cells.

    This function creates a visualization showing the Isoperimetric Quotient (IPQ)
    compactness of Maidenhead cells across the globe. IPQ measures how close each cell
    is to being circular, with values closer to 0.785 indicating more regular squares.

    Args:
        maidenhead_gpd: GeoDataFrame from maidenheadinspect function
    """
    fig, ax = plt.subplots(figsize=(10, 5))
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("bottom", size="5%", pad=0.1)
    # vmin, vmax, vcenter = maidenhead_gpd['ipq'].min(), maidenhead_gpd['ipq'].max(), np.mean([maidenhead_gpd['ipq'].min(), maidenhead_gpd['ipq'].max()])
    norm = TwoSlopeNorm(vmin=VMIN_QUAD, vcenter=VCENTER_QUAD, vmax=VMAX_QUAD)
    maidenhead_gpd = maidenhead_gpd[
        ~maidenhead_gpd["crossed"]
    ]  # remove cells that cross the dateline
    maidenhead_gpd.to_crs("proj=moll").plot(
        column="ipq",
        ax=ax,
        norm=norm,
        legend=True,
        cax=cax,
        cmap="viridis",
        legend_kwds={"orientation": "horizontal"},
    )
    world_countries = gpd.read_file(
        "https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/world_countries.geojson"
    )
    world_countries.boundary.to_crs("proj=moll").plot(
        color=None, edgecolor="black", linewidth=0.2, ax=ax
    )
    ax.axis("off")
    cb_ax = fig.axes[1]
    cb_ax.tick_params(labelsize=14)
    cb_ax.set_xlabel(xlabel="Maidenhead IPQ Compactness", fontsize=14)
    ax.margins(0)
    ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
    plt.tight_layout()


def maidenhead_norm_area_hist(maidenhead_gpd):
    """
    Plot histogram of normalized area for Maidenhead cells.

    This function creates a histogram visualization showing the distribution
    of normalized areas for Maidenhead cells, helping to understand area variations
    and identify patterns in area distortion.

    Args:
        maidenhead_gpd: GeoDataFrame from maidenheadinspect function
    """
    # Filter out cells that cross the dateline
    maidenhead_gpd_filtered = maidenhead_gpd[~maidenhead_gpd["crossed"]]

    # Create the histogram with color ramp
    fig, ax = plt.subplots(figsize=(10, 6))

    # Get histogram data
    counts, bins, patches = ax.hist(
        maidenhead_gpd_filtered["norm_area"], bins=50, alpha=0.7, edgecolor="black"
    )

    # Create color ramp using the same normalization as the map function
    vmin, vmax, vcenter = (
        maidenhead_gpd_filtered["norm_area"].min(),
        maidenhead_gpd_filtered["norm_area"].max(),
        1,
    )
    norm = TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)

    # Apply colors to histogram bars using the same color mapping as the map
    for i, patch in enumerate(patches):
        # Use the center of each bin for color mapping
        bin_center = (bins[i] + bins[i + 1]) / 2
        color = plt.cm.RdYlBu_r(norm(bin_center))
        patch.set_facecolor(color)

    # Add reference line at mean area (norm_area = 1)
    ax.axvline(
        x=1, color="red", linestyle="--", linewidth=2, label="Mean Area (norm_area = 1)"
    )

    # Add statistics text box
    stats_text = f"Mean: {maidenhead_gpd_filtered['norm_area'].mean():.3f}\nStd: {maidenhead_gpd_filtered['norm_area'].std():.3f}\nMin: {maidenhead_gpd_filtered['norm_area'].min():.3f}\nMax: {maidenhead_gpd_filtered['norm_area'].max():.3f}"
    ax.text(
        0.02,
        0.98,
        stats_text,
        transform=ax.transAxes,
        fontsize=12,
        verticalalignment="top",
        bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8),
    )

    # Customize the plot
    ax.set_xlabel("Maidenhead normalized area", fontsize=14)
    ax.set_ylabel("Number of cells", fontsize=14)
    ax.legend(fontsize=12)
    ax.grid(True, alpha=0.3)

    plt.tight_layout()


def maidenhead_compactness_hist(maidenhead_gpd):
    """
    Plot histogram of IPQ compactness for Maidenhead cells.

    This function creates a histogram visualization showing the distribution
    of Isoperimetric Quotient (IPQ) compactness values for Maidenhead cells, helping
    to understand how close cells are to being regular squares.

    Args:
        maidenhead_gpd: GeoDataFrame from maidenheadinspect function
    """
    # Filter out cells that cross the dateline
    maidenhead_gpd_filtered = maidenhead_gpd[~maidenhead_gpd["crossed"]]

    # Create the histogram with color ramp
    fig, ax = plt.subplots(figsize=(10, 6))

    # Get histogram data
    counts, bins, patches = ax.hist(
        maidenhead_gpd_filtered["ipq"], bins=50, alpha=0.7, edgecolor="black"
    )

    # Create color ramp using the same normalization as the map function
    norm = TwoSlopeNorm(vmin=VMIN_QUAD, vcenter=VCENTER_QUAD, vmax=VMAX_QUAD)

    # Apply colors to histogram bars using the same color mapping as the map
    for i, patch in enumerate(patches):
        # Use the center of each bin for color mapping
        bin_center = (bins[i] + bins[i + 1]) / 2
        color = plt.cm.viridis(norm(bin_center))
        patch.set_facecolor(color)

    # Add reference line at ideal square IPQ value (0.785)
    ax.axvline(
        x=0.785,
        color="red",
        linestyle="--",
        linewidth=2,
        label="Ideal Square (IPQ = 0.785)",
    )

    # Add statistics text box
    stats_text = f"Mean: {maidenhead_gpd_filtered['ipq'].mean():.3f}\nStd: {maidenhead_gpd_filtered['ipq'].std():.3f}\nMin: {maidenhead_gpd_filtered['ipq'].min():.3f}\nMax: {maidenhead_gpd_filtered['ipq'].max():.3f}"
    ax.text(
        0.02,
        0.98,
        stats_text,
        transform=ax.transAxes,
        fontsize=12,
        verticalalignment="top",
        bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8),
    )

    # Customize the plot
    ax.set_xlabel("Maidenhead IPQ Compactness", fontsize=14)
    ax.set_ylabel("Number of cells", fontsize=14)
    ax.legend(fontsize=12)
    ax.grid(True, alpha=0.3)

    plt.tight_layout()


def maidenheadinspect_cli():
    """
    Command-line interface for Maidenhead cell inspection.

    CLI options:
      -r, --resolution: Maidenhead resolution level (1-4)
    """
    parser = argparse.ArgumentParser(add_help=False)
    parser.add_argument("-r", "--resolution", dest="resolution", type=int, default=0)
    args, _ = parser.parse_known_args()  # type: ignore
    resolution = args.resolution
    print(maidenheadinspect(resolution))


if __name__ == "__main__":
    maidenheadstats_cli()
