"""
This module provides functions for generating statistics for GARS 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.garsgrid import garsgrid
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.utils.io import gars_num_cells

min_res = DGGS_TYPES["gars"]["min_res"]
max_res = DGGS_TYPES["gars"]["max_res"]


def gars_metrics(res, unit: str = "m"):  # length unit is km, area unit is km2
    """
    Calculate metrics for GARS DGGS cells.

    Args:
        res: 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'}")

    # GARS grid has 43200 (180x240) cells at base level
    # Each subdivision adds 10x10 = 100 cells per parent cell
    num_cells = gars_num_cells(res)
    # Calculate area in km² first
    avg_cell_area = AUTHALIC_AREA / num_cells  # cell area in km²
    avg_edge_len = math.sqrt(avg_cell_area)  # edge length in km
    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 garsstats(unit: str = "m"):  # length unit is km, area unit is km2
    """
    Generate statistics for GARS DGGS cells.

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

    Returns:
        pandas.DataFrame: DataFrame containing GARS 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 = gars_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 garsstats_cli():
    """
    Command-line interface for generating GARS 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_args()

    unit = args.unit

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

    # Display the DataFrame
    print(df)


def garsinspect(res):  # length unit is km, area unit is km2
    """
    Generate comprehensive inspection data for GARS DGGS cells at a given resolution.

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

    Args:
        res: GARS resolution level (0-4)

    Returns:
        geopandas.GeoDataFrame: DataFrame containing GARS cell inspection data with columns:
            - gars: GARS 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
    """
    gars_gpd = garsgrid(res, output_format="gpd")
    gars_gpd["crossed"] = gars_gpd["geometry"].apply(check_crossing_geom)
    mean_area = gars_gpd["cell_area"].mean()
    # Calculate normalized area
    gars_gpd["norm_area"] = gars_gpd["cell_area"] / mean_area
    # Calculate IPQ compactness using the standard formula: CI = 4πA/P²
    gars_gpd["ipq"] = (
        4 * np.pi * gars_gpd["cell_area"] / (gars_gpd["cell_perimeter"] ** 2)
    )
    # Calculate zonal standardized compactness
    gars_gpd["zsc"] = (
        np.sqrt(
            4 * np.pi * gars_gpd["cell_area"]
            - np.power(gars_gpd["cell_area"], 2) / np.power(6378137, 2)
        )
        / gars_gpd["cell_perimeter"]
    )
    return gars_gpd


def gars_norm_area(gars_gpd):
    """
    Plot normalized area map for GARS cells.

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

    Args:
        gars_gpd: GeoDataFrame from garsinspect 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 = gars_gpd["norm_area"].min(), gars_gpd["norm_area"].max(), 1
    norm = TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)
    gars_gpd = gars_gpd[~gars_gpd["crossed"]]  # remove cells that cross the dateline
    gars_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="GARS Normalized Area", fontsize=14)
    ax.margins(0)
    ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
    plt.tight_layout()


def gars_compactness(gars_gpd):
    """
    Plot IPQ compactness map for GARS cells.

    This function creates a visualization showing the Isoperimetric Quotient (IPQ)
    compactness of GARS 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:
        gars_gpd: GeoDataFrame from garsinspect 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 = gars_gpd['ipq'].min(), gars_gpd['ipq'].max(), np.mean([gars_gpd['ipq'].min(), gars_gpd['ipq'].max()])
    norm = TwoSlopeNorm(vmin=VMIN_QUAD, vcenter=VCENTER_QUAD, vmax=VMAX_QUAD)
    gars_gpd = gars_gpd[~gars_gpd["crossed"]]  # remove cells that cross the dateline
    gars_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="GARS IPQ Compactness", fontsize=14)
    ax.margins(0)
    ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
    plt.tight_layout()


def gars_norm_area_hist(gars_gpd):
    """
    Plot histogram of normalized area for GARS cells.

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

    Args:
        gars_gpd: GeoDataFrame from garsinspect function
    """
    # Filter out cells that cross the dateline
    gars_gpd_filtered = gars_gpd[~gars_gpd["crossed"]]

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

    # Get histogram data
    counts, bins, patches = ax.hist(
        gars_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 = (
        gars_gpd_filtered["norm_area"].min(),
        gars_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: {gars_gpd_filtered['norm_area'].mean():.3f}\nStd: {gars_gpd_filtered['norm_area'].std():.3f}\nMin: {gars_gpd_filtered['norm_area'].min():.3f}\nMax: {gars_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("GARS 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 gars_compactness_hist(gars_gpd):
    """
    Plot histogram of IPQ compactness for GARS cells.

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

    Args:
        gars_gpd: GeoDataFrame from garsinspect function
    """
    # Filter out cells that cross the dateline
    gars_gpd_filtered = gars_gpd[~gars_gpd["crossed"]]

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

    # Get histogram data
    counts, bins, patches = ax.hist(
        gars_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: {gars_gpd_filtered['ipq'].mean():.3f}\nStd: {gars_gpd_filtered['ipq'].std():.3f}\nMin: {gars_gpd_filtered['ipq'].min():.3f}\nMax: {gars_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("GARS 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 garsinspect_cli():
    """
    Command-line interface for GARS cell inspection.

    CLI options:
      -r, --resolution: GARS resolution level (0-4)
    """
    parser = argparse.ArgumentParser(add_help=False)
    parser.add_argument("-r", "--resolution", dest="resolution", type=int, default=0)
    args = parser.parse_args()  # type: ignore
    resolution = args.resolution
    print(garsinspect(resolution))


if __name__ == "__main__":
    garsstats_cli()
