# -*- coding: utf-8 -*-
"""
SpecPipe - Vegetation Indices - SR R750/R705 (Simple Ratio R750/R705)

Copyright (c) 2025 Siwei Luo. MIT License.
"""
import numpy as np
import pandas as pd
from typing import Annotated, Any

from ..specio import simple_type_validator, arraylike_validator

# # For local test
# from specpipe.specio import simple_type_validator, arraylike_validator


# %% Vegetation index function


@simple_type_validator
def sr_salinity(
    spec_array: Annotated[Any, arraylike_validator(ndim=2)],
    wavelength: Annotated[Any, arraylike_validator(ndim=1)],
    axis: int = 0,
) -> pd.DataFrame:
    """
    Compute salinity-sensitive SR R750/R705 (Simple Ratio R750/R705) of 2D arraylike of 1D spectra data series.
    The calculation is based on:
        S. Hamzeh, A.A. Naseri, S.K. AlaviPanah, B. Mojaradi, H.M. Bartholomeus, J.G.P.W. Clevers, M. Behzad,
        Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices,
        International Journal of Applied Earth Observation and Geoinformation,
        Volume 21,
        2013,
        Pages 282-290,
        ISSN 1569-8432,
        https://doi.org/10.1016/j.jag.2012.07.002.

    Parameters
    ----------
    spec_array : 2D arraylike
        2D arraylike of 1D spectral data series.
    wavelength : 1D arraylike
        Wavelengths for the spectra data.
    axis : int, optional
        Axis of spectral data series index, must be 0 or 1. If 0, each row represents a sample spectrum.

    Returns
    -------
    pd.DataFrame
        Dataframe containing spectral indices of the samples.
    """
    # Validate input spectral data array
    spec_array = np.array(spec_array).astype('float32')
    if np.isnan(spec_array).any():
        raise ValueError('spec_array contains NaN values.')

    # Validate axis
    if axis == 1:
        spec_array = spec_array.T
    elif axis != 0:
        raise ValueError(f'axis must be 0 or 1, got: {axis}.')
    wavelength = np.array(wavelength).astype('float32')

    # Validate data and wavelengths
    if len(wavelength) != spec_array.shape[1]:
        raise ValueError(
            f'Length of given wavelength does not match the band number of spec_array, \
                got wavelength length: {len(wavelength)}, \
                got band number of given spec_array: {spec_array.shape[1]}'
        )
    band1 = 705
    band2 = 750
    if (min(wavelength) > band1) | (max(wavelength) < band2):
        raise ValueError(
            'Index requires wavelength range (band1, band2) '
            + f'exceeds given wavelength range ({min(wavelength)}, {max(wavelength)}).'
        )

    # Get indices of the wavelength
    band2_ind = (np.abs(wavelength - band2)).argmin()
    band1_ind = (np.abs(wavelength - band1)).argmin()

    # Band values
    band2_values = spec_array[:, band2_ind]
    band1_values = spec_array[:, band1_ind]
    band2_values[band2_values == 0] = 1e-15
    band1_values[band1_values == 0] = 1e-15

    # Compute vegetation indices
    vi_values = band2_values / band1_values
    vi_values = np.array([vi_values])

    if axis == 0:
        df_vi = pd.DataFrame(vi_values.T, columns=['SR_salinity'])
    else:
        df_vi = pd.DataFrame(vi_values, index=['SR_salinity'])

    return df_vi


# %% Local test

# from specpipe.vegeind.demo_data import create_vegeind_demo_data

# specdata = create_vegeind_demo_data()
# vidata = sr_salinity(specdata, wavelength=specdata.columns)
