import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it
from polsartools.utils.convert_matrices import C3_T3_mat
from .fp_infiles import fp_c3t3files

@time_it
def rvifp(infolder,  window_size=1, outType="tif", cog_flag=False, 
          cog_overviews = [2, 4, 8, 16], write_flag=True, 
          max_workers=None,block_size=(512, 512),
          progress_callback=None,  # for QGIS plugin
            ):
    """Calculate Radar Vegetation Index (RVI) from full-pol SAR data.

    This function computes the Radar Vegetation Index (RVI) using full-polarimetric
    SAR data. RVI is one of the earliest and most widely used polarimetric vegetation
    indices, providing a measure of vegetation density and randomness of scattering.

    Examples
    --------
    >>> # Basic usage with default parameters
    >>> rvifp("/path/to/fullpol_data")
    
    >>> # Advanced usage with custom parameters
    >>> rvifp(
    ...     infolder="/path/to/fullpol_data",
    ...     window_size=5,
    ...     outType="tif",
    ...     cog_flag=True,
    ...     block_size=(1024, 1024)
    ... )


    Parameters
    ----------
    infolder : str
        Path to the input folder containing full-pol T3 or C3 matrix files.
    window_size : int, default=1
        Size of the spatial averaging window. Larger windows reduce speckle noise
        but decrease spatial resolution.
    outType : {'tif', 'bin'}, default='tif'
        Output file format:
        - 'tif': GeoTIFF format with georeferencing information
        - 'bin': Raw binary format
    cog_flag : bool, default=False
        If True, creates a Cloud Optimized GeoTIFF (COG) with internal tiling
        and overviews for efficient web access.
    cog_overviews : list[int], default=[2, 4, 8, 16]
        Overview levels for COG creation. Each number represents the
        decimation factor for that overview level.
    write_flag : bool, default=True
        If True, writes results to disk. If False, only processes data in memory.
    max_workers : int | None, default=None
        Maximum number of parallel processing workers. If None, uses
        CPU count - 1 workers.
    block_size : tuple[int, int], default=(512, 512)
        Size of processing blocks (rows, cols) for parallel computation.
        Larger blocks use more memory but may be more efficient.

    Returns
    -------
    None
        Writes one output file to disk:
        - 'rvifp.tif' or 'rvifp.bin': RVI values

    """
    input_filepaths = fp_c3t3files(infolder)
    output_filepaths = []
    if outType == "bin":
        output_filepaths.append(os.path.join(infolder, "rvifp.bin"))
    else:
        output_filepaths.append(os.path.join(infolder, "rvifp.tif"))
    
    process_chunks_parallel(input_filepaths, list(output_filepaths), 
                            window_size=window_size, write_flag=write_flag,
                        processing_func=process_chunk_rvifp,block_size=block_size, 
                        max_workers=max_workers,  num_outputs=len(output_filepaths),
                        cog_flag=cog_flag,
                        cog_overviews=cog_overviews,
                        progress_callback=progress_callback
                        )

def process_chunk_rvifp(chunks, window_size,input_filepaths,*args):

    if 'T11' in input_filepaths[0] and 'T22' in input_filepaths[5] and 'T33' in input_filepaths[8]:
        t11_T1 = np.array(chunks[0])
        t12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
        t13_T1 = np.array(chunks[3])+1j*np.array(chunks[4])
        t21_T1 = np.conj(t12_T1)
        t22_T1 = np.array(chunks[5])
        t23_T1 = np.array(chunks[6])+1j*np.array(chunks[7])
        t31_T1 = np.conj(t13_T1)
        t32_T1 = np.conj(t23_T1)
        t33_T1 = np.array(chunks[8])

        T_T1 = np.array([[t11_T1, t12_T1, t13_T1], 
                     [t21_T1, t22_T1, t23_T1], 
                     [t31_T1, t32_T1, t33_T1]])


    if 'C11' in input_filepaths[0] and 'C22' in input_filepaths[5] and 'C33' in input_filepaths[8]:
        C11 = np.array(chunks[0])
        C12 = np.array(chunks[1])+1j*np.array(chunks[2])
        C13 = np.array(chunks[3])+1j*np.array(chunks[4])
        C21 = np.conj(C12)
        C22 = np.array(chunks[5])
        C23 = np.array(chunks[6])+1j*np.array(chunks[7])
        C31 = np.conj(C13)
        C32 = np.conj(C23)
        C33 = np.array(chunks[8])
        C3 = np.array([[C11, C12, C13], 
                         [C21, C22, C23], 
                         [C31, C32, C33]])

        T_T1 = C3_T3_mat(C3)

    if window_size>1:
        kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)

        t11f = conv2d(T_T1[0,0,:,:],kernel)
        t12f = conv2d(np.real(T_T1[0,1,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,1,:,:]),kernel)
        t13f = conv2d(np.real(T_T1[0,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,2,:,:]),kernel)
        
        t21f = np.conj(t12f) 
        t22f = conv2d(T_T1[1,1,:,:],kernel)
        t23f = conv2d(np.real(T_T1[1,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[1,2,:,:]),kernel)

        t31f = np.conj(t13f) 
        t32f = np.conj(t23f) 
        t33f = conv2d(T_T1[2,2,:,:],kernel)

        T_T1 = np.array([[t11f, t12f, t13f], [t21f, t22f, t23f], [t31f, t32f, t33f]])


    reshaped_arr = T_T1.reshape(3, 3, -1).transpose(2, 0, 1)
    eigenvalues = np.linalg.eigvals(reshaped_arr)
    sorted_eigenvalues = np.sort(eigenvalues, axis=1)[:, ::-1]
    sorted_eigenvalues = sorted_eigenvalues.reshape(T_T1.shape[2], T_T1.shape[3], 3)
    
    p1 = sorted_eigenvalues[:,:,0]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
    p2 = sorted_eigenvalues[:,:,1]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
    p3 = sorted_eigenvalues[:,:,2]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
    
    p1[p1<0] = 0
    p2[p2<0] = 0
    p3[p3<0] = 0
        
    p1[p1>1] = 1
    p2[p2>1] = 1
    p3[p3>1] = 1

    
    rvi = np.real((4*p3)/(p1 + p2 + p3))

    # idx = np.argwhere(rvi>1)

    # rvi[idx] = (3/4)*rvi[idx]
    # rvi[~idx] = rvi[~idx]
    # rvi[rvi==0] = np.nan

    # rvi = np.real(rvi)
    
    rvi[rvi > 1] *= 0.75
    rvi[rvi == 0] = np.nan
    rvi = np.real(rvi)

    return rvi.astype(np.float32)
