import os
import multiprocessing
from multiprocessing import Pool
from turtle import width

import numpy as np

from moleculekit.molecule import Molecule
from scipy.spatial import KDTree
from sklearn.cluster import AgglomerativeClustering


def create_grid_fromBB(boundingBox, voxelSize=1):
    """Create a grid from a bounding box.

    Parameters
    ----------
    boundingBox : list
        List of the form [xmin, xmax, ymin, ymax, zmin, zmax]
    voxelSize : float
        Size of the voxels in Angstrom

    Returns
    -------
    grid : numpy.ndarray
        Grid of shape (nx, ny, nz)
    box_N : numpy.ndarray
        Number of voxels in each dimension

    """
    # increase grid by 0.5 to sample everything
    xrange = np.arange(boundingBox[0][0], boundingBox[1][0] + 0.5, step=voxelSize)
    yrange = np.arange(boundingBox[0][1], boundingBox[1][1] + 0.5, step=voxelSize)
    zrange = np.arange(boundingBox[0][2], boundingBox[1][2] + 0.5, step=voxelSize)

    gridpoints = np.zeros((xrange.shape[0] * yrange.shape[0] * zrange.shape[0], 3))
    i = 0
    for x in xrange:
        for y in yrange:
            for z in zrange:
                gridpoints[i][0] = x
                gridpoints[i][1] = y
                gridpoints[i][2] = z
                i += 1
    return gridpoints, (xrange.shape[0], yrange.shape[0], zrange.shape[0])


def get_bb(points):
    """Return bounding box from a set of points (N,3)

    Parameters
    ----------
    points : numpy.ndarray
        Set of points (N,3)

    Returns
    -------
    boundingBox : list
        List of the form [xmin, xmax, ymin, ymax, zmin, zmax]

    """
    minx = np.min(points[:, 0])
    maxx = np.max(points[:, 0])

    miny = np.min(points[:, 1])
    maxy = np.max(points[:, 1])

    minz = np.min(points[:, 2])
    maxz = np.max(points[:, 2])
    bb = [[minx, miny, minz], [maxx, maxy, maxz]]
    return bb


def get_all_protein_resids(pdb_file):
    """Return all protein residues from a pdb file

    Parameters
    ----------
    pdb_file : str
        Path to pdb file

    Returns
    -------
    resids : numpy.ndarray
        Array of protein resids old -> new

    """
    try:
        prot = Molecule(pdb_file)
    except:
        exit("could not read file")
    prot.filter("protein")
    return prot.get("index", sel="name CA")


def get_all_metalbinding_resids(pdb_file):
    """Return all metal binding residues from a pdb file

    Parameters
    ----------
    pdb_file : str
        Path to pdb file

    Returns
    -------
    resids : numpy.ndarray
        id of resids that are metal binding

    """

    try:
        prot = Molecule(pdb_file)
    except:
        exit("could not read file")
    prot.filter("protein")
    return prot.get(
        "index",
        sel="name CA and resid 94 96 119"
        #sel="name CA and resname HIS HID HIE HIP CYS CYX GLU GLH GLN ASP ASH ASN GLN MET",
    )


def compute_average_p_fast(point, cutoff=1):
    """Using KDTree find the closest gridpoints

    Parameters
    ----------
    point : numpy.ndarray
        Point of shape (3,)
    cutoff : float
        Cutoff distance in Angstrom

    Returns
    -------
    average_p : numpy.ndarray
        Average probability of shape (1,)"""
    p = 0
    nearest_neighbors, indices = tree.query(
        point, k=15, distance_upper_bound=cutoff, workers=1
    )
    if np.min(nearest_neighbors) != np.inf:
        p = np.mean(output_v[indices[nearest_neighbors != np.inf]])
    return p


def get_probability_mean(grid, prot_centers, pvalues):
    """Compute the mean probability of all gridpoints from the globalgrid based on the individual boxes

    Parameters
    ----------
    grid : numpy.ndarray
        Grid of shape (nx, ny, nz)
    prot_centers : numpy.ndarray
        Protein centers of shape (N,3)
    pvalues : numpy.ndarray
        Probability values of shape (N,1)

    Returns
    -------
    mean_p : numpy.ndarray
        Mean probability over grid of shape (nx, ny, nz)
    """
    global output_v
    output_v = pvalues
    global prot_v
    prot_v = prot_centers
    cpuCount = multiprocessing.cpu_count()

    global tree
    tree = KDTree(prot_v)
    p = Pool(cpuCount)
    results = p.map(compute_average_p_fast, grid)
    return np.array(results)


def write_cubefile(bb, pvalues, box_N, outname="Metal3D_pmap.cube", gridres=1):
    """Write a cube file from a probability map
    The cube specification from gaussian is used, distance are converted to bohr

    Parameters
    ----------
    bb : list
        List of the form [xmin, xmax, ymin, ymax, zmin, zmax]
    pvalues : numpy.ndarray
        Probability values of shape (nx, ny, nz)
    box_N : tuple
        Number of voxels in each dimension
    outname : str
        Name of the output file
    gridres:float
        Resolution of the grid used for writing the voxels

    """

    with open(outname, "w") as cube:
        cube.write(" Metal3D Cube File\n")
        cube.write(" Outer Loop: X, Middle Loop y, inner Loop z\n")

        angstromToBohr = 1.89
        cube.write(
            f"    1   {bb[0][0]*angstromToBohr: .6f}  {bb[0][1]*angstromToBohr: .6f}   {bb[0][2]*angstromToBohr: .6f}\n"
        )
        cube.write(
            f"{str(box_N[0]).rjust(5)}    {1.890000*gridres:.9f}    0.000000    0.000000\n"
        )
        cube.write(
            f"{str(box_N[1]).rjust(5)}    0.000000    {1.890000*gridres:.9f}    0.000000\n"
        )
        cube.write(
            f"{str(box_N[2]).rjust(5)}    0.000000    0.000000    {1.890000*gridres:.9f}\n"
        )
        cube.write("    1    1.000000    0.000000    0.000000    0.000000\n")

        o = pvalues.reshape(box_N)
        for x in range(box_N[0]):
            for y in range(box_N[1]):
                for z in range(box_N[2]):
                    cube.write(f" {o[x][y][z]: .5E}")
                    if z % 6 == 5:
                        cube.write("\n")
                cube.write("\n")


def find_unique_sites(
    pvalues, grid, writeprobes=False, probefile="probes.pdb", threshold=5, p=0.75
):
    """The probability voxels are points and the voxel clouds may contain multiple metals
    This function finds the unique sites and returns the coordinates of the unique sites with the highest p for each cluster.
    It uses the AgglomerativeClustering algorithm to find the unique sites.
    The threshold is the maximum distance between two points in the same cluster it can be changed to get more metal points.

    Parameters
    ----------
    pvalues : numpy.ndarray
        Probability values of shape (N, 1)
    grid : numpy.ndarray
        Grid of shape (N, 3)
    writeprobes : bool
        If True, write the probes to a pdb file
    probefile : str
        Name of the output file
    threshold : float
        Maximum distance between two points in the same cluster
    p : float
        Minimum probability of a point to be considered a unique site

    """

    points = grid[pvalues > p]
    point_p = pvalues[pvalues > p]
    if len(points) == 0:
        return "no metals found"
    clustering = AgglomerativeClustering(
        n_clusters=None, linkage="complete", distance_threshold=threshold
    ).fit(points)

    message = f"min metal p={p}, n(metals) found: {clustering.n_clusters_}"

    sites = []
    for i in range(clustering.n_clusters_):
        c_points = points[clustering.labels_ == i]
        c_points_p = point_p[clustering.labels_ == i]

        position = c_points[np.argmax(c_points_p)]
        sites.append((position, np.max(c_points_p)))
    if writeprobes:
        print(f"writing probes to {probefile}")
        with open(probefile, "w") as f:
            for i, site in enumerate(sites):
                f.write(
                    f"HETATM  {i+1:3} ZN    ZN A {i+1:3}    {site[0][0]: 8.3f}{site[0][1]: 8.3f}{site[0][2]: 8.3f}  {site[1]:.2f}  0.0           ZN2+\n"
                )
    return message