import asyncio 
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
from astropy.wcs import WCS
import tqdm
from multiprocessing.pool import ThreadPool
from chan_psf import solve_for_locations, solve_for_locations_eintp


psfe = np.array([1.8, 1.9, 3.0, 4.0, 6.0, 7.0, 8.0, 9.0])

def prepare_psf(evt):
    """
    find all unique psf for observation and load in single 3d data cuve
    return data cube with events slices indexes
    """
    u, ui = np.unique(evt["psf_cube"], return_inverse=True)
    data = np.array([np.load(p[3:])[:, ::-1,::-1].copy() for p in u])
    return data, ui

def select_xychunksize(wcs, halfpsfsize=36./3600.):
    """
    get wcs and find wcs pixel size of psf reach
    """
    sizex = int(abs(halfpsfsize/wcs.wcs.cdelt[1])) + 2
    sizey = int(abs(halfpsfsize/wcs.wcs.cdelt[0])) + 2
    print(sizex, sizey)
    return sizex, sizey

def read_wcs(h):
    """
    read events wcs header
    """
    w = WCS(naxis=2)
    w.wcs.ctype = [h["TCTYP11"], h["TCTYP12"]]
    w.wcs.crval = [h["TCRVL11"], h["TCRVL12"]]
    w.wcs.cdelt = [h["TCDLT11"], h["TCDLT12"]]
    w.wcs.crpix = [h["TCRPX11"], h["TCRPX12"]]
    w = WCS(w.to_header())
    return w


def create_neighboring_blocks(x, y, sizex, sizey):
    """
    schematically all sky is splitted on squares, which are approximatelly ~ 10 times greater then the psf
    events for corresponding square are joined :: squer + diluttaion of psf reach

    coordinate system with events and all required coefficiets are fed to psf solver
    current psf size is 25*0.5 arcsec (with up to \sqrt(2) factor in case of worst rolls -> 36''

    """
    """
    event list already contains x and y for each event
    """
    iix = (x//sizex + 0.5).astype(int)
    iiy = (y//sizey + 0.5).astype(int)
    isx, isy = np.mgrid[-1:2:1, -1:2:1]
    oidx = np.repeat(np.arange(x.size), 9)
    xyu, iixy, xyc = np.unique(np.array([np.repeat(iix, 9) + np.tile(isx.ravel(), x.size), 
                                         np.repeat(iiy, 9)+  np.tile(isy.ravel(), x.size)]), axis=1, return_counts=True, return_inverse=True)

    sord = np.argsort(iixy)
    return oidx[sord], xyu, xyc


def make_srccount_and_detmap(emap, evt, h, wcs=None):
    psfdata, ui = prepare_psf(evt)
    if wcs is None:
        wcs = read_wcs(h)
        x, y = evt["x"], evt["y"]
    else:
        ewcs = read_wcs(h)
        x, y = wcs.all_world2pix(ewcs.all_pix2world(np.array([x, y]).T, 0), 0).T

    eidx = np.searchsorted(psfe*1e3, evt["ENERGY"])
    eidx = np.maximum((evt["ENERGY"]/1000. - psfe[eidx])/(psfe[eidx + 1] - psfe[eidx]), 0.)
    sizex, sizey = select_xychunksize(wcs)
    iidx, xyu, cts = create_neighboring_blocks(x, y, sizex, sizey)
    cc = np.zeros(cts.size + 1, int)
    cc[1:] = np.cumsum(cts)
    cmap, pmap = np.zeros(emap.shape, float), np.zeros(emap.shape, float)
    #xe, ye, pk, roll, psfi = np.copy(evt["x"][iidx]), np.copy(evt["y"][iidx]), np.copy((evt["quant_eff"]/evt["bkg_model"])[iidx]), np.copy(evt["roll_pnt"][iidx]), np.copy(ui[iidx])
    xe = np.copy(x[iidx]).astype(float)
    ye = np.copy(y[iidx]).astype(float)
    ee = np.copy(eidx[iidx]).astype(float)
    pk = np.copy(evt["src_spec"][iidx]/evt["bkg_spec"][iidx]).astype(float)
    roll = np.copy(np.deg2rad(evt["roll_pnt"][iidx])).astype(float)
    psfi = np.copy(ui[iidx])

    yg, xg = np.mgrid[0:sizey:1, 0:sizex:1]

    def worker(ixys):
        i, (xs, ys) = ixys
        eloc = emap[ys*sizey:ys*sizey+sizey, xs*sizex:xs*sizex+sizex]
        mask = eloc > 0.
        xl = (xg[mask] + xs*sizex).astype(float)
        yl = (yg[mask] + ys*sizey).astype(float)
        ell = (eloc[mask]).astype(float)
        if np.any(mask):
            cr, pr = solve_for_locations_eintp(psfi[cc[i]:cc[i+1]], ee[cc[i]:cc[i + 1]], xe[cc[i]:cc[i+1]], ye[cc[i]:cc[i+1]], roll[cc[i]:cc[i+1]], pk[cc[i]:cc[i+1]], xl, yl, ell, psfdata)
        else:
            xl, yl, cr, pr = np.empty(0, int),np.empty(0, int),np.empty(0, float),np.empty(0, float)
        return xl.astype(int), yl.astype(int), cr, pr


    tpool = ThreadPool(8)
    for xl, yl, cr, pr in tqdm.tqdm(tpool.imap_unordered(worker, enumerate(xyu.T)), total=xyu.shape[1]):
        cmap[yl, xl] = cr
        pmap[yl, xl] = pr

    return wcs, cmap, pmap


if __name__ == "__main__":
    p1 = fits.open("test.fits")
    #emap = fits.getdata("exp.map.gz") #np.full((8192, 8192), 10000.)
    emap = fits.getdata("eR_spec_asp_0.fits.gz") #np.full((8192, 8192), 10000.)

    wcs, cmap, pmap = make_srccount_and_detmap(emap, p1[1].data, p1[1].header)
    fits.HDUList([fits.PrimaryHDU(), fits.ImageHDU(pmap - cmap, header=p1[1].header), fits.ImageHDU(cmap, header=p1[1].header)]).writeto("tmap4.fits.gz", overwrite=True)
    #fits.ImageHDU(data=pmap, header=wcs.to_header()).writeto("tmap4.fits.gz", overwrite=True)