'''deconvolving 2D images'''

import numpy as np
import h5py
import argparse
from scipy.signal import convolve
from scipy.ndimage import gaussian_filter
from deconvolve_func import DeconvolveWithBead as DWB

parser = argparse.ArgumentParser()
parser.add_argument('bead', type = str, help = "original file")
parser.add_argument('--output', type = str, required = True, help = "file name for the output of images")
parser.add_argument('-pd', '--plot_data', type = str, required = True, help = "data storage file name, iterations added automatically")
parser.add_argument('-it', '--iterations', type = int, required = True, help = "nr of iterations")
parser.add_argument('-in', '--intensity', type = float, nargs = '+', required = True, help = "image intensity; division of the signal by intensity")
parser.add_argument('-k', '--kernel', type = float, nargs = '+', required = True, help = "kernel g sigma values")
parser.add_argument('--vox', type = float, nargs = '+', default = [1.0, 1.0, 1.0], help = "voxel values in micrometers, default [1.0, 1.0, 1.0]")

args = parser.parse_args()


#--------- importing the image, that will become the GROUND TRUTH ------
with h5py.File(args.bead, 'r') as hdf5_file:
    original = hdf5_file['t0/channel0'][:]

print("algse pildi integraal", np.sum(original))

#--------- creating the 2D gaussian PSF----------------------------------
points = np.max(original)
point_sources = np.zeros(original.shape)

center_x = original.shape[0] // 2
center_y = original.shape[1] // 2

point_sources[center_x, center_y] = points
#point_sources[90, 110] = points
psf = gaussian_filter(point_sources, sigma = 2.4)

print("psf integraal", np.sum(psf))
if not np.isclose(np.sum(psf), 1.0, atol=1e-6):
    psf /= np.sum(psf) # normaliseerin, et piksli väärtused oleksid samas suurusjärgus
print("psf-integraal-uuesti", np.sum(psf))

#-------------- DECONVOLUTION--------------------------------------------------------------
with h5py.File(args.output, 'w') as hdf5_file:
    'erinevad mürad ### signal intensity #################################################'
    intensity = args.intensity

    for i in intensity:
        scaled_original = original / i if i!=0 else original.copy()
        scaled_original = scaled_original.astype(np.float64)
        image =  gaussian_filter(scaled_original, sigma = 2.4)#convolve(scaled_original, psf, mode='same')
        image[image <= 0] = 0

        "rakendan pildile Poissoni müra #################################################"
        if i == 0:
            noisy_image = image.copy()          
        else:
            noisy_image = np.random.poisson(lam = image, size = image.shape).astype(np.float64)

        hdf5_file.create_dataset(f"noise_level_{i:08.3f}", data = noisy_image)
        hdf5_file.create_dataset(f"scaled_original_{i:08.3f}", data = scaled_original)

        '''kernelid erinevate sigmadega - pildi taastamine ##################'''
        g_sigma = args.kernel
        vox = np.array(args.vox)
        mses = []
        mssim = []
        
        for j in g_sigma:
            image_kerneled = gaussian_filter(noisy_image, sigma = j)
            psf_kerneled = gaussian_filter(psf, sigma = j)
            print(np.sum(image_kerneled),np.sum(noisy_image), np.sum(psf_kerneled), np.sum(psf))
            'dekonvolveerin - taastan pilti ######################################'
            deconv = DWB(image = image_kerneled, voxel_size = vox, bead = scaled_original)

            dec = deconv.deconvolve_rl(n_iterations = args.iterations,
                bead = scaled_original,
                psf = psf_kerneled,
                im = image_kerneled)

            hdf5_file.create_dataset(f'intensity_{i:08.3f}/SIGMA_{j:03.1f}', data = dec[0])
            hdf5_file.create_dataset(f"intensity_{i:08.3f}/minimum_image_{j:03.1f}", data = dec[4])

            mses.append(dec[1])
            mssim.append(dec[2])


        print("---------------lõpp-pildi integraal", np.sum(dec[0]))       
        data = np.column_stack((np.column_stack(mses),np.column_stack(mssim)))
        np.save(f"{args.plot_data}_{args.iterations}_{i:08.3f}.npy", np.column_stack((dec[3], data)))