import torch
import sys
sys.path.append('./src')
import models
from datasets import *
from tqdm import tqdm
from matplotlib import pyplot as plt

torch.manual_seed(42)
torch.cuda.manual_seed(42)
np.random.seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

configs = {}
configs['SiemenStarLowerLeft'] = {
    'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerLeft_old/Clusters_2Photon_CS7_chunk{i}.h5' for i in range(200)],
    'modelVersion': '251124',
    'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
    'nSize': 7,
}

configs['SiemenStarLowerRight'] = {
    'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerRight/2Photon_CS7_chunk{i}.h5' for i in range(320)], ### 320 files
    'modelVersion': '251124',
    'roi': [235, 345, 110, 220], # x_min, x_max, y_min, y_max,
    'nSize': 7,
}

task = 'SiemenStarLowerRight'
config = configs[task]

BinningFactor = 10
Roi = configs[task]['roi']
X_st, X_ed, Y_st, Y_ed = Roi
mlSuperFrame = np.zeros(((Y_ed-Y_st)*BinningFactor, (X_ed-X_st)*BinningFactor))
countFrame = np.zeros((Y_ed-Y_st, X_ed-X_st))
subpixelDistribution = np.zeros((BinningFactor, BinningFactor))

if __name__ == "__main__":
    model = models.get_double_photon_model_class(config['modelVersion'])().cuda()
    model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/doublePhoton{config["modelVersion"]}_15keV_Noise0.13keV_E300.pth', weights_only=True))
    nChunks = np.ceil(len(config['dataFiles']) / 16).astype(int)
    for idxChunk in range(nChunks):
        predictions = []
        referencePoints = []
        stFileIdx = idxChunk * 16
        edFileIdx = min((idxChunk + 1) * 16, len(config['dataFiles']))
        sampleFiles = config['dataFiles'][stFileIdx : edFileIdx]
        print(f'Processing files {stFileIdx} to {edFileIdx}...')
        dataset = doublePhotonInferenceDataset(
            sampleFiles, 
            sampleRatio=1., 
            datasetName='Inference',
            nSize=config['nSize']
        )
        dataLoader = torch.utils.data.DataLoader(
            dataset,
            batch_size=8192,
            shuffle=False,
            num_workers=16,
            pin_memory=True,
        )

        referencePoints.append(dataset.referencePoint)

        with torch.no_grad():
            for batch in tqdm(dataLoader):
                inputs, _ = batch
                inputs = inputs.cuda()
                outputs = model(inputs).view(-1, 2)  # 2B x 2
                predictions.append(outputs.cpu())

        predictions = torch.cat(predictions, dim=0)
        predictions += torch.tensor([config['nSize']/2., config['nSize']/2.]).unsqueeze(0)  # adjust back to original coordinate system
        print(f'mean x = {torch.mean(predictions[:, 0])}, std x = {torch.std(predictions[:, 0])}')
        print(f'mean y = {torch.mean(predictions[:, 1])}, std y = {torch.std(predictions[:, 1])}')
        referencePoints = np.concatenate(referencePoints, axis=0) ### the lower-left corner of the cluster in absolute coordinate
        ### duplicate reference points for 2-photon clusters
        referencePoints = np.repeat(referencePoints, 2, axis=0)
        absolutePositions = predictions.numpy() + referencePoints
        
        hit_x = np.floor((absolutePositions[:, 0] - Roi[0]) * BinningFactor).astype(int)
        hit_x = np.clip(hit_x, 0, mlSuperFrame.shape[1]-1)
        hit_y = np.floor((absolutePositions[:, 1] - Roi[2]) * BinningFactor).astype(int)
        hit_y = np.clip(hit_y, 0, mlSuperFrame.shape[0]-1)
        np.add.at(mlSuperFrame, (hit_y, hit_x), 1)

        np.add.at(countFrame, ((referencePoints[:, 1] - Roi[2]).astype(int),
                            (referencePoints[:, 0] - Roi[0]).astype(int)), 1)

        np.add.at(subpixelDistribution,
                (np.floor((absolutePositions[:, 1] % 1) * BinningFactor).astype(int),
                np.floor((absolutePositions[:, 0] % 1) * BinningFactor).astype(int)), 1)

    import os
    os.makedirs(f'InferenceResults/{task}', exist_ok=True)

    plt.imshow(mlSuperFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
    plt.colorbar()
    plt.savefig(f'InferenceResults/{task}/ML_2Photon_superFrame.png', dpi=300)
    np.save(f'InferenceResults/{task}/ML_2Photon_superFrame.npy', mlSuperFrame)
    plt.clf()

    plt.imshow(countFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
    plt.colorbar()
    plt.savefig(f'InferenceResults/{task}/count_2Photon_Frame.png', dpi=300)
    np.save(f'InferenceResults/{task}/count_2Photon_Frame.npy', countFrame)

    plt.clf()
    plt.imshow(subpixelDistribution, origin='lower')
    plt.colorbar()
    plt.savefig(f'InferenceResults/{task}/subpixel_2Photon_Distribution.png', dpi=300)
    np.save(f'InferenceResults/{task}/subpixel_2Photon_Distribution.npy', subpixelDistribution)