import os
import numpy as np
from ROOT import TH1D, TH2D, TCanvas, TF1
from multiprocessing import Pool
from array import array
import h5py
from multiprocessing import Pool
import aare

_cfg = {} ### global config
_aduToKev3DMap = None ### global caliFile
_pedestalAduFrame = None ### global pedestalAduFrame
_noiseEneFrame = None ### global noiseEneFrame
nChunks = 16

def init(cfg):
    global _cfg
    global _aduToKev3DMap
    _cfg = cfg
    _aduToKev3DMap = cfg['caliFile']
    Roi = cfg['Roi']
    global nChunks
    nChunks = _cfg.get('NChunks', 16)

def convertAduFrameToEnergyFrame(aduFrame):
    if _aduToKev3DMap is None:
        return aduFrame
    NX, NY = _cfg['NX'], _cfg['NY']
    if _aduToKev3DMap.shape[0] == 1640:
        idxFrame = (np.abs(aduFrame//10)).astype(np.int32)
        idxFrame[idxFrame > _aduToKev3DMap.shape[0]-2] = _aduToKev3DMap.shape[0]-2
        NY, NX = _aduToKev3DMap.shape[1:3]
        _energyFrame0 = _aduToKev3DMap[idxFrame, np.arange(NY).reshape(-1, 1), np.arange(NX)]
        _energyFrame1 = _aduToKev3DMap[idxFrame+1, np.arange(NY).reshape(-1, 1), np.arange(NX)]
        energyFrame = _energyFrame0 + (_energyFrame1 - _energyFrame0)/10 * (np.abs(aduFrame) - idxFrame*10)
        energyFrame *= np.sign(aduFrame)

    else:
        aduPerBin = 10
        nExtraBins = 1000 // aduPerBin
        idxFrame = (aduFrame//aduPerBin).astype(np.int32) + nExtraBins
        idxFrame[idxFrame > _aduToKev3DMap.shape[0]-2] = _aduToKev3DMap.shape[0]-2
        NY, NX = _aduToKev3DMap.shape[1:3]
        _energyFrame0 = _aduToKev3DMap[idxFrame, np.arange(NY).reshape(-1, 1), np.arange(NX)]
        _energyFrame1 = _aduToKev3DMap[idxFrame+1, np.arange(NY).reshape(-1, 1), np.arange(NX)]
        energyFrame = _energyFrame0 + (_energyFrame1 - _energyFrame0)/aduPerBin * (aduFrame - (idxFrame-nExtraBins)*aduPerBin)
    if 'energyScalingFactor' in _cfg:
        energyFrame *= _cfg['energyScalingFactor']
    return energyFrame

def bookHistograms(energy, suffix='', energyBinWidth=0.1, isMC=False):
    histMaxEnergy = energy * 5.5
    nEnergyBins = int(histMaxEnergy // energyBinWidth)
    roi_x0, roi_x1, roi_y0, roi_y1 = _cfg['Roi'][0], _cfg['Roi'][1], _cfg['Roi'][2], _cfg['Roi'][3]
    roi_width = roi_x1 - roi_x0
    roi_height = roi_y1 - roi_y0
    interpolationBins = 10

    histograms = {}
    histograms['h1_ClusterEnergy'] = TH1D(
        f'h1_ClusterEnergy{suffix}', f'h1_ClusterEnergy{suffix}', nEnergyBins, -1, histMaxEnergy
    )
    histograms['h1_PixelEnergy'] = TH1D(
        f'h1_PixelEnergy{suffix}', f'h1_PixelEnergy{suffix}', nEnergyBins, -1, histMaxEnergy
    )
    histograms['h1_CenterPixelEnergy'] = TH1D(
        f'h1_CenterPixelEnergy{suffix}', f'h1_CenterPixelEnergy{suffix}', nEnergyBins, -1, histMaxEnergy
    )
    histograms['h2_Qc'] = TH2D(
        f'h2_Qc{suffix}', f'h2_Qc{suffix}', 201, 0, 1, 201, 0, 1
    )
    histograms['h2_counts'] = TH2D(
        f'h2_counts{suffix}', f'h2_counts{suffix}', roi_width, roi_x0, roi_x1, roi_height, roi_y0, roi_y1
    )
    histograms['h2_HitsSumFrame'] = TH2D(
        f'h2_HitsSumFrame{suffix}', f'h2_HitsSumFrame{suffix}', roi_width, roi_x0, roi_x1, roi_height, roi_y0, roi_y1
    )
    histograms['h2_interpolated'] = TH2D(
        f'h2_interpolated{suffix}', f'h2_interpolated{suffix}', roi_width * interpolationBins, roi_x0, roi_x1, roi_height * interpolationBins, roi_y0, roi_y1
    )
    histograms['h2_interpolatedSubpixel'] = TH2D(
        f'h2_interpolatedSubpixel{suffix}', f'h2_interpolatedSubpixel{suffix}', interpolationBins, 0, 1, interpolationBins, 0, 1
    )

    for hist in histograms.values():
        hist.SetDirectory(0)

    histograms['h1_ClusterEnergy'].SetTitle('Cluster Energy;Energy [keV];Counts')
    histograms['h1_PixelEnergy'].SetTitle('Pixel Energy;Energy [keV];Counts')
    histograms['h1_CenterPixelEnergy'].SetTitle('Center Pixel Energy;Energy [keV];Counts')
    histograms['h2_Qc'].SetTitle('Charge weighted center;#eta_x;#eta_y;Counts')

    if isMC:
        histograms['h1_ChargeCollectionEfficiency'] = TH1D(f'h1_ChargeCollectionEfficiency{suffix}', f'h1_ChargeCollectionEfficiency{suffix}', 400, 0.8, 1.01)
        histograms['h1_ChargeCollectionEfficiency'].SetDirectory(0)
        for key, hist in list(histograms.items()):
            histograms[key] = hist.Clone()

    return histograms

def _processFrames(idxChunk): ### for both single and double photon events, using 
    startFrame, endFrame = _cfg['NFrame'] * idxChunk // nChunks, min(_cfg['NFrame'] * (idxChunk + 1) // nChunks, _cfg['NFrame'])
    print(f'Processing frames from {startFrame} to {endFrame}...')
    Energy = _cfg['energy']
    selectionRange = _cfg['selectionRange']
    signalFileNames = _cfg['signalFileNames']
    NX = _cfg['NX']
    NY = _cfg['NY']
    headerSize = _cfg['headerSize']
    Roi = _cfg['Roi']
    nFramePerFile = os.path.getsize(f'{signalFileNames[0]}') // (NX * NY + 56) // 2
    _hists = bookHistograms(Energy, f'_{idxChunk}')

    ### create a cluster finder
    nxROI = Roi[1] - Roi[0]
    nyROI = Roi[3] - Roi[2]
    CF = aare.VarClusterFinder((nxROI, nyROI), 5) ### arg1: frame size, arg2: threshold
    CF.set_peripheralThresholdFactor(2)
    CF.set_noiseMap(_noiseEneFrame)

    if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
        h5_file = h5py.File(f'clusters_chunk{idxChunk}.h5', 'w')
        # 创建可扩展数据集
        dset_clusters = h5_file.create_dataset(
            'clusters', (0, 3, 3), maxshape=(None, 3, 3), dtype='f4',
            chunks=True, compression='gzip'
        )
        dset_refs = h5_file.create_dataset(
            'referencePoint', (0, 2), maxshape=(None, 2), dtype='i4',
            chunks=True
        )

        buffer_size = 50_000
        cluster_list = []
        refpoint_list = []
    def flush_h5_buffer():
        if not cluster_list:
            return
        old_len = dset_clusters.shape[0]
        new_len = old_len + len(cluster_list)
        dset_clusters.resize((new_len, 3, 3))
        dset_refs.resize((new_len, 2))
        dset_clusters[old_len:new_len] = cluster_list
        dset_refs[old_len:new_len] = refpoint_list
        cluster_list.clear()
        refpoint_list.clear()

    for idxFrame in range(startFrame, endFrame):
        if idxFrame % 10000 == 0:
            print(f'Processing frame {idxFrame}...')
        idxFile, idxFrame = divmod(idxFrame, nFramePerFile)
        try:
            if 'nFiber' in _cfg and _cfg['nFiber'] == 2:
                offset = (headerSize + idxFrame * (NX * NY//2 + headerSize)) * 2
                fileName1 = signalFileNames[idxFile]
                fileName2 = fileName1.replace('d0', 'd1')
                _signalAduFrame1 = np.fromfile(f'{fileName1}', dtype=np.uint16, offset=offset, count=NX * NY //2).astype(np.int32).reshape(NY//2, NX)
                _signalAduFrame2 = np.fromfile(f'{fileName2}', dtype=np.uint16, offset=offset, count=NX * NY //2).astype(np.int32).reshape(NY//2, NX)
                signalAduFrame = np.concatenate([_signalAduFrame1, _signalAduFrame2], axis=0)
                del _signalAduFrame1, _signalAduFrame2
            else:
                offset = (headerSize + idxFrame * (NX * NY + headerSize)) * 2
                signalAduFrame = np.fromfile(f'{signalFileNames[idxFile]}', dtype=np.uint16, offset=offset, count=NX * NY).astype(np.int32).reshape(NX, NY)
        except Exception as e:
            print(f'Error reading frame {idxFrame} from file {signalFileNames[idxFile]}: {e}, stop processing this chunk ({startFrame}-{endFrame})')
            return _hists

        signalAduFrame = signalAduFrame - _pedestalAduFrame
        signalEneFrame = convertAduFrameToEnergyFrame(signalAduFrame)

        CF.find_clusters_X(signalEneFrame[Roi[2]:Roi[3], Roi[0]:Roi[1]])
        clusters = CF.hits()
        for idxCluster in range(len(clusters)):
            energy = clusters['energy'][idxCluster]
            _hists['h1_ClusterEnergy'].Fill(energy)
            xs = clusters['cols'][idxCluster]
            ys = clusters['rows'][idxCluster]
            enes = clusters['enes'][idxCluster]
            if energy < 5:
                continue
            for i in range(len(xs)):
                _hists['h1_PixelEnergy'].Fill(enes[i])
                _hists['h2_HitsSumFrame'].Fill(xs[i]+Roi[0], ys[i]+Roi[2], enes[i])

    if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
        flush_h5_buffer()
        h5_file.close()
    return _hists

def process():
    with Pool(_cfg['NThread'], maxtasksperchild=1) as p:
        results = p.map(_processFrames, range(nChunks))

    global hists
    hists = bookHistograms(_cfg['energy'])
    for h in hists.values():
        h.SetDirectory(0)
        
    for _hists in results:
        hists['h1_ClusterEnergy'].Add(_hists['h1_ClusterEnergy'])
        hists['h1_PixelEnergy'].Add(_hists['h1_PixelEnergy'])
        hists['h1_CenterPixelEnergy'].Add(_hists['h1_CenterPixelEnergy'])
        hists['h2_Qc'].Add(_hists['h2_Qc'])
        hists['h2_interpolated'].Add(_hists['h2_interpolated'])
        hists['h2_interpolatedSubpixel'].Add(_hists['h2_interpolatedSubpixel'])
        hists['h2_counts'].Add(_hists['h2_counts'])
        hists['h2_HitsSumFrame'].Add(_hists['h2_HitsSumFrame'])
        del _hists

def getPedestalAndNoise():
    NX, NY = _cfg['NX'], _cfg['NY']
    pedestalFileName = _cfg['pedestalFileName']
    if 'nFiber' in _cfg and _cfg['nFiber'] == 2:
        pedestalFileName2 = pedestalFileName.replace('d0', 'd1')
        _pedestalAduFrames1 = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
        _pedestalAduFrames1 = _pedestalAduFrames1.reshape(-1, NX * NY //2 + 56)
        _pedestalAduFrames1 = _pedestalAduFrames1[:, 56:].reshape(-1, NY//2, NX)
        _pedestalAduFrames2 = np.fromfile(f'{pedestalFileName2}', dtype=np.uint16).astype(np.int32)
        _pedestalAduFrames2 = _pedestalAduFrames2.reshape(-1, NX * NY //2 + 56)
        _pedestalAduFrames2 = _pedestalAduFrames2[:, 56:].reshape(-1, NY//2, NX)
        pedestalAduFrames = np.concatenate([_pedestalAduFrames1, _pedestalAduFrames2], axis=1)
        del _pedestalAduFrames1, _pedestalAduFrames2
    else:
        pedestalAduFrames = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
        pedestalAduFrames = pedestalAduFrames.reshape(-1, NX * NY + 56)
        pedestalAduFrames = pedestalAduFrames[:, 56:].reshape(-1, NX, NY)

    pedestalAduFrames = pedestalAduFrames[pedestalAduFrames.shape[0]//10:]  # skip the first 10% frames
    global _pedestalAduFrame, _noiseEneFrame
    _pedestalAduFrame = np.mean(pedestalAduFrames, axis=0)
    noiseAduFrame = np.std(pedestalAduFrames, axis=0, ddof=1)
    with Pool(16) as p:
        pedestalEneFrames = p.map(convertAduFrameToEnergyFrame, pedestalAduFrames)
    _noiseEneFrame = np.std(pedestalEneFrames, axis=0, ddof=1)
    print(f'Average noise = {np.mean(_noiseEneFrame):.3f} keV; {np.mean(noiseAduFrame):.3f} ADU')
    del pedestalAduFrames, pedestalEneFrames

def getPedestalAndNoise_simplified():
    NX, NY = _cfg['NX'], _cfg['NY']
    pedestalFileName = _cfg['pedestalFileName']
    pedestalAduFrames = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
    pedestalAduFrames = pedestalAduFrames.reshape(-1, NX * NY + 56)
    pedestalAduFrames = pedestalAduFrames[:, 56:].reshape(-1, NX, NY)
    pedestalAduFrames = pedestalAduFrames[pedestalAduFrames.shape[0]//10:]  # skip the first 10% frames
    global _pedestalAduFrame, _noiseEneFrame
    _pedestalAduFrame = np.mean(pedestalAduFrames, axis=0)
    noiseAduFrame = np.std(pedestalAduFrames, axis=0, ddof=1)
    _noiseEneFrame = convertAduFrameToEnergyFrame(noiseAduFrame)
    
    print(f'Average noise = {np.mean(_noiseEneFrame):.3f} keV; {np.mean(noiseAduFrame):.3f} ADU')
    del pedestalAduFrames