How to use the hydrafloods.processing.hfCollection function in hydrafloods

iters = ee.List.sequence(0,nIters-1)

            sims = ee.ImageCollection(iters.map(_threholdWrapper))
            probs = sims.sum().divide(nIters).rename(['probability'])
            water = probs.select(['probability']).gt(probTreshold).rename('water')
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = geeutils.globalOtsu(self.downscaled,target_date,self.region,**kwargs)\
                .And(hand.lt(30))

        return mapResult



class Landsat(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Landsat, self).__init__(*args,**kwargs)

        self.collection = self.collection.map(self._qaMask)\
            .select(BANDREMAP.get('landsat'),BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        return

    def _qaMask(self,img):
        qaCloud = geeutils.extractBits(img.select('pixel_qa'),5,5,'qa').neq(1)
        qaShadow = geeutils.extractBits(img.select('pixel_qa'),3,3,'qa').neq(1)
        mask = qaCloud.And(qaShadow)
        t = ee.Date(img.get('system:time_start'))
        nDays = t.difference(INITIME,'day')
        time = ee.Image(nDays).int16().rename('time')

self.iniTime = time_start
        self.endTime = time_end
        self.id = collectionid

        self.collection = ee.ImageCollection(self.id)\
                            .filterBounds(self.region)\
                            .filterDate(self.iniTime,self.endTime)

        return

    def clip(self,img):
        return img.clip(self.region)



class Sentinel1(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Sentinel1, self).__init__(*args,**kwargs)

        self.collection = self.collection\
                            .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))\
                            .select('VV')

        return


    def waterMap(self,target_date,**kwargs):
        mapResult = geeutils.bootstrapOtsu(self.collection,target_date,**kwargs)

        return mapResult

    def _maskEdges(self,img):

if probablistic:
            iters = ee.List.sequence(0,nIters-1)

            sims = ee.ImageCollection(iters.map(_downscaleWrapper))
            probs = sims.sum().divide(nIters).rename(['probability','error'])
            water = probs.select(['probability'],['water']).gt(probTreshold)
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = downscale.bathtub(inImage,hand,permanent)

        return mapResult



class Viirs(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Viirs, self).__init__(*args,**kwargs)

        self.collection = self.collection.map(self._qaMask)\
            .select(BANDREMAP.get('viirs'),BANDREMAP.get('new'))\
            .map(self.clip)\
            .map(geeutils.addIndices)

        return

    def _qaMask(self,img):
        viewing = img.select('SensorZenith').abs().multiply(0.01).lt(45)
        clouds = geeutils.extractBits(img.select('QF1'),2,3,'cloud_qa').lte(2)
        shadows = geeutils.extractBits(img.select('QF2'),3,3,'shadow_qa').eq(0)
        aerosols = geeutils.extractBits(img.select('QF2'),4,4,'aerosol_qa').eq(0)
        snows = geeutils.extractBits(img.select('QF2'),5,5,'snow_qa').eq(0)

return


    def waterMap(self,target_date,**kwargs):
        mapResult = geeutils.bootstrapOtsu(self.collection,target_date,**kwargs)

        return mapResult

    def _maskEdges(self,img):
        angles = img.select('angle')
        return img.updateMask(angles.lt(45).And(angles.gt(31)))



class Atms(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Atms, self).__init__(*args,**kwargs)

        return


    def extract(self,date,region,outdir='./',creds=None,gridding_radius=50000):
        files = fetch.atms(date,region,outdir,creds)
        geotiffs = list(map(lambda x: preprocess.atms(x,gridding_radius), files))
        return geotiffs


    def load(self,files,gcsBucket='',eeAsset=''):
        if gcsBucket[-1] != '/':
            gcsBucket += '/'
        if eeAsset[-1] != '/':

iters = ee.List.sequence(0,nIters-1)

            sims = ee.ImageCollection(iters.map(_threholdWrapper))
            probs = sims.sum().divide(nIters).rename(['probability'])
            water = probs.select(['probability']).gt(probTreshold).rename('water')
            mapResult = water.addBands(probs.multiply(10000).uint16())

        else:
            mapResult = geeutils.globalOtsu(self.downscaled,target_date,self.region,**kwargs)\
                .And(hand.lt(30))

        return mapResult



class Modis(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Modis, self).__init__(*args,**kwargs)

        self.collection = self.collection.map(self._qaMask)\
            .select(BANDREMAP.get('modis'),BANDREMAP.get('new'))\
            .map(self.clip)\
            .map(geeutils.addIndices)

        return

    def _qaMask(self,img):
        viewing = img.select('SensorZenith').abs().multiply(0.01).lt(45)
        clouds = geeutils.extractBits(img.select('state_1km'),10,10,'cloud_qa').neq(1)
        shadows = geeutils.extractBits(img.select('state_1km'),2,2,'shadow_qa').eq(0)
        aerosols = geeutils.extractBits(img.select('state_1km'),6,7,'aerosol_qa').neq(0)
        snows = geeutils.extractBits(img.select('state_1km'),15,15,'snow_qa').eq(0)

return

    def _qaMask(self,img):
        qaCloud = geeutils.extractBits(img.select('pixel_qa'),5,5,'qa').neq(1)
        qaShadow = geeutils.extractBits(img.select('pixel_qa'),3,3,'qa').neq(1)
        mask = qaCloud.And(qaShadow)
        t = ee.Date(img.get('system:time_start'))
        nDays = t.difference(INITIME,'day')
        time = ee.Image(nDays).int16().rename('time')
        return img.updateMask(mask).addBands(time).uint16()




class Sentinel2(hfCollection):
    def __init__(self,*args,**kwargs):
        super(Sentinel2, self).__init__(*args,**kwargs)

        self.collection = self.collection.map(self._qaMask)\
            .select(BANDREMAP.get('sen2'),BANDREMAP.get('new'))\
            .map(geeutils.addIndices)

        return

    def _qaMask(self,img):
        sclImg = img.select('SCL') # Scene Classification Map
        mask = sclImg.gte(4).And(sclImg.lte(6))
        t = ee.Date(img.get('system:time_start'))
        nDays = t.difference(INITIME,'day')
        time = ee.Image(nDays).int16().rename('time')
        return img.updateMask(mask).addBands(time).uint16()