How to use the tifffile.tifffile function in tifffile

d = {key: {k: astype(v[0]) if len(v) == 1 else astype(v)
                       for k, v in dd.items()}}
        if t.attrib:
            d[key].update((at + k, astype(v)) for k, v in t.attrib.items())
        if t.text:
            text = t.text.strip()
            if children or t.attrib:
                if text:
                    d[key][tx + 'value'] = astype(text)
            else:
                d[key] = astype(text)
        return d

    return etree2dict(etree.fromstring(xml))

tifff.xml2dict = xml2dict


def astype(value):
    if isinstance(value, six.string_types) and (value[0].isdigit() or value[-1].isdigit()):
        if value.isdigit():
            return int(value)
        else:
            try:
                return float(value)
            except:
                try:
                    return tifff.asbool(value)
                except:
                    return value
    else:
        try:

flatThetaDataset[flatImageCounter] = rotmax
                flatImageName = samplename + str(flatImageCounter +offset +1).zfill(4) + "." + "tif"
                flatImageFullPath = tifdir + "/" + flatImageName
                with tifffile(flatImageFullPath) as tif:
                        imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        flatImagesDataset[flatImageCounter, :, :] = imageAs2DArray

        rawImagesDataset = exchangeGrp.create_dataset('data', (nprj*grid_steps, height, width), 'uint16')
        dataThetaDataset = exchangeGrp.create_dataset('theta_data', (nprj*grid_steps,), 'float') # Create dataset
        for rawDataImageCounter in range(0, nprj*grid_steps):
                #print rawDataImageCounter//grid_steps
                dataThetaDataset[rawDataImageCounter] = rotmin+(rawDataImageCounter//grid_steps)*angularStep
                print "Projection " + str(rawDataImageCounter)
                rawDataImageName = samplename + str(rawDataImageCounter +ndrk +nflt*grid_steps +1).zfill(4) + "." + "tif"
                rawDataImageFullPath = tifdir + "/" + rawDataImageName
                with tifffile(rawDataImageFullPath) as tif:
                        imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        rawImagesDataset[rawDataImageCounter, :, :] = imageAs2DArray

        imageFile.close() # Close file
        endtime = time.clock()
        print "time to write", str(endtime - starttime), "sec"
        print "speed", 2*(20+400+1441)*2048*2048/((endtime - starttime)*1000000000), "Gigabyte/s"

grid_periods = int(linelist[3])
                                interferometerGrp.create_dataset('number_of_grid_periods', data = int(linelist[3]))
		
        FILE.close()
	
        imageAs2DArray = np.zeros((height,width), dtype=np.uint16)
                
        starttime = time.clock()
        darkImagesDataset = exchangeGrp.create_dataset('data_dark', (ndrk, height, width), 'uint16') # Create dataset
        darkThetaDataset = exchangeGrp.create_dataset('theta_dark', (ndrk,), 'float') # Create dataset
        for darkImageCounter in range(0, ndrk):
                print "Dark " + str(darkImageCounter)
                darkThetaDataset[darkImageCounter] = rotmin
                darkImageName = samplename + str(darkImageCounter +1).zfill(4) + "." + "tif"
                darkImageFullPath = tifdir + "/" + darkImageName
                with tifffile(darkImageFullPath) as tif:
                        imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        darkImagesDataset[darkImageCounter, :, :] = imageAs2DArray # Fill dataset

        flatImagesDataset = exchangeGrp.create_dataset('data_white', (2*nflt*grid_steps, height, width), 'uint16')
        flatThetaDataset = exchangeGrp.create_dataset('theta_flat', (2*nflt*grid_steps,), 'float') # Create dataset
        for flatImageCounter in range(0, 2*nflt*grid_steps):
                print "Flat " + str(flatImageCounter)
                if flatImageCounter < nflt*grid_steps:
                        offset = ndrk
                        flatThetaDataset[flatImageCounter] = rotmin
                else:
                        offset = ndrk+nprj*grid_steps
                        flatThetaDataset[flatImageCounter] = rotmax
                flatImageName = samplename + str(flatImageCounter +offset +1).zfill(4) + "." + "tif"
                flatImageFullPath = tifdir + "/" + flatImageName
                with tifffile(flatImageFullPath) as tif:

imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        darkImagesDataset[darkImageCounter, :, :] = imageAs2DArray # Fill dataset

        flatImagesDataset = exchangeGrp.create_dataset('data_white', (2*nflt*grid_steps, height, width), 'uint16')
        flatThetaDataset = exchangeGrp.create_dataset('theta_flat', (2*nflt*grid_steps,), 'float') # Create dataset
        for flatImageCounter in range(0, 2*nflt*grid_steps):
                print "Flat " + str(flatImageCounter)
                if flatImageCounter < nflt*grid_steps:
                        offset = ndrk
                        flatThetaDataset[flatImageCounter] = rotmin
                else:
                        offset = ndrk+nprj*grid_steps
                        flatThetaDataset[flatImageCounter] = rotmax
                flatImageName = samplename + str(flatImageCounter +offset +1).zfill(4) + "." + "tif"
                flatImageFullPath = tifdir + "/" + flatImageName
                with tifffile(flatImageFullPath) as tif:
                        imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        flatImagesDataset[flatImageCounter, :, :] = imageAs2DArray

        rawImagesDataset = exchangeGrp.create_dataset('data', (nprj*grid_steps, height, width), 'uint16')
        dataThetaDataset = exchangeGrp.create_dataset('theta_data', (nprj*grid_steps,), 'float') # Create dataset
        for rawDataImageCounter in range(0, nprj*grid_steps):
                #print rawDataImageCounter//grid_steps
                dataThetaDataset[rawDataImageCounter] = rotmin+(rawDataImageCounter//grid_steps)*angularStep
                print "Projection " + str(rawDataImageCounter)
                rawDataImageName = samplename + str(rawDataImageCounter +ndrk +nflt*grid_steps +1).zfill(4) + "." + "tif"
                rawDataImageFullPath = tifdir + "/" + rawDataImageName
                with tifffile(rawDataImageFullPath) as tif:
                        imageAs2DArray = tif.asarray()  # image is a np.ndarray
                        rawImagesDataset[rawDataImageCounter, :, :] = imageAs2DArray

        imageFile.close() # Close file