# -*- coding: utf-8 -*-
#
#    Project: Azimuthal integration
#             https://github.com/silx-kit/pyFAI
#
#    Copyright (C) 2012-2023 European Synchrotron Radiation Facility, Grenoble, France
#
#    Principal author:       Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
#  Permission is hereby granted, free of charge, to any person obtaining a copy
#  of this software and associated documentation files (the "Software"), to deal
#  in the Software without restriction, including without limitation the rights
#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#  copies of the Software, and to permit persons to whom the Software is
#  furnished to do so, subject to the following conditions:
#  .
#  The above copyright notice and this permission notice shall be included in
#  all copies or substantial portions of the Software.
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#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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__author__ = "Jérôme Kieffer"
__license__ = "MIT"
__date__ = "24/04/2024"
__copyright__ = "2012-2021, ESRF, Grenoble"
__contact__ = "jerome.kieffer@esrf.fr"

import logging
from collections import OrderedDict
import numpy
from . import pyopencl, dtype_converter
from ..utils import calc_checksum
if pyopencl:
    mf = pyopencl.mem_flags
else:
    raise ImportError("pyopencl is not installed")
from ..containers import Integrate1dtpl, Integrate2dtpl, ErrorModel
from . import processing, OpenclProcessing
EventDescription = processing.EventDescription
BufferDescription = processing.BufferDescription

logger = logging.getLogger(__name__)


class OCL_LUT_Integrator(OpenclProcessing):
    """Class in charge of doing a sparse-matrix multiplication in OpenCL
    using the LUT representation of the matrix.
    It also performs the preprocessing using the preproc kernel
    """
    BLOCK_SIZE = 32
    buffers = [BufferDescription("output", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("output4", 4, numpy.float32, mf.READ_WRITE),
               BufferDescription("tmp", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("image_raw", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("image", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("variance", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("dark", 1, numpy.float32, mf.READ_WRITE),
               BufferDescription("dark_variance", 1, numpy.float32, mf.READ_ONLY),
               BufferDescription("flat", 1, numpy.float32, mf.READ_ONLY),
               BufferDescription("polarization", 1, numpy.float32, mf.READ_ONLY),
               BufferDescription("solidangle", 1, numpy.float32, mf.READ_ONLY),
               BufferDescription("absorption", 1, numpy.float32, mf.READ_ONLY),
               BufferDescription("mask", 1, numpy.int8, mf.READ_ONLY),
               ]
    kernel_files = ["silx:opencl/doubleword.cl",
                    "pyfai:openCL/preprocess.cl",
                    "pyfai:openCL/memset.cl",
                    "pyfai:openCL/ocl_azim_LUT.cl"
                    ]
    mapping = {numpy.int8: "s8_to_float",
               numpy.uint8: "u8_to_float",
               numpy.int16: "s16_to_float",
               numpy.uint16: "u16_to_float",
               numpy.uint32: "u32_to_float",
               numpy.uintc: "u32_to_float",
               numpy.int32: "s32_to_float",
               numpy.intc: "s32_to_float"
               }

    def __init__(self, lut, image_size, checksum=None,
                 empty=None, unit=None, bin_centers=None, azim_centers=None, mask_checksum=None,
                 ctx=None, devicetype="all", platformid=None, deviceid=None,
                 block_size=None, profile=False):
        """Constructor of the OCL_LUT_Integrator class

        :param lut: array of int32 - float32 with shape (nbins, lut_size) with indexes and coefficients
        :param image_size: Expected image size: image.size
        :param checksum: pre-calculated checksum of the LUT to prevent re-calculating it :)
        :param empty: value to be assigned to bins without contribution from any pixel
        :param unit: Storage for the unit related to the LUT
        :param bin_centers: the radial position of the bin_center, place_holder
        :param azim_centers: the radial position of the bin_center, place_holder
        :param mask_checksum: placeholder for the checksum of the mask
        :param ctx: actual working context, left to None for automatic
                    initialization from device type or platformid/deviceid
        :param devicetype: type of device, can be "CPU", "GPU", "ACC" or "ALL"
        :param platformid: integer with the platform_identifier, as given by clinfo
        :param deviceid: Integer with the device identifier, as given by clinfo
        :param block_size: preferred workgroup size, may vary depending on the outpcome of the compilation
        :param profile: switch on profiling to be able to profile at the kernel level,
                        store profiling elements (makes code slightly slower)
        """
        OpenclProcessing.__init__(self, ctx=ctx, devicetype=devicetype,
                                  platformid=platformid, deviceid=deviceid,
                                  block_size=block_size, profile=profile)
        self._lut = lut
        self.nbytes = lut.nbytes
        self.bins, self.lut_size = lut.shape
        self.size = image_size
        self.empty = empty or 0  # numpy.nan
        self.unit = unit
        self.space = tuple(str(u).split("_")[0] for u in unit) if isinstance(unit, (list, tuple)) else  str(unit).split("_")[0]
        self.bin_centers = bin_centers
        self.azim_centers = azim_centers
        # a few place-folders
        self.mask_checksum = mask_checksum
        self.pos0_range = self.pos1_range = None

        if not checksum:
            checksum = calc_checksum(self._lut)
        self.on_device = {"lut": checksum,
                          "dark": None,
                          "flat": None,
                          "polarization": None,
                          "solidangle": None,
                          "absorption": None}

        self.BLOCK_SIZE = min(self.BLOCK_SIZE, self.device.max_work_group_size)
        self.workgroup_size = self.BLOCK_SIZE,  # Note this is a tuple
        self.wdim_bins = int(self.bins + self.BLOCK_SIZE - 1) // self.BLOCK_SIZE * self.BLOCK_SIZE,
        self.wdim_data = int(self.size + self.BLOCK_SIZE - 1) // self.BLOCK_SIZE * self.BLOCK_SIZE,

        self.buffers = [BufferDescription(i.name, i.size * self.size, i.dtype, i.flags)
                        for i in self.__class__.buffers]
        self.buffers += [BufferDescription("lut", self.bins * self.lut_size,
                                           [("bins", numpy.float32),
                                            ("lut_size", numpy.int32)], mf.READ_ONLY),
                         BufferDescription("sum_data", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("sum_count", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("averint", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("sem", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("std", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("merged", self.bins, numpy.float32, mf.WRITE_ONLY),
                         BufferDescription("merged8", (self.bins, 8), numpy.float32, mf.WRITE_ONLY),
                         ]
        self.allocate_buffers()
        self.buffer_dtype = {i.name:numpy.dtype(i.dtype) for i in self.buffers}
        self.compile_kernels()
        self.set_kernel_arguments()
        if self.device.type == "CPU":
            ev = pyopencl.enqueue_copy(self.queue, self.cl_mem["lut"], lut)
        else:
            ev = pyopencl.enqueue_copy(self.queue, self.cl_mem["lut"], lut.T.copy())
        self.profile_add(ev, "copy LUT")

    @property
    def checksum(self):
        return self.on_device.get("data")

    @property
    def check_mask(self):
        return self.mask_checksum is not None

    def __copy__(self):
        """Shallow copy of the object

        :return: copy of the object
        """
        return self.__class__(self._lut, self.size,
                              checksum=self.on_device.get("lut"),
                              empty=self.empty,
                              ctx=self.ctx,
                              block_size=self.block_size,
                              profile=self.profile)

    def __deepcopy__(self, memo=None):
        """deep copy of the object

        :return: deepcopy of the object
        """
        if memo is None:
            memo = {}
        new_lut = self._lut.copy()
        memo[id(self._lut)] = new_lut
        new_obj = self.__class__(new_lut, self.size,
                                 checksum=self.on_device.get("lut"),
                                 empty=self.empty,
                                 ctx=self.ctx,
                                 block_size=self.block_size,
                                 profile=self.profile)
        memo[id(self)] = new_obj
        return new_obj

    def compile_kernels(self, kernel_file=None):
        """
        Call the OpenCL compiler
        :param kernel_file: path to the kernel (by default use the one in the resources directory)
        """
        # concatenate all needed source files into a single openCL module
        kernel_file = kernel_file or self.kernel_files[-1]
        kernels = self.kernel_files[:-1] + [kernel_file]

        compile_options = "-D NBINS=%i  -D NIMAGE=%i -D NLUT=%i -D ON_CPU=%i" % \
                          (self.bins, self.size, self.lut_size, int(self.device.type == "CPU"))

        default_compiler_options = self.get_compiler_options(x87_volatile=True)
        if default_compiler_options:
            compile_options += " " + default_compiler_options
        OpenclProcessing.compile_kernels(self, kernels, compile_options)

    def set_kernel_arguments(self):
        """Tie arguments of OpenCL kernel-functions to the actual kernels

        set_kernel_arguments() is a private method, called by configure().
        It uses the dictionary _cl_kernel_args.
        Note that by default, since TthRange is disabled, the integration kernels have tth_min_max tied to the tthRange argument slot.
        When setRange is called it replaces that argument with tthRange low and upper bounds. When unsetRange is called, the argument slot
        is reset to tth_min_max.
        """
        self.cl_kernel_args["corrections"] = OrderedDict((("image", self.cl_mem["image"]),
                                                          ("do_dark", numpy.int8(0)),
                                                          ("dark", self.cl_mem["dark"]),
                                                          ("do_flat", numpy.int8(0)),
                                                          ("flat", self.cl_mem["flat"]),
                                                          ("do_solidangle", numpy.int8(0)),
                                                          ("solidangle", self.cl_mem["solidangle"]),
                                                          ("do_polarization", numpy.int8(0)),
                                                          ("polarization", self.cl_mem["polarization"]),
                                                          ("do_absorption", numpy.int8(0)),
                                                          ("absorption", self.cl_mem["absorption"]),
                                                          ("do_mask", numpy.int8(0)),
                                                          ("mask", self.cl_mem["mask"]),
                                                          ("do_dummy", numpy.int8(0)),
                                                          ("dummy", numpy.float32(0)),
                                                          ("delta_dummy", numpy.float32(0)),
                                                          ("normalization_factor", numpy.float32(1.0)),
                                                          ("apply_normalization", numpy.int8(0)),
                                                          ("output", self.cl_mem["output"])))

        self.cl_kernel_args["lut_integrate"] = OrderedDict((("output", self.cl_mem["output"]),
                                                            ("lut", self.cl_mem["lut"]),
                                                            ("do_dummy", numpy.int8(0)),
                                                            ("dummy", numpy.float32(0)),
                                                            ("coef_power", numpy.int32(1)),
                                                            ("sum_data", self.cl_mem["sum_data"]),
                                                            ("sum_count", self.cl_mem["sum_count"]),
                                                            ("merged", self.cl_mem["merged"])))
        self.cl_kernel_args["corrections4"] = OrderedDict((("image", self.cl_mem["image"]),
                                                           ("error_model", numpy.int8(0)),
                                                           ("variance", self.cl_mem["variance"]),
                                                           ("do_dark", numpy.int8(0)),
                                                           ("dark", self.cl_mem["dark"]),
                                                           ("do_dark_variance", numpy.int8(0)),
                                                           ("dark_variance", self.cl_mem["dark_variance"]),
                                                           ("do_flat", numpy.int8(0)),
                                                           ("flat", self.cl_mem["flat"]),
                                                           ("do_solidangle", numpy.int8(0)),
                                                           ("solidangle", self.cl_mem["solidangle"]),
                                                           ("do_polarization", numpy.int8(0)),
                                                           ("polarization", self.cl_mem["polarization"]),
                                                           ("do_absorption", numpy.int8(0)),
                                                           ("absorption", self.cl_mem["absorption"]),
                                                           ("do_mask", numpy.int8(0)),
                                                           ("mask", self.cl_mem["mask"]),
                                                           ("do_dummy", numpy.int8(0)),
                                                           ("dummy", numpy.float32(0)),
                                                           ("delta_dummy", numpy.float32(0)),
                                                           ("normalization_factor", numpy.float32(1.0)),
                                                           ("apply_normalization", numpy.int8(0)),
                                                           ("output4", self.cl_mem["output4"])))
        self.cl_kernel_args["lut_integrate4"] = OrderedDict((("output4", self.cl_mem["output4"]),
                                                            ("lut", self.cl_mem["lut"]),
                                                            ("empty", numpy.float32(self.empty)),
                                                            ("merged8", self.cl_mem["merged8"]),
                                                            ("averint", self.cl_mem["averint"]),
                                                            ("std", self.cl_mem["std"]),
                                                            ("sem", self.cl_mem["sem"]),
                                                            ))

        self.cl_kernel_args["memset_out"] = OrderedDict(((i, self.cl_mem[i]) for i in ("sum_data", "sum_count", "merged")))
        self.cl_kernel_args["memset_ng"] = OrderedDict(((i, self.cl_mem[i]) for i in ("averint", "sem", "merged8")))
        self.cl_kernel_args["u8_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))
        self.cl_kernel_args["s8_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))
        self.cl_kernel_args["u16_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))
        self.cl_kernel_args["s16_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))
        self.cl_kernel_args["u32_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))
        self.cl_kernel_args["s32_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("image_raw", "image")))

    def send_buffer(self, data, dest, checksum=None):
        """Send a numpy array to the device, including the cast on the device if possible

        :param data: numpy array with data
        :param dest: name of the buffer as registered in the class
        :param convert: if True (default) convert dtype on GPU, if false, leave as it is.
        :return: the actual buffer where the data were sent
        """

        dest_type = self.buffer_dtype[dest]
        events = []
        if (data.dtype == dest_type) or (data.dtype.itemsize > dest_type.itemsize):
            copy_image = pyopencl.enqueue_copy(self.queue, self.cl_mem[dest], numpy.ascontiguousarray(data, dest_type))
            events.append(EventDescription("copy %s" % dest, copy_image))
        else:
            copy_image = pyopencl.enqueue_copy(self.queue, self.cl_mem["image_raw"], numpy.ascontiguousarray(data))
            kernel = self.kernels.get_kernel(self.mapping[data.dtype.type])
            cast_to_float = kernel(self.queue, (self.size,), None, self.cl_mem["image_raw"], self.cl_mem[dest])
            events += [EventDescription("copy raw %s" % dest, copy_image), EventDescription("cast to float", cast_to_float)]
        self.profile_multi(events)
        if checksum is not None:
            self.on_device[dest] = checksum

    def integrate_legacy(self, data, dummy=None, delta_dummy=None,
                         dark=None, flat=None, solidangle=None, polarization=None, absorption=None,
                         dark_checksum=None, flat_checksum=None, solidangle_checksum=None,
                         polarization_checksum=None, absorption_checksum=None,
                         preprocess_only=False, safe=True,
                         normalization_factor=1.0, coef_power=1,
                         out_merged=None, out_sum_data=None, out_sum_count=None):
        """
        Before performing azimuthal integration, the preprocessing is:

        .. math::

            data = (data - dark) / (flat * solidangle * polarization * absorption)

        Integration is performed using the LUT representation of the look-up table

        :param dark: array of same shape as data for pre-processing
        :param flat: array of same shape as data for pre-processing
        :param solidangle: array of same shape as data for pre-processing
        :param polarization: array of same shape as data for pre-processing
        :param dark_checksum: CRC32 checksum of the given array
        :param flat_checksum: CRC32 checksum of the given array
        :param solidangle_checksum: CRC32 checksum of the given array
        :param polarization_checksum: CRC32 checksum of the given array
        :param safe: if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
        :param preprocess_only: return the dark subtracted; flat field & solidangle & polarization corrected image, else
        :param normalization_factor: divide raw signal by this value
        :param coef_power: set to 2 for variance propagation, leave to 1 for mean calculation
        :param out_merged: destination array or pyopencl array for averaged data
        :param out_sum_data: destination array or pyopencl array for sum of all data
        :param out_sum_count: destination array or pyopencl array for sum of the number of pixels
        :return: averaged data, weighted histogram, unweighted histogram
        """
        events = []
        with self.sem:
            self.send_buffer(data, "image")
            memset = self.kernels.memset_out(self.queue, self.wdim_bins, self.workgroup_size, *list(self.cl_kernel_args["memset_out"].values()))
            events.append(EventDescription("memset", memset))
            kw_corr = self.cl_kernel_args["corrections"]
            kw_int = self.cl_kernel_args["lut_integrate"]
            if dummy is not None:
                do_dummy = numpy.int8(1)
                dummy = numpy.float32(dummy)
                if delta_dummy is None:
                    delta_dummy = numpy.float32(0.0)
                else:
                    delta_dummy = numpy.float32(abs(delta_dummy))
            else:
                do_dummy = numpy.int8(0)
                dummy = numpy.float32(self.empty)
                delta_dummy = numpy.float32(0.0)

            kw_corr["do_dummy"] = do_dummy
            kw_corr["dummy"] = dummy
            kw_corr["delta_dummy"] = delta_dummy
            kw_corr["normalization_factor"] = numpy.float32(normalization_factor)
            kw_int["do_dummy"] = do_dummy
            kw_int["dummy"] = dummy
            kw_int["coef_power"] = numpy.int32(coef_power)

            if dark is not None:
                do_dark = numpy.int8(1)
                if not dark_checksum:
                    dark_checksum = calc_checksum(dark, safe)
                if dark_checksum != self.on_device["dark"]:
                    self.send_buffer(dark, "dark", dark_checksum)
            else:
                do_dark = numpy.int8(0)
            kw_corr["do_dark"] = do_dark
            if flat is not None:
                do_flat = numpy.int8(1)
                if not flat_checksum:
                    flat_checksum = calc_checksum(flat, safe)
                if self.on_device["flat"] != flat_checksum:
                    self.send_buffer(flat, "flat", flat_checksum)
            else:
                do_flat = numpy.int8(0)
            kw_corr["do_flat"] = do_flat

            if solidangle is not None:
                do_solidangle = numpy.int8(1)
                if not solidangle_checksum:
                    solidangle_checksum = calc_checksum(solidangle, safe)
                if solidangle_checksum != self.on_device["solidangle"]:
                    self.send_buffer(solidangle, "solidangle", solidangle_checksum)
            else:
                do_solidangle = numpy.int8(0)
            kw_corr['do_solidangle'] = do_solidangle

            if polarization is not None:
                do_polarization = numpy.int8(1)
                if not polarization_checksum:
                    polarization_checksum = calc_checksum(polarization, safe)
                if polarization_checksum != self.on_device["polarization"]:
                    self.send_buffer(polarization, "polarization", polarization_checksum)
                    self.on_device["polarization"] = polarization_checksum
            else:
                do_polarization = numpy.int8(0)
            kw_corr["do_polarization"] = do_polarization

            if absorption is not None:
                do_absorption = numpy.int8(1)
                if not absorption_checksum:
                    absorption_checksum = calc_checksum(absorption, safe)
                if absorption_checksum != self.on_device["absorption"]:
                    self.send_buffer(absorption, "absorption", absorption_checksum)
            else:
                do_absorption = numpy.int8(0)
            kw_corr["do_absorption"] = do_absorption

            ev = self.kernels.corrections(self.queue, self.wdim_data, self.workgroup_size, *list(kw_corr.values()))
            events.append(EventDescription("corrections", ev))

            if preprocess_only:
                image = numpy.empty(data.shape, dtype=numpy.float32)
                ev = pyopencl.enqueue_copy(self.queue, image, self.cl_mem["output"])
                events.append(EventDescription("copy D->H image", ev))
                ev.wait()
                self.profile_multi(events)
                return image
            integrate = self.kernels.lut_integrate(self.queue, self.wdim_bins, self.workgroup_size, *list(kw_int.values()))
            events.append(EventDescription("integrate", integrate))
            if out_merged is None:
                merged = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_merged is False:
                merged = None
            else:
                merged = out_merged.data
            if out_sum_count is None:
                sum_count = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_sum_count is False:
                sum_count = None
            else:
                sum_count = out_sum_count.data
            if out_sum_data is None:
                sum_data = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_sum_data is False:
                sum_data = None
            else:
                sum_data = out_sum_data.data
            if merged is not None:
                ev = pyopencl.enqueue_copy(self.queue, merged, self.cl_mem["merged"])
                events.append(EventDescription("copy D->H merged", ev))
            if sum_data is not None:
                ev = pyopencl.enqueue_copy(self.queue, sum_data, self.cl_mem["sum_data"])
                events.append(EventDescription("copy D->H sum_data", ev))
            if sum_count is not None:
                ev = pyopencl.enqueue_copy(self.queue, sum_count, self.cl_mem["sum_count"])
                events.append(EventDescription("copy D->H sum_count", ev))
            ev.wait()
        self.profile_multi(events)
        return merged, sum_data, sum_count

    integrate = integrate_legacy

    def integrate_ng(self, data, dark=None, dummy=None, delta_dummy=None,
                     error_model=ErrorModel.NO, variance=None, dark_variance=None,
                     flat=None, solidangle=None, polarization=None, absorption=None,
                     dark_checksum=None, flat_checksum=None, solidangle_checksum=None,
                     polarization_checksum=None, absorption_checksum=None, dark_variance_checksum=None,
                     safe=True,
                     normalization_factor=1.0, weighted_average=True,
                     out_avgint=None, out_sem=None, out_std=None, out_merged=None):
        """
        Before performing azimuthal integration with proper variance propagation, the preprocessing is:

        .. math::

            signal = (raw - dark)
            variance = variance + dark_variance
            normalization  = normalization_factor*(flat * solidangle * polarization * absortoption)
            count = number of pixel contributing

        Integration is performed using the LUT representation of the look-up table on all
        arrays: signal, variance, normalization and count

        :param dark: array of same shape as data for pre-processing
        :param dummy: value for invalid data
        :param delta_dummy: precesion for dummy assessement
        :param error_model: select the ErrorModel (defined in enum), use POISSON to enforce variance=signal
        :param variance: array of same shape as data for pre-processing
        :param dark_variance: array of same shape as data for pre-processing
        :param flat: array of same shape as data for pre-processing
        :param solidangle: array of same shape as data for pre-processing
        :param polarization: array of same shape as data for pre-processing
        :param dark_checksum: CRC32 checksum of the given array
        :param flat_checksum: CRC32 checksum of the given array
        :param solidangle_checksum: CRC32 checksum of the given array
        :param polarization_checksum: CRC32 checksum of the given array
        :param safe: if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
        :param preprocess_only: return the dark subtracted; flat field & solidangle & polarization corrected image, else
        :param normalization_factor: divide raw signal by this value
        :param bool weighted_average: set to False to use an unweighted mean (similar to legacy) instead of the weighted average. WIP
        :param out_avgint: destination array or pyopencl array for average intensity
        :param out_sem: destination array or pyopencl array for standard deviation (of mean)
        :param out_std: destination array or pyopencl array for standard deviation (of pixels)
        :param out_merged: destination array or pyopencl array for averaged data (float8!)
        :return: large namedtuple with out_avgint, out_sem, out_merged ...
        """
        events = []
        with self.sem:
            kernel_correction_name = "corrections4"
            corrections4 = self.kernels.corrections4
            kw_corr = self.cl_kernel_args[kernel_correction_name]
            self.send_buffer(data, "image")

            wg = self.workgroup_size
            wdim_bins = int(self.bins + wg[0] - 1) // wg[0] * wg[0],
            memset = self.kernels.memset_out(self.queue, wdim_bins, wg, *list(self.cl_kernel_args["memset_ng"].values()))
            events.append(EventDescription("memset_ng", memset))

            kw_int = self.cl_kernel_args["lut_integrate4"]

            if dummy is not None:
                do_dummy = numpy.int8(1)
                dummy = numpy.float32(dummy)
                if delta_dummy is None:
                    delta_dummy = numpy.float32(0.0)
                else:
                    delta_dummy = numpy.float32(abs(delta_dummy))
            else:
                do_dummy = numpy.int8(0)
                dummy = numpy.float32(self.empty)
                delta_dummy = numpy.float32(0.0)

            kw_corr["do_dummy"] = do_dummy
            kw_corr["dummy"] = dummy
            kw_corr["delta_dummy"] = delta_dummy
            kw_corr["normalization_factor"] = numpy.float32(normalization_factor)
            kw_corr["error_model"] = numpy.int8(error_model.value)
            if variance is not None:
                self.send_buffer(variance, "variance")
            if dark_variance is not None:
                if not dark_variance_checksum:
                    dark_variance_checksum = calc_checksum(dark_variance, safe)
                if dark_variance_checksum != self.on_device["dark_variance"]:
                    self.send_buffer(dark_variance, "dark_variance", dark_variance_checksum)
            else:
                do_dark = numpy.int8(0)
            kw_corr["do_dark"] = do_dark

            if dark is not None:
                do_dark = numpy.int8(1)
                # TODO: what is do_checksum=False and image not on device ...
                if not dark_checksum:
                    dark_checksum = calc_checksum(dark, safe)
                if dark_checksum != self.on_device["dark"]:
                    self.send_buffer(dark, "dark", dark_checksum)
            else:
                do_dark = numpy.int8(0)
            kw_corr["do_dark"] = do_dark

            if flat is not None:
                do_flat = numpy.int8(1)
                if not flat_checksum:
                    flat_checksum = calc_checksum(flat, safe)
                if self.on_device["flat"] != flat_checksum:
                    self.send_buffer(flat, "flat", flat_checksum)
            else:
                do_flat = numpy.int8(0)
            kw_corr["do_flat"] = do_flat

            if solidangle is not None:
                do_solidangle = numpy.int8(1)
                if not solidangle_checksum:
                    solidangle_checksum = calc_checksum(solidangle, safe)
                if solidangle_checksum != self.on_device["solidangle"]:
                    self.send_buffer(solidangle, "solidangle", solidangle_checksum)
            else:
                do_solidangle = numpy.int8(0)
            kw_corr["do_solidangle"] = do_solidangle

            if polarization is not None:
                do_polarization = numpy.int8(1)
                if not polarization_checksum:
                    polarization_checksum = calc_checksum(polarization, safe)
                if polarization_checksum != self.on_device["polarization"]:
                    self.send_buffer(polarization, "polarization", polarization_checksum)
            else:
                do_polarization = numpy.int8(0)
            kw_corr["do_polarization"] = do_polarization

            if absorption is not None:
                do_absorption = numpy.int8(1)
                if not absorption_checksum:
                    absorption_checksum = calc_checksum(absorption, safe)
                if absorption_checksum != self.on_device["absorption"]:
                    self.send_buffer(absorption, "absorption", absorption_checksum)
            else:
                do_absorption = numpy.int8(0)
            kw_corr["do_absorption"] = do_absorption
            kw_corr["apply_normalization"] = numpy.int8(not weighted_average)

            ev = corrections4(self.queue, self.wdim_data, self.workgroup_size, *list(kw_corr.values()))
            events.append(EventDescription(kernel_correction_name, ev))

            kw_int["empty"] = dummy
            integrate = self.kernels.lut_integrate4(self.queue, wdim_bins, self.workgroup_size, *kw_int.values())
            events.append(EventDescription("integrate4", integrate))

            if out_merged is None:
                merged = numpy.empty((self.bins, 8), dtype=numpy.float32)
            elif out_merged is False:
                merged = None
            else:
                merged = out_merged.data

            if out_avgint is None:
                avgint = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_avgint is False:
                avgint = None
            else:
                avgint = out_avgint.data

            if out_sem is None:
                sem = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_sem is False:
                sem = None
            else:
                sem = out_sem.data

            if out_std is None:
                std = numpy.empty(self.bins, dtype=numpy.float32)
            elif out_std is False:
                std = None
            else:
                std = out_std.data

            ev = pyopencl.enqueue_copy(self.queue, avgint, self.cl_mem["averint"])
            events.append(EventDescription("copy D->H avgint", ev))
            if std is not None:
                ev = pyopencl.enqueue_copy(self.queue, std, self.cl_mem["std"])
                events.append(EventDescription("copy D->H std", ev))
            if sem is not None:
                ev = pyopencl.enqueue_copy(self.queue, sem, self.cl_mem["sem"])
                events.append(EventDescription("copy D->H sem", ev))
            if self.azim_centers is None:
                if merged is None:
                    res = Integrate1dtpl(self.bin_centers, avgint, sem, None, None, None, None)
                else:
                    ev = pyopencl.enqueue_copy(self.queue, merged, self.cl_mem["merged8"])
                    events.append(EventDescription("copy D->H merged8", ev))
                    res = Integrate1dtpl(self.bin_centers, avgint, sem, merged[:, 0], merged[:, 2], merged[:, 4], merged[:, 6],
                                         std, sem, merged[:, 7])
            else:  # 2D case
                outshape = self.bin_centers.size, self.azim_centers.size
                if merged is None:  # "radial azimuthal intensity sigma"
                    res = Integrate2dtpl(self.bin_centers, self.azim_centers,
                                         avgint.reshape(outshape).T,
                                         sem.reshape(outshape).T if sem is not None else None,
                                         None, None, None, None,
                                         std.reshape(outshape).T if std is not None else None,
                                         sem.reshape(outshape).T if sem is not None else None,
                                         None,)
                else:
                    ev = pyopencl.enqueue_copy(self.queue, merged, self.cl_mem["merged8"])
                    events.append(EventDescription("copy D->H merged8", ev))
                    res = Integrate2dtpl(self.bin_centers, self.azim_centers,
                                         avgint.reshape(outshape).T,
                                         sem.reshape(outshape).T if sem is not None  else None,
                                         merged[:, 0].reshape(outshape).T,
                                         merged[:, 2].reshape(outshape).T if error_model else None,
                                         merged[:, 4].reshape(outshape).T,
                                         merged[:, 6].reshape(outshape).T,
                                         std.reshape(outshape).T if std is not None  else None,
                                         sem.reshape(outshape).T if sem is not None  else None,
                                         merged[:, 7].reshape(outshape).T,
                                         )
        self.profile_multi(events)
        return res
