# -*- coding: utf-8 -*-
#
#    Project: SILX
#             https://github.com/silx-kit/silx
#
#    Copyright (C) 2012-2019 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.
#  .
#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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#  THE SOFTWARE.

"""A module for performing basic statistical analysis (min, max, mean, std) on
large data where numpy is not very efficient.
"""

__author__ = "Jerome Kieffer"
__license__ = "MIT"
__date__ = "19/05/2021"
__copyright__ = "2012-2019, ESRF, Grenoble"
__contact__ = "jerome.kieffer@esrf.fr"

import logging
import numpy
from collections import OrderedDict, namedtuple
from math import sqrt

from .common import pyopencl
from .processing import EventDescription, OpenclProcessing, BufferDescription
from .utils import concatenate_cl_kernel

if pyopencl:
    mf = pyopencl.mem_flags
    from pyopencl.reduction import ReductionKernel
    try:
        from pyopencl import cltypes
    except ImportError:
        v = pyopencl.array.vec()
        float8 = v.float8
    else:
        float8 = cltypes.float8

else:
    raise ImportError("pyopencl is not installed")
logger = logging.getLogger(__name__)

StatResults = namedtuple("StatResults", ["min", "max", "cnt", "sum", "mean",
                                         "var", "std"])
zero8 = "(float8)(FLT_MAX, -FLT_MAX, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f)"
#                    min      max    cnt  cnt_e  sum   sum_e  var  var_e


class Statistics(OpenclProcessing):
    """A class for doing statistical analysis using OpenCL

    :param List[int] size: Shape of input data to treat
    :param numpy.dtype dtype: Input data type
    :param numpy.ndarray template: Data template to extract size & dtype
    :param ctx: Actual working context, left to None for automatic
                initialization from device type or platformid/deviceid
    :param str devicetype: Type of device, can be "CPU", "GPU", "ACC" or "ALL"
    :param int platformid: Platform identifier as given by clinfo
    :param int deviceid: Device identifier as given by clinfo
    :param int block_size:
        Preferred workgroup size, may vary depending on the outcome of the compilation
    :param bool profile:
        Switch on profiling to be able to profile at the kernel level,
        store profiling elements (makes code slightly slower)
    """
    buffers = [
        BufferDescription("raw", 1, numpy.float32, mf.READ_ONLY),
        BufferDescription("converted", 1, numpy.float32, mf.READ_WRITE),
    ]
    kernel_files = ["preprocess.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.int32: "s32_to_float"}

    def __init__(self, size=None, dtype=None, template=None,
                 ctx=None, devicetype="all", platformid=None, deviceid=None,
                 block_size=None, profile=False
                 ):
        OpenclProcessing.__init__(self, ctx=ctx, devicetype=devicetype,
                                  platformid=platformid, deviceid=deviceid,
                                  block_size=block_size, profile=profile)
        self.size = size
        self.dtype = dtype
        if template is not None:
            self.size = template.size
            self.dtype = template.dtype

        self.buffers = [BufferDescription(i.name, i.size * self.size, i.dtype, i.flags)
                        for i in self.__class__.buffers]

        self.allocate_buffers(use_array=True)
        self.compile_kernels()
        self.set_kernel_arguments()

    def set_kernel_arguments(self):
        """Parametrize all kernel arguments"""
        for val in self.mapping.values():
            self.cl_kernel_args[val] = OrderedDict(((i, self.cl_mem[i]) for i in ("raw", "converted")))

    def compile_kernels(self):
        """Compile the kernel"""
        OpenclProcessing.compile_kernels(self,
                                         self.kernel_files,
                                         "-D NIMAGE=%i" % self.size)
        compiler_options = self.get_compiler_options(x87_volatile=True)
        src = concatenate_cl_kernel(("doubleword.cl", "statistics.cl"))
        self.reduction_comp = ReductionKernel(self.ctx,
                                              dtype_out=float8,
                                              neutral=zero8,
                                              map_expr="map_statistics(data, i)",
                                              reduce_expr="reduce_statistics(a,b)",
                                              arguments="__global float *data",
                                              preamble=src,
                                              options=compiler_options)
        self.reduction_simple = ReductionKernel(self.ctx,
                                                dtype_out=float8,
                                                neutral=zero8,
                                                map_expr="map_statistics(data, i)",
                                                reduce_expr="reduce_statistics_simple(a,b)",
                                                arguments="__global float *data",
                                                preamble=src,
                                                options=compiler_options)

        if "cl_khr_fp64" in self.device.extensions:
            self.reduction_double = ReductionKernel(self.ctx,
                                                    dtype_out=float8,
                                                    neutral=zero8,
                                                    map_expr="map_statistics(data, i)",
                                                    reduce_expr="reduce_statistics_double(a,b)",
                                                    arguments="__global float *data",
                                                    preamble=src,
                                                    options=compiler_options)
        else:
            logger.info("Device %s does not support double-precision arithmetics, fall-back on compensated one", self.device)
            self.reduction_double = self.reduction_comp

    def send_buffer(self, data, dest):
        """
        Send a numpy array to the device, including the cast on the device if
        possible

        :param numpy.ndarray data: numpy array with data
        :param dest: name of the buffer as registered in the class
        """
        logger.info("send data to %s", dest)
        dest_type = numpy.dtype([i.dtype for i in self.buffers if i.name == dest][0])
        events = []
        if (data.dtype == dest_type) or (data.dtype.itemsize > dest_type.itemsize):
            copy_image = pyopencl.enqueue_copy(self.queue,
                                               self.cl_mem[dest].data,
                                               numpy.ascontiguousarray(data, dest_type))
            events.append(EventDescription("copy H->D %s" % dest, copy_image))
        else:
            copy_image = pyopencl.enqueue_copy(self.queue,
                                               self.cl_mem["raw"].data,
                                               numpy.ascontiguousarray(data))
            kernel = getattr(self.program, self.mapping[data.dtype.type])
            cast_to_float = kernel(self.queue,
                                   (self.size,),
                                   None,
                                   self.cl_mem["raw"].data,
                                   self.cl_mem[dest].data)
            events += [
                EventDescription("copy H->D raw", copy_image),
                EventDescription(f"cast to float {dest}", cast_to_float)
            ]
        if self.profile:
            self.events += events
        return events

    def process(self, data, comp=True):
        """Actually calculate the statics on the data

        :param numpy.ndarray data: numpy array with the image
        :param comp: use Kahan compensated arithmetics for the calculation 
        :return: Statistics named tuple
        :rtype: StatResults
        """
        if data.ndim != 1:
            data = data.ravel()
        size = data.size
        assert size <= self.size, "size is OK"
        events = []
        if comp is True:
            comp = "comp"
        elif comp is False:
            comp = "single"
        else:
            comp = comp.lower()
        with self.sem:
            self.send_buffer(data, "converted")
            if comp in ("single", "fp32", "float32"):
                reduction = self.reduction_simple
            elif comp in ("double", "fp64", "float64"):
                reduction = self.reduction_double
            else:
                reduction = self.reduction_comp
            res_d, evt = reduction(self.cl_mem["converted"][:self.size],
                                   queue=self.queue,
                                   return_event=True)
            events.append(EventDescription(f"statistical reduction {comp}", evt))
            if self.profile:
                self.events += events
            res_h = res_d.get()
        min_ = 1.0 * res_h["s0"]
        max_ = 1.0 * res_h["s1"]
        count = 1.0 * res_h["s2"] + res_h["s3"]
        sum_ = 1.0 * res_h["s4"] + res_h["s5"]
        m2 = 1.0 * res_h["s6"] + res_h["s7"]
        var = m2 / (count - 1.0)
        res = StatResults(min_,
                          max_,
                          count,
                          sum_,
                          sum_ / count,
                          var,
                          sqrt(var))
        return res

    __call__ = process
