#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
import pyopencl as cl
from weights import GaussianDispersion
from sasmodel import card

def set_precision(src, qx, qy, dtype):
    qx = np.ascontiguousarray(qx, dtype=dtype)
    qy = np.ascontiguousarray(qy, dtype=dtype)
    if np.dtype(dtype) == np.dtype('float32'):
        header = """\
#define real float
"""
    else:
        header = """\
#pragma OPENCL EXTENSION cl_khr_fp64: enable
#define real double
"""
    return header+src, qx, qy

def set_precision_1d(src, q, dtype):
    q = np.ascontiguousarray(q, dtype=dtype)
    if np.dtype(dtype) == np.dtype('float32'):
        header = """\
#define real float
"""
    else:
        header = """\
#pragma OPENCL EXTENSION cl_khr_fp64: enable
#define real double
"""
    return header+src, q

class GpuCylinder(object):
    PARS = {
        'scale':1,'radius':1,'length':1,'sldCyl':1e-6,'sldSolv':0,'background':0,
        'cyl_theta':0,'cyl_phi':0,
    }
    PD_PARS = ['radius', 'length', 'cyl_theta', 'cyl_phi']

    def __init__(self, qx, qy, dtype='float32'):

        #create context, queue, and build program
        ctx,_queue = card()
        src, qx, qy = set_precision(open('NR_BessJ1.cpp').read()+"\n"+open('Kernel-Cylinder.cpp').read(), qx, qy, dtype=dtype)
        self.prg = cl.Program(ctx, src).build()
        self.qx, self.qy = qx, qy

        #buffers
        mf = cl.mem_flags
        self.qx_b = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=self.qx)
        self.qy_b = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=self.qy)
        self.res_b = cl.Buffer(ctx, mf.WRITE_ONLY, qx.nbytes)
        self.res = np.empty_like(self.qx)

    def eval(self, pars):

        _ctx,queue = card()
        radius, length, cyl_theta, cyl_phi = \
            [GaussianDispersion(int(pars[base+'_pd_n']), pars[base+'_pd'], pars[base+'_pd_nsigma'])
             for base in GpuCylinder.PD_PARS]

        #Get the weights for each
        radius.value, radius.weight = radius.get_weights(pars['radius'], 0, 10000, True)
        length.value, length.weight = length.get_weights(pars['length'], 0, 10000, True)
        cyl_theta.value, cyl_theta.weight = cyl_theta.get_weights(pars['cyl_theta'], -90, 180, False)
        cyl_phi.value, cyl_phi.weight = cyl_phi.get_weights(pars['cyl_phi'], -90, 180, False)

        #Perform the computation, with all weight points
        sum, norm, norm_vol, vol = 0.0, 0.0, 0.0, 0.0
        size = len(cyl_theta.weight)
        sub = pars['sldCyl'] - pars['sldSolv']

        real = np.float32 if self.qx.dtype == np.dtype('float32') else np.float64
        #Loop over radius, length, theta, phi weight points
        for i in xrange(len(radius.weight)):
            for j in xrange(len(length.weight)):
                for k in xrange(len(cyl_theta.weight)):
                    for l in xrange(len(cyl_phi.weight)):
                        self.prg.CylinderKernel(queue, self.qx.shape, None, self.qx_b, self.qy_b, self.res_b, real(sub),
                                           real(radius.value[i]), real(length.value[j]), real(pars['scale']),
                                           real(radius.weight[i]), real(length.weight[j]), real(cyl_theta.weight[k]),
                                           real(cyl_phi.weight[l]), real(cyl_theta.value[k]), real(cyl_phi.value[l]),
                                           np.uint32(self.qx.size), np.uint32(size))
                        cl.enqueue_copy(queue, self.res, self.res_b)
                        sum += self.res
                        vol += radius.weight[i]*length.weight[j]*pow(radius.value[i], 2)*length.value[j]
                        norm_vol += radius.weight[i]*length.weight[j]
                        norm += radius.weight[i]*length.weight[j]*cyl_theta.weight[k]*cyl_phi.weight[l]

       # if size > 1:
        #    norm /= math.asin(1.0)
        if vol != 0.0 and norm_vol != 0.0:
            sum *= norm_vol/vol

        return sum/norm+pars['background']

class OneDGpuCylinder(object):
    PARS = {
        'scale':1,'radius':1,'length':1,'sldCyl':1e-6,'sldSolv':0,'background':0,
        'bolim':0, 'uplim':90
    }
    PD_PARS = ['radius', 'length']

    def __init__(self, q, dtype='float32'):

        #create context, queue, and build program
        ctx,_queue = card()
        trala = open('NR_BessJ1.cpp').read()+"\n"+open('OneDCyl_Kfun.cpp').read()+"\n"+open('Kernel-OneDCylinder.cpp').read()
        src, self.q = set_precision_1d(trala, q, dtype=dtype)
        self.prg = cl.Program(ctx, src).build()

        #buffers
        mf = cl.mem_flags
        self.q_b = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=self.q)
        self.res_b = cl.Buffer(ctx, mf.WRITE_ONLY, q.nbytes)
        self.res = np.empty_like(self.q)

    def eval(self, pars):

        _ctx,queue = card()
        radius, length = \
            [GaussianDispersion(int(pars[base+'_pd_n']), pars[base+'_pd'], pars[base+'_pd_nsigma'])
             for base in OneDGpuCylinder.PD_PARS]

        #Get the weights for each
        radius.value, radius.weight = radius.get_weights(pars['radius'], 0, 10000, True)
        length.value, length.weight = length.get_weights(pars['length'], 0, 10000, True)

        #Perform the computation, with all weight points
        sum, norm, vol = 0.0, 0.0, 0.0,
        sub = pars['sldCyl'] - pars['sldSolv']

        real = np.float32 if self.q.dtype == np.dtype('float32') else np.float64
        #Loop over radius, length, theta, phi weight points
        for r in xrange(len(radius.weight)):
            for l in xrange(len(length.weight)):
                        self.prg.OneDCylKernel(queue, self.q.shape, None, self.q_b, self.res_b, real(sub),
                                           real(length.value[l]), real(radius.value[r]), real(pars['scale']),
                                           np.uint32(self.q.size), real(pars['uplim']), real(pars['bolim']))
                        cl.enqueue_copy(queue, self.res, self.res_b)
                        sum += radius.weight[r]*length.weight[l]*self.res*pow(radius.value[r],2)*length.value[l]
                        vol += radius.weight[r]*length.weight[l] *pow(radius.value[r],2)*length.value[l]
                        norm += radius.weight[r]*length.weight[l]

        if vol != 0.0 and norm != 0.0:
            sum *= norm/vol

        return sum/norm + pars['background']