from __future__ import absolute_import
from __future__ import print_function
import pyopencl as cl
import pyopencl.array as cl_array
import numpy
import numpy.linalg as la

ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)

n = 10
a_gpu = cl_array.to_device(queue,
        ( numpy.random.randn(n) + 1j*numpy.random.randn(n)
            ).astype(numpy.complex64))
b_gpu = cl_array.to_device(queue,
        ( numpy.random.randn(n) + 1j*numpy.random.randn(n)
            ).astype(numpy.complex64))

from pyopencl.elementwise import ElementwiseKernel
complex_prod = ElementwiseKernel(ctx,
        "float a, "
        "float2 *x, "
        "float2 *y, "
        "float2 *z",
        "z[i] = a * complex_mul(x[i], y[i])",
        "complex_prod",
        preamble="""
        #define complex_ctr(x, y) (float2)(x, y)
        #define complex_mul(a, b) complex_ctr(mad(-(a).y, (b).y, (a).x * (b).x), mad((a).y, (b).x, (a).x * (b).y))
        #define complex_div_scalar(a, b) complex_ctr((a).x / (b), (a).y / (b))
        #define conj(a) complex_ctr((a).x, -(a).y)
        #define conj_transp(a) complex_ctr(-(a).y, (a).x)
        #define conj_transp_and_mul(a, b) complex_ctr(-(a).y * (b), (a).x * (b))
        """)

complex_add = ElementwiseKernel(ctx,
        "float2 *x, "
        "float2 *y, "
        "float2 *z",
        "z[i] = x[i] + y[i]",
        "complex_add")

real_part = ElementwiseKernel(ctx,
        "float2 *x, float *z",
        "z[i] = x[i].x",
        "real_part")

c_gpu = cl_array.empty_like(a_gpu)
complex_prod(5, a_gpu, b_gpu, c_gpu)

c_gpu_real = cl_array.empty(queue, len(a_gpu), dtype=numpy.float32)
real_part(c_gpu, c_gpu_real)
print(c_gpu.get().real - c_gpu_real.get())

print(la.norm(c_gpu.get() - (5*a_gpu.get()*b_gpu.get())))
assert la.norm(c_gpu.get() - (5*a_gpu.get()*b_gpu.get())) < 1e-5
