import numpy as np
import numpy.ma as ma
from pycuda.tools import make_default_context
from pycuda.compiler import SourceModule
from pycuda.characterize import sizeof
import pycuda.driver as cuda
from pycuda import gpuarray
from copy import copy
from itertools import izip
from chroma.tools import timeit
from os.path import dirname

import chroma.src
from chroma.geometry import standard_wavelengths
from chroma.color import map_to_color
import sys
import event

cuda.init()

def to_float3(arr):
    return arr.astype(np.float32).view(gpuarray.vec.float3)

def boolean_argsort(condition):
    '''Returns two arrays of indicies indicating which elements of the
    boolean array `condition` should be exchanged so that all of the
    True entries occur before the false entries.

       Returns: tuple (a,b, ntrue) which give the indices for elements
       to exchange in a and b, and the number of true entries in the array
       in ntrue.  This function in general requires fewer swaps than 
       numpy.argsort.
    '''

    true_indices = condition.nonzero()[0][::-1] # reverse
    false_indices = (~condition).nonzero()[0]
    
    length = min(len(true_indices), len(false_indices))
    
    cut_index = np.searchsorted((true_indices[:length] - false_indices[:length]) <= 0, True)
    return (true_indices[:cut_index], false_indices[:cut_index], len(true_indices))


def chunk_iterator(nelements, nthreads_per_block, max_blocks):
    '''Iterator that yields tuples with the values requried to process
    a long array in multiple kernel passes on the GPU.

       Each yielded value is (first_index, elements_this_iteration, nblocks_this_iteration)

    >>> list(chunk_iterator(300, 32, 2))
    [(0, 64, 2), (64, 64, 2), (128, 64, 2), (192, 64, 2), (256, 9, 1)]
    '''
    first = 0
    while first < nelements:
        elements_left = nelements - first
        blocks = int(elements_left // nthreads_per_block)
        if elements_left % nthreads_per_block != 0:
            blocks += 1 #Round up only if needed
        blocks = min(max_blocks, blocks)
        elements_this_round = min(elements_left, blocks * nthreads_per_block)

        yield (first, elements_this_round, blocks)
        first += elements_this_round
    

def format_size(size):
    if size < 1e3:
        return '%.1f%s' % (size, ' ')
    elif size < 1e6:
        return '%.1f%s' % (size/1e3, 'K')
    elif size < 1e9:
        return '%.1f%s' % (size/1e6, 'M')
    else:
        return '%.1f%s' % (size/1e9, 'G')

def format_array(name, array):
    return '%-15s %6s %6s' % \
        (name, format_size(len(array)), format_size(array.nbytes))

class CUDAFuncs(object):
    '''simple container class for GPU functions as attributes'''
    def __init__(self, cuda_module, func_names):
        '''Extract __global__ functions listed in func_names from
        the PyCUDA module object.  The functions are assigned
        to attributes of this object with the same name.'''
        for name in func_names:
            setattr(self, name, cuda_module.get_function(name))

class GPU(object):
    def __init__(self, device_id=None, nthreads_per_block=64, max_blocks=1024):
        '''Initialize a GPU context on the specified device.  If device_id==None,
        the default device is used.'''

        if device_id is None:
            self.context = make_default_context()
        else:
            device = cuda.Device(device_id)
            self.context = device.make_context()

        print 'device %s' % self.context.get_device().name()

        self.nthreads_per_block = nthreads_per_block
        self.max_blocks = max_blocks

        self.context.set_cache_config(cuda.func_cache.PREFER_L1)

        cuda_options = ['-I' + dirname(chroma.src.__file__), '--use_fast_math', '--ptxas-options=-v']

        self.module = SourceModule(chroma.src.kernel, options=cuda_options, no_extern_c=True)
        self.kernels = CUDAFuncs(self.module, ['ray_trace', 'ray_trace_alpha', 'rotate', 'rotate_around_point', 'translate', 'update_xyz_lookup', 'update_xyz_image', 'process_image', 'init_rng'])

        self.geo_funcs = CUDAFuncs(self.module, ['set_wavelength_range', 'set_material', 'set_surface', 'set_global_mesh_variables', 'color_solids'])

        self.prop_funcs = CUDAFuncs(self.module, ['init_rng', 'propagate', 'swap'])
        self.daq_module = SourceModule(chroma.src.daq, options=cuda_options, no_extern_c=True)
        self.daq_funcs = CUDAFuncs(self.daq_module, 
                              ['reset_earliest_time_int', 'run_daq',
                               'convert_sortable_int_to_float', 'bin_hits'])

    def print_device_usage(self):
        print 'device usage:'
        print format_array('vertices', self.vertices_gpu)
        print format_array('triangles', self.triangles_gpu)
        print format_array('lower_bounds', self.lower_bounds_gpu)
        print format_array('upper_bounds', self.upper_bounds_gpu)
        print format_array('node_map', self.node_map_gpu)
        print format_array('node_map_end', self.node_map_end_gpu)
        print '%-15s %6s %6s' % ('total', '', format_size(self.vertices_gpu.nbytes + self.triangles_gpu.nbytes + self.lower_bounds_gpu.nbytes + self.upper_bounds_gpu.nbytes + self.node_map_gpu.nbytes + self.node_map_end_gpu.nbytes))

    def load_geometry(self, geometry, print_usage=True):
        if not hasattr(geometry, 'mesh'):
            print 'WARNING: building geometry with 8-bits'
            geometry.build(bits=8)

        self.geo_funcs.set_wavelength_range(np.float32(standard_wavelengths[0]), np.float32(standard_wavelengths[-1]), np.float32(standard_wavelengths[1]-standard_wavelengths[0]), np.uint32(standard_wavelengths.size), block=(1,1,1), grid=(1,1))

        self.materials = []
        for i in range(len(geometry.unique_materials)):
            material = copy(geometry.unique_materials[i])

            if material is None:
                raise Exception('one or more triangles is missing a material.')

            material.refractive_index_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, material.refractive_index[:,0], material.refractive_index[:,1]).astype(np.float32))
            material.absorption_length_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, material.absorption_length[:,0], material.absorption_length[:,1]).astype(np.float32))
            material.scattering_length_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, material.scattering_length[:,0], material.scattering_length[:,1]).astype(np.float32))

            self.geo_funcs.set_material(np.int32(i), material.refractive_index_gpu, material.absorption_length_gpu, material.scattering_length_gpu, block=(1,1,1), grid=(1,1))

            self.materials.append(material)

        self.surfaces = []
        for i in range(len(geometry.unique_surfaces)):
            surface = copy(geometry.unique_surfaces[i])

            if surface is None:
                continue

            surface.detect_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, surface.detect[:,0], surface.detect[:,1]).astype(np.float32))
            surface.absorb_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, surface.absorb[:,0], surface.absorb[:,1]).astype(np.float32))
            surface.reflect_diffuse_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, surface.reflect_diffuse[:,0], surface.reflect_diffuse[:,1]).astype(np.float32))
            surface.reflect_specular_gpu = gpuarray.to_gpu(np.interp(standard_wavelengths, surface.reflect_specular[:,0], surface.reflect_specular[:,1]).astype(np.float32))

            self.geo_funcs.set_surface(np.int32(i), surface.detect_gpu, surface.absorb_gpu, surface.reflect_diffuse_gpu, surface.reflect_specular_gpu, block=(1,1,1), grid=(1,1))

            self.surfaces.append(surface)

        self.vertices_gpu = gpuarray.to_gpu(to_float3(geometry.mesh.vertices))

        triangles = \
            np.empty(len(geometry.mesh.triangles), dtype=gpuarray.vec.uint4)
        triangles['x'] = geometry.mesh.triangles[:,0]
        triangles['y'] = geometry.mesh.triangles[:,1]
        triangles['z'] = geometry.mesh.triangles[:,2]
        triangles['w'] = ((geometry.material1_index & 0xff) << 24) | ((geometry.material2_index & 0xff) << 16) | ((geometry.surface_index & 0xff) << 8)
        self.triangles_gpu = gpuarray.to_gpu(triangles)

        self.lower_bounds_gpu = gpuarray.to_gpu(to_float3(geometry.lower_bounds))

        self.upper_bounds_gpu = gpuarray.to_gpu(to_float3(geometry.upper_bounds))

        self.colors_gpu = gpuarray.to_gpu(geometry.colors.astype(np.uint32))
        self.node_map_gpu = gpuarray.to_gpu(geometry.node_map.astype(np.uint32))
        self.node_map_end_gpu = gpuarray.to_gpu(geometry.node_map_end.astype(np.uint32))
        self.solid_id_map_gpu = gpuarray.to_gpu(geometry.solid_id.astype(np.uint32))

        self.geo_funcs.set_global_mesh_variables(self.triangles_gpu, self.vertices_gpu, self.colors_gpu, np.uint32(geometry.node_map.size-1), np.uint32(geometry.first_node), self.lower_bounds_gpu, self.upper_bounds_gpu, block=(1,1,1), grid=(1,1))

        self.node_map_tex = self.module.get_texref('node_map')
        self.node_map_end_tex = self.module.get_texref('node_map_end')

        self.node_map_tex.set_address(self.node_map_gpu.gpudata, self.node_map_gpu.nbytes)
        self.node_map_end_tex.set_address(self.node_map_end_gpu.gpudata, self.node_map_end_gpu.nbytes)

        self.node_map_tex.set_format(cuda.array_format.UNSIGNED_INT32, 1)
        self.node_map_end_tex.set_format(cuda.array_format.UNSIGNED_INT32, 1)

        self.geometry = geometry

        if print_usage:
            print 'average of %f child nodes per node' % (np.mean(geometry.node_map_end[geometry.first_node:] - geometry.node_map[geometry.first_node:]))
            print '%i nodes with one child' % (np.count_nonzero((geometry.node_map_end[geometry.first_node:] - geometry.node_map[geometry.first_node:]) == 1))
            print '%i leaf nodes with one child' % (np.count_nonzero((geometry.node_map_end[:geometry.first_node] - geometry.node_map[:geometry.first_node]) == 1))
            self.print_device_usage()

    def reset_colors(self):
        self.colors_gpu.set_async(self.geometry.colors.astype(np.uint32))

    def color_solids(self, solid_hit, colors):
        solid_hit_gpu = gpuarray.to_gpu(np.array(solid_hit, dtype=np.bool))
        solid_colors_gpu = gpuarray.to_gpu(np.array(colors, dtype=np.uint32))

        for first_triangle, triangles_this_round, blocks in chunk_iterator(self.triangles_gpu.size, self.nthreads_per_block, self.max_blocks):
            self.geo_funcs.color_solids(np.int32(first_triangle),
                                        np.int32(triangles_this_round),
                                        self.solid_id_map_gpu, 
                                        solid_hit_gpu, 
                                        solid_colors_gpu, 
                                        block=(self.nthreads_per_block,1,1), 
                                        grid=(blocks,1))
        self.context.synchronize()

    def setup_propagate(self, seed=1):
        self.rng_states_gpu = cuda.mem_alloc(self.nthreads_per_block * self.max_blocks
                                             * sizeof('curandStateXORWOW', '#include <curand_kernel.h>'))
        self.prop_funcs.init_rng(np.int32(self.max_blocks * self.nthreads_per_block), self.rng_states_gpu, 
                                 np.uint64(seed), np.uint64(0), 
                                 block=(self.nthreads_per_block,1,1), 
                                 grid=(self.max_blocks,1))
        #self.context.synchronize()

    def load_photons(self, photons):
        '''Load photons onto the GPU from an event.Photons object.

           If photon.histories or photon.last_hit_triangles are set to none,
           they will be initialized to 0 and -1 on the GPU, respectively.
        '''
        self.nphotons = len(photons.positions)
        assert len(photons.directions) == self.nphotons
        assert len(photons.polarizations) == self.nphotons
        assert len(photons.wavelengths) == self.nphotons

        self.positions_gpu = gpuarray.to_gpu(to_float3(photons.positions))
        self.directions_gpu = gpuarray.to_gpu(to_float3(photons.directions))
        self.polarizations_gpu = gpuarray.to_gpu(to_float3(photons.polarizations))
        self.wavelengths_gpu = gpuarray.to_gpu(photons.wavelengths.astype(np.float32))

        if photons.times is not None:
            self.times_gpu = gpuarray.to_gpu(photons.times.astype(np.float32))
        else:
            self.times_gpu = gpuarray.zeros(self.nphotons, dtype=np.float32)

        if photons.histories is not None:
            self.histories_gpu = gpuarray.to_gpu(photons.histories.astype(np.uint32))
        else:
            self.histories_gpu = gpuarray.zeros(self.nphotons, dtype=np.uint32)

        if photons.last_hit_triangles is not None:
            self.last_hit_triangles_gpu = gpuarray.to_gpu(photons.last_hit_triangles.astype(np.int32))
        else:
            self.last_hit_triangles_gpu = gpuarray.empty(self.nphotons, dtype=np.int32)
            self.last_hit_triangles_gpu.fill(-1)


    def propagate(self, max_steps=10):
        '''Propagate photons on GPU to termination or max_steps, whichever comes first.

        May be called repeatedly without reloading photon information if single-stepping
        through photon history.
        '''
        nphotons = self.nphotons
        step = 0
        input_queue = np.zeros(shape=self.nphotons+1, dtype=np.uint32)
        input_queue[1:] = np.arange(self.nphotons, dtype=np.uint32)
        input_queue_gpu = gpuarray.to_gpu(input_queue)
        output_queue = np.zeros(shape=self.nphotons+1, dtype=np.uint32)
        output_queue[0] = 1
        output_queue_gpu = gpuarray.to_gpu(output_queue)


        while step < max_steps:
            ## Just finish the rest of the steps if the # of photons is low
            if nphotons < self.nthreads_per_block * 16 * 8:
                nsteps = max_steps - step
            else:
                nsteps = 1

            for first_photon, photons_this_round, blocks in chunk_iterator(nphotons, self.nthreads_per_block, self.max_blocks):
                self.prop_funcs.propagate(np.int32(first_photon),
                                          np.int32(photons_this_round),
                                          input_queue_gpu[1:],
                                          output_queue_gpu,
                                          self.rng_states_gpu, self.positions_gpu, 
                                          self.directions_gpu, 
                                          self.wavelengths_gpu, self.polarizations_gpu, 
                                          self.times_gpu, self.histories_gpu, 
                                          self.last_hit_triangles_gpu, 
                                          np.int32(nsteps), 
                                          block=(self.nthreads_per_block,1,1),
                                          grid=(blocks, 1))
        
            step += nsteps

            if step < max_steps:
                temp = input_queue_gpu
                input_queue_gpu = output_queue_gpu
                output_queue_gpu = temp
                output_queue_gpu[:1].set(np.uint32(1))
                nphotons = input_queue_gpu[:1].get()[0] - 1
        
        if gpuarray.max(self.histories_gpu).get() & (1 << 31):
            print >>sys.stderr, "WARNING: ABORTED PHOTONS IN THIS EVENT"

        if 'profile' in __builtins__:
            self.context.synchronize()

    def get_photons(self):
        '''Returns a dictionary of current photon state information.

        Contents of dictionary have the same names as the parameters to load_photons().
        '''
        return event.Photons(positions=self.positions_gpu.get().view(np.float32).reshape(self.positions_gpu.size, 3),
                             directions=self.directions_gpu.get().view(np.float32).reshape(self.directions_gpu.size, 3),
                             polarizations=self.polarizations_gpu.get().view(np.float32).reshape(self.polarizations_gpu.size, 3),
                             wavelengths=self.wavelengths_gpu.get(),
                             times=self.times_gpu.get(),
                             histories=self.histories_gpu.get(),
                             last_hit_triangles=self.last_hit_triangles_gpu.get())
    
    def setup_daq(self, max_pmt_id, pmt_rms=1.2e-9):
        self.earliest_time_gpu = gpuarray.GPUArray(shape=(max_pmt_id+1,), dtype=np.float32)
        self.earliest_time_int_gpu = gpuarray.GPUArray(shape=self.earliest_time_gpu.shape, 
                                                       dtype=np.uint32)
        self.channel_history_gpu = gpuarray.zeros_like(self.earliest_time_int_gpu)
        self.channel_q_gpu = gpuarray.zeros_like(self.earliest_time_int_gpu)
        self.daq_pmt_rms = pmt_rms

    def run_daq(self):
        self.daq_funcs.reset_earliest_time_int(np.float32(1e9), 
                                               np.int32(len(self.earliest_time_int_gpu)), 
                                               self.earliest_time_int_gpu, 
                                               block=(self.nthreads_per_block,1,1), 
                                               grid=(len(self.earliest_time_int_gpu)//self.nthreads_per_block+1,1))
        self.channel_q_gpu.fill(0)
        self.channel_history_gpu.fill(0)

        #self.context.synchronize()
        for first_photon, photons_this_round, blocks in chunk_iterator(self.nphotons, self.nthreads_per_block, self.max_blocks):
            self.daq_funcs.run_daq(self.rng_states_gpu, np.uint32(0x1 << 2), 
                                   np.float32(self.daq_pmt_rms), 
                                   np.int32(first_photon),
                                   np.int32(photons_this_round), 
                                   self.times_gpu, self.histories_gpu,
                                   self.last_hit_triangles_gpu, 
                                   self.solid_id_map_gpu, 
                                   np.int32(len(self.earliest_time_int_gpu)), 
                                   self.earliest_time_int_gpu,
                                   self.channel_q_gpu,
                                   self.channel_history_gpu,
                                   block=(self.nthreads_per_block,1,1), grid=(blocks,1))
        #self.context.synchronize()
        self.daq_funcs.convert_sortable_int_to_float(np.int32(len(self.earliest_time_int_gpu)), 
                                                     self.earliest_time_int_gpu, 
                                                     self.earliest_time_gpu, 
                                                     block=(self.nthreads_per_block,1,1), 
                                                     grid=(len(self.earliest_time_int_gpu)//self.nthreads_per_block+1,1))
        if 'profile' in __builtins__:
            self.context.synchronize()
        

    def get_hits(self):
        t = self.earliest_time_gpu.get()
        # For now, assume all channels with small enough hit time were hit
        return event.Channels(hit=t<1e8, t=t, 
                        q=self.channel_q_gpu.get().astype(np.float32),
                        histories=self.channel_history_gpu.get())

    def setup_pdf(self, max_pmt_id, tbins, trange, qbins, qrange):
        '''Setup GPU arrays to hold PDF information.

           max_pmt_id: int, largest PMT id #
           tbins: number of time bins
           trange: tuple of (min, max) time in PDF
           qbins: number of charge bins
           qrange: tuple of (min, max) charge in PDF
        '''
        self.events_in_histogram = 0
        self.hitcount_gpu = gpuarray.zeros(max_pmt_id+1, dtype=np.uint32)
        self.pdf_gpu = gpuarray.zeros(shape=(max_pmt_id+1, tbins, qbins), 
                                      dtype=np.uint32)
        self.tbins = tbins
        self.trange = trange
        self.qbins = qbins
        self.qrange = qrange

    def clear_pdf(self):
        '''Rezero the PDF counters.'''
        self.hitcount_gpu.fill(0)
        self.pdf_gpu.fill(0)

    def add_hits_to_pdf(self):
        '''Put the most recent results of run_daq() into the PDF.'''

        self.daq_funcs.bin_hits(np.int32(len(self.hitcount_gpu)),
                                self.channel_q_gpu,
                                self.earliest_time_gpu,
                                self.hitcount_gpu,
                                np.int32(self.tbins),
                                np.float32(self.trange[0]),
                                np.float32(self.trange[1]),
                                np.int32(self.qbins),
                                np.float32(self.qrange[0]),
                                np.float32(self.qrange[1]),
                                self.pdf_gpu,
                                block=(self.nthreads_per_block,1,1), 
                                grid=(len(self.earliest_time_gpu)//self.nthreads_per_block+1,1))


        self.events_in_histogram += 1

    def get_pdfs(self):
        '''Returns the 1D hitcount array and the 3D [channel, time, charge] histogram'''
        return self.hitcount_gpu.get(), self.pdf_gpu.get()

    def __del__(self):
        self.context.pop()