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

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"""
Module for internal utility functions.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)


__author__ = "Doga Gursoy"
__copyright__ = "Copyright (c) 2015-2016, UChicago Argonne, LLC."
__version__ = "0.1.0"
__docformat__ = 'restructuredtext en'
__all__ = ['as_ndarray',
           'as_dtype',
           'as_float32',
           'as_int32',
           'as_uint8',
           'as_uint16',
           'as_c_float_p',
           'as_c_int',
           'as_c_int_p',
           'as_c_float',
           'as_c_char_p',
           'as_c_void_p',
           'as_sharedmem',
           'is_sharedmem',
           'is_contiguous',
           'empty_shared_array']


import ctypes
import numpy as np
import multiprocessing as mp
import logging

logger = logging.getLogger(__name__)


def as_ndarray(arr, dtype=None):
    if not isinstance(arr, np.ndarray):
        arr = np.array(arr, dtype=dtype)
    return arr


def as_dtype(arr, dtype):
    if not arr.dtype == dtype:
        arr = np.array(arr, dtype=dtype)
    return arr


def as_float32(arr):
    arr = as_ndarray(arr, np.float32)
    return as_dtype(arr, np.float32)


def as_int32(arr):
    arr = as_ndarray(arr, np.int32)
    return as_dtype(arr, np.int32)


def as_uint16(arr):
    arr = as_ndarray(arr, np.uint16)
    return as_dtype(arr, np.uint16)


def as_uint8(arr):
    arr = as_ndarray(arr, np.uint8)
    return as_dtype(arr, np.uint8)


def as_c_float_p(arr):
    c_float_p = ctypes.POINTER(ctypes.c_float)
    return arr.ctypes.data_as(c_float_p)


def as_c_int(arr):
    return ctypes.c_int(arr)


def as_c_int_p(arr):
    c_int_p = ctypes.POINTER(ctypes.c_int)
    return arr.ctypes.data_as(c_int_p)


def as_c_float(arr):
    return ctypes.c_float(arr)


def as_c_char_p(arr):
    c_char_p = ctypes.POINTER(ctypes.c_char)
    return arr.ctypes.data_as(c_char_p)


def as_c_void_p():
    return ctypes.POINTER(ctypes.c_void_p)


def as_sharedmem(arr, copy=False):
    # first check to see if it already a shared array
    if not copy and is_sharedmem(arr):
        return arr
    # get ctype from numpy array
    ctype = np.ctypeslib._typecodes[arr.__array_interface__['typestr']]
    # create shared ctypes object with no lock
    shared_obj = mp.RawArray(ctype, arr.size)
    # create numpy array from shared object
    #shared_arr = np.ctypeslib.as_array(shared_obj)
    shared_arr = np.frombuffer(shared_obj, dtype=arr.dtype)
    shared_arr = np.reshape(shared_arr, arr.shape)
    # copy data to shared array
    shared_arr[:] = arr[:]
    return shared_arr


def is_sharedmem(arr):
    # attempt to determine if data is in shared memory
    try:
        base = arr.base
        if base is None:
            return False
        elif type(base).__module__.startswith('multiprocessing.sharedctypes'):
            return True
        else:
            return is_sharedmem(base)
    except:
        return False


def is_contiguous(arr):
    return arr.flags.c_contiguous


def empty_shared_array(shape, dtype=np.float32):
    # create a shared ndarray with the provided shape and type
    # get ctype from np dtype
    temp_arr = np.empty((1), dtype)
    ctype = np.ctypeslib._typecodes[temp_arr.__array_interface__['typestr']]
    # create shared ctypes object with no lock
    size = 1
    for dim in shape:
        size *= dim
    shared_obj = mp.RawArray(ctype, size)
    # create numpy array from shared object
    arr = np.frombuffer(shared_obj, dtype)
    arr = arr.reshape(shape)
    return arr
