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dtype.py
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/
dtype.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# #########################################################################
# Copyright (c) 2015-2019, UChicago Argonne, LLC. All rights reserved. #
# #
# Copyright 2015-2019. UChicago Argonne, LLC. This software was produced #
# under U.S. Government contract DE-AC02-06CH11357 for Argonne National #
# Laboratory (ANL), which is operated by UChicago Argonne, LLC for the #
# U.S. Department of Energy. The U.S. Government has rights to use, #
# reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR #
# UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR #
# ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is #
# modified to produce derivative works, such modified software should #
# be clearly marked, so as not to confuse it with the version available #
# from ANL. #
# #
# Additionally, redistribution and use in source and binary forms, with #
# or without modification, are permitted provided that the following #
# conditions are met: #
# #
# * Redistributions of source code must retain the above copyright #
# notice, this list of conditions and the following disclaimer. #
# #
# * Redistributions in binary form must reproduce the above copyright #
# notice, this list of conditions and the following disclaimer in #
# the documentation and/or other materials provided with the #
# distribution. #
# #
# * Neither the name of UChicago Argonne, LLC, Argonne National #
# Laboratory, ANL, the U.S. Government, nor the names of its #
# contributors may be used to endorse or promote products derived #
# from this software without specific prior written permission. #
# #
# THIS SOFTWARE IS PROVIDED BY UChicago Argonne, LLC AND CONTRIBUTORS #
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS #
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago #
# Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, #
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, #
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; #
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT #
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN #
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #
# POSSIBILITY OF SUCH DAMAGE. #
# #########################################################################
"""
Module for internal utility functions.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import ctypes
import logging
import multiprocessing as mp
import numpy as np
logger = logging.getLogger(__name__)
__author__ = "Doga Gursoy"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = 'restructuredtext en'
__all__ = [
'as_ndarray',
'as_dtype',
'as_float32',
'as_int32',
'as_uint8',
'as_uint16',
'as_c_float_p',
'as_c_bool_p',
'as_c_uint8_p',
'as_c_uint16_p',
'as_c_int',
'as_c_int_p',
'as_c_float',
'as_c_char_p',
'as_c_void_p',
'as_c_size_t',
]
def as_ndarray(arr, dtype=None, copy=False):
if not isinstance(arr, np.ndarray):
arr = np.array(arr, dtype=dtype, copy=copy)
return arr
def as_dtype(arr, dtype, copy=False):
if not arr.dtype == dtype:
arr = np.array(arr, dtype=dtype, copy=copy)
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_bool_p(arr):
c_bool_p = ctypes.POINTER(ctypes.c_bool)
return arr.ctypes.data_as(c_bool_p)
def as_c_uint8_p(arr):
c_uint8_p = ctypes.POINTER(ctypes.c_uint8)
return arr.ctypes.data_as(c_uint8_p)
def as_c_uint16_p(arr):
c_uint16_p = ctypes.POINTER(ctypes.c_uint16)
return arr.ctypes.data_as(c_uint16_p)
def as_c_int(arr):
return ctypes.c_int(arr)
def as_c_long(arr):
return ctypes.c_long(arr)
def as_c_int_p(arr):
arr = arr.astype(np.intc, copy=False)
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):
return ctypes.c_char_p(arr.encode())
def as_c_void_p():
return ctypes.POINTER(ctypes.c_void_p)
def as_c_size_t(arr):
return ctypes.c_size_t(arr)
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
temp_arr = np.empty((1), dtype=arr.dtype)
ctype = type(np.ctypeslib.as_ctypes(temp_arr)._type_())
# 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 to_numpy_array(obj, dtype, shape):
return np.frombuffer(obj, dtype=dtype).reshape(shape)
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 get_shared_mem(arr):
try:
while isinstance(arr, np.ndarray):
arr = arr.base
except:
pass
return arr
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 = type(np.ctypeslib.as_ctypes(temp_arr)._type_())
# create shared ctypes object with no lock
size = 1
for dim in shape:
size *= dim
shared_obj = mp.RawArray(ctype, int(size))
# create numpy array from shared object
arr = np.frombuffer(shared_obj, dtype)
arr = arr.reshape(shape)
return arr