source/tomopy/util/dtype.py
#!/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)
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