Francesc Alted
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
###################################################################
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# Numexpr - Fast numerical array expression evaluator for NumPy.
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#
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# License: MIT
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# Author: See AUTHORS.txt
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#
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# See LICENSE.txt and LICENSES/*.txt for details about copyright and
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# rights to use.
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####################################################################
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"""
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Numexpr is a fast numerical expression evaluator for NumPy. With it,
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expressions that operate on arrays (like "3*a+4*b") are accelerated
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and use less memory than doing the same calculation in Python.
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See:
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https://github.com/pydata/numexpr
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for more info about it.
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"""
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from __config__ import show as show_config, get_info
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if get_info('mkl'):
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use_vml = True
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else:
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use_vml = False
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from cpuinfo import cpu
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if cpu.is_AMD() or cpu.is_Intel():
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is_cpu_amd_intel = True
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else:
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is_cpu_amd_intel = False
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import os, os.path
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import platform
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from numexpr.expressions import E
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from numexpr.necompiler import NumExpr, disassemble, evaluate
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from numexpr.tests import test, print_versions
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from numexpr.utils import (
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get_vml_version, set_vml_accuracy_mode, set_vml_num_threads,
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set_num_threads, detect_number_of_cores)
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# Detect the number of cores
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ncores = detect_number_of_cores()
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# Initialize the number of threads to be used
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# If this is modified, please update the note in:
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# https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide
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try:
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nthreads = int(os.environ['NUMEXPR_NUM_THREADS'])
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except KeyError:
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nthreads = ncores
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# Check that we don't activate too many threads at the same time.
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# 8 seems a sensible value.
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if nthreads > 8:
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nthreads = 8
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# Check that we don't surpass the MAX_THREADS in interpreter.cpp
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if nthreads > 4096:
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nthreads = 4096
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if 'sparc' in platform.machine():
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import warnings
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warnings.warn('The number of threads have been set to 1 because problems related '
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'to threading have been reported on some sparc machine. '
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'The number of threads can be changes using the "set_num_threads" '
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'function.')
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set_num_threads(1)
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else:
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set_num_threads(nthreads)
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# The default for VML is 1 thread (see #39)
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set_vml_num_threads(1)
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import version
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dirname = os.path.dirname(__file__)
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__version__ = version.version
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