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{ | |
"metadata": { | |
"name": "Queries2" | |
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""" | |
Benchmark that compares the storing of objects in both Blaze and PyTables | |
""" | |
import os.path | |
import shutil | |
from time import time | |
import blaze | |
import tables |
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Using 2.7.6: | |
$ python2 ~/mem_prof.py | |
memory deallocated: 0.0 MB | |
Using 3.4b1: | |
$ python3 ~/mem_prof.py | |
memory deallocated: -1040.09765625 MB |
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{ | |
"metadata": { | |
"name": "" | |
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"nbformat_minor": 0, | |
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{ | |
"metadata": { | |
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"signature": "sha256:5a6759998dc5cecdfc269873ed0afe13bceff1f78f0178535b0e07efc6161892" | |
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:82e5b24036955db348e69e8d773df9940f1cc361f8dff79db3f0b5602fd6cf67" | |
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"worksheets": [ | |
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# Benchmark to compare the times for storing numpy arrays in a key-value. | |
# The main point is to compare numpy serialization vs a bcolz approach. | |
from __future__ import print_function | |
import sys | |
import os | |
import os.path | |
import subprocess | |
import getopt |
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import iarray as ia | |
from time import time | |
import numpy as np | |
import numexpr as ne | |
from numba import jit | |
from itertools import zip_longest as zip | |
import py2llvm as llvm | |
from py2llvm import float64, int32, Array | |
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Python 3.7.3 (default, Mar 27 2019, 22:11:17) | |
Type 'copyright', 'credits' or 'license' for more information | |
IPython 7.6.1 -- An enhanced Interactive Python. Type '?' for help. | |
In [1]: import numpy as np | |
In [2]: import numexpr as ne | |
In [3]: import numba |
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