forked from numpy/numpy
/
bench_core.py
178 lines (123 loc) · 4.17 KB
/
bench_core.py
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from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Core(Benchmark):
def setup(self):
self.l100 = range(100)
self.l50 = range(50)
self.l = [np.arange(1000), np.arange(1000)]
self.l_view = [memoryview(a) for a in self.l]
self.l10x10 = np.ones((10, 10))
def time_array_1(self):
np.array(1)
def time_array_empty(self):
np.array([])
def time_array_l1(self):
np.array([1])
def time_array_l100(self):
np.array(self.l100)
def time_array_l(self):
np.array(self.l)
def time_array_l_view(self):
np.array(self.l_view)
def time_vstack_l(self):
np.vstack(self.l)
def time_hstack_l(self):
np.hstack(self.l)
def time_dstack_l(self):
np.dstack(self.l)
def time_arange_100(self):
np.arange(100)
def time_zeros_100(self):
np.zeros(100)
def time_ones_100(self):
np.ones(100)
def time_empty_100(self):
np.empty(100)
def time_eye_100(self):
np.eye(100)
def time_identity_100(self):
np.identity(100)
def time_eye_3000(self):
np.eye(3000)
def time_identity_3000(self):
np.identity(3000)
def time_diag_l100(self):
np.diag(self.l100)
def time_diagflat_l100(self):
np.diagflat(self.l100)
def time_diagflat_l50_l50(self):
np.diagflat([self.l50, self.l50])
def time_triu_l10x10(self):
np.triu(self.l10x10)
def time_tril_l10x10(self):
np.tril(self.l10x10)
class Temporaries(Benchmark):
def setup(self):
self.amid = np.ones(50000)
self.bmid = np.ones(50000)
self.alarge = np.ones(1000000)
self.blarge = np.ones(1000000)
def time_mid(self):
(self.amid * 2) + self.bmid
def time_mid2(self):
(self.amid + self.bmid) - 2
def time_large(self):
(self.alarge * 2) + self.blarge
def time_large2(self):
(self.alarge + self.blarge) - 2
class CorrConv(Benchmark):
params = [[50, 1000, int(1e5)],
[10, 100, 1000, int(1e4)],
['valid', 'same', 'full']]
param_names = ['size1', 'size2', 'mode']
def setup(self, size1, size2, mode):
self.x1 = np.linspace(0, 1, num=size1)
self.x2 = np.cos(np.linspace(0, 2*np.pi, num=size2))
def time_correlate(self, size1, size2, mode):
np.correlate(self.x1, self.x2, mode=mode)
def time_convolve(self, size1, size2, mode):
np.convolve(self.x1, self.x2, mode=mode)
class CountNonzero(Benchmark):
param_names = ['numaxes', 'size', 'dtype']
params = [
[1, 2, 3],
[100, 10000, 1000000],
[bool, int, str, object]
]
def setup(self, numaxes, size, dtype):
self.x = np.arange(numaxes * size).reshape(numaxes, size)
self.x = (self.x % 3).astype(dtype)
def time_count_nonzero(self, numaxes, size, dtype):
np.count_nonzero(self.x)
def time_count_nonzero_axis(self, numaxes, size, dtype):
np.count_nonzero(self.x, axis=self.x.ndim - 1)
def time_count_nonzero_multi_axis(self, numaxes, size, dtype):
if self.x.ndim >= 2:
np.count_nonzero(self.x, axis=(
self.x.ndim - 1, self.x.ndim - 2))
class PackBits(Benchmark):
param_names = ['dtype']
params = [[bool, np.uintp]]
def setup(self, dtype):
self.d = np.ones(10000, dtype=dtype)
self.d2 = np.ones((200, 1000), dtype=dtype)
def time_packbits(self, dtype):
np.packbits(self.d)
def time_packbits_axis0(self, dtype):
np.packbits(self.d2, axis=0)
def time_packbits_axis1(self, dtype):
np.packbits(self.d2, axis=1)
class UnpackBits(Benchmark):
def setup(self):
self.d = np.ones(10000, dtype=np.uint8)
self.d2 = np.ones((200, 1000), dtype=np.uint8)
def time_unpackbits(self):
np.unpackbits(self.d)
def time_unpackbits_axis0(self):
np.unpackbits(self.d2, axis=0)
def time_unpackbits_axis1(self):
np.unpackbits(self.d2, axis=1)
class Indices(Benchmark):
def time_indices(self):
np.indices((1000, 500))