nschloe

import numpy as np
import perfplot
from scipy.linalg.blas import ddot


def np_dot(data):
    x, y = data
    return np.dot(x, y)

nschloe

import numpy as np
import perfplot
from scipy.linalg.blas import daxpy

a = 1.3


def np_axpy(data):
    x, y = data
    return a * x + y

nschloe

import perfplot
import numpy as np


def div(ab):
    a, b = ab
    return a / b


def mul(ab):

nschloe

import math
import numpy as np
import perfplot
import random


def plain_loop(n):
    inside = 0
    random.seed()
    for _ in range(n):

nschloe

import perfplot
import numpy as np
from scipy.sparse import spdiags


def setup_dense(n):
    A = np.random.rand(n, n) + 1j * np.random.rand(n, n)
    AH = A.T.conj()
    x = np.random.rand(n) + 1j * np.random.rand(n)
    return A, AH, x

nschloe

import numpy as np
import perfplot


def dot(xy):
    x, y = xy
    return np.dot(x, y)


def einsum(xy):

nschloe

import numpy as np
import scipy.optimize

np.random.seed(0)
V_orig = np.random.rand(50, 3)


def func(x):
    Vx = V_orig @ x
    return np.dot(Vx, Vx)

nschloe

import numpy as np

import perfplot


def dot(a, b):
    a_shape = a.shape
    b_shape = b.shape
    b = b.reshape(b.shape[0], -1)
    return np.dot(a, b).reshape(a_shape[:-1] + b_shape[1:])

nschloe

import numpy
import perfplot


def setup(n):
    pts = numpy.random.rand(3, n, 2)
    e = numpy.array([
        pts[2] - pts[1],
        pts[0] - pts[2],
        pts[1] - pts[0],

nschloe

import perfplot
import numpy


def setup(n):
    a = numpy.random.rand(n, 3)
    b = numpy.ascontiguousarray(a.T)
    return a, b