nschloe

import perfplot
import numpy as np

rng = np.random.default_rng(0)


def setup(n):
    return [rng.random(n) for _ in range(10)]

nschloe

import perfplot
import numpy as np


def householder(A):
    m, n = A.shape
    R = A.copy()
    Q = np.identity(m)

    for j in range(n):

nschloe

import numpy as np
import perfplot

rng = np.random.default_rng(1)


def setup(n):
    U = rng.random((n, 10))
    return U, np.ascontiguousarray(U.T)

nschloe

import perfplot
import numpy as np


def inv(AX):
    A, X = AX
    Ainv = np.linalg.inv(A)
    return Ainv @ X @ Ainv.T

nschloe

from math import sqrt


fibonacci = [1, 1]
phi = (sqrt(5) - 1) / 2
print(phi)
print()

errors = []

nschloe

import random
import perfplot

lst = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"]
string = "abcdefghijkl"


def list_lookup(data):
    return [lst[n] for n in data]

nschloe

import perfplot
import numpy as np


threshold = 0.5
# threshold = 1.0e-3

def div_where(data):
    a, b = data
    return a / np.where(b > threshold, b, 1.0)

nschloe

from numpy import pi, cos, sin


def f(z, s_z, c_z):
    return (0.5 - s_z) * cos(pi / 2 * z ** 2) - (0.5 - c_z) * sin(pi / 2 * z ** 2)


def f_asymp(z):
    zp2 = (pi * z ** 2) ** 2
    r = 1.0

nschloe

import perfplot
import numpy as np
import npx


A = np.array([[0.0, 1.0, 0.0], [125 / 29, -125 / 29, 0.0], [0.0, 50 / 29, -50 / 29]])


def manual(data):
    fx, fy, fz = data

nschloe

import numpy as np
import perfplot


def mult(x):
    return x * (1.0 / np.pi)


def div(x):
    return x / np.pi