Nick ODell nickodell

Docs Performance Investigation Plan

Date: 2025-10-16

The following are a set of suggested troubleshooting ideas for diagnosing the problem with the docs.scipy.org server.

(Note: The following instructions assume that you are running Ubuntu 20.04 LTS, which is my best guess for what the server is running based on its Apache version / server banner.)

Linux Performance in 60000 ms

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	scipy version: 1.16.2
	numpy version: 2.3.3
	pandas version: 2.3.3

	ODR_NOISY: ['Sum of squares convergence']
	ODR_CLEAN: ['Sum of squares convergence']

	beta[0] beta[1] beta[2]
	initial 1.0000 85.0000 0.0200
	ODR noisy 4.6002 87.6715 0.0140

	// command gcc -Wall -Wextra -pedantic test_sgnd.c -lm -o test_sgnd_c && ./test_sgnd_c
	#include <stdio.h>
	#include <math.h>

	// Test sgnd = dp*(dx/ABS(dx)) for all combinations of [1, 0, -0, 1]
	int main(void)
	{
	const double dp[4] = { 1.0, 0.0, -0.0, -1.0 };
	const double dx[4] = { 1.0, 0.0, -0.0, -1.0 };

	FROM archlinux:latest
	RUN pacman -Syu --noconfirm git base-devel
	RUN useradd nodell \
	&& mkdir /builddir \
	&& chown nodell:nodell /builddir
	USER nodell
	WORKDIR /builddir
	RUN git clone https://aur.archlinux.org/python-scipy-mkl-tbb.git
	USER root
	RUN pacman -Su --noconfirm cython gcc-fortran meson-python pybind11 python-build python-installer python-pythran python-pytest python-hypothesis

	# 1.14.1 was benchmarked outside of SciPy's build directory
	# It was done with the following command
	for i in $(seq 1 7); do python3 bug-22655b.py; done
	# This runs the benchmark fewer times than normal, but this is fine because 1.14.1 is very clearly faster.

	// ==UserScript==
	// @name Remove Search Pulsing
	// @version 1
	// @grant none
	// @match https://stackoverflow.com/*
	// ==/UserScript==


	(new MutationObserver(check)).observe(document, {childList: true, subtree: true});

	import numpy as np
	import cv2
	import scipy.ndimage
	import matplotlib.pyplot as plt

	img = cv2.imread('HjJto.jpg')
	img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

	blue = np.array((81, 93, 147)).reshape(1, 1, 3)
	rmse = np.sqrt(((img_rgb - blue)**2).mean(axis=2))

	concatenated = data[['day', 'month', 'year', 'time']].astype(str).agg(' '.join, axis=1)
	data['date'] = pd.to_datetime(concatenated)