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import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
from datetime import datetime


# Function to parse dates with different formats
def parse_date_flexible(date_str):
    for fmt in ("%b %d %Y", "%B %d %Y", "%b %d, %Y", "%B %d, %Y"):

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One-time differentiable function (locally linearized function):

def f_linearized(x):
    _, f_lin = jax.lax.stop_gradient(jax.linearize(f, x))
    return f_lin(x)

Differentiate with respect to internal variable ("perturbation trick"):

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#!/usr/bin/env python3
import os
import tempfile
import subprocess
import fitz  # PyMuPDF for reading PDFs
import re
import statistics
import argparse

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#!/bin/bash
set -e
# you can directly run this with: bash <(curl  -Ls "https://gist.githubusercontent.com/jkbjh/c83e5bf6d708f590f77ccdd6b2ef0a40/raw/mujoco-210-user-install.sh?$(date +%s)")
# (the date query parameter is to to prevent caching when downloading through curl.)
export START_DIR=$(pwd)
export MJ_DIR=$HOME/.mujoco

# Check if the file exists
if [ -e "$MJ_DIR/mujoco210/bin/compile" ]; then
  echo "mujoco already there, not downloading."

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#!/usr/bin/env python
import argparse
import re
import subprocess
import sys
import uuid

url_rex = re.compile(
    "^(?P<user>.*?)@(?P<host>.*?):(?:(?P<port>[0-9]*?))?(?P<path>.*?/.*?)$"
)

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#!/usr/bin/env python
import argparse
import subprocess
import shlex
import shutil
import sys


def check_programs(*progs):
    all_success = True

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#!/usr/bin/env python3
import pandas as pd
import matplotlib.pyplot as plt
import countryinfo  # for populations

FUTURE = 28
POPULATION = {
    "Czechia": countryinfo.CountryInfo("Czech Republic").population(),
}

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#!/bin/bash
set -e
set -x
# you can directly run this with: bash <(curl  -Ls "https://gist.githubusercontent.com/jkbjh/c83e5bf6d708f590f77ccdd6b2ef0a40/raw/mujoco-210-user-install.sh?$(date +%s)")
# (the date query parameter is to to prevent caching when downloading through curl.)
export START_DIR=$(pwd)
export MJ_DIR=$HOME/.mujoco
mkdir -p $MJ_DIR
pushd $MJ_DIR
curl -LJ 'https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz' -o $MJ_DIR/mujoco210_linux.tgz

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import collections


JointInfo = collections.namedtuple(
    "JointInfo",
    [
        "index",
        "name",
        "type",
        "q_index",

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# a selection of small numpy helper functions
import joblib


def apply_along_axes(func, data, axes):
    """
    apply function along axes and use joblib parallel to execute the function on the selections.
    might result in a large allocation for reconstructing the result.
    only makes sense if the sub-executions are sufficiently costly.
    """