rldotai

import io, tokenize


def extract_comments(code: str | io.TextIOBase) -> str:
    """
    Extract comments from a piece of Python code, returning a string of
    *just* the comments.

    Example:
        >>> extract_comments(r'''

rldotai

"""
Renaming files to make them more consistent/easier to type.
"""
import unicodedata
import re
from pathlib import Path


# You can use whatever function you prefer, but Django's slugify is pretty slick 
def slugify(value, allow_unicode=False):

rldotai

import operator
from functools import reduce


def get(obj, key):
    """Get the item within `obj` corresponding to `key`. 
    If `obj` is subscriptable, return `obj[key]`, otherwise assume `key` 
    refers to an attribute and return `obj.key` instead.
    """
    if hasattr(obj, '__getitem__'):

rldotai

%%html
<canvas id="canvas"></canvas>
<script>
/* A function that reports the width of a Jupyter notebook cells */
function reportWidth() {
    /* Get an output area element */ 
    let el = document.querySelector("div.output_subarea.output_text.output_stream.output_stdout > pre");
    
    /* Determine the width of the output area, in pixels */
    let output_width = el.clientWidth;

rldotai

from colorama import Fore, Back, Style


foregrounds_dark = {
    i: Fore.__dict__[i]
    for i in dir(Fore)
    if not (i.startswith("_") or i.startswith("LIGHT") or i.startswith("RESET"))
}

foregrounds_light = {

rldotai

"""
Calculate the number of distinct floats between two numbers via bit packing and
unpacking.
--------------------------------------------------------------------------------
>>> distinct_floats(1, 0)
4607182418800017408
>>> distinct_floats(-1, 0)
4616189618054758400
>>> distinct_floats(0, float('inf'))
9218868437227405312

rldotai

import numpy as np
from matplotlib.colors import Normalize


class FixedMidpointNorm(Normalize):
    def __init__(self, vcenter):
        """
        Normalize data with a set center.

        For plotting diverging colormaps around a particular value, at the 

rldotai

"""
********************************************************************************
Matplotlib can create some great plots, although it is often a hassle to 
customize said plots after their creation, particularly if you're creating a 
large number of them, or regenerating them for minor changes (e.g. scaling)
is inconvenient.

I've been experimenting with generating "minimal" plots, which contain only the
actual representation of the data.
Insofar as I can add titles/labels in LaTeX, this allows me to embed the plots

rldotai

"""
Here we imagine that you've plotted some things, added a legend, and now
want to be able to save that legend separately.
Perhaps you want to rescale things later, or maybe you've plotted a bunch
of similar graphs and want to have a common legend for all of them, which
can be awkard to do in matplotlib.

Here is one method for doing this:
    0. Plot some things, set up a legend
    1. Get information about the legend, in particular its bounding box

rldotai

import itertools


def is_iterable(x) -> bool:
    """Return `True` if `x` is iterable."""
    try:
        iter(x)
        return True
    except TypeError:
        return False