ma-ric

#!/usr/bin/env python3

def flatten(t):
    """
    Generator flattening the structure
    
    >>> list(flatten([2, [2, "test", (4, 5, [7], [2, [6, 2, 6, [6], 4]], 6)]]))
    [2, 2, "test", 4, 5, 7, 2, 6, 2, 6, 6, 4, 6]
    """
    from collections.abc import Iterable

tebeka

#!/usr/bin/env python2
'''Serving dynamic images with Pandas and matplotlib (using flask).'''

import matplotlib
matplotlib.use('Agg')

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from cStringIO import StringIO

pouyatafti

#include <string.h>
#include <unistd.h>

char *moomin[] = {
	"                 .   ,",
	"ASCII art (c) Maija Haavisto (@DiamonDie)",
	"",
	"            \"._.'        `' mh/VK",
	"          `.   `,      `.  .'",
	"          .   .'     `.   `' ;",

tonyfast

tonyfast

rhettinger

'Emulate max() as fully as possible in pure Python.'

# https://stackoverflow.com/questions/69997857/implementation-of-max-function-in-python/69997876#69997876
# https://github.com/python/mypy/issues/7231

from typing import TypeVar, Any, Iterator, Iterable, Optional
from typing import Union, Protocol, Callable, cast, Tuple, overload


class SupportsGT(Protocol):

dweinstein

#+TITLE: Template org-mode document for export to LaTeX/PDF
#+AUTHOR: David Weinstein

#+LaTeX_HEADER: \usepackage[left=1in,top=1in,right=1in,bottom=1.5in]{geometry}
#+LaTeX_HEADER: \usepackage{palatino}
#+LaTeX_HEADER: \usepackage{fancyhdr}
#+LaTeX_HEADER: \usepackage{sectsty}
#+LaTeX_HEADER: \usepackage{engord}
#+LaTeX_HEADER: \usepackage{cite}
#+LaTeX_HEADER: \usepackage{graphicx}

dannvix

#!/usr/bin/env python3

import math

# >> Discrete Fourier transform for sampled signals
# x [in]: sampled signals, a list of magnitudes (real numbers)
# yr [out]: real parts of the sinusoids
# yi [out]: imaginary parts of the sinusoids
def dft(x):
  N, yr, yi = len(x), [], []

xero

                        ..:77I777777777777777777777I. .                         
                     ..?77777777777777777777777777777$+..                       
                   . ~7777777I7777777777777777777777777$~..                     
                    .7I7777...7777777777777777777777777$7+.                     
                   .?7777..   ..77777777777777777777777$$7.                     
                   .77777       777777777777777777777$$$$$I                     
                   .77777..  ...7777777777777777777$$$$$$$$                     
                   .7777777  .77$777777777777777777$$$$$$$$                     
                   .77777777777777777777777777777$$$$$$$$$$                     
                   .77777777777777777777777777$$$$$$$$$$$$$                     

StuartGordonReid

def linear_complexity(self, bin_data, block_size=500):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    The focus of this test is the length of a linear feedback shift register (LFSR). The purpose of this test is to
    determine whether or not the sequence is complex enough to be considered random. Random sequences are
    characterized by longer LFSRs. An LFSR that is too short implies non-randomness.

    :param bin_data: a binary string