havron

import re

def sanitize(regex):
    if regex.startswith('r"'):
        regex = regex[2:]
    elif regex.startswith('"'):
        regex = regex[1:]
    if regex.endswith('"'):
        regex = regex[:-1]
    return regex

havron

import json

def json_load(path: str) -> dict:
    if not path:
        return {}
    try:
        with open(path, 'r') as p:
            return json.load(p)  # expect dict
    except (FileNotFoundError, json.decoder.JSONDecodeError) as e:
        print(f"[WARNING] No recipients discovered. Reason: {e}")

havron

<!-- On Squarespace config -->

<!-- In your blog post (text section), make an "insert point" (teardrop on the side) and choose code block. -->
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A very long blog post, please see minor point[<div id="footnote-1-back"><sup>1</sup></div>](my_site.com/slug/#footnote-1)
A second but more major point[<div id="footnote-2-back"><sup>2</sup></div>](my_site.com/slug/#footnote-2)

havron

#!/usr/bin/env python3

import argparse
import random


def main(path, samplesize):
    """
    Prints random samples in order of selection.

havron

https://superuser.com/a/752510

havron

curl -s -o /dev/null -w "%{http_code}" http://cuonly.cs.cornell.edu/ | grep -q 200 || sudo vpnc-connect

havron

#!/usr/bin/env python3
# coding: utf-8

import pandas as pd
import sys
import os

# pass in infile and CMSX assignment name as commandline args
if len(sys.argv) < 3:
    print("Usage: python3 {}".format(os.path.basename(__file__))\

havron

du -cha --max-depth=2 /home/ | grep -E "M|G" | sort -rh

havron

from sklearn.model_selection import KFold
from sklearn.base import clone

def cross_validate(features, labels, nsplits, model):
    '''Returns tuple of (scores : list, average_score : float) over K folds.
        Keyword arguments:
        nsplits : int -- the number of folds to perform cross validation on. 
        model -- an object that is the model for CV. Assumes fit() and score() methods,
                 akin to sklearn model APIs.
        features : pandas.DataFrame -- a Pandas DataFrame containing preprocessed training data features.

havron

'''
    Get full human-readable labels for Android permissions. 
    Run `adb shell pm list permissions -g -f > permissions_dump.txt` on an Android device, 
    then run this Gist to convert to pandas-friendly CSV.
'''
from rsonlite import simpleparse
import pandas as pd
OUTFILE = 'android_permissions.csv'
def permission_labels(permissions_dumpfile):
    print('Parsing permissions dumpfile...')