ehoppmann

#!/usr/bin/env python3
from bs4 import BeautifulSoup
import dateparser
import csv

soup = BeautifulSoup(open('WeightRecorder - 2016-09-18.xml'), "lxml")

def kgtolbs(kg):
    return float(kg) * 2.20462262185

ehoppmann

#!/bin/bash

grep -A10000000 ListenHTTPS /etc/pound/pound.cfg \
| sed -n "s/^.*HeadRequire\s*\"Host\:\.\*\(.*\)\.\*.*$/\1/p" \
> domains.txt

ehoppmann

# MIT License

# Copyright (c) 2016 Chandler Abraham

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

ehoppmann

"""Plots a Pandas dataframe as a heatmap"""
import matplotlib as mpl
import matplotlib.pyplot as plt

def heatmap(df,
            edgecolors='w',
            cmap=mpl.cm.RdBu,
            log=False,vmin=0,vmax=500):    
    width = len(df.columns)/4
    height = len(df.index)/4

ehoppmann

from typing import Iterable, Callable, List

def ordered_set(iterable: Iterable, key: Callable=lambda x: x) -> List:
    """
    Returns a new list containing all unique items from the iterable. A custom key function can
    be specified to customize the uniqueness constraint, for example to return the first unique
    element from the input list based on the second item in each element, one could pass
    `lambda x: x[1]`.
    """
    out_list = []

ehoppmann

COST_GB_SEC = 0.00001667

cost = 0
with open('logs.log', 'r') as f:
    logs = f.readlines()
billing = [i.split('\t') for i in logs if '\tBilled Duration:' in i]

for i in billing:
    gb = float(i[3].lstrip('Memory Size: ').rstrip(' MB')) / 1024
    sec = float(i[2].lstrip('Billed Duration: ').rstrip(' ms ')) / 1000

ehoppmann

# -*- coding: utf-8 -*-
# -*- mode: python -*-
# Adapted from mpl_toolkits.axes_grid1
# LICENSE: Python Software Foundation (http://docs.python.org/license.html)

from matplotlib.offsetbox import AnchoredOffsetbox
class AnchoredScaleBar(AnchoredOffsetbox):
    def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
                 pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None, 
                 **kwargs):

ehoppmann

from __future__ import division
import numpy as np
import scipy.stats.kde as kde

def hpd_grid(sample, alpha=0.05, roundto=2):
    """Calculate highest posterior density (HPD) of array for given alpha. 
    The HPD is the minimum width Bayesian credible interval (BCI). 
    The function works for multimodal distributions, returning more than one mode

    Parameters

ehoppmann

Kimbie

#F3E3CD,#F3E3CD,#F3951D,#DA3D61,#F26328,#183E1C,#DA3D61,#F26328

Monokai

#222222,#2F2F2F,#F92772,#FFFFFF,#A6E22D,#FFFFFF,#66D9EF,#BE84F2

Juice Bar

#86A34E,#94AF63,#FFFFFF,#6D8B42,#94AF63,#FFFFFF,#FFB10A,#DFA044

ehoppmann

#!/usr/bin/env python3
# based on https://github.com/cherrypy/cherrypy/blob/0857fa81eb0ab647c7b59a019338bab057f7748b/cherrypy/process/wspbus.py#L305
import sys
import os
import time

_startup_cwd = os.getcwd()

def _do_execv():
    args = sys.argv[:]