bhpayne

# Python3
import multiprocessing
print(multiprocessing.cpu_count())

def serial_function(some_variable):
    reslt=some_variable*5
    return reslt

list_of_variables=[3, 5]

bhpayne

import requests
import json
import datetime
import time
import matplotlib.pyplot as plt
import matplotlib.dates as md
import pandas
print('pandas',pandas.__version__)
import numpy
print('numpy',numpy.__version__)

bhpayne

import multiprocessing

def serial_func(arg1):
    return arg1*2

if __name__ == '__main__':
    start_time=time.time()
    res_list=[]
    with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:   # start worker processes
        res_list = pool.map(serial_func,[3,4,5]) # see https://docs.python.org/3/library/multiprocessing.html

bhpayne

from inspect import stack, currentframe, getframeinfo  # file name and line number


def prntln(*args):
    """
    https://stackoverflow.com/questions/24438976/python-debugging-get-filename-and-line-number-from-which-a-function-is-called
    """
    caller = getframeinfo(stack()[1][0])
    print(caller.filename, caller.lineno, args)

bhpayne

from functools import wraps
import errno
import signal


def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):
    """
    https://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish

    see also

bhpayne

To turn off detection of MITM attacks, edit ~.ssh/config to include

StrictHostKeyChecking=no
UserKnownHostsFile=/dev/null

Alternatively, on the command line use

ssh -o "StrictHostKeyChecking no" -o LogLevel=ERROR name@server

bhpayne

There are many Git workflows out there, I heavily suggest also reading the atlassian.com [Git Workflow][article] article as there is more detail than presented here.

The two prevailing workflows are [Gitflow][gitflow] and [feature branches][feature]. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited.

When using Bash in the command line, it leaves a bit to be desired when it comes to awareness of state. I would suggest following these instructions on [setting up GIT Bash autocompletion][git-auto].

Basic branching

When working with a centralized workflow the concepts are simple, master represented the official history and is always deployable. With each new scope of work, aka feature, the developer is to create a new branch. For clarity, make sure to use descriptive names like transaction-fail-message or github-oauth for your branches.

bhpayne

Keybase proof

I hereby claim:

• I am bhpayne on github.
• I am benislocated (https://keybase.io/benislocated) on keybase.
• I have a public key ASDplRUB-egGgrAKV7O5OizXr1hBRFTAsD2oE5bDS0OudAo

To claim this, I am signing this object:

bhpayne

function lcount() {
    count_py_files=`find . -type f | grep \.py$ | wc -l`
    count_c_files=`find . -type f | grep \.c$ | wc -l`
    count_csv_files=`find . -type f | grep \.csv$ | wc -l`
    count_png_files=`find . -type f | grep \.png$ | wc -l`
    count_html_files=`find . -type f | grep \.html$ | wc -l`
    count_sh_files=`find . -type f | grep \.sh$ | wc -l`
    count_xml_files=`find . -type f | grep \.xml$ | wc -l`
    py_line_count=`find . -name "*.py" -type f -exec grep . {} \; | sed -n '/^# /!p' | wc -l`
    tex_line_count=`find . -name "*.tex" -type f -exec grep . {} \; | sed -n '/^% /!p' | wc -l`

bhpayne

import time

from flask import Flask, request, g, render_template

app = Flask(__name__)
app.config['DEBUG'] = True

@app.before_request
def before_request():
    g.request_start_time = time.time()