rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!

nrollr

# UPDATED 17 February 2019
# Redirect all HTTP traffic to HTTPS
server {
   listen 80;
   listen [::]:80; 	
   server_name www.domain.com domain.com;
   return 301 https://$host$request_uri;
}

# SSL configuration

michiel

#
# Wide-open CORS config for nginx
#
location / {

     if ($request_method = 'OPTIONS') {

        add_header 'Access-Control-Allow-Origin' '*';
        
        #

maraujop

# -*- coding: utf-8 -*-
from django import forms

from crispy_forms.helper import FormHelper
from crispy_forms.layout import Layout, Div, Submit, HTML, Button, Row, Field
from crispy_forms.bootstrap import AppendedText, PrependedText, FormActions


class MessageForm(forms.Form):
    text_input = forms.CharField()

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

kripken

int doubler(int x) {
  return 2 * x;
}

acolyer

A collection of The Morning Paper posts relating to containers, operating systems etc.

In order of first appearance in The Morning Paper.

• On the duality of operating system structures
• Why threads are a bad idea
• Why events are a bad idea
• Unikernels: library operating systems for the cloud
• The scalable commutativity rule: designing scalable software for multicore processors
• Jitsu: just-in time summoning of unikernels

mangecoeur

Easy parallel python with concurrent.futures

As of version 3.3, python includes the very promising concurrent.futures module, with elegant context managers for running tasks concurrently. Thanks to the simple and consistent interface you can use both threads and processes with minimal effort.

For most CPU bound tasks - anything that is heavy number crunching - you want your program to use all the CPUs in your PC. The simplest way to get a CPU bound task to run in parallel is to use the ProcessPoolExecutor, which will create enough sub-processes to keep all your CPUs busy.

We use the context manager thusly:

with concurrent.futures.ProcessPoolExecutor() as executor:

alyssonbruno

import io
import mmap
from typing import Optional

_DEFAULTBS=io.DEFAULT_BUFFER_SIZE

def clone(input_file: Optional[str]='/dev/zero', output_file: Optional[str]='/dev/null', block_size: Optional[int]=_DEFAULTBS, count: Optional[int]=None, verbose: Optional[bool]=False):
    """Make a clone of one file or device. Emule the command dd, without type conversion
        >>> clone(output_file='/tmp/swap.img',bs=512,count=8*2*1024*1024*1024)