nanounanue

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

nanounanue

#!/usr/bin/python

import os
import sys
import boto3

# get an access token, local (from) directory, and S3 (to) directory
# from the command-line
local_directory, bucket, destination = sys.argv[1:4]

nanounanue

# -*- coding: utf-8 -*-
import datetime as dt
import numpy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as md
import locale
from functools import reduce
locale.setlocale(locale.LC_ALL, "")
matplotlib.rcParams['font.family']='IPAPGothic'

nanounanue

import psycopg2 as pg
import pandas.io.sql as psql
 
# get connected to the database
connection = pg.connect("dbname=mydatabase user=postgres")
 
dataframe = psql.frame_query("SELECT * FROM <tablename>", connection)

nanounanue

#!/bin/sh
# Userland mode (~$USER/), (~/).

# ~/.fonts is now deprecated and that
#FONT_HOME=~/.fonts
# ~/.local/share/fonts should be used instead
FONT_HOME=~/.local/share/fonts

echo "installing fonts at $PWD to $FONT_HOME"
mkdir -p "$FONT_HOME/adobe-fonts/source-code-pro"

nanounanue

# Install R
sudo apt-get update
sudo apt-get install gdebi libxml2-dev libssl-dev libcurl4-openssl-dev libopenblas-dev r-base r-base-dev

# Install RStudio
cd ~/Downloads
wget https://download1.rstudio.org/rstudio-xenial-1.1.383-amd64.deb
sudo gdebi rstudio-xenial-1.1.383-amd64.deb
printf '\nexport QT_STYLE_OVERRIDE=gtk\n' | sudo tee -a ~/.profile

nanounanue

import logging
import flask

app = flask.Flask(__name__)

@app.before_first_request
def setup_logging():
    if not app.debug:
        # In production mode, add log handler to sys.stderr.
        app.logger.addHandler(logging.StreamHandler())

nanounanue

par(mar=c(0,0,0,0))
plot(0,0,xlim=c(0,28),ylim=c(0,1),
       xaxt="n",yaxt="n",bty="n",xlab="",ylab="",type="n")
i <- 1
for(j in 1:20)
{
  test <- (6+j):26
  train <- 1:(5+j)
  arrows(0,1-j/20,27,1-j/20,0.05)
  points(train,rep(1-j/20,length(train)),pch=19,col="blue")

nanounanue

"""
AWS Batch wrapper for Luigi

From the AWS website:

    AWS Batch enables you to run batch computing workloads on the AWS Cloud. 

    Batch computing is a common way for developers, scientists, and engineers 
    to access large amounts of compute resources, and AWS Batch removes the 
    undifferentiated heavy lifting of configuring and managing the required 

nanounanue

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm