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## regular case
foo <- function(a, b, c)  a + b - c ## does something
foo2 <- function(b, c)  b + c ## also some function
foo(a=1, b=2, c=5)
foo2(b=2, c=5)  ## repeating list of multiple arguments

## passing a list
arg.list <- list(b=2, c=5)
do.call(foo, c(list(a=1), arg.list))
do.call(foo2, arg.list)

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#!/bin/bash

# setup AWS CLI first
#  http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-set-up.html

# configure AWS CLI (e.g. use IAM role for S3 access)
export AWS_DEFAULT_REGION=us-east-1
export AWS_ACCESS_KEY_ID=IDHERE
export AWS_SECRET_ACCESS_KEY=KeyHere

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## Problem
## https://www.reddit.com/r/CoinBase/comments/18b1oqe/comment/krtzg2n/
## As a workaround, use old API (as of 2024-02)
## https://github.com/danpaquin/coinbasepro-python
## (Python 3.7) pip install cbpro
import json
import cbpro

# supply API credentials
auth_client = cbpro.AuthenticatedClient(key, secret, passphrase)

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# phusion baseimage initiates cron properly

# Use phusion/baseimage as base image. To make your builds reproducible, make
# sure you lock down to a specific version, not to `latest`!
# See https://github.com/phusion/baseimage-docker/blob/master/Changelog.md for
# a list of version numbers.
FROM phusion/baseimage:0.9.16

# Use baseimage-docker's init system.
CMD ["/sbin/my_init"]

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## https://superuser.com/a/377407
## starting second (7) to ending timestamp (06:05:00)
ffmpeg -ss 7 -i input.mkv -to 06:05:00 -c copy output.mkv

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This gist contains lists of modules available in

• Python 2.7
• Python 3.6
• Python 3.7

in AWS Lambda.

It also contains the code to run in Lambda to generate these lists. In addition there is a less_versbose module in the code that you can call to get a list of the top level modules installed and the version of those modules (if they contain a version

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# IMPORTS
import pandas as pd
import math
import os.path
import time
from bitmex import bitmex
from binance.client import Client
from datetime import timedelta, datetime
from dateutil import parser
from tqdm import tqdm_notebook #(Optional, used for progress-bars)

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library(caret)

## select training indices preserving class distribution
in.train <- createDataPartition(yclass, p=0.8, list=FALSE)
summary(factor(yclass))
ytra <- yclass[in.train]; summary(factor(ytra))
ytst <- yclass[-in.train]; summary(factor(ytst))

## standardize features: training parameters of scaling for test-part
Xtra <- scale(X[in.train,])

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The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

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# start julia specifying how many cores to make available
##$ julia -p 4

# julia multicore start command in a shell script
##$ julia -p $(nproc)
# http://stackoverflow.com/questions/2314750/how-to-assign-the-output-of-a-shell-command-to-a-variable

# alternatively add local cores dynamically (e.g. after starting julia single-core)
##$ julia
addprocs_local(3)