Faraz Yashar fny

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

MIT License Comment Headers Formatted for Various Languages

A convenient reference for adding a MIT license to a work.

All prepared texts are hard wrapped at 80 characters unless stated otherwise.

A convenient reference for adding a LGPL license to a work.

All prepared texts are hard wrapped at 80 characters unless stated otherwise.

I hereby claim:

I am fny on github.
I am fny (https://keybase.io/fny) on keybase.
I have a public key whose fingerprint is 477B 6E2F 415F 02BC AA6C 44E0 1E9C CF9B 131E 417C

To claim this, I am signing this object:

	import subprocess
	import requests
	import psutil

	def get_notebooks(host, port, token = None):
	sessions_url = f'http://{host}:{port}/api/sessions/?token={token}' if token else f'http://{host}:{port}/api/sessions'
	response = requests.get(sessions_url).json()
	notebooks = [{'kernel_id': notebook['kernel']['id'],
	'path': notebook['notebook']['path']} for notebook in response]

	function concat_with_header() {
	# Quoted suffix to pattern match for concatenation (e.g. '*.csv')
	local suffix="${1}"
	# Name of the output file
	local output="${2:-combined.out}"
	# Number of lines to use for the header
	local header_length="${3:-1}"
	# Grab the header from the first file
	local header=`echo -e "$(ls -b *$suffix \| head -n$header_length)"`
	head -1 $header_file > $output; tail -n +"`expr $header_length + 1`" -q *$suffix >> $output


	package greenlight.etc


	import java.security.SecureRandom
	import kotlin.math.abs

	val BASE58_ALPHABET = "23456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"

	fun randomBase58(length: Int): String {

	[Unit]
	Description=AutoSSH tunnel service
	After=network-online.target sshd.service

	[Service]
	Environment="AUTOSSH_GATETIME=0"
	Environment="AUTOSSH_PORT=0"
	ExecStart=/usr/bin/autossh -o "ServerAliveInterval 10" -o "ServerAliveCountMax 3" -N -R 2222:localhost:22 -R 8888:localhost:8888 [email protected]

	[Install]

	import numpy as np

	def shrink_df(df, categorize=False, verbose=False):
	"""Reduces the memory use of a data frame by using more compact types.
	Args:
	df (pandas.DataFrame): The dataframe
	categorize (bool): Whether strings should be converted to categorical values.
	Note this may cause memory use to increase slightly.
	verbose (bool): Whether to print memory savings to stdout.

	# icd10_map_charlson from https://github.com/jackwasey/icd/blob/master/data/icd10_map_charlson.RData

	df = data.frame("comorbidity" = character(0), "icd10" = character(0), "score" = numeric(0), stringsAsFactors = FALSE)

	scores = data.frame(
	MI = 1,
	CHF = 1,
	PVD = 1,
	Stroke = 1,
	Dementia = 1,