J535D165

{
    "version": "2.0.0",
    "tasks": [
        {
            "label": "startBackendServer",
            "type": "shell",
            "command": "${command:python.interpreterPath}",
            "args": ["-m",
                "flask",
                "run",

J535D165

import xml.etree.ElementTree as ET

import requests

# curl -LH "Accept: application/vnd.datacite.datacite+xml" https://doi.org/10.34894/KEIQRD

def get_datapublisher_from_doi(doi):
    """Get the publisher from the DOI.

    Arguments

vankesteren

# load packages
library(tidyverse)
library(sf)

# read data
flow <- read_csv("https://data.mendeley.com/public-files/datasets/rtn8t47t6j/files/7f85a66d-a80d-4e3d-ada8-694a9c6e0a28/file_downloaded")
gem <- st_read("WFS:https://geodata.nationaalgeoregister.nl/wijkenbuurten2018/wfs?&request=GetCapabilities&service=WFS", 
               "wijkenbuurten2018:gemeenten2018")

# clean gemeente dataset

lebrice

"""A simple tool to add the name of downloaded paper pdf's in front of the id.
(Written by fabrice.normandin@gmail.com)


If there are multiple downloads of same paper, replaces the original with the
latest download. This can be useful in a downloads folder filled with copies.
For instance:
"""

import glob

chrisjsimpson

#!/env/python
# coding=utf8

"""
Converting Degrees, Minutes, Seconds formatted coordinate strings to decimal. 

Formula:
DEC = (DEG + (MIN * 1/60) + (SEC * 1/60 * 1/60))

Assumes S/W are negative. 

33eyes

Running jupyter notebook with nohup

This is helpful when running jupyter notebook on remote server (AWS) and tunneling into it, because with nohup the notebook process continues to run on the server even if you get disconnected from it (any running notebook code continues to run on the server without interruption, and just needs to be re-tunneled into).

Start jupyter notebook on remote server

In the remote server terminal, run:

nohup jupyter notebook &

joelbecker

from datetime import datetime
from random import randrange

import recordlinkage as rl
import recordlinkage.algorithms.conflict_resolution as cr
from recordlinkage.datasets import load_febrl4

dfA, dfB = load_febrl4()

# Adapt dataset for example

inventionate

# Stat Installation

# XCode CLT
xcode-select --install

# Update Homebrew
brew update

# Check for broken dependencies and/or outdated packages
brew doctor

fgilio

/*
 * Handling Errors using async/await
 * Has to be used inside an async function
 */
try {
    const response = await axios.get('https://your.site/api/v1/bla/ble/bli');
    // Success 🎉
    console.log(response);
} catch (error) {
    // Error 😨

jakechen

Introduction

This quick guide describes how to create a Jupyter Notebook in AWS EC2 then how to access it remotely using SSH tunneling. This method is preferred since you do not open any additional ports besides 22, requires little-to-no configuration, and is generally more straight-forward.

Pre-requisites

This current version assumes basic familiarity with cloud computing, AWS services, and Jupyter Notebook. Mostly because this version won't have images and won't dive too deep into each individual step.

Steps

Spin-up EC2 instance with "Deep Learning" AMI

1. Log into EC2 console and click "Launch Instance" button.
2. Inside "AWS Marketplace", select the "Deep Learning AMI" from AWS. I use this AMI because most of the stuff you'll need is installed already.