nkrabben

import json
import pandas as pd

with open('Downloads/xfrcollective.json', 'r') as f:
	data = json.load(f)
	df = pd.DataFrame(data['collection_items'])
	df.to_csv('Downloads/xfrcollective.csv', index=False)

nkrabben

import json
import csv

with open('Downloads/xfrcollective.json', 'r') as f:
	data = json.load(f)

with open('Downloads/xfrcollective.csv', 'w') as f:
	header = ['addeddate', 'backup_location', 'collection', 'color', 'contact', 'contributor', 'coverage', 'creator', 'curation', 'date', 'description', 'designer', 'identifier', 'illustrator', 'language', 'licenseurl', 'mediatype', 'ocr', 'ppi', 'publicdate', 'publisher', 'rights', 'runtime', 'scanner', 'sound', 'sponsor', 'subject', 'title', 'updatedate', 'updater', 'uploader', 'year']
	writer = csv.writer(f, header)
	writer.writerow(header)

nkrabben

import argparse
import bagit

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
            "directory",
            nargs="+",
            help=_(
                "Directory which will be scanned for files and hashed."

nkrabben

import os
import subprocess
import multiprocessing
import timeit


def work(drive):
	cmd = [
		'snowball',
    		'cp',

nkrabben

import os
import subprocess
import multiprocessing
import timeit


def work(drive):
	cmd = [
		'find',
		drive,

nkrabben

import os
import subprocess
import multiprocessing
import timeit


def work(drive):
	cmd = [
		'snowball',
		'cp',

nkrabben

#!/usr/bin/env/python

import os
import argparse
import subprocess
import multiprocessing


### For extra utitily when running this script remotely
# nohup transcode.py -d directory/of/mov -e mov -o directory/of/future/mkv &

nkrabben

#!/usr/bin/env python3

from pymediainfo import MediaInfo
import argparse
import os
import glob
import logging
import csv
import re

nkrabben

library(readr)
library(dplyr)
library(tidyr)
library(factoextra)

# Read data, drop last 3 columns that don't ask about skills
osf_url = "https://files.osf.io/v1/resources/zndwq/providers/osfstorage/570be0eab83f6901d62b19d9"
responses <- read_csv(osf_url)[,1:30]

# Reshape raw data into number of responses per importance level