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app.get('/tweets',
	require('connect-ensure-login').ensureLoggedIn(),
	function(req, res) {

	    // Create client instance to connect to Twitter
	    // We pass in the access token we got from the OAuth process
	    var T = new Twit({
		consumer_key: process.env['TWITTER_CONSUMER_KEY'],
		consumer_secret: process.env['TWITTER_CONSUMER_SECRET'],
		access_token: req.user.token,

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app.get('/tweets',
        require('connect-ensure-login').ensureLoggedIn(),
        function(req, res) {

            var T = new Twit({
                consumer_key: process.env['TWITTER_CONSUMER_KEY'],
                consumer_secret: process.env['TWITTER_CONSUMER_SECRET'],
                access_token: req.user.token,
                access_token_secret: req.user.tokenSecret,
                timeout_ms:           60*1000,  // optional HTTP request timeout to apply to all requests.

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require('dotenv').config();

var express = require('express');
var passport = require('passport');
var Strategy = require('passport-twitter').Strategy;
var Twit = require('twit');
var axios = require('axios');

// Tone analyser creds
tone_url = process.env['TONE_URL'],

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import json
import os 
import requests
import librosa
import io
import numpy as np

SAMPLE_RATE = 44100

# function to measure two waveforms with one offset by a certian amount

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Using a docker image for a cloud function

% ic fn action create calculate_alignment calculate_alignment.py --docker hammertoe/librosa_ml:latest

Invoking the function

% ic fn action invoke calculate_alignment -p referece leader.wav -p part sarah.wav

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# A simple hello world function
% cat hello.py
def main(args):
    name = args.get("name", "unknown")
    return {"message": f"Hello {name}!"}
    
# create an action
% ic fn action create hello hello.py
ok: created action hello

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import argparse
import requests
import json
from urllib.parse import urlparse

parser = argparse.ArgumentParser(description="Fetch content from a coil post")
parser.add_argument("url", help="URL to fetch from Coil")
args = parser.parse_args()

url_parts = urlparse(args.url)

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(pytorch2) (base) Matts-iMac:facial_alignment matt$ ls input/
IMG_0720.JPG	IMG_1784.JPG	IMG_2697_2.JPG	IMG_3251.JPG	IMG_5523_2.JPG	IMG_7489.JPG	IMG_7836.JPG
IMG_1148.JPG	IMG_1908.JPG	IMG_2918.JPG	IMG_3651.JPG	IMG_7480.JPG	IMG_7490.JPG	IMG_9029.JPG
IMG_1215.JPG	IMG_1910.JPG	IMG_3028.JPG	IMG_3833.JPG	IMG_7482.JPG	IMG_7630.JPG	IMG_9622.JPG
IMG_1574.JPG	IMG_2041.JPG	IMG_3250.JPG	IMG_5520_2.JPG	IMG_7485.JPG	IMG_7633.JPG	IMG_9919.JPG
(pytorch2) (base) Matts-iMac:facial_alignment matt$ python align_faces.py --shape-predictor shape_predictor_68_face_landmarks.dat --images input/*.JPG
processing: input/IMG_0720.JPG
processing: input/IMG_1148.JPG
processing: input/IMG_1215.JPG
processing: input/IMG_1574.JPG

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# USAGE
# python align_faces.py --shape-predictor shape_predictor_68_face_landmarks.dat input/example_*.jpg

# import the necessary packages
from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
import argparse
import imutils
import dlib
import cv2

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input	output
16	1
17	1
18	1
19	1
20	1
21	0
22	0
23	0