johanastborg

//https://docs.google.com/presentation/d/1bnDuaUqqDM8uh_dZD_JbfCIMtA5U4iP8mazT4h21yDI

function onOpen() {
  let ui = SlidesApp.getUi();
  ui.createMenu('Custom Menu')
      .addItem('Populate Donut Chart', 'showPrompt')
      .addSeparator()
      .addSubMenu(ui.createMenu('Sub-menu')
      .addItem('Second item', 'menuItem2'))

johanastborg

import re

# Ref: https://cloud.google.com/endpoints/docs/grpc/transcoding

regexparse = r'option\ \(google.api.http\)\ =\s{\s+(\w+: \"[\S]+)\s+(\w+:\s\"[\S]+\s+)?};'

with open('http_bookstore.proto','r') as file:
    content = file.read()

[print(x.group()) for x in re.finditer(regexparse, content)]

johanastborg

const express = require('express');
const bodyParser = require('body-parser');
const path = require('path');
const Busboy = require('busboy');
const os = require('os');
const fs = require('fs');

// create express app
const app = express();
var public = path.join(__dirname, 'public');

johanastborg

import json
from flask import Flask
from keras.models import load_model
from utils import preprocess 

model = load_model('model.h5')
app = Flask(__name__) 

@app.route('/forecast', methods=['POST'])
def forecast(): 

johanastborg

ATVI
ADBE
AMD
ALXN
ALGN
GOOGL
GOOG
AMZN
AMGN
ADI

johanastborg

const dir = '/tmp';
// list content
fs.readdir(dir, (_, files) => { console.log(files.join('\n')) });
// delete files (ignore permission errors)
fs.readdir(dir, (_, files) => { files.forEach(file => { fs.unlink(`${dir}/${file}`, _ => _)})});

johanastborg

function expectedRowCount(ts1, ts2) {
    let d1 = new Date(ts1);
    let d2 = new Date(ts2);
    let ms_diff = d2 - d1;
    return ms_diff / (3.6e6) + 1;
}
// expectedRowCount('2020-09-07 10:00:00 UTC', '2020-09-10 06:00:00 UTC');
// => 69

johanastborg

import numpy as np

def pow(x, n):
    return np.exp(n * np.log(x))

def cnd(x):
    a1 =  0.31938153
    a2 = -0.356563782
    a3 =  1.781477937
    a4 = -1.821255978

johanastborg

import nltk

#sentence = """John waited for us at the bus stop."""

def parse(sentence):
    tokens = nltk.word_tokenize(sentence)
    tagged = nltk.pos_tag(tokens)

    print(tagged)

johanastborg

def angle2(xx,yy,A): 
    t = (xx.T@A@yy)/(np.sqrt(xx@A@xx.T*yy.T@A@yy)) 
    return np.arccos(t)

def angle1(xx,yy): 
    t = (xx.T@yy)/(np.sqrt(xx@xx.T*yy.T@yy)) 
    return np.arccos(t)