kyliepace

model Book {
  id                      String   @id @default(auto()) @map("_id") @db.ObjectId
  authors                 EmbeddedAuthor[]
  translators             EmbeddedAuthor[] 
  createdAt               DateTime @default(now())
  updatedAt               DateTime @updatedAt
  ISBN                    String? @unique
}

kyliepace

model Character {
  id        String  @id @default(auto()) @map("_id") @db.ObjectId
  books     Book[] @relation(fields: [bookIds], references: [id])
  bookIds   String[] @db.ObjectId
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
}

model Book {
  id                      String   @id @default(auto()) @map("_id") @db.ObjectId

kyliepace

import {
  Resolver,
  Query,
  Mutation,
  Args,
} from '@nestjs/graphql'
import { Inject } from '@nestjs/common';
import { Book } from '@prisma/client'
import { NewBookInput } from './dto/new-book.input'
import { BookStock } from './dto/book-stock.input'

kyliepace

function truncateNumber(number: Float, decimalPlaces: Int): Float {

  const reg = new RegExp("^-?\\d+(?:\\.\\d{0," + decimalPlaces + "})?", "g")
  
  const stringedNumber = number.toString().match(reg)[0]
  
  const dotIndex = stringedNumber.indexOf(".")
  const remainingDecimals = decimalPlaces - (stringedNumber.length - dotIndex) + 1
  

kyliepace

---
eleventyExcludeFromCollections: true
permalink: /robots.txt
---
User-agent: *
Allow: /
Sitemap: {{ site.url }}/sitemap.xml

kyliepace

---
permalink: /sitemap.xml
eleventyExcludeFromCollections: true
---
<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
{% for item in collections.all %}
  <url>
    <loc>{{ site.url }}{{ item.url }}</loc>
    <lastmod>{{ item.date | htmlDateString }}</lastmod>

kyliepace

from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, to_json, col, unbase64, base64, split, expr
from pyspark.sql.types import StructField, StructType, StringType, BooleanType, ArrayType, DateType

# TO-DO: create a StructType for the Kafka redis-server topic which has all changes made to Redis - before Spark 3.0.0, schema inference is not automatic
redisSchema = StructType(
    [
        StructField("key", StringType()),
        StructField("existType", StringType()),
        StructField("Ch", BooleanType()),

kyliepace

from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, to_json, col, unbase64, base64, split, expr
from pyspark.sql.types import StructField, StructType, StringType, FloatType, BooleanType, ArrayType, DateType

# create deposit a kafka message schema StructType including the following JSON elements:
# {"accountNumber":"703934969","amount":415.94,"dateAndTime":"Sep 29, 2020, 10:06:23 AM"}
# Cast the amount as a FloatType

depositMessageSchema = StructType(
    [

kyliepace

map.on('load', async () => {
  map.addSource('greenspace_tiles', {
    type: 'vector',
    url: `mapbox://${mapboxUsername}.${mapID}`
  });

  map.addLayer({
    id: 'greenspaces',
    type: 'fill',
    source: 'greenspace_tiles',

kyliepace

// node.js express.js server
const express = require('express');
const bodyParser = require('body-parser');
const routes = require('./routes.js');
const port = process.env.PORT || 3000;
const app = express();
const http = require('http').Server(app);

app.use(bodyParser.json())
app.use(bodyParser.urlencoded({ extended: true }))