penland365

//In this case, we are decoding a type `Teams` from a third party service
//If `Teams` has any elements, the presented JSON is an Array of `Team`
//If `Teams` is empty, is is suppoed to be Optional.  However, sometimes when `Teams` is empty the service returns an empty JSON object

import argonaut._, Argonaut._

case class Team(id: Int, name: String)

object Team {
 implicit val decodeTeamJson: DecodeJson[Team] = { DecodeJson(h => for {

penland365

package com.twitter.finagle.dispatch

import com.twitter.finagle.context.Contexts
import com.twitter.finagle.tracing.{Annotation, Trace}
import com.twitter.finagle.transport.Transport
import com.twitter.finagle.{Service, NoStacktrace, CancelledRequestException}
import com.twitter.util._
import java.util.concurrent.atomic.AtomicReference
import scala.collection.mutable.ListBuffer

penland365

// Which of these is a better style? Is there much of a difference?

def createMicrosoftAudio0(utterance: Utterance, svc: MsSynthSvc = MicrosoftSpeech.Synthesize): Future[SynthesizedSpeech] = {
  if(!utterance.text.isDefined) return Future.exception(new UtteranceTextNotFound(utterance))  
  val req = SynthesizeRequest(utterance.convo.id, utterance.text.get)
  for {
    resp <- svc(req)
  } yield SynthesizedSpeed(resp._1, resp._2)
}

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private RecognitionConfig buildRecogintionConfig {
  return com.google.cloud.speech.v1.RecognitionConfig.newBuilder()
    .setEncoding(LINEAR16)
    .setLanguageCode("en-US")
    .setSampleRateHertz(16000)
    .addSpeechContexts(speechContexts) // attached 
    .build();
}

private StreamingRecognitionConfig buildStreamingRecognitionConfig {

penland365

// so you have something like this?
def respond(state: EngineState): Future[EngineState] = {
  
}

def respond(state: EngineState): Future[EngineState] = {
    state.transaction match {
      case Throw(_)            =>
        val transaction = Transaction(state.request.conversation, UnrecognisedCommand, Vector.empty)
        val request = ResponseRequest(state.request.conversation, transaction)

penland365

(cons (clojure graphql))

Graphqls' declarative schemas and stringified strong typing cons* together perfectly with Clojure's dynamic typing, homoiconicity**, and easy concurrency.

We will take our first steps into the Clojure ecosystem and how it can work closely with Graphql. We will show how to create a simple Clojure app, how to read a LISP, how to understand de-structuring, and how to read an EDN, extensible data notation, file.

penland365

penland365

Louisiana Tech Bulldogs 2020 Staff and Offense Scouting Report

Prepared by: penland365 Finalized: 09/07/2020

Table of Contents

• Staff Overview
• Roster Overview
• Offense