The document describes ray tracing and how it can be implemented in a modular way using ZIO. It discusses:
1. The key components of ray tracing including a world (spheres, light source, camera), incident rays, reflected rays, discarded rays, and colored pixels on a canvas.
2. Options for computing rays, including computing all rays or only those from the camera through the canvas.
3. How transformations can be handled in a module with services to apply transformations and compose them.
4. Other modules including for camera, world, and rastering to generate colored pixels from the world and camera. The overall architecture is designed to be composable using ZIO.
Free monads and free applicatives have proven an incredibly useful tool in repertoire of the functional programmer: they separate concerns, encourage denotational semantics for program specification, allow easy and type-safe mocking of purely functional code, and allow dynamic introspection and optimization.
Despite these benefits, free monads are notoriously constrained: by themselves, they cannot handle parallelism (only sequentiality), and because they provide only a monad, richer structures (such as monads that fail, or monads that support alternation) cannot be expressed without crude hacks that limit composability and expressiveness.
In this session, John A. De Goes shows how the free monad can be deconstructed for its individual features, and then rebuilt using a more powerful technique that enables more extensibility. The resulting structure — no longer technically a "free monad" — allows reification of as few or as many aspects of computation as are necessary to model the problem domain.
After the session, attendees will know how to augment their existing free programs to add parallelism, racing, failure, and other aspects of computation as required by their problem. In addition, through this thorough deconstruction and reconstruction of the free monad, attendees will have a very deep understanding of reified computation and why the free monad has the structure and limitations it does.
ZIO Schedule: Conquering Flakiness & Recurrence with Pure Functional ProgrammingJohn De Goes
As professional software engineers, sometimes messy details of the real world stand in the way of us delivering principled software. Flaky connections, unreliable services, and bulletproof job scheduling in the presence of non-determinism and failure all tricky problems that discourage us from writing principled software. Yet sometimes the shortcuts we take to solve these problems result in downtime for the business and sleepless nights for us.
In this brand-new presentation, created exclusively for Scala in the City, John A. De Goes will show how functional programming can help bring order to even the most chaotic systems. Using ZIO, a new zero-dependency Scala library for building massively scalable asynchronous and concurrent applications, John will demonstrate how functional programming leverages reified effects and algebras to solve the trickiest of reliability and scheduling problems in a principled, composable, flexible way.
Join John for an evening of fun and functional programming as you explore fresh ways of thinking about reliability and scheduling, and come out of the talk with valuable skills for using ZIO to solve the everyday problems you encounter at work.
Découverte d'algèbre, d'interpréteur par la création d'un DSL pour la base de données Aerospike. Le tout est implémenté dans le langage Scala avec un style fonctionnel.
Free monads and free applicatives have proven an incredibly useful tool in repertoire of the functional programmer: they separate concerns, encourage denotational semantics for program specification, allow easy and type-safe mocking of purely functional code, and allow dynamic introspection and optimization.
Despite these benefits, free monads are notoriously constrained: by themselves, they cannot handle parallelism (only sequentiality), and because they provide only a monad, richer structures (such as monads that fail, or monads that support alternation) cannot be expressed without crude hacks that limit composability and expressiveness.
In this session, John A. De Goes shows how the free monad can be deconstructed for its individual features, and then rebuilt using a more powerful technique that enables more extensibility. The resulting structure — no longer technically a "free monad" — allows reification of as few or as many aspects of computation as are necessary to model the problem domain.
After the session, attendees will know how to augment their existing free programs to add parallelism, racing, failure, and other aspects of computation as required by their problem. In addition, through this thorough deconstruction and reconstruction of the free monad, attendees will have a very deep understanding of reified computation and why the free monad has the structure and limitations it does.
ZIO Schedule: Conquering Flakiness & Recurrence with Pure Functional ProgrammingJohn De Goes
As professional software engineers, sometimes messy details of the real world stand in the way of us delivering principled software. Flaky connections, unreliable services, and bulletproof job scheduling in the presence of non-determinism and failure all tricky problems that discourage us from writing principled software. Yet sometimes the shortcuts we take to solve these problems result in downtime for the business and sleepless nights for us.
In this brand-new presentation, created exclusively for Scala in the City, John A. De Goes will show how functional programming can help bring order to even the most chaotic systems. Using ZIO, a new zero-dependency Scala library for building massively scalable asynchronous and concurrent applications, John will demonstrate how functional programming leverages reified effects and algebras to solve the trickiest of reliability and scheduling problems in a principled, composable, flexible way.
Join John for an evening of fun and functional programming as you explore fresh ways of thinking about reliability and scheduling, and come out of the talk with valuable skills for using ZIO to solve the everyday problems you encounter at work.
Découverte d'algèbre, d'interpréteur par la création d'un DSL pour la base de données Aerospike. Le tout est implémenté dans le langage Scala avec un style fonctionnel.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
Moore's Law may be dead, but CPUs acquire more cores every year. If you want to minimize response latency in your application, you need to use every last core - without wasting resources that would destroy performance and throughput. Traditional locks grind threads to a halt and are prone to deadlocks, and although actors provide a saner alternative, they compose poorly and are at best weakly-typed.
In this presentation, created exclusively for Scalar Conf, John reveals a new alternative to the horrors of traditional concurrency, directly comparing it to other leading approaches in Scala. Witness the beauty, simplicity, and power of declarative concurrency, as you discover how functional programming is the most practical solution to solving the tough challenges of concurrency.
There are many popular stream libraries for Scala developers, including Akka Streams, scalaz-stream, fs2, plus others in the Java ecosystem. While all excellent choices for building reactive Scala applications, their reliance on effects makes them particularly difficult to test and reason about. In this talk, long-time Scala functional programmer John A. De Goes takes to the stage to demonstrate a new approach to modeling streams that requires less machinery and has more reasoning power, composability, flexibility, and testability than many other approaches. By attending the talk, you'll learn how the best stream library may be the one you get for (co)free!
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
Moore's Law may be dead, but CPUs acquire more cores every year. If you want to minimize response latency in your application, you need to use every last core - without wasting resources that would destroy performance and throughput. Traditional locks grind threads to a halt and are prone to deadlocks, and although actors provide a saner alternative, they compose poorly and are at best weakly-typed.
In this presentation, created exclusively for Scalar Conf, John reveals a new alternative to the horrors of traditional concurrency, directly comparing it to other leading approaches in Scala. Witness the beauty, simplicity, and power of declarative concurrency, as you discover how functional programming is the most practical solution to solving the tough challenges of concurrency.
There are many popular stream libraries for Scala developers, including Akka Streams, scalaz-stream, fs2, plus others in the Java ecosystem. While all excellent choices for building reactive Scala applications, their reliance on effects makes them particularly difficult to test and reason about. In this talk, long-time Scala functional programmer John A. De Goes takes to the stage to demonstrate a new approach to modeling streams that requires less machinery and has more reasoning power, composability, flexibility, and testability than many other approaches. By attending the talk, you'll learn how the best stream library may be the one you get for (co)free!
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
Expressing Types as values using GADTS in scala and various other techniques. Using Schemas in this style allows subsequent derivation ot Typeclass instances (and other fun things)
If you want to play around with a minimal example of the involved code here you go: https://scalafiddle.io/sf/YSrSwQy/1
As a part of kotlinx.coroutines 1.2 Flow was introduced as a cold asynchronous data stream. How does it work? What we can do with it? Is it that we waiting so long for replacing Rx? The talk has answers to them.
Laziness, trampolines, monoids and other functional amenities: this is not yo...Codemotion
by Mario Fusco - Lambdas are the main feature introduced with Java 8, but the biggest part of Java developers are still not very familliar with the most common functional idioms and patterns. The purpose of this talk is presenting with practical examples concepts like high-order functions, currying, functions composition, persistent data structures, lazy evaluation, recursion, trampolines and monoids showing how to implement them in Java and how thinking functionally can help us to design and develop more readable, reusable, performant, parallelizable and in a word better, code.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
12. Ray tracing
Options:
1. Compute all the rays (and
discard most of them)
2. Compute only the rays outgoing
from the camera through the
canvas, and determine how
they behave on the surfaces
13. Ray
case class Ray(origin: Pt, direction: Vec) {
def positionAt(t: Double): Pt =
origin + (direction * t)
}
20. Transformations Module
val rotatedPt =
for {
rotateX <- ATModule.>.rotateX(math.Pi / 2)
_ <- Log.>.info("rotated of π/2")
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
21. Transformations Module
val rotatedPt: ZIO[ATModule with Log, ATError, Pt] =
for {
rotateX <- ATModule.>.rotateX(math.Pi / 2)
_ <- Log.>.info("rotated of π/2")
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
22. Transformations Module - Live
val rotated: ZIO[ATModule, ATError, Vec] =
for {
rotateX <- ATModule.>.rotateX(math.Pi/2)
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
23. Transformations Module - Live
val rotated: ZIO[ATModule, ATError, Vec] =
for {
rotateX <- ATModule.>.rotateX(math.Pi/2)
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
→ Vec(x, y, z)
24. Transformations Module - Live
val rotated: ZIO[ATModule, ATError, Vec] =
for {
rotateX <- ATModule.>.rotateX(math.Pi/2)
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
→ Vec(x, y, z)
→ Pt(x, y, z)⠀
25. Transformations Module - Live
val rotated: ZIO[ATModule, ATError, Vec] =
for {
rotateX <- ATModule.>.rotateX(math.Pi/2)
res <- ATModule.>.applyTf(rotateX, Pt(1, 1, 1))
} yield res
→ Vec(x, y, z)
→ Pt(x, y, z)⠀
→ ⠀
29. World
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
30. World
→ Sphere.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
31. World
→ Sphere.canonical
→ Plane.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
32. World
→ Sphere.canonical
→ Plane.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
33. World
→ Sphere.canonical
→ Plane.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
34. World
→ Sphere.canonical
→ Plane.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
35. World
→ Sphere.canonical
→ Plane.canonical
sealed trait Shape {
def transformation: AT
def material: Material
}
case class Sphere(transformation: AT, material: Material) extends Shape
case class Plane(transformation: AT, material: Material) extends Shape
36. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
37. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
38. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
39. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
40. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
41. World
Make a world
object Sphere {
def make(center: Pt, radius: Double, mat: Material): ZIO[ATModule, ATError, Sphere] = for {
scale <- ATModule.>.scale(radius, radius, radius)
translate <- ATModule.>.translate(center.x, center.y, center.z)
composed <- ATModule.>.compose(scale, translate)
} yield Sphere(composed, mat)
}
object Plane {
def make(...): ZIO[ATModule, ATError, Plane] = ???
}
case class World(pointLight: PointLight, objects: List[Shape])
→ Everything requires ATModule
42. World Rendering - Top Down
Rastering - Generate a stream of colored pixels
@accessible(">")
trait RasteringModule {
val rasteringModule: RasteringModule.Service[Any]
}
object RasteringModule {
trait Service[R] {
def raster(world: World, camera: Camera):
ZStream[R, RayTracerError, ColoredPixel]
}
43. World Rendering - Top Down
Rastering - Generate a stream of colored pixels
@accessible(">")
trait RasteringModule {
val rasteringModule: RasteringModule.Service[Any]
}
object RasteringModule {
trait Service[R] {
def raster(world: World, camera: Camera):
ZStream[R, RayTracerError, ColoredPixel]
}
44. World Rendering - Top Down
Rastering - Generate a stream of colored pixels
@accessible(">")
trait RasteringModule {
val rasteringModule: RasteringModule.Service[Any]
}
object RasteringModule {
trait Service[R] {
def raster(world: World, camera: Camera):
ZStream[R, RayTracerError, ColoredPixel]
}
45. World Rendering - Top Down
Rastering - Live
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
46. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
47. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
48. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
49. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
50. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
→ World module - Color per ray
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
51. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
→ World module - Color per ray
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
52. World Rendering - Top Down
Rastering - Live
→ Camera module - Ray per pixel
object CameraModule {
trait Service[R] {
def rayForPixel(
camera: Camera, px: Int, py: Int
): ZIO[R, Nothing, Ray]
}
}
→ World module - Color per ray
object WorldModule {
trait Service[R] {
def colorForRay(
world: World, ray: Ray
): ZIO[R, RayTracerError, Color]
}
}
53. World Rendering - Top Down
Rastering Live - Module Dependency
trait LiveRasteringModule extends RasteringModule {
val cameraModule: CameraModule.Service[Any]
val worldModule: WorldModule.Service[Any]
val rasteringModule: Service[Any] = new Service[Any] {
def raster(world: World, camera: Camera): ZStream[Any, RayTracerError, ColoredPixel] = {
val pixels: Stream[Nothing, (Int, Int)] = ???
pixels.mapM{
case (px, py) =>
for {
ray <- cameraModule.rayForPixel(camera, px, py)
color <- worldModule.colorForRay(world, ray)
} yield data.ColoredPixel(Pixel(px, py), color)
}
}
}
}
54. World Rendering - Top Down
Rastering Live - Module Dependency
trait LiveRasteringModule extends RasteringModule {
val cameraModule: CameraModule.Service[Any]
val worldModule: WorldModule.Service[Any]
val rasteringModule: Service[Any] = new Service[Any] {
def raster(world: World, camera: Camera): ZStream[Any, RayTracerError, ColoredPixel] = {
val pixels: Stream[Nothing, (Int, Int)] = ???
pixels.mapM{
case (px, py) =>
for {
ray <- cameraModule.rayForPixel(camera, px, py)
color <- worldModule.colorForRay(world, ray)
} yield data.ColoredPixel(Pixel(px, py), color)
}
}
}
}
55. World Rendering - Top Down
Rastering Live - Module Dependency
trait LiveRasteringModule extends RasteringModule {
val cameraModule: CameraModule.Service[Any]
val worldModule: WorldModule.Service[Any]
val rasteringModule: Service[Any] = new Service[Any] {
def raster(world: World, camera: Camera): ZStream[Any, RayTracerError, ColoredPixel] = {
val pixels: Stream[Nothing, (Int, Int)] = ???
pixels.mapM{
case (px, py) =>
for {
ray <- cameraModule.rayForPixel(camera, px, py)
color <- worldModule.colorForRay(world, ray)
} yield data.ColoredPixel(Pixel(px, py), color)
}
}
}
}
56. Test LiveRasteringModule
1 - Define the method under test
val world = /* prepare a world */
val camera = /* prepare a camera */
val appUnderTest: ZIO[RasteringModule, RayTracerError, List[ColoredPixel]] =
RasteringModule.>.raster(world, camera).runCollect
57. Test LiveRasteringModule
2 - Annotate the modules as mockable
import zio.macros.annotation.mockable
@mockable
trait CameraModule { ... }
@mockable
trait WorldModule { ... }
59. Test LiveRasteringModule
4 - Build the environment for the code under test
val appUnderTest: ZIO[RasteringModule, RayTracerError, List[ColoredPixel]] =
RasteringModule.>.raster(world, camera).runCollect
appUnderTest.provideManaged(
worldModuleExp.managedEnv.zipWith(cameraModuleExp.managedEnv) { (wm, cm) =>
new LiveRasteringModule {
val cameraModule: CameraModule.Service[Any] = cm.cameraModule
val worldModule: WorldModule.Service[Any] = wm.worldModule
}
}
)
60. Test LiveRasteringModule
4 - Build the environment for the code under test
val appUnderTest: ZIO[RasteringModule, RayTracerError, List[ColoredPixel]] =
RasteringModule.>.raster(world, camera).runCollect
appUnderTest.provideManaged(
worldModuleExp.managedEnv.zipWith(cameraModuleExp.managedEnv) { (wm, cm) =>
new LiveRasteringModule {
val cameraModule: CameraModule.Service[Any] = cm.cameraModule
val worldModule: WorldModule.Service[Any] = wm.worldModule
}
}
)
61. Test LiveRasteringModule
5 - Assert on the results
assert(res, equalTo(List(
ColoredPixel(Pixel(0, 0), Color.red),
ColoredPixel(Pixel(0, 1), Color.green),
ColoredPixel(Pixel(1, 0), Color.blue),
ColoredPixel(Pixel(1, 1), Color.white),
))
)
62. Test LiveRasteringModule
suite("LiveRasteringModule") {
testM("raster should rely on cameraModule and world module") {
for {
(worldModuleExp, cameraModuleExp) <- RasteringModuleMocks.mockExpectations(world, camera)
res <- appUnderTest.provideManaged(
worldModuleExp.managedEnv.zipWith(cameraModuleExp.managedEnv) { (wm, cm) =>
new LiveRasteringModule {
val cameraModule: CameraModule.Service[Any] = cm.cameraModule
val worldModule: WorldModule.Service[Any] = wm.worldModule
}
}
)
} yield assert(res, equalTo(List(
ColoredPixel(Pixel(0, 0), Color.red),
ColoredPixel(Pixel(0, 1), Color.green)
)))
}
}
70. Live WorldModule
PhongReflectionModule
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldModule: Service[Any] = new Service[Any] {
def colorForRay(
world: World, ray: Ray, remaining: Ref[Int]
): ZIO[Any, RayTracerError, Color] = {
/* use other modules */
}
}
}
71. Live WorldModule
PhongReflectionModule
case class PhongComponents(
ambient: Color, diffuse: Color, reflective: Color
) {
def toColor: Color = ambient + diffuse + reflective
}
trait BlackWhite extends PhongReflectionModule {
val phongReflectionModule: Service[Any] =
new Service[Any] {
def lighting(
pointLight: PointLight, hitComps: HitComps, inShadow: Boolean
): UIO[PhongComponents] = {
if (inShadow) UIO(PhongComponents.allBlack)
else UIO(PhongComponents.allWhite)
}
}
}
72. Live WorldModule
PhongReflectionModule
case class PhongComponents(
ambient: Color, diffuse: Color, reflective: Color
) {
def toColor: Color = ambient + diffuse + reflective
}
trait BlackWhite extends PhongReflectionModule {
val phongReflectionModule: Service[Any] =
new Service[Any] {
def lighting(
pointLight: PointLight, hitComps: HitComps, inShadow: Boolean
): UIO[PhongComponents] = {
if (inShadow) UIO(PhongComponents.allBlack)
else UIO(PhongComponents.allWhite)
}
}
}
73. Display the first canvas / 1
def drawOnCanvasWithCamera(world: World, camera: Camera, canvas: Canvas) =
for {
coloredPointsStream <- RasteringModule.>.raster(world, camera)
_ <- coloredPointsStream.mapM(cp => canvas.update(cp)).run(Sink.drain)
} yield ()
def program(viewFrom: Pt) =
for {
camera <- cameraFor(viewFrom: Pt)
w <- world
canvas <- Canvas.create()
_ <- drawOnCanvasWithCamera(w, camera, canvas)
_ <- CanvasSerializer.>.serialize(canvas, 255)
} yield ()
74. Display the first canvas / 2
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
program(Pt(2, 2, -10))
.provide(
new CanvasSerializer.PNGCanvasSerializer
with RasteringModule.ChunkRasteringModule
with ATModule.Live
)
// Members declared in zio.blocking.Blocking
// [error] val blocking: zio.blocking.Blocking.Service[Any] = ???
// [error]
// [error] // Members declared in modules.RasteringModule.ChunkRasteringModule
// [error] val cameraModule: modules.CameraModule.Service[Any] = ???
// [error] val worldModule: modules.WorldModule.Service[Any] = ???
// [error]
// [error] // Members declared in geometry.affine.ATModule.Live
// [error] val matrixModule: geometry.matrix.MatrixModule.Service[Any] = ???
75. Display the first canvas / 2
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
program(Pt(2, 2, -10))
.provide(
new CanvasSerializer.PNGCanvasSerializer
with RasteringModule.ChunkRasteringModule
with ATModule.Live
)
// Members declared in zio.blocking.Blocking
// [error] val blocking: zio.blocking.Blocking.Service[Any] = ???
// [error]
// [error] // Members declared in modules.RasteringModule.ChunkRasteringModule
// [error] val cameraModule: modules.CameraModule.Service[Any] = ???
// [error] val worldModule: modules.WorldModule.Service[Any] = ???
// [error]
// [error] // Members declared in geometry.affine.ATModule.Live
// [error] val matrixModule: geometry.matrix.MatrixModule.Service[Any] = ???
76. Display the first canvas / 2
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
program(Pt(2, 2, -10))
.provide(
new CanvasSerializer.PNGCanvasSerializer
with RasteringModule.ChunkRasteringModule
with ATModule.Live
)
// Members declared in zio.blocking.Blocking
// [error] val blocking: zio.blocking.Blocking.Service[Any] = ???
// [error]
// [error] // Members declared in modules.RasteringModule.ChunkRasteringModule
// [error] val cameraModule: modules.CameraModule.Service[Any] = ???
// [error] val worldModule: modules.WorldModule.Service[Any] = ???
// [error]
// [error] // Members declared in geometry.affine.ATModule.Live
// [error] val matrixModule: geometry.matrix.MatrixModule.Service[Any] = ???
77. Display the first canvas / 3
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
program(Pt(2, 2, -10))
.provide(
new CanvasSerializer.PNGCanvasSerializer
with RasteringModule.ChunkRasteringModule
with ATModule.Live
with CameraModule.Live
with MatrixModule.BreezeLive
with WorldModule.Live
)
)
// [error] // Members declared in io.tuliplogic.raytracer.ops.model.modules.WorldModule.Live
// [error] val phongReflectionModule: io.tuliplogic.raytracer.ops.model.modules.PhongReflectionModule.Service[Any] = ???
// [error] val worldHitCompsModule: io.tuliplogic.raytracer.ops.model.modules.WorldHitCompsModule.Service[Any] = ???
// [error] val worldReflectionModule: io.tuliplogic.raytracer.ops.model.modules.WorldReflectionModule.Service[Any] = ???
// [error] val worldRefractionModule: io.tuliplogic.raytracer.ops.model.modules.WorldRefractionModule.Service[Any] = ???
// [error] val worldTopologyModule: io.tuliplogic.raytracer.ops.model.modules.WorldTopologyModule.Service[Any] = ???
78. Display the first canvas - /4
Group modules in trait
type BasicModules =
NormalReflectModule.Live
with RayModule.Live
with ATModule.Live
with MatrixModule.BreezeLive
with WorldModule.Live
with WorldTopologyModule.Live
with WorldHitCompsModule.Live
with CameraModule.Live
with RasteringModule.Live
with Blocking.Live
79. Display the first canvas - /4
Group modules in trait
type BasicModules =
NormalReflectModule.Live
with RayModule.Live
with ATModule.Live
with MatrixModule.BreezeLive
with WorldModule.Live
with WorldTopologyModule.Live
with WorldHitCompsModule.Live
with CameraModule.Live
with RasteringModule.Live
with Blocking.Live
80. Display the first canvas - /5
Group modules
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
program(Pt(2, 2, -10))
.provide(new BasicModules with PhongReflectionModule.BlackWhite)
81. Display the first canvas - /6
Group modules
def program(viewFrom: Pt):
ZIO[CanvasSerializer with RasteringModule with ATModule, RayTracerError, Unit]
def run(args: List[String]): ZIO[ZEnv, Nothing, Int] =
ZIO.traverse(-18 to -6)(z => program(Pt(2, 2, z))
.provide(
new BasicModules with PhongReflectionModule.BlackWhite
)
).foldM(err =>
console.putStrLn(s"Execution failed with: $err").as(1),
_ => UIO.succeed(0)
)
82. Live PhongReflectionModule
With LightDiffusion
trait Live extends PhongReflectionModule {
val aTModule: ATModule.Service[Any]
val normalReflectModule: NormalReflectModule.Service[Any]
val lightDiffusionModule: LightDiffusionModule.Service[Any]
}
87. Reflect the ligtht source
Describe material properties
case class Material(
color: Color, // the basic color
ambient: Double, // ∈ [0, 1]
diffuse: Double, // ∈ [0, 1]
specular: Double, // ∈ [0, 1]
shininess: Double, // ∈ [10, 200]
)
88. Live PhongReflectionModule
With LightDiffusion and LightReflection
trait Live extends PhongReflectionModule {
val aTModule: ATModule.Service[Any]
val normalReflectModule: NormalReflectModule.Service[Any]
val lightDiffusionModule: LightDiffusionModule.Service[Any]
val lightReflectionModule: LightReflectionModule.Service[Any]
}
89. Spheres reflect light source
program(
from = Pt(57, 20, z),
to = Pt(20, 0, 20)
).provide {
new BasicModules
with PhongReflectionModule.Live
with LightReflectionModule.Live
}
90. Reflective surfaces
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldReflectionModule: WorldReflectionModule.Service[Any]
91. Reflective surfaces
Use WorldReflection
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldReflectionModule: WorldReflectionModule.Service[Any]
val worldModule: Service[Any] =
new Service[Any] {
def colorForRay(world: World, ray: Ray): ZIO[Any, RayTracerError, Color] =
{
/* ... */
for {
color <- /* standard computation of color */
reflectedColor <- worldReflectionModule.reflectedColor(world, hc)
} yield color + reflectedColor
}
}
}
92. Reflective surfaces
Use WorldReflection
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldReflectionModule: WorldReflectionModule.Service[Any]
val worldModule: Service[Any] =
new Service[Any] {
def colorForRay(world: World, ray: Ray): ZIO[Any, RayTracerError, Color] =
{
/* ... */
for {
color <- /* standard computation of color */
reflectedColor <- worldReflectionModule.reflectedColor(world, hc)
} yield color + reflectedColor
}
}
}
93. Reflective surfaces
Use WorldReflection
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldReflectionModule: WorldReflectionModule.Service[Any]
val worldModule: Service[Any] =
new Service[Any] {
def colorForRay(world: World, ray: Ray): ZIO[Any, RayTracerError, Color] =
{
/* ... */
for {
color <- /* standard computation of color */
reflectedColor <- worldReflectionModule.reflectedColor(world, hc)
} yield color + reflectedColor
}
}
}
94. Reflective surfaces
Use WorldReflection
trait Live extends WorldModule {
val worldTopologyModule: WorldTopologyModule.Service[Any]
val worldHitCompsModule: WorldHitCompsModule.Service[Any]
val phongReflectionModule: PhongReflectionModule.Service[Any]
val worldReflectionModule: WorldReflectionModule.Service[Any]
val worldModule: Service[Any] =
new Service[Any] {
def colorForRay(world: World, ray: Ray): ZIO[Any, RayTracerError, Color] =
{
/* ... */
for {
color <- /* standard computation of color */
reflectedColor <- worldReflectionModule.reflectedColor(world, hc)
} yield color + reflectedColor
}
}
}
95. Implement WorldReflection
Live
trait Live extends WorldReflectionModule {
val worldModule: WorldModule.Service[Any]
val worldReflectionModule = new WorldReflectionModule.Service[Any] {
def reflectedColor(world: World, hitComps: HitComps, remaining: Int):
ZIO[Any, RayTracerError, Color] =
if (hitComps.shape.material.reflective == 0) {
UIO(Color.black)
} else {
val reflRay = Ray(hitComps.overPoint, hitComps.rayReflectV)
worldModule.colorForRay(world, reflRay, remaining).map(c =>
c * hitComps.shape.material.reflective
)
}
}
}
96. Implement WorldReflection
Live
trait Live extends WorldReflectionModule {
val worldModule: WorldModule.Service[Any]
val worldReflectionModule = new WorldReflectionModule.Service[Any] {
def reflectedColor(world: World, hitComps: HitComps, remaining: Int):
ZIO[Any, RayTracerError, Color] =
if (hitComps.shape.material.reflective == 0) {
UIO(Color.black)
} else {
val reflRay = Ray(hitComps.overPoint, hitComps.rayReflectV)
worldModule.colorForRay(world, reflRay, remaining).map(c =>
c * hitComps.shape.material.reflective
)
}
}
}
97. Implement WorldReflection
Live
trait Live extends WorldReflectionModule {
val worldModule: WorldModule.Service[Any]
val worldReflectionModule = new WorldReflectionModule.Service[Any] {
def reflectedColor(world: World, hitComps: HitComps, remaining: Int):
ZIO[Any, RayTracerError, Color] =
if (hitComps.shape.material.reflective == 0) {
UIO(Color.black)
} else {
val reflRay = Ray(hitComps.overPoint, hitComps.rayReflectV)
worldModule.colorForRay(world, reflRay, remaining).map(c =>
c * hitComps.shape.material.reflective
)
}
}
}
98. Implement WorldReflection
Live
trait Live extends WorldReflectionModule {
val worldModule: WorldModule.Service[Any]
val worldReflectionModule = new WorldReflectionModule.Service[Any] {
def reflectedColor(world: World, hitComps: HitComps, remaining: Int):
ZIO[Any, RayTracerError, Color] =
if (hitComps.shape.material.reflective == 0) {
UIO(Color.black)
} else {
val reflRay = Ray(hitComps.overPoint, hitComps.rayReflectV)
worldModule.colorForRay(world, reflRay, remaining).map(c =>
c * hitComps.shape.material.reflective
)
}
}
}
106. Conclusion - Environmental Effects
ZIO[R, E, A]
Build purely functional, testable, modular applications
→ Do not require HKT, typeclasses, etc
107. Conclusion - Environmental Effects
ZIO[R, E, A]
Build purely functional, testable, modular applications
→ Do not require HKT, typeclasses, etc
→ Do not abuse typeclasses
108. Conclusion - Environmental Effects
ZIO[R, E, A]
Build purely functional, testable, modular applications
→ Do not require HKT, typeclasses, etc
→ Do not abuse typeclasses
→ Can group capabilities
109. Conclusion - Environmental Effects
ZIO[R, E, A]
Build purely functional, testable, modular applications
→ Do not require HKT, typeclasses, etc
→ Do not abuse typeclasses
→ Can group capabilities
→ Can provide capabilities one at a time - provideSome
110. Conclusion - Environmental Effects
ZIO[R, E, A]
Build purely functional, testable, modular applications
→ Do not require HKT, typeclasses, etc
→ Do not abuse typeclasses
→ Can group capabilities
→ Can provide capabilities one at a time - provideSome
→ Are not dependent on implicits (survive refactoring)