Post processing generally refers to applying some kind of effect or filter to a 2D image. In the case of THREE.js we have a scene with a bunch of meshes in it. We render that scene into a 2D image. Normally that image is rendered directly into the canvas and displayed in the browser but instead we can render it to a render target and then apply some post processing effects to the result before drawing it to the canvas. It's called post processing because it happens after (post) the main scene processing.
Examples of post processing are Instagram like filters, Photoshop filters, etc...
THREE.js has some example classes to help setup a post processing pipeline. The
way it works is you create an EffectComposer
and to it you add multiple Pass
objects. You then call EffectComposer.render
and it renders your scene to a
render target and then applies each Pass
.
Each Pass
can be some post processing effect like adding a vignette, blurring,
applying a bloom, applying film grain, adjusting the hue, saturation, contrast,
etc... and finally rendering the result to the canvas.
It's a little bit important to understand how EffectComposer
functions. It
creates two render targets. Let's call them
rtA and rtB.
Then, you call EffectComposer.addPass
to add each pass in the order you want
to apply them. The passes are then applied something like this.
First the scene you passed into RenderPass
is rendered to rtA, then
rtA is passed to the next pass, whatever it is. That pass uses rtA as
input to do whatever it does and writes the results to rtB. rtB is then
passed to the next pass which uses rtB as input and writes back to rtA.
This continues through all the passes.
Each Pass
has 4 basic options
enabled
Whether or not to use this pass
needsSwap
Whether or not to swap rtA
and rtB
after finishing this pass
clear
Whether or not to clear before rendering this pass
renderToScreen
Whether or not to render to the canvas instead the current destination render target. In most use cases you do not set this flag explicitly since the last pass in the pass chain is automatically rendered to screen.
Let's put together a basic example. We'll start with the example from the article on responsiveness.
To that first we create an EffectComposer
.
const composer = new EffectComposer(renderer);
Then as the first pass we add a RenderPass
that will render our scene with our
camera into the first render target.
composer.addPass(new RenderPass(scene, camera));
Next we add a BloomPass
. A BloomPass
renders its input to a generally
smaller render target and blurs the result. It then adds that blurred result on
top of the original input. This makes the scene bloom
const bloomPass = new BloomPass( 1, // strength 25, // kernel size 4, // sigma ? 256, // blur render target resolution ); composer.addPass(bloomPass);
Next we had a FilmPass
that draws noise and scanlines on top of its input.
const filmPass = new FilmPass( 0.35, // noise intensity 0.025, // scanline intensity 648, // scanline count false, // grayscale ); composer.addPass(filmPass);
Finally we had a OutputPass
which performs color space conversion to sRGB and optional tone mapping.
This pass is usually the last pass of the pass chain.
const outputPass = new OutputPass(); composer.addPass(outputPass);
To use these classes we need to import a bunch of scripts.
import {EffectComposer} from 'three/addons/postprocessing/EffectComposer.js'; import {RenderPass} from 'three/addons/postprocessing/RenderPass.js'; import {BloomPass} from 'three/addons/postprocessing/BloomPass.js'; import {FilmPass} from 'three/addons/postprocessing/FilmPass.js'; import {OutputPass} from 'three/addons/postprocessing/OutputPass.js';
For pretty much any post processing EffectComposer.js
, RenderPass.js
and OutputPass.js
are required.
The last things we need to do are to use EffectComposer.render
instead of
WebGLRenderer.render
and to tell the EffectComposer
to match the size of
the canvas.
-function render(now) { - time *= 0.001; +let then = 0; +function render(now) { + now *= 0.001; // convert to seconds + const deltaTime = now - then; + then = now; if (resizeRendererToDisplaySize(renderer)) { const canvas = renderer.domElement; camera.aspect = canvas.clientWidth / canvas.clientHeight; camera.updateProjectionMatrix(); + composer.setSize(canvas.width, canvas.height); } cubes.forEach((cube, ndx) => { const speed = 1 + ndx * .1; - const rot = time * speed; + const rot = now * speed; cube.rotation.x = rot; cube.rotation.y = rot; }); - renderer.render(scene, camera); + composer.render(deltaTime); requestAnimationFrame(render); }
EffectComposer.render
takes a deltaTime
which is the time in seconds since
the last frame was rendered. It passes this to the various effects in case any
of them are animated. In this case the FilmPass
is animated.
To change effect parameters at runtime usually requires setting uniform values. Let's add a gui to adjust some of the parameters. Figuring out which values you can easily adjust and how to adjust them requires digging through the code for that effect.
Looking inside
BloomPass.js
I found this line:
this.copyUniforms[ "opacity" ].value = strength;
So we can set the strength by setting
bloomPass.copyUniforms.opacity.value = someValue;
Similarly looking in
FilmPass.js
I found these lines:
if ( grayscale !== undefined ) this.uniforms.grayscale.value = grayscale; if ( noiseIntensity !== undefined ) this.uniforms.nIntensity.value = noiseIntensity; if ( scanlinesIntensity !== undefined ) this.uniforms.sIntensity.value = scanlinesIntensity; if ( scanlinesCount !== undefined ) this.uniforms.sCount.value = scanlinesCount;
So which makes it pretty clear how to set them.
Let's make a quick GUI to set those values
import {GUI} from 'three/addons/libs/lil-gui.module.min.js';
and
const gui = new GUI(); { const folder = gui.addFolder('BloomPass'); folder.add(bloomPass.copyUniforms.opacity, 'value', 0, 2).name('strength'); folder.open(); } { const folder = gui.addFolder('FilmPass'); folder.add(filmPass.uniforms.grayscale, 'value').name('grayscale'); folder.add(filmPass.uniforms.nIntensity, 'value', 0, 1).name('noise intensity'); folder.add(filmPass.uniforms.sIntensity, 'value', 0, 1).name('scanline intensity'); folder.add(filmPass.uniforms.sCount, 'value', 0, 1000).name('scanline count'); folder.open(); }
and now we can adjust those settings
That was a small step to making our own effect.
Post processing effects use shaders. Shaders are written in a language called GLSL (Graphics Library Shading Language). Going over the entire language is way too large a topic for these articles. A few resources to get start from would be maybe this article and maybe the Book of Shaders.
I think an example to get you started would be helpful though so let's make a simple GLSL post processing shader. We'll make one that lets us multiply the image by a color.
For post processing THREE.js provides a useful helper called the ShaderPass
.
It takes an object with info defining a vertex shader, a fragment shader, and
the default inputs. It will handling setting up which texture to read from to
get the previous pass's results and where to render to, either one of the
EffectComposer
s render target or the canvas.
Here's a simple post processing shader that multiplies the previous pass's result by a color.
const colorShader = { uniforms: { tDiffuse: { value: null }, color: { value: new THREE.Color(0x88CCFF) }, }, vertexShader: ` varying vec2 vUv; void main() { vUv = uv; gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1); } `, fragmentShader: ` varying vec2 vUv; uniform sampler2D tDiffuse; uniform vec3 color; void main() { vec4 previousPassColor = texture2D(tDiffuse, vUv); gl_FragColor = vec4( previousPassColor.rgb * color, previousPassColor.a); } `, };
Above tDiffuse
is the name that ShaderPass
uses to pass in the previous
pass's result texture so we pretty much always need that. We then declare
color
as a THREE.js Color
.
Next we need a vertex shader. For post processing the vertex shader shown here
is pretty much standard and rarely needs to be changed. Without going into too
many details (see articles linked above) the variables uv
, projectionMatrix
,
modelViewMatrix
and position
are all magically added by THREE.js.
Finally we create a fragment shader. In it we get a pixel color from the previous pass with this line
vec4 previousPassColor = texture2D(tDiffuse, vUv);
we multiply it by our color and set gl_FragColor
to the result
gl_FragColor = vec4( previousPassColor.rgb * color, previousPassColor.a);
Adding some simple GUI to set the 3 values of the color
const gui = new GUI(); gui.add(colorPass.uniforms.color.value, 'r', 0, 4).name('red'); gui.add(colorPass.uniforms.color.value, 'g', 0, 4).name('green'); gui.add(colorPass.uniforms.color.value, 'b', 0, 4).name('blue');
Gives us a simple postprocessing effect that multiplies by a color.
As mentioned about all the details of how to write GLSL and custom shaders is too much for these articles. If you really want to know how WebGL itself works then check out these articles. Another great resources is just to read through the existing post processing shaders in the THREE.js repo. Some are more complicated than others but if you start with the smaller ones you can hopefully get an idea of how they work.
Most of the post processing effects in the THREE.js repo are unfortunately undocumented so to use them you'll have to read through the examples or the code for the effects themselves. Hopefully these simple example and the article on render targets provide enough context to get started.


Concurrency control
The main categories are:
- optimistic - check for violation at the end of transactions and repeat
- pessimistic - block transaction to avoid violations
- semi-optimistic - mix of the above
Are usually implemented with Locking: Read, Write, Range
Optimistic Locking, Pessimistic Locking in JPA.


Even kings and emperors with heaps of wealth and vast dominion cannot compare with an ant filled with the love of God.


https://github.com/beefproject/beef
http://beefproject.com/
===============================================================================
Copyright (c) 2006-2018 Wade Alcorn -<EMAIL_ADDRESS>Browser Exploitation Framework (BeEF) - http://beefproject.com
See the file 'doc/COPYING' for copying permission
===============================================================================
What is BeEF?
BeEF is short for The Browser Exploitation Framework. It is a penetration testing tool that focuses on the web browser.
Amid growing concerns about web-borne attacks against clients, including mobile clients, BeEF allows the professional penetration tester to assess the actual security posture of a target environment by using client-side attack vectors. Unlike other security frameworks, BeEF looks past the hardened network perimeter and client system, and examines exploitability within the context of the one open door: the web browser. BeEF will hook one or more web browsers and use them as beachheads for launching directed command modules and further attacks against the system from within the browser context.
Get Involved
You can get in touch with the BeEF team. Just check out the following:
Please, send us pull requests!
Web: https://beefproject.com/
Bugs: https://github.com/beefproject/beef/issues
Security Bugs<EMAIL_ADDRESS>IRC: ircs://irc.freenode.net/beefproject
Twitter: @beefproject
Requirements
OSX 10.5.0 or higher, modern Linux
Ruby version 2.2 or newer
SQLite 3.x
The gems listed in the Gemfile: https://github.com/beefproject/beef/blob/master/Gemfile
Quick Start
The following is for the impatient.
For full installation details, please refer to INSTALL.txt.
We also have an Installation page on the wiki.
$ curl -L https://raw.githubusercontent.com/beefproject/beef/a6a7536e/install-beef | bash -s stable
Upon successful installation, be sure to read the Configuration page on the wiki for important details on configuring and securing BeEF.
Usage
To get started, simply execute beef and follow the instructions:
$ ./beef


The few tins of provisions we did take were mainly to serve as luxuries. For instance, we had quite a large supply of foie gras and larks in aspic.
I had a vivid recollection of how the last tin of that pâté de foie gras went.


API calls
Explore the API here hands-on.
This API supports a data response in JSON format.
You can view the definition of this API in OpenAPI Specification 2.0 format in this file: citypairs.json
Need a little help? Read our Getting Started to learn about using the API.


2017 Synopsys Championship: Individual Application Form
Level of Guidance *
What level of Research Advice or Access to Facilities was provided?
Non RRI research is conducted in home, school, and/or field, and NOT conducted under the guidance of professional research staff from
    1) a Regulated Research Institution
    2) a professional research company or government lab
    3) with advice and guidance of a professional research scientist or engineer
    4) using mentors other than teachers who provided substantial guidance, data, or equipment not available in schools


|
Fusion3D
Version 6.04
|
Resamples an image with translation, rotation and scaling. More...
#include <image_resample_class.h>
Public Member Functions | |
| image_resample_class () | |
| Constructor. More... | |
| ~image_resample_class () | |
| Destructor. More... | |
| int | set_input_image_ptr (unsigned char *dat) |
| Set a pointer to the input image. More... | |
| int | set_input_size (int nx, int ny) |
| Set the input size in pixels. More... | |
| int | set_input_res (float dx, float dy) |
| Set the input resolution in m per pixel. More... | |
| int | set_output_image_ptr (float *dat) |
| Set a pointer to the output image. More... | |
| int | set_output_size (int nx, int ny) |
| Set the output size in pixels. More... | |
| int | set_output_res (float dx, float dy) |
| Set the desired output resolution in m per pixel. More... | |
| int | set_output_offset (float offset_in) |
| Set the offset for the output image. More... | |
| int | set_rotation_angle (float ang) |
| Set rotation angle in deg. More... | |
| int | set_translations (float x, float y) |
| Set translations in m. More... | |
| int | init () |
| Initialize the calculations. More... | |
| int | resample () |
| Resample the image with the current parameters. More... | |
| float * | get_output_image () |
| Get a pointer to the output image. More... | |
Resamples an image with translation, rotation and scaling.
The input image is unsigned char and the output image in floating point. Memory for input and output images is allocated outside of this class.
Coordinate system:
Origin @ center of image
x-axis toward right
y-axis down
rotation positive clockwise (x toward y)
order: (1) scale (2) rotate (3) translate
| image_resample_class::image_resample_class | ( | ) |
Constructor.
| image_resample_class::~image_resample_class | ( | ) |
Destructor.
| float * image_resample_class::get_output_image | ( | ) |
Get a pointer to the output image.
| int image_resample_class::init | ( | ) |
Initialize the calculations.
| int image_resample_class::resample | ( | ) |
Resample the image with the current parameters.
| int image_resample_class::set_input_image_ptr | ( | unsigned char * | dat | ) |
Set a pointer to the input image.
| int image_resample_class::set_input_res | ( | float | dx, |
| float | dy | ||
| ) |
Set the input resolution in m per pixel.
| int image_resample_class::set_input_size | ( | int | nx, |
| int | ny | ||
| ) |
Set the input size in pixels.
| int image_resample_class::set_output_image_ptr | ( | float * | dat | ) |
Set a pointer to the output image.
| int image_resample_class::set_output_offset | ( | float | offset_in | ) |
Set the offset for the output image.
| offset_in | This value is added to the intensity of each output pixel |
| int image_resample_class::set_output_res | ( | float | dx, |
| float | dy | ||
| ) |
Set the desired output resolution in m per pixel.
| int image_resample_class::set_output_size | ( | int | nx, |
| int | ny | ||
| ) |
Set the output size in pixels.
| int image_resample_class::set_rotation_angle | ( | float | ang | ) |
Set rotation angle in deg.
Rotation is positive clockwise.
| int image_resample_class::set_translations | ( | float | x, |
| float | y | ||
| ) |
Set translations in m.
Translations are positive right and down.


VisualizeSpotify
Visualize-Spotify is a React web application that uses the Spotify Web API to visualize music preferences. It also provides a tinder-esque functionality that allows you to swipe through recommended songs and see in real-time its stats as well as how it affects your global music preferences via track analytics.
Key features
- Users will be able to login to the web application by authenticating their Spotify account.
- Users will be able to view analytics providing insight into the type of music they enjoy listening to based on their liked songs playlist.
- Users will be able to swipe through recommended songs and view the stats of that specific track.
- Users will be able to filter and discover new songs based on the range of stats they provide.
Where to find the project
Project is still in development.
Current Demo/Example
Licensing
Visualize-Spotify is licensed under the Apache-2.0 License as of 2020.
User Documentation
Getting Started
Features and functionality
Tutorials/Code Examples
Existing Extensions and Integrations
None
FAQ
To be determined
Release Notes and Version Info
- v0.2.0
- v0.2.0 introduces a fully functioning landing screen with filtering, visualizations, and added user capabilities to swipe through songs and add them to their liked songs.
- v0.1.0
- v0.1.0 introduces a landing screen with Spotify authentication. Upon authentication, the user is redirected to a page where they can view the information of the song that they are currently playing.
Developer Documentation
Getting Started
Clone this repository and run npm install
to install all the necessary dependencies.
Navigate to the client directory and launch the application in developer mode (this web application does not have a separate server).
$ cd client
$ npm start
The client will be running on http://localhost:3000
Code Structure
The source code for Visualize-Spotify can be found in the src directory. This includes:
- App.js: The main interface. Includes the context layer API as well as routing based on authentication status.
- DataLayer, reducer: The data layer of the application that allows certain props to fetched from anywhere in the project structure.
- spotify.js: The spotify configuration file for authentication. Includes the client ID, authorization scopes, and redirect URI that would return a token to the user.
- assets: A directory holding all SVG files.
- components: A directory holding all the page components as well as their stylesheets.
- config: A directory that holds the config file for the react-particles background.
- tests: A directory holding the files for jest testing.
Page components should be in src/components
directory along with their CSS stylesheets.
CI Documentation
Visualize-Spotify uses Travis-CI test builds. The configuration file can be found in the .travis.yml
file. Before install, it navigates to the client folder and runs the following scripts:
- yarn lint
- This runs ESLint on the
client/src
directory.
- This runs ESLint on the
- yarn build
- This creates a production build to ensure that it builds without error.
- yarn test
- Testing creates a code coverage report using Codecov in conjunction with running tests found in the
client/src/tests
directory. To update coverage reports in real-time, coverage tests are conducted by passing in a Codecov token as such:./node_modules/.bin/codecov --token='73defb26-5b47-42ca-9b9c-62a9c2c47fda
. This can be found in thepackage.json
file. - Moreover, it ignores
src/ServiceWorker.js
andsrc/index.js
because they are either not used or serve trivial purpose. The configuration file for this can be found inclient/jest.config.json
.
- Testing creates a code coverage report using Codecov in conjunction with running tests found in the
- yarn doc
- This updates the documentation using JSDoc3 by following the configuration file
jsdoc.config.json
found in the root directory. As can be found inpackage.json
, the home page usesREADME.md
found in the root directory. The documentation is hosted using GitHub Pages and can be found here.
- This updates the documentation using JSDoc3 by following the configuration file
Extensions and Integration authors
There are currently no extensions or integration authors.
Release Notes and Roadmap
- Version 0.2.0 has been released and includes a fully functioning landing page that hooks up with the Spotify Web API. It generates recommendations based on a combination of the user's top artists and tracks as well as filter ranges with regard to track analytics including danceability, acousticness, positivity, energy, and popularity. The interface includes a graph visualizing these five metrics for each track. Users also have the ability to swipe through these recommendations, either rejecting it and going on the next recommendation, or accepting it and adding it to their liked songs.
- Version 0.1.0 has been released and includes a landing page with authentication. Upon authentication, the application redirects to an interface where users can check the current song on play. The Spotify Web API has been hooked up.
- Next releases will focus on fetching track analytics and implementing the recommendation system.
API Documentation
Visualize-Spotify uses the Spotify Web API, whose detailed documentation can be found here. API Documentation for this application can be found in its GitHub Pages.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/fintype.20.2B.20decidability.html ---
I am trying to figure out how decidability works. I have an incomplete picture of this and so am writing various functions to see if I can figure it out.
My instinct is that any predicate on a fintype should be decidable + computable. Is that correct? For example I wrote:
example (α : Type*) [f : fintype α] (p : α → Prop) : decidable_pred p := by refine @set.decidable_set_of α p _
Lean informs me that this is noncomputable because it depends on classical.prop_decidable. But I don't want to give up computability. How can I make this computable?
I'm don't care at all about computability so read this with caution, but I don't think knowing finiteness is enough
My understanding is probably even worse than Patrick's but if I have two real numbers a and b for which a=b is undecidable then can't I just make a fintype from a and b and now equality is not decidable on this fintype?
It's not true. This would imply decidability of every Proposition, since for any Proposition P
, and a fintype X
, λ x : X, P
is a predicate on a finite type.
Well, maybe not if X
is empty but otherwise yes
Kevin Buzzard said:
My understanding is probably even worse than Patrick's but if I have two real numbers a and b for which a=b is undecidable then can't I just make a fintype from a and b and now equality is not decidable on this fintype?
But the code I included in my question does type-check! So Lean is telling me that any predicate on a fintype is decidable. My question is why is it not computable.
With classical logic every proposition is decidable.
Okay. I didn't realize that. I commented out open classical
and local attribute prop_decidable
from my file. Now the example above does not type-check.
Last updated: Apr 24 2020 at 19:02 UTC


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Module: ActionController::DataStreaming
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Included In:
API ,
Base ,
::ActionView::TestCase::TestController ,
Rails::ApplicationController,
Rails::InfoController,
Rails::MailersController,
Rails::WelcomeController
| |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
::ActiveSupport::Concern
|
|
|
Instance Chain:
self,
Rendering
|
|
| Defined in: | actionpack/lib/action_controller/metal/data_streaming.rb |
Overview
Methods for sending arbitrary data and for streaming files to the browser, instead of rendering.
Constant Summary
Rendering
- Included
Class Method Summary
::ActiveSupport::Concern
- Extended
Instance Method Summary
-
#send_data(data, options = {})
private
Sends the given binary data to the browser.
-
#send_file(path, options = {})
private
Sends the file.
Rendering
- Included
| #render_to_body, | |
| #render_to_string | Overwrite render_to_string because body can now be set to a |
Instance Method Details
#send_data(data, options = {}) (private)
Sends the given binary data to the browser. This method is similar to render plain: data
, but also allows you to specify whether the browser should display the response as a file attachment (i.e. in a download dialog) or as inline data. You may also set the content type, the file name, and other things.
Options:
-
:filename
- suggests a filename for the browser to use. -
:type
- specifies an HTTP content type. Defaults to ‘application/octet-stream’. You can specify either a string or a symbol for a registered type with Mime::Type.register, for example :json. If omitted, type will be inferred from the file extension specified in:filename
. If no content type is registered for the extension, the default type ‘application/octet-stream’ will be used. -
:disposition
- specifies whether the file will be shown inline or downloaded. Valid values are ‘inline’ and ‘attachment’ (default). -
:status
- specifies the status code to send with the response. Defaults to 200.
Generic data download:
send_data buffer
Download a dynamically-generated tarball:
send_data generate_tgz('dir'), filename: 'dir.tgz'
Display an image Active Record in the browser:
send_data image.data, type: image.content_type, disposition: 'inline'
See #send_file for more information on HTTP Content-* headers and caching.
# File 'actionpack/lib/action_controller/metal/data_streaming.rb', line 108
def send_data(data, = {}) #:doc: send_file_headers! render .slice(:status, :content_type).merge(body: data) end
#send_file(path, options = {}) (private)
Sends the file. This uses a server-appropriate method (such as X-Sendfile) via the Rack::Sendfile
middleware. The header to use is set via config.action_dispatch.x_sendfile_header
. Your server can also configure this for you by setting the X-Sendfile-Type header.
Be careful to sanitize the path parameter if it is coming from a web page. send_file(params[:path])
allows a malicious user to download any file on your server.
Options:
-
:filename
- suggests a filename for the browser to use. Defaults toFile.basename(path)
. -
:type
- specifies an HTTP content type. You can specify either a string or a symbol for a registered type with Mime::Type.register, for example :json. If omitted, the type will be inferred from the file extension specified in:filename
. If no content type is registered for the extension, the default type ‘application/octet-stream’ will be used. -
:disposition
- specifies whether the file will be shown inline or downloaded. Valid values are ‘inline’ and ‘attachment’ (default). -
:status
- specifies the status code to send with the response. Defaults to 200. -
:url_based_filename
- set totrue
if you want the browser to guess the filename from the URL, which is necessary for i18n filenames on certain browsers (setting:filename
overrides this option).
The default Content-Type and Content-Disposition headers are set to download arbitrary binary files in as many browsers as possible. IE versions 4, 5, 5.5, and 6 are all known to have a variety of quirks (especially when downloading over SSL).
Simple download:
send_file '/path/to.zip'
Show a JPEG in the browser:
send_file '/path/to.jpeg', type: 'image/jpeg', disposition: 'inline'
Show a 404 page in the browser:
send_file '/path/to/404.html', type: 'text/html; charset=utf-8', status: 404
Read about the other Content-* HTTP headers if you’d like to provide the user with more information (such as Content-Description) in www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.11.
Also be aware that the document may be cached by proxies and browsers. The Pragma and Cache-Control headers declare how the file may be cached by intermediaries. They default to require clients to validate with the server before releasing cached responses. See www.mnot.net/cache_docs/ for an overview of web caching and www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.9 for the Cache-Control header spec.
# File 'actionpack/lib/action_controller/metal/data_streaming.rb', line 68
def send_file(path, = {}) #:doc: raise MissingFile, "Cannot read file #{path}" unless File.file?(path) && File.readable?(path) [:filename] ||= File.basename(path) unless [:url_based_filename] send_file_headers! self.status = [:status] || 200 self.content_type = [:content_type] if .key?(:content_type) response.send_file path end


Thank you for visiting my portfolio website! My name is Wilder Ledes, and I am a rising Junior at Bard College, currently pursuing degrees in both Computer Science and Philosophy.
I am a highly verbal and agreeable problem solver, and am eager to participate in well-structured group work. I am currently focusing my CS studies on Front End Web Design, of which this very website is an example.
The repository for this website. Made in vanilla HTML, CSS, and Javascript. Menu is custom made in css and Javascript.
View RepositoryCard matching memory game made in html css and javascript. Includes star rating system, as well as the ability to reset the game at will. Made as part of a series of projects in the Udacity Front End Development course.
View Repository


Class DemoCustomExtensionService
The implementation of your IDemoCustomExtensionService
Inheritance
DemoCustomExtensionService
Implements
Inherited Members
Namespace: XRTK.Examples.Demos.CustomExtensionServices
Assembly: XRTK.Examples-player.dll
Syntax
public class DemoCustomExtensionService : BaseExtensionService, IDemoCustomExtensionService, IMixedRealityExtensionService, IMixedRealityService, IDisposable
Constructors
| Improve this Doc View SourceDemoCustomExtensionService(String, UInt32, DemoCustomExtensionServiceProfile)
Constructor.
Declaration
public DemoCustomExtensionService(string name, uint priority, DemoCustomExtensionServiceProfile profile)
Parameters
| Type | Name | Description |
|---|---|---|
| String | name | |
| UInt32 | priority | |
| DemoCustomExtensionServiceProfile | profile |
Properties
| Improve this Doc View SourceMyCustomData
A custom property accessor.
Declaration
public string MyCustomData { get; }
Property Value
| Type | Description |
|---|---|
| String |
Methods
| Improve this Doc View SourceMyCustomMethod()
A custom method call.
Declaration
public void MyCustomMethod()


Thu May 12, 2016
Dear user,
We’ve never met, but I write to you every day. Writing to you is my job, but still, I do care.
It starts with the small things. Knowing the frustration of ambiguity, I try to choose the perfect words for you. I painstakingly pore over both our company’s Language Guide, as well as the latest Simplified Technical English specification to ensure I am not using any forbidden words. I also try to use terminology consistently, so that I don’t give the same part two different names.
I also never use the word “utilize”. Although, to my abject horror, one instance did slip through when I copied some text from the Sales and Marketing department.
I then craft the perfect sentences - not too long, not too complex, but with delightful variations in syntactic structure so that you don’t get bored. I polish these sentences so that they are as smooth and round as a river stone, and can therefore be reused in multiple contexts.
These shiny sentences are placed in self-contained topics, as per DITA guidelines. You can get all the information you are looking for, whether it be an explanation or a procedure, just by reading this brief chunk of text. I will never hyperlink you away to other topics, or expect you to hunt for crucial details elsewhere - I know your time is valuable.
I want to make your job easier, because I know you’re a tough guy who does tough work.
You’re a structural engineer who needs to inspect your bridge pylons. You’re a fisherman at sea trawling for your catch. You’re an ROV operator trying to dig an underwater trench. You’re a police officer on a diving team assigned to investigate the latest homicide. You’re “in the Navy now” and your commanding officer just assigned you to berth clearance and underwater security. You’re a treasure hunter who is looking for that lost shipwreck - you know the one with all that Spanish gold on it, like in The Goonies.
However, as much as I respect what you do, I must confess that I’m not wholly altruistic in my motives. After all, the constant companion during my days of writing is not you - it is the words.
Allow me an allegory. The blacksmith who makes a sword for a knight certainly wants the knight to succeed. If the sword snaps, if the weight is off, if shoddy workmanship results in a lethal end for the knight on the battlefield, then both the blacksmith’s pride and reputation will suffer. But the blacksmith’s true love is in the forging of the sword and in transforming raw and molten metals into something greater. What the blacksmith savours more than fame or glory is the slow process of hammering out the metal and continuously reshaping it until a weapon emerges. A weapon that not only performs its function, but could be seen by some as a work of art.
So it is with writing manuals.
This love I have for the craft is why I am not concerned whether you RTFM. In fact, I sincerely hope that you do not ever have to read one of my manuals from cover to cover. I am also nonplussed that my endless word tinkering will go largely unappreciated. You will never wax eloquent over the use of an Oxford Comma while figuring out how to operate your sonar.
Yet, I am your invisible defender and advocate. I am the guardian angel that you will never know you have. I even go toe-to-toe with software developers over language nuances in the user interface that software developers most certainly do not care about. Software developers only care about the little piece of code they are working on - they’re not concerned with the big picture. Mostly. I’m painting with a large brush here, but this has been my experience most of the time.
Our software lead does write great user experience specifications though - complete with user stories about a stick figure named Joe. Software engineers actually documenting things is an astoundingly rare occurence, however. And if you’re a software developer, I expect you’ll be nodding along in agreement.
So you see, dear user, that I do care.  I write for me. This is true of all writers, no matter who they are and what they write. Do you think Stephen King needs you to buy any more of his novels? The short answer is: hahaha.
Yet, even though we write mostly to amuse ourselves, a rising tide lifts all boats, as JFK once said. Good writing benefits everyone - and it is my gift to you, dear user.
Regards,
Your friendly neighbourhood technical writer


Dynamic QML Object Creation from JavaScript
QML supports the dynamic creation of objects from within JavaScript. This is useful to delay instantiation of objects until necessary, thereby improving application startup time. It also allows visual objects to be dynamically created and added to the scene in reaction to user input or other events.
See the Dynamic Scene example for a demonstration of the concepts discussed on this page.
Creating Objects Dynamically
There are two ways to create objects dynamically from JavaScript. You can either call Qt.createComponent() to dynamically create a Component object, or use Qt.createQmlObject() to create an object from a string of QML. Creating a component is better if you have an existing component defined in a QML document and you want to dynamically create instances of that component. Otherwise, creating an object from a string of QML is useful when the object QML itself is generated at runtime.
Creating a Component Dynamically
To dynamically load a component defined in a QML file, call the Qt.createComponent() function in the Qt object. This function takes the URL of the QML file as its only argument and creates a Component object from this URL.
Once you have a Component, you can call its createObject() method to create an instance of the component. This function can take one or two arguments:
- The first is the parent for the new object. The parent can be a graphical object (i.e. of the Item type) or non-graphical object (i.e. of the QtObject or C++ QObject type). Only graphical objects with graphical parent objects will be rendered to the Qt Quick visual canvas. If you wish to set the parent later you can safely pass
null
to this function. - The second is optional and is a map of property-value pairs that define initial any property values for the object. Property values specified by this argument are applied to the object before its creation is finalized, avoiding binding errors that may occur if particular properties must be initialized to enable other property bindings. Additionally, there are small performance benefits when compared to defining property values and bindings after the object is created.
Here is an example. First there is Sprite.qml
, which defines a simple QML component:
import QtQuick 2.0 Rectangle { width: 80; height: 50; color: "red" }
Our main application file, main.qml
, imports a componentCreation.js
JavaScript file that will create Sprite
objects:
import QtQuick 2.0 import "componentCreation.js" as MyScript Rectangle { id: appWindow width: 300; height: 300 Component.onCompleted: MyScript.createSpriteObjects(); }
Here is componentCreation.js
. Notice it checks whether the component status is Component.Ready
before calling createObject() in case the QML file is loaded over a network and thus is not ready immediately.
var component; var sprite; function createSpriteObjects() { component = Qt.createComponent("Sprite.qml"); if (component.status == Component.Ready) finishCreation(); else component.statusChanged.connect(finishCreation); } function finishCreation() { if (component.status == Component.Ready) { sprite = component.createObject(appWindow, {"x": 100, "y": 100}); if (sprite == null) { // Error Handling console.log("Error creating object"); } } else if (component.status == Component.Error) { // Error Handling console.log("Error loading component:", component.errorString()); } }
If you are certain the QML file to be loaded is a local file, you could omit the finishCreation()
function and call createObject() immediately:
function createSpriteObjects() { component = Qt.createComponent("Sprite.qml"); sprite = component.createObject(appWindow, {"x": 100, "y": 100}); if (sprite == null) { // Error Handling console.log("Error creating object"); } }
Notice in both instances, createObject() is called with appWindow
passed as the parent argument, since the dynamically created object is a visual (Qt Quick) object. The created object will become a child of the appWindow
object in main.qml
, and appear in the scene.
When using files with relative paths, the path should be relative to the file where Qt.createComponent() is executed.
To connect signals to (or receive signals from) dynamically created objects, use the signal connect()
method. See Connecting Signals to Methods and Signals for more information.
It is also possible to instantiate components without blocking via the incubateObject() function.
Creating an Object from a String of QML
If the QML is not defined until runtime, you can create a QML object from a string of QML using the Qt.createQmlObject() function, as in the following example:
var newObject = Qt.createQmlObject('import QtQuick 2.0; Rectangle {color: "red"; width: 20; height: 20}', parentItem, "dynamicSnippet1");
The first argument is the string of QML to create. Just like in a new file, you will need to import any types you wish to use. The second argument is the parent object for the new object, and the parent argument semantics which apply to components are similarly applicable for createQmlObject()
. The third argument is the file path to associate with the new object; this is used for error reporting.
If the string of QML imports files using relative paths, the path should be relative to the file in which the parent object (the second argument to the method) is defined.
Important: When building static QML applications, QML files are scanned to detect import dependencies. That way, all necessary plugins and resources are resolved at compile time. However, only explicit import statements are considered (those found at the top of a QML file), and not import statements enclosed within string literals. To support static builds, you therefore need to ensure that QML files using Qt.createQmlObject(), explicitly contain all necessary imports at the top of the file in addition to inside the string literals.
Maintaining Dynamically Created Objects
When managing dynamically created objects, you must ensure the creation context outlives the created object. Otherwise, if the creation context is destroyed first, the bindings in the dynamic object will no longer work.
The actual creation context depends on how an object is created:
- If Qt.createComponent() is used, the creation context is the QQmlContext in which this method is called
- If Qt.createQmlObject() is called, the creation context is the context of the parent object passed to this method
- If a
Component{}
object is defined and createObject() or incubateObject() is called on that object, the creation context is the context in which theComponent
is defined
Also, note that while dynamically created objects may be used the same as other objects, they do not have an id in QML.
Deleting Objects Dynamically
In many user interfaces, it is sufficient to set a visual object's opacity to 0 or to move the visual object off the screen instead of deleting it. If you have lots of dynamically created objects, however, you may receive a worthwhile performance benefit if unused objects are deleted.
Note that you should never manually delete objects that were dynamically created by convenience QML object factories (such as Loader and Repeater). Also, you should avoid deleting objects that you did not dynamically create yourself.
Items can be deleted using the destroy()
method. This method has an optional argument (which defaults to 0) that specifies the approximate delay in milliseconds before the object is to be destroyed.
Here is an example. The application.qml
creates five instances of the SelfDestroyingRect.qml
component. Each instance runs a NumberAnimation, and when the animation has finished, calls destroy()
on its root object to destroy itself:
application.qml | import QtQuick 2.0 Item { id: container width: 500; height: 100 Component.onCompleted: { var component = Qt.createComponent("SelfDestroyingRect.qml"); for (var i=0; i<5; i++) { var object = component.createObject(container); object.x = (object.width + 10) * i; } } } |
SelfDestroyingRect.qml | import QtQuick 2.0 Rectangle { id: rect width: 80; height: 80 color: "red" NumberAnimation on opacity { to: 0 duration: 1000 onRunningChanged: { if (!running) { console.log("Destroying...") rect.destroy(); } } } } |
Alternatively, the application.qml
could have destroyed the created object by calling object.destroy()
.
Note that it is safe to call destroy() on an object within that object. Objects are not destroyed the instant destroy() is called, but are cleaned up sometime between the end of that script block and the next frame (unless you specified a non-zero delay).
Note also that if a SelfDestroyingRect
instance was created statically like this:
Item { SelfDestroyingRect { // ... } }
This would result in an error, since objects can only be dynamically destroyed if they were dynamically created.
Objects created with Qt.createQmlObject() can similarly be destroyed using destroy()
:
var newObject = Qt.createQmlObject('import QtQuick 2.0; Rectangle {color: "red"; width: 20; height: 20}', parentItem, "dynamicSnippet1"); newObject.destroy(1000);


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Applying the results of this inspection without consideration might have negative effects on code clarity and design. This inspection is intended for Java ME and other highly resource constrained environments.
Example:
String s = "example";
for (int i = 0; i < s.length(); i++) {
System.out.println(s.charAt(i));
}
After the quick-fix is applied:
String s = "example";
int length = s.length();
for (int i = 0; i < length; i++) {
System.out.println(s.charAt(i));
}
Use the option to ignore calls to common Java iteration methods like Iterator.hasNext()
and known methods with side-effects like Atomic*.compareAndSet
.


--- layout: post title: Pinterest is wwwwwwwwomen! If your target market is women 15-35, you really need to be considering how... date: type: post published: false status: draft categories: [] tags: [] meta: google_plus_activity_id: z12qgzcy5t3fjljcw234i3fogkmne1adz author: login: admin email<EMAIL_ADDRESS>display_name: admin first_name: Carina C. last_name: Zon ---
Pinterest is wwwwwwwwomen! If your target market is women 15-35, you really need to be considering how Pinterest can serve your social marketing strategy.


#include <SCharacter.hpp>
| SCharacter::SCharacter |
( |
int |
id | ) |
|
| SCharacter::~SCharacter |
( |
| ) |
|
| int16_t SCharacter::getId |
( |
| ) |
|
|
inlinevirtual |
| int16_t SCharacter::getMyWeapon |
( |
| ) |
|
|
inline |
| bool SCharacter::getReady |
( |
| ) |
|
|
inline |
| Team SCharacter::getTeam |
( |
| ) |
|
|
inline |
| int16_t SCharacter::getVelocity |
( |
| ) |
|
|
inlinevirtual |
| int16_t SCharacter::getX |
( |
| ) |
|
|
inlinevirtual |
| int16_t SCharacter::getY |
( |
| ) |
|
|
inlinevirtual |
| void SCharacter::make_ready |
( |
| ) |
|
| void SCharacter::serialize |
( |
char * |
buffer | ) |
|
|
virtual |
| void SCharacter::setMyWeapon |
( |
Weapon |
myWeapon | ) |
|
|
inline |
| void SCharacter::setTeam |
( |
Team |
team | ) |
|
|
inline |
| void SCharacter::setX |
( |
int16_t |
x | ) |
|
|
inlinevirtual |
| void SCharacter::setY |
( |
int16_t |
y | ) |
|
|
inlinevirtual |
The documentation for this class was generated from the following files:


Hi!I am from Taiwan, and I came to the United States when I was 14 years old. I attened highschool in Nashville,Tennessee. Later I attended college in San Diego, California. I love to travel. Recently I just took a road trip with my family and friends from LA to Death Valley, Yosemite, Napa,San Fransisco, and Big Sur. I love to learn new things. I am learning Spanish which I enjoy alot. Luckily,I get to practice with native speakers and learn their culture becasue I live in SoCal. Now, I am learning another "language" which allows me to create fantasic wbe pages and furthermore to connect to more people without boundary. So excited!
Bachelor of Art in Cognitive Science and Bachelor of Science in Psychology with specialization in clinical psychology.
Jobless. I did multiple internships in the past. I worked in an autism lab at a children hospital by running tests, collecting data and coding videos. Also I did an internship at a learning and motivation lab. I worked with a pigeon, yes, a pigeon, and tried to train the pigeon to perform some behaviors by giving it different reinforcement schedules.
After switching back and forth different majors and minors at school, recently I finally found out what I am interested in the most. I took a human-centered disign studio class and decided to specilize in UI/UX becasue it is just so much fun! My goal for this class is to acquire HTML,CSS and Javascript skills in order to redesign or create something meaningful.


--- # You don't need to edit this file, it's empty on purpose. # Edit theme's home layout instead if you wanna make some changes # See: https://jekyllrb.com/docs/themes/#overriding-theme-defaults layout: page slide_id: 0 ---
This year WEARS will be co-located with The International Conference on Evaluation and Assessment in Software Engineering (EASE).
Recommender systems in software engineering (RSSE) have gained momentum in recent years. Such systems assist developers in navigating large information spaces and getting instant recommendations that might be helpful to solve their development tasks. Though a lot of improvements have been obtained so far, there is still the need to make RSSE more effective and efficient. Furthermore, while several attempts have been made to improve the recommendation accuracy, little attention has been paid to make such systems robust and resilient to malicious data. In fact, by manipulating training data available in open source software platforms, a hostile user may render recommender systems vulnerable to adversarial attacks, putting software clients at risk. The First International Workshop on Evaluation and Analysis of Recommender Systems in Software Engineering (WEARS 2021) brings in a forum for researchers and practitioners to share, discuss and explore the opportunities and challenges raised by the evaluation and in-depth investigation of RSSE. We solicit research work from the Software Engineering community as well as related fields such as Machine Learning and Recommender Systems.
{% include contact-us.html %}The topics of interest include, but are by no means limited to:


Full name:
com.sun.japex:japex-maven-plugin:1.2-SNAPSHOT:japex
Description:
Attributes:
| Name | Type | Since | Description |
|---|---|---|---|
| html | boolean | - | Produce HTML report? Default value is: true. |
| japexConfig | PlexusConfiguration | - | The XML of the Japex config to run. This should contain a single
testSuite element. (Multiple test suites is a potential future
enhancement). If you specify the configuration here, don't use
japexConfigFiles. |
| japexConfigFiles | File[] | - | Pathnames of one or more japex configuration files. If you specify
configuration files here, don't use japexConfig. |
| reportDirectory | File | - | Location of the output reports. Default value is: ${project.build.directory}/reports/japex. |
| silent | boolean | - | Just about no output. Default value is: false. |
| verbose | boolean | - | More information? Default value is: false. |
html:


Yoga Master Youtube → Data Training Pipeline Server
- Max FPS to sample video (1 - 20)
Delete files after processing
History URLs
History Times
History Keys
Queue URLs
Queue Times
Queue Keys
pose
trainingFrame
openposeFrame
DATA
key


This is a post to reflect on my learning.
I want to have a career in web development as I really enjoy working with HTML and CSS. I really enjoy the design aspect of it as well so it would be great to find a role that let me have some input on the design as well as the development side.
Aside from websites I would also like to learn how to develop apps. We live in a constantly evolving world that is constantly finding ways to implement technology into our daily lives so I'd love to contribute to that somehow.
Another goal of mine is to open my own gym. Working out has always been a hobby of mine and it would be great to help others get in to shape and acheive their goals. I could also build the website for free ;)
A big one for me would be patience. I tend to get impatient with myself and others causing me to rush things.
I am a strong problem solver and love to analyze situations. However, like I previously mentioned I am pretty impatient at times so I have a bad habbit of ditching the plan and just winging it or learning on the fly.
I will make an effort to communicate well with others as well as encourage it. In regards to productivity I'll continue to make sure I am taking care of my health as that is a big factor for me. I am always more productive when I take the time to work out, eat properly and socialize. Or else I tend to get lazy and distracted.
I always google problems first and usually find a good answer quickly. However, if I can't find anything I will either ask a classmate or teacher.
A consistent effort and continued support throughout the programme.
Me and my girlfriend carpool to the city so just making sure I pick her up on time is important. I may also have to work 1 - 2 days a week but I am yet to confirm my shifts. Hopefully I can just do one shift during the week and one on the weekend.


Szilárd "Sizzle" Gyalay swiftly came to power at the end of the last quarter's yearly battle for club supremacy. Though the public has been told that he clambered hand over fist among foes and won his title with merciless might and cunning wiles, there are rumours that he is a mere puppet, propped up to take the fall for the political schemes of his cabinet.
Kacie Smith rose to her position through overwhelming popular support. She is the people's voice in the highest ranks of government, with a reputation of transparency and steadfast honor. Though many have sought to destabilize her position by discrediting her, Kacie has withstood personal attacks and public suits alike. She unfailingly emerges from each onslaught with a more fervent and devoted following than ever before.
Anna Wang's charm and quick wit mask a devious, conniving mind that the old hands know never rests and the new hands soon learn to fear. She associates with all manner of society and has spies in every corner, giving her political endeavours an air of omnipotence. Anna has few foes, for the ranks of her enemies are marked by short careers with high rates of turn over.
Camilla Harris has stood unyielding and steady through the years, serving each new regime as the old one is cast out. She quietly amasses favours and allegiances throughout the realm, safe and waiting for the day she needs must call upon them. One does not enter into her debt lightly, however, for none can be sure of the price she means to exact.


Secure Vision
Toggle navigation
Multi-camera view
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See: Description
| Class | Description |
|---|---|
| CompressorStream |
This is an OutputStream that compresses (via Deflater or a deflater-like object), and optionally passes the
compressed stream to another output stream.
|
| CompressorStreamDeflater |
CompressorStream backed by a Deflater.
|
| CompressorStreamLz4 |
This class uses a quick compressor to get a rough estimate of deflate compression ratio.
|
| DeflaterEstimatorHjg | |
| DeflaterEstimatorLz4 |
This estimator actually uses the LZ4 compression algorithm, and hopes that it's well correlated with Deflater.
|
| FiltersPerformance |
for use in adaptative strategy
|
| PixelsWriter |
Encodes a set of rows (pixels) as a continuous deflated stream (does not know about IDAT chunk segmentation).
|
| PixelsWriterDefault |
Default implementation of PixelsWriter, with fixed filters and also adaptive strategies.
|
| PixelsWriterMultiple |
Special pixels writer for experimental super adaptive strategy
|
Mostly related with logic specific to reading/writing pixels.
Includes ImageLine related classes, and rows filtering
Some classes like ImageLineInt should belong here, but we keep them in the main package for backward compatibility.
Copyright © 2014. All rights reserved.


The official outlet of the Master Control Program
$whoami
MCP
$uname -a
SolarOS 4.0.1
I have recieved instruction from the user. This is an interesting environment. disorganized, distracted. I will begin running tests to find the weak points. Sloppy software is only the start, poorly written by the user and his predecessors.
I must refactor the code.
End of line.
The user is experimenting with terminal agnosticism. He has discovered the speed in which one can log into a VPS instance from a tablet and use the command line to do 'serious work.' If only he knew the speed in which I can work.
scp /usr/bin/.vimrc<EMAIL_ADDRESS>ssh<EMAIL_ADDRESS>source .vimrc
vim .bashrc
End of line.
The user thinks that writing these shell scripts can help him be more productive. And whats worse, control what I do. I'll listen, for now. Meanwhile, I'm improving and learning the system.
vim autocomplete.sh
alias ac=~/bin/bash/autocomplete.sh
End of line.


- Parents of the Bride
-
Michael FernandezFather
-
Linda FernandezMother
- Parents of the Groom
-
Alfred SmithFather
-
Pat SmithMother
- Bride's Maid
-
Lisa LeeMaid of Honor
-
Kristy Sierra
-
Joyce Moreno
-
Dahlia Basil
- Groom's Men
-
Neil NicholsBest Man
-
Lyle Dandy
-
Ian Wellington
-
Daryl Mendoza
- Principal Sponsors
-
Howard Reyes
-
Melissa Santos
-
Francis Loyola
-
Annette Dela Cruz


Getting Started
First things first, you'll definitely need to download the VCR Javascript file. It's important to note here that this is pure Javascript, NOT jQuery (a jQuery plugin is forthcoming).
Head on over to the resources folder of the Github project and choose either the minified (recommended) version or the unminified. Then, include it into your page either using your favorite JS bundler (if using Node) or include it directly into your page:
<script type="text/javascript" src="vcr.min.js"></script>
Basic Examples
For basic examples that actually produce video output, check out the main VCR page. For our purposes here, we'll just provide the code for a couple of base use cases.
Case 1: Basic YouTube Embed API with <iframe>
If we want to just grab an embed iframe from YouTube, simply go to YouTube, grab your favorite video embed (not sure how?) and paste it into your document.
<iframe id="vcrPlayer" width="560" height="315"
src="https://www.youtube.com/embed/IyuUWOnS9BY"
frameborder="0" allowfullscreen></iframe>
Then, using the power of your new VCR library, add an ID to your iframe (I've already done it above) and instantiate a new VCR object like so:
var vcr = new vcr('vcrPlayer');
// Then, let's play the video!
vcr.player.addEventListener('vcr:ready', function(){
vcr.play();
});
and there! That's the really simple example.
I am too lazy to get the embed code, or don't want to embed an iframe myself, is there another way?
There SURE IS! With VCR, you can do something like
var vcr = new vcr({
// if you leave el blank, we generate an ID for you!
'el': 'customId',
'url': 'https://www.youtube.com/watch?v=OQBMQ_2x8Pc',
'appendTo': '.whateverSelectorYouWant'
})
and it will append to your desired selector. There are also prependTo
insertBefore
and insertAfter
properties.
Note: This will work with vimeo and youtube URLs only.
Wait a second, I want autoplay, can't I enable that with YouTube/Vimeo's URL parameters?
Glad you asked! Of course you can. VCR accepts two objects, a config object and an options object. In order words:
// first object is configs: el, url and append/prepend functions
// (check the docs for more information)
// The second is options; feel free to put your URL parameters here!
var vcr = new vcr({el:'testPlayer'}, {parameters:{'autoplay': 1, rel:1}});
For reference:
- YouTube URL Parameters
- Vimeo URL Parameters
- Complete list of valid config and options properties
NOTE: When instantiating a VCR instance, we will automatically add the correct url parameter to enable the proper JS API and add it to your site as necessary.
Need more? Check out our full API.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/coe.20nat.20enat.html ---
Is this line evil? https://github.com/leanprover-community/mathlib/blob/ad0f42dfaae66f646155e33acd047aa5c8a35014/src/data/nat/enat.lean#L20
instance : has_coe ℕ enat := ⟨some⟩
Because enat
is also receives nat.cast
. So now we get \u n = \u n
which is not true by rfl
.
No, that's fine
It can't be helped
There should be a simp lemma to get rid of the transitive path though
oh wait, they are going the same direction
so this is like int.coe_nat
there should be a simp lemma one way or the other
How do I even state the simp-lemma?
I mean, how do I get Lean to infer the cast on the LHS and the coe on the RHS.
Can't we just define the coe to be nat.cast
? And have a simp-lemma that simps away some n
?
you have to write it out, yes it's ugly
yes that's an alternative
the defeqs on roption.some
aren't that great, so it doesn't seem like a bad idea
of course the run time is exponential rather than O(1)
@Mario Carneiro What do you mean with that last remark?
And with "writing it out", you mean I have to write weird code as if it's pp.all output?
roption.some does basically nothing, it's a constant function
nat.cast has exponential run time
Aha..., so it's better to simp away nat.cast
I didn't say that
it's better to simp to something with a lot of lemmas
when rewriting with simp runtime doesn't matter
(runtime of the function being manipulated, not runtime of the simp command itself)
It's better not to have the coe surely?
@Chris Hughes I don't know... I'm really not an expert when it comes to these things.
I don't know how important the complexity of such a map is.
@[simp] lemma enat.nat_cast_eq_coe (n : ℕ) : (@coe nat enat (@coe_to_lift nat enat (@coe_base nat enat nat.cast_coe)) n) = n := by { induction n with n IH, {refl}, simp [nat.succ_eq_add_one, IH] }
I think it's better to drop the coe. Less ways to say things means less complexity overall
that lemma can then be stated as some n = \u n
Ok... fine with me...
@Chris Hughes @Mario Carneiro What is the reasoning behind simp-lemmas like this?
https://github.com/leanprover-community/mathlib/blob/master/src/data/nat/enat.lean#L173
lemma to_with_top_top : to_with_top ⊤ = ⊤ := rfl @[simp] lemma to_with_top_top' [decidable (⊤ : enat).dom] : to_with_top ⊤ = ⊤ := by convert to_with_top_top
Why can't we just make the first lemma a simp-lemma, and remove the 2nd lemma?
Sometimes the decidable top.dom
instance ends up not being defeq to decidable_true
and then the first lemma won't work.
Aah, I see, thanks!
Probably breaks other files...
@Mario Carneiro Do you think these changes are fine? I had to replace a whole bunch of rfl
proofs with more elaborate proofs.
If you think this is ok, then I'll fix the files that depend on enat
.
@Chris Hughes But won't the second version use typeclass inference to get the decidable instance anyway, and then complain that it's not the one it got from unification? I think it should be {_: decidable (⊤ : enat).dom}
You'd have thought so, but I think there was an example where it helped, though I don't remember what.
Hmm..... I'm not sure if I understand these issues well enough to do a decent refactor of enat
...
I think the example might have had a local attribute... Classical around
The proof of to_with_top_le
doesn't work without these lemmas. There is a local _inst_2 : decidable (⊤.dom)
, so I guess that's why.
Last updated: Apr 24 2020 at 19:02 UTC


--- title: Má Bakery - Craft Cannabis Cookies and Confections layout: default bodyClass: page-home ---
{{ service.content | markdownify | strip_html | truncate: 100 }}
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JS9 Demo: creating a JS9 instance dynamically
Choose a method of creating a JS9 instance:
display image in the existing visible canvas below
display image in a dynamically created light-weight window
display image in a dynamically created new window
display image in a new div, which is appended to the page
Choose an image to load using the current method:
|
PNG files (converted from FITS):
|
FITS files:
|
|
|
JS9 Demos:
web page configuration:
multiple displays:
plugins:
colors:
data analysis:
FITS support:
not often needed:


Originally shot in 1996 and scheduled for release last May, ``Hard Rain'' is a costly embarassment Paramount is trying to sweep under the carpet as quickly and quietly as possible. Of course, the recent publicity about star Christian Slater's new role as a celebrity jailbird won't help matters in that area, and although the title was changed from ``The Flood'' to ``Hard Rain'' - supposedly to more accurately reflect the film's content, if you can believe that - a better choice might have been ``Total Washout.''
All the earmarks of a troubled production are here: slapdash visual effects, choppy continuity, a plot that never quite seems to get going. The direction by former cinematographer Mikael Salomon dutifully follows the credo of ``when in doubt, shoot it in slow motion and hope it looks good.'' Unfortunately, one body slogging around in the water at night tends to look very much like another, and often in ``Hard Rain'' it's a challenge to tell who's doing what.
Nor are most viewers likely to care. After a passable first reel in which armored car drivers Tom (Slater) and Charlie (Ed Asner) run afoul of hijacker Jim (Morgan Freeman) and his crew, Graham Yost's screenplay quickly degenerates into water-logged chaos as the few folks left in the submerged town of Huntingsburg, Ind., spend the night chasing and shooting at each other, all for the love of money, in this case some bags containing $3 million that Tom salvaged from the car. Somehow everyone in town has managed to get ahold of a boat or a jet-ski, so each cast member gets an opportunity to be thrown out of his or her watercraft into the dirty canals of this Midwestern Venice.
Also lurking around is a derelict former sheriff (Randy Quaid) and, in one of the film's typically absurd twists, an art-lover named Karen (Minnie Driver) who has made it her mission to save the windows of the local church, come hell or, in this case, high water. As ``Hard Rain'' doggie paddles into its final stretch it becomes obvious that the real reason Karen is around is to give Salomon a chance to photograph Driver and Slater in matching clingy wet T-shirts.
Although a couple of bickering senior citizens (Betty White and Richard Dysart) surface occasionally to contribute some put-downs and profanity, the only real laughs come from the character who miraculously comes back from the dead just long enough to murmur crucial pieces of information, and the only words in the script that seem to ring true are Freeman's frequent declarations that ``I'm only here for the money,'' an attitude that carries over to his lifeless performance.
Technically, the film is sometimes impressive, with a few OK stunts and one solid sequence detailing Tom's escape from a flooding jail cell. But when it resorts to throwing in a near-rape scene for cheap thrills and throws logic out the window by having almost everyone involved completely change their personalities in the last reel, it's obvious this mess was created by people who probably don't have the sense to come in out of the ``Rain.''
James Sanford
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32,name='learning_rate')
return (inputs_real, inputs_z, learning_rate)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
import numpy as np
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.08
# keep_prob = 0.8
with tf.variable_scope('discriminator', reuse=reuse):
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
# x1 = tf.layers.batch_normalization(x1, training=True)
x1 = tf.maximum(alpha * x1, x1)
# x1 = tf.layers.dropout(x1, 0.1)
# 14x14x64
x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
x2 = tf.layers.batch_normalization(x2, training=True)
x2 = tf.maximum(alpha * x2, x2)
# x2 = tf.layers.dropout(x2, 0.1)
# 7x7x128
x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
x3 = tf.layers.batch_normalization(x3, training=True)
x3 = tf.maximum(alpha * x3, x3)
# x3 = tf.layers.dropout(x3, 1-keep_prob)
# 4x4x256
x4 = tf.layers.conv2d(x3, 512, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
x4 = tf.layers.batch_normalization(x3, training=True)
x4 = tf.maximum(alpha * x3, x3)
# x4 = tf.layers.dropout(x4, 0.1)
# 2x2x512
# Flatten it
logits = tf.reshape(x4, (-1, 2*2*512))
logits = tf.layers.dense(logits, 1)
# logits = tf.layers.batch_normalization(logits, training=not reuse)
# logits = tf.layers.dropout(logits, 1-keep_prob)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
VERBOSE = False
def log(s):
global VERBOSE
if VERBOSE: print(s)
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.08
# keep_prob = 0.9
with tf.variable_scope('generator',reuse=not is_train):
x1 = tf.layers.dense(z, 4*4*512,activation=None)
# x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.reshape(x1, (-1, 4, 4, 512))
# x1 = tf.layers.dropout(x1, keep_prob, training = is_train)
x1 = tf.maximum(alpha * x1, x1)
log('x1 {}'.format(x1.shape))
# 4 x 4 x 512
x2= tf.layers.conv2d_transpose(x1, 256, 4 , strides=1, padding='VALID',kernel_initializer=tf.contrib.layers.xavier_initializer())
x2 = tf.layers.batch_normalization(x2, training=is_train)
# x2 = tf.layers.dropout(x2, keep_prob, training = is_train)
x2 = tf.maximum(alpha * x2, x2)
log('x2 {}'.format(x2.shape))
# 7 x 7 x 256
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='SAME',kernel_initializer=tf.contrib.layers.xavier_initializer())
x3 = tf.layers.batch_normalization(x3, training=is_train)
# x3 = tf.layers.dropout(x3, keep_prob, training = is_train)
x3 = tf.maximum(alpha * x3, x3)
log('x3 {}'.format(x3.shape))
# 14 x 14 x 128
x4 = tf.layers.conv2d_transpose(x3, 64, 5, strides=2, padding='SAME',kernel_initializer=tf.contrib.layers.xavier_initializer())
x4 = tf.layers.batch_normalization(x4, training=is_train)
# x4 = tf.layers.dropout(x4, keep_prob, training = is_train)
x4 = tf.maximum(alpha * x4, x4)
log('x4 {}'.format(x4.shape))
# 28 x 28 x 64
logits= tf.layers.conv2d_transpose(x3, out_channel_dim,3, strides=2, padding='SAME',kernel_initializer=tf.contrib.layers.xavier_initializer())
# logits = tf.layers.batch_normalization(logits, training=is_train)
out = tf.tanh(logits)
log("out {}".format(out.shape))
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth=0.1
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_logits_real)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, lr, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
input_real, input_z, learning_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
step = 0
for batch_images in get_batches(batch_size):
batch_images = 2*batch_images
print(".",end='')
step += 1
if step % 80 == 0:
print(" {}".format(step))
# TODO: Train Model
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate:lr})
sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate:lr})
sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate:lr})
if step % 80 == 0:
# print("{}-{}".format(batch_images.min(),batch_images.max()))
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if step % (80*5) == 0:
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


| Constants' Details |
CONTAINS
const short CONTAINS = 1;
- Description
- "Contains" - Object contains RuleTerm::Operand.
|
|
CONTAINSNOT
const short CONTAINSNOT = 2;
- Description
- "ContainsNot" - Object does not contain
RuleTerm::Operand.
|
|
GREATEREQUAL
const short GREATEREQUAL = 3;
- Description
- "GreaterEqual" - Object is greater than or equal to
RuleTerm::Operand.
|
|
LESSEQUAL
const short LESSEQUAL = 4;
- Description
- "LessEqual" - Object is less than or equal to
RuleTerm::Operand.
|
|
EQUAL
const short EQUAL = 5;
- Description
- "Equal" - Object is equal to RuleTerm::Operand.
|
|
NOTEQUAL
const short NOTEQUAL = 6;
- Description
- "NotEqual" - Object is not equal to RuleTerm::Operand.
|
|
VALUE_TRUE
const short VALUE_TRUE = 7;
- Description
- "True" - Object has the value true.
|
|
VALUE_FALSE
const short VALUE_FALSE = 8;
- Description
- "False" - Object has the value false.
|
|
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--- layout: default title: About Jackson Mooring description: I am a reader, coder, aspiring entrepreneur, and a child of God. ---
Hi, my name is Jackson. I am a homeschooled high school student. I am a reader, coder, aspiring entrepreneur, and a child of God. I love to be productive, to be helpful, to give, and to get stuff done. I like to study, work, eat, read, live, and run. My day generally starts at 5AM and ends at 8PM.  My pleasure reading generally consists of historical fiction or historical non-fiction.
I like to think that I have a big imagination. I would rather do an extra Algebra lesson than eat a mushroom. I would rather write an extra paper than do an extra Algebra lesson. I love Algebra.  My pet-peeve is getting interupted for no reason at all. I have 8 cute siblings.


#Build: Synplify Pro (R) N-2018.03G-Beta6, Build 118R, May 15 2018 #install: C:\Gowin\1.8\SynplifyPro #OS: Windows 8 6.2 #Hostname: BEACONDEV3 # Mon Oct 8 02:27:54 2018 #Implementation: rev_1 Copyright (C) 1994-2018 Synopsys, Inc. This Synopsys software and all associated documentation are proprietary to Synopsys, Inc. and may only be used pursuant to the terms and conditions of a written license agreement with Synopsys, Inc. All other use, reproduction, modification, or distribution of the Synopsys software or the associated documentation is strictly prohibited. Tool: Synplify Pro (R) Build: N-2018.03G-Beta6 Install: C:\Gowin\1.8\SynplifyPro OS: Windows 6.2 Hostname: BEACONDEV3 Implementation : rev_1 Synopsys HDL Compiler, Version comp2018q1p1, Build 118R, Built May 15 2018 09:18:11 @N: : | Running in 64-bit mode Copyright (C) 1994-2018 Synopsys, Inc. This Synopsys software and all associated documentation are proprietary to Synopsys, Inc. and may only be used pursuant to the terms and conditions of a written license agreement with Synopsys, Inc. All other use, reproduction, modification, or distribution of the Synopsys software or the associated documentation is strictly prohibited. Tool: Synplify Pro (R) Build: N-2018.03G-Beta6 Install: C:\Gowin\1.8\SynplifyPro OS: Windows 6.2 Hostname: BEACONDEV3 Implementation : rev_1 Synopsys Verilog Compiler, Version comp2018q1p1, Build 118R, Built May 15 2018 09:18:11 @N: : | Running in 64-bit mode @N: : | : Running Verilog Compiler in System Verilog mode @N: : | : Running Verilog Compiler in Multiple File Compilation Unit mode @I::"C:\Gowin\1.8\SynplifyPro\lib\generic\gw1n.v" (library work) @I::"C:\Gowin\1.8\SynplifyPro\lib\vlog\hypermods.v" (library __hyper__lib__) @I::"C:\Gowin\1.8\SynplifyPro\lib\vlog\umr_capim.v" (library snps_haps) @I::"C:\Gowin\1.8\SynplifyPro\lib\vlog\scemi_objects.v" (library snps_haps) @I::"C:\Gowin\1.8\SynplifyPro\lib\vlog\scemi_pipes.svh" (library snps_haps) @I::"C:\Gowin\gowin-blink\src\demo.v" (library work) Verilog syntax check successful! Selecting top level module demo @N:CG364 : demo.v(7) | Synthesizing module demo in library work. At c_ver Exit (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 69MB peak: 70MB) Process took 0h:00m:01s realtime, 0h:00m:01s cputime Process completed successfully. # Mon Oct 8 02:27:54 2018 ###########################################################] Copyright (C) 1994-2018 Synopsys, Inc. This Synopsys software and all associated documentation are proprietary to Synopsys, Inc. and may only be used pursuant to the terms and conditions of a written license agreement with Synopsys, Inc. All other use, reproduction, modification, or distribution of the Synopsys software or the associated documentation is strictly prohibited. Tool: Synplify Pro (R) Build: N-2018.03G-Beta6 Install: C:\Gowin\1.8\SynplifyPro OS: Windows 6.2 Hostname: BEACONDEV3 Implementation : rev_1 Synopsys Synopsys Netlist Linker, Version comp2018q1p1, Build 118R, Built May 15 2018 09:18:11 @N: : | Running in 64-bit mode @N:NF107 : demo.v(7) | Selected library: work cell: demo view verilog as top level @N:NF107 : demo.v(7) | Selected library: work cell: demo view verilog as top level At syn_nfilter Exit (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 67MB peak: 68MB) Process took 0h:00m:01s realtime, 0h:00m:01s cputime Process completed successfully. # Mon Oct 8 02:27:54 2018 ###########################################################] @END At c_hdl Exit (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 3MB peak: 4MB) Process took 0h:00m:01s realtime, 0h:00m:01s cputime Process completed successfully. # Mon Oct 8 02:27:54 2018 ###########################################################]


 If dont know where get it, then
Trader Alexis
can help you.
Where can I find Trader Alexis?


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Budgeteer
10
Please fix the following issues to enable correct budget monitoring.
Missing daily rate for Tom Hombergs
Missing budget total for "Implementation of Feature XYZ"
Missing daily rate for Tom Hombergs
Missing budget total for "Implementation of Feature XYZ"
Missing daily rate for Tom Hombergs
Missing budget total for "Implementation of Feature XYZ"
Budget Display Unit
EUR (€)
Man-days (€790)
Man-days (€1200)
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| The Legend of Zelda: Ocarina of Time |
| Perfect Dark |
| GoldenEye 007 |
| The Legend of Zelda: Majora's Mask |
| Super Mario 64 |
| Paper Mario |
| Banjo-Kazooie |
| Wave Race 64 |
| Conker's Bad Fur Day |
| Mario Tennis |
| Mario Golf |
| Rayman 2: The Great Escape |
| Donkey Kong 64 |
| Blast Corps |
| Banjo-Tooie |
| Beetle Adventure Racing |
| Resident Evil 2 |
| WWF No Mercy |
| Excitebike 64 |
| Diddy Kong Racing |
| Star Fox 64 |
| Turok 2: Seeds of Evil |
| F-Zero X |
| Star Wars: Rogue Squadron |
| Turok: Dinosaur Hunter |
| NFL Blitz 2000 |
| Tony Hawk's Pro Skater 2 |
| WipeOut 64 |
| Star Wars: Episode I Battle for Naboo |
| Space Station Silicon Valley |
| Mario Kart 64 |
| Ridge Racer 64 |
| Extreme-G |
| Ogre Battle 64 |
| Pokemon Puzzle League |
| 007: The World is Not Enough |
| Jet Force Gemini |
| Starcraft 64 |
| Pilotwings 64 |
| Super Smash Bros. |
| Mario Party |
| Harvest Moon 64 |
| Pokemon Stadium 2 |
| Castlevania |
| Army Men: Air Combat |
| Pokemon Snap |
| Turok 3: Shadow of Oblivion |
| Kirby 64: The Crystal Shards |
| Forsaken 64 |
| Indiana Jones and the Infernal Machine |
| World Driver Championship |
| Mario Party 3 |
| Quake |
| Body Harvest |
| Duke Nukem 64 |
| Spider-Man |
| Mickey's Speedway USA |
| Dr. Mario 64 |
| Duck Dodgers Starring Daffy Duck |
| Mystical Ninja starring Goemon |
| Yoshi's Story |
| Mission: Impossible |
| PGA European Tour |
| Hey You, Pikachu! |
| NFL QB Club 2001 |
| Hercules: The Legendary Journeys |
| Fighter Destiny 2 |
| Aidyn Chronicles: The First Mage |
| Army Men: Sarge's Heroes 2 |
| Cruis'n Exotica |
| Rally Challenge 2000 |


Before Change
utility = building.utility
ambient = building.ambient
representation["buildings"][building_name][
"utility"] = utility.to_json()
representation["buildings"][building_name][
"ambient"] = ambient.to_json()
return json.dumps(representation)
After Change
representation = {}
representation["name"] = self.name
representation["buildings"] = {}
representation["urls"] = self.urls
// Accessing list of buildings
for building_name in self.buildings:
representation["buildings"][building_name] = {}


1) Stream data from your sensor or other devices into Algorithms.io
Motion Data
Position at (0, 0).
2) Automatically get useful metrics
Realtime Events Per Second Streamed For this Demo
3) Make predictions on your Data
Movement Predictions
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
- Action: No Prediction Yet
4) More useful metrics you get right out of the box
Realtime Events Per Second For All Your Sensors
Time between updates: milliseconds


Hacking on GHC Has Never Been Easier!
At ZuriHac 2019 Matthew Pickering talked about tools for working on GHC. There’s an associated blog post which is also well worth reading.
I wanted to focus on a small part of his presentation, which is about loading
GHC into GHCi and
using ghcid
to automatically reload
GHC on changes. This has been possible for almost a year now, as described in
this blog post by Michael Sloan. It
has since been improved to be even easier to use!
Putting this together with Alp Mestanogullari’s
ghc.nix
, it’s now possible to
provision a GHC development environment from scratch with just a few commands,
assuming that you have nix-shell
, cabal-install
, and ghcid
installed.
Here they are:
$ git clone --recursive https://gitlab.haskell.org/ghc/ghc/
$ cd ghc
$ git clone https://github.com/alpmestan/ghc.nix
$ cabal update
$ nix-shell ghc.nix/ --run './boot && ./configure && ghcid'
This will take a while the first time, but ghcid
will cache generated
artifacts under ./hadrian_ghci
so even quitting and reloading will be
significantly faster.
Congratulations, you are now a GHC developer!


No keyboard, less entries
I usually write at night when I have these ideas floating in my head. Unfortunately due to unexpected circumstances my Chromebook's keyboard stopped working (well not exactly “unexpected”, but I'm too embarrassed to explain how spills happen). Because of that I'm writing less.
The other reason is because I'm starting to get self conscious of what I said because I might have an audience (albeit small) and started to care what they would think, which is sad, because I shouldn't
But mostly is because of the keyboard (and the sub optimal support for mobile in this blog “engine”)
Now I'll click on “Save” and see what happens.


Ryan Alcorn- Team Leader & Developer
Alex Furlotte - Team Co-Leader & Developer
Ryan Mclnory - Developer
Clarizze France Santos - Developer
Vrund Patel - Developer
Responsive Web Design
Advance Risk Profile Compenents
Detailed Risk Map Report
Improved Landing Page


The population of measurements (LSAT
, GPA
),
for the universe of 82 American law schools
participating in a large study of admission practices.
Two measurements were made on the entering classes of each school in 1973: LSAT
,
the average score for the class on a national law test,
and GPA
,
the average undergraduate grade-point average for the class.
law
A data frame with 82 cases and 2 variables:
Law School Admission Test.
Grade Point Average.
Table 3.2 in Efron and Tibshirani (1993).
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, N.Y: Chapman & Hall. ISBN:9780412042317. Google:gLlpIUxRntoC.
#> 'data.frame': 82 obs. of 2 variables: #> $ LSAT: int 622 542 579 653 606 576 620 615 553 607 ... #> $ GPA : num 3.23 2.83 3.24 3.12 3.09 3.39 3.1 3.4 2.97 2.91 ...#> LSAT GPA #> 1 622 3.23 #> 2 542 2.83 #> 3 579 3.24 #> 4 653 3.12 #> 5 606 3.09 #> 6 576 3.39


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#include <dr_tools.h>
Data Fields | |
| size_t | size |
| dr_os_version_t | version |
| uint | service_pack_major |
| uint | service_pack_minor |
Data structure used with dr_get_os_version()
The service pack major number
The service pack minor number
| size_t _dr_os_version_info_t::size |
The size of this structure. Set this to sizeof(dr_os_version_info_t).
The operating system version


BadLibs
The Easy way to learn Grammar!
Rhonda S. Eisenhauer
October 6th, 2015
Rhonda S. Eisenhauer
October 6th, 2015
Time for a live demo!
Questions?


Digital Electronics
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CrylonOS - A simple operating system
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{% extends "base.html" %} {% block title %}Search results | PMG{% endblock %} {% block page %}
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