If you think the 18-month shoot of "Eyes Wide Shut" is out of the ordinary for perfectionist director Stanley Kubrick consider that "The Shining" also took two years to get onscreen; the film began shooting in May of 1978 and was released in June of 1980. Early reviews were decidedly mixed, with some critics wondering what attracted Kubrick to Stephen King's tale of murder and madness in a remote, snowbound hotel in the first place and why, if he had to make the picture at all, Kubrick had given it such a deliberate, dreamy pace and allowed Jack Nicholson to go so far over the top. Though "The Shining" performed fairly well at the box office, it continued to be controversial: King panned it in the press for the liberties Kubrick had taken with his book, and instead of Oscar nominations, Kubrick and Shelley Duvall instead found themselves in the running for the uncoveted Golden Raspberry Awards as Worst Director and Worst Actress respectively. Just for the record, both lost. As demonstrated by an excellent website devoted to "The Shining" at brain.mindbuilder.com/mkraft/shining/index.html, the movie's reputation has improved substantially in the past 18 years. Nicholson fans hail his work here as one of his landmark funny/scary performances and Kubrick admirers who once dismissed the movie as a misstep have since learned to appreciate its special brand of psychological terror. Few directors would dare to make a horror movie in which the first hour is devoted almost entirely to backstory and character development, but that's one of the reasons Kubrick is, well, Kubrick. Its unconventionality makes "The Shining" one of those rare thrillers that actually benefits from repeated viewings, although the movie is so unnerving many viewers have never made it all the way through even once. Frustrated writer and recovering alcoholic Jack Torrance (Nicholson), his colorless wife Wendy (Duvall) and young son Danny (Danny Lloyd) agree to move into the mammoth Overlook Hotel and to look after the property during the winter season when the mountain resort is shut down. Almost immediately however, the bad vibes begin to flow, and Danny's psychic powers go into overdrive, conjuring up visions of blood-spattered walls, sinister-looking twin girls and the cryptic message "REDRUM." Though the recent "Shining" TV mini-series was more faithful to King's original and was perhaps more frightening in the traditional sense, few films have ever built up and sustained an eerie atmosphere as successful as Kubrick does here. Although The Overlook is an enormous place with multiple floors and seemingly endless hallways (which we are forced to explore repeatedly in one Steadicam tracking shot after another) Kubrick makes it clear the hotel is as constraining as a prison cell -- a nicely appointed cell, perhaps, but a cell nonetheless. By the movie's midpoint, the feeling of claustrophobia is inescapable, and when Jack's mind finally snaps and he begins stalking Wendy and Danny, "The Shining" becomes a true nerve-wracker. Of course, nobody plays madness quite like Nicholson, and you can see him laying the groundwork in this manic performance for such later wackos as Darryl Van Horn in "The Witches of Eastwick" and the Joker in "Batman." With her fey Texas accent and rag-doll smile, Duvall is truly irritating, but that actually works in the actress' favor; she daringly challenges us to root for Wendy. Scatman Crothers is also effective as the Overlook veteran who has something in common with Danny. Is "The Shining" really the masterpiece Kubrick worshippers would have you believe it is? No. Even though the tension is sustained throughout, the first reviews were right about the movie being overlong, and Jack flies off the handle a bit too abruptly rather than gradually sinking into psychotic behavior as he did in the book. The ending may initially strike you as clever, but the more you think about it the less satisfying it seems. But there's never been a shocker quite like "The Shining," and it seems likely to remain one-of-a-kind. Like all of Kubrick's films, whether you love it or hate it, it's sure to be a conversation-starter. James Sanford
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---
layout: post
title: Best practices of software development - I) Programming Techniques
date: 2012-06-29 18:15:01.000000000 +02:00
type: post
published: true
status: publish
categories:
- Coding
tags:
- best practices
- coding
- software development
meta:
_edit_last: '1'
_thumbnail_id: '2005'
dsq_thread_id: '745037512'
author:
login: bbWebdesign
email<EMAIL_ADDRESS>display_name: Bob
first_name: Bob
last_name: Belderbos
---
The coming weeks I will have 3 articles for you about best practices of software development. Following these methods could lead to more efficient development and better quality code. In this post: Programming Techniques and Strategies.
Purpose and planning
The purpose of this series is to have some understanding of how to write quality code to become a better developer. It should be a theoretical reference to go back to from time to time. It should complement the practical day-to-day learning.
Potentially I will follow up with a 4th article about Objected-oriented Coding, whenever I have some more experience with it.
Programming Techniques and Strategies
50% to 75% of development time goes into testing and validation of code. Why? Software development is a complicated process. In this posts some techniques to handle this.
Complexity
4 things that define uncontrolled complexity and should be limited:
- Coupling: the more dependencies of subsystems the more complex the system overall and the easier bugs get introduced;
- Control flows; the number of independent paths (see also Cyclomatic complexity);
- Complexity of data structures;
- The overall size of the program.
To avoid or reduce complexity a developer can:
- Partition: break the program up in smaller units or classes;
- Add hierarchy to your programs. A good example is the OSI model: similar communication functions are grouped into logical layers. In programs, units are black boxes and are self contained. This way they are easier to test. Use local (not global) variables. Build in privacy: data should be local to each blackbox element (method or function);
- Limit functionality at the start. Adding functionality scales up complexity in a non-linear fashion;
- Use an iterative development process: have early and frequent feedback loops. See further down ...
Elegant software
Good software ought to be non verbose and elegant. The most efficient piece of software for a particular purpose uses the least amount of code and the smallest number of abstracted entities.
Human factor
The human factor is important. Pair programming can reduce the amount of bugs introduced into the system. Same with peer reviews and collective code ownership (if communicated well and version controlled).
Software quality
Good software can be measured by the following key metrics:
- External factors: availability, efficiency, flexibility, integrity/ security, interoperability, reliability and usability.
- Internal factors: maintainability (average time to correct faults), reusability (well documented, platform independent, modular), readability, portability, testability.
Defensive programming
The best prevention measure is to write robust code that anticipates problems, validates data inputs and terminates gracefully providing debug info to de developer.
To be defensive a developer should use:
- Assertions: test for errors that should never occur;
- Input validations: test ALL data inputs (whether it comes from a database, users, or from methods);
- Error handling: these errors are expected but it depends the application how to handle them (a cash withdrawal machine should handle errors differently than a word processing program);
- Error containment: shut down parts of the program to prevent damage to other parts.
Software development methodologies:
There are different types of development styles:
- The Waterfall model is a sequential design process with its roots in the manufacturing industry. Progress flows from the top to the bottom. Requirements, design, implementation, verification and maintenance, each stage is completed before moving to the next one. The risk here is when requirements change throughout the process. In that case you would have to go through most of the process again.
- Iterative development is cyclic and a response to the weaknesses of the Waterfall model. It is more flexible because it works with smaller iterations. It is adaptive, starts with a small version, changes per cycle are smaller so easier to correct if wrong. The customer has an early demo ready.
- Agile methods are iterative and incremental. They are adaptive rather than predictive and people-oriented rather than process driven. The work is done in small self-organizing, cross-functional teams.
- Extreme Programming is a type of agile software development. It is characterized by simplicity and clarity in code (using coding standards), rapid iterations, team work (pair programming), refactoring and unit testing, frequent feedback from the customer (changes are a fact of life) and 40-hour workweeks (no overtime).
More resources
Two must-reads on this topic are: The Pragmatic Programmer: From Journeyman to Master and Clean Code: A Handbook of Agile Software Craftsmanship.
Two of my previous posts that are related: Some tips to make a developer's life easier and Becoming a good debugger.


In addition to the methods listed above, class
RVG::Group
also implements the styles method, the shape methods and the transform methods.
RVG::Group.new [ { |self| drawing method calls } ] -> group
Drawing objects defined within a group share the transforms
and styles defined on the group. Groups can be nested
arbitrarily deep. RVG::Group.new
is commonly
invoked within another group or RVG object by calling the
g
method (RVG#g,
RVG::Group#g) , which nests the group within
the target object.
RVG::Group.new
yields to a block if one is
present, passing self
as an argument.
An RVG::Group
object
See the tutorial for examples of the most common uses of groups. In this example, I use RVG::Group.new to construct a "stand-alone" group that is then referenced in multiple calls to the use method.
group.desc ->
string
group.desc = string
desc
attribute to
assign a text description to the group.
group.metadata ->
string
group.metadata = string
metadata
attribute to
assign additional metadata to the group.
group.title -> string
group.title = string
title
attribute to
assign a title to the group.
group.g [{|grp| ...}] -> group
Calls new to construct a new group and nests it within the target group. Yields to a block if one is present, passing the new group as an argument.
Returns the new group, so other methods in this class can be chained to this method.
group.image(raster_image, width=nil, height=nil, x=0, y=0) -> image
Calls RVG::Image.new to construct an image and adds it to the group.
Returns the new image, so RVG::Image
methods
can be chained to this method.
group.rvg(width, height, x=0, y=0) [{|new_rvg| ...}] -> rvg
Calls RVG.new to construct a new RVG object and adds it to the group. See the rvg method in the RVG class.
The RVG
object, so other RVG
methods can be chained to this method.
group.text(x=0, y=0, text=nil) [{|text| ...}] -> text
Calls RVG::Text.new to construct a text object and adds it to the group. Yields to a block if one is present, passing the new text object as an argument.
The RVG::Text
object, so other
RVG::Text
methods can be chained to this
method.
group.use(obj, x=0, y=0, width=nil, height=nil) -> use
Calls RVG::Use.new to
constructs a use
object and adds it to the RVG
object.
When the referenced argument is an RVG object, width and height can be
used to specify the width and height of the viewport. If
present and non-nil, these arguments override any width and
height specified when the RVG object was created. You must
specify the viewport size either when creating the RVG object
or when referencing it with use
.
See RVG::Use.new
The RVG::Use
object, so other
RVG::Use
methods can be chained to this
method.


Five stories you might have missed from the weekend
by Jack QuannBillionaire Michael Bloomberg joins race to become the next US president
The former Mayor of New York Michael Bloomberg has announced a bid for the 2020 Democratic presidential nomination.
It follows weeks of speculation that the billionaire would run for president.
On twitter this afternoon, he said he was “running for president to defeat Donald Trump and rebuild America.”
County Armagh man charged over discovery of 39 bodies in Essex truck
A man from County Armagh has been charged with human trafficking offences in connection with the discovery of 39 bodies in a refrigerated truck in the UK last month.
23-year-old Christopher Kennedy from Corkley Road in Darkley was arrested in Buckinghamshire on Friday.
This morning, Essex Police said he would be charged with conspiracy to arrange or facilitate the travel of people with a view to exploitation and conspiracy to facilitate the commission of a breach of UK immigration law.
Dublin Chamber warns against strict new laws on e-scooters
The Dublin Chamber of Commerce is warning against the introduction of overly restrictive regulations on e-scooters.
It comes as Dublin City Council calls for e-scooter users to be forced to sit a test, get tax and insurance and wear protective clothing.
The opposing views are contained in submissions both bodies made to the Government’s public consultation on e-scooter use.
Government to develop new national policy for island around Ireland
The Government is aiming to put together a new national policy for the development of the islands around Ireland.
The Tánaiste launched a public consultation process on what the policy should look like this afternoon.
It will be the first new cross-departmental policy for the islands in over two decades and the Government said it will “set a roadmap for the long-term sustainability and development of our offshore communities.”
Musk down €700m after Tesla Cybertruck launch
Elon Musk is down around €700m today after the launch of the company’s new electric pickup truck was marred by an “armour glass” window fail.
Shares in the firm fell after two of the new Cybertruck’s windows were smashed during a demonstration of how strong they are.
After the truck was unveiled in California, Mr Musk told the audience: “You want a truck that's really tough, not fake tough.”


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WITNESS LIST Judicial Affairs Committee February 19, 2001-2:00P HB 587 For: Bates, Patty (Self and Greater Dallas Community Relations Commission) Bledsoe, Gary (Texas NAACP) Briskman, Mark L. (Anti-Defamation League, N TX, OK & SW Regions) Brittain, Dean John C. (Self) Brooks, Joel (American Jewish Congress & TX Alliance for Hate Crimes Legislation) Byrd, Stella (Self and The James Byrd Family) Byrd, Sr., James (Self) Foshage, Rev. Ron (The Most Reverend Curtis Guillory) Gura, Ruth-Ellen (Travis Co. District Attorney) Harrell, William (ACLU of Texas) Harris, Steve (Self) Hinojosa, Walter (Texas AFL-CIO) Levy, Professor Martin (Self) Lyons, Rev. Kenneth O. (Byrd Family & a friend of Father Ron Foshage) Malcolm, Molly Beth (TX Democratic Party) Martino, Kelly (Self) Nevelow, Donna (Self) Overstreet, Morris (Self) Rigby, Jim (Self) Ross, Maria (Self) Saldana, Cruz (Self) Smoot, Samantha (Texas Freedom Network) Truax, Noah (Self) Against: Armstrong, Bo (TX Eagle Forum) Bryan, Thomas (Self) Levin, Marc (Young Conservatives of TX, TX Eagle Forum, American Freedom Center) Markham, Anne (Self) Zook, Nathan (Self) On: Meteyer, Rudy (Young Comnservatives of TX, College Repubs., Libertarian Longhorns & Students for Colorblind Soc'y) HB 587 Committee Substitute (Thompson/CSHB 587) For: Levy, Professor Martin (Self)


Your first Chrome/Push App
Now that the UnifiedPush server is up and running, time to create a sample application that will exercise AeroGear’s Chrome Push library support.
Let’s get started!
Creating the application
Since a Chrome Packaged Application is just a Web Application consisting of HTML, Javascript and CSS, you can use any editor you feel comfortable with.
Here is an example of what the directory structure will look like
We will create the remaining files.
The first thing we need to do is create a manifest.json file and add the gcm permission:
{
"name": "NAME OF YOUR APP",
"version": "1",
"manifest_version": 2,
"description": "DESCRIPTION OF YOUR APP",
"icons": {
"16": "images/icon-16.png",
"128": "images/icon-128.png"
},
"app": {
"background": {
"scripts": ["scripts/main.js", "scripts/jquery.min.js", "scripts/aerogear.js"]
}
},
"permissions": ["gcm"]
}
| To Learn more about what the manifest file is, go here |
Now we need to create our "background event" script, which will be our main.js file.
chrome.app.runtime.onLaunched.addListener(function() {
chrome.app.window.create('index.html', {
width: 500,
height: 309
});
});
// This will happen when the app is first Installed
chrome.runtime.onInstalled.addListener(function() {
// First we need to register our senderId with GCM and then send the returned registrationId to the UnifiedPush Server
// This code could also go in the 'onLaunched' method
chrome.gcm.register(['YOUR_SENDERID'], function (registrationId) {
/*
Set our Variant ID and Secret and the location of the UnifiedPush Server
*/
var variantId = "VARIANT_ID",
variantSecret = "VARIANT_SECRET",
pushServerURL = "http://localhost:8080/ag-push";
// Once we have the registrationId we can then register with the UnifiedPush Server using
// the AeroGear.js library
var client = AeroGear.UnifiedPushClient(
variantId,
variantSecret,
pushServerURL
),settings = {};
// The channelId will be the "device token"
settings.metadata = {
deviceToken: registrationId
};
// Register with the UnifiedPush Server
client.registerWithPushServer( settings );
});
});
The above code is responsible for registering with the UnifiedPush Server once the application is installed.
To learn more about the Chrome App Lifcycle, go here
With the "background" code out of the way, let now lets create a basic HTML page and call it index.html
<!doctype html>

<meta charset="utf-8">
<title>AeroGear Chrome App</title>

AeroGear Chrome App

We also need to create our app.js:
// Here we listen for incoming messages from GCM and then we will just add them to our list
chrome.gcm.onMessage.addListener( function( message ) {
var ul = document.querySelector('#messages ul'),
li = document.createElement('li');
li.innerHTML = message.data.alert;
ul.appendChild( li );
});
Now that we have our application up an running time to send messages using the AeroGear UnifiedPush Server!


--- layout: cohort title: Cohort 1 | Student Work | Essays cohortlink: one cohort: ONE --- Student Work
We wrote essays at the end of each unit to analyse what we had learned. We wrote essays on the books Persepolis, House of Spirits, and Cry the Beloved Country.


Scaling Flink automatically with Reactive Mode
May 6, 2021 - Robert Metzger (@rmetzger_)Introduction #
Streaming jobs which run for several days or longer usually experience variations in workload during their lifetime. These variations can originate from seasonal spikes, such as day vs. night, weekdays vs. weekend or holidays vs. non-holidays, sudden events or simply the growing popularity of your product. Although some of these variations are more predictable than others, in all cases there is a change in job resource demand that needs to be addressed if you want to ensure the same quality of service for your customers.
A simple way of quantifying the mismatch between the required resources and the available resources is to measure the space between the actual load and the number of available workers. As pictured below, in the case of static resource allocation, you can see that there’s a big gap between the actual load and the available workers — hence, we are wasting resources. For elastic resource allocation, the gap between the red and black line is consistently small.
Manually rescaling a Flink job has been possible since Flink 1.2 introduced rescalable state, which allows you to stop-and-restore a job with a different parallelism. For example, if your job is running with a parallelism of p=100 and your load increases, you can restart it with p=200 to cope with the additional data.
The problem with this approach is that you have to orchestrate a rescale operation with custom tools by yourself, including error handling and similar tasks.
Reactive Mode introduces a new option in Flink 1.13: You monitor your Flink cluster and add or remove resources depending on some metrics, Flink will do the rest. Reactive Mode is a mode where JobManager will try to use all TaskManager resources available.
The big benefit of Reactive Mode is that you don’t need any specific knowledge to scale Flink anymore. Flink basically behaves like a fleet of servers (e.g. webservers, caches, batch processing) that you can expand or shrink as you wish. Since this is such a common pattern, there is a lot of infrastructure available for handling such cases: all major cloud providers offer utilities to monitor specific metrics and automatically scale a set of machines accordingly. For example, this would be provided through Auto Scaling groups in AWS, and Managed Instance groups in Google Cloud. Similarly, Kubernetes provides Horizontal Pod Autoscalers.
What is interesting, as a side note, is that unlike most auto scalable “fleets of servers”, Flink is a stateful system, often processing valuable data requiring strong correctness guarantees (comparable to a database). But, unlike many traditional databases, Flink is resilient enough (through checkpointing and state backups) to adjust to changing workloads by just adding or removing resources, with very little requirements (i.e. simple blob store for state backups).
Getting Started #
If you want to try out Reactive Mode yourself locally, follow these steps using a Flink 1.13.0 distribution:
# These instructions assume you are in the root directory of a Flink distribution.
# Put Job into usrlib/ directory
mkdir usrlib
cp ./examples/streaming/TopSpeedWindowing.jar usrlib/
# Submit Job in Reactive Mode
./bin/standalone-job.sh start -Dscheduler-mode=reactive -Dexecution.checkpointing.interval="10s" -j org.apache.flink.streaming.examples.windowing.TopSpeedWindowing
# Start first TaskManager
./bin/taskmanager.sh start
You have now started a Flink job in Reactive Mode. The web interface shows that the job is running on one TaskManager. If you want to scale up the job, simply add another TaskManager to the cluster:
# Start additional TaskManager
./bin/taskmanager.sh start
To scale down, remove a TaskManager instance:
# Remove a TaskManager
./bin/taskmanager.sh stop
Reactive Mode also works when deploying Flink on Docker or using the standalone Kubernetes deployment (both only as application clusters).
Demo on Kubernetes #
In this section, we want to demonstrate the new Reactive Mode in a real-world scenario. You can use this demo as a starting point for your own scalable deployment of Flink on Kubernetes, or as a template for building your own deployment using a different setup.
The Setup #
The central idea of this demo is to use a Kubernetes Horizontal Pod Autoscaler, which monitors the CPU load of all TaskManager pods and adjusts their replication factor accordingly. On high CPU load, the autoscaler should add more TaskManagers, distributing the load across more machines. On low load, it should stop TaskManagers to save resources.
The whole setup is presented here:
Let’s discuss the components:
Flink
- The JobManager is deployed as a Kubernetes job. We are submitting a container that is based on the official Flink Docker image, but has the jar file of our job added to it. The Flink job simply reads data from a Kafka topic and does some expensive math operations per event received. We use these math operations to generate high CPU loads, without requiring a large Kafka deployment.
- The TaskManager(s) are deployed as a Kubernetes deployment, which is scaled through a Horizontal Pod Autoscaler. In this experiment, the autoscaler is monitoring the CPU load of the pods in the deployment. The number of pods is adjusted between 1 and 15 pods by the autoscaler.
Additional Components:
- We have a Zookeeper and Kafka deployment (each with one pod) to provide a Kafka topic that serves as the input for the Flink job.
- The Data Generator pod produces simple string messages at a adjustable rate to the Kafka topic. In this experiment, the rate is following a sine wave.
- For monitoring, we are deploying Prometheus and Grafana.
The entire setup is available on GitHub if you want to try this out yourself.
Results #
We’ve deployed all the above components on a hosted Kubernetes cluster, running it for several days. The results are best examined based on the following Grafana dashboard:
Reactive Mode Experiment Results
Let’s take a closer look at the dashboard:
-
On the top left, you can see the Kafka consumer lag, reported by Flink’s Kafka consumer (source), which reports the queue size of unprocessed messages. A high lag means that Flink is not processing messages as fast as they are produced: we need to scale up.
The lag is usually following the throughput of data coming from Kafka. When the throughput is the highest, the reported lag is at ~75k messages. In low throughput times, it is basically at zero.
-
On the top right, you’ll see the throughput, measured in records per second, as reported by Flink. The throughput is roughly following a sine wave, peaking at 6k messages per second, and going down to almost zero.
-
The bottom left chart shows the CPU load per TaskManager. We’ve added this metric to the dashboard because this is what the pod autoscaler in Kubernetes will use to decide on the replica count of the TaskManager deployment. You can see that, as soon as a certain CPU load is reached, additional TaskManagers are started.
-
In the bottom right chart, you can see the TaskManager count over time. When the throughput (and CPU load) is peaking, we’re running on 5 TaskManagers (with some peaks up to even 8). On low throughput, we’re running the minimal number of just one TaskManager. This chart showcases nicely that Reactive Mode is working as expected in this experiment: the number of TaskManagers is adjusting to the load on the system.
Lessons Learned: Configuring a low heartbeat timeout for a smooth scale down #
When we initially started with the experiment, we noticed some anomalies in the behavior of Flink, depicted in this chart:
Reactive Mode not scaling down properly
In all the charts, we see sudden spikes or drops: The consumer lag is going to up to 600k messages (that’s 8 times more than the usual 75k lag we observe at peak), the throughput seems to peak (and drop). On the “Number of TaskManagers” chart, we see that we are not following the throughput line very nicely. We are wasting resources by allocating too many TaskManagers for the given at rate.
We see that these issues are only occurring when the load is decreasing, and Reactive Mode is supposed to scale down. So what is happening here?
The Flink JobManager is sending periodic heartbeats to the TaskManagers, to check if they are still alive. These heartbeats have a default timeout of 50 seconds. This value might seem high, but in high load scenarios, there might be network congestions, garbage collection pauses or other disruptions that cause slow heartbeats. We don’t want to consider a TaskManager dead just because of a temporary disruption.
However, this default value is causing problems in this experiment: When the Kubernetes autoscaler notices that the CPU load has gone down, it will reduce the replica count of the TaskManager deployment, stopping at least one TaskManager instance. Flink will almost immediately stop processing messages, because of the connection loss in the data transport layer of Flink. However, the JobManager will wait for 50 seconds (the default heartbeat timeout) before the TaskManager is considered dead.
During this waiting period, the throughput is at zero and messages are queuing in Kafka (causing spikes in the consumer lag). Once Flink is running again, Flink will try to catch up on the queued messages, causing a spike in CPU load. The autoscaler notices this load spike and allocates more TaskManagers.
We are only seeing this effect on scale down, because a scale down is much more disruptive than scaling up. Scale up, which means adding TaskManagers, is disrupting the processing only for the duration of a job restart (which is fast since our application state are just a few bytes for the Kafka offsets), while scaling down is disrupting the processing for roughly 50 seconds.
To mitigate this issue, we have reduced the heartbeat.timeout
in our experiment to 8 seconds. Additionally, we are looking into improving the behavior of the JobManager to detect TaskManager losses better and faster.
Conclusion #
In this blog post, we’ve introduced Reactive Mode, a big step forward in Flink’s ability to dynamically adjust to changing workloads, reducing resource utilization and overall costs. The blog post demonstrated Reactive Mode on Kubernetes, including some lessons learned.
Reactive Mode is new feature in Flink 1.13 and is currently in the MVP (Minimal Viable Product) phase of product development. Before experimenting with it, or using it in production, please check the documentation, in particular the current limitations section. In this phase, the biggest limitation is that only standalone application mode deployments are supported (i.e. no active resource managers or session clusters).
The community is actively looking for feedback on this feature, to continue improving Flink’s resource elasticity. If you have any feedback, please reach out to the dev@ mailing list or to me personally on Twitter.


Full Stack of Politics
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1. Choose a topic
add
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add
Defence


Binded ng-grid to the Backand REST API
This directive provides out-of-the-box CRUD functioality, advanced sorting and filtering
All settings can be provided as attributes in the ngback-ng-grid
or through the query string parameters.
view-name
(Defaults: null) :
A name of a table or view from the database
filter-options
(Defaults: null) :
uses BackAnd REST API filter options: {fieldName, operator, value}
input-style
(Defaults: null) :
css style
gridConfigCompleted
Emit when the configuration of the grid finishes loading from the Backand REST API and before the configuration of the ng-grid
gridDataCompleted
Emit when the data of the grid finishes loading from the Backand REST API and before the loading of the rows into ng-grid
setCellTemplate
Emit when setting the cellTemplate option of ng-grid

 Static Grid Example <ngback-ng-grid view-name="'Employees'" input-style="{'height': 400}"></ngback-ng-grid>

 Dynamic Grid Example <ngback-ng-grid view-name="viewName" input-style="inputStyle"></ngback-ng-grid>

Binded Form to BackAnd REST API
This directive provide you a quick Form connected to your database
All settings can be provided as attributes in the ngback-ng-grid
or through the query string parameters.
view-name
(Defaults: null) :
A name of a table or view from the database
row-id
(Defaults: null) :
The row primary key, if provided then the form is in EDIT mode otherwise it is in a CREATE mode.
if the primary key of the row has multiple column values then they should be comma delimited in the primary key columns' order
default-fields-values
(Defaults: null) :
Default value for each field: {fieldName, value}

 Static Form Example

 Dynamic Form Example

Binded dashboard to BackAnd REST API
This directive provide you a quick dashboard display based on your settings in the console
All settings can be provided as attributes in the ngback-ng-grid
or through the query string parameters.
dashboard-id
(Defaults: null) :
An id of a dashboard from BackAnd REST API configuration

 Dashboard Example <ngback-dashboard dashboard-id="1"></ngback-dashboard>


Because you love chubby animals as much as I do!
This app uses Flickrs API to find photos of fat animals.
Be warned: Sometimes results might not be what you expect.


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{% extends "layout.html" %} {% block content %}
This is a simple web application having basic features like :


{# The script searchindex.js contains the code Search.setIndex(...) where the content is an object built from IndexBuilder.freeze(). So we need to setup the Search.setIndex function beforehand just to store the data. This should all be finished when onload fires, and at that point the code in searchbox.js will pull the data out of Search.store and build the actual index and callbacks. #}


Hello, my name is James Doss
Most of my employment experience isn't in the tech field. But I have always been a creative person, from music, video production, and now learning on becoming a full stack developer. I enjoy learning new things, and being challenged. And look
forward on things to come, as I go through this process.
Skills so far
HTML5/CSS3
Bootstrap


For a generic introduction to the computation of ERP and ERF see Epoching and averaging (ERP/ERF). Here we cover the specifics of EEG, namely:
- setting the reference
- using standard montages
mne.channels.Montage()
- Evoked arithmetic (e.g. differences)
import mne
from mne.datasets import sample
Setup for reading the raw data
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
raw = mne.io.read_raw_fif(raw_fname, add_eeg_ref=True, preload=True)
Out:
Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Current compensation grade : 0
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
Let’s restrict the data to the EEG channels
raw.pick_types(meg=False, eeg=True, eog=True)
By looking at the measurement info you will see that we have now 59 EEG channels and 1 EOG channel
print(raw.info)
Out:
<Info | 20 non-empty fields
bads : list | 0 items
buffer_size_sec : numpy.float64 | 13.3196808772
ch_names : list | EEG 001, EEG 002, EEG 003, EEG 004, EEG 005, EEG 006, ...
chs : list | 60 items (EEG: 59, EOG: 1)
comps : list | 0 items
custom_ref_applied : bool | False
dev_head_t : 'mne.transforms.Transform | 3 items
dig : list | 146 items
events : list | 0 items
file_id : dict | 4 items
filename : unicode | /home/ubun.../sample_audvis_filt-0-40_raw.fif
highpass : float | 0.10000000149 Hz
hpi_meas : list | 1 items
hpi_results : list | 1 items
lowpass : float | 40.0 Hz
meas_date : numpy.ndarray | 2002-12-03 19:01:10
meas_id : dict | 4 items
nchan : int | 60
projs : list | PCA-v1: off, PCA-v2: off, PCA-v3: off, ...
sfreq : float |<PHONE_NUMBER>59 Hz
acq_pars : NoneType
acq_stim : NoneType
ctf_head_t : NoneType
description : NoneType
dev_ctf_t : NoneType
experimenter : NoneType
hpi_subsystem : NoneType
line_freq : NoneType
proj_id : NoneType
proj_name : NoneType
subject_info : NoneType
xplotter_layout : NoneType
>
In practice it’s quite common to have some EEG channels that are actually
EOG channels. To change a channel type you can use the
mne.io.Raw.set_channel_types()
method. For example
to treat an EOG channel as EEG you can change its type using
raw.set_channel_types(mapping={'EOG 061': 'eeg'})
print(raw.info)
Out:
<Info | 20 non-empty fields
bads : list | 0 items
buffer_size_sec : numpy.float64 | 13.3196808772
ch_names : list | EEG 001, EEG 002, EEG 003, EEG 004, EEG 005, EEG 006, ...
chs : list | 60 items (EEG: 60)
comps : list | 0 items
custom_ref_applied : bool | False
dev_head_t : 'mne.transforms.Transform | 3 items
dig : list | 146 items
events : list | 0 items
file_id : dict | 4 items
filename : unicode | /home/ubun.../sample_audvis_filt-0-40_raw.fif
highpass : float | 0.10000000149 Hz
hpi_meas : list | 1 items
hpi_results : list | 1 items
lowpass : float | 40.0 Hz
meas_date : numpy.ndarray | 2002-12-03 19:01:10
meas_id : dict | 4 items
nchan : int | 60
projs : list | PCA-v1: off, PCA-v2: off, PCA-v3: off, ...
sfreq : float |<PHONE_NUMBER>59 Hz
acq_pars : NoneType
acq_stim : NoneType
ctf_head_t : NoneType
description : NoneType
dev_ctf_t : NoneType
experimenter : NoneType
hpi_subsystem : NoneType
line_freq : NoneType
proj_id : NoneType
proj_name : NoneType
subject_info : NoneType
xplotter_layout : NoneType
>
And to change the nameo of the EOG channel
raw.rename_channels(mapping={'EOG 061': 'EOG'})
Let’s reset the EOG channel back to EOG type.
raw.set_channel_types(mapping={'EOG': 'eog'})
The EEG channels in the sample dataset already have locations. These locations are available in the ‘loc’ of each channel description. For the first channel we get
print(raw.info['chs'][0]['loc'])
Out:
[-0.03737009 0.10568011 0.07333875 0.00235201 0.11096951 -0.03500458
0. 1. 0. 0. 0. 1. ]
And it’s actually possible to plot the channel locations using
the mne.io.Raw.plot_sensors()
method
raw.plot_sensors()
raw.plot_sensors('3d') # in 3D
In the case where your data don’t have locations you can set them
using a mne.channels.Montage()
. MNE comes with a set of default
montages. To read one of them do:
montage = mne.channels.read_montage('standard_1020')
print(montage)
Out:
<Montage | standard_1020 - 97 channels: LPA, RPA, Nz ...>
To apply a montage on your data use the mne.io.set_montage()
function. Here don’t actually call this function as our demo dataset
already contains good EEG channel locations.
Next we’ll explore the definition of the reference.
Let’s first remove the reference from our Raw object.
This explicitly prevents MNE from adding a default EEG average reference required for source localization.
raw_no_ref, _ = mne.io.set_eeg_reference(raw, [])
Out:
Applying a custom EEG reference.
Removing existing average EEG reference projection.
We next define Epochs and compute an ERP for the left auditory condition.
reject = dict(eeg=180e-6, eog=150e-6)
event_id, tmin, tmax = {'left/auditory': 1}, -0.2, 0.5
events = mne.read_events(event_fname)
epochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,
reject=reject)
evoked_no_ref = mne.Epochs(raw_no_ref, **epochs_params).average()
del raw_no_ref # save memory
title = 'EEG Original reference'
evoked_no_ref.plot(titles=dict(eeg=title))
evoked_no_ref.plot_topomap(times=[0.1], size=3., title=title)
Out:
72 matching events found
Applying baseline correction (mode: mean)
3 projection items activated
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EEG : [u'EEG 007']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EEG : [u'EEG 001', u'EEG 002', u'EEG 003', u'EEG 007']
Rejecting epoch based on EEG : [u'EEG 001', u'EEG 002', u'EEG 003', u'EEG 007']
Average reference: This is normally added by default, but can also be added explicitly.
raw_car, _ = mne.io.set_eeg_reference(raw)
evoked_car = mne.Epochs(raw_car, **epochs_params).average()
del raw_car # save memory
title = 'EEG Average reference'
evoked_car.plot(titles=dict(eeg=title))
evoked_car.plot_topomap(times=[0.1], size=3., title=title)
Out:
An average reference projection was already added. The data has been left untouched.
72 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 1)
4 projection items activated
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Custom reference: Use the mean of channels EEG 001 and EEG 002 as a reference
raw_custom, _ = mne.io.set_eeg_reference(raw, ['EEG 001', 'EEG 002'])
evoked_custom = mne.Epochs(raw_custom, **epochs_params).average()
del raw_custom # save memory
title = 'EEG Custom reference'
evoked_custom.plot(titles=dict(eeg=title))
evoked_custom.plot_topomap(times=[0.1], size=3., title=title)
Out:
Applying a custom EEG reference.
Removing existing average EEG reference projection.
72 matching events found
Applying baseline correction (mode: mean)
3 projection items activated
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Trial subsets from Epochs can be selected using ‘tags’ separated by ‘/’. Evoked objects support basic arithmetic. First, we create an Epochs object containing 4 conditions.
event_id = {'left/auditory': 1, 'right/auditory': 2,
'left/visual': 3, 'right/visual': 4}
epochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,
reject=reject)
epochs = mne.Epochs(raw, **epochs_params)
print(epochs)
Out:
288 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 1)
4 projection items activated
<Epochs | n_events : 288 (good & bad), tmin : -0.199795213158 (s), tmax : 0.499488032896 (s), baseline : (None, 0),
'left/auditory': 72, 'left/visual': 73, 'right/auditory': 73, 'right/visual': 70>
Next, we create averages of stimulation-left vs stimulation-right trials. We can use basic arithmetic to, for example, construct and plot difference ERPs.
left, right = epochs["left"].average(), epochs["right"].average()
(left - right).plot_joint() # create and plot difference ERP
Out:
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EEG : [u'EEG 001']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Note that by default, this is a trial-weighted average. If you have
imbalanced trial numbers, consider either equalizing the number of events per
condition (using Epochs.equalize_event_counts
), or the combine_evoked
function.
As an example, first, we create individual ERPs for each condition.
aud_l = epochs["auditory", "left"].average()
aud_r = epochs["auditory", "right"].average()
vis_l = epochs["visual", "left"].average()
vis_r = epochs["visual", "right"].average()
all_evokeds = [aud_l, aud_r, vis_l, vis_r]
# This could have been much simplified with a list comprehension:
# all_evokeds = [epochs[cond] for cond in event_id]
# Then, we construct and plot an unweighted average of left vs. right trials.
mne.combine_evoked(all_evokeds, weights=(1, -1, 1, -1)).plot_joint()
Out:
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EEG : [u'EEG 001']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Often, it makes sense to store Evoked objects in a dictionary or a list - either different conditions, or different subjects.
# If they are stored in a list, they can be easily averaged, for example,
# for a grand average across subjects (or conditions).
grand_average = mne.grand_average(all_evokeds)
mne.write_evokeds('/tmp/tmp-ave.fif', all_evokeds)
# If Evokeds objects are stored in a dictionary, they can be retrieved by name.
all_evokeds = dict((cond, epochs[cond].average()) for cond in event_id)
print(all_evokeds['left/auditory'])
# Besides for explicit access, this can be used for example to set titles.
for cond in all_evokeds:
all_evokeds[cond].plot_joint(title=cond)
Out:
Identiying common channels ...
all channels are corresponding, nothing to do.
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EEG : [u'EEG 001']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
Rejecting epoch based on EOG : ['EOG']
<Evoked | comment : 'left/auditory', kind : average, time : [-0.199795, 0.499488], n_epochs : 56, n_channels x n_times : 59 x 106>
Total running time of the script: (0 minutes 13.190 seconds)
plot_eeg_erp.py
plot_eeg_erp.ipynb


Module Example1
Module description.
The first sentence (up to the first dot) is the summary. All following sentences form the complete description. You can use HTML.
Functions
| Example1->myFunction(filename) | This documents the function "myFunction". |


WaxaM works commonly in the following web component languages and are recognized industry leaders in the development and trainings associated with
Know how to make web components but unsure of the best way to deploy? Talk to our team about our recommendations around how to ship web components for maximal resuse!


So, the community stopped waiting for DICE to implement it and coded their own
FREE CAMERA (spectator) MODE for BATTLEFIELD 3!
When they laumch it, the second version will be right here for you:
http://theblue.co/freecam.php
You just need to create an account on their page.
Hell yea, I'm really looking forward to seeing machinimas using this tool :)
Needless to say: DON'T USE THIS ON RANKED / PB ENABLED SERVERS!
ORFK
Global Moderator
wel.. i already have my Key & Tool :-D
but do you realy have to regist a account? i didn't have to regist me, i just hat to request a key and they have send me one Oo
Topicstarter
juress
Theme Developer
ORFK wrote:
wel.. i already have my Key & Tool :-D
but do you realy have to regist a account? i didn't have to regist me, i just hat to request a key and they have send me one Oo
That was the V1 process. Now they will launch the V2 and it will be available for everyone with ACC... At least thats what they wrote on their page :P
Meaurk
Global Moderator
I don't like the "Using this tool in a Punkbuster or ranked server will get you banned." sentence. Not using it, there are a few more tools to capture at BF3, and those will not get you banned. :-)
Topicstarter
juress
Theme Developer
Meaurk wrote:
I don't like the "Using this tool in a Punkbuster or ranked server will get you banned." sentence. Not using it, there are a few more tools to capture at BF3, and those will not get you banned. :-)
This is not a capture software, its a free camera mode.
It is supposed to be used for making videos and streaming matches, not capturing your gameplay.


Men told to desist from posting social media videos in Mullingar court appearance
Judge Flann Brennan granted free legal aid to all three accused.
by John FitzSimonsTHREE ATHLONE MEN, who were arrested by gardaí after alleged threatening videos were posted to social media sites, have appeared before Mullingar District Court this morning.
All three, Alan Joyce (22), Bernard Joyce (49), and John Joyce (26), of Blackberry Lane, Athlone were remanded on bail to appear before Athlone District Court on 18 December next.
All three men were charged with breaching Section 6 of the Public Order Act at Jim’s Field, Montree, Athlone on 16 October last.
Both Alan Joyce and John Joyce were further charged with possession of articles and knives, contrary to Section 9 (1) and (7) of the Firearms and Offensive Weapons Act on the same occasion.
Bernard Joyce was charged with having a knife which was sharply pointed also at Jim’s Fields, Montree on the same date.
Garda Niall Smyth gave evidence of arrest, charge and caution in relation to Alan Joyce and John Joyce while Garda Gavin Conway gave evidence of arresting Bernard Joyce.
Paul Connellan, solicitor appeared for all three accused.
Appearing for the DPP, Sergeant Kevin O’Brien said there was no garda objection to bail.
Conditions imposed on the accused included that they sign on daily at Athlone Garda Station, reside at the addresses provided, observe a curfew between the hours of 10pm and 7am, provide mobile phone numbers to the gardaí and remain contactable, remain within the jurisdiction and surrender their passports.
They were further ordered to provide cash bail of €500 and to desist from making videos and posting them on social media.
Judge Flann Brennan granted free legal aid to all three accused.
Comments are closed as legal proceedings are ongoing.


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| DOSE: |
2.5 hits |
oral |
LSD |
(blotter / tab) |
This experience took place early in the year 2000 in a small town in north central Florida. The night began with my attendance to a keg party consisting of a near-full keg, an apartment that I had never been to, and about 25 college aged people (about one-third of which I knew). I had already consumed about 4 or 5 beers, but I have a high tolerance for alcohol, so the effects were not overpowering.
Not long after I was at the party, I was engaged in a conversation with a friend-of-a-friend that I had heard of, but never actually met. We talked about music and the local drug scene for about 15 minutes or so, when he brought up the subject of acid. Not soon after, he sold me 5 tabs of paper that he said were quite potent for a cheap $10. I was suprised at the low price, but split the tabs with my close friend Eric, each of us eating 2 and a half tabs.
We soon left the party to go to our friend Jennifer's house to listen to some new house music Jennifer had bought and to play her old, original nintendo system with classic games (tetris, etc). I rode with Eric.
We got there and played Legend of Cage and listened to Jennifer spin her house records. I soon started feeling a bit strange, but I had tripped many times before this, and it was not anything new. I smoked a few cigarettes and kept playing nintendo, noticing that no one had said a word for quite a while (at least 10 minutes). Two new people soon came by, bringing with them burgers from Krystal's (a fast food chain). Justin, one of the newcomers, announced directly to me that he was tripping pretty hard (and wasn't particularly happy about his trip), but that he was 'always up for eating krystals.' I was familiar that the chemical composition of LSD was crystalline, and it made me a bit curious of his statement. He then repeated the statement a few more times and laughed a scary, demonic-type laugh. I had seen Justin a few times before this, but I was not his good friend, and had never seen him like this.
Not soon after, I began to trip extremely hard, and I got a strong feeling that the people I were with, even my friends, had an evil nature to them. I concluded this based on the things they said and the looks they gave me. It seemed that whenever I would think this, whoever I was thinking of would look at me and give me an evil, knowing smile.
I became more and more frightened of the people and situation in Jennifer's house, and I had a strong feeling that I needed to leave soon or I would start freaking out. I looked at Eric, and mentioned the idea of leaving soon. Immediately, Justin asked Eric where his keys were. It was then that I realized Eric had lost his keys, and I was stuck in this house with these people. The situation was now elevated, and I was soon so freaked that I fled the house alone without anyone noticing.
I found myself under the street light outside. I looked up into the artificial light, wondering what I had done and what on earth I was going to do with myself. I looked back to the ground, and saw something I will never forget. There were three small black kittens circling me and meowing silently. I bent down to touch them, and they kept circling, not affected whatsoever by my petting. I began to really freak and I started running in the direction of my house, which was about 5 miles away.
I soon found myself in the middle of a mid-high class neighborhood at about 5:30 Saturday morning. The sky was beginning to take on a reddish-pink hue, yet it still retained it's dark-blue night shade. I wondered how I had gotten there; I was supposed to be in my own neighborhood. Yet I felt like I wasn't in a neighborhood at all, but in another dimension or reality. After staring around intently at this interesting new place, I lost all previous fear and anxiety, and things began to take on an unexplainable mathematical clarity. I was not visually hallucinating, however. I was just able to look at the road, the blades of grass, the gentle breeze, and the trees, seeing them all as extremely complex mathematical equations and expressions.
I was overwhelmed by the sensations, and I collapsed into the yard of an unknown resident. As my body hit the grass, I suddenly felt as if I had lost my mortal body and my consciousness had elevated into the afterlife.
And then it happened. The colors and shapes of everything around me turned into giant fractals. MY history and existence was an entire world of chaos math and neverending, intelligent fractals, expanding and developing into infinity. My mind was at a peace I can never describe - to this day, I honestly believe that equations, fractals, and math govern our universe and compose the afterlife. Is it my own personal LSD psychosis, or had I seen the afterlife?
I have not and will never eat LSD again, for fear of my own sanity. I still attend the University of Florida, and I maintain good grades, but I frequently think back to that strange, revolutionary experience, and I always will. I honestly believe it was not just a giant, drug-induced hallucination, but was an actual glimpse into further dimensions and realities.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Executing Named Queries using Hibernate.
Use Hibernate named queries to perform database operations safely.
Hibernate named queries provide a data access and manipulation mechanism that closely ties the query content to the Java code defining the objects that the query is executing against. It also removes the actual query language from Java code, which is a common tactic and creates certain maintainability issues. A security related advantage to named queries is that dynamic data must be bound, which prevents SQL injection attacks. The code below shows how to both define and use named queries in Hibernate.
To use named queries with Hibernate:
Define the entity class and the named queries. Define your entity class and use the @NamedQuery (or @NamedQueries with multiple @NamedQuery's) annotation to define each named query. The examples below are straightforward.
@Entity @NamedQuery( name="Product.findAllProductsByProductName", queryString="from Product pro WHERE pro.name = :productName" ) public class Product { ... } @Entity @NamedQueries({ @NamedQuery(name="Product.findAllProducts", queryString="from Product pro"), @NamedQuery(name="Product.findAllProductsByProductId", queryString="from Product pro where pro.id = :productId"), @NamedQuery(name="Product.findAllProductsByProductName", queryString="from Product pro where pro.name = :productName"), @NamedQuery(name="Product.findAllProductsByProductPrice", queryString="from Product pro where pro.price between :minPrice and :maxPrice") }) public class Product { ... }
Consider defining named queries in XML configuration files. An alternative to using the Entity and NamedQuery annotations is to define the named queries in the hibernate XML configuration mapping files. An example is below.
<!-- product.hbm.xml --> <hibernate-mapping> <class name="com.myapp.domain.Product" table="Product"> <id ...> <property ...> <property ...> <property ...> ... </class> <query name="Product.findAllProductsByProductName"> <![CDATA[from Product pro where pro.name = :productName]]> </query> <query name="Product.findAllProductsByProductPrice"> <![CDATA[from Product pro where pro.price between :minPrice and :maxPrice]]> </query> .... </hibernate-mapping>
Use the defined named queries in code. If there is a need for dynamic data, use the parameter binding mechanism included in the query implementation.
-------------------------- Example using dynamic data -------------------------- //get hibernate session Session session = ...; //get dynamic data //request parameters should be validated before use - this is a simple example String productName = request.getParameter("product_name"); //use entity manager to retrieve named query Query queryProductsByName = session.createNamedQuery("Product.findAllProductsByProductName"); //set dynamic data for query queryProductsByName.setString("productName", productName); //execute query and get results List products = queryProductsByName.list(); -------------------------- Example without using dynamic data -------------------------- //get hibernate session Session session = ...; //use entity manager to retrieve named query Query queryProducts = session.createNamedQuery("Product.findAllProducts", Product.class); //execute query and get results List products = queryProducts.list();
The examples above show how to use named queries within Hibernate to create an environment where the queries are defined closely with the data they are operating against and that are safely used by requiring bound parameters for dynamic data. The use of other types of queries works in an identical manner, such as delete, update, insert, etc.


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