The Dynamic Host Configuration Protocol module for ns-3.12 was RFC non-compliant with only DHCP discover and DHCP offer implemented. I ported it to the latest ns-3 version, and modified it to follow RFC.
I have implemented support for IPv6 in the existing Optimised Link State Routing Protocol in ns-3.25
I have implemented support for IPv6 in the existing Destination Sequenced Distance Vector Routing Protocol in ns-3.25
I updated the python 2 compatible Bake source tree to make it compatible with python 3.
I modified ns-3 python utilities to make them compatible with python 3
I implemented LEDBAT as a TCP congestion control flavour for ns-3.
I modified the existing tmix ns-3 for ns-3.21 to worked with latest ns-3 versions. Also, I have worked to remove hard coded chunks, provide support for felix connection vectors and shuffling on tmix traces.
Worked on the implementation of a suite to evaluate TCP variants in ns-3 using real time traffic generated by Tmix. It was based on an IETF internet draft of same name.
I modified the bake system according to this functional specification for supporting contributed code .
The distributed hash tree based multipath routing protocol for MANET was ported from ns-2 to ns-3.
I implemented the previously unimplemented parallel resolution process of the Bake source code.
With CastXML replacing gccxml, I am currently working to update the API scanning code flow of ns-3 to work with the same.
A java based IDE which runs on top of a python backend, can be deployed in a local area network with linux server. Read more
A Django based website for a customer to customer renting portal. Read more
I am working to optimize TCP congestion control algorithm to improve its performance for wireless networks.
Transfered a video file over two nodes of the ORBIT testbed. Read more
Implemented Visual and Extended Visual Cryptography with and without Pixel Expansion. Read more
Studied the scheduler and deadlock management algorithms of Berkeley's Akaros, and solved real life problems of multiple consumer-multiple producer and synchronisation.
Used the operating system framework COSMOS to build a simple operating system in C#/X#.
An android application which allows patients to check their symptoms against a set of diseases.
The survey looked into different flavours of linux and other operating systems which aim to provided security to their users.
This project implemented counting semaphore using existing Mutex lock in Baremetal OS.
A publication accepted by ICRCICN 2015, Kolkata, to be published in IEEE Xplore. The project tried to develop an efficient algorithm for software data analysis.Read more
This project aimed to optimise the round robin algorithm for better performance in multicore operating systems using genetic algorithm. The paper based on the project was accepted in COMNET 2015, Ahmedabad, India.
A short paper written on implementation of OLSR for IPv6 addreses in ns-3 accepted in WNS3, Seattle, Washington. Read more
A publication accepted by ACIIDS 2017, Kanazawa, Japan. The project used colour and shape based feature extraction from human eye and Boosted classification to diagnose different diseases with 95% accuracy.
Accepted at WNS3 2017, Portugal. Manuscript can be found here
This project tried to develop a framework to collect entropy from end devices and generate cryptographic keys in cloud based smart cities. A manuscript on it was accepted for presented and to be included in IEEE Xplore at CICT 2017, Ghaziabad.
In Summer of 2015, I interned at Moog Technology Center Bangalore to build a embedded operating system for a TEGRA Jetson K1 board to use the cores effectively. It was a great learning experience for me. It was my first exposure to working in industry and I really enjoyed it. It started on 1st June 2015, in Electronic City of Bangalore. I can divide the whole internship into two phases both of 15 days as the internship ended after a month. The first phase was the knowledge gathering phase where I looked into the existing multicore operating systems. These included Akaros, Barrelfish, Baremetal etc. The biggest challenge of this internship was to use minimal space, as it had to be a part of embedded system. The next phase was to use this gathered knowledge to design the system. The development language for this internship was C.
I interned at the Microsoft Indian Development Center, Hyderabad. I worked on cool development project by under Foundry Program by Microsoft Garage. I worked with different Microsoft services like Azure, Cognitive services, LUIS etc. At the end of the internship, I was offered a placement offer and will be joining Microsoft as SDE in summer 2017.
I am the convenor of Web Enthusiasts' Club NITK for the session 2016-17. Web Enthusiasts' Club NITK is a group of people who love codes. Find more about Web Enthusiasts' Club NITK
I am associated with WiNG NITK for many different student projects currently taken up by the group.
Campus Ambassador for National Institute of Technology, Karnataka at Campus Geeks Connect -GeeksForGeeks 2016. Check them out on facebook
I was the joint convenor at NITK Annual Tech Fest Engineer 2016. I worked with the computer science committee, responsible for several offline and online computer science based events.
Became a memeber of the NITK Student Chapter of Institute of Engineer's in 2014. IE is a group of people who are interested in different fields. It known for its versitality. Find more about IE
In January of 2017, I started writing about my experiences with programming, coding and hacking. This blog is not an attempt to put something which is new. This blog is just a medium to circulate some tricks which I picked up over time and which a lot of people generally miss. Go to the blog


Runs a model
Available features are buffer, intersect, difference, combine and union.
Need to select the model, based on selection possible layers will appear. After choosing the layer/layers user can hit a create button to get the model.
Distance for the buffer can be given by user otherwise model will create using default distance.
User guidelines to create JSON file :
1) Type of functionalities/operations must be a string's and always small letters with correct spelling eg. buffer, intersect...
2) The id must be an integer eg. 1, 2.
3) If the layer taking input using from the previous operation, thus the value of the layer should be integer of that operation
4)The operation must take input from preperformed or preprocessed operations
parameters :
1) Layer names must be string's
2) Buffer distance must be integer and can not be a negetive value
3) User definable parmeters must be boolean
4) Similarly for dissolve also
Json file example
{ "model_name" : "my_model",
"operations" : [
{
"type": "buffer",
"id" : 1,
"parameters" :{
"l1": "input_layer1",
"radius": {
"default_dist" : 50,
"user_defineable" : false,
"units" : "kilometres",
"dissolve" : true
}
}
},
{
"type": "difference",
"id" : 2,
"parameters" : {
"l1": "input_layer3",
"l2": 1
}
}
]
}


My web blog, and talk me gTalk You can find me in twitter, github and linkedin Prestige urele and visited 3Reis, and you'll probably find me as "cogumm" everywhere else. Thanks!


--- layout: post title: Some thoughts on Tapestry 5 date: type: post published: false status: draft categories: - English tags: - Tapestry5 meta: _edit_last: '1' author: login: Eladamari email<EMAIL_ADDRESS>display_name: Michał Gruca first_name: Michał last_name: Gruca ---
So after nearly 2 months of working with T5 it's time to share some experience.
For those of you who don't know me so well, let me explain that for last 2-3 years I was promoting heavily T5 whenever I had occasion. I gave some speeches at Tricity JUG, wrote couple posts on this blog and tried to create as much buzz as I could. My guess is that most people think about me as a fanboy, and probably they are right. In my opinion I've got a lot of points why I prefer to work with T5 rather than with other frameworks. On the same time I've never had problem to work with e.g. Spring or struts, and I must admit that in those 3 years Spring started to catch up with T5.
Right, so back to the main topic. I've finally got occasion to look at large project code base written in T5 and here are some thoughts I want to share.
Local state of pages
Let's start from thing that I thought that is one of greatest strenghts of Tapestry. I radically changed my mind now and think that it may be greatest curse for your application.
By default all fields in pages and their values are bounded to user session. There is no threat of sharing state between users if we'll start using fields in a page objects. And we don't even have to clean up after ourselves. How is that possible?
What Tapestry do in background is packing all variables into Map and put them into session. When user comes back to page, values are being repopulated from map. Howard described that several times on his blog.
Debbugger issues
Cleanliness
Pure java
Testability
Overall experience


Particle Size Data Constraints
pdI value for each entry must be less than 1.0.
Z-Average value for each entry must be greater than or equal to 1.0 nm.
Upload an IDRI Excel file containing particle size data.


Having downloaded the XML Query Update Test Suite (XQUTS), constructed your test harness, and executed the test cases, we ask that you send us your results. As we will be generating reports from all of the results that are submitted, we ask that you send us your results in an XML document with a very specific format.
We have included several documents in the XQUTS zip file that you downloaded:
YourDirectory ReportingResults Build.xml Results.xml XQUTSReport.html XQUTSReportSimple.html XQTSResult.xsd XQUTSResults.xsl XQUTSSampleResult.xml
We ask that the document that you send us begin with a test-suite-result element and validate using the schema defined by XQTSResult.xsd. We have provided a sample result file for you to look at, XQUTSSampleResult.xml.  You can tell us about your organization and who is submitting these results.
The implementation element also allows you to tell us which features of XQuery Update your implementation supports, the values of implementation-defined items, and whether you have augmented or overwritten properties in the static and dynamic context. The names of all of these items are provided in the feature, implementation-defined-item, and context-property elements of XQUTSCatalog.xml, respectively.
You might wish information about the results you submit to be anonymous. If you set the anonymous-result-column attribute to 'true', then the column that contains your results will have the heading 'Anonymous'. If the anonymous attribute in the organization element is set to 'true', then the reports that we generate will not contain any header information that identifies your implementation.
You may provide information about how you transformed the queries and how you compared your results with the expected results in the test-run element. These elements, along with the otherComments element, allow you to provide free text within one or more p elements.
For each test case in XQUTSCatalog.xml, you should report your result in a test-case element. Results can be pass, fail, not applicable, not tested, or cannot tell. You can provide a comment with each result, if you desire. This looks something like the following:
<test-case name="id-insert-expr-001" result='pass' /> <test-case name="id-insert-expr-002" result='fail' /> <test-case name="id-insert-expr-003" result='not tested' comment='This was hard.' />
We have provided the XQUTSResults.xsl style sheet, so that you can generate your own report from the XML result document that you have produced. We have provided the Build.xml Ant script that invokes the transformation for you. The Results.xml document contains a result element for each test result that is to be included in the report. The result element contains the URL of the XML document that contains the test results. XQUTSReport.html and XQUTSReportSimple.html were produced from the sample report document that we provided.
Copyright © 2008-2009 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark, document use and software licensing rules apply. Your interactions with this site are in accordance with our public and Member privacy statements.


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Note: Before importing a calendar, you must use your calendaring program to export its information as a file. When you do so, make note of the file's location.
When working with Outlook calendars, for best results, export the
calendar as a Windows .csv
file, and when importing,
choose Microsoft Outlook
as the type of calendar file.
To import a calendar file:
Schedule
.
Import
.
Note: If the Import
option doesn't
appear, you don't have permission to import a calendar. In some sites'
calendars, including your My Workspace, you may be able to change your
permission settings. To do so, click Permissions
, check the
boxes under "import", and then click Save
.
Microsoft Outlook
, Meeting Maker
,
or Generic calendar import (comma-separate
values)
), and then click Continue
. Browse...
and browse to the calendar
file you want to import. When you have selected it, click
Continue
. Import events for selected groups
,
and then select the desired group. Import Events
to add the imported items.


Loader SearchBar
Flutter widget integrating search field feature into app bar, allowing to receive query change callbacks and automatically load new data set into ListView. It replaces standard AppBar widget and needs to be placed underneath Scaffold element in the widget tree to work properly.
Getting started
To start using SearchBar insert it in place of an AppBar element in the Scaffold widget. Regardless of the use case, defaultAppBar named argument has to be specified, which basically is a widget that will be displayed whenever SearchBar is not in activated state:
@override
Widget build(BuildContext context) {
return Scaffold(
appBar: SearchBar(
defaultAppBar: AppBar(
leading: IconButton(
icon: Icon(Icons.menu),
onPressed: _openDrawer,
),
title: Text('Default app bar title'),
),
...
),
body: _body,
drawer: _drawer,
);
}
Optional attributes
- searchHint - hint string being displayed until user inputs any text
- iconified - boolean value indicating way of representing non-activated SearchBar:
- true if widget should be showed as an action item in defaultAppBar
- false if widget should be merged with defaultAppBar (only leading icon of the default widget and search input field are displayed in such case)
- autofocus - boolean value determining if search text field should get focus whenever it becomes visible
- attrs - SearchBarAttrs class instance allowing to specify part of exact values used during widget building (e.g. search bar colors, text size, border radius)
- onActivatedChanged - callback function receiving widget's current state as a boolean value; triggered whenever user begins or cancels/ends search action
Query callbacks
To get notified about user input specify onQueryChanged and/or onQuerySubmitted callback functions that receive current query string as an argument:
appBar: SearchBar(
...
onQueryChanged: (query) => _handleQueryChanged(context, query),
onQuerySubmitted: (query) => _handleQuerySubmitted(context, query),
),
QuerySetLoader
By passing QuerySetLoader object as an argument one can additionally benefit from search results being automatically built as ListView widget whenever search query changes:
appBar: SearchBar(
...
loader: QuerySetLoader<Item>(
querySetCall: _getItemListForQuery,
itemBuilder: _buildItemWidget,
loadOnEachChange: true,
animateChanges: true,
),
),
List<Item> _getItemListForQuery(String query) { ... }
Widget _buildItemWidget(Item item) { ... }
- querySetCall - function transforming search query into list of items being then rendered in ListView (required)
- itemBuilder - function creating Widget object for received item, called during ListView building for each element of the results set (required)
- loadOnEachChange - boolean value indicating whether querySetCall should be triggered on each query change; if false query set is loaded once user submits query
- animateChanges - determines whether ListView's insert and remove operations should be animated
Contributions
Any kind of contribution to the project is welcomed.
Feel free to request new features, report encountered bugs or pull request changes you've made.
To do so, make use of Pull requests and Issues tabs.
Libraries
- loader_search_bar
- Flutter widget integrating search field feature into app bar, allowing to receive query change callbacks and automatically load new data set into ListView.


I knew that Rayman origins was going to be fun when I first saw the grim reaper in the snoring tree. It was quite clear that he had been thrashed. I could see the bandages on his head and a black eye .  He hid away in shame when I approached him and came out mocking me when I turned my back. So there you go a toothless grim reaper. He is pretty hilarious and silly. Silly is an apt word to describe rayman origins.
The plot of the game is silly. Rayman and his friends are sleeping peacefully on the snoring tree (sleeping on the snoring tree lol) and well they don't sleep quiet. Their collective snoring is quite musical and charming but the denizens of the living dead are not amused.  Rayman manages to escape. He must now save electoons and nymphs to restore order to his world.
The characters in origins are silly as well. The creator is a laid back guy with a large beard. You can hop on his beard and he often comes up with funny quips. The magical hat is a guide and gives out tips on the game if you slap or jump on it. The tongue of characters in the game is foreign. They speak gibberish though the manner in which they speak often makes their intentions clear. Nymphs for instance have a very seductive and feminine voice. The creator grumbles like an old man.
There is a silliness to music and sound effects. Jumping on enemies will make them utter painful though funny squeaks. Each stage in the game has unique music. The gibberish jungle has the chirp of the birds in the background. In Mystical pique one can hear solemn prayers of the meditating monks while in the gourmand land the chefs sing ballads. The music in each stage perfectly captures the essence of that stage.
Rayman origins is a platformer. You go about your business jumping over stuff while collecting shiny lums. There are lots of hidden items within the stages . These are usually kept in hard to reach places . However the game makes it clear when you are near something valuable . For instance if you are near the electoon cage you can hear a help me distress call. Likewise if you are near a legend piece you can hear a sound effect. These indications make the levels a lot more fun to explore. Oh and that pose that rayman gives when you clear a level is priceless.
The developers keep things interesting by providing different play modes. Treasure chases are like time trials where rayman has to chase down the treasure chest. If you fall too behind the chest outruns you. And if you move too quick you might get hit and loose your life.These sections are much more challenging than the normal platforming parts and there is a lot of emphasis on the timing of your jumps. The reward of catching one of the chests is that you get a tooth that you can offer to the grim reaper. Give him all of his teeth and he will open the way to the land of the living dead. Talk about bribing the guard!
Then there are flying levels where rayman hops on to a mosquito and shoots through the enemies. During these fights the pace of the game is greatly increased and you need to have good reflexes to make it through . These shooting levels are some of my favorite in the game.
More variety is added in the form of boss battles. The technique of fighting them is nearly the same in all the cases. You have to dodge their attack until a weak spot is exposed and then focus your blows there. All of them however have different attack patterns and it takes a couple tries to understand it. However the game does not have enough of them. I wish there were more bosses since they are so well done. Their animation, their design and their eccentric personalities are quite engaging.
I had lot of fun while playing rayman origins.Even while writing this article I cracked up a couple of times when I remembered something funny. If you like platformers then rayman origins is a must play. If you don't like them then this is the game that might change your mind.
You might also like


--- title: About ---
The Open Shop Channel was started in March 2018 by dhtdht020, who wanted to make a service to download homebrew apps with your Wii again.
The original plan for the project was to make a Wii Shop Channel replacement, which was a lot of work that we didn't enjoy and not fun to work on.
An additional service was planned called Club Spot, which gave you perks for using the Open Shop Channel.
What we originally envisioned the Open Shop Channel to be was ambitious. It wasn't gonna work out.
We eventually changed our plans to revive the Homebrew Browser, an app to download homebrew.
The Open Shop Channel has been possible thanks to these people.


Owl Numerical System¶
- Introduction
- Installation
- Interacting with Owl
- Architecture
- Modules & Basics
- N-Dimensional Array
- Matrix Manipulation
- Broadcasting Operation
- Indexing and Slicing
- Function Naming Conventions
- Operators
- Maths Module
- Statistics Module
- Linear Algebra
- Fast Fourier Transform
- Computation Graph
- Lazy Evaluation and Dataflow
- Algorithmic Differentiation
- Optimisation Engine
- Neural Network
- Plot Figures
- Scripting and Zoo System
- Sparse Data Structures
- Dataframe for Tabular Data
- Compile into Javascript
- Constants and Metric System
- Testing Framework
- Natural Language Processing
- Performance Comparison
- Case - Google Inception V3
- Case - Neural Style Transfer
- Community
- Proposed Sub-Projects
- Project 1. Maths and Stats in the Base library
- Project 2. Linear Algebra in the Base library
- Project 3. Ndarray in the Base library
- Project 4. Probabilistic Programming
- Project 5. Machine Learning Applications
- Project 6. Deep Neural Network
- Project 7. Optimisation Engine
- Project 8. Signal Processing & ODE
- Project 9. Dynamic Graph Optimisation
- Project 10. GPU Computing
- Project 11. Data Processing and Visualisation
- Project 12. Computation Graph Exchange Format
- Project 13. Distributed ML on Unikernel for IoT
- Project 14. Sublinear Algorithms in Owl
- Finished Sub-Projects
- Probabilistic Synchronous Parallel
- Supporting Browser-based Machine Learning
- Adaptable Asynchrony in Distributed Learning
- Applications of Linear Types
- Composing Data Analytical Services
- Computer Vision & Performance Optimisation of Computation Graph
- Automatic Parameter Tuning for OpenMP
- Run Your Owl Computation on TensorFlow
- Ordinary Differential Equation Solver
- How-To?
- Frequently Asked Questions
- References


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SRS Guide - DEPRECATED
Credits
The following recommendations are based on chapter 4.7 of Real-Time Systems Design and Analysis (Third Edition), by Phillip A. Laplante, published by Wiley & Sons in 2004.
IEEE Standard 830-1998
There are many ways to organize the SRS, but IEEE Standard 830-1998 is the IEEE’s Recommended Practice for Software Requirements Specifications (SRS), and provides a template of what an SRS should look like.
The SRS is described as a “binding contract among designers, programmers, customers, and testers,” and it encompasses different design views or paradigms for system design. The recommended design views include some combination of decomposition, dependency, interface, and detail descriptions.
Basic issues
The basic issues that the SRS writer(s) shall address are the following:
- Functionality - What is the software supposed to do?
- External interfaces - How does the software interact with people, the system’s hardware, other hardware, and other software?
- Performance - What is the speed, availability, response time, recovery time of various software functions, etc.?
- Attributes - What are the portability, correctness, maintainability, security, etc. considerations?
- Design constraints imposed on an implementation - Are there any required standards in effect, implementation language, policies for database integrity, resource limits, operating environment(s) etc.?
Limitations
The SRS writer(s) should avoid placing either design or project requirements in the SRS.
The SRS writer(s) should be careful not to go beyond the bounds of that role. This means the SRS:
- Should correctly define all of the software requirements. A software requirement may exist because of the nature of the task to be solved or because of a special characteristic of the project.
- Should not describe any design or implementation details. These should be described in the design stage of the project.
- Should not impose additional constraints on the software. These are properly specified in other documents such as a software quality assurance plan.
Therefore, a properly written SRS limits the range of valid designs, but does not specify any particular design.
Good requirements
Whatever approach is used in organizing the SRS, the IEEE 830 standard describes the characteristics of good requirements. That is, good requirements must be.
-
Correct - They must correctly describe the system behavior.
-
Unambiguous - The requirements must be clear, not subject to different interpretations.
-
Complete - There must be no missing requirements. Ordinarily, the note TBD [to be defined (later)] is unacceptable in a requirements document. IEEE 830 sets out some exceptions to this rule. Consistent One requirement must not contradict another. Consistency can be checked using formal methods, as illustrated previously.
-
Ranked - for importance and/or stability Not every requirement is as critical as another. By ranking the requirements, designers will find guidance in making trade-off decisions.
-
Verifiable - A requirement that cannot be verified is a requirement that cannot be shown to have been met.
-
Modifiable - The requirements need to be written in such a way so as to be easy to change. In many ways, this approach is similar to the information hiding principle.
-
Traceable - The benefits of traceability cannot be overstated. The requirements provide the starting point for the traceability chain. Numbering the requirements in hierarchical fashion can aid in this regard.
Best practices
To meet these criteria and to write clear requirements documentation, there are several best practices that the requirements engineer can follow. These are as follows:
- Invent and use a standard format and use it for all requirements.
- Use language in a consistent way.
- Use shall for mandatory requirements.
- Use should for desirable requirements.
- Use text highlighting to identify key parts of the requirement.
- Avoid the use of technical language unless it is warranted.
To illustrate, consider the following bad requirements:
- “The systems shall be completely reliable.”
- “The system shall be modular.”
- “The system shall be maintainable.”
- “The system will be fast.”
- “Errors shall be less than 99%.”
These requirements are bad for a number of reasons. None are verifiable, for example, how is “reliability” supposed to be measured? Even requirement 5 is vague, that is, what does “less than 99% mean”?
Content
Table of contents
The recommended table of contents for an SRS is:
1. Introduction
1.1 Purpose
1.2 Scope
1.3 Definitions and Acronyms
1.4 References
1.5 Overview
2. Overall Description
2.1 Product Perspective
2.2 Product Functions
2.3 User Characteristics
2.4 Constraints
2.5 Assumptions and Dependencies
3. Specific Requirements
Appendices
Index
Purpose
This subsection should
- Delineate the purpose of the SRS;
- Specify the intended audience for the SRS.
Scope
This subsection should
- Identify the software product(s) to be produced by name (e.g., Host DBMS, Report Generator, etc.);
- Explain what the software product(s) will, and, if necessary, will not do;
- Describe the application of the software being specified, including relevant benefits, objectives, and goals;
- Be consistent with similar statements in higher-level specifications (e.g., the system requirements specification), if they exist.
Definitions, acronyms, and abbreviations
This subsection should provide the definitions of all terms, acronyms, and abbreviations required to properly interpret the SRS. This information may be provided by reference to one or more appendixes in the SRS or by reference to other documents.
References
This subsection should
- Provide a complete list of all documents referenced elsewhere in the SRS;
- Identify each document by title, report number (if applicable), date, and publishing organization;
- Specify the sources from which the references can be obtained.
Overview
This subsection should
- Describe what the rest of the SRS contains;
- Explain how the SRS is organized.
Product perspective
This subsection of the SRS should put the product into perspective with other related products. If the product is independent and totally self-contained, it should be so stated here. If the SRS defines a product that is a component of a larger system, as frequently occurs, then this subsection should relate the requirements of that larger system to functionality of the software and should identify interfaces between that system and the software.
A block diagram showing the major components of the larger system, interconnections, and external interfaces can be helpful.
This subsection should also describe how the software operates inside various constraints. For example, these constraints could include
- System interfaces;
- User interfaces;
- Hardware interfaces;
- Software interfaces;
- Communications interfaces;
- Memory;
- Operations;
- Site adaptation requirements
(for details, please see the original document)
Product functions
This subsection of the SRS should provide a summary of the major functions that the software will perform.
User characteristics
This subsection of the SRS should describe those general characteristics of the intended users of the product including educational level, experience, and technical expertise. It should not be used to state specific requirements, but rather should provide the reasons why certain specific requirements are later specified in Section 3 of the SRS.
Constraints
This subsection of the SRS should provide a general description of any other items that will limit the developer’s options. These include
- Regulatory policies;
- Hardware limitations (e.g., signal timing requirements);
- Interfaces to other applications;
- Parallel operation;
- Audit functions;
- Control functions;
- Higher-order language requirements;
- Signal handshake protocols (e.g., XON-XOFF, ACK-NACK);
- Reliability requirements;
- Criticality of the application;
- Safety and security considerations.
Assumptions and dependencies
This subsection of the SRS should list each of the factors that affect the requirements stated in the SRS. These factors are not design constraints on the software but are, rather, any changes to them that can affect the requirements in the SRS. For example, an assumption may be that a specific operating system will be available on the hardware designated for the software product. If, in fact, the operating system is not available, the SRS would then have to change accordingly.
Specific requirements
This section of the SRS should contain all of the software requirements to a level of detail suficient to enable designers to design a system to satisfy those requirements, and testers to test that the system satisfies those requirements.
These requirements should include at a minimum a description of every input (stimulus) into the system, every output (response) from the system, and all functions performed by the system in response to an input or in support of an output.
- Specific requirements should be stated in conformance with all the characteristics described in 4.3.
- Specific requirements should be cross-referenced to earlier documents that relate.
- All requirements should be uniquely identifiable.
- Careful attention should be given to organizing the requirements to maximize readability
Various items that comprise requirements
- External interfaces
- Functions
- Performance requirements
- Logical database requirements
- Design constraints
- Standards compliance
- Software system attributes
- Reliability
- Availability
- Security
- Maintainability
- Portability
- Organizing the specific requirements
- System mode
- User class
- Objects
- Feature
- Stimulus
- Response
- Functional hierarchy
- Additional comments
Appendixes
The appendixes are not always considered part of the actual SRS and are not always necessary. They may include
- Sample input/output formats, descriptions of cost analysis studies, or results of user surveys;
- Supporting or background information that can help the readers of the SRS;
- A description of the problems to be solved by the software;
- Special packaging instructions for the code and the media to meet security, export, initial loading, or other requirements.
Categories of Words
Imperatives
Imperatives found in requirements specifications and their purpose
-
Shall - Dictates provision of fundamental capability
-
Must - Establishes performance requirements or constraints
-
Must not - Establishes performance requirements or constraints
-
Is required to - Used in specifications statements written in passive voice
-
Are applicable - Used to include, by reference, standards or other documentation as an addition to the requirements being specified
-
Responsible for - Used as an imperative for systems whose architectures are already defined
-
Will - Generally used to cite things that the operational or development environment are to provide to the capability being specified
-
Should - Not recommended for use
Continuances
Continuances follow an imperative and introduce the specification of requirements at a lower level. Continuances include:
- “Below”
- “As follows”
- “Following”
- “Listed”
- “In particular”
- “Support”
Options
Options give the developer latitude in satisfying the specifications, and include:
Weak phrases
Weak phrases, which should be avoided in SRS, include:
- “Adequate”
- “As a minimum”
- “As applicable”
- “Be able to”
- “Be capable”
- “But not limited to”
- “Capability of”
- “Capability to”
- “Effective”
- “If practical”
- “Normal”
- “Provide for”
- “Timely”
- “TBD”
Requirements validation
Verification of the software product means ensuring that the software is conforming to the SRS. Verification is akin to asking the question “Am I building the software right?” in that it requires satisfaction of requirements.
Requirements validation, on the other hand, is tantamount to asking the question “Am I building the right software?” Too often, software engineers deliver a system that conforms to the SRS, only to discover that it is not what the customer really wanted.
Performing requirements validation involves checking the following:
- Validity, that is, does the system provide the functions that best support the customer’s needs?
- Consistency, that is, are there any requirements conflicts?
- Completeness, in other words, are all functions required by the customer included?
- Realism, or, can the requirements be implemented given available budget and technology?
- Verifiability, that is, can the requirements be checked?
There are a number of ways of checking the SRS for conformance to the IEEE 830 best practices and for validity. These approaches include:
- Requirements reviews.
- Systematic manual analysis of the requirements.
- Prototyping.
- Using an executable model of the system to check requirements.
- Test-case generation.
- Developing tests for requirements to check testability.
- Automated consistency analysis.
- Checking the consistency of a structured requirements description.
Further readings


Syntax:
and form* => result*
Arguments and Values:
form---a form.
results---the values resulting from the evaluation of the last form, or the symbols nil or t.
Description:
The macro and evaluates each form one at a time from left to right. As soon as any form evaluates to nil, and returns nil without evaluating the remaining forms. If all forms but the last evaluate to true values, and returns the results produced by evaluating the last form.
If no forms are supplied, (and) returns t.
and passes back multiple values from the last subform but not from subforms other than the last.
Examples:
(if (and (>= n 0) (< n (length a-simple-vector)) (eq (elt a-simple-vector n) 'foo)) (princ "Foo!"))The above expression prints Foo! if element n of a-simple-vector is the symbol foo, provided also that n is indeed a valid index for a-simple-vector. Because and guarantees left-to-right testing of its parts, elt is not called if n is out of range.
(setq temp1 1 temp2 1 temp3 1) => 1 (and (incf temp1) (incf temp2) (incf temp3)) => 2 (and (eql 2 temp1) (eql 2 temp2) (eql 2 temp3)) => true (decf temp3) => 1 (and (decf temp1) (decf temp2) (eq temp3 'nil) (decf temp3)) => NIL (and (eql temp1 temp2) (eql temp2 temp3)) => true (and) => T
Affected By: None.
Exceptional Situations: None.
See Also:
Notes:
(and form) == (let () form) (and form1 form2 ...) == (when form1 (and form2 ...))


Process: {{$ctrl.state.process.name}}
show json
Details
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- Last Modified
- {{$ctrl.state.process.mtime | date:'fullDate'}}
- Id
- {{$ctrl.state.process.id}}
- Templated Used
- {{$ctrl.state.process.template_name}}
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At screen pixel sizes the rasterizer has trouble rendering some of the finer details, but with anti-alias turned on the results are much better. In the "H" below, the one on the left (bitmap) does not show the double horizontal bar of the H very well, while it is well rendered in the one on the right. The results are more extreme in the example above.


--- title: "Classify structured data with feature columns" type: docs menu: main: name: "Classify structured data" identifier: "tutorials-advanced-structured-classify" parent: "tutorials-advanced-structured-top" weight: 20 editor_options: chunk_output_type: console ---
This tutorial demonstrates how to classify structured data (e.g. tabular data in a CSV). We will use Keras to define the model, and feature columns as a bridge to map from columns in a CSV to features used to train the model. This tutorial contains complete code to:
We will use a small dataset provided by the Cleveland Clinic Foundation for Heart Disease. There are several hundred rows in the CSV. Each row describes a patient, and each column describes an attribute. We will use this information to predict whether a patient has heart disease, which in this dataset is a binary classification task.
Following is a description of this dataset. Notice there are both numeric and categorical columns.
Column Description Feature Type Data Type Age Age in years Numerical integer Sex (1 = male; 0 = female) Categorical integer CP Chest pain type (0, 1, 2, 3, 4) Categorical integer Trestbpd Resting blood pressure (in mm Hg on admission to the hospital) Numerical integer Chol Serum cholestoral in mg/dl Numerical integer FBS (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) Categorical integer RestECG Resting electrocardiographic results (0, 1, 2) Categorical integer Thalach Maximum heart rate achieved Numerical integer Exang Exercise induced angina (1 = yes; 0 = no) Categorical integer Oldpeak ST depression induced by exercise relative to rest Numerical integer Slope The slope of the peak exercise ST segment Numerical float CA Number of major vessels (0-3) colored by flourosopy Numerical integer Thal 3 = normal; 6 = fixed defect; 7 = reversable defect Categorical string Target Diagnosis of heart disease (1 = true; 0 = false) Classification integer
We will use Keras and TensorFlow datasets.
## ── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.0 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
We will use read_csv
in order to read the csv file to R.
heart <- pins::pin("https://storage.googleapis.com/applied-dl/heart.csv", "heart")
df <- read_csv(heart)
## Parsed with column specification:
## cols(
## age = col_double(),
## sex = col_double(),
## cp = col_double(),
## trestbps = col_double(),
## chol = col_double(),
## fbs = col_double(),
## restecg = col_double(),
## thalach = col_double(),
## exang = col_double(),
## oldpeak = col_double(),
## slope = col_double(),
## ca = col_double(),
## thal = col_character(),
## target = col_double()
## )
## Observations: 303
## Variables: 14
## $ age <dbl> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44,…
## $ sex <dbl> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0…
## $ cp <dbl> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3…
## $ trestbps <dbl> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, …
## $ chol <dbl> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, …
## $ fbs <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0…
## $ restecg <dbl> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0…
## $ thalach <dbl> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, …
## $ exang <dbl> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ oldpeak <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, …
## $ slope <dbl> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1…
## $ ca <dbl> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ thal <chr> "fixed", "normal", "reversible", "normal", "normal", "n…
## $ target <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
We are going to use the rsample
package to split the data into train, validation
and test sets.
# first we split between training and testing sets
split <- initial_split(df, prop = 4/5)
train <- training(split)
test <- testing(split)
# the we split the training set into validation and training
split <- initial_split(train, prop = 4/5)
train <- training(split)
val <- testing(split)
## [1] 195
## [1] 48
## [1] 60
Next, we will wrap the dataframes with tfdatasets. This will enable us to use feature columns as a bridge to map from the columns in the dataframe to features used to train the model. If we were working with a very large CSV file (so large that it does not fit into memory), we would use tfdatasets to read it from disk directly. That is not covered in this tutorial.
Now that we have created the input pipeline, let’s call it to see the format of the data it returns. We have used a small batch size to keep the output readable.
## List of 14
## $ age :tf.Tensor([54. 60. 52. 66.  0. 1. 1. 1.], shape=(5,), dtype=float32)
## $ cp :tf.Tensor([2. 3. 4. 4. 4.], shape=(5,), dtype=float32)
## $ trestbps:tf.Tensor([132. 102. 128. 112. 110.], shape=(5,), dtype=float32)
## $ chol :tf.Tensor([288. 318. 255. 212. 239.], shape=(5,), dtype=float32)
## $ fbs :tf.Tensor([1. 0. 0. 0. 0.], shape=(5,), dtype=float32)
## $ restecg :tf.Tensor([2. 0. 0. 2. 0.], shape=(5,), dtype=float32)
## $ thalach :tf.Tensor([159. 160. 161. 132. 126.], shape=(5,), dtype=float32)
## $ exang :tf.Tensor([1. 0. 1. 1. 1.], shape=(5,), dtype=float32)
## $ oldpeak :tf.Tensor([0. 0. 0. 0.1 2.8], shape=(5,), dtype=float32)
## $ slope :tf.Tensor([1. 1. 1. 1. 2.], shape=(5,), dtype=float32)
## $ ca :tf.Tensor([1. 1. 1. 1. 1.], shape=(5,), dtype=float32)
## $ thal :tf.Tensor([b'normal' b'normal' b'reversible' b'normal' b'reversible'], shape=(5,), dtype=string)
## $ target :tf.Tensor([0. 0. 0. 1. 1.], shape=(5,), dtype=float32)
We can see that the dataset returns a list of column names (from the dataframe) that map to column values from rows in the dataframe.
We want to train a model to predict the target
variable using Keras but, before
that we need to prepare the data. We need to transform the categorical variables
into some form of dense variable, we usually want to normalize all numeric columns too.
The feature spec interface works with data.frame
s or TensorFlow datasets objects.
Let’s start creating our feature specification:
The first thing we need to do after creating the feature_spec is decide on the variables’ types.
We can do this by adding steps to the spec
object.
spec <- spec %>%
step_numeric_column(
all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
normalizer_fn = scaler_standard()
) %>%
step_categorical_column_with_vocabulary_list(thal)
The following steps can be used to define the variable type:
step_numeric_column
to define numeric variablesstep_categorical_with_vocabulary_list
for categorical variables with a fixed vocabularystep_categorical_column_with_hash_bucket
for categorical variables using the hash trickstep_categorical_column_with_identity
to store categorical variables as integersstep_categorical_column_with_vocabulary_file
when you have the possible vocabulary in a fileWhen using step_categorical_column_with_vocabulary_list
you can also provide a vocabulary
argument
with the fixed vocabulary. The recipe will find all the unique values in the dataset and use it
as the vocabulary.
You can also specify a normalizer_fn
to the step_numeric_column
. In this case the variable will be
transformed by the feature column. Note that the transformation will occur in the TensorFlow Graph,
so it must use only TensorFlow ops. Like in the example we offer pre-made normalizers - and they will
compute the normalizing function during the recipe preparation.
You can also use selectors like:
starts_with()
, ends_with()
, matches()
etc. (from tidyselect)all_numeric()
to select all numeric variablesall_nominal()
to select all stringshas_type("float32")
to select based on TensorFlow variable type.Now we can print the recipe:
## ── Feature Spec ────────────────────────────────────────────────────────────────────────────────────────
## A feature_spec with 8 steps.
## Fitted: FALSE
## ── Steps ───────────────────────────────────────────────────────────────────────────────────────────────
## StepCategoricalColumnWithVocabularyList: thal
## StepNumericColumn: age, trestbps, chol, thalach, oldpeak, slope, ca
## ── Dense features ──────────────────────────────────────────────────────────────────────────────────────
## Feature spec must be fitted before we can detect the dense features.
After specifying the types of the columns you can add transformation steps. For example you may want to bucketize a numeric column:
spec <- spec %>%
step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65))
You can also specify the kind of numeric representation that you want to use for your categorical variables.
Another common transformation is to add interactions between variables using crossed columns.
spec <- spec %>%
step_crossed_column(thal_and_age = c(thal, bucketized_age), hash_bucket_size = 1000) %>%
step_indicator_column(thal_and_age)
Note that the crossed_column
is a categorical column, so we need to also specify what
kind of numeric tranformation we want to use. Also note that we can name the transformed
variables - each step uses a default naming for columns, eg. bucketized_age
is the
default name when you use step_bucketized_column
with column called age
.
With the above code we have created our recipe. Note we can also define the recipe by chaining a sequence of methods:
spec <- feature_spec(train_ds, target ~ .) %>%
step_numeric_column(
all_numeric(), -cp, -restecg, -exang, -sex, -fbs,
normalizer_fn = scaler_standard()
) %>%
step_categorical_column_with_vocabulary_list(thal) %>%
step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65)) %>%
step_indicator_column(thal) %>%
step_embedding_column(thal, dimension = 2) %>%
step_crossed_column(c(thal, bucketized_age), hash_bucket_size = 10) %>%
step_indicator_column(crossed_thal_bucketized_age)
After defining the recipe we need to fit
it. It’s when fitting that we compute the vocabulary
list for categorical variables or find the mean and standard deviation for the normalizing functions.
Fitting involves evaluating the full dataset, so if you have provided the vocabulary list and
your columns are already normalized you can skip the fitting step (TODO).
In our case, we will fit the feature spec, since we didn’t specify the vocabulary list for the categorical variables.
After preparing we can see the list of dense features that were defined:
## List of 11
## $ age :NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x136510ea0>)
## $ trestbps :NumericColumn(key='trestbps', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311f2ea0>)
## $ chol :NumericColumn(key='chol', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311f2158>)
## $ thalach :NumericColumn(key='thalach', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311f2f28>)
## $ oldpeak :NumericColumn(key='oldpeak', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311ec048>)
## $ slope :NumericColumn(key='slope', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311ec0d0>)
## $ ca :NumericColumn(key='ca', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x1311ec158>)
## $ bucketized_age :BucketizedColumn(source_column=NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x136510ea0>), boundaries=(18.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0))
## $ indicator_thal :IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='thal', vocabulary_list=('fixed', 'normal', 'reversible'), dtype=tf.string, default_value=-1, num_oov_buckets=0))
## $ embedding_thal :EmbeddingColumn(categorical_column=VocabularyListCategoricalColumn(key='thal', vocabulary_list=('fixed', 'normal', 'reversible'), dtype=tf.string, default_value=-1, num_oov_buckets=0), dimension=2, combiner='mean', initializer=<tensorflow.python.ops.init_ops.TruncatedNormal>, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True)
## $ indicator_crossed_thal_bucketized_age:IndicatorColumn(categorical_column=CrossedColumn(keys=(VocabularyListCategoricalColumn(key='thal', vocabulary_list=('fixed', 'normal', 'reversible'), dtype=tf.string, default_value=-1, num_oov_buckets=0), BucketizedColumn(source_column=NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function make_python_function.<locals>.python_function at 0x136510ea0>), boundaries=(18.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0))), hash_bucket_size=10.0, hash_key=None))
Now we are ready to define our model in Keras. We will use a specialized layer_dense_features
that
knows what to do with the feature columns specification.
We also use a new layer_input_from_dataset
that is useful to create a Keras input object copying the structure from a data.frame
or TensorFlow dataset.
We can finally train the model on the dataset:
history <- model %>%
fit(
dataset_use_spec(train_ds, spec = spec_prep),
epochs = 15,
validation_data = dataset_use_spec(val_ds, spec_prep),
verbose = 2
)
## Epoch 1/15
## 39/39 - 1s - loss: 0.6738 - binary_accuracy: 0.6513 - val_loss: 0.0000e+00 - val_binary_accuracy: 0.0000e+00
## Epoch 2/15
## 39/39 - 0s - loss: 0.5535 - binary_accuracy: 0.7692 - val_loss: 0.5300 - val_binary_accuracy: 0.7708
## Epoch 3/15
## 39/39 - 0s - loss: 0.4731 - binary_accuracy: 0.8103 - val_loss: 0.4800 - val_binary_accuracy: 0.7917
## Epoch 4/15
## 39/39 - 0s - loss: 0.4254 - binary_accuracy: 0.8359 - val_loss: 0.4566 - val_binary_accuracy: 0.7917
## Epoch 5/15
## 39/39 - 0s - loss: 0.3922 - binary_accuracy: 0.8410 - val_loss: 0.4395 - val_binary_accuracy: 0.8125
## Epoch 6/15
## 39/39 - 0s - loss: 0.3678 - binary_accuracy: 0.8564 - val_loss: 0.4326 - val_binary_accuracy: 0.7917
## Epoch 7/15
## 39/39 - 0s - loss: 0.3600 - binary_accuracy: 0.8615 - val_loss: 0.4208 - val_binary_accuracy: 0.8125
## Epoch 8/15
## 39/39 - 0s - loss: 0.3469 - binary_accuracy: 0.8615 - val_loss: 0.4167 - val_binary_accuracy: 0.7917
## Epoch 9/15
## 39/39 - 0s - loss: 0.3404 - binary_accuracy: 0.8615 - val_loss: 0.4076 - val_binary_accuracy: 0.7917
## Epoch 10/15
## 39/39 - 0s - loss: 0.3308 - binary_accuracy: 0.8667 - val_loss: 0.4115 - val_binary_accuracy: 0.7917
## Epoch 11/15
## 39/39 - 0s - loss: 0.3109 - binary_accuracy: 0.8615 - val_loss: 0.4136 - val_binary_accuracy: 0.7917
## Epoch 12/15
## 39/39 - 0s - loss: 0.3239 - binary_accuracy: 0.8667 - val_loss: 0.4033 - val_binary_accuracy: 0.7917
## Epoch 13/15
## 39/39 - 0s - loss: 0.3176 - binary_accuracy: 0.8564 - val_loss: 0.4046 - val_binary_accuracy: 0.7917
## Epoch 14/15
## 39/39 - 0s - loss: 0.3171 - binary_accuracy: 0.8615 - val_loss: 0.3979 - val_binary_accuracy: 0.7917
## Epoch 15/15
## 39/39 - 0s - loss: 0.3040 - binary_accuracy: 0.8615 - val_loss: 0.3967 - val_binary_accuracy: 0.7917
Finally we can make predictions in the test set and calculate performance metrics like the AUC of the ROC curve:
## [1] 0.890538


WITNESS LIST Natural Resources Committee August 3, 2006-11:00A Municipal Utility Districts For: Parrish, Donna (Self) On: Allen, Joe B. (Self) Haag, Phil (Self) Hodges, Allen (Hodges/Leonard Family) Holcomb, Doug (Texas Commission on Environmental Quality) Howe, Donna (Capital Area Suburban Exchange) Huffman, Laura (City of Austin) Hughes, Ellen (Self) Icken, Andrew (City of Houston) Jordan, Douglas (Association of Water Board Directors Texas & Harris County MUD 102) Lee, Donald (Texas Conference of Urban Counties) Morgan, Tom (Texas Association of Realtors) Nelson, W.F. "Ted" (Self) Norman, Scott (Texas Association of Builders) Page, Michael G. (Self) Perez, Noe E. (Self) Schindewolf, Jimmie (North Harris County Regional Water Authority) West, Robert G. "Bob" (Self) White, Connie (Self) Wiley, Jeffrey C. (Herbert W Appel; Greater Fort Bend Economic Development Council)


This document is the API specification for the Reactor Addons libraries, a set of artifacts that complement the Reactor-Core library.


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Public Member Functions |
Public Attributes |
Protected Member Functions |
Protected Attributes |
Friends |
List of all members
ARTrackable2D Class Reference
ARTrackable2D marker type of ARTrackable. More...
#include <ARTrackable2D.h>
Inheritance diagram for ARTrackable2D:
Collaboration diagram for ARTrackable2D:
Public Member Functions | |
| int | getRefImageWidth () |
| int | getRefImageHeight () |
| std::shared_ptr< unsigned char * > | getRefImage () |
| ARTrackable2D () | |
| ~ARTrackable2D () | |
| bool | load (const char *dataSetPathname_in, int trackableId) |
| bool | updateWith2DTrackingResults (int detectedPage, float P[3][4], const ARdouble transL2R[3][4]) |
| void | setHeight (const float height) |
| float | getHeight () |
| Public Member Functions inherited from ARTrackable | |
| ARTrackable (TrackableType type) | |
| Constructor takes the type of this marker. More... | |
| virtual | ~ARTrackable () |
| void | setPositionScalefactor (ARdouble scale) |
| ARdouble | positionScalefactor () |
| virtual bool | update (const ARdouble transL2R[3][4]=NULL) |
| Completes an update begun in the parent class, performing filtering, generating OpenGL view matrix and notifying listeners (just a log message at the moment). More... | |
| ARTrackableAppearance * | getPattern (int n) |
| Returns the specified pattern within this marker. More... | |
| void | setFiltered (bool flag) |
| bool | isFiltered () |
| ARdouble | filterSampleRate () |
| void | setFilterSampleRate (ARdouble rate) |
| ARdouble | filterCutoffFrequency () |
| void | setFilterCutoffFrequency (ARdouble freq) |
Public Attributes | |
| int | pageNo |
| char * | datasetPathname |
| Public Attributes inherited from ARTrackable | |
| int | UID |
| Internal unique ID (note: not the same as ARToolKit pattern ID) More... | |
| TrackableType | type |
| Type of marker: single, multi, ... More... | |
| bool | visiblePrev |
| Whether or not the marker was visible prior to last update. More... | |
| bool | visible |
| Whether or not the marker is visible at current time. More... | |
| ARdouble | transformationMatrix [16] |
| Transformation suitable for use in OpenGL. More... | |
| ARdouble | transformationMatrixR [16] |
| Transformation suitable for use in OpenGL. More... | |
| int | patternCount |
| Number of patterns in this marker (1 for single) More... | |
| ARTrackableAppearance ** | patterns |
| Array of pointers to patterns. More... | |
Protected Member Functions | |
| bool | unload () |
| Protected Member Functions inherited from ARTrackable | |
| void | allocatePatterns (int count) |
| Allocates space for patterns within this marker. More... | |
| void | freePatterns () |
| Frees allocated patterns and resets the pattern count to zero. More... | |
Protected Attributes | |
| std::shared_ptr< unsigned char * > | m_refImage |
| int | m_refImageX |
| int | m_refImageY |
| Protected Attributes inherited from ARTrackable | |
| ARdouble | trans [3][4] |
| Transformation from camera to this marker. If stereo, transform from left camera to this marker. More... | |
| ARdouble | m_positionScaleFactor |
Friends | |
| class | ARTracker2D |
Additional Inherited Members | |
| Public Types inherited from ARTrackable | |
| enum | TrackableType { TwoD, Square, MultiSquare, InstantOn } |
Detailed Description
ARTrackable2D marker type of ARTrackable.
Constructor & Destructor Documentation
| ARTrackable2D::ARTrackable2D | ( | ) |
| ARTrackable2D::~ARTrackable2D | ( | ) |
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Member Function Documentation
| float ARTrackable2D::getHeight | ( | ) |
|
inline |
|
inline |
|
inline |
| bool ARTrackable2D::load | ( | const char * | dataSetPathname_in, |
| int | trackableId | ||
| ) |
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| void ARTrackable2D::setHeight | ( | const float | height | ) |
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|
protected |
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| bool ARTrackable2D::updateWith2DTrackingResults | ( | int | detectedPage, |
| float | P[3][4], | ||
| const ARdouble | transL2R[3][4] | ||
| ) |
Here is the call graph for this function:
Friends And Related Function Documentation
|
friend |
Member Data Documentation
| char* ARTrackable2D::datasetPathname |
|
protected |
|
protected |
|
protected |
| int ARTrackable2D::pageNo |
The documentation for this class was generated from the following files:
- /bitrise/src/Source/AR6/include/AR6/ARTrackable2D.h
- /bitrise/src/Source/AR6/ARTrackable2D.cpp


Constructor
new Timeline(element, paramsopt)
Constructs a timeline
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
element |
string | css query selector of the container dom element into which the chart is placed. | ||
params |
Object |
<optional> |
options. | |
lineHeight |
Object |
<optional> |
25 | the hight of a bar in a timeline in px. |
scale |
Object |
<optional> |
options for the scale at the top of the timelines | |
scale.from |
number |
<optional> |
0 | the time in minutes at which the timeline should start. |
scale.to |
number |
<optional> |
1440 | the time in minutes at which the timeline should end. |
scale.interval |
number |
<optional> |
240 | the interval at which labels are shown on the scale. |
scale.intervalStart |
number |
<optional> |
0 | the point at which the interval starts counting. |
data |
array |
<optional> |
the data to be displayed. | |
data.timelines |
Array.<Object> |
<optional> |
each object represents one timeline. For multiple timelines under each other, have multiple objects. | |
data.timelines[].label |
string |
<optional> |
the label to the right of the timeline. | |
data.timelines[].values |
Array.<Object> | the values (marked time slots). | ||
data.timelines[].values[].start |
number | the point at which the time slot starts in minutes. | ||
data.timelines[].values[].length |
number | the point at which the time slot ends in minutes. | ||
data.timelines[].values[].title |
string |
<optional> |
the title of the time slot. | |
data.timelines[].colors |
Array.<string> |
<optional> |
['#7cd6fd', '#5e64ff', '#743ee2', '#ff5858', '#ffa00a', '#feef72', '#28a745', '#98d85b', '#b554ff', '#ffa3ef', '#36114C', '#bdd3e6', '#f0f4f7', '#b8c2cc'] | the colors for the timeline. |
padding |
Object |
<optional> |
padding in all directions of the chart. | |
padding.top |
number | string |
<optional> |
top padding for the chart. | |
padding.right |
number | string |
<optional> |
right padding for the chart. | |
padding.bottom |
number | string |
<optional> |
bottom padding for the chart. | |
padding.left |
number | string |
<optional> |
left padding for the chart. | |
colors |
Object |
<optional> |
custom colors | |
colors.background |
string |
<optional> |
"#E3E6E9" | the color of the background of the bars (not the color of background of the whole chart). |
colors.text |
string |
<optional> |
"black" | the color of the text. |
round |
string |
<optional> |
true | if the timeline is round. |
font |
string |
<optional> |
'Roboto' | the font for all writing. Font must be imported separately. |
hover |
Object |
<optional> |
options for the hover effect. | |
hover.visible |
boolean |
<optional> |
true | whether the titles should be shown on hover or not. |
hover.callback |
function |
<optional> |
function that returns html that is displayed in the hover effect. Receives (title, start, end). | |
legend |
Object |
<optional> |
options for the legend. | |
legend.visible |
boolean |
<optional> |
true | whether a legend should be shown underneath the timelines. |
legend.distance |
number |
<optional> |
15 | distance from the last timeline to the legend in px. Always set to 0 if params.legend.visible === false. |
legend.textColor |
number |
<optional> |
"white" | the color of the text in the legend. |
legend.textWidth |
number |
<optional> |
"variable" | distance between the legend text and the legend. |
distance |
'variable' | number |
<optional> |
'variable' | whether the distance between timelines should be variable (based on svg size) or a fixed number of px. |
adjustSize |
boolean |
<optional> |
false | whether the size of the container should be adjusted based on the needed space. Only works if params.distance != 'variable'. |
Will throw an error if the container element is not found.
Methods
setData()
Replaces the existing data with new data.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
params.data |
array |
<optional> |
the data to be displayed. | |
timelines |
Array.<Object> |
<optional> |
each object represents one timeline. For multiple timelines under each other, have multiple objects. | |
timelines[].label |
string |
<optional> |
the label to the right of the timeline. | |
timelines[].values |
Array.<Object> | the values (marked time slots). | ||
timelines[].values[].start |
number | the point at which the time slot starts in minutes. | ||
timelines[].values[].length |
number | the point at which the time slot ends in minutes. | ||
timelines[].values[].title |
string |
<optional> |
the title of the time slot. | |
timelines[].colors |
Array.<string> |
<optional> |
['#7cd6fd', '#5e64ff', '#743ee2', '#ff5858', '#ffa00a', '#feef72', '#28a745', '#98d85b', '#b554ff', '#ffa3ef', '#36114C', '#bdd3e6', '#f0f4f7', '#b8c2cc'] | the colors for the timeline. |


SETLEDS(1) SETLEDS(1) NAME setleds - set the keyboard leds SYNOPSIS setleds [-v] [-L] [-D] [-F] [{+|-}num] [{+|-}caps] [{+|-}scroll] DESCRIPTION Setleds reports and changes the led flag settings of a VT (namely Num‐ Lock, CapsLock and ScrollLock). Without arguments, setleds prints the current settings. With arguments, it sets or clears the indicated flags (and leaves the others unchanged). The settings before and after the change are reported if the -v flag is given. The led flag settings are specific for each VT (and the VT corresponding to stdin is used). By default (or with option -F), setleds will only change the VT flags (and their setting may be reflected by the keyboard leds). With option -D, setleds will change both the VT flags and their default settings (so that a subsequent reset will not undo the change). This might be useful for people who always want to have numlock set. With option -L, setleds will not touch the VT flags, but only change the leds. From this moment on, the leds will no longer reflect the VT flags (but display whatever is put into them). The command setleds -L (without further arguments) will restore the situation in which the leds reflect the VT flags. One might use setleds in /etc/rc to define the initial and default state of NumLock, e.g. by INITTY=/dev/tty[1-8] for tty in $INITTY; do setleds -D +num < $tty done OPTIONS -num +num Clear or set NumLock. (At present, the NumLock setting influences the interpretation of keypad keys. Pressing the NumLock key com‐ plements the NumLock setting.) -caps +caps Clear or set CapsLock. (At present, the CapsLock setting comple‐ ments the Shift key when applied to letters. Pressing the Cap‐ sLock key complements the CapsLock setting.) -scroll +scroll Clear or set ScrollLock. (At present, pressing the ScrollLock key (or ^S/^Q) stops/starts console output.) BUGS In keyboard application mode the NumLock key does not influence the Num‐ Lock flag setting. SEE ALSO loadkeys(1) 24 Sep 1994 SETLEDS(1)


Phase 3 crawler
Input
Choose input JSON file
Links:
/
Songs:
Current link:
Current song:
Crawl only the links unique within mixer.cz.
Run
Pause
Download result
Debug
Delay between links:
ms


Class TorrentTracker
Represents the torrent tracker information.
Inherited Members
Namespace: QBittorrent.Client
Assembly: QBittorrent.Client.dll
Syntax
public class TorrentTracker
Properties
| Improve this Doc View SourceAdditionalData
Additional properties not handled by this library.
Declaration
[JsonExtensionData]
public IDictionary<string, JToken> AdditionalData { get; set; }
Property Value
| Type | Description |
|---|---|
| IDictionary<String, Newtonsoft.Json.Linq.JToken> |
CompletedDownloads
Number of completed downloads for current torrent reported by the tracker
Declaration
[JsonProperty("num_downloaded")]
[JsonConverter(typeof(NegativeToNullConverter))]
[ApiLevel(ApiLevel.V2, MinVersion = "2.2.0")]
public int? CompletedDownloads { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<Int32> |
Leeches
Number of leeches for current torrent reported by the tracker
Declaration
[JsonProperty("num_leeches")]
[JsonConverter(typeof(NegativeToNullConverter))]
[ApiLevel(ApiLevel.V2, MinVersion = "2.2.0")]
public int? Leeches { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<Int32> |
Message
Tracker message (there is no way of knowing what this message is - it's up to tracker admins)
Declaration
[JsonProperty("msg")]
public string Message { get; set; }
Property Value
| Type | Description |
|---|---|
| String |
Peers
Number of peers for current torrent reported by the tracker
Declaration
[JsonProperty("num_peers")]
[JsonConverter(typeof(NegativeToNullConverter))]
public int? Peers { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<Int32> |
Seeds
Number of seeds for current torrent reported by the tracker
Declaration
[JsonProperty("num_seeds")]
[JsonConverter(typeof(NegativeToNullConverter))]
[ApiLevel(ApiLevel.V2, MinVersion = "2.2.0")]
public int? Seeds { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<Int32> |
Status
Tracker status as string
Declaration
[JsonIgnore]
public string Status { get; set; }
Property Value
| Type | Description |
|---|---|
| String |
Remarks
Until API v2.2.0 this property might return localized status string.
Starting from API v2.2.0 this property always returns English status string.
See Also
| Improve this Doc View SourceTier
Tracker priority tier. Lower tier trackers are tried before higher tiers
Declaration
[JsonProperty("tier")]
[ApiLevel(ApiLevel.V2, MinVersion = "2.2.0")]
public int? Tier { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<Int32> |
TrackerStatus
Tracker status
Declaration
[JsonIgnore]
public TorrentTrackerStatus? TrackerStatus { get; set; }
Property Value
| Type | Description |
|---|---|
| Nullable<TorrentTrackerStatus> |
Remarks
Until API v2.2.0 this property might always return null on non-English localizations.
See Also
| Improve this Doc View SourceUrl
Tracker URL
Declaration
[JsonProperty("url")]
public Uri Url { get; set; }
Property Value
| Type | Description |
|---|---|
| Uri |


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I thought you had a lot of potential, but then you had to go and pull a stunt like this. You really didn't strike me as a rat. I guess I need to upgrade my "ratdar".
I'll have to find someone else with whom to exchange recipes. I suppose it's all for the best. You won't have access to many exotic ingredients where you are going anyway.
However, I would feel bad if I left you with nothing, so I have a surprise for you: I've arranged for you to keep your spatula!
Remember how I told you that crêpes can be so much more than dessert? This is your chance to take advantage of this fact! Getting hold of some butter and buckwheat flour shouldn't be a problem, and you will be cooking up delicious crêpes for everyone in no time!
Just take whatever other foodstuff you can find and use it for fillings! For special occasions, you may be able to convince the guards to fetch some extra ingredients for you. If you can resist the temptation to rat on the other inmates, you might even be able to make some new friends. Good luck with that.
Salvinu Manduca
Senior Consultant


When you work with us, you are part of a team of commited collaborators. All aspects of our software development process are intergrated , interactive and agile.
Our approach unifies design, development and product managent to create exceptional products.
We start each project by understanding your business goals, the users needs and the product requirements.
we help translate them into active and elegant solutions.
Before we sign a contract or write a line of code, our team will spend the neccessary time needed to understand your product vission.
The outcome of the scoping session a proposal or high-level statement of work.
based on your requirements, we can provide a complete offering or just the piece you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
DESIGNS
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
DEVELOPMENT
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
PRODUCT MANAGEMENT
Ontario Security
Pyramids
Jim Carrey
Calculator
Burned
Giraffe Restaurant


|
ICEfaces ACE Components 3.3.0 Java API Documentation |
||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.faces.component.UIComponent javax.faces.component.UIComponentBase javax.faces.component.UIColumn org.icefaces.ace.component.column.ColumnBase
public class ColumnBase
Component that represents a column in an ace:dataTable.
For more information, see the Column Wiki Documentation.
| Nested Class Summary | |
|---|---|
protected static class |
ColumnBase.PropertyKeys
|
| Field Summary | |
|---|---|
static java.lang.String |
COMPONENT_TYPE
|
static java.lang.String |
RENDERER_TYPE
|
| Fields inherited from class javax.faces.component.UIColumn |
|---|
COMPONENT_FAMILY |
| Fields inherited from class javax.faces.component.UIComponent |
|---|
ATTRS_WITH_DECLARED_DEFAULT_VALUES, BEANINFO_KEY, bindings, COMPOSITE_COMPONENT_TYPE_KEY, COMPOSITE_FACET_NAME, CURRENT_COMPONENT, CURRENT_COMPOSITE_COMPONENT, FACETS_KEY, HONOR_CURRENT_COMPONENT_ATTRIBUTES_PARAM_NAME, VIEW_LOCATION_KEY |
| Constructor Summary | |
|---|---|
ColumnBase()
|
| Method Summary | |
|---|---|
int |
getColspan()
Return the value of the colspan property. |
java.lang.String |
getFamily()
|
java.lang.Object |
getFilterBy()
Return the value of the filterBy property. |
java.lang.String |
getFilterMatchMode()
Return the value of the filterMatchMode property. |
java.lang.Object |
getFilterOptions()
Return the value of the filterOptions property. |
java.lang.String |
getFilterStyle()
Return the value of the filterStyle property. |
java.lang.String |
getFilterStyleClass()
Return the value of the filterStyleClass property. |
java.lang.String |
getFilterValue()
Return the value of the filterValue property. |
java.lang.String |
getFooterText()
Return the value of the footerText property. |
java.lang.Object |
getGroupBy()
Return the value of the groupBy property. |
java.lang.String |
getHeaderText()
Return the value of the headerText property. |
java.lang.Integer |
getPinningOrder()
Return the value of the pinningOrder property. |
int |
getRowspan()
Return the value of the rowspan property. |
java.lang.Object |
getSelectBy()
Return the value of the selectBy property. |
java.lang.Object |
getSortBy()
Return the value of the sortBy property. |
java.util.Comparator |
getSortFunction()
Return the value of the sortFunction property. |
java.lang.Integer |
getSortPriority()
Return the value of the sortPriority property. |
java.lang.String |
getStyle()
Return the value of the style property. |
java.lang.String |
getStyleClass()
Return the value of the styleClass property. |
boolean |
isConfigurable()
Return the value of the configurable property. |
boolean |
isReorderable()
Return the value of the reorderable property. |
java.lang.Boolean |
isSortAscending()
Return the value of the sortAscending property. |
boolean |
isStacked()
Return the value of the stacked property. |
void |
setColspan(int colspan)
Set the value of the colspan property. |
void |
setConfigurable(boolean configurable)
Set the value of the configurable property. |
void |
setFilterBy(java.lang.Object filterBy)
Set the value of the filterBy property. |
void |
setFilterMatchMode(java.lang.String filterMatchMode)
Set the value of the filterMatchMode property. |
void |
setFilterOptions(java.lang.Object filterOptions)
Set the value of the filterOptions property. |
void |
setFilterStyle(java.lang.String filterStyle)
Set the value of the filterStyle property. |
void |
setFilterStyleClass(java.lang.String filterStyleClass)
Set the value of the filterStyleClass property. |
void |
setFilterValue(java.lang.String filterValue)
Set the value of the filterValue property. |
void |
setFooterText(java.lang.String footerText)
Set the value of the footerText property. |
void |
setGroupBy(java.lang.Object groupBy)
Set the value of the groupBy property. |
void |
setHeaderText(java.lang.String headerText)
Set the value of the headerText property. |
void |
setPinningOrder(java.lang.Integer pinningOrder)
Set the value of the pinningOrder property. |
void |
setReorderable(boolean reorderable)
Set the value of the reorderable property. |
void |
setRowspan(int rowspan)
Set the value of the rowspan property. |
void |
setSelectBy(java.lang.Object selectBy)
Set the value of the selectBy property. |
void |
setSortAscending(java.lang.Boolean sortAscending)
Set the value of the sortAscending property. |
void |
setSortBy(java.lang.Object sortBy)
Set the value of the sortBy property. |
void |
setSortFunction(java.util.Comparator sortFunction)
Set the value of the sortFunction property. |
void |
setSortPriority(java.lang.Integer sortPriority)
Set the value of the sortPriority property. |
void |
setStacked(boolean stacked)
Set the value of the stacked property. |
void |
setStyle(java.lang.String style)
Set the value of the style property. |
void |
setStyleClass(java.lang.String styleClass)
Set the value of the styleClass property. |
| Methods inherited from class javax.faces.component.UIColumn |
|---|
getFooter, getHeader, setFooter, setHeader |
| Methods inherited from class javax.faces.component.UIComponentBase |
|---|
addClientBehavior, addFacesListener, broadcast, clearInitialState, decode, encodeBegin, encodeChildren, encodeEnd, findComponent, getAttributes, getChildCount, getChildren, getClientBehaviors, getClientId, getDefaultEventName, getEventNames, getFacesContext, getFacesListeners, getFacet, getFacetCount, getFacets, getFacetsAndChildren, getId, getListenersForEventClass, getParent, getRenderer, getRendererType, getRendersChildren, getValueBinding, invokeOnComponent, isRendered, isTransient, markInitialState, processDecodes, processRestoreState, processSaveState, processUpdates, processValidators, queueEvent, removeFacesListener, restoreAttachedState, restoreState, saveAttachedState, saveState, setId, setParent, setRendered, setRendererType, setTransient, setValueBinding, subscribeToEvent, unsubscribeFromEvent |
| Methods inherited from class javax.faces.component.UIComponent |
|---|
encodeAll, getClientId, getCompositeComponentParent, getContainerClientId, getCurrentComponent, getCurrentCompositeComponent, getNamingContainer, getResourceBundleMap, getStateHelper, getStateHelper, getTransientStateHelper, getTransientStateHelper, getValueExpression, initialStateMarked, isCompositeComponent, isInView, isVisitable, popComponentFromEL, processEvent, pushComponentToEL, restoreTransientState, saveTransientState, setInView, setValueExpression, visitTree |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final java.lang.String COMPONENT_TYPE
public static final java.lang.String RENDERER_TYPE
| Constructor Detail |
|---|
public ColumnBase()
| Method Detail |
|---|
public java.lang.String getFamily()
getFamily
in class javax.faces.component.UIColumn
public void setColspan(int colspan)
Set the value of the colspan
property.
Contents: Defines the number of columns the rendered cell spans. Only significant to Column components within a column group.
setColspan
in interface IColumn
public int getColspan()
Return the value of the colspan
property.
Contents: Defines the number of columns the rendered cell spans. Only significant to Column components within a column group. Default = '1'.
getColspan
in interface IColumn
public void setConfigurable(boolean configurable)
Set the value of the configurable
property.
Contents: When disabled, this column is excluded from the list of columns available for configuration on a TableConfigPanel component.
setConfigurable
in interface IColumn
public boolean isConfigurable()
Return the value of the configurable
property.
Contents: When disabled, this column is excluded from the list of columns available for configuration on a TableConfigPanel component. Default = 'true'.
isConfigurable
in interface IColumn
public void setFilterBy(java.lang.Object filterBy)
Set the value of the filterBy
property.
Contents: Defines a ValueExpression of the value of this row to use when filtering this column. Setting this attribute, or the "value" attribute for a column enables filtering.
setFilterBy
in interface IColumn
public java.lang.Object getFilterBy()
Return the value of the filterBy
property.
Contents: Defines a ValueExpression of the value of this row to use when filtering this column. Setting this attribute, or the "value" attribute for a column enables filtering.
getFilterBy
in interface IColumn
public void setFilterMatchMode(java.lang.String filterMatchMode)
Set the value of the filterMatchMode
property.
Contents: Defines the method of filter comparison used, default is "startsWith". Types available include: "contains", "exact", "startsWith" and "endsWith".
setFilterMatchMode
in interface IColumn
public java.lang.String getFilterMatchMode()
Return the value of the filterMatchMode
property.
Contents: Defines the method of filter comparison used, default is "startsWith". Types available include: "contains", "exact", "startsWith" and "endsWith". Default = 'startsWith'.
getFilterMatchMode
in interface IColumn
public void setFilterOptions(java.lang.Object filterOptions)
Set the value of the filterOptions
property.
Contents: Defines a collection of SelectItem objects for use as filter choices.
setFilterOptions
in interface IColumn
public java.lang.Object getFilterOptions()
Return the value of the filterOptions
property.
Contents: Defines a collection of SelectItem objects for use as filter choices.
getFilterOptions
in interface IColumn
public void setFilterStyle(java.lang.String filterStyle)
Set the value of the filterStyle
property.
Contents: Defines additional CSS rules to be applied to the filter text input.
setFilterStyle
in interface IColumn
public java.lang.String getFilterStyle()
Return the value of the filterStyle
property.
Contents: Defines additional CSS rules to be applied to the filter text input.
getFilterStyle
in interface IColumn
public void setFilterStyleClass(java.lang.String filterStyleClass)
Set the value of the filterStyleClass
property.
Contents: Defines supplementary CSS classes to add to those already applied on this component.
setFilterStyleClass
in interface IColumn
public java.lang.String getFilterStyleClass()
Return the value of the filterStyleClass
property.
Contents: Defines supplementary CSS classes to add to those already applied on this component.
getFilterStyleClass
in interface IColumn
public void setFilterValue(java.lang.String filterValue)
Set the value of the filterValue
property.
Contents: Defines the string input filtering this column, coming from the client, or from the application via a value binding.
setFilterValue
in interface IColumn
public java.lang.String getFilterValue()
Return the value of the filterValue
property.
Contents: Defines the string input filtering this column, coming from the client, or from the application via a value binding.
getFilterValue
in interface IColumn
public void setFooterText(java.lang.String footerText)
Set the value of the footerText
property.
Contents: Defines a plain text footer with less effort than adding a facet.
setFooterText
in interface IColumn
public java.lang.String getFooterText()
Return the value of the footerText
property.
Contents: Defines a plain text footer with less effort than adding a facet.
getFooterText
in interface IColumn
public void setGroupBy(java.lang.Object groupBy)
Set the value of the groupBy
property.
Contents: Defines a value expression representing the value of this column per row when determining if the values of this column in sibling rows are equal. If they are equal, span the rows witha single column cell.
setGroupBy
in interface IColumn
public java.lang.Object getGroupBy()
Return the value of the groupBy
property.
Contents: Defines a value expression representing the value of this column per row when determining if the values of this column in sibling rows are equal. If they are equal, span the rows witha single column cell.
getGroupBy
in interface IColumn
public void setHeaderText(java.lang.String headerText)
Set the value of the headerText
property.
Contents: Defines a plain text header with less effort than adding a facet.
setHeaderText
in interface IColumn
public java.lang.String getHeaderText()
Return the value of the headerText
property.
Contents: Defines a plain text header with less effort than adding a facet.
getHeaderText
in interface IColumn
public void setPinningOrder(java.lang.Integer pinningOrder)
Set the value of the pinningOrder
property.
Contents: Define the position of this column in the pinned column region when column pinning and scrolling are enabled at the table level. Having a value in this field indicates a column is pinned. 1 indexed.
setPinningOrder
in interface IColumn
public java.lang.Integer getPinningOrder()
Return the value of the pinningOrder
property.
Contents: Define the position of this column in the pinned column region when column pinning and scrolling are enabled at the table level. Having a value in this field indicates a column is pinned. 1 indexed.
getPinningOrder
in interface IColumn
public void setReorderable(boolean reorderable)
Set the value of the reorderable
property.
Contents: Enables per-column control of column ordering when the attribute ("reorderableColumns") is true at the table level.
setReorderable
in interface IColumn
public boolean isReorderable()
Return the value of the reorderable
property.
Contents: Enables per-column control of column ordering when the attribute ("reorderableColumns") is true at the table level. Default = 'true'.
isReorderable
in interface IColumn
public void setRowspan(int rowspan)
Set the value of the rowspan
property.
Contents: Defines the number of rows the rendered cell spans. Only significant to Column components within a column group.
setRowspan
in interface IColumn
public int getRowspan()
Return the value of the rowspan
property.
Contents: Defines the number of rows the rendered cell spans. Only significant to Column components within a column group. Default = '1'.
getRowspan
in interface IColumn
public void setSelectBy(java.lang.Object selectBy)
Set the value of the selectBy
property.
Contents: Defines a value expression representing the value of this column per row when using cell selection. Defining this attribute, or the "value" attribute, enables cell selection for this column when also enabled at the table level.
setSelectBy
in interface IColumn
public java.lang.Object getSelectBy()
Return the value of the selectBy
property.
Contents: Defines a value expression representing the value of this column per row when using cell selection. Defining this attribute, or the "value" attribute, enables cell selection for this column when also enabled at the table level.
getSelectBy
in interface IColumn
public void setSortAscending(java.lang.Boolean sortAscending)
Set the value of the sortAscending
property.
Contents: Defines the direction of column values during sorting. The column directions incoming from the client during a sort request overwrite any set by the application. Processing the sorted columns is done by the component whenever a client edits a sort control or the application calls table.applySorting().
setSortAscending
in interface IColumn
public java.lang.Boolean isSortAscending()
Return the value of the sortAscending
property.
Contents: Defines the direction of column values during sorting. The column directions incoming from the client during a sort request overwrite any set by the application. Processing the sorted columns is done by the component whenever a client edits a sort control or the application calls table.applySorting(). Default = 'false'.
isSortAscending
in interface IColumn
public void setSortBy(java.lang.Object sortBy)
Set the value of the sortBy
property.
Contents: Defines a value expression representing the value of this column per row when sorting. Setting this attribute, or the "value" attribute for a column enables sorting.
setSortBy
in interface IColumn
public java.lang.Object getSortBy()
Return the value of the sortBy
property.
Contents: Defines a value expression representing the value of this column per row when sorting. Setting this attribute, or the "value" attribute for a column enables sorting.
getSortBy
in interface IColumn
public void setSortFunction(java.util.Comparator sortFunction)
Set the value of the sortFunction
property.
Contents: Defines an alternate method of sorting. Sort this column using a Comparator
setSortFunction
in interface IColumn
public java.util.Comparator getSortFunction()
Return the value of the sortFunction
property.
Contents: Defines an alternate method of sorting. Sort this column using a Comparator
getSortFunction
in interface IColumn
public void setSortPriority(java.lang.Integer sortPriority)
Set the value of the sortPriority
property.
Contents: Defines the priority of a column during sorting. The column priorities incoming from the client during a sort request overwrite any set by the application. Processing the sorted columns is done by the component whenever a client edits a sort control or the application calls table.applySorting().
setSortPriority
in interface IColumn
public java.lang.Integer getSortPriority()
Return the value of the sortPriority
property.
Contents: Defines the priority of a column during sorting. The column priorities incoming from the client during a sort request overwrite any set by the application. Processing the sorted columns is done by the component whenever a client edits a sort control or the application calls table.applySorting().
getSortPriority
in interface IColumn
public void setStacked(boolean stacked)
Set the value of the stacked
property.
Contents: When enabled, this column is rendered underneath the previous column.
setStacked
in interface IColumn
public boolean isStacked()
Return the value of the stacked
property.
Contents: When enabled, this column is rendered underneath the previous column.
isStacked
in interface IColumn
public void setStyle(java.lang.String style)
Set the value of the style
property.
Contents: Custom inline CSS styles to use for this component. These styles are generally applied to the root DOM element of the component. This is intended for per-component basic style customizations. Note that due to browser CSS precedence rules, CSS rendered on a DOM element will take precedence over the external stylesheets used to provide the ThemeRoller theme on this component. If the CSS properties applied with this attribute do not affect the DOM element you want to style, you may need to create a custom theme styleClass for the theme CSS class that targets the particular DOM elements you wish to customize.
setStyle
in interface IColumn
public java.lang.String getStyle()
Return the value of the style
property.
Contents: Custom inline CSS styles to use for this component. These styles are generally applied to the root DOM element of the component. This is intended for per-component basic style customizations. Note that due to browser CSS precedence rules, CSS rendered on a DOM element will take precedence over the external stylesheets used to provide the ThemeRoller theme on this component. If the CSS properties applied with this attribute do not affect the DOM element you want to style, you may need to create a custom theme styleClass for the theme CSS class that targets the particular DOM elements you wish to customize.
getStyle
in interface IColumn
public void setStyleClass(java.lang.String styleClass)
Set the value of the styleClass
property.
Contents: Custom CSS style class(es) to use for this component. These style classes can be defined in your page or in a theme CSS file.
setStyleClass
in interface IColumn
public java.lang.String getStyleClass()
Return the value of the styleClass
property.
Contents: Custom CSS style class(es) to use for this component. These style classes can be defined in your page or in a theme CSS file.
getStyleClass
in interface IColumn
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Colossians 1:4
Since we heard of your faith in Christ Jesus, and of the love which ye have to all the saints,


Open the console and click the links to see what was watched. You'll have to actually follow-through with the actions to see the events get fired in the console.


{% extends "think/simple-base.html" %} {% block content %}
Change the colors of this thought by selecting a color box and then using the color picker to change the color.


I usually do not do commission work for free, but I dislike the idea of someone having to spend ages on something I could do in a few minutes, and I had those minutes to spare. So voilà, lj_batch_replace.rb:
#!/usr/bin/env ruby# By Henrik Nyh 2007-09-11.# Free to modify and redistribute with due credit.# Change these:USERNAME='frank'PASSWORD='sesame'REPLACE_FROM='myoldsite.com'REPLACE_UNTO='mynewsite.com'# Leave these alone unless you know what you're doing:require'rubygems'require'livejournal/login'require'livejournal/entry'# So the livejournal gem doesn't choke:# http://henrik.nyh.se/2007/04/ruby-livejournal-batch-private#commentsLiveJournal::Entry::KNOWN_EXTRA_PROPS<<"used_rte"puts"Logging in..."user=LiveJournal::User.new(USERNAME,PASSWORD)login=LiveJournal::Request::Login.new(user)login.runputs"Login response:"login.dumpresponseputs"Getting entries..."entries=LiveJournal::Request::GetEvents.new(user,:recent=>-1).run.valuesentries=entries.sort_by{|e|e.time}puts"Processing entries..."entries.eachdo|e|puts"#{e.time}#{e.subject}"e.event=e.event.gsub(REPLACE_FROM,REPLACE_UNTO)LiveJournal::Request::EditEvent.new(user,e).runputse.eventendputs"All done."
As with the batch private script, you need to install a Ruby interpreter, install RubyGems, install the LiveJournal gem (e.g. sudo gem install livejournal), configure the values at the top of the script and then run it (e.g. ruby lj_batch_replace.rb).


I am a full stack web-developer and graphic designer specializing in Javascript and Python. With 10 years of Photoshop experience, an affinity for aesthetics and a love for logic, my skillset has equipped me to create simple yet user friendly programs. I believe the beauty of things are in their details and approach each project with the goal to build it better than expected. During my time with CodeCrew, I have become more social and able to explain code in a way that is relative to many. Being a software developer has increased my confidence, expanded my creativity and boosted my career opportunities. I look forward to creating functional applications that solve every day problems and make an impact to every user.


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