Portfolio
CLI PROJECT
CLI PROJECT is a ruby program that requires command line interface interaction (thus the name). It demonstrates OOP, DRY, and SRP.
Description:
The program provides either a list of photographers to choose from or a random photographer which it then displays the photographer name, photographer URL and the URL of an original picture.Installation Instructions:
Clone the CLI Project from CLI PROJECT into it's own folder. Request an API from Pexels API and add it to your .env file, which you must create. Type the following in your terminal or command prompt window: gem install dotenv bundle install bin/run *Verified to work with ruby 2.7.1*Contributors Guide:
Thank you to Pexels for the API. Thank you to httparty, dotenv and pry from Ruby GemsLicense:
MITCLI PROGRAM Run Through
CLI PROGRAM Walkthrough
BoardGames Top
Description:
BoardGames is a Sinatra GEM running on ruby program. It demonstrates CRUD and SRP. The program is a series of HTML pages that demonstrate a game's CMS (Content Managment System).Installation Instructions:
Clone BoardGames into it's own folder. Type the following in your terminal or command prompt window: bundle install shotgun Open URL that shotgun says to Explore the web pages.Contributors Guide:
Thank you to Pexels and the artist Pixabay for providing the background art.License:
MITBoardGames LogIn Page
BoardGames Walkthrough
kimkohelhayes.com aka MyWebsite Top
Description:
MyWebsite is primarily an HTML project, with over 40% CSS and less than 1% Java Script. It is intendended to be a visual CV, displaying my Resume, Blog, Porfolio, About and Contact pages.Contributors Guide:
Thank you to Pure for the framework.Thank you to Pexels and the artist picjumbo for the artwork on Contact.Stretch Goals:
I own the Domain name kimkohelhayes.com The plan is to migrate this site to ruby on rails then host it on Heroku or AWS.License:
My image, blogs, portfolio and email are mine. Everything Else: MITwinchestertrimshop.com Top
Description:
winchestertrimshop is 56% an HTML project, with over 43% CSS and will include some JS. It is intendended to drive customer traffic to a small automotive upholstery shop. It will include Home, Gallery, About and Contact.Contributors Guide:
Thank you to Munna Bhakta for contributing to teach me so much! Thank you to Bootstrap for the framework.Stretch Goals:
The owner of Winchester Trim Shop owns the domain name winchestertrimshop.com The plan is to migrate this site to ruby on rails then host it on AWS.License:
This web site is © Winchester Trim Shop. Design is © Kimberly A Kohel-Hayes.railsWTS Top
Table of contents
General info
This project is a website for an automotive upholstery shop. It allows users (the shop workers) to log in via google or to create new users and log in on the site. A user can create a vehicle owner, cars and campers that belong to that owner. A user can edit it's profile and add an avatar. A user can add up to 2 pictures to each car and camper. A user can see the oldest and the newest car and camper and their owner on record on the owner's home page.
Technologies
railsWTS is created with:
- Ruby version: 2.6.1
- Ruby on Rails version: 6.0.3
- Bootstrap version: 3.4.1
- Omniauth Google Oauth 2 version: 0.8.0
Setup
To run this project, fork and clone it from:
hereHow to Fork and Clone
Click on fork and then click on the green box that says code on it and clone the the repository. Open a terminal of your choice in the directory where you want the app and enter the following commands.
mkdir folder_name
cd folder_name
git clone YOU_PASTE_THE_CODE_YOU_COPIED_FROM_BENEATH_THE_GREEN_BUTTON
Google API Setup
Go to the Google Developers ConsoleSelect or make your project.
Go to Credentials, then select the "OAuth consent screen" tab on top, and provide an 'EMAIL ADDRESS' and a 'PRODUCT NAME'.
Wait 10 minutes for changes to take effect.
- Create a `.env` file in the main directory of the railsWTS project.
- Put your client ID and Secred into your `.env` file as shown below
GOOGLE_CLIENT_ID=
GOOGLE_CLIENT_SECRET=
Bundle && Yarn
The next steps are to install your Gemfile and make sure that yarn is up to date. In the terminal of your choice...
bundle
If yarn complains, follow the directions to update it, if not, then you should be OK.
Database Migration
Next you will need to migrate your database. In the terminal please type the following.
rails db:migrate
Start your Server
In order to start your rails server and display the railsWTS app in the browser please type the following in your terminal.
rails s
Now open a browser tab and enter this url
http://localhost:3000/login
Thank You!
railsWTS Walkthrough
Java Script Project Top
Table of contents
General info
This project is a allows for the creation and display of recipes. It allows a user to create and display recipes with instructions. You can add many ingredients to each recipe.
Technologies
railsWTS is created with:
- Frontend is Java Script
- Backend is Ruby on Rails version: 6.0.3
Setup
To run this project, create a new folder.
Fork and clone the frontend from: here
Fork and clone the backend from: here
Navigate to the 'js_project_backend' folder in terminal and run the following:
- bundle install
- rails db:migrate
- rails db:seed
Running the App
In the terminal that you have open at the 'js_project_backend' folder, run rails s in your terminal to access and use the Rails API. Find the 'js_project_frontend' folder and open index.html in your browser.Have fun!
License
Copyright (c) 2021 Kim Kohel-Hayes
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Thank You!
JS_Project Walkthrough
Concentration Top
Table of contents
General Info
Play a Concentration game.
Flatiron Portfolio Project -- Module 4: React and Redux with Rails API
Setup
Make a folder on your system,
Fork and clone the frontend from: here
Fork and clone the backend from: here
Clone them into the same folder or directory.
Navigate to the 'concentration' folder in the terminal and run:
npm install
Navigate to the 'concentration-backend' folder in the terminal and run the following:
bundle install
rails db:migrate
rails db:seed
Running the App
In the terminal that you have open at the 'concentration-backend' folder, run
rails s -p 3001
in your terminal to access and use the Rails API. Find the 'concentration' folder and run
npm start
Have fun!
License
Copyright (c) 2021 Kim Kohel-Hayes


Myterious sci-fi MMO Project C now has a new name: Life Beyond
by Emily GeraDarewise Entertainment’s Project C got a name change.
Life Beyond is the official name of the upcoming sci-fi MMO once known only as Project C.
Described as a combination of Eve Online, a third-person action game, and an immersive sim, in Life Beyond players explore a planet called Corvus where they develop new societies and make individual and collective decisions that impact the game.
Life Beyond will be a cross-platform title available on PC, mobile, Xbox One and PS4. The game uses Improbable’s SpatialOS to enable an “unprecedented level of simulation in the game world,” according to the independent studio Darewise.
Have your first look at in-game footage from Life Beyond below.


Hosted Payment Page is an easy way to collect direct payments from your customers without a cart or invoice. Using Hosted Payment Page is easy!
Click the button to see Hosted Payment Page in action. Keep reading for more details on how to customize it to meet your needs.
To place a test payment, you can use the payment details below:
You can create links manually using the documentation provided below, or even easier, use the Link Builder tool.
Hosted Payment Page allows a number of customizations, such as:
To set your preferences, login to your account, go to Apps> Hosted Payment Page. Click on the arrow next to "Launch" and select "Settings" and "Style"
Below are several examples, you can mix and match parameters to provide the options you want.
A basic payment page where the customer supplies the amount and currency
{{data.src}}/#/link
This URL has no parameters and the user will be asked to supply the amount to pay directly.
Supply the amount, allow the user to change the amount
{{data.src}}/#/link?currency=USD&total=100.00
If you don't supply the currency, your account's default payment currency will be used.
Supply the amount, don't allow the user to change the amount
{{data.src}}/#/link?currency=USD&subtotal=100.00
By providing the amount as a subtotal, the amount on the page will not be editable by the user.
Supply the amount, shipping and tax
{{data.src}}/#/link?currency=USD&subtotal=100.00&shipping=5&tax=7.97
If you provide shipping and / or tax, subtotal is required. The total will be calculated and shown to the user.
Supply the amount, payment reference and description
{{data.src}}/#/link?currency=USD&total=100.00&reference=ABCD-1234&description=July+Invoice+Payment
The reference and description should be URL encoded.
Supply the amount, and customer data
{{data.src}}/#/link?currency=USD&total=100.00&name=John+Doe&company_name=Acme+Inc
You can provide name
, company_name
and email
Any other parameters you provide will be saved with the payment as meta data
{{data.src}}/#/link?currency=USD&total=100.00&my-data=whatever&this=that&more=stuff
The values you provide will be recorded and available for your records when you view the payment.


{% extends 'base.html' %} {% block title %} - About{% endblock %} {% block content %}
You have just met NotFlix, a free movie database and recommendation website.
This website is simply a side project, that aims at displaying a fixed
dataset of movies, and provide recommendations about other movies to watch.
The short answer is: for fun! I love working on tech side projects, mixing some Machine Learning and Full Stack development. I thought I could learn a lot while working on a movie recommendation website using existing data for training Machine Learning algorithms.
I saw many posts and tutorials about creating movie recommendations algorithms, but few of them teach how to run them in production. It's a very different challenge since users won't want to wait 10 seconds before your fancy very-deep-learning algorithm outputs its results. It's all about finding a trade off between accuracy and computation complexity.
I also wanted to work on a larger python project, involving multiple packages and modules, and see how I could organize all my code to keep it simple and readable. It's definitely not perfect yet, but I am working on it as I go.Here are the technologies I used so far:
| Programming Languages |
|
|---|---|
| Machine Learning |
|
| Databases |
|
| Deployment |
|


---
layout: post
title: ToxoDB 6.3 Released
category: [ToxoDB]
---
We are pleased to announce the release
of ToxoDB 6.3
This release of ToxoDB contains the
following new datasets:
- Histone 3 monomethyl-Lysine 4 (H3K4me1) chromatin
Immunoprecipitation chip data identifying active transcriptional
units.
This data was generated by Mathieu Gissot, J. Eduardo Fajardo, Louis M.
Weiss, Andras Fiser and Kami Kim (Albert Einstein College of Medicine)
and was graciously provided pre-publication to the Toxoplasma research community via
ToxoDB.
- This data is available as genome browser tracks. An example
region of the Toxoplasma
genome with these tracks turned on can be accessed here.
- T. gondii (VEG) and N. caninum (Liverpool) tachyzoite
RNA-sequence data. This data was generated by Adam Reid, Mandy
Sanders, Mike Quail, Matthew Berriman, Arnab Pain (Wellcome Trust
Sanger Institute) Rebecca Norton, Dong Xia, Sarah Vermont and Jonathan
Wastling (University of Liverpool). This data was graciously
provided pre-publication to the Toxoplasma
research community via
ToxoDB.
- Genes in ToxoDB may be identified using this data based on
their expression percentile. This
search
is available under the "Transcript Expression" section in ToxoDB. In addition, expression
coverage and percentile graphs are available on gene pages.
- RNA-sequence coverage graphs may also be visualized in the genome
browser. Data has been mapped to both the VEG and ME49
genomes.


resampling
resample_df(df, key_cols, strat_cols = NULL, n = NULL, key_col_name = "KEY", replace = TRUE)
| df | data frame |
|---|---|
| key_cols | key columns to resample on |
| strat_cols | columns to maintain proportion for stratification |
| n | number of unique sampled keys, defaults to match dataset |
| key_col_name | name of outputted key column. Default to "KEY" |
| replace | whether to stratify with replacement |
This function is valuable when generating a large simulated population where you goal is to create resampled sub-populations in addition to being able to maintain certain stratifications of factors like covariate distributions
A new keyed column will be created (defaults to name 'KEY') that contains the uniquely created new samples. This allows one to easily compare against the key'd columns. Eg, if you would like to see how many times a particular individual was resampled you can check the original ID column against the number of key's associated with that ID number.
library(PKPDmisc) library(dplyr, quiet = TRUE) # simple example resampling by ID maintaining Gender distribution, with 10 individuals resample_df(sd_oral_richpk, key_cols = "ID", strat_cols = "Gender", n = 10)#> # A tibble: 120 x 10 #> KEY ID Time Amt Conc Age Weight Gender Race Dose #> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <fct> <fct> <int> #> 1 1 5 0 5000 0 50.9 73.8 Female Caucasian 5000 #> 2 1 5 0.25 0 17.6 50.9 73.8 Female Caucasian 5000 #> 3 1 5 0.5 0 28.7 50.9 73.8 Female Caucasian 5000 #> 4 1 5 1 0 46.8 50.9 73.8 Female Caucasian 5000 #> 5 1 5 2 0 51.6 50.9 73.8 Female Caucasian 5000 #> 6 1 5 3 0 50.8 50.9 73.8 Female Caucasian 5000 #> 7 1 5 4 0 49.1 50.9 73.8 Female Caucasian 5000 #> 8 1 5 6 0 25.6 50.9 73.8 Female Caucasian 5000 #> 9 1 5 8 0 24.4 50.9 73.8 Female Caucasian 5000 #> 10 1 5 12 0 17.3 50.9 73.8 Female Caucasian 5000 #> # ... with 110 more rows# for a more complex example lets resample "simulated" data with multiple replicates subset_data <- sd_oral_richpk %>% filter(ID < 20) # make 'simulated' data with 5 replicates and combine to single dataframe rep_dat <- lapply(1:5, function(x) { subset_data %>% mutate(REP = x) }) %>% bind_rows() # now when we resample we also want to maintain the ID+REP relationship as resampling # just the ID would give all rows associated for an ID with all reps, rather than # a single "unit" of ID/REP resample_df(rep_dat, key_cols = c("ID", "REP"))#> # A tibble: 1,140 x 11 #> KEY ID Time Amt Conc Age Weight Gender Race Dose REP #> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <fct> <fct> <int> <int> #> 1 1 8 0 5000 0 56.7 85.0 Male Asian 5000 5 #> 2 1 8 0.25 0 16.3 56.7 85.0 Male Asian 5000 5 #> 3 1 8 0.5 0 23.0 56.7 85.0 Male Asian 5000 5 #> 4 1 8 1 0 32.4 56.7 85.0 Male Asian 5000 5 #> 5 1 8 2 0 36.4 56.7 85.0 Male Asian 5000 5 #> 6 1 8 3 0 44.4 56.7 85.0 Male Asian 5000 5 #> 7 1 8 4 0 37.0 56.7 85.0 Male Asian 5000 5 #> 8 1 8 6 0 27.6 56.7 85.0 Male Asian 5000 5 #> 9 1 8 8 0 26.4 56.7 85.0 Male Asian 5000 5 #> 10 1 8 12 0 18.8 56.7 85.0 Male Asian 5000 5 #> # ... with 1,130 more rows#> # A tibble: 2 x 2 #> Gender n #> <fct> <int> #> 1 Female 300 #> 2 Male 840#> # A tibble: 2 x 2 #> Gender n #> <fct> <int> #> 1 Female 300 #> 2 Male 840#> # A tibble: 8 x 3 #> # Groups: Gender [?] #> Gender Race n #> <fct> <fct> <int> #> 1 Female Caucasian 180 #> 2 Female Hispanic 60 #> 3 Female Other 60 #> 4 Male Asian 120 #> 5 Male Black 180 #> 6 Male Caucasian 60 #> 7 Male Hispanic 180 #> 8 Male Other 300resample_df(rep_dat, key_cols=c("ID", "REP"), strat_cols=c("Gender", "Race")) %>% group_by(Gender, Race) %>% tally#> # A tibble: 8 x 3 #> # Groups: Gender [?] #> Gender Race n #> <fct> <fct> <int> #> 1 Female Caucasian 180 #> 2 Female Hispanic 60 #> 3 Female Other 60 #> 4 Male Asian 120 #> 5 Male Black 180 #> 6 Male Caucasian 60 #> 7 Male Hispanic 180 #> 8 Male Other 300


There's currently a bug with the Nominatim object which we use in the next video. To fix that please change these lines:
from geopy.geocoders import Nominatim nom = Nominatim()
to these:
from geopy.geocoders import ArcGIS nom = ArcGIS()
The rest of the code remains the same.


GroupFund Bank was originally promoted in 2016 by GroupFund Limited, an Indian financial institution, and was its wholly-owned subsidiary.. GroupFund was formed in 2016 at the initiative of the World Bank, the Government of India and representatives of Indian industry. The principal objective was to create a development financial institution for providing medium-term and long-term project financing to Indian businesses.
This Web Application is developed by AA ltd.
Back to HomePage


Install
rake install
Merb
In the before the app loads block add:
dependency "sequel_versioned", "0.0.1"
Sequel Plugin
Schema
The fact table needs for a versioned dimension both dimension_id and dimension_version attributes, the versioned dimension must have a fact_id.
For a versioned collection the fact table needs collection_version, and the collection objects must have a fact_id.
Model
The fact table needs to have a many_to_one association with each versioned dimension or collection.
To load the plugin in your Fact model
is(:versioned_fact, {:collections => [Collection], :dimensions => [Dimension]})
Then in your versioned models is :versioned_dimension or is :versioned_collection depending on what you need.
The method version! is available on the fact model, along with fetch_dimension and fetch_collection.
Example
Say we have a blog posts and comments, all of which need to be versioned. So post would be our fact table, with post_detail as it’s dimension. Each post would also have a versioned_collection of comments.
class Post < Sequel::Model set_schema do foreign_key :post_detail, :table => :post_details end is(:versioned_fact, {:collections => [Comment], :dimensions => [PostDetail]}) one_to_many :comments one_to_many :post_details def body fetch_post_detail.body end end
The Post table would have to have the following fields defined: post_detail_id, post_detail_version, comments_version.
So when a new post is created a corresponding post_detail is also created.
To version the post and the comments, the method version! is provided on instances of the Post class.
class PostDetail < Sequel::Model set_schema do foreign_key :post_id, :table => :posts end is :versioned_dimension many_to_one :post end class Comments < Sequel::Model set_schema do foreign_key :post_id, :table => :posts end is :versioned_collection many_to_one :post end
The comments and post_details table would have post_id and version defined.


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The layouts provided by Anime Dreamz are all original and designed
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If you are to use one of AD's 'FREE' layouts it is required you link to AD on your site in one way or another. These were worked very hard on and Anime Dreamz would like to recieve credit for our effort. Please do consider and leaving the links on the pages so we can keep providing for the anime community. Best regards, ------------------------------------------------------ Ok guys now you have your layout. Next get your HOST ~ ------------------------------------------------------ Modification: This is the webmaster @ Anime Dreamz which also runs Proeffect.com *So please replace your links to Proeffect.com* Thanks. |
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Welcome to Lab number two(2)!
I'm another paragraph; what color am I?
I'm a list item; what color am I?
#5 - more paragraphs
I'm a paragraph; what color am I?
I'm another paragraph; what color am I?
I'm a list item; what color am I?
#6 - modification
Tikal: Mayan Ruins in the Guatemalan Jungle
Tikal is the largest of the ancient ruined cities of the Mayan civilization. It is a really awesome, magical place. If you go to Tikal, you have three main lodging options:


|
From time to time, I need to know the value of a specific UNIX environment variable. Of course, that’s easy enough to do if you know the exact spelling of the variable, as in the case of the current working directory:
However, what if I’m unsure of the spelling, or I simply want to see all of the
variables that are set for my current environment? That’s easy enough to find
as well, using the
That’s pretty effective and since the environment variables are sorted, it makes it easy to find a specific setting. But,
Here you can see that this little script simply prints the contents of the For sanity, let’s check the current setting of that variable just so we can see if it matches our expectations:
That was what I expected. So, let’s say I want this script to use another setting for that environment variable. I’ll try to set it first before calling the script:
But, wait! That didn’t do anything. That’s because the The way to get around this is to pass the
Now it uses the variable as expected. Keep in mind that it didn’t set this
variable permanently, but instead just for the execution of the utility we
passed it, which in this case was the To prove it, let’s take a look at the
Based on the information above, if you ever need to review your current
environment, or set it for a single run of a utility, it’s easy enough to do
using the |


{% extends 'layout-af.html' %} {% block pageTitle %} Support when leaving the armed forces {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
Find information and support to help you move from military healthcare to civilian healthcare after leaving the armed forces.
Leaving the armed forces can be a daunting process to undertake. Understanding what options you have and what actions you can take will help make your transition from military healthcare to civilian healthcare as smooth as possible.
After your discharge date, you will move from military healthcare to civilian and veteran healthcare.
Most of your treatment will come through NHS services, which includes dedicated healthcare services designed to support veterans.
This includes services that support veterans mental health.
If you are unsure of what to do when you leave the armed forces, there are several actions you can take to make your transition from military to civilian healthcare easier.
You can get 3 documents when leaving the armed forces that can help continue your healthcare without disadvantage. These are:
You will usually receive the NHS welcome letter and the form during your final medical assessment at the end of your discharge period. If you do not receive them, ask a doctor at a medical centre to get them for you as soon as possible.
You will not usually be given a paper copy of your summary medical records unless you ask a doctor at your medical centre. It is better to ask for this earlier in your discharge period as it may take a while to arrive if they agree to provide a copy.
If you receive specialist healthcare treatment or are medically discharged, it is important to ask a medical officer about how you can continue your treatment after your discharge date.
There are services who can provide support and treatment to you during your discharge period and after your discharge date. These include:
Ask a medical officer about getting a referral to these services if you need them.
It is recommended that you register with a GP 1 to 3 months before you are discharged, or as soon as possible after this if you have not already.
Some GP surgeries are veteran friendly. This means they have a better understanding of veteran-specific issues and health services.
You can contact any GP surgery before registering to find out if it is veteran friendly.
Registering as soon as you can means your GP surgery may be able to request your complete military medical record from Defence Medical Services (DMS) before the end of your discharge period.
Your GP surgery will automatically receive any previous NHS medical records you may have when you register.
Once your GP surgery has your full military medical records, a GP can give you better healthcare advice, as well as quickly refer you to any specialist care you might need.
When you first visit a GP at your NHS GP surgery, it is important to give them the medical summary form (FMed133A) you received when you were discharged from the armed forces. This form includes the details needed for them to request your complete military GP medical records from DMS.
Once you have given them your form, you can check with a GP at a later date to make sure your full medical records have been requested.
It usually takes several months for your GP surgery to receive your military GP records.
You can also give them a paper copy of your summary medical records if you got one during your discharge period. They might need this if they do not have access to your full military medical records yet.
Once a GP has your medical records, they will be aware of your medical history and can give you the treatment you need.
You can also give them your NHS welcome letter. This provides information to them about how to help you.
If you or a GP has lost the form containing the information on how they can access your full medical records, a GP can email to arrange its transferral instead. They need your permission to do this.
For Royal Navy/Royal Marines<EMAIL_ADDRESS>For British Army<EMAIL_ADDRESS>For Royal Air Force<EMAIL_ADDRESS>When you first visit your GP surgery, it is important to tell a GP or other clinical member of staff that you are a veteran.
Ask them to record this in your NHS medical records if it is not already there. They can do this by putting a specific code that represents veterans into your medical records.
Being flagged as a veteran means you are more likely to receive specialist veteran care and consideration, and GPs can refer you to specialist veteran services if you need them.
Your NHS welcome letter will also encourage you to find a dentist.
It is important to do this as soon as possible.
You may need to find a dentist for your family too.
Finding a dentist sooner rather than later allows you to continue getting dental treatment with less of a wait. It also means you can get urgent treatment closer to when you need it.
Find more information about getting dental care
You will not get free healthcare from the NHS after your discharge date from the UK armed forces if you are:
You will receive free healthcare up to your discharge date.
In order to receive free healthcare through the NHS after this date, you must gain indefinite leave by using the immigration rules and process.
There are many services and organisations that can support you as you prepare to leave the armed forces, or after you have left.
These services cover various issues, including healthcare, welfare, financial support and family care.
The Veterans Welfare Service (VWS) provides one-to-one advice and support to all veterans, their families and carers. This includes veterans who have been medically discharged.
VWS also supports serving personnel who need help applying for financial support.
It is run by Veterans UK, which is part of the Ministry of Defence (MoD).
GOV.UK: Find more information and get in touch with the Veterans Welfare Service
www.gov.uk
Defence Transition Services (DTS) help service leavers and their families by providing tailored one-to-one advice and support to help you as you leave the armed forces.
It is run by Veterans UK, which is part of the MoD.
DTS work closely with the service leaver and their family to access support specific to your situation. They can introduce you to other services and organisations if you need help from them, and will help identify, assess and support your individual and family needs.
There are many ways to access DTS, including:
GOV.UK: Find more information about DTS, including how to access it
ww.gov.uk
Veterans UK is part of the MoD.
They provide free support for serving personnel, reservists, veterans and their families, including:
GOV.UK: Find more information about Veterans UK
www.gov.uk
www.gov.uk
Integrated Personal Commissioning is an approach to personalised care for armed forces personnel who have complex physical, neurological or mental health conditions as a result of injury whilst in service.
This includes:
This service is only available for service leavers with specific health and well-being needs. You cannot self-refer.
If you are eligible, you can only access this service through a referral from medical staff while you are on the Defence Recovery Pathway, including if you are at a Personnel Recovery Centre or a Defence Medical Rehabilitation Centre.
Find more information on armed forces personnel in transition: Integrated Personal Commissioning
If you are a veteran with specific physical or mental health problems, a GP may be able to refer you to a number of specialist services during or after your transition from the armed forces.
There are many different types of services, including:
Find more information on veterans healthcare and specialist services


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$def with()
Login with your Dropbox account. It becomes way easier to plan Thanksgiving trip or next vacation.
We find how you travel and what you like during a trip from your photos on Dropbox. We will recommend you places you would like yet have never been.
Graduate student at UC Berkeley. Main research area is big data processing.
Another graduate student at UC Berkeley. Main research area is HCI and CSCW.


Example to_hdf calls¶
Initialize the simulation with the tardis_example.yml
configuration
file.
In [2]:
from tardis.io.config_reader import Configuration
from tardis.model import Radial1DModel
from tardis.simulation import Simulation
# Must have the tardis_example folder in the working directory.
config_fname = 'tardis_example/tardis_example.yml'
tardis_config = Configuration.from_yaml(config_fname)
model = Radial1DModel(tardis_config)
simulation = Simulation(tardis_config)
tardis.io.config_reader - INFO - Reading Atomic Data from tardis_example/kurucz_cd23_chianti_H_He.h5
tardis.atomic - INFO - Read Atom Data with UUID=5ca3035ca8b311e3bb684437e69d75d7 and MD5=21095dd25faa1683f4c90c911a00c3f8
tardis.io.config_reader - INFO - "initial_t_inner" is not specified in the plasma section - initializing to 9933.95199592 K with given luminosity
tardis.io.config_reader - WARNING - No "species" given - ignoring other NLTE options given:
OrderedDict([('coronal_approximation', False), ('classical_nebular', False)])
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
Run the simulation while storing all its iterations to an HDF file.
The first parameter is the path where the HDF file should be stored. The second parameter determines which properties will be stored. When its value is ‘input’, only Input plasma properties will be stored. The third parameter, hdf_last_only, if True will only store the last iteration of the simulation, otherwise every iteration will be stored.
In [3]:
simulation.legacy_run_simulation(model, '/tmp/full_example.hdf', 'full', hdf_last_only=False)
tardis.simulation.base - INFO - Remaining run 20
tardis.simulation.base - INFO - Luminosity emitted = 7.96033e+42 erg / s Luminosity absorbed = 2.64155e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 9926.501965 10178.847299 0.400392 0.497507
5 9852.611678 10369.295730 0.211205 0.187775
10 9779.813302 10216.758348 0.142695 0.114941
15 9708.082813 9970.054217 0.104556 0.084019
tardis.simulation.base - INFO - t_inner 9933.952 K -- next t_inner 11459.378 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
py.warnings - WARNING - /home/ftsamis/anaconda2/envs/tardis/lib/python2.7/site-packages/pandas/io/pytables.py:2577: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->[0]]
warnings.warn(ws, PerformanceWarning)
tardis.simulation.base - INFO - Remaining run 19
tardis.simulation.base - INFO - Luminosity emitted = 1.40992e+43 erg / s Luminosity absorbed = 4.66880e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10178.847299 11571.630571 0.497507 0.528891
5 10369.295730 11639.658719 0.187775 0.212796
10 10216.758348 11307.611990 0.114941 0.135974
15 9970.054217 11014.938341 0.084019 0.100935
tardis.simulation.base - INFO - t_inner 11459.378 K -- next t_inner 9932.734 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 18
tardis.simulation.base - INFO - Luminosity emitted = 8.08011e+42 erg / s Luminosity absorbed = 2.52343e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11571.630571 10470.275968 0.528891 0.437266
5 11639.658719 10807.317458 0.212796 0.161611
10 11307.611990 10595.281489 0.135974 0.101195
15 11014.938341 10227.284716 0.100935 0.077164
tardis.simulation.base - INFO - t_inner 9932.734 K -- next t_inner 11372.729 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 17
tardis.simulation.base - INFO - Luminosity emitted = 1.36647e+43 erg / s Luminosity absorbed = 4.54437e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10470.275968 11608.335004 0.437266 0.505287
5 10807.317458 11697.606272 0.161611 0.200153
10 10595.281489 11435.859233 0.101195 0.126663
15 10227.284716 11050.826908 0.077164 0.095893
tardis.simulation.base - INFO - t_inner 11372.729 K -- next t_inner 10013.126 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 16
tardis.simulation.base - INFO - Luminosity emitted = 8.30340e+42 erg / s Luminosity absorbed = 2.63966e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11608.335004 10565.362018 0.505287 0.443312
5 11697.606272 10983.556971 0.200153 0.158241
10 11435.859233 10750.431319 0.126663 0.098824
15 11050.826908 10356.249101 0.095893 0.076076
tardis.simulation.base - INFO - t_inner 10013.126 K -- next t_inner 11309.576 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 15
tardis.simulation.base - INFO - Luminosity emitted = 1.34658e+43 erg / s Luminosity absorbed = 4.34429e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10565.362018 11478.282430 0.443312 0.513052
5 10983.556971 11755.783299 0.158241 0.194266
10 10750.431319 11448.648154 0.098824 0.124291
15 10356.249101 11096.197414 0.076076 0.093099
tardis.simulation.base - INFO - t_inner 11309.576 K -- next t_inner 10030.795 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 14
tardis.simulation.base - INFO - Luminosity emitted = 8.34165e+42 erg / s Luminosity absorbed = 2.67751e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11478.282430 10623.738370 0.513052 0.433783
5 11755.783299 11070.101670 0.194266 0.155108
10 11448.648154 10808.067614 0.124291 0.097959
15 11096.197414 10405.862182 0.093099 0.074787
tardis.simulation.base - INFO - t_inner 10030.795 K -- next t_inner 11303.531 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 13
tardis.simulation.base - INFO - Luminosity emitted = 1.33656e+43 erg / s Luminosity absorbed = 4.40498e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10623.738370 11498.818693 0.433783 0.508315
5 11070.101670 11666.501329 0.155108 0.197676
10 10808.067614 11442.955962 0.097959 0.123946
15 10405.862182 11020.034976 0.074787 0.095226
tardis.simulation.base - INFO - t_inner 11303.531 K -- next t_inner 10062.914 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 12
tardis.simulation.base - INFO - Luminosity emitted = 8.46354e+42 erg / s Luminosity absorbed = 2.69714e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11498.818693 10647.866376 0.508315 0.436795
5 11666.501329 11045.489008 0.197676 0.157595
10 11442.955962 10847.411669 0.123946 0.098109
15 11020.034976 10380.366497 0.095226 0.077053
tardis.simulation.base - INFO - t_inner 10062.914 K -- next t_inner 11257.771 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 11
tardis.simulation.base - INFO - Luminosity emitted = 1.31182e+43 erg / s Luminosity absorbed = 4.36200e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10647.866376 11497.910067 0.436795 0.506422
5 11045.489008 11708.194588 0.157595 0.194059
10 10847.411669 11369.876746 0.098109 0.124261
15 10380.366497 11074.943400 0.077053 0.091596
tardis.simulation.base - INFO - t_inner 11257.771 K -- next t_inner 10116.260 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 10
tardis.simulation.base - INFO - Luminosity emitted = 8.64601e+42 erg / s Luminosity absorbed = 2.75713e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11497.910067 10658.108570 0.506422 0.442424
5 11708.194588 10976.263048 0.194059 0.164630
10 11369.876746 10843.425806 0.124261 0.099291
15 11074.943400 10435.716429 0.091596 0.076242
tardis.simulation.base - INFO - t_inner 10116.260 K -- next t_inner 11197.386 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 9
tardis.simulation.base - INFO - Luminosity emitted = 1.29557e+43 erg / s Luminosity absorbed = 4.15837e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10658.108570 11478.296887 0.442424 0.493588
5 10976.263048 11679.173270 0.164630 0.191540
10 10843.425806 11490.833906 0.099291 0.118832
15 10435.716429 11110.058621 0.076242 0.089509
tardis.simulation.base - INFO - t_inner 11197.386 K -- next t_inner 10124.899 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 8
tardis.simulation.base - INFO - Luminosity emitted = 8.65501e+42 erg / s Luminosity absorbed = 2.78370e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11478.296887 10677.156574 0.493588 0.440400
5 11679.173270 11111.367299 0.191540 0.158564
10 11490.833906 10872.011689 0.118832 0.098784
15 11110.058621 10450.659577 0.089509 0.076628
tardis.simulation.base - INFO - t_inner 10124.899 K -- next t_inner 11201.122 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 7
tardis.simulation.base - INFO - Luminosity emitted = 1.28875e+43 erg / s Luminosity absorbed = 4.24569e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10677.156574 11497.715452 0.440400 0.491399
5 11111.367299 11679.730149 0.158564 0.191832
10 10872.011689 11406.165607 0.098784 0.120992
15 10450.659577 11110.848584 0.076628 0.089157
tardis.simulation.base - INFO - t_inner 11201.122 K -- next t_inner 10155.063 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 6
tardis.simulation.base - INFO - Luminosity emitted = 8.75375e+42 erg / s Luminosity absorbed = 2.82292e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11497.715452 10659.800239 0.491399 0.452245
5 11679.730149 11052.553607 0.191832 0.164140
10 11406.165607 10786.600051 0.120992 0.102983
15 11110.848584 10402.919519 0.089157 0.078084
tardis.simulation.base - INFO - t_inner 10155.063 K -- next t_inner 11170.949 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 5
tardis.simulation.base - INFO - Luminosity emitted = 1.27525e+43 erg / s Luminosity absorbed = 4.19494e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10659.800239 11418.366335 0.452245 0.502809
5 11052.553607 11651.982083 0.164140 0.190958
10 10786.600051 11383.484120 0.102983 0.120709
15 10402.919519 11015.171956 0.078084 0.091113
tardis.simulation.base - INFO - t_inner 11170.949 K -- next t_inner 10181.150 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 4
tardis.simulation.base - INFO - Luminosity emitted = 8.90701e+42 erg / s Luminosity absorbed = 2.79118e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11418.366335 10708.718613 0.502809 0.446783
5 11651.982083 11168.510192 0.190958 0.158326
10 11383.484120 10980.154176 0.120709 0.098042
15 11015.171956 10446.799182 0.091113 0.078283
tardis.simulation.base - INFO - t_inner 10181.150 K -- next t_inner 11102.876 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 3
tardis.simulation.base - INFO - Luminosity emitted = 1.24835e+43 erg / s Luminosity absorbed = 4.05721e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 10708.718613 11427.883632 0.446783 0.489513
5 11168.510192 11606.341840 0.158326 0.189838
10 10980.154176 11386.202955 0.098042 0.118663
15 10446.799182 11072.393582 0.078283 0.087588
tardis.simulation.base - INFO - t_inner 11102.876 K -- next t_inner 10227.566 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Remaining run 2
tardis.simulation.base - INFO - Luminosity emitted = 9.03062e+42 erg / s Luminosity absorbed = 2.88133e+42 erg / s Luminosity requested = 1.05928e+43 erg / s
tardis.simulation.base - INFO - Plasma stratification:
t_rad next_t_rad w next_w
Shell
0 11427.883632 10851.556588 0.489513 0.434476
5 11606.341840 11231.251082 0.189838 0.156921
10 11386.202955 10841.393480 0.118663 0.103295
15 11072.393582 10574.899924 0.087588 0.075600
tardis.simulation.base - INFO - t_inner 10227.566 K -- next t_inner 11076.900 K
tardis.model - INFO - Calculating J_blues for radiative_rates_type=dilute-blackbody
tardis.simulation.base - INFO - Doing last run
tardis.simulation.base - INFO - Finished in 19 iterations and took 61.07 s
Open the stored HDF file with pandas and print its structure.

For example to access model.t_inner
of the 9th iteration of the
simulation, one would need to do the following.
Note: Quantities are always stored as their SI values.
In [9]:
print data['/simulation9/model/scalars']['t_inner']
11257.7713721
Breakdown of the various to_hdf methods¶
Every module in TARDIS has its own to_hdf
method responsible to
store its own data to an HDF file.
Plasma¶
The following call will store every plasma property to
/tmp/plasma_output.hdf
under /parent/plasma
In [3]:
model.plasma.to_hdf('/tmp/plasma_output.hdf', path='parent')
py.warnings - WARNING - /home/ftsamis/anaconda2/envs/tardis/lib/python2.7/site-packages/pandas/io/pytables.py:2577: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->[0]]
warnings.warn(ws, PerformanceWarning)
In [4]:
import pandas
with pandas.HDFStore('/tmp/plasma_output.hdf') as data:
print data
<class 'pandas.io.pytables.HDFStore'>
File path: /tmp/plasma_output.hdf
/parent/plasma/abundance frame (shape->[6,20])
/parent/plasma/atomic_mass series (shape->[6])
/parent/plasma/beta_rad series (shape->[20])
/parent/plasma/beta_sobolev frame (shape->[29224,20])
/parent/plasma/density series (shape->[20])
/parent/plasma/electron_densities series (shape->[20])
/parent/plasma/excitation_energy series (shape->[4439])
/parent/plasma/f_lu series (shape->[29224])
/parent/plasma/g series (shape->[4439])
/parent/plasma/g_electron series (shape->[20])
/parent/plasma/general_level_boltzmann_factor frame (shape->[4439,20])
/parent/plasma/ion_number_density frame (shape->[94,20])
/parent/plasma/ionization_data frame (shape->[88,1])
/parent/plasma/j_blues frame (shape->[29224,20])
/parent/plasma/level_boltzmann_factor frame (shape->[4439,20])
/parent/plasma/level_number_density frame (shape->[4439,20])
/parent/plasma/levels frame (shape->[1,1])
/parent/plasma/lines frame (shape->[29224,12])
/parent/plasma/lines_lower_level_index series (shape->[29224])
/parent/plasma/lines_upper_level_index series (shape->[29224])
/parent/plasma/metastability series (shape->[4439])
/parent/plasma/nu series (shape->[29224])
/parent/plasma/number_density frame (shape->[6,20])
/parent/plasma/partition_function frame (shape->[94,20])
/parent/plasma/phi frame (shape->[88,20])
/parent/plasma/scalars series (shape->[2])
/parent/plasma/selected_atoms series (shape->[6])
/parent/plasma/stimulated_emission_factor frame (shape->[29224,20])
/parent/plasma/t_electrons series (shape->[20])
/parent/plasma/t_rad series (shape->[20])
/parent/plasma/tau_sobolevs frame (shape->[29224,20])
/parent/plasma/transition_probabilities frame (shape->[87672,20])
/parent/plasma/w series (shape->[20])
/parent/plasma/wavelength_cm series (shape->[29224])
Plasma’s to_hdf
method can also accept a collection
parameter
which can specify which types of plasma properties will be stored. For
example if we wanted to only store Input plasma properties, we would do
the following:
In [11]:
from tardis.plasma.properties.base import Input
model.plasma.to_hdf('/tmp/plasma_input_output.hdf', collection=[Input])
In [12]:
import pandas
with pandas.HDFStore('/tmp/plasma_input_output.hdf') as data:
print data
<class 'pandas.io.pytables.HDFStore'>
File path: /tmp/plasma_input_output.hdf
/plasma/abundance frame (shape->[6,20])
/plasma/density series (shape->[20])
/plasma/j_blues frame (shape->[29224,20])
/plasma/scalars series (shape->[2])
/plasma/t_rad series (shape->[20])
/plasma/w series (shape->[20])
Model¶
The following call will store properties of the Radial1DModel
to
/tmp/model_output.hdf
under /model
. Additionally, it will
automatically call model.plasma.to_hdf
, since plasma is also a
property of the model.
In [ ]:
model.to_hdf('/tmp/model_output.hdf')
MontecarloRunner¶
The following call will store properties of the MontecarloRunner
to
/tmp/runner_output.hdf
under /runner
In [7]:
simulation.runner.to_hdf('/tmp/runner_output.hdf')
In [8]:
import pandas
with pandas.HDFStore('/tmp/runner_output.hdf') as data:
print data
<class 'pandas.io.pytables.HDFStore'>
File path: /tmp/runner_output.hdf
/runner/j_estimator series (shape->[20])
/runner/nu_bar_estimator series (shape->[20])
/runner/output_energy series (shape->[100000])
/runner/output_nu series (shape->[100000])


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The measurements at recursive resolvers (M4 and M6) are based on DNS traces collected at:
The measurements at authoritative resolvers (M8) are based on DNS traces collected at:


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The singleton instance of the Feature discovery API.
The log manager.
The singleton instance of the Platform API.
The strategy to use when activating modules during navigation.
List the events that an Animator should raise.
Specifies the available logging levels.
Provides helpers for working with metadata.
The status of a Pipeline.
Decorator: Indicates that the decorated class/object is a custom resolver.
A context used when invoking a binding's callable API to notify the binding that the context is a "source update".
Decorator: Indicates that the decorated class/object is a view strategy.
Creates an instance of Error that aggregates and preserves an innerError.
The error message.
The inner error message to aggregate.
Indicates to not wrap the inner error if it itself already has an innerError.
The Error instance.
Adds an appender capable of processing logs and channeling them to an output.
An appender instance to begin processing logs with.
Decorator: Specifies the dependency should load all instances of the given key.
Decorator: Directs the TypeScript transpiler to write-out type metadata for the decorated class.
Decorator: Specifies a custom HtmlBehaviorResource instance or an object that overrides various implementation details of the default HtmlBehaviorResource.
The customized HtmlBehaviorResource or an object to override the default with.
Decorator: Specifies that a property is bindable through HTML.
The name of the property, or a configuration object.
Decorator: Indicates that the decorated class is a binding behavior.
The name of the binding behavior.
Generate a query string from an object.
Object containing the keys and values to be used.
Boolean Use the old URI template standard (RFC6570)
The generated query string, excluding leading '?'.
camel-cases a string.
Creates a behavior property that references an immediate content child element that matches the provided selector.
Creates a behavior property that references an array of immediate content child elements that matches the provided selector.
Decorator: Indicates that the decorated property is computed from other properties.
The names of the properties the decorated property is computed from. Simple property names, not expressions.
Configures a global EA by merging functionality into the Aurelia instance.
The Aurelia Framework configuration object used to configure the plugin.
Decorator: Internal decorator used to mixin binding APIs.
Decorator: Indicates that the custom element should be rendered without its element container.
Creates an overrideContext object with the supplied bindingContext and optional parent overrideContext.
Creates a scope object for testing purposes.
Decorator: Indicates that the decorated class is a custom attribute.
The name of the custom attribute.
The default binding mode to use when the attribute is bound wtih .bind.
Decorator: Indicates that the decorated class is a custom element.
The name of the custom element.
Declares a property's dependencies.
Enables applying decorators, particularly for use when there is no syntax support in the language, such as with ES5 and ES2016.
The decorators to apply.
Decorator: Enables marking methods as deprecated.
Options for how the deprected decorator should function at runtime.
Decorator: Specifies that the decorated custom attribute has options that are dynamic, based on their presence in HTML and not statically known.
Decorator: Indicates that the decorated class provides element configuration to the EventManager for one or more Web Components.
Internal API that adds a binding to the connect queue.
Decorator: Specifies the dependency to create a factory method, that can accept optional arguments
An internal API used by Aurelia's array observation components.
Gets the instance of a logger associated with a particular id (or creates one if it doesn't already exist).
The id of the logger you wish to get an instance of.
The instance of the logger, or creates a new logger if none exists for that id.
Returns whether a property's dependencies have been declared.
Includes EA functionality into an object instance.
The object to mix Event Aggregator functionality into.
Enables initializing a specific implementation of the Platform Abstraction Layer (PAL).
Allows providing a callback which configures the three PAL singletons with their platform-specific implementations.
Decorator: Specifies the dependencies that should be injected by the DI Container into the decoratored class/function.
Decorator: Provides a view template, directly inline, for the component. Be sure to wrap the markup in a template element.
The markup for the view.
A list of dependencies that the template has.
A base url from which the dependencies will be loaded.
Decorator: Specifies that the decorated item should be called as a factory function, rather than a constructor.
Decorator: Specifies a custom Invoker for the decorated item.
Determines if the provided object is a navigation command. A navigation command is anything with a navigate method.
The object to check.
Joins two paths.
The first path.
The second path.
The joined path.
Decorator: Specifies the dependency should be lazy loaded
An internal API used by Aurelia's array observation components.
Decorator: Enables mixing behaior into a class.
An object with keys for each method to mix into the target class.
Decorator: Specifies the dependency as a new instance
Decorator: Indicates that the component has no view.
Decorator: Specifies that a property is observable.
The name of the property, or a configuration object.
Decorator: Specifies the dependency as optional
Decorator: Specifies the dependency to look at the parent container for resolution
Parse a query string.
The query string to parse.
Object with keys and values mapped from the query string.
Decorator: Enables custom processing of the attributes on an element before the framework inspects them.
Pass a function which can provide custom processing of the content.
Decorator: Enables custom processing of the content that is places inside the custom element by its consumer.
Pass a boolean to direct the template compiler to not process the content placed inside this element. Alternatively, pass a function which can provide custom processing of the content. This function should then return a boolean indicating whether the compiler should also process the content.
Decorator: Creates a protocol.
The name of the protocol.
The validation function or options object used in configuring the protocol.
Decorator: Specifies a custom registration strategy for the decorated class/function.
Calculates a path relative to a file.
The relative path.
The file path.
The calculated path.
Decorator: Specifies a resource instance that describes the decorated class.
The resource instance.
Sets the level of logging for ALL the application loggers.
Matches a value of logLevel specifying the level of logging.
Decorator: Specifies to register the decorated item with a "singleton" lieftime.
Decorator: Adds efficient subscription management methods to the decorated class's prototype.
Decorator: Applied to custom attributes. Indicates that whatever element the attribute is placed on should be converted into a template and that this attribute controls the instantiation of the template.
Decorator: Specifies to register the decorated item with a "transient" lifetime.
Decorator: Indicates that the custom element should render its view in Shadow DOM. This decorator may change slightly when Aurelia updates to Shadow DOM v1.
Decorator: Provides a relative path to a view for the component.
The path to the view.
Decorator: Associates a custom view strategy with the component.
The view strategy instance.
Decorator: Indicates that the decorated class is a value converter.
The name of the value converter.
Decorator: Provides the ability to add resources to the related View
Same as:
Either: strings with moduleIds, Objects with 'src' and optionally 'as' properties or one of the classes of the module to be included.
Generated using TypeDoc
The singleton instance of the Dom API.


Export this node in JSON format. You can import this JSON format in other CONNECT platform instances.
Import from a JSON file of an exported node. This will override the contents of your node.


Hello, We are Yars groub
Yars it is group of students studied 201 course and need to practice CSS and HTML and JavaScript
Stand for :
S
R
A
Y
Yazan Aljamal :
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Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Yasmin Adaileh :
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Ammar Badwan :
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Raghad Najjar :
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Sondos Abu-Elayyan :
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type specimen book.


Download This Plugin
Back To jQueryScript.Net
jQuery Tabular Input Plugin Examples
Basic 5x5 table
With controls
Add New Row
Delete Last Row
Add New Column
Delete Last Column
With table header


{% include "wagtailadmin/panels/object_list.html" %} {% load wagtail_opengraph_image_generator_tags wagtailimages_tags %} {% get_existing_og_image self.instance as og_img %} {% get_og_image_generator_setting 'CREATE_AUTOMATICALLY' as CREATE_AUTOMATICALLY %} {% if og_img %}
Since you have WAGTAIL_OG_GENERATOR_CREATE_AUTOMATICALLY
set to True
, a new image will be created and saved automatically when you save this page.
Please click the button to generate a preview of your OpenGraph image for this page.
Saving a new image will overwrite any existing image.


Support for Dockstore is provided by grants from Genome Canada, the National Institutes of Health, and the Province of Ontario.
{{ funder.content }}


R/crunchy-ui.R
crunchyPublicBody.Rd
crunchyServer()
and crunchyBody()
allow you to protect your app with
Crunch authentication and authorization. Add these UI contents to your
shiny::shinyUI()
body to display different content for visitors who are
not authenticated with Crunch (crunchyPublicBody()
) or who are
authenticated but not authorized to access your app
(crunchyUnauthorizedBody
).
crunchyPublicBody(...) crunchyUnauthorizedBody(...)
| ... | UI elements for your app, to be conditionally rendered |
|---|
An empty string; these functions are called for their side effects of
registering the UI elements so that crunchyServer()
can render them as
appropriate.
crunchyBody()
; setCrunchyAuthorization()
for governing who is
authorized to view your app.
# NOT RUN { # This is the example from crunchyBody(), adding these alternate bodies: shinyUI(fluidPage( tags$head( # This is content that will always be rendered tags$title("My secure app") ), crunchyBody( # This is content that only is rendered if the user is authorized fluidRow( column(6, h1("Column 1")), column(6, h1("Column 2")) ) ), crunchyPublicBody( # This is shown to visitors who are not logged into Crunch at all h1("Please log into Crunch.") ), crunchyUnauthorizedBody( # This is for Crunch users who don't meet your authorization criteria # Perhaps they don't have access to a particular dataset h1("You don't have access to this app."), tags$div("Contact<EMAIL_ADDRESS>) )) # }