text:
---
title: e-Commerce and LifeStyle
tags: ["application", "lifestle", "commerce"]
weight: 1
description: >
  e-Commerce and LifeStyle
aliases:
  - /modules/bigdataapplications/2019/applications/lifestyle
---

Recommender systems operate under the hood of such widely recognized
sites as Amazon, eBay, Monster and Netflix where everything is a
recommendation. This involves a symbiotic relationship between vendor
and buyer whereby the buyer provides the vendor with information about
their preferences, while the vendor then offers recommendations tailored
to match their needs. Kaggle competitions h improve the success of the
Netflix and other recommender systems. Attention is paid to models that
are used to compare how changes to the systems affect their overall
performance. It is interesting that the humble ranking has become such a
dominant driver of the world's economy. More examples of recommender
systems are given from Google News, Retail stores and in depth Yahoo!
covering the multi-faceted criteria used in deciding recommendations on
web sites.

The formulation of recommendations in terms of points in a space or bag
is given where bags of item properties, user properties, rankings and
users are useful. Detail is given on basic principles behind recommender
systems: user-based collaborative filtering, which uses similarities in
user rankings to predict their interests, and the Pearson correlation,
used to statistically quantify correlations between users viewed as
points in a space of items. Items are viewed as points in a space of
users in item-based collaborative filtering. The Cosine Similarity is
introduced, the difference between implicit and explicit ratings and the
k Nearest Neighbors algorithm. General features like the curse of
dimensionality in high dimensions are discussed. A simple Python k
Nearest Neighbor code and its application to an artificial data set in 3
dimensions is given. Results are visualized in Matplotlib in 2D and with
Plotviz in 3D. The concept of a training and a testing set are
introduced with training set pre labeled. Recommender system are used to
discuss clustering with k-means based clustering methods used and their
results examined in Plotviz. The original labelling is compared to
clustering results and extension to 28 clusters given. General issues in
clustering are discussed including local optima, the use of annealing to
avoid this and value of heuristic algorithms.

## Recommender Systems {#s-recommender}

We introduce Recommender systems as an optimization technology used in a
variety of applications and contexts online. They operate in the
background of such widely recognized sites as Amazon, eBay, Monster and
Netflix where everything is a recommendation. This involves a symbiotic
relationship between vendor and buyer whereby the buyer provides the
vendor with information about their preferences, while the vendor then
offers recommendations tailored to match their needs, to the benefit of
both.

There follows an exploration of the Kaggle competition site, other
recommender systems and Netflix, as well as competitions held to improve
the success of the Netflix recommender system. Finally attention is paid
to models that are used to compare how changes to the systems affect
their overall performance. It is interesting how the humble ranking has
become such a dominant driver of the world's economy.

[![Presentation](images/presentation.png) Lifestyle Recommender
(45)](https://drive.google.com/open?id=0B6wqDMIyK2P7YkIwczVfQlJqVG8)

### Recommender Systems as an Optimization Problem

We define a set of general recommender systems as matching of items to
people or perhaps collections of items to collections of people where
items can be other people, products in a store, movies, jobs, events,
web pages etc. We present this as "yet another optimization problem".

[![Video](images/video.png) Recommender Systems I
(8:06)](https://www.youtube.com/watch?v=kO023BIW2dw)

### Recommender Systems Introduction

We give a general discussion of recommender systems and point out that
they are particularly valuable in long tail of tems (to be recommended)
that are not commonly known. We pose them as a rating system and relate
them to information retrieval rating systems. We can contrast
recommender systems based on user profile and context; the most familiar
collaborative filtering of others ranking; item properties; knowledge
and hybrid cases mixing some or all of these.

[![Video](images/video.png) Recommender Systems Introduction
(12:56)](https://youtu.be/KbjBKrzFYKg)

### Kaggle Competitions

We look at Kaggle competitions with examples from web site. In
particular we discuss an Irvine class project involving ranking jokes.

[![Video](images/video.png) Kaggle Competitions: (3:36)](https://youtu.be/DFH7GPrbsJA)

> ![Warning](images/warning.png) *Please not that we typically do not accept any projects
> using kaggle data for this classes. This class is not about winning a
> kaggle competition and if done wrong it does not fullfill the minimum
> requiremnt for this class. Please consult with the instructor.*

### Examples of Recommender Systems

We go through a list of 9 recommender systems from the same Irvine
class.

[![Video](images/video.png) Examples of Recommender Systems
(1:00)](https://youtu.be/1Eh1epQj-EQ)

### Netflix on Recommender Systems

We summarize some interesting points from a tutorial from Netflix for
whom *everything is a recommendation*. Rankings are given in multiple
categories and categories that reflect user interests are especially
important. Criteria used include explicit user preferences, implicit
based on ratings and hybrid methods as well as freshness and diversity.
Netflix tries to explain the rationale of its recommendations. We give
some data on Netflix operations and some methods used in its recommender
systems. We describe the famous Netflix Kaggle competition to improve
its rating system. The analogy to maximizing click through rate is given
and the objectives of optimization are given.

[![Video](images/video.png) Netflix on Recommender Systems
(14:20)](https://www.youtube.com/watch?v=ModhdIT9D24)

Next we go through Netflix's methodology in letting data speak for
itself in optimizing the recommender engine. An example iis given on
choosing self produced movies. A/B testing is discussed with examples
showing how testing does allow optimizing of sophisticated criteria.
This lesson is concluded by comments on Netflix technology and the full
spectrum of issues that are involved including user interface, data, AB
testing, systems and architectures. We comment on optimizing for a
household rather than optimizing for individuals in household.

[![Video](images/video.png) Consumer Data Science (13:04)](https://youtu.be/B8cjaOQ57LI)

### Other Examples of Recommender Systems

We continue the discussion of recommender systems and their use in
e-commerce. More examples are given from Google News, Retail stores and
in depth Yahoo! covering the multi-faceted criteria used in deciding
recommendations on web sites. Then the formulation of recommendations in
terms of points in a space or bag is given.

Here bags of item properties, user properties, rankings and users are
useful. Then we go into detail on basic principles behind recommender
systems: user-based collaborative filtering, which uses similarities in
user rankings to predict their interests, and the Pearson correlation,
used to statistically quantify correlations between users viewed as
points in a space of items.

[![Presentation](images/presentation.png) Lifestyle Recommender
(49)](https://drive.google.com/open?id=0B6wqDMIyK2P7UVloVElaZ2FXcTg)

We start with a quick recap of recommender systems from previous unit;
what they are with brief examples.

[![Video](images/video.png) Recap and Examples of Recommender Systems
(5:48)](https://www.youtube.com/watch?v=PwS8UE4TDS4)

#### Examples of Recommender Systems

We give 2 examples in more detail: namely Google News and Markdown in
Retail.

[![Video](images/video.png) Examples of Recommender Systems
(8:34)](https://youtu.be/og07mH9fU0M)

#### Recommender Systems in Yahoo Use Case Example

We describe in greatest detail the methods used to optimize Yahoo web
sites. There are two lessons discussing general approach and a third
lesson examines a particular personalized Yahoo page with its different
components. We point out the different criteria that must be blended in
making decisions; these criteria include analysis of what user does
after a particular page is clicked; is the user satisfied and cannot
that we quantified by purchase decisions etc. We need to choose
Articles, ads, modules, movies, users, updates, etc to optimize metrics
such as relevance score, CTR, revenue, engagement.These lesson stress
that if though we have big data, the recommender data is sparse. We
discuss the approach that involves both batch (offline) and on-line
(real time) components.

[![Video](images/video.png) Recap of Recommender Systems II
(8:46)](https://youtu.be/FBn7HpGFNvg)

[![Video](images/video.png) Recap of Recommender Systems III
(10:48)](https://youtu.be/VS2Y4lAiP5A)

[![Video](images/video.png) Case Study of Recommender systems
(3:21)](https://youtu.be/HrRJWEF8EfU)

#### User-based nearest-neighbor collaborative filtering

Collaborative filtering is a core approach to recommender systems. There
is user-based and item-based collaborative filtering and here we discuss
the user-based case. Here similarities in user rankings allow one to
predict their interests, and typically this quantified by the Pearson
correlation, used to statistically quantify correlations between users.

[![Video](images/video.png) User-based nearest-neighbor collaborative filtering I
(7:20)](https://youtu.be/lsf_AE-8dSk)

[![Video](images/video.png) User-based nearest-neighbor collaborative filtering II
(7:29)](https://youtu.be/U7-qeX2ItPk)

#### Vector Space Formulation of Recommender Systems

We go through recommender systems thinking of them as formulated in a
funny vector space. This suggests using clustering to make
recommendations.

[![Video](images/video.png) Vector Space Formulation of Recommender Systems new
(9:06)](https://youtu.be/IlQUZOXlaSU)

### Resources

-   <http://pages.cs.wisc.edu/~beechung/icml11-tutorial/>

## Item-based Collaborative Filtering and its Technologies

We move on to item-based collaborative filtering where items are viewed
as points in a space of users. The Cosine Similarity is introduced, the
difference between implicit and explicit ratings and the k Nearest
Neighbors algorithm. General features like the curse of dimensionality
in high dimensions are discussed.

[![Presentation](images/presentation.png) Lifestyle Filtering
(18)](https://drive.google.com/open?id=0B6wqDMIyK2P7UExxVFc5YlpOZ28)

### Item-based Collaborative Filtering

We covered user-based collaborative filtering in the previous unit. Here
we start by discussing memory-based real time and model based offline
(batch) approaches. Now we look at item-based collaborative filtering
where items are viewed in the space of users and the cosine measure is
used to quantify distances. WE discuss optimizations and how batch
processing can help. We discuss different Likert ranking scales and
issues with new items that do not have a significant number of rankings.

[![Video](images/video.png) Item Based Filtering
(11:18)](https://www.youtube.com/watch?v=HTdYGaOTlFI)

[![Video](images/video.png) k Nearest Neighbors and High Dimensional Spaces
(7:16)](https://youtu.be/SM8EJdAa4mw)

### k-Nearest Neighbors and High Dimensional Spaces {#s-knn}

We define the k Nearest Neighbor algorithms and present the Python
software but do not use it. We give examples from Wikipedia and describe
performance issues. This algorithm illustrates the curse of
dimensionality. If items were a real vectors in a low dimension space,
there would be faster solution methods.

[![Video](images/video.png) k Nearest Neighbors and High Dimensional Spaces
(10:03)](https://youtu.be/2NqUsDGQDy8)

#### Recommender Systems - K-Neighbors

Next we provide some sample Python code for the k Nearest Neighbor and its application to
an artificial data set in 3 dimensions. Results are visualized in
Matplotlib in 2D and with Plotviz in 3D. The concept of training and
testing sets are introduced with training set pre-labelled.
This lesson is adapted from the Python k Nearest Neighbor code found on the
web associated with a book by Harrington on Machine Learning [??]. There are
two data sets. First we consider a set of 4 2D vectors divided into two
categories (clusters) and use k=3 Nearest Neighbor algorithm to classify
3 test points. Second we consider a 3D dataset that has already been
classified and show how to normalize. In this lesson we just use
Matplotlib to give 2D plots.

The lesson goes through an example of using k NN classification
algorithm by dividing dataset into 2 subsets. One is training set with
initial classification; the other is test point to be classified by k=3
NN using training set. The code records fraction of points with a
different classification from that input. One can experiment with
different sizes of the two subsets. The Python implementation of
algorithm is analyzed in detail.

#### Plotviz

The clustering methods are used and their results examined in
Plotviz. The original labelling is compared to clustering results and
extension to 28 clusters given.  General issues in clustering are
discussed including local optima, the use of annealing to avoid this
and value of heuristic algorithms.

#### Files

- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/kNN.py>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/kNN_Driver.py>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/dating_test_set2.txt>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/clusterFinal-M3-C3Dating-ReClustered.pviz>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/dating_rating_original_labels.pviz>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/knn/clusterFinal-M30-C28.pviz>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/plotviz/clusterfinal_m3_c3dating_reclustered.pviz>
- <https://github.com/cloudmesh-community/book/blob/master/examples/python/plotviz/fungi_lsu_3_15_to_3_26_zeroidx.pviz>

### Resources k-means

-   <http://www.slideshare.net/xamat/building-largescale-realworld-recommender-systems-recsys2012-tutorial> [@www-slideshare-building]
-   <http://www.ifi.uzh.ch/ce/teaching/spring2012/16-Recommender-Systems_Slides.pdf> [@www-ifi-teaching]
-   <https://www.kaggle.com/> [@www-kaggle]
-   <http://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/CS77B_w12.html> [@www-ics-uci-welling]
-   [Jeff
    Hammerbacher](https://berkeleydatascience.files.wordpress.com/2012/01/20120117berkeley1.pdf)[@20120117berkeley1]
-   <http://www.techworld.com/news/apps/netflix-foretells-house-of-cards-success-with-cassandra-big-data-engine-3437514/> [@www-techworld-netflix]
-   <https://en.wikipedia.org/wiki/A/B_testing> [@wikipedia-ABtesting]
-   <http://www.infoq.com/presentations/Netflix-Architecture> [@www-infoq-architec]


text:
One Cerner Style Icons Changelog
=========

## v1.19.1 - 8/6/2018
* Minor update to 0179
	* Knocked out exlamation from triangle to fix "all gray" triangle.

## v1.19.0 - 7/2/2018
* New Icon Added for: Expand Row - 0183_arrowsInTwoLines_a.svg
	* new terra component for treeview has included funcitonality to expand or collapse the hierarchy (show or hide all children)... icons are needed to allow this functionality
* New Icon Added for: Collapse Row - 0184_arrowsOutTwoLines_a.svg
	* new terra component for treeview has included funcitonality to expand or collapse the hierarchy (show or hide all children)... icons are needed to allow this functionality
* New Icon Added for: Back and Forward in time - doubleChevronLeft & doubleChevronRight
	* In the Timeline Framework, we have a navigation concept using the chevron icons. A single chevron moves the view in that direction by one column, and a double chevron moves the view by a larger segment.

## v1.18.0 - 6/20/2018
* New Icon Added for: Simulation - 0182_monitor_a.svg
	* The icon will be used within Learning Framework to indicate content type as Simulation.
* New Icon Added for: Half Star Rating - 0056_star_a_half.svg
	* The icon will be used within Home Care Analytics for star rating system

## v1.17.0 - 5/24/2018
* Non-themable Terra icons had several legibility issues and so a "Low Light" version was needed. Below are the added icons in the low light color palette.
	* success_low_light
	* success_inverse_low_light
	* noRisk_low_light
	* scheduled_low_light
	* alert_low_light
	* warning_low_light
	* moderate_low_light
	* critical_low_light
	* critical_inverse_low_light
	* information_low_light
	* information_inverse_low_light
	* error_low_light
	* incomplete_low_light
	* incomplete_inverse_low_light
	* help_low_light
	* help_inverse_low_light
	* unexpected_low_light
	* dueSoon_low_light
	* overDue_low_light
	* high_low_light
	* high_inverse_low_light
	* low_low_light
	* noRisk_low_light
	* abnormal_low_light
	* deviceCheck_low_light
	* deviceAlert_low_light
	* diamond_low_light
	* pharmacyReject_low_light
	* bedAssigned_low_light
	* dischargeComplete_low_light
	* dischargeComingDue_low_light
	* dischargeOverDue_low_light
	* drugDrug_low_light
* Added new assets for Symbols, that will be used for the purpose of graphing libraries, like Carbon.
	* circle_symbol
	* diamond_symbol
	* plus_symbol
	* rectangle_symbol
	* square_symbol
	* teardrop_symbol
	* teardropUp_symbol
	* thinDiamond_symbol
	* triangle_symbol
	* triangleDown_symbol
	* x_symbol
* Updated exterior stroke weight on 'light outline' icons
	* noRisk
	* noRisk_low_light
	* moderate
	* moderate_low_light
	* high
	* high_low_light
	* high_inverse
	* high_inverse_low_light
	* low
	* low_low_light
	* abnormal
	* abnormal_low_light
	* partiallyMet
	* lowRisk
	* lowRisk_low_light

## v1.16.1 - 4/12/2018

* Swapped icons: flipHorizontal and flipVertical to accurately reflect icon use.
	* flipVertical becomes 0174_twoTrianglesRight_a.svg
	* flipHorizontal becomes 0174_twoTrianglesUp_a.svg
## v1.16.0 - 4/2/2018

* Added 'incomplete_inverse, to be used in the context of a selected table row.
* Added 'partiallyMet', where insurance has indicated that they will cover some of the visit, but not all of it. This will represent a state between the current two icons: Met, Not Met.

## v1.15.0 - 3/12/2018

* Added 'drugDrug', to be used in the context of a specific orderable, on a list of recommended (or saved) orders.
	* src/icons/0179_pill_exclamation_i_heavyTriangleYellow.svg

* Added 'secondaryProvider', units have a primary caregiver assigned who is responsible for the majority of care. Frequently they will also have a secondary caregiver assigned to them. In our assignment application, we allow the user to make both of these assignments.
	* src/icons/0180_people_a_light.svg

## v1.14.0 - 3/12/2018

* Added 'rollup' icon when an action header has many different icons and either has been pulled to a smaller responsive size or has too many icons for the space - they are all rolled up into this icon.
	* srsc/icons/0036_ellipsesVertical_a.svg
* Adding icon to hide items within a workspace
	* src/icons/0159_eye_i.svg
* Used to fit component to width
	* src/icons/0177_arrowsLeftRightLines_a.svg
* Used to fit component to scale
	* src/icons/0178_arrowsUpDownRightLeftLines_a.svg

## v1.13.1 - 2/20/2018

* Correct extension nameing bug in the csv

## v1.13.0 - 2/12/2018

* New icon added - 0176_stopwatch_pause_i.svg
	* The icon will be used in Transfer Center to indicate that a transfer request is on hold
* New icon added - 0037_exclamation_i_heavyDiamondWhite.svg
	* The icon will display on a selected row to improve visibility within Population Health Management
* New icon added - 0124_statusArrowUp_i_lightDiamondWhite.svg
	* The icon will display on a selected row to improve visibility within Population Health Management

## v1.12.0 - 1/8/2018

* Swapped icon numbers 0172 and 0173
	* Icon 0172 now accurately reflects the status of dischargeComingDue
	* Icon 0172 now accurately reflects the status of dischargeOverDue

## v1.11.0 - 1/8/2018

* Updated icons 0124
	* Included more space between arrows and diamond container shape for clarity.
* New Alias added for icon - 0037_exclamation_i_lightCircleGray.svg
	* Icon will be used to represent Moderate in the Risk indicator family
* New Icon Added - 0003_check_i_lightCircle.svg
	* Icon will be used to represent noRisk wihtin Risk indicator family.
* New Icon Added - 0169
	* Used within Transfer Center to indicate a bed has been requested.
* New Icon Added - 0170
	* Used within Transfer Center to indicate a bed assignment has been made.
* New Icon Added - 0171
	* Used within Cerner Care Team Assignments to indicate a patient's discharge has been completed
* New Icon Added - 0172
	* Used within Cerner Care Team Assignments to indicate a patient is past due on their discharge
* New Icon Added - 0173
	* Used within Cerner Care Team Assignments to indicate a patient's discharge is coming due
* New Icon Added - 0174
	* flipHorizontal - used within Media Gallery component to flip image horizontally
	* flipVertical - used within Media Gallery  omponent to flip image vertically
* New Icon Added - 0175
	* rotateRight - used within Media Gallery component to rotate an image right
	* rotateLeft - used within Media Gallery component o rotate an image left

## v1.10.0 - 1/8/2018

* New Icon Variant Added - 0039
	* Added a default black version of icon 0039, which will be used as a 404 error glyph.

## v1.9.0 - 1/5/2018

* New Icon Added - 0169
	* The icon will be used to represent a bed has been requested for a patient
* New Icon Added - 0170
	* The icon will be used to represent a bed has been assigned to a patient

## v1.8.0 - 11/21/2017

* New Icon Added - 0168
	* The icon will be used to represent an external link. Selecting the icon will take the user outside of their current application/solution.

## v1.7.1 - 11/20/2017

* Minor change to icon file name: src/icons/0116_cancel_a.svg
	* The icon previously did not have a usage in the file name. Included icon usage for action

## v1.7.0 - 11/2/2017

* New Icon Added: src/icons/0167_speakerCurvedLines_a.svg
	* Icon will be used to represent the concept of audio.
* New Icon Added: src/icons/0061_questionMark_i_lightCircle.svg
	* Icon will be used to represent when a question has been unanswered within Ontology Mapping.
* Icon Alias Update: Added the additional alias, featuredOutline, to 0056_star_a_light_off.svg

## v1.6.0 - 10/11/2017

* New Icon Added: src/icons/0166_pause_a.svg
* New Icon Added: src/icons/0127_bell_a_light_off.svg
	* This outlined version will be utilized as an off state for “reminders”

* Icon Updated: src/icons/0020_curvedDoubleArrowLeft_a.svg
	* Accessibility concerns with previous icon version. New icon version includes
more space between the arrow shape and the second arrow.

* Icon Name Updated: src/icons/0127_bell_a_heavy_on.svg
	* Updated naming convention to reflect toggle state documentation

* Icon Name Updated: src/icons/0056_star_a_light_off.svg
	* Changed icon variant from “outline” to “light”
	* Changed icon state to off

* Icon Name Updated: src/icons/0056_star_a_on.svg
	* Changed icon state to on

* Icon Name Updated: src/icons/0165_pinDown_a_on.svg
	* changed icon type from “t” to “a”

* Icon Name Updated: src/icons/0165_pinDiagonal_a_off.svg
	* changed icon type from “t” to “a”

## v1.5.0 - 9/27/2017

* Icon Added: src/icons/0165_pinDown_t.svg
* Icon Added: src/icons/0165_pinDiagonal_t.svg

## v1.4.3 - 9/05/2017

* Icon Updated: src/icons/0061_questionMark_i_diamond_light.svg

## v1.4.1 - 8/30/2017

* Icon Added: src/icons/0162_phoneArrowRight_i.svg
* Icon Added: src/icons/0163_phoneArrowLeft_i.svg
* Icon Added: src/icons/0164_phoneDownCurvedArrow_i.svg

## v1.3.1 - 8/03/2017

* Icon Added: src/icons/0139_phoneDown_a.svg

## v1.3.0 - 8/02/2017

* Icon Added: src/icons/0161_microphone_a.svg
* Icon Added: src/icons/0161_microphone_i_slash.svg
* Icon Added: src/icons/0150_videoCamera_i_slash.svg

## v1.2.4 - 7/24/2017

* Icon Added: src/icons/0160_arrowLeftarrowRight_i.svg

## v1.2.3 - 7/21/2017

* Icon Added: src/icons/0159_eye_i_slash.svg

## v1.2.2 - 6/15/2017

* Icon Updated: src/icons/0037_exclamation_i_lightCircleGray.svg
* Icon Updated: src/icons/0037_exclamation_i_heavyDiamondRed.svg
* Icon Updated: src/icons/0061_questionMark_i_diamond_light.svg
* Icon Updated: src/icons/0061_questionMark_a.svg,themeable

## v1.2.1 - 5/25/2017

* Icon Updated: src/icons/0147_circle_i_light.svg

## v1.2.0 - 5/23/2017

* Icon Added: src/icons/0157_exclamation_i_lightCircleGray.svg
* Icon Added: src/icons/0158_pencilSquare_a.svg

## v1.1.0 - 5/18/2017

* Icon Added: 0156_questionMark_i_diamond_light.svg

## v1.0.0 - 3/28/2017

* Initial stable release

repository: ossVerifier/one-cerner-style-icons

branch_name: refs/heads/master

extension: md

license_type: permissive

repo_name: ossVerifier/one-cerner-style-icons


text:
# yang-express

YANG model-driven Express framework

Minimalistic web framework leveraging powerful YANG schema expressions
according to [RFC 6020](http://tools.ietf.org/html/rfc6020). Generates
dynamic model-driven interfaces with flexible plugin system.

  [![NPM Version][npm-image]][npm-url]
  [![NPM Downloads][downloads-image]][downloads-url]

## Installation

```bash
$ npm install -g yang-express
```
The preferred installation is *global* for easy access to the
`yang-express` utility but can also be used as a dependency module to
enable YANG model-driven express app as part of your project.

For development/testing, clone from repo and initialize:

```bash
$ git clone https://github.com/corenova/yang-express
$ cd yang-express
$ npm install
```

## Features

* Robust model-driven routing
* Hotplug runtime models
* [Dynamic interface generators](#dynamic-interface-generators)
* Hierarchical (deeply nested) data tree
* Adaptive validations
* Flexibe RPCs and notifications

## Quick Start

```bash
$ yang-express example/example-petstore.yang
```

The above example will import the `example-petstore` YANG module and
start an instance of `yang-express` listening on port 5000 with
`restjson` feature enabled.

```
  Usage: yang-express [options] modules...

  Options:
      -p, --port <number>    Run yang-express on <port> (default: 5000)
      -f, --feature <name>   Enable one or more features: (restjson, openapi, etc.)
```

You can run `yang-express` inside your own project and it will
dynamically import one or more `modules` and route them using the
`feature` plugins specified.

You can also use it as a library module:

```coffeescript
require 'yang-js'
opts =
  port: 5000
  feature: [ 'restjson', 'openapi' ]
  modules: [ 'ietf-yang-library' ]
express = require('yang-express').eval()
express.in('run')
  .invoke opts
  .then  (res) -> console.log "running"
  .catch (err) -> console.error err
```

For more information on programmatic usage, be sure to take a look at
the References listed below.

## References

This module is a YANG model-driven data module, which is essentially a
composition of the [YANG Schema](./yang-express.yang) and
[Control Binding](./src/yang-exress.coffee).  It is designed to model
middleware routing runtime configuration and can be utilized with or
without an actual [Express](http://expressjs.com) instance.

- [Apiary Documentation](http://docs.yangexpress.apiary.io)
- [Using YANG with JavaScript](http://github.com/corenova/yang-js)
- [Using Model API](http://github.com/corenova/yang-js#model-instance)

## Examples

**PetStore** is a simple example based on the provided spec sample in the
[OpenAPI Specification 2.0](http://github.com/OAI/OpenAPPI-Specification)
project.

```bash
$ npm run example:petstore
```

When the `yang-express` app runs, it will auto-generate the data model
using the [example-petstore.yang](./example/example-petstore.yang)
schema and dynamically route the following endpoints utilizing the
[restjson](./src/feature/restjson.coffee) dynamic interface
generator:

endpoint        | methods    | description
---             | ---        | ---
/pet            | **CRUMDO** | operate on the pet collection
/pet/:id        | **RUMDO**  | operate on a specific pet
/pet/:id/:leaf  | **RUMDO**  | operate on a pet's attribute
/pet/:leaf      | **RUMDO**  | bulk operate attributes*

This example runs using the
[sample data](./config/example-petstore.yaml) found inside the
`config` directory.

### CRUMDO

- C: CREATE (POST)
- R: READ (GET)
- U: UPDATE (PUT)
- M: MODIFY (PATCH)
- D: DELETE
- O: OPTIONS

Alternative API endpoints can be fully-qualified `/petstore:pet/...`
as well as prefix-qualified `/ps:pet/...`. This is the suggested
convention when using multiple models that may have namespace
conflict (if mounted together at '/').

**Note**: Bulk operation on all matching attributes can be used to set a new
value for every matching attribute in the collection.

## Dynamic Interface Generators

name | description
--- | ---
[restjson](./src/feature/restjson.coffee)   | REST/JSON API
[openapi](./src/feature/openapi.coffee)     | OpenAPI/Swagger 2.0 spec
[websocket](./src/feature/websocket.coffee) | [socket.io](http://socket.io)

## Tests

To run the test suite, first install the dependencies, then run `npm
test`:
```
$ npm install
$ npm test
```

## License
  [Apache 2.0](LICENSE)

This software is brought to you by
[Corenova Technologies](http://www.corenova.com). We'd love to hear
your feedback.  Please feel free to reach me at<EMAIL_ADDRESS>anytime with questions, suggestions, etc.

[npm-image]: https://img.shields.io/npm/v/yang-express.svg
[npm-url]: https://npmjs.org/package/yang-express
[downloads-image]: https://img.shields.io/npm/dt/yang-express.svg
[downloads-url]: https://npmjs.org/package/yang-express

repository: corenova/yang-express

gha_language: JavaScript

repo_name: corenova/yang-express


text:
# NvmeService

## Properties

Name | Type | Description | Notes
------------ | ------------- | ------------- | -------------
**Links** | [**InlineResponse201Links**](inline_response_201__links.md) |  | [optional]
**Enabled** | **bool** | The administrative state of the NVMe service. The NVMe service can be disabled to block all NVMe connectivity to the SVM.<br/> This is optional in POST and PATCH. The default setting is _true_ (enabled) in POST.  | [optional] [default to true]
**Svm** | [**AuditSvm**](audit_svm.md) |  | [optional]

[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

license: MIT

gha_language: Go

gha_license_id: NOASSERTION


text:
# Sample Markdown

# Heading

This file contains some example markdown syntax, more can be found all over the [internet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).

## Sub Heading 1

It's a _hug_, Michael. I'm **hugging** you. That's why you always leave a note! But I bought a yearbook ad from you, doesn't that mean anything anymore? Army had half a day.

[I hear the jury's still out on science](http://arresteddevelopment.wikia.com/wiki/G.O.B.). Whoa, this guy's straight? Across from where? There's only one man I've ever called a coward, and that's Brian Doyle Murray. No, what I'm calling you is a television actor.

### Sub Heading 2

He'll want to use your yacht, and I don't want this thing smelling like fish. I don't understand the question, and I won't respond to it. I'm half machine. I'm a monster. Steve Holt!

## Quote/Note

> This is a quote or more commonly used as a notification box. You can use **bold**, _italics_ and [links](http://google.com) here too.

## Images

Images will be converted into binary format and embedded in the document so they can be viewed offline. Image paths are relative to the location of the markdown file.

![Persistance of Memory](persistance-of-memory.png "Persistence of Memory - Dali")

## Lists

### Numbered Lists

1. Task A
2. Task B
3. Task C

### Unordered lists

* Salt
* Pepper
* Sugar

## References

Reference style links can be made using the `^` character in reference style links[^1]. The numbering[^3] will be sorted out automatically if the references are numbered in the wrong order[^2].

[^1]: [First reference](#)
[^3]: [Second reference](#)
[^2]: [Third reference](#)

## Code

```python
import os

def main():
    print('Hello World')

main()
```

## Table

Column 1 | Column 2 | Column 3
--- | --- | ---
A | B | C
1 | 2 | 3
D | E | F
4 | 5 | 6

## Mathematic equation

Uses [MathJax](https://www.mathjax.org/) for rendering and [TeX](https://en.wikipedia.org/wiki/TeX) syntax for markup. Automatically detects lines enclosed with `$$` and adds the library if it is detected. Only display format is supported, not single `$` inline format. Note also that the equation must have newlines preceding and following it to render properly.

$$
e^{i\pi} = -1
$$

$$\sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6}$$


text:
# Rolling-a-dice
This is a classic rolling the dice game created with Python

## Technologies Used
Python code

## How to use?
Open the file me.py in your Python text editor

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)

repository: Anugya-Gogoi/Rolling-a-dice

repo_name: Anugya-Gogoi/Rolling-a-dice


text:
# Demo of a Self-Driving Car Steering Model for ClusterOne

This is a basic self-steering car model implemented in TensorFlow. It is used as a demo project to get started with the [ClusterOne](https://clusterone.com) deep learning computation platform.

Follow the [Getting Started guide](https://docs.clusterone.com/v1.0/docs/getting-started) for ClusterOne and read the author's blog post about building this demo [here](https://medium.com/towards-data-science/what-i-learnt-building-a-simple-self-steering-car-in-tensorflow-c8d7cab6f6d) and [here](https://medium.com/@malomarrec/how-to-write-distributed-tensorflow-code-with-an-example-on-tensorport-70bf3306adcb).

## Table of Contents

- [Install](#install)
- [Usage](#usage)
- [License](#license)

## Install

To run this project, you need:

- [Python](https://python.org) 2.7 or 3.5 (or higher).
- [Git](https://git-scm.com/) and [Git Large File Storage (LFS)](https://git-lfs.github.com/)
- The TensorFlow Python library. Get it with `pip install tensorflow`
- The ClusterOne Python library. Install it with `pip install clusterone`
- A ClusterOne account. Sign up [here](https://clusterone.com) for free if you don't have an account yet.

### Create the dataset

The model uses the Comma dataset, download it [here](https://tensorport-public.s3.amazonaws.com/comma-train.zip). Unzip the downloaded file and `cd` into the directory of the data.

Create a new repository with `git init`.

Use Git LFS to deal with the large files in the dataset: `git lfs track *.h5`.

Add and commit the changes with `git add -A`. This can take a moment due to the data size.

Log into your ClusterOne account using `just login`.

Create a new dataset with `just create dataset`.

### Get the code ready

Clone this repository to your local machine.

In [main_tf.py](/main_tf.py), change the flags at the top of the script to their appropriate values:

- `CLUSTERONE_USERNAME`: your ClusterOne username. This should be something like `johndoe`, not your email address!
- `LOCAL_LOG_LOCATION`: a location where you want the log files to be stored when running ClusterOne on your local machine. Example: `~/Documents/self-driving-demo/logs/`
- `LOCAL_DATASET_LOCATION`: the location of the comma dataset you downloaded above. Example: `~/Documents/data/comma`

## Usage

You can run the model either on your local machine or online on the ClusterOne platform. Running a model is simple with the `just` command line tool that comes included with the ClusterOne Python package. Running the model locally is recommended for testing before uploading the code to ClusterOne.

### Run locally

`cd` into the repository folder, then run the model on your local machine with:

```shell
just run --local --requirements requirements.txt --mode single-node
```

In this example, single-node execution is selected. To create a distributed job, use the `--mode distributed` option instead of `--mode single-node`. This will simulate a distributed environment on your local machine.

### Run on ClusterOne

To run the model on ClusterOne, you first need a ClusterOne account. Log in with `just login`.

Create a new project with `just create project`.

Push the project code to ClusterOne with `git push clusterone master`.

Then, navigate to your dataset folder and push the dataset to ClusterOne as well using `git push clusterone master`. This transfer will probably take a little while since the dataset is pretty large.

When the upload is complete, run the model on ClusterOne using:

```bash
$ just create job --name first-job --project YOUR_USERNAME/YOUR_PROJECT_NAME \
--datasets YOUR_USERNAME/YOUR_DATASET_NAME --module main_tf \
--framework-version 1.0.0 --requirements requirements.txt \
--mode distributed
--worker-type p2.xlarge --worker-replicas 3 \
--ps-type c4.2xlarge --ps-replicas 1 \
--time-limit 1h --description "This is the first run"
```

You can monitor the execution of your job on ClusterOne using `just watch`.

Instead of running the model from the command line, you can also use ClusterOne's graphical web interface [Matrix](https://clusterone.com/matrix).

For a more detailed guide on how to run this project on ClusterOne, check out the [Getting Started guide](https://docs.clusterone.com/v1.0/docs/getting-started). To learn more about ClusterOne, visit our [website](https://clusterone.com).

## License

[MIT](LICENSE) © ClusterOne Inc.

Comma dataset and [data_reader.py](utils/data_reader.py) by [comma.ai](https://github.com/commaai/research), licensed as [specified](LICENSE_COMMA).

repository: jb-brown/clusterone

repo_name: jb-brown/clusterone


text:
## import

* import containerpage
* import

* two mode :
    * save Model in sql (Remote)
    * convert Model to json (Local)

* add sync button

* sync database(in process...)
    * resolve ref

* fn, fnServer

* template (processing...)

* log system

* change css file at web (maybe with sass)

* sync folder to Web
    * server App folder by server
    * class
* cms-element :

* intergrate with git and facebook
    * invite
    * create game (giai do)

* test gridlayout for container

```
<Button text="Link" class="link" [nsRouterLink]="['/MainPage']"></Button>
```

* update

* refactor code to module (processing...)

* another way to save data (better than file)

repository: anhoev/cms-mobile

repo_name: anhoev/cms-mobile


text:
# @loopback/rest

The REST API package for
[loopback-next](https://github.com/strongloop/loopback-next).

## Overview

This component provides a REST server for your application instances, complete
with:

- new custom routing engine (special thanks to @bajtos)!
- tools for defining your application routes
- OpenAPI 3.0 spec (`openapi.json`/`openapi.yaml`) generation using
  `@loopback/openapi-v3`
- a default sequence implementation to manage the request and response lifecycle

## Installation

To use this package, you'll need to install `@loopback/rest`.

```sh
npm i @loopback/rest
```

## Basic Use

Here's a basic "Hello World" application using `@loopback/rest`:

```ts
import {RestApplication, RestServer} from '@loopback/rest';

const app = new RestApplication();
app.handler(({request, response}, sequence) => {
  sequence.send(response, 'hello world');
});

(async function start() {
  await app.start();

  const rest = await app.getServer(RestServer);
  const port = await server.get('rest.port');
  console.log(`Server is running at http://<IP_ADDRESS>:${port}`);
})();
```

## Configuration

The rest package is configured by passing a `rest` property inside of your
Application options.

```ts
const app = new RestApplication({
  rest: {
    port: 3001,
  },
});
```

### `rest` options

| Property | Type            | Purpose                                                                                |
| -------- | --------------- | --------------------------------------------------------------------------------------------------------- |
| port     | number          | Specify the port on which the RestServer will listen for traffic.                                         |
| sequence | SequenceHandler | Use a custom SequenceHandler to change the behavior of the RestServer for the request-response lifecycle. |

## Contributions

- [Guidelines](https://github.com/strongloop/loopback-next/blob/master/docs/CONTRIBUTING.md)
- [Join the team](https://github.com/strongloop/loopback-next/issues/110)

## Tests

Run `npm test` from the root folder.

## Contributors

See
[all contributors](https://github.com/strongloop/loopback-next/graphs/contributors).

## License

MIT

repository: heartstchr/loopback-next

repo_name: heartstchr/loopback-next


text:
# OpenShift Container Platform 4.4 on Azure with Microsoft Windows Server 2019/1809 Nodes

**Developer Preview**

This document is tested against a Fedora release 31 Linux host for running all
the Linux commands.

Please note that a command preceded by `>` is to be run in a PowerShell window
on a Windows instance, and a command preceded by `$` is to be run on a Linux
console (localhost).

## Pre-requisites

Complete these prerequisites before proceeding:

1. [Install prerequisite packages](../ocp-4-4-with-windows-server.md#installation-of-prerequisite-packages)
2. [Bring up the OpenShift cluster with ovn-kubernetes](../ocp-4-4-with-windows-server.md#bring-up-the-openshift-cluster-with-ovn-kubernetes)
3. [Configure Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest)
4. [Create a service principal](https://docs.openshift.com/container-platform/4.4/installing/installing_azure/installing-azure-account.html#installation-azure-service-principal_installing-azure-account)
5. [Create the install-config](../ocp-4-4-with-windows-server.md#create-the-install-config)
6. [Create manifests](../ocp-4-4-with-windows-server.md#create-manifests)
7. [Configure OVNKubernetes on a Hybrid cluster](../ocp-4-4-with-windows-server.md#configuring-ovnkubernetes-on-a-hybrid-cluster)
8. [Disable Azure rate limiting](#disable-azure-rate-limiting)
9. [Create the cluster](../ocp-4-4-with-windows-server.md#create-the-cluster)
10. [Verify Hybrid networking](../ocp-4-4-with-windows-server.md#verify-hybrid-networking)

## Bring up the Windows node

Launch a *Windows 2019 Server Datacenter with Containers* instance and add it to
the cluster.

### Windows Node Installer

Windows Node Installer (WNI) is a tool that can automate the process of
launching a Windows instance and prepare it for bootstrapping. For more details,
refer to the
[README](https://github.com/openshift/windows-machine-config-bootstrapper/tree/release-4.4/tools/windows-node-installer).

Download WNI v4.4.3-alpha on your Linux host and ensure you can execute it:

```sh
$ wget https://github.com/openshift/windows-machine-config-bootstrapper/releases/download/v4.4.3-alpha/wni
$ chmod +x wni
```

Azure stores the credentials as a JSON file at `~/.azure/osServicePrincipal.json`
by default. `dir` is an optional argument. The directory path provided should
exist on the system. Default is the current directory.

The following command creates the instance in your Azure account and stores
credentials for logging into the instance in the directory path:

```sh
$ ./wni azure create --kubeconfig <path_to_openshift_cluster>/auth/kubeconfig --credentials ~/.azure/osServicePrincipal.json --image-id MicrosoftWindowsServer:WindowsServer:2019-Datacenter-with-Containers:latest --instance-type Standard_D2s_v3 --dir <directory>
```

This command outputs the private and public IP required for creating the host
file in the [Setup Ansible connection](#setup-ansible-connection) step.

You can now [setup Ansible](#setup-ansible-connection) before moving on to the
next steps.

If the Windows node cannot be brought up due to some reason using the automated
Windows Node Installer (WNI) tool, the Windows node can also be brought up
manually using the steps outlined in the [Appendix](#appendix) section.

#### Setup Ansible connection

Now we can use Ansible to configure the Windows host. On the Linux host, install
`ansible` and `pywinrm`, as well as `selinux-python` bindings:

**Note:** This step assumes that python3 is installed on the system. Ansible
will not work without python.

```sh
$ sudo dnf install python3-libselinux
$ pip install ansible==2.9 pywinrm selinux --user
```

Create a hosts file with the following information:

```
[win]
<public_ip> ansible_password=<password> private_ip=<private_ip>

[win:vars]
ansible_user=<username>
cluster_address=<cluster_address>
ansible_connection=winrm
ansible_ssh_port=5986
ansible_winrm_server_cert_validation=ignore
```

`<cluster_address>` is the cluster endpoint. It is the combination of your cluster
name and the base domain for your cluster.

```sh
$ oc cluster-info | head -n1 | sed 's/.*\/\/api.//g'| sed 's/:.*//g'
```

`<public_ip>` is the public IP address of the Windows instance and can
be found under <Windows_node_name>-ip (look at the screenshot below).
Please provide the IPv4 public IP here as `<public_ip>`.

![](../images/azure-public-ip.png)

`<private_ip>` is the private IP of the node and can be found in the output of
the Windows node creation using the automated [WNI](#windows-node-installer)
tool.

`<username>` and `<password>` are the login credentials for Windows instance. Note
the username listed here must have administrative privileges.

Here is an example hosts file:

```
[win]
<IP_ADDRESS> ansible_password='mypassword’ private_ip=<IP_ADDRESS>

[win:vars]
ansible_user=core
cluster_address=winc-cluster.winc.azure.devcluster.openshift.com
ansible_connection=winrm
ansible_ssh_port=5986
ansible_winrm_server_cert_validation=ignore
```

Test if Ansible is able to communicate with the Windows instance with the
following command:

```sh
$ ansible win -i <name_of_the_hosts_file> -m win_ping -v
```

**Note:** If you do not want to provide the password in the hosts file, you can
provide the same as an extra variable to any ansible command. For example, the
above command could be executed as:

```sh
$ ansible win -i <name_of_the_hosts_file> -m win_ping -v --extra-vars "ansible_password=<password>"
```

### Bootstrap the Windows node

On a Linux host, run the Ansible Playbook that transfers the necessary files
onto the Windows instance and bootstraps it so that it can join the cluster as a
worker node.

**Note:** Playbook assumes you have `jq `installed. Your active RDP connection
might be disrupted during the execution of the playbook.

Clone the GitHub repository to download the Ansible playbook and all the required
dependencies.

```sh
$ git clone https://github.com/openshift/windows-machine-config-bootstrapper.git
$ git fetch && git checkout release-4.4
```

Run the Ansible playbook to bootstrap the windows worker node. Make sure you
have at least 6GB of free space in the `/tmp` directory.

```sh
$ ansible-playbook -i <path_to_hosts_file> windows-machine-config-bootstrapper/tools/ansible/tasks/wsu/main.yaml -v
```

```sh
$ oc get nodes -l kubernetes.io/os=windows
```

You can now see the Windows instance has joined the cluster:

```sh
NAME                                        STATUS   ROLES    AGE     VERSION
winworker-obm7a                             Ready    worker   2m11s   v1.17.1
```

#### API rate limit exceeded error when running WSU
WSU playbook uses GitHub API to fetch releases for WMCB. You might encounter an
API rate limit exceeded error while running WSU playbook in `TASK [Get release]`
and `TASK [Get latest 0.8.x cni plugins version]`. The issue occurs due to
GitHub rate-limiting unauthenticated requests at 60 requests per hour. As a
workaround, wait for the rate-limit to reset (at most 1 hour) before running the
playbook again.

## Test Windows workload

You can now create a pod that can be deployed on a Windows instance. Here is an example
[WebServer](https://gist.githubusercontent.com/suhanime/683ee7b5a2f55c11e3a26a4223170582/raw/d893db98944bf615fccfe73e6e4fb19549a362a5/WinWebServer.yaml)
deployment to create a pod.

**Note:** Given the size of Windows images, it is recommended to pull the Docker
image `mcr.microsoft.com/windows/servercore:ltsc2019` on the instance first,
before creating the pods.

On a Windows instance, run the following command in a PowerShell window:

```pwsh
> docker pull mcr.microsoft.com/windows/servercore:ltsc2019
```

**Note:** Refer [RDP section](#rdp-setup) to set up and RDP into your Windows
node.

On the Linux host, deploy the pods:

```sh
$ oc create -f https://gist.githubusercontent.com/suhanime/683ee7b5a2f55c11e3a26a4223170582/raw/d893db98944bf615fccfe73e6e4fb19549a362a5/WinWebServer.yaml -n default
```

Once the deployment has been created, you can check the status of the pods:

```sh
$ oc get pods -n default
NAME                             READY   STATUS    RESTARTS   AGE
win-webserver-6f5bdc5b95-x65tq   1/1     Running   0          14m
```

You have created a service of
[LoadBalancer](https://kubernetes.io/docs/concepts/services-networking/service/#loadbalancer)
type:

```sh
$ oc get service win-webserver -n default
NAME            TYPE           CLUSTER-IP    EXTERNAL-IP   PORT(S)        AGE
win-webserver   LoadBalancer   <IP_ADDRESS>  <IP_ADDRESS>  80:31412/TCP   17m
```

```sh
$ curl <IP_ADDRESS>:80
<html><body><H1>Windows Container Web Server</H1><p>IP <IP_ADDRESS> callerCount 4 </body></html>
```
### Deploying in a namespace other than default

To deploy into a different namespace, SCC must be disabled in that
namespace. This should never be used in production, and any namespace that this
has been done to should not be used to run Linux pods.

To skip SCC for a namespace, the label `openshift.io/run-level = 1` should be
applied to the namespace. This applies to both Linux and Windows pods, and
thus Linux pods should not be deployed into this namespace.

For example, to create a new project and apply the label, run the following
commands:

```sh
$ oc new-project <project_name>
$ oc label namespace <project_name> "openshift.io/run-level=1"
```

## Appendix

### Bring up the Windows node using the Azure portal

1. Log in to the [Azure portal](https://portal.azure.com/).

2. Navigate to the resource group related to your cluster to see the masters and
workers running.

3. Select *Create a resource* in the left menu:

    ![](../images/azure-create-resource.png)

4. Select *Virtual Machine* under Compute:

    ![](../images/azure-vm.png)

5. Provide your cluster’s resource group and its region as the *Resource Group*
and *Region*. This ensures the instance is created in the same region and is a
part of the OpenShift cluster. The image should be *Windows Server 2019 Datacenter with Containers*
and the size should be *Standard_D2s_v3*.

    **Note:** The image might not be populated in the drop down automatically.

    ![](../images/azure-resource-group-region.png)

    ![](../images/azure-admin-account.png)

6. In the Networking tab, ensure the Virtual network points to the same
virtual network to which your cluster belongs, and the subnet points to the *worker
subnet* of the cluster. You should also have a public IP created, so you can RDP
into the instance:

    ![](../images/azure-networking.png)

7. In the Load balancing section, enable the load balancer of type *Azure load
balancer* and place the virtual machine behind the same load balancer as other
worker nodes — a load balancer which is neither the `internal-lb` nor the
`public-lb` as highlighted in the image below.

    ![](../images/azure-set-load-balancer.png)

    You might see 3 different load balancers similar to the ones shown in the
    image. Select the load balancer which corresponds to the name of your
    cluster. In the image below, `pmahajan-az-msj6n` is the load balancer that
    needs to be picked up for this example workflow.

    ![](../images/azure-load-balancer.png)

8. Under the Tags section, pick the tag of your cluster and set the value to
`owned`:

    ![](../images/azure-tags.png)

9. Review and create the instance:

    ![](../images/azure-create-instance.png)

    The portal will notify you when the instance is ready:

    ![](../images/azure-create-instance-complete.png)

10. Now you must set the security groups that will allow you to RDP to the node.
Under your resource group, find the Network Security Group for the
`<cluster name>-node-nsg`; this is the worker network security group.

    ![](../images/azure-security-groups.png)

11. Select the *Inbound security rules* on the left panel:

    ![](../images/azure-inbound-security-rules.png)

12. Add the following rules that will allow you to RDP into the instance,
remotely manage with Ansible, and open up communications from within the
cluster. The *Source IP* for RDP and WinRM is the public IP address of your
machine followed by `/32`.

    You can get your public IP address by running the following command:

    ```sh
    $ curl ifconfig.co
    ```

    Example security rule to allow access through RDP:

    ![](../images/azure-security-rdp.png)

    Example security rule for WinRM:

    ![](../images/azure-security-winrm.png)

    Example security rule to open up communication within the cluster:

    ![](../images/azure-security-comms.png)

    The final rules should look like the following:

    ![](../images/azure-security-rules-final.png)

13. Note the private IP of your Windows worker node. This is required
for creating the host file in the [Setup Ansible Connection](#setup-ansible-connection)
step.

    Now you can [setup Ansible](#setup-ansible-connection)

#### RDP Setup

##### Install an RDP tool:

For Fedora, we use `freerdp`.

```sh
$ sudo dnf install freerdp
```

You can see the public IP by looking at the `<name_of_windows_instance>-ip`
under your resource group.

The user sets up the password while creating the virtual machine. There is no
way to recover this once the machine is created. You can reset the password
through *Azure Portal* → *Virtual Machine* → *Settings* → *Reset
Password*.

![](../images/azure-public-ip-info.png)

```sh
$ xfreerdp /u:<username of Windows instance> /v:<Public IP of Windows instance> /p:'<password>' /f
```

You can now see a Windows screen display.

#### Setup on Windows instance

##### Enable WinRM

Open a PowerShell window and execute the following commands:

```pwsh
> $url = "https://raw.githubusercontent.com/ansible/ansible/devel/examples/scripts/ConfigureRemotingForAnsible.ps1"
$file = "$env:temp\ConfigureRemotingForAnsible.ps1"
(New-Object -TypeName System.Net.WebClient).DownloadFile($url,  $file)
powershell.exe -ExecutionPolicy ByPass -File $file
```

##### Enable Console logs

Open TCP port 10250 in the Windows Firewall so that logs can be viewed in
console and using `oc logs`.

On your Windows instance, execute the following in a PowerShell window:

```pwsh
> New-NetFirewallRule -DisplayName "Enable console logs" -Direction Inbound -Action Allow -Protocol TCP -LocalPort 10250 -EdgeTraversalPolicy Allow
```

## Reference

### Disable Azure rate limiting

On bringing up the cluster with network type OVNKubernetes, you might encounter
an error related to
[Rate Limiting on Azure API](https://bugzilla.redhat.com/show_bug.cgi?id=1782516)
calls.

```sh
Error updating load balancer with new hosts map[sumehta-winc8-2tk76-master-0:{}
sumehta-winc8-2tk76-master-1:{} sumehta-winc8-2tk76-master-2:{}
sumehta-winc8-2tk76-worker-centralus1-mmjkr:{}
sumehta-winc8-2tk76-worker-centralus2-qptsj:{}
sumehta-winc8-2tk76-worker-centralus3-pq5sk:{} winnode:{}]:
ensure(openshift-ingress/router-default):
backendPoolID(/subscriptions/52432811-04323-4342f-92309-ff3452333f0/resourceGroups/sumehta-winc8-2tk76-rg
/providers/Microsoft.Network/loadBalancers/sumehta-winc8-2tk76/backendAddressPools/sumehta-winc8-2tk76)
- failed to ensure host in pool: "azure - cloud provider rate limited(read) for operation:NicGet"
```

As a temporary workaround, you can modify the cloud provider manifests to
disable rate limiting on Azure API calls.

In `manifests/cloud-provider-config.yaml` change the following parameters to
disable rate limiting.

```yml
"cloudProviderRateLimit": false,
"cloudProviderRateLimitQPS": 0,
"cloudProviderRateLimitBucket": 0,
"cloudProviderRateLimitQPSWrite": 0,
"cloudProviderRateLimitBucketWrite": 0
```

Here is an example of `manifests/cloud-provider-config.yaml` with the changes:

```yml
apiVersion: v1
data:
  config: "{"cloud": "AzurePublicCloud","tenantId": "63544359-b35345d-4353453d-353453e-d534435345534e",
  "aadClientId":
    "",
  "aadClientSecret": "",
  "aadClientCertPath": "",
  "aadClientCertPassword":
    "",
  "useManagedIdentityExtension": true,
  "userAssignedIdentityID":
    "",
  "subscriptionId": "5f343433-04fa-1234-4567-ffd8a354533f0",
  "resourceGroup":
    "pmahajan-az-44bwz-rg",
  "location": "centralus",
  "vnetName": "pmahajan-az-44bwz-vnet",
  "vnetResourceGroup":
    "pmahajan-az-44bwz-rg",
  "subnetName": "pmahajan-az-44bwz-worker-subnet",
  "securityGroupName":
    "pmahajan-az-44bwz-node-nsg",
  "routeTableName": "pmahajan-az-44bwz-node-routetable",
  "primaryAvailabilitySetName":
    "",
  "vmType": "",
  "primaryScaleSetName": "",
  "cloudProviderBackoff":
    true,
  "cloudProviderBackoffRetries": 0,
  "cloudProviderBackoffExponent":
    0,
  "cloudProviderBackoffDuration": 6,
  "cloudProviderBackoffJitter":
    0,
  "cloudProviderRateLimit": false,
  "cloudProviderRateLimitQPS": 0,
  "cloudProviderRateLimitBucket":
    0,
  "cloudProviderRateLimitQPSWrite": 0,
  "cloudProviderRateLimitBucketWrite":
    0,
  "useInstanceMetadata": true,
  "loadBalancerSku": "standard",
  "excludeMasterFromStandardLB":
    null,
  "disableOutboundSNAT": null,
  "maximumLoadBalancerRuleCount": 0
}
"
kind: ConfigMap
metadata:
  creationTimestamp: null
  name: cloud-provider-config
  namespace: openshift-config
```


text:
---
layout: post
title: October - Chicago, New York and New Jersey
date: 2019-11-15
author: April
tags: Chicago, braces, New York, New Jersey, boat problems, autopilot
thumb: https://user-images.githubusercontent.com/13930968/81184200-20f80d80-8f7e-11ea-9791-01aa90bbb710.jpeg
backgrounds:
- https://user-images.githubusercontent.com/13930968/92014800-d022d680-ed1d-11ea-83bb-756a807b0923.jpeg
- https://user-images.githubusercontent.com/13930968/81184178-1b022c80-8f7e-11ea-860d-90e86da7c17a.jpeg
- https://user-images.githubusercontent.com/13930968/81184212-26555800-8f7e-11ea-814f-fd427b09b708.jpeg
---
![audrey before and after](https://user-images.githubusercontent.com/13930968/92014800-d022d680-ed1d-11ea-83bb-756a807b0923.jpeg)
before and after

After 16 months in braces, Audrey had them removed! Yay! She will be using Invisalign at night now for the unforeseeable future. She is really happy with the results, but funnily enough, she was becoming attached to the way she looked in braces, and may have even shed a tear when they were gone.

![downtown NYC](https://user-images.githubusercontent.com/13930968/81184207-23f2fe00-8f7e-11ea-94f3-d4a2768b6cdf.jpeg)

As soon as we got back to New York, GranMiri and Papa joined us on Lark for a visit. We even were able to have a fun day with Aunt Amy!

![Statue of Liberty with GM and P](https://user-images.githubusercontent.com/13930968/81184200-20f80d80-8f7e-11ea-9791-01aa90bbb710.jpeg)

![Central Park with Aunt Amy](https://user-images.githubusercontent.com/13930968/81184166-150c4b80-8f7e-11ea-801c-96be302bed88.jpeg)
exploring Central Park with Aunt Amy

![AMNH with Traveler](https://user-images.githubusercontent.com/13930968/81184178-1b022c80-8f7e-11ea-860d-90e86da7c17a.jpeg)
Bonus! Traveler's in town!

Our original plan was to skip straight from NYC to Puerto Rico… but boat plans are written in sand and a broken auto pilot had us pull into Cape May, New Jersey instead.

![Harry Potter Halloween with Aiden](https://user-images.githubusercontent.com/13930968/81184190-1dfd1d00-8f7e-11ea-9f15-6113cba49bb7.jpeg)
Silver lining, Traveler joined us in Cape May for some Harry Potter Halloween fun!

![Trick or Treating with Traveler](https://user-images.githubusercontent.com/13930968/81184199-205f7700-8f7e-11ea-8ab6-d57d07b65b78.jpeg)


text:
__Description__: The `setTransform` method allows for the transformation of the results via a function or an array of functions. This allows for a cleaner method of transforming the result data compared to `setCallback`. The function will be passed the result.

```js
function (res) {
  //make your transfomation and return
  return res;
}
```

This is how the transform functions process the data, pretty simple.
```js
res = _.reduce([Transform Functions], function (str, funk) {
  return funk(str);
}, res);
```

__Note__

+ You don't nessicarly need to return the res, and if you don't it will be omited.
+ `this` is binded to the instance
+ `setTransform(<Function>, {reset: false, once: false})`

repository: ctr-lang/ctr

gha_language: CSS

repo_name: ctr-lang/ctr


text:
---
title: Mock Behaviors
page_title: Mock Behaviors | JustMock Documentation
description: Mock Behaviors
previous_url: /basic-usage-mock-behaviors.html
slug: justmock/basic-usage/mock-behaviors
tags: mock,behaviors
published: True
position: 0
---

# Mock Behaviors

This article provides a quick overview of the four mock behaviors in __Telerik® JustMock__. For more details about each mock behavior, follow the links below.

__RecursiveLoose Behavior__

This is the default mock behavior. It takes care of members at all times by recursively creating empty stubs for them. Mocks with RecursiveLoose behavior will never return `null` on any of the mocks in the dependency chain even if none of them are arranged.

[Read more...]({%slug justmock/basic-usage/mock-behaviors/recursiveloose%})

__Loose Behavior__

Unless otherwise arranged, the members of loose mocks act like stubs - they return the default value of their return type - null for reference types and the default value for value types.

[Read more...]({%slug justmock/basic-usage/mock-behaviors/loose%})

__CallOriginal Behavior__

A mock with behavior CallOriginal will follow the original implementation of the mocked instance.

[Read more...]({%slug justmock/basic-usage/mock-behaviors/calloriginal%})

__Strict Behavior__

When a strict mock is created, you are only allowed to call arranged methods. Every other call will result in `MockException`.

[Read more...]({%slug justmock/basic-usage/mock-behaviors/strict%})

## See Also

 * [Arrange Act Assert]({%slug justmock/basic-usage/arrange-act-assert%})

 * [JustMock API Basics]({%slug justmock/basic-usage/mock%})

repository: Prophasi/justmock-docs

repo_name: Prophasi/justmock-docs


text:
What's Ryu
==========
Ryu is a component-based software defined networking framework.

Ryu provides software components with well defined API's that make it
easy for developers to create new network management and control
applications. Ryu supports various protocols for managing network
devices, such as OpenFlow, Netconf, OF-config, etc. About OpenFlow,
Ryu supports fully 1.0, 1.2, 1.3, 1.4, 1.5 and Nicira Extensions.

All of the code is freely available under the Apache 2.0 license. Ryu
is fully written in Python.

Quick Start
===========
Installing Ryu is quite easy::

   % pip install ryu

If you prefer to install Ryu from the source code::

   % git clone git://github.com/osrg/ryu.git
   % cd ryu; pip install .

If you want to write your Ryu application, have a look at
`Writing ryu application <http://ryu.readthedocs.io/en/latest/writing_ryu_app.html>`_ document.
After writing your application, just type::

   % ryu-manager yourapp.py

Optional Requirements
=====================

Some functions of ryu require extra packages:

- OF-Config requires lxml and ncclient
- NETCONF requires paramiko
- BGP speaker (SSH console) requires paramiko
- Zebra protocol service (database) requires SQLAlchemy

If you want to use these functions, please install the requirements::

    % pip install -r tools/optional-requires

Please refer to tools/optional-requires for details.

Prerequisites
=============
If you got some error messages at the installation stage, please confirm
dependencies for building the required Python packages.

On Ubuntu(16.04 LTS or later)::

  % apt install gcc python-dev libffi-dev libssl-dev libxml2-dev libxslt1-dev zlib1g-dev

Support
=======
Ryu Official site is `<http://osrg.github.io/ryu/>`_.

If you have any
questions, suggestions, and patches, the mailing list is available at
`ryu-devel ML
<https://lists.sourceforge.net/lists/listinfo/ryu-devel>`_.
`The ML archive at Gmane <http://dir.gmane.org/gmane.network.ryu.devel>`_
is also available.

repository: thurtsdn/ryu

branch_name: refs/heads/tt-exp

extension: rst

gha_language: Python

repo_name: thurtsdn/ryu


text:
# Deprecated

# TimeRecord.js
  TimeRecord is a time library for nodejs

## Install

Install via NPM:
```
npm install timerecord
```
Then:
```javascript
var TimeRecord = require("timerecord");
```

## Example

```javascript
var oldDate = new Date("2013/07/17 13:52:37.596");
var newDate = new Date("2013/07/18 21:36:15.400");

//compare two date objects
var ctr = TimeRecord.Compare(newDate,oldDate).getJson();

console.log( "Time difference between two dates: \n" +
			ctr.year + " years, " +
			ctr.day + " days, " +
			ctr.hour + " hours, " +
			ctr.minute + " mins, " +
			ctr.second + " seconds, " +
			ctr.millionsecond + " millionseconds (" +
			(ctr.isnegative? "overtime":"remaining time") + ")" );

```

output:

```
Time difference between two dates:
0 years, 1 days, 7 hours, 43 mins, 37 seconds, 804 millionseconds (remaining time)
```

## Test
`make test`
or
`npm test`

## Credits

(The MIT License)

Copyright (c) 2013 Martin Chan (http://twitter.com/osiutino)

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.


text:
---
layout: post
author: Owen Rumney
date: 2013-09-27
title: New blog, new platform
description: Moving to a new platform for my blog
image: '/assets/img/owen.png'
tags: [blogging]
categories: [Blogging]
---

An interesting chain of events happened last week; [@thecodejunkie](http://twitter.com/thecodejunkie) relaunched his blog, I happen to follow his twitter feed and he happened to mention it.

The chain continued, I followed the link to his blog for a look and read the post there, [I am back and this time I am taking control](http://thecodejunkie.com/2013/09/10/i-am-back-and-this-time-i-am-taking-control/), the content struck a chord about claiming back a blog.

My interest was further piqued by my new found use of Azure and the static generation method discussed in the article.

I forked [Sandra.Snow](https://github.com/sandra/sandra.snow) on github and plugged away deploying the sample solution and asking questions till I got it working... thanks to [@PhillipHaydon](https://twitter.com/philliphaydon) for quickly helping me out.

Interestingly enough, the thing I've found most enjoyable about the whole process is learning about Markdown... Other than the odd stackoverflow post, I had not had any use for it. Now, I'm sitting on the train with Sublime Text 2 open and I can quickly write my post in relatively clear text, which is awesome!

extension: markdown

gha_language: HTML


text:
<!--
page_number: true
footer: Guillaume Lobet & Xavier Draye || ACELI formation || Image analysis pipeline
-->

# Introduction to ImageJ

> Improve your image analysis pipeline

</br>

http://bit.ly/intro-imagej

</br>

2017-04-24

Xavier Draye and Guillaume Lobet

---

# Image analysis pipeline

Image analysis is only one of the step in the global data analysis pipeline:

1. Image acquisition
2. Image analysis
3. Data analysis

Sometimes it is easier to improve previous or future steps rather than spend too much time on the image analysis in itself...

---

# Improve image acquisition

- Better lighting conditions

- Keep same disctance between camera and object

- Increase contrast between background and object

- Reduce the number of artefact in the object

- Try different sensors (visible, infrared, multispectral)

---

## Improve the image acquisition

![](https://github.com/plantmodelling/intro-imagej/blob/master/img/leaf.png?raw=true)

---

## Improve the image acquisition

![](https://github.com/plantmodelling/intro-imagej/blob/master/img/root.png?raw=true)

---

## Improve the image acquisition

| ![](http://www.plantmodelling.xyz/content/5-blog/17-acquiring-root-images/logo.jpg) | A. Camera </br> B. Fixed distance </br> C. Black background </br> D. Scale |
| ---- | ---- |

---

# Improve image analysis

- Calibrate your pipeline

- Find a robust ground-truth dataset
	- manual measurements
	- simulated images

- Estimate the error

---

## Estimate the error

![](https://github.com/plantmodelling/intro-imagej/blob/master/img/error.png?raw=true)

---

# Improve data processing

- Filter out noisy data

- Correctly identify outlyers

repository: plantmodelling/intro-imagej

repo_name: plantmodelling/intro-imagej


text:
# kprobes based linux kernel process monitor

## Brief

Implementation of basic kprobes demo kernel module. The  module places kprobes probe on do_exit.
On each exited process, the module will print a log with the process name and exit code.

Example output:
``` text
[ 3692.905970] handler_pre : handler_pre: name [sudo], pid [6250], exit_code [0]
[ 3692.906176] handler_pre : handler_pre: name [make], pid [6249], exit_code [0]
[ 3696.337482] handler_pre : handler_pre: name [dircolors], pid [6255], exit_code [0]
[ 3696.608100] handler_pre : handler_pre: name [systemd-udevd], pid [6252], exit_code [0]
[ 3707.276686] handler_pre : handler_pre: name [kworker/dying], pid [2688], exit_code [0]
[ 3711.833541] handler_pre : handler_pre: name [pool], pid [6253], exit_code [0]
[ 3711.983803] handler_pre : handler_pre: name [Backgro~Pool #3], pid [3513], exit_code [0]
[ 3712.709814] handler_pre : handler_pre: name [git], pid [6258], exit_code [0]

```

## Usage

In order to install the kenrel module use the following steps:

Step 1 - compile the kernel module object file.
```
make
```

Step 2 - Install the kernel module into the kernel.

```
make install
```

Step 3 - Observe the logs printed by the module.

```
sudo dmesg -w
```

To uninstall the module please use the following command:

``` bash
make uninstall
```

## Links

[kprobes documentation](https://www.kernel.org/doc/Documentation/kprobes.txt)

[x64 registers struct](https://elixir.bootlin.com/linux/latest/source/arch/x86/include/uapi/asm/ptrace.h#L44)

[_do_fork source](https://elixir.bootlin.com/linux/v4.5/source/kernel/fork.c#L1691)

[_do_fork guide](https://s-matyukevich.github.io/raspberry-pi-os/docs/lesson04/linux/fork.html)

[x64 regirster struct](https://elixir.bootlin.com/linux/latest/source/arch/x86/include/uapi/asm/ptrace.h#L44)

[x64 gcc ABI](https://www.systutorials.com/x86-64-calling-convention-by-gcc/)

</br>

## Kprobes notes

Probe handlers are run with preemption disabled or interrupt disabled,
which depends on the architecture and optimization state.
In any case, your handler should not yield the CPU (e.g., by attempting
to acquire a semaphore, or waiting I/O).

repository: paranlee/linux-kernel-kprobes-demo

repo_name: paranlee/linux-kernel-kprobes-demo


text:
# meinHaushalt 🏡

meinHaushalt is an Application for MacOS and Windows that helps you and your
household members to keep track of their spendings and earnings and become more financially responsible.

This application was built as part of a lecture at Aalen University within 3 weeks.

## 🌟 Features
### Admin
* Single Admin who manages the system
* Create and delete users
* Reset password of users
* Unlock locked users
* Create and delete categories for all users to use

### Users
* Each user with their own space - protected by a own password and not visible to the Admin
* Create and delete own means of payment
* Change personal details

### Transactions
* Add, alter and delete transactions
* Transaction consisting of Date, Type (Spending/Earning),
Price, Description, Category, Means of Payment and Source
* Show transactions in certain timeperiod in table
* Sort table by date or category
* Group table by category
* Show balance of transactions

### Persistence
* User data is locally stored using a SQLite-Database

## 🆕 Upcoming Features
* Export transactions to Excel-File
* Search in Transaction-Table

## 🛑 Known Bugs
* On Windows only: Database can only be altered when application is run as administrator
* Let me know if you found any bugs and I will try to fix them

## 🖼 More Screenshots

branch_name: refs/heads/main


text:
# General Information:

Zechariah often wrote prophecy in the form of poetry. Hebrew poetry uses different kinds of parallelism. Here Yahweh begins speaking. (See: [[writing-poetry]] and [[figs-parallelism]])

repository: mandolyte/uw-obsidian

repo_name: mandolyte/uw-obsidian


text:
---
title: Organization Accounts
tags:
  - administration
  - project
  - organizations
  - team management
  - teams
  - account

redirect_from:
  - /administration/organizations/
menus:
  general/account:
    title: Organization Accounts
    weight: 2
---

* include a table of contents
{:toc}

Organizations simplify and enhance team management as well as subscription management for (larger) teams on Codeship.

You can define arbitrary teams and add them to any organization project and add Codeship accounts to those teams. You can also provide read-only access to some of your team members.

We currently offer the following roles (though more roles are already on our todo list):

* **Owners** have control over all aspects of an organization. From changing the subscription to managing organization projects and teams.
* **Managers** have control over team and project management of an organization. They can add and remove projects and manage the organization teams by adding new team members or assigning projects to teams. They have access to all projects and are able to change the project configuration.
* **Project Managers** can manage projects the team is assigned to. They can debug builds, update test settings, or manage deployments.
* **Contributors** have read-only access to their projects. This means that they can view the project dashboard and build details but are not allowed to change project settings or open debug builds.

## Creating an Organization

* Click on your name in the navigation bar at the top and click the green _Create Organization_ button.
* Choose an available name and you're done!

![Creating an Organization]({{ site.baseurl }}/images/administration/create_organization.png)

## Managing Teams

On the _Teams_ tab of the organization settings, you can manage your different teams, add new teams and invite or remove team members from the available teams.

Two teams are created for each organization by default:
* _Owners_, containing only the user who created the organization by default. You can however add any other Codeship account to the _Owners_ team as well.
* _Managers_, containing nobody by default.

If you want to create a new team, click the _Create new team_ button and select the appropriate role.

![Creating a Team]({{ site.baseurl }}/images/administration/create_team.png)

Once you have created a new team, you can invite your colleagues and add new team members via their email address as well as any existing projects.

If you need to change the team settings (e.g., the name or the role), hover over the team card and click the gear icon showing on the right hand side.

## Adding Projects

You can either add a project via the _Select project_ dropdown at the top, or via the _Create a new project_ button in the organization's project settings. Please make sure the correct account (either organization or your private account) is selected first, as it's currently not possible to transfer projects to another account.

Once the project is created, you can add it to any of your teams. Members of the _Owners_ and _Managers_ team will have access to all projects by default.

![Adding a project to a team]({{ site.baseurl }}/images/administration/add_project_to_team.png)

## Transfer A Project To An Organization

You can transfer your project to another account by navigating to:

***Project Settings > General***

A user with appropriate permission for the target account needs to confirm the transfer if you do not have project creation rights in the target account. When confirming the transfer, the user can choose which of the current team members to keep and which to remove.

If you want to **bulk transfer projects**, please reach out to our support via [helpdesk.codeship.com](https://helpdesk.codeship.com).

![Importing a personal project to an organization]({{ site.baseurl }}/images/administration/import_projects.png)

## Removing A User From All Teams And Projects

To remove a team member from all organization teams and projects, click into your team management page. At the top, you will see a link for "Show all Members".

From the "Show all Members" screen, changes to a any team member will apply to all teams and projects, organization-wide.

![Importing a personal project to an organization]({{ site.baseurl }}/images/administration/remove_all.png)

## Accessing Invoices

For all users assigned the **owner** role in an organization, you can access invoices by clicking on your name in the top right and selecting an organization from the drop-down. Then, on the sidebar underneath the organization name, you will see a tab for "Invoices".

![View invoices]({{ site.baseurl }}/images/general/download-invoice.png)

repository: RushinthJohn/documentation

repo_name: RushinthJohn/documentation


text:
# Change Log
All notable changes to XLMediaZoom will be documented in this file.

### [v2.0.1](https://github.com/xmartlabs/XLMediaZoom/releases/tag/v2.0.1)
#### Support for iOS 7
This version fix some issues when running on iPhone with iOS 7. For iOS 7, it's possible specify to use blur effect over background view.

### [v3.0.0](https://github.com/xmartlabs/XLMediaZoom/releases/tag/v2.0.1)
#### Swift 3 migration
* Updated the codebase to Swift3.
* Updated the readme to match new code.
* Added podspec and cartfile with new version (3.0.0)
* Added new example project.

[xmartlabs]: https://xmartlabs.com


text:
# Experimenting with Eclipse Microprofile Configuration

This repository contains a super simple JAX-RS application to use in testing the
[Eclipse Microprofile Config][mpconfig] library.

The implementation of the Microprofile Config API is the current version of the
[Geronimo component][impl] found in the Apache Subversion repository.

To get it running:

1. Check out the config implementation and ensure it builds and is installed to
   your local Maven repository
2. Build this module: `mvn clean package`
3. Deploy the `mpconfig.war` file to a JavaEE 7 compliant app server like [TomEE][tomee] or
[Payara][payara].

[mpconfig]: https://github.com/eclipse/microprofile-config
[impl]: https://svn.apache.org/repos/asf/geronimo/components/config/trunk/
[tomee]: https://tomee.apache.org
[payara]: https://www.payara.fish

repository: ashri/eclipse-microprofile-example

repo_name: ashri/eclipse-microprofile-example


text:
# [Focus UI](https://focus.pagie.co)

[![CircleCI](https://circleci.com/gh/pagieco/focus-ui/tree/master.svg?style=svg)](https://circleci.com/gh/pagieco/focus-ui/tree/master)

Our design system helps us work together to build a great experience for all of Pagie's users.

## Using the Vue components

### Installation

Run the following command using [npm](https://www.npmjs.com/):

```bash
npm install @pagie/focus-ui --save
```

If you prefer [Yarn](https://yarnpkg.com/en/), use the following command instead:

```bash
yarn add @pagie/focus-ui
```

##

Check out these practical guides to help you understand how to design for the Pagie platform using Focus.

repository: pagieco/focus-ui

gha_license_id: MIT

repo_name: pagieco/focus-ui


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# Setup VR Study

A computer with Windows 10 installed is required. The AR experiment is intended to only support the **Microsoft HoloLens Gen1** at the current stage.

## Basic PC Setup

- Clone this Repository
- Install [Visual Studio 2019](https://visualstudio.microsoft.com)
- Install [Unity 2019.2.13f1](https://unity3d.com/get-unity/download/archive)
- Install the workloads: Desktop development with C++ and Universal Windows Platform (UWP)
development
- You need at least the "Windows 10 SDK (10.0.xxxxx) Components! We later build for 10.0.18362.0
- Select "UWP build support" & IL2CPP for Hololens in the Build settings
- Connect Microsoft Xbox one Bluetooth Controller with the HoloLens

## Open Project & install required packages

Open the project with Unity and wait till it's fully loaded. All required packages are already included.

- Open the project with Unity and wait till it's fully loaded.
  - You'll might get several error messages. This will be fixed in the process
- Close "MRTK Project Configurator"-dialog for the moment.
- Open File -> Build Settings
  - Select "UWP"
  - Target Platform: HoloLens
  - Architecture: x86
  - Press Switch Platform
- Open "Mixed Reality Toolkit" -> Utilities -> "Configure Unity Project"
  - Apply
  - Wait for completion
- Open Windows -> "Package Manager", search for "Windows Mixed Reality" and install.
- Open Edit -> Projects Setings -> Audio and make sure "MS HRTF Spatializer" is selected at *Spatializer Plugin*
- Restart Unity
- Open Scene Assets/_project/Scenes/Performance
- If vibration cues should be involved:
  - Select in Hierarchy: Feedback -> Controller -> Transition. Look for "Tactor Controller (Script)" in the Inspector and Change "IP" to the IP of your raspberry, "Port" to 5005 and "Topic" to "/actuators"
Press play to check if scene can run.

## Build and deploy
- Open File -> Build Settings
- Make Sure only Scene "_projects/Scenes/Performance" is selected
- Press "Build"
- Select empty or create a new directory to build to.
- Wait for build.
- Open directory.
- Open multimodal-hololens.sln inside build-directory (Not the one inside your regular project folder)
- Wait for Visual Studio to load project
- At the top change Config to "Release", "x86" and "Device"
- Unfold "multimodal-hololens (Universal Windows)" and open "Package.appxmanifest
- Select tab "Capabilities" and enable:
  - Internet (Client and Server)
  - Internet (Client)
  - Low-Level-Devices
  - Private Area Network (Client and Server)
  - Spatial Perception
- Connect Hololens via usb to your computer.
- Right click on "multimodal-hololens (Universal Windows)" -> deploy

## Start Application on Hololens

- Make sure your XboX-controlleris connected to your Hololens
- Start the application "multimodal-hololens"

## Log files for Study
All log files can be found on the hololens via Device Portal. Open Device Portal and go to System -> File explorer -> LocalAppData -> multimodal-hololens -> LocalState. We create 2 files for every participant: "Study-PerformanceX.csv" and "Study-PrimaryX.csv". Number at the end will be increased sequently. If you delete all files it starts at zero again. If you delete Log File for Participant 2 out of 5. The next log file will be 2 again. Log files will be generated every time you start the application. On a re-deploy, all existing data will be lost!


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# oneplace-task

[![Build Status](https://travis-ci.com/OnePlc/PLC_X_Task.svg?branch=master)](https://travis-ci.com/OnePlc/PLC_X_Task)
[![Coverage Status](https://coveralls.io/repos/github/OnePlc/PLC_X_Task/badge.svg?branch=master)](https://coveralls.io/github/OnePlc/PLC_X_Task?branch=master)
![Packagist Downloads](https://img.shields.io/packagist/dt/oneplace/oneplace-task)
![Stable Version](https://img.shields.io/packagist/v/oneplace/oneplace-task)
![GitHub Release Date](https://img.shields.io/github/release-date/oneplc/plc_x_task)
![Open Issues](https://img.shields.io/github/issues-raw/oneplc/plc_x_task)
![Discourse status](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.1plc.ch)

## Introduction

This is the Task Module for onePlace Software Framework based on Laminas Project (former Zend Framework)

Create your web based apps on the fly! onePlace brings you extended Entity and Table Models,
View Helpers and much more - so you can build your Webapp to manage database based data,
in almost any layout you can image.

onePlace is built for maximum freedom of choice for your app, and so only small
parts of laminas mvc are utilized. a lot is done from scratch to give you more freedom / customization.

## Task Module

This task module is a starting point for your own onePlace modules.
It expands on [oneplace-core](https://github.com/OnePlc/PLC_X_Core) and uses the onePlace / Laminas MVC layer and module systems.

## Installation

The easiest way to install onePlace Task is via composer
```shell script
composer require oneplace/oneplace-task
```

## Getting started

how to utilize user manager

how to create own modules

## Documentation

Documentation will be extended soon.