DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Skills Minister calls on employers to inspire young people as he responds to an Ofsted report on careers guidance in schools.
Skills Minister Matthew Hancock today called for all schools and colleges to follow the example of the best and provide inspiring careers advice to young people.
He also said it was vital that employers worked with schools and colleges to ensure all young people received high-quality careers advice - with employers taking centre stage in inspiring young people to get on in life.
His comments came on the day his ‘Inspiration vision’ and the government response to 2 reports on careers were published. Ofsted ’s ‘Going in the right direction?’ was published today (10 September 2013) and the National Careers Council report ‘An aspirational nation’ was published in June.
Matthew Hancock’s ‘Inspiration vision’ sets the tone for the government’s position on careers guidance: all schools and colleges should do as the best do, put young people in touch with the world of work to inspire them about their futures and help them make fully informed decisions.
The government gave schools and colleges the responsibility to provide independent and impartial careers advice because heads and teachers know their pupils best. The previous system was replaced as it was often costly, patchy and of poor quality. Critics include Alan Milburn, formerly the chair of the Panel on Fair Access to the Professions, who found very little support for the previous system (see notes to editors).
In today’s response to the Ofsted and National Careers Council reports, the government is setting out how it will help schools and colleges carry out this duty.
The government will:
Matthew Hancock said:
Today we are announcing new action to improve careers guidance by holding schools to account.
People with fulfilling careers are the ones who can really show young people what it is like to succeed in the world of work. That is why I want more employers involved in providing high-quality careers advice to the future workforce.
We gave schools and colleges the responsibility for securing good careers advice for their pupils because they know them best. Ofsted highlighted excellent careers advice already being provided by schools, but I want all schools to do as the best do - inspiring young people, providing work experience and putting them in touch with employers.
There is also a challenge to us all: to respond to the world as it changes, to inspire, to motivate, to encourage and to create a skilled workforce to compete in the global race.
The National Careers Service will also be improved to give young people a greater understanding of the full range of options available to them. It will use its local networks to bring employers, schools, charities and social enterprises together. The government has maintained its budget for 2015 so it can continue to provide online, telephone and face-to-face support for adults of all ages. Its activities will be extended to improve the careers resources that are available to schools, young people and parents.
The government’s measures respond to the recommendations made by Ofsted and the National Careers Council reports. Ofsted pointed to good examples of careers guidance provided schools and colleges in its report. It made recommendations for the government, schools, local authorities, employers and the National Careers Service so all schools can provide high-quality careers guidance.
The National Careers Council report called for a culture change in careers provision for young people. It also called for an improvement of the National Careers Service, so it makes young people aware of the full range of options available to them.
The government response and ‘Inspiration vision’ have been published on GOV.UK.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 10 September 2013


Kristopia
Kristopia helps you connect and share with the people in your life
It is free and always be
First Name
Middle Name
Last Name
Password
Date fo birth


Vivek@fb
Vivek@linkedIn
Oh No ! You failed to save Contractor Nesamani !!!!
You are appointed as apparacentee :)


1. Initialise Database
The first step to
Minstagram
is to initialise the Databases.
Install Database


Goldeneye A review by Joe Chamberlain
Starring Pierce Brosnan; Sean Bean; Izabella Scorupco; Famke Janssen & Joe Don Baker This latest film in the 007 series finds the actor Pierce Brosnan in the title role of the British secret agent. If a person were to compare Brosnan' s portrayal of Bond to that of his predecessors, the most accurate description would be to say that Brosnan is a cross between Roger Moore and Sean Connery. Brosnan has the serious side that Connery brought to the character. Happily, the element of campy humor that was present when Moore played the Bond has returned. This is a welcome change from Timothy Dalton's brooding incarnation in the previous two movies.
The premise of Goldeneye is simple. The Goldeneye is a killer satellite left over from the cold war era that gets stolen by the usual collection of villains that are always present in a good Bond flick. This includes one of the most original "Bond girls" in a long while, played by the beautiful Famke Janssen. Her outstanding trait is her ability to kill men using her thighs. (Enough said) And while on the subject of female cast members, British actress Judi Dench has taken over the role of "M", Bond's boss.
As with all Bond films, Goldeneye has its fair share of nifty spy gadgets and car chases (and tank chases), enough to satisfy this James Bond fan anyway. Unlike the previous Bond outing, this one never leaves you with the feeling that the writers were struggling with the script.
All in all a solid value for your entertainment buck, and probably reason that producers have signed Brosnan to star in the next bond film. Bond fans will love this movie, and even folks who don't normally like James Bond films are bound to have a good time as well.
8/10
Visit The Movie Guy http://members.tripod.com/~MovieGuy/index.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


OVERVIEW * I collected all of our design patterns for Realtor.com and organized them into a library file so everyone could easily leverage a set of coordinated in our source format, Sketch. * This was a huge benefit for our design team in efficiency and brought a new perspective to our overall work.
LESSONS LEARNED * Patterns are a vital tool for a design team. * I wrote about the benefits of patterns here. * Creating a collection of your design patterns enables a powerful view of the overall system, which leads to better perspective when working on individual parts.
PROJECT STORY I love trying new design tools and processes. I end up getting the software questions from my teammates. I’m not an expert, but I enjoy the challenge of learning new tools and sharing those skills with my teammates. If an issue arrises I can usually figure out a solution. Earlier this year our UX design team switch completely from Photoshop to Sketch. As we got used to Sketch, we noticed how useful pre-set styles and symbols were for speeding up our work. After demonstrating these advantages and the potential benefit to the team, management was interested. I was tasked with collecting and organizing the design patterns used on our Realtor.com product, primarily from the Responsive Redesign project. So I systematically collected and organized these patterns, in Sketch format, and organized them using the Atomic system to create our Pattern Library file, aka ‘sticker sheet’.
[atomic pic]
This was an instant success for our team. Even the first draft with limited patterns was useful to team members. Several versions later, we are now all able to quickly grab our standard parts and compose a mockup in record speed. Instead of wasting time re-designing a form or wondering what dimension a listing card should be, we just cut/paste from the pattern library. As our styles and patterns evolve for new projects, the library will evolve along side. In addition to the efficiency gain, the team also gained a new perspective by being able to view all of our patterns together. Previously we had partial documentation of styles and patterns but it quickly became obsolete so we stopped referring to them. This pattern library is flexible enough to be kept current with minimal effort. Now one document can provide the overview of our design patterns so each designer knows the context for their work. Next we would like to document interactive and motion design elements in a similar fashion.
Atomic Design Reference: * http://bradfrost.com/blog/post/atomic-web-design * https://blog.hannahcdesign.com/how-to-maintain-product-ux-consistency-71f58df0a91f?ref=hackingui#.eap5ppkkm


Software Developer
The official website of the Puglisi Laboratory of Structural Biology at Stanford University.
Trails500 is an easy to use, single page web application that allows users to connect, explore, and share experiences through photos.
Inheritage is a fun, easy to use application that allows the general public to access ethnically relevant genetic information.
This React Native application is designed to help you keep track of all the things in life that make you move. (Work still in progress)


Only members from your organisation will be able to see a breakdown of your finances.
Please complete your project finances.
This is a breakdown of your project costs and sources of funding for this project.


Wheeeeeeee!!!11one Imma axe u a question.
Does Sam have 4 cats?
Answer to question on shows up here.
http://AdamFTC.github.io/Airplane02
http://alsoucy.github.io/my-guessing-game/


Part 1: Scraper Setup
NOTES
Better inheritance example
http://stackoverflow.com/questions/575217/whats-a-good-example-for-class-inheritance
Building the scraper setup portion of the tutorial.
Define our Items
In our items.py
file, scrapy needs us to define containers for the data that we plan to scrape. If you have worked through the Django tutorial at one point, you'll see that the items.py
is similar to models.py
in Django.
First, using scrapy’s item module, we import Item
and `Field:
1 | from scrapy.item import Item, Field
|
Simple enough. Now we’ll create a class, and name it after the kind of data that we’ll scrape, LivingSocialDeal
:
1 2 | class LivingSocialDeal(Item):
"""Livingsocial container (dictionary-like object) for scraped data"""
|
For our LivingSocialDeal
class, we inherit from Item
- which basically takes come pre-defined objects that scrapy has already built for us.
For the curious
You can think of class inheritance like the birds and the bees. For instance, we can have a base class, class Human(object)
, that will have some human attributes - like a function for running, for bathing, eating, etc. Then we can inherit from the Human
class to make a new class, class Superwoman(Human)
. Because we inherit from Human
, we can still access the running, eating, bathing functions. But - perhaps Superwoman runs faster than the average human, so we can redefine the running function. This basically rewrites over Human’s running function.
Or maybe we want to add to Human
’s eating()
method by adding an intact of 1000 more calories (being a Superwoman requires a lot of energy!). We can define an eating()
function within our Superwoman
class, then call super()
on the method,
Perhaps Superwomen should also fly. We can define a separate Flying(object)
class. Now, when we define our Superwoman()
class, we can inherit both from Human
and Flying
– called multiple inheritance: class Superwoman(Human, Flying)
.
Back to the tutorial
Let’s add some items that we actually want to collect. We assign them to Field()
because that is how we specify metadata to scrapy:
1 2 3 4 5 6 7 8 9 | class LivingSocialDeal(Item):
"""Livingsocial container (dictionary-like object) for scraped data"""
title = Field()
description = Field()
link = Field()
category = Field()
location = Field()
original_price = Field()
price = Field()
|
Nothing too hard - that was it. In scrapy, there are no other field types, unlike Django. So, we’re sort of stuck with Field()
.
Let’s play around with this in the Python terminal. Make sure your ScrapeProj
virtualenv is activated.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | >>> from scrapy.item import Item, Field
>>> from items import LivingSocialDeal
>>> deal = LivingSocialDeal(title="$20 off yoga classes", category="health")
>>> print deal
LivingSocialDeal(title='$20 off yoga classes', category='health')
>>> deal['title']
'$20 off yoga classes'
>>> deal.get('title')
'$20 off yoga classes'
>>> deal['category']
'health'
>>> deal['location'] = "New York"
>>> deal['location']
'New York'
|
The scrapy Item
class behaves very similar to Python’s dictionaries with the ability to get keys and values.
Now that it’s all setup, let’s setup our spider →


Attempt to Shame Trump Supporter on Daily Show Panel Completely Backfires
by Leah BarkoukisComedy Central host Roy Wood Jr. sat down with a panel of African-American voters to get their take on what black voters want in a presidential candidate, and their responses seemed to surprise him.
The similarity among the six African-American voters he sat down with in Atlanta last week is that they voted for President Obama in 2012, with the exception of one who was too young at the time.
“Which candidate do you think best understands the needs of the black voter?” he asked.
One voter said Sen. Elizabeth Warren and two said Sen. Bernie Sanders, before the host got to “Chris” who said Donald Trump.
After laughter and one co-panelist saying “huh?”, Wood Jr. asked him to explain.
"Some of the stuff I've heard he's already doing, like prison reform," he replied. "He’s been hanging around black people as long as I can remember. They loved him until he ran for president as a Republican. Trump was the man.”
Others disagreed, but then later in the segment after discussing the other candidates, Wood Jr. asked how many still thought Trump will win in 2020. Four out of the six raised their hands.
“Do you honestly think any of these Democratic candidates can beat Trump?” he followed up.
One respondent said perhaps if Democrats rallied around one candidate that person could beat the president.
“So, we all need to agree on who that one candidate is and then all the other candidates support them. Tell me who’s that candidate,” he said.
Everyone rattled off a different name, much to Wood Jr’s disappointment. Then Chris stepped in and said, “Trump 2020, baby!”
The host then walked off the set.
"And there's a lesson for Democrats," he said.  If you want our trust, make promises you can keep, walk it like you talk it, and above all, keep the faith."
He then said a "prayer" that Chris's truck wouldn't start on Election Day so he wouldn't be able to case his vote for Trump.


Environment not found.
This environment is protected and cannot be scheduled.
You can still set schedules against particular ASGs or Instances where required.
Note: This setting applies to all ASGs in this environment unless they have a specific schedule tag set. Specific schedules on particular ASGs or Instances will always take priority and override this setting.


If I were to track the number of copies that are made in school each year, I think it would turn into a fantastic conversion problem for my students. How deep would we be able to fill a room with used copy paper? I’m sure we’d all have our eyes opened at the amount of material that goes through our classrooms each year.
Schools are also looking to go as paperless as they can in order to cut costs. While I don’t agree with the rationale for cutting paper, it is an important factor to consider when we think about the amount of waste we put into the environment. But, the infrastructure required to go completely paperless is often out of reach. There are some ways you can cut down on paper in your class, even if you can’t go completely paperless.
Use your space – This year, I was introduced to the neon Expo white board marker, and they have changed my life. These markers write on top of the lab tables and wipe off easily with a damp paper towel. Kids can now do scratch work on the table, and then call me over to check their progress. Plus, kids always love being able to write on things we usually tell them not to write on. I have also seen teachers use regular white board markers on desks that have polyurethane veneers. (Be sure to test it yourself before handing out 30 markers, though). Neon dry erase, my photo.
$2 interactive whiteboard – Frank Noschese wrote this post a couple of years ago, and I went out and did the same for my class last year. I think for my classroom, it cost me $35. And, Lowe’s or Home Depot will cut the boards for free. Double win.
Track large handouts – I provide note packets for each unit my students go through. This is a lot of paper, considering each packet is 4-5 pages (double sided, of course) and 120 students looking for a copy with each new unit. So, I print them once, and then post the packet on Google Drive. The problem I had, is that students would often lose the packets, and then they would tell me that they never got one in the first place. In the moment, I would usually go print another one, which led to a lot of wasted paper. Now, I keep track of who receives a copy by having them sign their name on my grade sheet. It saves me extra copies, plus, now they can’t pull the wool over my eyes and they manage to keep track of their papers the first time around. Checkout signatures, my photo
Class sets – This seems silly, but go back to making class sets of materials. I do this with quizzes, and I ask students to either do the question on the desk or on scrap paper. Which leads me to…
Keep scrap paper – I started keeping a scrap paper bin in order to reuse as much as I could. Oftentimes, a student would make one mistake and then throw their paper away. Now, it gets trimmed and thrown into the scrap bin. Now, rather than using notebook paper to complete an assignment or exit slip, students grab some scrap and we can reuse perfectly good white space. Scrap box, my photo.
Post it on the Internet – This one seems to counter the title, but ask yourself, “Do my students really need a hard copy of these questions?” Often, I say no. Now, with that being said, I definitely recognize the benefit of having something right in front of you. But, find ways to create prompts or questions that can be put online as a Google Doc and then answered in writing on scrap, in their notes, or even on the table. It’s easy to throw up on the projector if you don’t have computers available, and it’s there for homework if you run out of class time.
These are just a few simple suggestions. Any others are welcome in the comments.
Written on February 1st , 2013 by Brian Bennett Categorized in: All Creative Teaching
Leave a comment


Getting Started
Requirements
Installation
In this tutorial, we will create a new virtual environment with conda
to install
compas_assembly
. We will name the environment assembly
, but you can use any
name you like.
If you wish to install compas_assembly
in an already existing environment, just
skip the first step and simply activate the environment of your choice.
1. Create a virtual environment and install COMPAS
$ conda create -n assembly -c conda-forge python=3.6 COMPAS
2. Activate the environment
On Mac
$ source activate assembly
On Windows
$ activate assembly
3. Install dependencies
On Mac
pip install shapely
On Windows
conda install -n assembly -c conda-forge shapely
4. Fork package
Go to https://github.com/BlockResearchGroup/compas_assembly and fork the repository to your personal account. This will simplify the development process when you will start making changes to the code or when you want to start contributing.
5. Clone forked package
Clone the forked package to a location on your computer.
$ mkdir ~/Code/COMPAS-packages
$ cd ~/Code/COMPAS-packages
$ git clone https://github.com/<your-username>/compas_assembly.git
6. Install cloned package
$ cd compas_assembly
$ pip install -r requirements-dev.txt
7. Check installation
Start an interactive Python session in the Terminal.
>>> import compas
>>> import compas_assembly
8. Install packages for Rhino
On Mac
$ python -m compas_rhinomac.install -p compas compas_assembly
On Windows
$ python -m compas_rhino.install -p compas compas_assembly
9. Install solvers
To evaluate the stability of an assembly or to compute the contact forces at the interfaces between the blocks required for static equilibrium, you will need to install an equilibrium solver.


Django Software As A Service Project Starter Template
Table of Contents
1 SaaS Definition
A software as a service, offering some software online as a paid service. Income generating software. A project supporting user management, subscriptions, payments methods, Per User Data CRUD, isolated db and runtime environments, 3rd party integrations.
An Intuitive, Modern, Responsive UX, User Friendliness. Easy Deployment, Testing, Extensibility, Modularity, OS independent
1.1 Target Project Attributes
- Easy Deployment
- Automated Testing and CI
- Estensible
- Modular
- OS Independent
- Multiple Data Storage Technologies Support
- 3rd Party Services Integrations
- Data Analytics and Usage Stats
- User Friendly, Intuitive, Sleek, Modern, Responsive
- Data Driven, Reactive
- Asynchronous, Realtime
- Multi-Language Support, Internalization
2 Implementing the SaaS
2.1 Prospective Technologies and Frameworks
2.1.1 TODO Data and Database Layer
- Sqlite3
- MySQL
- PostgreSQL
- MongoDB
- NoSQL
- Amazon s3 buckets
- Google Cloud Firebase
2.1.2 TODO Backend Controller
- Django
- Flask
- Laravel
- Nodejs
2.1.3 TODO Frontend View
- Jinja2 templates
- React.js
- Vue.js
- Angular.js
- Bootstrap
- Font-Awesome
2.2 Django Framework
2.2.1 Modules
- django-allauth
- django-social
- django-stripe
- django-heroku
- django-multitenants
- django-rest-framework


class ClustersController
ClusterController is responsible for the clusters' retrieval, creation, and deletion.
Public Instance Methods
Creates new cluster entity and sends cluster create request to DISCO if everything filled correctly(cluster parameters and infrastructure password). After success redirects to the clusters page. After failure redirects to the new cluster creation page. Failure may occure since:
1) Cluster parameters were not filled correctly, in this case: "Cluster details were not filled correctly" warning will show up. 2) Incorrect password was typed or DISCO connection problem occured, in this case: "DISCO connection error" warning will show up.
# File app/controllers/clusters_controller.rb, line 49 def create # Retrieves chosen infrastructure and creates new cluster entity which belongs to that infrastructure infrastructure = Infrastructure.find(params[:cluster][:infrastructure_id]) cluster = infrastructure.clusters.build(cluster_params) # Retrieves chosen master and slave image from database to reference them from new cluster entity cluster.master_image = Image.find(params[:cluster][:master_image]) cluster.slave_image = Image.find(params[:cluster][:slave_image]) # Retrieves chosen master and slave flavor from database to reference them from new cluster entity cluster.master_flavor = Flavor.find(params[:cluster][:master_flavor]) cluster.slave_flavor = Flavor.find(params[:cluster][:slave_flavor]) # If new cluster is properly configured it will be saved in database if cluster.save # Then we send request to the DISCO to create a new cluster response = create_req(params[:cluster], infrastructure) if response.code == "201" # If the request was accepted, continues to add chosen framework(s) to the new cluster entity get_frameworks cluster # Gets cluster uuid, so we can get detailed information from the DISCO of the current cluster cluster.get_uuid response.header # Starts a background job which will update state of the cluster as deployment proceeds ClusterUpdateJob.perform_later(infrastructure, current_user[:id], cluster[:id], params[:cluster][:password]) sleep(1) redirect_to clusters_path return else # In case of failed DISCO connection, new cluster is deleted from database cluster.delete flash[:danger] = "DISCO connection error" Rails.logger.debug "DISCO connection error" end else flash[:warning] = "Cluster details were not filled correctly" Rails.logger.debug "Cluster details were not filled correctly" end # Retrieves infrastructures and putting them to adapters so in case of failure in creation, we can render them again @infrastructures = current_user.infrastructures.all @adapters = { "Choose" => 0 } @infrastructures.each { |inf| @adapters[inf.name] = inf.id } if @infrastructures redirect_to clusters_new_path end
Deletes chosen cluster from stack and database if success response returned from DISCO framework
# File app/controllers/clusters_controller.rb, line 92 def destroy # Chosen cluster is retrieved from the database according to its uuid uuid = params[:delete][:uuid] cluster = Cluster.find_by(uuid: uuid) # Retrieves cluster's infrastructure to get credentials infrastructure = Infrastructure.find(cluster.infrastructure_id) # Sends DISCO delete request using credentials, password, and cluster uuid response = delete_req(infrastructure, params[:delete][:password], uuid) if response.code != "200" flash[:danger] = "DISCO connection error" else # After a successful delete request cluster will be deleted from database cluster.delete flash[:success] = "The cluster is being deleted" end redirect_to clusters_path end
Retrieves current user clusters and groups from database
# File app/controllers/clusters_controller.rb, line 18 def index @clusters = current_user.clusters.all @groups = current_user.groups.all end
Retrieves current user infrastructures and puts them to the adapter dictionary(hash) so user can select needed infrastructure
# File app/controllers/clusters_controller.rb, line 26 def new @infrastructures = current_user.infrastructures.all @adapters = { "Choose" => 0 } @infrastructures.each { |inf| @adapters[inf.name] = inf.id } if @infrastructures end
Retrieves data and renders a form for a cluster creation Called by AJAX Get request from dashboard
# File app/controllers/clusters_controller.rb, line 115 def render_form @infrastructure_id = params[:infrastructure_id] if @infrastructure_id != "0" @frameworks = Framework.all @images = Image.where(infrastructure_id: @infrastructure_id) @flavors = Flavor.where(infrastructure_id: @infrastructure_id) @keypairs = Keypair.where(infrastructure_id: @infrastructure_id) end respond_to do |format| format.js end end
(This is not used currently) Retrieves selected cluster from database
# File app/controllers/clusters_controller.rb, line 35 def show @cluster = Cluster.find(params[:cluster][:id]) end


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App version: $VERSION
nRF Loader is an app that uploads new firmware to a device running Nordic Semiconductor's DFU service. A number of binary files are embedded in this app which can be written to any device advertising the appropriate service.
A .bin file can be opened directly with nRF Loader from other iOS apps, so if you have an application as a .bin file in an email or in Dropbox, you can open it with nRF Loader to upload the application to a target.
The source code for this app is available at nordicsemi.com, so its functionality can be embedded in a custom app that also supports the primary use case for a device.
The following steps tell you how to upload an application to a target.


core
/
com.mo.stratego.model.game
/
TurnCounter
/
firstTurn
firstTurn
var
firstTurn
:
GameState
Player who starts the game.


Programmable Input/Output Words¶
Programmable input/output (PIO) on the RP2040 (i.e. the Raspberry Pi Pico) provides a means to input to or output from pins in a very high-speed fashion at some speed up to the system clock of 125 MHz. There are two PIO peripherals, PIO0
and PIO1
, each of which contain four state machines.
PIO’s may have up to 32 PIO instructions in their memory, which are 16 bits in size each. PIO state machines may be set to wrap from a top instruction to a bottom instruction automatically, unless a jmp
instruction is executed at the top address. Instructions may be loaded into a PIO’s instruction memory with pio-instr-mem!
or pio-instr-relocate-mem!
. Instructions may also be fed into a state machine to be executed immediately with sm-instr!
. The address to execute PIO instructions at may be set for a state machine with sm-addr!
Up to four PIO state machines may be enabled, disabled, or reset at a time with sm-enable
, sm-disable
, or sm-restart
respectively. These take a bitset of four bits where the position of each bit corresponds to the index of the state machine to enable, disable, or restart.
Each PIO state machine has four 32-bit registers, an input shift register (ISR), an output shift register (OSR), an X register, and a Y register. They also have a 5-bit program counter (PC). These are all initialized to zero.
Each PIO state machine has an RX FIFO and a TX FIFO of four 32-bit values each. These are initialized to empty. Note that the RX FIFO and TX FIFO on a PIO state machine may be joined into a single unidirectional FIFO consisting of eight 32-bit values. The RX FIFO for a state machine may be pushed to from a state machine’s ISR register, which is 32-bits in size. The TX FIFO for a state machine may be pulled from to a state machine’s OSR register, which is also 32-bits in size.
PIO state machines may automatically pull from its TX FIFO after a threshold number of bits have been shifted out of its OSR register. They may also automatically push to its RX FIFO after a threshold number of bits have been shifted into its ISR register.
The clock divider for a state machine is set with sm-clkdiv!
, which takes a fractional component (from 0 to 255) and an integral component (from 0 to 65536) to divide the system clock by for the clock rate of the state machine in question. Note that if the integral clock divisor is 0 it is treated as 65536, and in those cases the fractional clock divisor must be 0.
PIO state machines may either have an optional delay associated with each PIO instruction, or may have sideset enabled, where they may set the state of up to five output pins each cycle simultaneous with whatever other operations they are carrying out. sm-sideset-pins!
sets the number of higher bits in this field will be used for sideset, with the remaining lower bits used for delay.
PIO assembler words compile PIO instructions to here
as 16 bits per instruction. There are two different basic types of PIO instructions - instructions without an associated delay or sideset, and instructions with an associated delay or sideset. The latter kind of instruction is marked with an +
in its assembling word.
pio
¶
The pio
module contains the following words:
Constants¶
on
¶
( – state )
On state.
off
¶
( – state )
Off state.
right
¶
( – direction )
Right direction.
left
¶
( – direction )
Left direction.
PIO0
¶
( – pio )
PIO0.
PIO1
¶
( – pio )
PIO1.
IRQ0
¶
( – irq )
IRQ0 index.
IRQ1
¶
( – irq )
IRQ1 index.
PIO0_IRQ0
¶
( – irq )
PIO0 IRQ0 index.
PIO0_IRQ1
¶
( – irq )
PIO0 IRQ1 index.
PIO1_IRQ0
¶
( – irq )
PIO1 IRQ0 index.
PIO1_IRQ1
¶
( – irq )
PIO1 IRQ1 index.
PIO Words¶
pins-pio-alternate
¶
( pin-base pin-count – )
Configure GPIO pins starting from pin-base of a count up to pin-count to be in an alternate function state such that the PIO may make use to them. Note that the pins wrap around, e.g. 29 3 pins-pio-alternate
configures GPIO pins 29, 0, and 1. Note that this is unnecessary when configuring pins with sm-sideset-pins!
, sm-set-pins!
, or sm-out-pins!
as these internally call this word.
However, this is necessary with sm-in-pin-base!
because any number of pins may be used by the in
instruction and there exists no register to set specifying a number of pins to use with this instruction
pio-instr-mem!
¶
( addr count pio – )
Write count halfwords starting at addr to instruction memory starting at instruction 0.
pio-instr-relocate-mem!
¶
( addr count offset pio – )
Write count halfwords starting at addr to instruction memory starting at instruction offset, relocating any JMP instructions in the process so that a destination of 0 is relocated to offset and so on.
pio-interrupt-enable
¶
( interrupt-bits irq pio – )
Enable interrupts.
pio-interrupt-disable
¶
( interrupt-bits irq pio – )
Disable interrupts.
pio-interrupt-enable-force
¶
( interrupt-bits irq pio – )
Enable forcing interrupts.
pio-interrupt-disable-force
¶
( interrupt-bits irq pio – )
Disable forcing interrupts.
pio-interrupt-raw@
¶
( pio – interrupt-bits )
Get raw interrupts.
pio-interrupt@
¶
( irq pio – interrupt-bits )
Get interrupt status.
INT_SM
¶
( state-machine – index )
Interrupt bits.
INT_SM_TXNFULL
¶
( state-machine – index )
TXN not full interupt bits.
INT_SM_RXNEMPTY
¶
( state-machine – index )
RXN not empty interrupt bits.
State Machine Words¶
sm-enable
¶
( state-machine-bits pio – )
Enable state machines.
sm-disable
¶
( state-machine-bits pio – )
Disable state machines.
sm-restart
¶
( state-machine-bits pio – )
Restart state machines.
sm-clkdiv!
¶
( fractional integral state-machine pio – )
Set the clock divisor for a state machine.
sm-addr!
¶
( address state-machine pio – )
Set the address for a state machine.
sm-wrap!
¶
( bottom-address top-address state-machine pio – )
Set the wrapping of a state machine.
sm-out-sticky!
¶
( sticky state-machine pio – )
Set the sticky state of a state machine.
sm-sideset-high-enable!
¶
( on/off state-machine pio – )
Set sideset highest-bit enable for a state machine.
sm-sideset-pindir!
¶
( on/off state-machine pio – )
Set sideset data assserted to pin directions pit.
sm-jmp-pin!
¶
( pin state-machine pio – )
Set GPIO number to use as condition for JMP PIN.
sm-inline-out-enable!
¶
( pin state-machine pio – )
Set a bit of OUT data to use as an inline OUT enable.
sm-inline-out-enable-clear
¶
( state-machine pio – )
Disable using a bit of OUT data as an inline OUT enable.
sm-pull-threshold!
¶
( threshold state-machine pio – )
Set OSR threshold before autopull or conditional pull will take place; value may be from 1 to 32.
sm-push-threshold!
¶
( threshold state-machine pio – )
Set ISR threshold before autopush or conditional push will take place; value may be from 1 to 32.
sm-txf!
¶
( x state-machine pio – )
Write to the TX FIFO of a state machine.
sm-rxf@
¶
( state-machine pio – x )
Read from the RX FIFO of a state machine.
pins-pio-alternate
¶
( pin-base pin-count – )
Set pins to PIO mode.
sm-pin!
¶
( on/off pin state-machine pio – )
Set the on/off state of a pin for a state machine. Note that the state machine should be disabled.
sm-pindir!
¶
( out/in pin state-machine pio – )
State the out/in state of a pin for a state machine. Note that the state machine should be disabled.
sm-sideset-pins!
¶
( pin-base pin-count state-machine pio – )
Set the sideset pins for a state machine. Note that the state machine should be disabled.
sm-set-pins!
¶
( pin-base pin-count state-machine pio – )
Set the SET pins for a state machine. Note that the state machine should be disabled.
sm-out-pins!
¶
( pin-base pin-count state-machine pio – )
Set the OUT pins for a state machine. Note that the state machine should be disabled.
sm-in-pin-base!
¶
( pin-base state-machine pio – )
Set the IN pin base for a state machine. Note that the state machine should be disabled.
sm-rx-fifo-level@
¶
( state-machine pio – level )
Get a state machine RX FIFO level.
sm-tx-fifo-level@
¶
( state-machine pio – level )
Get a state machine TX FIFO level.
sm-join-rx-fifo!
¶
( join? state-machine pio – )
Set joining the RX FIFO and TX FIFO of a state machine into a single eight by 32-bit RX FIFO.
sm-join-tx-fifo!
¶
( join? state-machine pio – )
Set joining the RX FIFO and TX FIFO of a state machine into a single eight by 32-bit TX FIFO.
sm-out-shift-dir
¶
( direction state-machine pio – )
Set the output shift register direction.
sm-in-shift-dir
¶
( direction state-machine pio – )
Set the input shift register direction.
sm-autopull!
¶
( on/off state-machine pio – )
Set autopull on/off.
sm-autopush!
¶
( on/off state-machine pio – )
Set autopush on/off.
sm-instr!
¶
( addr count state-machine pio – )
Manually write instructions to a state machine.
PIO Assembler Words¶
jmp,
¶
( address condition – )
PIO JMP instruction. Branch, conditionally or unconditionally. Condition is the condition, with or without a decrement, to apply to whether to branch. Address is the PIO instruction address to branch to.
wait,
¶
( index source polarity – )
PIO WAIT instruction. Wait for a condition to be true. Polarity is whether to wait for a 1 or a 0. Source is whether to wait for an absolute GPIO pin, a pin relative to the absolute IN
pin base, or a PIO IRQ index. Index is the pin or PIO IRQ index to wait for.
in,
¶
( bit-count source – )
PIO IN instruction. Input bits from a source into the current state machine’s ISR register. Source is the source to shift bits into the ISR register for the current state machine into, according to the configured shift in direction. Bit-count is how many bits to shift, a value from 1 to 32. The ISR bit shift count is increased by the bit count, saturating at 32.
out,
¶
( bit-count destination – )
PIO OUT instruction. Output bits from the current state machine’s OSR register to a destination. Destination is the destination to shift bits into from the OSR register for the current state machine, according to the configured shift out direction. Bit-count is how many bits to shift, value from 1 to 32. The OSR bit shift count is increased by the bit count, saturating at 32.
push,
¶
( block if-full – )
PIO PUSH instruction. Push the contents of the current state machine’s ISR register into the current state machine’s RX FIFO and reset the ISR register to all zeros. If-full is the condition as to whether to conditionally push. Block is whether to stall if pushing is not possible due to the RX FIFO being full.
pull,
¶
( block if-empty – )
PIO PULL instruction. Pull a 32-bit value from the current state machine’s TX FIFO into the current state machine’s OSR register. If-empty is the condition as to whether to conditionally pull. Block is whether to stall if pulling is not possible due to the TX FIFO being empty.
mov,
¶
( source op destination – )
PIO MOV instruction. Transfer a value from source to destination, applying op (which may be a no-op) to the value in the process.
irq,
¶
( index set/wait – )
PIO IRQ instruction. Set or wait on a PIO IRQ index, from 0 to 7 before REL
is applied. Note that REL
can be used to mark a PIO IRQ as relative to the current state machine ID by adding the state machine ID to the lower two bits of the IRQ index, by way of module-4 addition on the two LSB’s.
set,
¶
( data destination – )
PIO SET instruction. Set destination to data, which is a value from $00 to $1F.
jmp+,
¶
( address delay/side-set condition – )
PIO JMP instruction with delay or side-set. Branch, conditionally or unconditionally. Condition is the condition, with or without a decrement, to apply to whether to branch. Address is the PIO instruction address to branch to. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
wait+,
¶
( index delay/side-set source polarity – )
PIO WAIT instruction with delay or side-set. Wait for a condition to be true. Polarity is whether to wait for a 1 or a 0. Source is whether to wait for an absolute GPIO pin, a pin relative to the absolute IN
pin base, or a PIO IRQ index. Index is the pin or PIO IRQ index to wait for. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
in+,
¶
( bit-count delay/side-set source – )
PIO IN instruction with delay or side-set. Input bits from a source into the current state machine’s ISR register. Source is the source to shift bits into the ISR register for the current state machine into, according to the configured shift in direction. Bit-count is how many bits to shift, a value from 1 to 32. The ISR bit shift count is increased by the bit count, saturating at 32. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
out+,
¶
( bit-count delay/side-set destination – )
PIO OUT instruction with delay or side-set. Output bits from the current state machine’s OSR register to a destination. Destination is the destination to shift bits into from the OSR register for the current state machine, according to the configured shift out direction. Bit-count is how many bits to shift, value from 1 to 32. The OSR bit shift count is increased by the bit count, saturating at 32. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
push+,
¶
( delay/side-set block if-full – )
PIO PUSH instruction with delay or side-set. Push the contents of the current state machine’s ISR register into the current state machine’s RX FIFO and reset the ISR register to all zeros. If-full is the condition as to whether to conditionally push. Block is whether to stall if pushing is not possible due to the RX FIFO being full. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
pull+,
¶
( delay/side-set block if-empty – )
PIO PULL instruction with delay or side-set. Pull a 32-bit value from the current state machine’s TX FIFO into the current state machine’s OSR register. If-empty is the condition as to whether to conditionally pull. Block is whether to stall if pulling is not possible due to the TX FIFO being empty.
mov+,
¶
( source delay/side-set op destination – )
PIO MOV instruction with delay or side-set. Transfer a value from source to destination, applying op (which may be a no-op) to the value in the process. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
irq+,
¶
( index delay/side-set set/wait – )
PIO IRQ instruction with delay or side-set. Set or wait on a PIO IRQ index, from 0 to 7 before REL
is applied. Note that REL
can be used to mark a PIO IRQ as relative to the current state machine ID by adding the state machine ID to the lower two bits of the IRQ index, by way of module-4 addition on the two LSB’s. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
set+,
¶
( data delay/side-set destination – )
PIO SET instruction with delay or side-set. Set destination to data, which is a value from $00 to $1F. Delay/side-set is either a number of state machine cycles to delay after the instruction is executed, or up to five bits to write to the pins configured to sideset, depending upon how the current state machine is configured.
COND_ALWAYS
¶
( – condition )
Always jump.
COND_X0=
¶
( – condition )
Jump if scratch X is zero.
COND_X1-
¶
( – condition )
Jump if scratch X is non-zero, post-decrement.
COND_Y0=
¶
( – condition )
Jump if scratch Y is zero.
COND_Y1-
¶
( – condition )
Jump if scratch Y is non-zero, post-decrement.
COND_XY<>
¶
( – condition )
Jump if scratch X not equal scratch Y.
COND_PIN
¶
( – condition )
Jump on input pin.
COND_IOSRE
¶
( – condition )
Jump on output shift register not empty.
WAIT_GPIO
¶
( – wait )
Wait for GPIO.
WAIT_PIN
¶
( – wait )
Wait for a pin.
WAIT_IRQ
¶
( – wait )
Wait for an IRQ.
IN_PINS
¶
( – in-source )
Pins input.
IN_X
¶
( – in-source )
Scratch register X input.
IN_Y
¶
( – in-source )
Scratch register Y input.
IN_NULL
¶
( – in-source )
NULL input (all zeros).
IN_ISR
¶
( – in-source )
ISR input.
IN_OSR
¶
( – in-source )
OSR input.
OUT_PINS
¶
( – out-destination )
Pins output.
OUT_X
¶
( – out-destination )
Scratch register X output.
OUT_Y
¶
( – out-destination )
Scratch register Y output.
OUT_NULL
¶
( – out-destination )
NULL output (discard data).
OUT_PINDIRS
¶
( – out-destination )
PINDIRs output.
OUT_PC
¶
( – out )
PC output (unconditional jump to shifted address).
OUT_ISR
¶
( – out )
ISR output (also sets ISR shift counter to bit count).
OUT_EXEC
¶
( – out )
Execute OSR shift data as instruction.
PUSH_NOT_FULL
¶
( – push-if-full-option )
Push data even if threshold is not met.
PUSH_IF_FULL
¶
( – push-if-full-option )
Do nothing unless the total input shift count has reached its threshold.
PUSH_NO_BLOCK
¶
( – push-block-option )
Do not stall execution if RX FIFO is full, instead drop data from ISR.
PUSH_BLOCK
¶
( – push-block-option )
Stall execution if RX FIFO is full.
PULL_NOT_EMPTY
¶
( – pull-if-full-option )
Pull data even if threshold is not met.
PULL_IF_EMPTY
¶
( – pull-if-full-option )
Do nothing unless the total output shift count has reached its threshold.
PULL_NO_BLOCK
¶
( – pull-block-option )
Do not stall execution if TX FIFO is empty, instead copy from scratch X.
PULL_BLOCK
¶
( – pull-block-option )
Stall execution if TX FIFO is empty.
MOV_DEST_PINS
¶
( – mov-destination )
Move to PINS.
MOV_DEST_X
¶
( – mov-destination )
Move to scratch register X.
MOV_DEST_Y
¶
( – mov-destination )
Move to scratch register Y.
MOV_DEST_EXEC
¶
( – mov-destination )
Move to EXEC (execute data as instruction).
MOV_DEST_PC
¶
( – mov-destination )
Move to PC (treat data as address for unconditional branch).
MOV_DEST_ISR
¶
( – mov-destination )
Move to ISR (input shift counter is reset to 0, i.e. empty).
MOV_DEST_OSR
¶
( – mov-destination )
Move to OSR (output shift counter is reset to 0, i.e. full).
MOV_OP_NONE
¶
( – mov-op )
Move operation none.
MOV_OP_INVERT
¶
( – mov-op )
Move operation invert.
MOV_OP_REVERSE
¶
( – mov-op )
Move operation bit-reverse.
MOV_SRC_PINS
¶
( – mov-op )
Move from PINS.
MOV_SRC_X
¶
( – mov-source )
Move from scratch register X.
MOV_SRC_Y
¶
( – mov-source )
Move from scratch register Y.
MOV_SRC_NULL
¶
( – mov-source )
Move from NULL.
MOV_SRC_STATUS
¶
( – mov-source )
Move from STATUS.
MOV_SRC_ISR
¶
( – mov-source )
Move from ISR.
MOV_SRC_OSR
¶
( – mov-source )
Move from OSR.
IRQ_SET
¶
( – irq-op )
Raise an IRQ.
IRQ_CLEAR
¶
( – irq-op )
Clear an IRQ.
IRQ_WAIT
¶
( – irq-op )
Wait for an IRQ to be lowered.
SET_PINS
¶
( – set-destination )
Set PINS.
SET_X
¶
( – set-destination )
Set scratch register X (5 LSBs are set to data, all others are cleared).
SET_Y
¶
( – set-destination )
Set scratch register Y (5 LSBs are set to data, all others are cleared).
SET_PINDIRS
¶
( – set-destination )
Set PINDIRS.
REL
¶
( irq – irq-rel )
Add the state machine ID to the lower two bits of the IRQ index, by way of module-4 addition on the two LSB’s.
Exceptions¶
x-sm-out-of-range
¶
( – )
State machine out of range exception.
x-pio-out-of-range
¶
( – )
PIO out of range exception.
x-too-many-instructions
¶
( – )
Too many instructions exception.
x-address-out-of-range
¶
( – )
Address out of range exception.
x-too-many-pins
¶
( – )
Too many pins exception.
x-pin-out-of-range
¶
( – )
Pin out of range exception.
x-clkdiv-out-of-range
¶
( – )
Clock divisor out of range exception.
x-irq-out-of-range
¶
( – )
IRQ out of range exception.
x-interrupt-out-of-range
¶
( – )
Interrupt out of range exception.
x-threshold-out-of-range
¶
( – )
Buffer threshold out of range exception.
x-bit-out-of-range
¶
( – )
Bit out of range exception.
x-relocate-out-of-range
¶
( – )
Relocation out of range exception


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Version 1.0.1
This document is the documentation for the Parameters XML files used in Ballard Arinc-429 Custom Device.
XML Schema used is called: Arinc429_Parameters_XML_Schema.xsd. Schema is located at location: …\NI VeriStand XXXX\Custom Devices\Ballard - Arinc-429\Data (XXXX being your NI-VeriStand version
For XML Schema File, go to XML Schema File.
The Ballard Arinc-429 Custom Device comes with an Assembly called: Parameters_Arinc429_XML_Tools.dll, located here: …\NI VeriStand XXXX\Custom Devices\Ballard - Arinc-429\Data (XXXX being your NI-VeriStand version). The assembly can be called to check any user defined XML Parameters file against the schema. Though, it's important to note that some extra checkngs are performed out from the Assembly itself.
Once the validation against XML Schema is done, some extra checking is performed. Below is a list of what is checked. Left Column gives the Error Code returned in case of Error. Right Column is the Description of the checking performed.
| Error | Description |
|---|---|
| 5010 | This checking confirms that, for a given Hardware Channel+Label combination, all parameters have a unique combination of Parameter settings: Hardware Channel, Label Decimal and Parameter Name. If there are multiple duplicates, the feature returns all duplicates. |
| 5011 | This checking confirms that, for a given Hardware Channel+Label combination, all parameters don't use a range of bits larger than 32. If there are some Parameters out of range, the feature returns their names. |
| 5012 | This checking confirms that, for a given Hardware Channel+Label combination, all Discrete parameters have a name per Bit. If there are some Discrete Parameters without name, the feature returns their names. |
| 5013 | This checking confirms that, for a given Tx Hardware Channel, all Parameters have a Default Value (per Bit for Discrete Parameter Type). If there are some Discrete Parameters without Default Value, the feature returns their names. |
| 5014 | This checking confirms that, for a given Hardware Channel+Label combination, all BCD parameters have a valid startBit+numberOfBits combination. If some BCD Parameters have invalid settings, the feature returns their names. |
| 5015 | This checking confirms that, for a given Tx Hardware Channel, all Labels have their createParityChannel set to false. If there are some Labels with createParityChannel set to true, the feature returns their names. |
| 5016 | This checking confirms that, for a given Tx Hardware Channel, all Parameters have their startBit set to a minimum value of 8. If there are some Parameters with startBit equal or lower than 7, the feature returns their names. |
For XLM Tags description, go to XML Tags.
For XML Parameters File Examples, go to XML Parameters Files Example.


Saxon implements the JAXP 1.2 API (originally known as TrAX), which is now documented as
a standard part of JDK 1.4.
Saxon implements the interfaces in the javax.xml.transform
package in full, including support for
SAX, DOM, and Stream input, and SAX, DOM, and Stream output.
Note: The transformation interfaces in JAXP 1.2 are identical to JAXP 1.1: the new version only affects the XML parser interface, adding options to control schema validation.
Saxon also implements part of the javax.xml.parsers
API.
Saxon no longer provides its own SAX parser,
however it does provide a DocumentBuilder
.
The DOM interfaces are limited by the capabilities of the Saxon DOM, specifically the fact that
it is read-only. Nevertheless, the DocumentBuilder may be used to construct a Saxon tree, or to
obtain an empty Document node which can be supplied in a DOMResult to hold the result of a
transformation.


The Best Chocolate Chip Cookies
Recipe by: My Grandma
Prep Time: 45 Min
INGREDIENTS
1 1/2 cup (3 sticks) softened butter, 1 cup brown sugar, 1 cup granulated sugar, 1 Tbl vanilla instant pudding powder, 2 Tbl milk, 2 Tbl vanilla extract, 2 eggs, 4 cups all purpose flour, 2 tsp baking soda, 1/2 tsp salt, 4 cups chocolate chips, 1 cup chopped walnuts or pecans(optional)
How to make Mama's Recipe:
- Preheat oven to 350 degrees.
- Beat butter and sugars together until light and fluffy.
- Stir in pudding mix milk and vanilla extract.
- Beat in eggs.
- Add dry ingredients and stir until combined.
- Stir in chocolate chips and nuts.

- Bake 8-10 minutes or until golden brown.


We intend to create a research based documentation as a complete idea set for the initiation and continuity of any business firm or just plan. The whole package of services is categorized into four major fields of a successful business organization and management.
1. Research as a service
This part focuses on analysis of market, competitors(national and international), feasibility of the product the company intends on producing and the projection of company status for a few years based on the budget and time the client can give to their business. Our trained AI engine will be used for accurate business projection and prediction.
2. Platform
This part of the Idea package extends the Research as a service and focuses on further business upliftment practices. Eydean provides all the technical as well as physical platforms for the smooth running of one’s organization. Technical platform package includes branding(logo and online marketing), Search Engine Optimization business strategies planning, websites management, internal software requirements fulfillment as well as suggestion on latest technologies to be adopted in the future. The physical platform package includes management of workspace, branding and business strategies planning and also getting to put the projection of Research on real time environment.
3. Human Resource collection and optimization
Eydean attempts to be complete business planner and manager. We thus provide the required human resources for your business within a suitable and feasible salary that our client can afford. After the human resource is hired by the company, the obligation of such manpower goes completely to the client itself.
4. Idea Eydean as an incubator.
Eydean targets to be a top notch incubator in Nepal promoting tech as well as non-technical entrepreneurship among Nepali youth. We also intend on incubating international organization with different rounds of funding.
Milestone
The four packages can be separated out and picked one at a time or can be collectively chosen. Generally, the last three packages extend the first part of the service that promotes business culture based on Research and analysis.


Natural ability
Like its previous form, the Ivysaur has a plant on its back, which becomes the source of most Pokemon attacks. True, unlike the bulb of the bulbasaur, its attacks are much stronger than the attacks of its pre-evolution. The hidden property is Chlorophyll.


Standard tree:
Table tree:
jquery.fancytree.js project home Link to this page Example Browser View source code


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GHOST QUIZ
If you died and became a ghost, where would you like to haunt?
connecticut
dumpster
meth lab
superstore
When would you like to die in an ideal world?


authors: Joseph Marcus, Hussein Al-Asadi
We're interested in addressing population continuity through time in population genetic / ancient DNA data. A great starting place to tackle this problem is to visualize the data! To this end we'd like to visualize the variogram for our data. Ideally, for each individual we would observe the age of the sample $t_i$ and their genotype at a single position in the genome $g_i$. The variogram is a plot of the expected genetic distance versus time lag for a pair of samples
$$ E\big((g_i - g_j)^2\big) \text{ vs. } |t_i - t_j| $$
$$ g_i \in \{0,1,2\} $$
to be clear we assume there are two alleles ($A$, $a$) at this location and $g_i$ denotes the count of the $A$ allele in sample $i$.
However, in our data we don't observe genotypes, we observe sequence reads. To start, here we show a overly simple model to generate data
$$ \begin{aligned} p(g_i = k) &= \frac{1}{3}, k = \{0, 1, 2\} \\ y_i | g_i &\sim Binomial\big(c_{i}, \frac{g_i}{2}\big) \end{aligned} $$
Here $y_i$ denotes the count of reads carrying the $A$ allele and $c_i$ is the observed total number of reads. Typically $c_i$ is 0 i.e. there is a lot of missing data. Now we can get some traction in visualizing the variogram by conditioning on the data we observe
$$ E\big((g_i - g_j)^2 | y_i, y_j\big) = \sum_{g_i \in \{0, 1, 2\}} \sum_{g_j \in \{0, 1, 2\}} (g_i - g_j)^2 p(g_i, g_j| y_i, y_j) = \sum_{g_i \in \{0, 1, 2\}} \sum_{g_j \in \{0, 1, 2\}} (g_i - g_j)^2 p(g_i|y_i) p(g_j|y_j) $$
show plots!
Of course, we'd like to use our biological knowledge of the generative process, particularly the fact that two individuals sampled close in time should have more similar genotypes than two people sampled far apart in time. Now, let $\mathbf{f}(\mathbf{t})$ be the frequency of the $A$ allele for the $n$ samples observed at different time points.
A standard model for the allele frequencies is
$$ \mathbf{f}(\mathbf{t}) | \mu, \theta \sim \mathcal{N}\big(\mathbf{\mu}, \mathbf{\Sigma}(\mathbf{t}; \theta)\big) $$
Given these frequencies we can make some simplifying approximations to estimate the variogram
$$ \begin{aligned} \mathbf{g} | \mathbf{f}(\mathbf{t}) &\sim \mathcal{N}\Bigg(2\mathbf{f}(\mathbf{t}), diag\Big(2\mathbf{f}(\mathbf{t}) \odot \big(\mathbf{1}-\mathbf{f}(\mathbf{t})\big)\Big)\Bigg) \\ y_i | \tilde{g}_i &\sim Binomial\Big(c_i, \frac{\tilde{g}_i}{2}\Big) \end{aligned} $$
where $\tilde{g}_i \in \{0, 1, 2\}$ denotes the rounded genotype and $\odot$ represents element wise multiplication. See Wen and Stephens 2010 for inspiration on the normal approximation to the genotypes and rounding trick. Recall, we are not interested in estimating $\mathbf{f}(\mathbf{t})$ so we integrate out it of the model
$$ \mathbf{g} | \mu, \theta \sim \mathcal{N}\Big(2\mu, \Phi(\mathbf{t}; \theta)\Big) $$
where $\Phi(\mathbf{t}; \theta) = Var\Big(\mathbb{E}\big(\mathbf{g} | \mathbf{f}(\mathbf{t})\big)\Big) + \mathbb{E}\Big(Var\big(\mathbf{g} | \mathbf{f}(\mathbf{t})\big)\Big)$. Rewriting our marginal model
$$ \begin{aligned} \mathbf{g} | \mu, \theta &\sim \mathcal{N}\Big(2\mu, \Phi(\mathbf{t}; \theta)\Big) \\ y_i | \tilde{g}_i &\sim Binomial\Big(c_i, \frac{\tilde{g}_i}{2}\Big) \end{aligned} $$
Now we would like to estimate our model parameters $\mu$ and $\theta$ by maximum likelihood. We can maximize the marginal likelihood, however this requires a sum over the discretized genotypes which is computationally intractable
$$ \begin{aligned} p(\mathbf{y} | \mu, \theta) &= \sum_\tilde{\mathbf{g}} p(\mathbf{y} | \tilde{\mathbf{g}}, \mu, \theta) p(\tilde{\mathbf{g}} | \mu, \theta) \\ &= \sum_\tilde{\mathbf{g}} \prod_{i=1}^n p(y_i|\tilde{g}_i, \mu, \theta)p(\tilde{\mathbf{g}} | \mu, \theta) \end{aligned} $$
Note that the sum over the rounded genotype will have $3^n$ terms! Thus it is intractable for any reasonable data. Therefore we need to come up with a another strategy to compute or approximate the marginal likelihood. Note that
$$ \sum_\tilde{\mathbf{g}} \prod_{i=1}^n p(y_i|\tilde{g}_i, \mu, \theta)p(\tilde{\mathbf{g}} | \mu, \theta) = E_{\tilde{\mathbf{g}}}\Big(\prod_{i=1}^n p(y_i|\tilde{g}_i, \mu, \theta)\Big) $$
Perhaps this motivates a monte carlo approach to estimate this expectation.


If you’re Developing your Rest API’s using Node.js or Express.js, Then You’re probably using MongoDB as Database for Storing Data and Probably with mongoose ODM for working with MongoDB Database.Now as you’re DB starts to grow and you have multiple collections and they are related to each other and soon, you need to work with array’s inside in MongoDB document and now you want to push and pop from that array, In this MongoDB How-To Tutorial, we are going to learn How to Push items into mongo Array with an example.
Consider the following document, which is USER document, which has an array of Friends:
{
“_id” : ObjectId(“5a0fa6f6e744c923ec159606”),
“updated_at” : ISODate(“2017-11-18T03:20:22.282Z”),
“created_at” : ISODate(“2017-11-18T03:20:22.282Z”),
“firstName” : “stackFame”,
“lastName” : “Media”,
“userName” : “stackFame”,
“email”<EMAIL_ADDRESS>“password” : “$2a10Ru5TVFL889Y5RjDhycly.74c2tFXUEOn0rghzq6OiAu8oQNuqZwu”,
“status” : 1,
“language” : “en”,
“friends”:[
5a0fa6f6e744c923ec159606,
5a0fa6f6e744c923ec159606,
5a0fa6f6e744c923ec159606, ]
}
Now, You want edit friends array, when you want to add or remove friend to friends array.First, we will push a friend into an array with the mongoose in nodejs app.
// find by document id and update and push item in array
users.findByIdAndUpdate(userID,
{$push: {friends: friend}},
{safe: true, upsert: true},
function(err, doc) {
if(err){
console.log(err);
}else{
//do stuff
}
}
);
and you’ve successfully pushed item in a MongoDB document.Now we will learn how to pop or remove an item from MongoDB document array.
// find by document id and update and pop or remove item in array
users.findByIdAndUpdate(userID,
{$pull: {friends: friend}},
{safe: true, upsert: true},
function(err, doc) {
if(err){
console.log(err);
}else{
//do stuff
}
}
);
and you’re successfully poped or removed an item in a MongoDB document.
Conclusion:
It’s very easy to handle data with mongoose and MongoDB.So that you’ve to spend less time with data handling in MongoDB, Focus on your business logic of Rest API’s.
Article first appeared on: stackfame.com


import pandas as pd
import numpy as np
import pickle
import sys; sys.path.append('modules')
from Visual import Visual as V
from LearningPipe import LearningPipe as lp
from KDE_naive_bayes import KDENB as nb
from sklearn.linear_model import LogisticRegression as lr
from sklearn.svm import SVC as svc
from sklearn.neighbors import KNeighborsClassifier as knn
from sklearn.ensemble import RandomForestClassifier as rf
from xgboost import XGBClassifier as xgb
The dataset is cleaned, standarized in the previous preprocessing phase and saved to npz files. We directly load them in this notebook.
In the this part of project, I will test 6 classifiers:
1. Naive Bayes
2. Logistic Regression
3. SVM
4. k-Nearest Neighbours
5. Random Forest
6. XGBoost
In different Feature spaces:
1. original
2. PCA
3. sparsePCA
4. FactorAnalysis
5. NMF
Before fitting models on the dataset, I use the extra tree model to select the most informative features to deduce the dimensionality. So the chance of overfitting and the running time of algorithms is reduced.
All the classifiers above have hyperparameter to tune except naive Bayes. To avoid overfitting, I will test each hyperparameter combination on the training set using 5-fold cross validation. And evaluate the performance using function explained below.
As explained with great detail in "Pattern Recognition and Machine Learning" (Bishop, 2011), the expected error can be expressed as:
$$Error = Bias^2 + Variance + (Noise)$$
In which the squared bias is the distance from expectation of prediction to the target value. In the case of classification, the distance from target value to correct prediction is 0 and to wrong prediction is 1. Since we are using 5-fold cross validation, $bias = mean(1 - accuracy) = 1 - mean(accuracy)$. And the variance is $var(accuracy)$.
*Because the noise term discribes the noise come with the data set, it is not reducible by tuning model, we don't consider it in the performance evaluation.
np.random.seed(seed=666)
clfNames = [
'Naive Bayes', 'Logistic Regression', 'SVM',
'k-Nearest Neighbours', 'Random Forest', 'XGBoost'
]
spaceNames = [
'original', 'PCA', 'sparsePCA', 'factorAnalysis', 'NMF'
]
dimPairs = [[0,1], [1,2], [2,3], [3,4]]
X_train_space = np.load("data/clean_dataset/X_train_space.npz")
X_test_space = np.load("data/clean_dataset/X_test_space.npz")
y_train = np.fromfile('data/clean_dataset/y_train', dtype=int)
y_test = np.fromfile('data/clean_dataset/y_test', dtype=int)
# feature selection
Xs_train = {}; Xs_test = {}
for space in spaceNames:
Xs_train[space], Xs_test[space] = lp.featureSelection(
X_train_space[space], X_test_space[space], y_train
)
print("Space: {0:17} n_features: {1}"\
.format(space, Xs_train[space].shape[1]))
We start with the simple generative model naive Bayes. As we can see in feature histograms below, each feature has slightly different distributions in 2 classes. So it makes sense to try naive Bayes.
But these features have more than 1 type of distributions. Some of them are normal, some of them are exponential and there are even ones not typical and multimodal. So we can't directly use Gaussian naive Bayes or Bernoulli naive Bayes. To accommodate various types of distributions, I implemented a naive Bayes classifier using Gaussian KDE as distribution estimator.
*Due to the large amount of time needed for grid searching, I will directly import the results generated before hand.
# v = V(5, 4, figsize=(18,10))
# v.plotHists(Xs_train, y_train, [0,1,2,3], spaceNames)
# models_nb = lp(nb, 'NaiveBayes', Xs_train, y_train)
# models_nb.gridSearching()
with open('data/performances/models_nb.pickle', 'rb') as f:
models_nb = pickle.load(f)
models_nb.results
As the grid searching results shows, although naive Bayes classifier performs a little better in sparsPCA space than in the other spaces, the accuracy is still quite low.
In general, naive Bayes is a relatively simple generative model. It simplifies and factorizes the joint distribution into the product of distributions of each single random variable, so it can not capture the relationships between variables thus the decision boundarys are always distributed parallel to axes.
As we can see in the scatter and decision boundary plots, the data points of 2 class are mixing together. Without new features, we can't make good predictions.
models_nb.plotDecisionContours(dimPairs, 4)
Next we move on to the simple discriminative classifier logistic regression.
From the scatter plots in feature engineering section we can tell the data points in any of those 5 spaces are not linear separable. In order to draw non-linear decision boundary and incorporate possible relationships between pairs of features, we can map the features into higher dimensional polynomial space. In this project, we will set degree equals 2.(We will test the situation of degree=3 with polynomial kernel in support vector machine)
Xs_train_poly = {}; Xs_test_poly = {}
for space in spaceNames:
Xs_train_poly[space], Xs_test_poly[space] = lp.polynomializeFeatures(
Xs_train[space], Xs_test[space], n_degree=2
)
Grid search is performed on parameters:
1. Regularization type(penalty): ['l1', 'l2']
2. Regularization coefficient(C): [1, 10, 100, 1000, 2000]
3. Maximum Iterations(max_iter): [100, 200, 500]
params_lr = {
'penalty':['l1', 'l2'],
'C':[1, 10, 100, 1000, 2000],
'max_iter':[100, 200, 500]
}
# models_lr = lp(lr, 'LogisticRegression', Xs_train_poly, y_train)
# models_lr.gridSearching(params=params_lr)
with open('data/performances/models_lr.pickle', 'rb') as f:
models_lr = pickle.load(f)
The performances are much better than that of naive Bayes classifiers. The mean accuracies are much higher and the variances are relatively low.
models_lr.results.head(n=10)
For each of the hyperparameters:
replaceCol = {'max_iter':'m_iter', 'penalty':'reg'}
models_lr.plotParamGrid(params_lr, replace=replaceCol)
models_lr.plotDecisionContours(dimPairs, 4)
models_lr.compareDecisionContour('factorAnalysis', [3,4], alpha=0.3)
In general, a linear classifier like logistic regression is too simple to achieve high accuracy even if we map the features into quadratic polynomial space. Thus the tuning of hyperparameters doesn't change the behavior of classifier a lot.
If we map them into higher dimensional space, the computation becomes expensive. This leads us to the support vector machine which deals with higher dimensional space more efficiently using kernel trick.
The main problem we have in previous 2 classifiers is about the features, they are not predictive enough. When we couldn't find more features, we can try to combine the existing ones in some mathmetical ways hoping to find some of them separating the classes well. And SVM helps us do this with kernel trick.
Grid search is performed on parameters:
1. kernel type: ['poly'(degree=3), 'rbf']
2. Error penalty(C): [10**1, 10**1.5, 10**2.0, 10**2.5, 10**3.5]
3. Kernel coefficient(gamma): [10**-3.0, 10**-2.0, 10**-1.5, 10**-1.0]
params_svc = {
'kernel':['poly', 'rbf'],
'C':(10**1, 10**1.5, 10**2.0, 10**2.5, 10**3.5),
'gamma':(10**-3.0, 10**-2.0, 10**-1.5, 10**-1.0),
'probability':[True]
}
# models_svc = lp(svc, 'SVM', Xs_train, y_train)
# models_svc.gridSearching(params=params_svc)
# models_svc.results = models_svc.results.drop('probability', axis=1)
with open('data/performances/models_svc.pickle', 'rb') as f:
models_svc = pickle.load(f)
Looking at the 10 best models, we can see the performance is not better than logistic regression. But the variances in sparsePCA feature space are all low.
models_svc.results.head(n=10)
For each of the hyperparameters:
models_svc.plotParamGrid(params_svc)
But the decision boundary plotting renders the classifiers almost useless because most of these svm models label most of the data points as unpopular. We will investigate this issue more by looking at positive and negative predictions in model selection section.
models_svc.plotDecisionContours(dimPairs, 4)
models_svc.compareDecisionContour('factorAnalysis', [3,4], alpha=0.3)
So far we have tested 3 classifiers, and the best ones are SVM(questionable) and logistic regression. But still, we hope to find models that predict better. What is in common about those 3 classifiers is that they are all feature-based. In other words, we have been manipulating features in different ways to make prediction. Maybe there is a limitation of this method for our dataset. So this time, we will try KNN which is instance-based, so that every prediction will be made by checking surrounding data points whose labels are already known. And this model has a very high representability.
Grid search is performed on parameters:
1. Number of neighbors: [20, 40, 80, 160]
2. Weighting methods: ['uniform', 'distance']
3. Distance metric: ['manhattan', 'euclidean', 'minkowski']
params_knn = {
'n_neighbors':[20, 40, 80, 160],
'weights':['uniform', 'distance'],
'metric':['manhattan', 'euclidean', 'minkowski']
}
# models_knn = lp(knn, 'k-NearestNeighbors', Xs_train, y_train)
# models_knn.gridSearching(params=params_knn)
with open('data/performances/models_knn.pickle', 'rb') as f:
models_knn = pickle.load(f)
Among the models in this family, the pattern of performances are quite different from the previous models. The accuracies are not as high as svm models in general, but the variances are all low, thus the expected loss are low.
models_knn.results.head(n=10)
For each of the hyperparameters:
replaceCol = {'n_neighbors':'n_ngbs'}
models_knn.plotParamGrid(params_knn, replace=replaceCol)
In general, benefit from its high representability, the knn models are able to separate the mixed data points better than the other models. This matchs the decision boundary plotting below, the boundaries are rough thus captures finer pattern.
models_knn.plotDecisionContours(dimPairs, 4)
By comparing the models before and after the hyperparameter tuning, we can see the decision boundary are smoothened mainly due to the increasing of neighbor number. Thus the models are less prone to overfitting.
models_knn.compareDecisionContour('sparsePCA', [3,4], alpha=0.3)
After trying both feature-based classifiers and instance-based classifiers, the performances are still not satisfying enough. We will try ensemble method which combines the decision of multiple classifiers. So the rest 2 classifiers we are testing, random forest and extra gradient boosting, are both tree based emsemble classifiers. The former one, random forest classifier simply combines decision by averaging the predictions of an assigned number of tree classifiers. And the latter one, extra gradient boosting, focus more on the data points which are difficult to classify.
Grid search is performed on parameters:
1. Number of trees: [100, 200, 400, 800, 1600]
2. Maximal depth of each tree: [10, 20, 30]
3. Minimal leaf node sample size: [2, 5, 10, 20, 40]
params_rf = {
'n_estimators':(100, 200, 400, 800, 1600),
'max_depth':(5, 10, 20, 30),
'min_samples_split':(2, 5, 10, 20, 40)
}
# models_rf = lp.LearningPipe(rf, 'RandomForest', Xs_train, y_train)
# models_rf.gridSearching(params=params_rf)
with open('data/performances/models_rf.pickle', 'rb') as f:
models_rf = pickle.load(f)
Investigaitng the performances, the random forest models are indeed doing quite well. The accuracies are all over 66% thus the expected loss are lower. Although the variances are higher than the other model families.
models_rf.results.head(n=10)
For each of the hyperparameters:
replaceCol = {'max_depth':'depth', 'min_samples_split':'leaf', 'n_estimators':'tree'}
models_rf.plotParamGrid(params_rf, replace=replaceCol)
Similar to naive Bayes, tree classifiers look at only one feature at one time so the decision boundaries are distributed parallel to the axes. But because random forest are combining decisions of many trees, so the model is much more flexible than the naive Bayes.
Compared with default model, we have much more trees in the forest and the leaf size are much bigger. Thus the decision boundary are much smoother.
models_rf.plotDecisionContours(dimPairs, 4)
models_rf.compareDecisionContour('original', [2,3], alpha=0.3)
The extra gradient boost is a popular emsemble model. By starting from simple classifiers, it keeps adding new tree classifiers based on the wrong predictions.
Grid search is performed on parameters:
1. Subsample ratio of each time of fitting: [0.2, 0.5, 0.8, 1]
2. Minimum loss reduction required to make a split(gamma): [0, 0.2, 0.5, 1]
3. Coefficient of L1 regularization(alpha): [0.001, 0.01, 0.1, 1]
params_xgb = {
'subsample':[0.2, 0.5, 0.8, 1],
'gamma':[0, 0.2, 0.5, 1],
'reg_alpha':[0.001, 0.01, 0.1, 1],
'silent':[1],
'nthread':[4],
'objective':['binary:logistic']
}
# models_xgb = lp.LearningPipe(xgb, 'ExtraGradientBoost', Xs_train, y_train)
# models_xgb.gridSearching(params=params_xgb)
# models_xgb.results = models_xgb.results.drop(['nthread', 'objective', 'silent'], axis=1)
with open('data/performances/models_xgb.pickle', 'rb') as f:
models_xgb = pickle.load(f)
Shown in the performance evaluations of extra gradient boost classfiers, the accuracies are even higher than the random forest classifiers but the variances are also high. High variances seems to be a common problem of emsemble methods on this dataset.
models_xgb.results.head(n=10)
For each of the hyperparameters:
replaceCol = {'subsample':'bag', 'reg_alpha':'reg_l1'}
models_xgb.plotParamGrid(params_xgb, replace=replaceCol)
models_xgb.plotDecisionContours(dimPairs, 4)
models_xgb.compareDecisionContour('original', [2,3], alpha=0.3)
Taking one model out of each family in each feature space with best hyperparameter setting, we can plot them out and compare their performances visually.
models = [
models_nb, models_lr, models_svc, models_knn, models_rf, models_xgb
]
performances = []
for model in models: performances.append(model.bestPerfs)
performances = pd.concat(performances, axis=0).reset_index()
# because mean accuracy, variance and expected loss are in different scale.
# So we need to standardize them before showing them in the line plotting.
# standardize = lambda sr: (sr - sr.mean()) / (sr.max() - sr.min())
# visuallize loss var and bias
# v = V(1, 1, figsize=(15, 8))
# v.plotLines(
# performances.index,
# [standardize(performances['expectedLoss']),
# standardize(performances['mean']),
# standardize(performances['variance'])]
# )
Ideally, we should select models with high mean accuracy and low variances. Judging from the plotting we can easily tell naive Bayes are not good for us. Although the variances are not steady, the random forest and extra gradient boost have the lowest expected losses.
The best 10 models are listed below.
bestPerfs = performances.sort_values('expectedLoss')\
.reset_index().head(n=12)
bestPerfs
Remember the SVM classifiers have a issue of label most of data points as one class. To avoid selecting models have such kind of behavior, we need to investigate the confusion matrix. Here, we use confusion histograms, which are the detail version of confusion matrix, show us how confident those 10 classifiers are when they are making predictions and how many predictions are correct or wrong.
# v = V(3, 4, figsize=(18, 12))
# v.plotConfusionHists(
# bestPerfs['confusionHist'],
# bestPerfs['clf'],
# bestPerfs['space']
# )
These confusion histograms assure us that none of these classifiers make unbalanced predictions. And when these classifiers make confident right predictions, they also make confident mistakes. None of them are obviously better than the others. So we may choose the one with lowest expected loss, which is extra gradient boost in original space, as our final model.


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Everything You Need to Know About Gold Medals
Olympic gold medals contain only about 1.34 percent gold, with the rest composed of sterling silver.
Lessons from trying to make content for men during Weinstein and me too.
Alana Hope Levinson . 22 th Oct 2016Journal for Science & Engineering
Journal for Science & Engineering
Journal for Science & Engineering
Olympic gold medals contain only about 1.34 percent gold, with the rest composed of sterling silver.
Olympic gold medals contain only about 1.34 percent gold, with the rest composed of sterling silver.
Olympic gold medals contain only about 1.34 percent gold, with the rest composed of sterling silver.


Each tab exposes it's own unique deep link that can be distributed or bookmarked. The Back and Forward buttons of the browser work as in a normal website.
Next: Extras »


Cora scooted herself backward across the floor about 4 feet, almost under the tv cabinet. While we were out of sight for about a minute...
10/27/2008, 9:51:13 PM
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Cora scooted herself backward across the floor about 4 feet, almost under the tv cabinet. While we were out of sight for about a minute...
10/27/2008, 9:51:13 PM
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link


AWS does not have native support for Kubernetes, however there are many organizations that have put together their own solutions and guides for setting up Kubernetes on AWS.
This guide uses kops to setup a cluster on AWS. This should be seen as a rough template you will use to setup and shape your cluster.
Create a IAM Role
This role will be used to give your CI host permission to create and destroy resources on AWS
AmazonEC2FullAccess
IAMFullAccess
AmazonS3FullAccess
AmazonVPCFullAccess
Route53FullAccess (Optional)
Create a new instance to use as your CI host. This node will deal with provisioning and tearing down the cluster.
This instance can be small (t2.micro for example).
When creating it, assign the IAM role created in step 1.
Once created, download ssh keys.
SSH to your CI host
Install kops and kubectl on your CI host
Follow the instructions here: https://github.com/kubernetes/kops/blob/master/docs/install.md
Choose a cluster name
Since we are not using pre-configured DNS we will use the suffix “.k8s.local”. Per the docs, if the DNS name ends in .k8s.local the cluster will use internal hosted DNS.
export NAME=<somename>.k8s.local
Setup an ssh keypair to use with the cluster
ssh-keygen
Create a S3 bucket to store your cluster configuration
Since we are on AWS we can use a S3 backing store. It is recommended to enabling versioning on the S3 bucket. We don’t need to pass this into the KOPS commands. It is automatically detected by the kops tool as an env variable.
export KOPS_STATE_STORE=s3://<your_s3_bucket_name_here>
Set the region to deploy in
export REGION=`curl -s http://<IP_ADDRESS>/latest/dynamic/instance-identity/document|grep region|awk -F\" '{print $4}'`
Install the AWS CLI:
sudo apt-get update sudo apt-get install awscli
Set the availability zones for the nodes
For this guide we will be allowing nodes to be deployed in all AZs:
.. code-block:: bash export ZONES=$(aws ec2 describe-availability-zones --region $REGION | grep ZoneName | awk '{print $2}' | tr -d '"')
Create the cluster
For a basic setup run the following (All sizes measured in GB):
kops create cluster $NAME \ --zones "$ZONES" \ --authorization RBAC \ --master-size t2.micro \ --master-volume-size 10 \ --node-size t2.medium \ --node-volume-size 10 \ --yes
For a more secure setup add the following params to the kops command:
--topology private \ --networking weave \
This creates a cluster where all of the masters and nodes are in private subnets and don’t have external IP addresses. A mis-configured security group or insecure ssh configuration is less likely to compromise the cluster. In order to SSH into your cluster you will need to set up a bastion node. Make sure you do that step below. If you have the default number of elastic IPs (10) you may need to put in a request to AWS support to bump up that limit. The alternative is reducing the number of zones specified.
More reading on this subject: https://github.com/kubernetes/kops/blob/master/docs/networking.md
Settings to consider (not covered in this guide):
--vpc Allows you to use a custom VPC or share a VPC https://github.com/kubernetes/kops/blob/master/docs/run_in_existing_vpc.md --master-count Spawns more masters in one or more VPCs This improves redudancy and reduces downtime during cluster upgrades --master-zones specify zones to run the master in --node-count Increases the total nodes created (default 2) --master/node-security-groups Allows you to specify additional security groups to put the masters and nodes in by default --ssh-access By default SSH access is open to the world (<IP_ADDRESS>). If you are using a private topology, this is not a problem. If you are using a public topology make sure your ssh keys are strong and you keep sshd up to date on your cluster's nodes.
Note
Consider setting a cloud budget for your AWS account in order to make sure you don’t accidentally spend more than you wish to.
Wait for the cluster to start-up
Running the ‘kops validate cluster’ command will tell us what the current state of setup is. If you see “can not get nodes” initially, just be patient as the cluster can’t report until a few basic services are up and running. Keep running ‘kops validate cluster’ until you see “Your cluster $NAME is ready” at the end of the output. time until kops validate cluster; do sleep 15 ; done can be used to automate the waiting process. If at any point you wish to destroy your cluster after this step, run kops delete cluster $NAME --yes
Running the ‘kops validate cluster’ command will tell us what the current state of setup is. If you see “can not get nodes” initially, just be patient as the cluster can’t report until a few basic services are up and running.
Keep running ‘kops validate cluster’ until you see “Your cluster $NAME is ready” at the end of the output.
time until kops validate cluster; do sleep 15 ; done
can be used to automate the waiting process.
If at any point you wish to destroy your cluster after this step, run kops delete cluster $NAME --yes
kops delete cluster $NAME --yes
Confirm that kubectl is connected to your Kubernetes cluster.
kubectl
Run: .. code-block:: bash kubectl get nodes You should see a list of two nodes, each beginning with ip. If you want to use kubectl and helm locally (necessary for step #3 in Setting up Helm): run the following on CI host: kops export kubecfg copy the contents of ~/.kube/config to the same place on your local system If you wish to put the kube config file in a different location, you will need to export KUBECONFIG=<other kube config location>
Run:
.. code-block:: bash kubectl get nodes
You should see a list of two nodes, each beginning with ip.
ip
If you want to use kubectl and helm locally (necessary for step #3 in Setting up Helm):
run the following on CI host: kops export kubecfg
kops export kubecfg
copy the contents of ~/.kube/config to the same place on your local system
~/.kube/config
If you wish to put the kube config file in a different location, you will need to
export KUBECONFIG=<other kube config location>
Configure ssh bastion (Skip this step if you did not go with the –topology private option above!)
Ideally we would simply be passing the –bastion flag into the kops command above. However that flag is not functioning as intended at the moment. https://github.com/kubernetes/kops/issues/2881 Instead we need to follow this guide: https://github.com/kubernetes/kops/blob/master/docs/examples/kops-tests-private-net-bastion-host.md#adding-a-bastion-host-to-our-cluster At this point there are a few public endpoints left open which need to be addressed Bastion ELB security group defaults to access from <IP_ADDRESS> API ELB security group defaults to access from <IP_ADDRESS>
Ideally we would simply be passing the –bastion flag into the kops command above. However that flag is not functioning as intended at the moment. https://github.com/kubernetes/kops/issues/2881
Instead we need to follow this guide: https://github.com/kubernetes/kops/blob/master/docs/examples/kops-tests-private-net-bastion-host.md#adding-a-bastion-host-to-our-cluster
At this point there are a few public endpoints left open which need to be addressed
Bastion ELB security group defaults to access from <IP_ADDRESS>
API ELB security group defaults to access from <IP_ADDRESS>
Enable dynamic storage on your Kubernetes cluster.
Create a file, storageclass.yml on your local computer, and enter this text: kind: StorageClass apiVersion: storage.k8s.io/v1 metadata: annotations: storageclass.beta.kubernetes.io/is-default-class: "true" name: gp2 provisioner: kubernetes.io/aws-ebs parameters: type: gp2 Next, run this command: kubectl apply -f storageclass.yml This enables dynamic provisioning of disks, allowing us to automatically assign a disk per user when they log in to JupyterHub.
Create a file, storageclass.yml on your local computer, and enter this text:
storageclass.yml
kind: StorageClass apiVersion: storage.k8s.io/v1 metadata: annotations: storageclass.beta.kubernetes.io/is-default-class: "true" name: gp2 provisioner: kubernetes.io/aws-ebs parameters: type: gp2
Next, run this command:
kubectl apply -f storageclass.yml
This enables dynamic provisioning of disks, allowing us to automatically assign a disk per user when they log in to JupyterHub.
There are simple methods for encrypting your Kubernetes cluster. Illustrated here are simple methods for encryption at rest and encryption in transit.
Encryption at Rest
Instead of performing step 13 above. Create the following storageclass.yml file on your local computer:
kind: StorageClass apiVersion: storage.k8s.io/v1 metadata: annotations: storageclass.beta.kubernetes.io/is-default-class: "true" name: gp2 provisioner: kubernetes.io/aws-ebs parameters: type: gp2 encrypted: "true"
The main difference is the addition of the line encrypted: "true" and make note that true is in double quotes.
encrypted: "true"
true
Next run these commands:
kubectl delete storageclass gp2 kubectl apply -f storageclass.yml
Kubernetes will not allow you to modify storageclass gp2 in order to add the encrypted flag so you will have to delete it first. This will encrypt any dynamic volumes (such as your notebook)created by Kubernetes, it will not encrypt the storage on the Kubernetes nodes themselves.
encrypted
Encryption in Transit
In step 9 above, set up the cluster with weave by including the --networking weave flag in the kops create command above. Then perform the following steps:
--networking weave
kops create
Verify weave is running:
kubectl --namespace kube-system get pods
You should see several pods of the form weave-net-abcde
weave-net-abcde
Create Kubernetes secret with a private password of sufficient strength. A random 128 bytes is used in this example:
openssl rand -hex 128 >weave-passwd kubectl create secret -n kube-system generic weave-passwd --from-file=./weave-passwd
It is important that the secret name and its value (taken from the filename) are the same. If they do not match you may get a ConfigError
ConfigError
Patch Weave with the password:
kubectl patch --namespace=kube-system daemonset/weave-net --type json -p '[ { "op": "add", "path": "/spec/template/spec/containers/0/env/0", "value": { "name": "WEAVE_PASSWORD", "valueFrom": { "secretKeyRef": { "key": "weave-passwd", "name": "weave-passwd" } } } } ]' If you want to remove the encryption you can use the following patch: kubectl patch --namespace=kube-system daemonset/weave-net --type json -p '[ { "op": "remove", "path": "/spec/template/spec/containers/0/env/0"} ]'
kubectl patch --namespace=kube-system daemonset/weave-net --type json -p '[ { "op": "add", "path": "/spec/template/spec/containers/0/env/0", "value": { "name": "WEAVE_PASSWORD", "valueFrom": { "secretKeyRef": { "key": "weave-passwd", "name": "weave-passwd" } } } } ]'
If you want to remove the encryption you can use the following patch:
kubectl patch --namespace=kube-system daemonset/weave-net --type json -p '[ { "op": "remove", "path": "/spec/template/spec/containers/0/env/0"} ]'
Check to see that the pods are restarted. To expedite the process you can delete the old pods.
You can verify encryption is turned on with the following command:
kubectl exec -n kube-system weave-net-<pod> -c weave -- /home/weave/weave --local status
You should see encryption: enabled
encryption: enabled
If you really want to insure encryption is working, you can listen on port 6783 of any node. If the traffic looks like gibberish, you know it is on.
6783
A shared volume is supposed to be mounted to multiple user pods, so we cannot use EBS. As an alternative, there’s AWS EFS:
Setting up EFS storage on AWS
Customizing User Storage
Congrats. Now that you have your Kubernetes cluster running, it’s time to begin Setup JupyterHub.


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New to the Financial domain and interested in Investment Management and Stock Market price analysis using Machine Learning. Understanding Financial statements is what keeps me up at night! Alongside this, I am interested to pursue a career in Investment Banking or Investment Management!
As the world is moving towards AI, it becomes a mandate to understand how ML & DL works and how we can use these technologies to enhance our day to day work. I try to implement Machine Learning in analysing the stocks and other possible management schemes.
An attempt to explain the complex world of Finance & Macro-Economics in as simple form as possible, alongside giving relatable examples from the daily routine in order to create a path for how one's activity connects eventually to global differences. Alongside this I also maintain a tech blog to write about the trending technologies.
I have been coding since 5 years and I love to code in Python, Java, C++ & Shell Scripting. Have coded in 18 different programming languages and have worked from system level code to UI/UX development. With FinTech becoming more than a buzz word I would like to Innovate more in the same!
July 2017 to Aug 2020
Worked on High Performance Computing, ML, DL, networking research on RoCE, Lustre setup, and much more.
Wrote 2 recognized Patents for Dell Internal Use.
May 2016 to August 2016
Worked on Indoor mapping and navigation using beacons and on iOS game development with integration to Shell co.
May 2015 to June 2015
Worked on MVC implementation, using Nette framework(UI & UX), with major and minor updates.
From: Cass Business School, London-UK
Session: 2020-21
Award: Cass Asia Award
From: University of Petroleum & Energy Studies, Dehradun-India
Session: 2013-17
Result: 3.12 (On the Scale of 4.00)
From: Dayawati Modi Academy, Meerut-India
Session: 20012-13
Result: 78%
From: Dayawati Modi Academy, Meerut-India
Session: 2010-11
Result: 9.4 (In the Scale of 10)
The dataset used for the demonstration is the Ditigal Music reviews from Amazon and the pre-trained word embedding is Google News' 300d vector. For a simple bidirectional LSTM algorithm I have set my feature sizes and train a tokenizer. First we Tokenize and get the data into a form that the model can read then Tokenizer creates a BOW encoding, which is then going to be fed into our Embedding matrix.
Build a Multilayer Convolutional Network to analyze the MNIST data set and added layers of CNN to increase the efficiency. Used Tensorboard to display the data analysis. MNIST ("Modified National Institute of Standards and Technology") is the de facto “Hello World” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms.
Accuracy Attained: 99.24%
Detecting Fraud Credit card transactions with optimized runtime and increased accuracy. The datasets contains transactions made by credit cards in September 2013 by european cardholders. It contains only numerical input variables which are the result of a PCA transformation.
Created a minimal install Linux based Operating System which includes all required packages pre-build, made slight modifications to the kernel and developed few Graphical User Interface Applications based on GNOME, which will help the user to interact with the Operating System and perform some basic operations like taking backup, system cleanup etc.
Project is distributed in 3 modules:
1~ Linux From Scratch OS Build
2~ Kernel Modifications
3~ Graphical User Interface Applications
I stay fasicinated by the pace at which Technology is coming into use to Analyze Financial data. So, I always stay up to date with my Analytical Abilities.
I have great footprints in Research domain and am keenly interested to produce or improvise workflow.
I have cleared the NDA exam for Indian Air Force along with Round 1 of SSB 11'
I have been associated with Microsoft as a Student Respresentative for 2 consecutive years during my UG.
I was the Technical Director for UPES-CSI student chapter succeeding my post of Technical Head.
Always been appreciated for my way of working with the Team.