An annex script will allow you to take over some aspects of another storefront, replacing it with zoovy functionality. For instance, you may not want to rebuild your entire storefront, but to use the zoovy API for cart and checkout. To do so, you would use one of the following scripts:
These differ from other apps in that the templates are in independant files. This is to minimize the footprint of the annex script.


A Radio For The Apocalypse
by Jonathan BennettThere’s been a spate of apocalypse related articles over the last few weeks, but when I saw an AM radio made from a hand-wound coil and an oxidized British penny, I couldn’t help but be impressed. We’ve covered foxhole radios, stereotypical radios that are cobbled together from found parts during wartime.
This example uses a variable capacitor for tuning, but that’s technically optional. All that’s really needed is a coil and something to work as a diode.  Though, note, not all pennies have that necessary coating of copper. If a penny has green oxide, it’s likely a candidate.
Need a quickly cobbled together AM radio? Have some wire and a penny? Yeah, watch the video below the break, and you’ll know how to make it happen. When the apocalypse comes, you’ll thank us.


Fall Japanese Curry
"The secret ingredient to her recipe was the one and only japanese golden curry roux cubes."
silent screams through
white noise streams
her dreams don’t seem to fade
the drying petals
caress her lonely face
she’s
White Chocolate Lavender Lava Cake
"That moist, delicate cake stuffed with a hot, creamy filling that just oozes out — pastry perfection."
dreams of firelit faces
and undefined cases.
he flirts with perfection,
incapable of accepting his grievances.
these crooked smiles and
tilted tiles. they
yearn for the warmth
of the damp winter night.
so be it,
beauty is seldom intentional
Peanut Butter and Apple Layer Cake
"There was something about the cold, crisp apples paired and the smooth nutty spread that I couldn’t get over."
it’s hard to forget,
screams of a sun that bathe in its own addiction
the supple rays who
coax the blind, oblivious to that
bottomless void that resides within.
pure bliss in
comfort, less the devil;
says the mind.
and we all know
better, but
where else can we go.
Lemon Miso Noodle Soup with Tempura Lemon
Meet the brad behind the butter...
Hello and welcome to you new home!  Here on Brad & Butter, I bake, shake, style, and snap tot tell inspiring stories. This blog is a manifestation of all the things in life at have inspired me to act upon something. And in turn, I hope that whatever I wrtie, bake, or shake can inspire other people. Click here to learn about me...
Read More


Welcome to the Bikesy – an innovative and open bike mapping system specially designed to find flat, safe, and fast routes anywhere in the SF Bay Area.
Avoid those uphill climbs.
San Francisco isn’t New York – they might have taller buildings, but we’ve got bigger hills. Since we couldn’t find another service that lets you choose slightly longer but less steep routes, we made our own. Even better, Bikesy automatically gives you an elevation profile for your ride to help you prepare for the tough parts.
Safety is our number one priority.
As any parent knows, it’s not just hills that matter. In a region with as much traffic as the Bay Area, you’ll want to stay on bike lanes or paths whenever possible. Bikesy gives the flattest AND safest ways to get around.
3 x 3 = 9.
We know that one size doesn’t fit all, and our simple interface automatically chooses nine routes for you to pick from, ranging from the safest and most flat to the shortest and steepest. It’s easy to choose among three hill tolerances and three levels of safety (safe, safer, and safest) to find a path that’s right for you.
We’re serious about open source.
This part is a little geeky, but our back-end and front-end code is completely open. Plus, all our data is from OpenStreetMap, so you can edit the underlying map if you think we’ve got it wrong. Don’t like what we’ve done? Take our code and do it yourself! Or use our API to power your own app.
We’re cutting edge.
We’ve implemented some cute tricks like automatic browser location detection that we think you’ll like.
Cute name, but doesn’t Google do it better?
Actually, there are a number of other bike mapping services that work in the Bay Area, and they’re all pretty good. We strongly encourage you to try out other services to find out how we’re different, but ours is the only site to give you a complete elevation profile for your trip and to let you choose from nine pre-computed routes. Also, think Google will give you their code or let you change their map? Not likely.
If you find some neat features someone else has implemented that you’d like to see on Bikesy, let us know.
Other bike route planners we know about:
Please, let us know if you missed any other bike route planners.
Wait, who are you?
Bikesy was started by BlinkTag Inc with very large contributions from Jerry Jariyasunant and Brandon Martin-Anderson.


Cocommutative Hopf Algebras
with Antipode
by
Moss Eisenberg Sweedler
B.S., Massachusetts Institute of Technology (1963)
SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY August, 1965
a Ae sf
Signature of Author —_— ee re HY Me TH. Department of Mathematics, August 31, 1965
Certified Dy ....8 7. ov otc e eee ee eee mw meer na ees e sae rvene Thesis Supervisgr ff
A bby Accepted by evens rese Side ee ece el NB a ee ew ee wears dbase sas sensvneses
Chairman, Departmental Committee on Graduate Students
{


CEoptim: Cross-Entropy R Package for Optimization
Optimization solver based on the Cross-Entropy method.
| Version: |
1.2 |
| Depends: |
MASS, msm, stats, sna |
| Published: |
2017-02-20 |
| Author: |
Tim Benham and Qibin Duan and Dirk P. Kroese and Benoit Liquet |
| Maintainer: |
Benoit Liquet <b.liquet at uq.edu.au> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: |
no |
| Citation: |
CEoptim citation info |
| In views: |
Optimization |
| CRAN checks: |
CEoptim results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=CEoptim
to link to this page.


Some of the services we provide.
Founded on the solid foundation of integrity, passion and the desire to provide solutions.
We have seven(7) pillars holding this Organisation. Love, Integrity, Passion, ....
copyrights © 2019 Cranium® All rights reserved.


TOPIC
#sub-topic's(Tip:use more than one sub-topics means more tags)
WORK
10-10-2020
SAVE
WRITE YOUR NOTES HERE.
2020 © COPYRIGHT.HARMAN SINGH PRODUCTIONS.ALL RIGHTS RESERVED


Click in the correct boxes of ‘Subject’ and ‘Predicate’ to colour them, that identifies the given words.
1)
jumped on his bed in excitement
3)
went to the amusement park yesterday
4)
danced about with pom poms in their hands
5)
That creaky wooden door
7)
are going for their friend’s wedding
8)
chased a squirrel in the backyard
9)
migrate during the winter to warm places


LETTERS TO THE EDITOR: Congress made border problem
by THE WASHINGTON TIMESANALYSIS/OPINION:
Who’s to blame for our illegal immigration problems? Who’s to blame for the American communities that are suffering from rampant crime, cartel violence, social and language problems in schools, drained health care and welfare systems, and drugs and gang activity in many parts of the country? There are several parties to blame.
First, it’s the illegal immigrants themselves and the criminal elements who help them cross our border. Our nation has laws that should be abided by. Law and order are used to protect our society in varied ways. Some illegal immigrants may be seeking a better life, but they should do so in a lawful manner. Others come with ulterior and insidious motives.
Second, Congress should be held accountable. Members of Congress — specifically the Democrats — have repeatedly resisted enacting legislation to reinforce current immigration laws and close loopholes.
America finally has a president who’s willing to address this challenging problem, but the Democrats resist his steadfast efforts. They support open borders and, in essence, lawlessness.
Third, Americans who voted for congressional Democrats are to blame. Many of these citizens are ignorant or indifferent of what’s really taking place in our nation. Along with them are American citizens who do not vote.
Come 2020, vote — and vote the Democrats out.
MICHAEL IMHOF
Aurora, Ill.
Sign up for Daily Opinion Newsletter
Copyright © 2019 The Washington Times, LLC. Click here for reprint permission.
Please read our comment policy before commenting.


Control The Arduino From An App Using BLE. This example shows how to make an app that controls an Arduino board.
Communication with the Arduino is made using Bluetooth Low Energy (BLE). Your Arduino board therefore needs a BLE shield for this example to work.
You can browse the source code for this example at the Evothings GitHub repository
The file index.html is the entry point of the app. File arduinoble.js contains functions for writing to the Arduino.
The file arduinoledonoffble/arduinoledonoffble.ino contains the Arduino BLE server code that listens for commands from the app.
This example runs in Evothings Viewer on Android or iOS.
You need to run the example in Evothings Viewer. Alternatively, you can make a Cordova application if you wish to distribute the app. You then need to include the Cordova plugin com.evothings.ble. Read more in the documentation of the Evothings Viewer.
An iOS device or an Android device with support for Bluetooth 4.0 (which includes BLE) is required. In addition Android 4.3 or later is needed. Please note that BLE support on Android is still not fully mature. As a result, you may experience difficulties running this example. If the app stops working, restart Evothings Viewer and/or reset Bluetooth on the device.
For the Arduino setup, you need:
Here is the sketch:
Here is a photo of a board configured to work with this example. Note that output pin 2 is used (it may look like pin 3 in the photo).
Follow these steps to get started with this example:


Myanmar faces genocide lawsuit at top UN court
KABUL: An Afghan official says a Pakistani delegation is in Kabul to meet Afghan authorities amid increased tensions.
Monday's meeting is the first since the Pakistani Embassy closed its consular section in Kabul earlier this month, citing unspecified security concerns.
Afghanistan and Pakistan have an uneasy relationship. Kabul blames Islamabad for supporting the Taliban in the country's protracted war, a charge Pakistan denies.
The two countries also trade accusations that each side is firing across their shared border. Cross-border clashes killed three Afghan women last month.
Kaber Haqmal, spokesman for the Afghan national security adviser, said the two sides discussed efforts to normalize relations.
He says Afghan national security adviser Hamdullah Mohib has met with Gen. Faiz Hameed, the head of Pakistan's intelligence agency, and Suhail Mahmood, Pakistan's foreign secretary.


EASTER 4 SUNDAY
Entrance Antiphon
The merciful love of the Lord fills the earth; by the word of the Lord the heavens were made, alleluia.
Collect
Almighty ever-living God, lead us to a share in the joys of heaven, so that the humble flock may reach where the brave Shepherd has gone before. Who lives and reigns with you...
FIRST READING Acts 2:14a.36-41
A reading from the Acts of the Apostles
[On the day of Pentecost,] Peter, standing with the
Eleven, lifted up his voice and said to the multitude, Let all the house of
The word of the Lord.
RESPONSORIAL PSALM Psalm 23:1-3a.3b-4.5.6 (R. 1)
R. The Lord is my shepherd; there is nothing I shall want.
Or Alleluia!
The LORD is my shepherd; there is nothing I shall
want. Fresh and green are the pastures where he gives me repose. Near restful
waters he leads me; he revives my soul.
R. The Lord is my
shepherd; there is nothing I shall want.
He guides me along the right path, for the sake of his
name. Though I should walk in the valley of the shadow of death, no evil would
I fear, for you are with me.
R. The Lord is my
shepherd; there is nothing I shall want.
You have prepared a table before me in the sight of
my foes. My head you have anointed with oil; my cup is overflowing.
R. The Lord is my
shepherd; there is nothing I shall want.
Surely goodness and mercy shall follow me all the
days of my life. In the LORD's own house shall I
dwell for length of days unending.
R. The Lord is my
shepherd; there is nothing I shall want.
SECOND READING 1 Peter 2:20-24
Beloved: If when you do right and suffer for it you take it patiently, you have God's approval. For to this you have been called, because Christ also suffered for you, leaving you an example, that you should follow in his steps. He committed no sin; no guile was found on his lips. When he was reviled, he did not revile in return; when he suffered, he did not threaten; but he trusted to him who judges justly. He himself bore our sins in his body on the tree, that we might die to sin and live to righteousness. By his wounds you have been healed. For you were straying like sheep, but have now returned to the Shepherd and Guardian of your souls.
The word of the Lord.
ALLELUIA John 10:14
R. Alleluia. I am the good shepherd, says the Lord; I know my own, and my own know me. R. Alleluia.
GOSPEL John 10:1-10
A reading from the holy Gospel according to John
At that time: Jesus said, "Truly, truly, I say to you, he who does not enter the sheepfold by the door but climbs in by another way, that man is a thief and a robber; but he who enters by the door is the shepherd of the sheep. To him the gatekeeper opens; the sheep hear his voice, and he calls his own sheep by name and leads them out. When he has brought out all his own, he goes before them, and the sheep follow him, for they know his voice. A stranger they will not follow, but they will flee from him, for they do not know the voice of strangers." This figure Jesus used with them, but they did not understand what he was saying to them.  All who came before me are thieves and robbers; but the sheep did not heed them. I am the door; if any one enters by me, he will be saved, and will go in and out and find pasture. The thief comes only to steal and kill and destroy; I came that they may have life, and have it abundantly.
The Gospel of the Lord.
FIRST READING Acts 4:8-12
A reading from the Acts of the Apostles
In those days: Peter, filled with the Holy Spirit,
said, "Rulers of the people and elders, if we are being examined today
concerning a good deed done to a cripple, by what means this man has been
healed, be it known to you all, and to all the people of Israel, that by the
name of Jesus Christ of Nazareth, whom you crucified, whom God raised from the
dead, by him this man is standing before you well. This is the stone which was
rejected by you builders, but which has become the head of the corner. And
there is salvation in no one else, for there is no other name under heaven
given among men by which we must be saved."
The word of the Lord.
RESPONSORIAL PSALM Psalm 118
R. The stone that the builders rejected has become the cornerstone.
Or Alleluia!
Give praise to the LORD, for he is good; his mercy
endures for ever. It is better to take refuge in the LORD than to trust in man;
it is better to take refuge in the LORD than to trust in princes.
R. The stone that the builders rejected has become the
cornerstone.
I will thank you, for you have answered, and you
are my saviour. The stone that the builders rejected has become the
cornerstone. By the LORD has this been done, a marvel in our eyes.
R. The stone that the builders rejected has become the
cornerstone.
Blest is he who comes in the name of the LORD. We
bless you from the house of the LORD.  My God, I
exalt you. Give praise to the LORD, for he is good; his mercy endures for ever.
R. The stone that the builders rejected has become the
cornerstone.
SECOND READING 1 John 3:1-2
A reading from the first Letter of Saint John
Beloved: See what love the Father has given us, that we should be called children of God; and so we are.
The reason why the world does not know us is that it did not know him. Beloved,
we are God's children now; it does not yet appear what we shall be, but we know
that when he appears we shall be like him, for we shall see him as he is.
The word of the Lord.
ALLELUIA John 10:14
R. Alleluia. I am the good shepherd, says the Lord; I know my own, and my own know me. R. Alleluia.
GOSPEL John 10:11-18
A reading from the holy Gospel according to John
At that time: Jesus said, “I am the good shepherd.
The good shepherd lays down his life for the sheep. He who is a hireling and
not a shepherd, whose own the sheep are not, sees the wolf coming and leaves
the sheep and flees; and the wolf snatches them and scatters them. He flees
because he is a hireling and cares nothing for the sheep. I am the good
shepherd; I know my own and my own know me, as the Father knows me and I know
the Father; and I lay down my life for the sheep. And I have other sheep, that are not of this fold; I must bring them also,
and they will heed my voice. So there shall be one flock, one shepherd. For
this reason the Father loves me, because I lay down my life, that I may take it
again. No one takes it from me, but I lay it down of my own accord. I have
power to lay it down, and I have power to take it again; this charge I have
received from my Father."
The Gospel of the Lord.
FIRST READING Acts 13:14.43-52
A reading from the Acts of the Apostles
In those days: Paul and Barnabas passed on from
Perga and came to Antioch of Pisidia. And on the sabbath
day they went into the synagogue and sat down. And many Jews and devout
converts to Judaism followed Paul and Barnabas, who spoke to them and urged
them to continue in the grace of God. The next sabbath
almost the whole city gathered together to hear the word of God. But when the
Jews saw the multitudes, they were filled with jealousy, and contradicted what
was spoken by Paul, and reviled him. And Paul and Barnabas spoke out boldly,
saying, "It was necessary that the word of God should be spoken first to
you. Since you thrust it from you, and judge yourselves unworthy of eternal
life, behold, we turn to the Gentiles. For so the Lord has commanded us,
saying, 'I have set you to be a light for the Gentiles, that you may bring
salvation to the uttermost parts of the earth.'" And when the Gentiles
heard this, they were glad and glorified the word of God; and as many as were
ordained to eternal life believed. And the word of the Lord spread throughout all the region. But the Jews incited the devout women of
high standing and the leading men of the city, and stirred up persecution
against Paul and Barnabas, and drove them out of their district. But they shook
off the dust from their feet against them, and went to Iconium. And the
disciples were filled with joy and with the Holy Spirit.
The word of the Lord.
RESPONSORIAL PSALM Psalm 100:1-2.3.5 (R. 3c)
R. We are his people, the sheep of his flock.
Or Alleluia!
Cry out with joy to the LORD, all the earth. Serve the
LORD with gladness. Come before him, singing for joy.
R. We are his people, the sheep of his flock.
Know that he, the LORD, is God. He made us; we
belong to him. We are his people, the sheep of his flock.
R. We are his people, the sheep of his flock.
Indeed, how good is the LORD, eternal his merciful love. He is faithful from age to age.
R. We are his people, the sheep of his flock.
SECOND READING Revelation 7:9.14-17
A reading from the Book of Revelation
I, John, looked, and behold, a great multitude which no man could number, from every nation, from all tribes and peoples and tongues, standing before the throne and before the Lamb, clothed in white robes, with palm branches in their hands. Then one of the elders said to me, "These are they who have come out of the great tribulation; they have washed their robes and made them white in the blood of the Lamb. Therefore are they before the throne of God, and serve him day and night within his temple; and he who sits upon the throne will shelter them with his presence. They shall hunger no more, neither thirst any more; the sun shall not strike them, nor any scorching heat. For the Lamb in the midst of the throne will be their shepherd, and he will guide them to springs of living water; and God will wipe away every tear from their eyes."
The word of the Lord.
ALLELUIA John 10:14
R. Alleluia. I am the good shepherd, says the Lord; I know my own, and my own know me. R. Alleluia.
 My Father, who has given them to me, is greater than all, and no one is able to snatch them out of the Father's hand. I and the Father are one."
The Gospel of the Lord.
Prayer over the Offerings
Grant, we pray, O Lord, that we may always find delight in these paschal mysteries, so that the renewal constantly at work within us may be the cause of our unending joy. Through Christ our Lord.
Communion Antiphon
The Good Shepherd has risen, who laid down his life for his sheep, and willingly died for his flock, alleluia.
 Who lives and reigns for ever and ever.


Prompt text
2020
Before
sure
unsure
sure
unsure
After
I think this advice is...
Click or Touch the tooltip above to move on...


Mishael’s names can be seen in many of the names of God in Scripture, such as El Shaddai (God Almighty) and El Elyon (Most High God). Other names that include that suffix are Gabriel (Strength of God) and Michael (He Who Is Like God).
In Week 2, Day 5 we learned about the unspoken name of God given to Moses as “I am” at the burning bush. Although “I am” in Hebrew is not pronounceable (YHWH has no vowels), it is sometimes pronounced Jehovah or Yahweh when vowels are added. The name Yahweh forms the suffix -aniah or -iah at the end of Hananiah and Azariah’s names.
We don’t know anything else about Daniel, Hananiah, Mishael, and Azariah’s families, but we do know that they made a conscious effort from the first day of their lives to teach them about God and let them know that worshipping the one true God was part of the culture they were born into.
I wonder if those families had a sense of the impending destruction of their surrounding culture and way of life. The Babylonians, under the leadership of King Nebuchadnezzar, invaded and destroyed Jerusalem in 605 b.c., destroying the city and the Temple. The four boys with God-centered names were probably in their early teen years when they became prisoners of war, taken back to the empire of Babylon when their beloved Jerusalem was destroyed.
Reread Daniel 1:3-5.
What were the qualities these young Hebrews possessed that caused the Babylonians to notice them and export them back to their country?
How long was their training period? What would happen to them at the end of it?
Nebuchadnezzar wanted to destroy the culture and convert the people. He wanted to make them Babylonian in culture, education, attitude, and religion. He knew that in order to do that he would need to conquer the hearts and minds of the leaders of the next generation, instilling in them a Babylonian way of life. He did this by taking an entire generation of leaders back to Babylon, where they would be treated not as prisoners of war or as slaves but as trainees for positions of leadership, influence, and power. He knew that if he could convert one generation away from their own religion and way of life, he would reach his goal.


Missão: sendo a área de profissionais domésticos uma das áreas que possibilita uma grande mudança social para a base da pirâmide em países que estão em desenvolvimento, nós tentamos oferecer uma tecnologia facilitando para sua escolha ser a certa.
Nossa Visão: ser a primeira opção de serviço de contratação e busca de empregos para Profissionais Domésticos no Brasil até 2016.
Nossos Valores: ética, iniciativa, inovação e, parceiros e clientes em primeiro lugar.
Nossos Segmentos: Cuidador de Idosos, Babás, Domésticas, Faxineiras, Caseiros, Cozinherias, Dogwalkers, Governantas, Jardineiros, Motoristas e Piscineiros.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
“Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.”
“Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.”
“Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.”
Basic customer support for small business
Basic customer support for small business
Basic customer support for small business
Basic customer support for small business


<IP_ADDRESS>
'AT*REF=0,512\r' +
'AT*PCMD=1,0,0,0,0,0\r'
According to tests, a satisfying control of the AR.Drone is reached by sending the ATcommands every 30 ms for smooth drone movements. To prevent the drone from considering the WIFI connection as lost, two consecutive commands must be sent within less than 2 seconds.
dgram = require 'dgram'
socket = dgram.createSocket 'udp4'
seq = 0
flying = false
emergency = false
setInterval ->
ref = (flying << 9) | (emergency << 8)
commands = [
"AT*REF=#{seq++},#{ref}"
"AT*PCMD=#{seq++},0,0,0,0,0"
].join('\r') + '\r'
console.log JSON.stringify commands
buffer = new Buffer commands
socket.send buffer, 0, buffer.length, 5556, '<IP_ADDRESS>'
, 30
setTimeout ->
emergency = false
, 1000
setTimeout ->
flying = true
, 3000
setTimeout ->
flying = false
, 6000
var arDrone = require('ar-drone');
var client = arDrone.createClient();
client.takeoff();
client
.after(5000, function() {
this.clockwise(0.5);
})
.after(3000, function() {
this.stop();
this.land();
});
var arDrone = require('ar-drone');
var client = arDrone.createClient();
client.createRepl();
var arDrone = require('ar-drone');
var client = arDrone.createClient();
client.on('navdata', console.log);


FUSS Examples
Hello world!
Show your text in a bold blue panel by using the FUSS .panel extension class
e.g.

 H1 Hello world!

Click the Tables button above to see how we change the default appearance of tables
Applying Fuss to a HTML Unordered list created the navigation bar at the top of these pages.
- Button 1
- Button 2
- Button 3
- Button 4
- Button 5
- Button 6
- Button 7
- Button 8
CSS layout for a 'Well' (generic information box). Set at 78% of width in CSS with a 10% margin & justified text. A custom style
which you can add to your own text blocks using
. Click on the classes button above for more examples


Module rustyGFX::object_parser
[−]
[src]
Copyright <2018>
Modules
| mat |
Copyright <2018> |
Structs
| Model |
struct name: Model description: this struct is designed to hold the meta data about a model being used by the program it holds the number of vertices, positions, texels, normals, and faces of the model |
Functions
| faces |
function name: faces inputs: the name of the file as a string outputs: a vector of the Face struct description: this function takes in a filename it get the piece of the file related to face data it then parses that into a vector of Face structs |
| fileSection |
function name: fileSection inputs: name of file as string, token to look for as start of line in the file outputs: a vector of strings that are the lines in the file related to the token description: this function gets all of the lines of a file that start with the token passed to it. |
| getObjectFileName |
function name: getObjectFileName inputs: the index into the list of names as usize outputs: the string that is the file name description: this function reads in the file that contains the names of the object files and then returns the one at the index or modulus of the index to avoid out of bounds |
| normals |
function name: normals inputs: the file name as a string outputs: a vector of Normal structs description: this function gets the normal data from a file and reorders it by index for openGL |
| posTex |
function name: posTex inputs: positions as a vector of Vertex structs, texels as a vector of Texture structs outputs: a vector of PosTex structs description: this function combines the position and texture data into a single list of data |
| positions |
function name: positions inputs: the name of the file as a string outputs: a vector of Vertex structs description: this function takes in a file name it runs vertices and faces to get vectors of vertices and faces and uses those to create a new ordering for the vertices that is acceptable for openGL to use in rendering |
| texels |
function name: texels inputs: the file name as a string outputs: a vector of Textures description: this function gets and reorders the textures based on the face data so that they can be used by openGL |
| unmodifiedNormals |
function name: unmodifiedNormals inputs: the name of the file to parse as a string outputs: a vector of the Normal struct description: this function takes the piece of a file related to normals and parses it into a vector of normal structs |
| unmodifiedTextures |
function name: unmodifiedTextures input: name of the file as a string output: a vector of the Texture struct description: this function takes a file name gets the section of that file related to texture data parses that into Texture structures adds those to a vector and returns that |
| vertices |
function name: vertices inputs: the file name as a string outputs: a vector of Vertex structures description: this function takes the name of a file gets the section of the file related to vertices and parses that section into a series of Vertex structures that |


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getColumns(ColumnExpression)
- Method in class com.njkremer.Sqlite.
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Returns a List of Maps which map from a field/database table column for the resulting query along with the passed in
ColumnExpression
.
getConnection()
- Static method in class com.njkremer.Sqlite.
DataConnectionManager
Gets the raw SQL Connection.
getCount()
- Method in class com.njkremer.Sqlite.
SqlExecutor
Used to end a
SqlExecutor.select(Class)
where you just want the count of the resulting query.
getDatabaseFormattedStringFromDate(Date)
- Static method in class com.njkremer.Sqlite.utils.
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getDateFromDatabaseFormattedString(String)
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getFieldName()
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getFirst()
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SqlExecutor
A convenience method to return the first object of a given query which would normally be returned by
SqlExecutor.getList()
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getFk()
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Relationship
getList()
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SqlExecutor
Returns a List of Objects of type T (as specified by class type passed into
SqlExecutor.select(Class)
) that result from the query built up with the
SqlStatement
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getterName()
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getType()
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greaterThan(Object)
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Used to specify a greater than comparison with the supplied value and the preceding field as specified with a
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Save Up To $150 On Google Pixel 4 & Get Google WiFi Free
Amazon has a rather unique deal going on right now for the Pixel 4 and Pixel 4 XL. You can save up to $150 on these new smartphones, and you're getting Google WiFi thrown in for free. This is for a single Google WiFi, and not a three-pack.
The Google Pixel 4 with Google WiFi comes in at $699 and the Google Pixel 4 XL with Google WiFi comes in at $749. So it's $100 off of the Pixel 4 and $150 off of the Pixel 4 XL.
You can get these for the same price without Google WiFi included too.
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Pixel 4 was released just a couple of months ago, back in October. It's currently the latest and greatest smartphone from Google. It comes in two sizes: Pixel 4 and Pixel 4 XL. The main differences between these two are the display sizes and battery capacities. With a 5.7-inch full HD+ display and 2800mAh capacity battery on the smaller Pixel 4. The Pixel 4 XL has a 6.3-inch Quad HD+ display with a 3700mAh capacity battery.
Otherwise, both of these sport the Snapdragon 855 chipset, 6GB of RAM and 64GB of storage. There is a 128GB model, but that is not part of this deal at this price.
What really makes the Pixel impressive though, is the camera. It's a 12-megapixel main sensor here and a 16-megapixel telephoto available as well. This is going to give you some really great Portrait shots, as well as some really good looking zoom shots. Instead of needing to crop after you take the photo, just zoom in and grab it.
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Google WiFi is the original mesh WiFi system from Google. It's going to offer you faster speeds, even with more devices on your network. Making it great for smart homes.
You can pick up the Google Pixel 4 and Google WiFi bundle from Amazon by clicking here. It's unclear how long this bundle is going to last.
Google Pixel 4 + Google WiFi - Amazon - $699
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Most salamanders are nocturnal, and hunt for insects, worms, and other small creatures.


This is the partial for view 2.
Showing of 'interpolate' filter: {{ 'Current version is v%VERSION%.' | interpolate }}
View 1


Hill'ffair
  It is a name which needs no introduction up here in the hills, it is the reason why most of northern India awaits the month of October, it is the name whose mention alone excites the listener to his core. Hill'ffair is the soul of NIT Hamirpur. A soul that is vibrant, contagious and exuberant. Hill’ffair is around the corner once again!!!
      And needless to say, it promises to be more extravagant this year than ever before. In the age where fests are in a perennial quest to provide all who bear witness an unforgettable and worth - cherishing experience, Hill’ffair has become synonymous with exhilarating joy and excitement that knows no bounds.


--- layout: jsdoc title: "Class: CacheExpirationPlugin" jsdocNav: - Modules: - workbox-background-sync - workbox-broadcast-cache-update - workbox-build - workbox-cache-expiration - workbox-cacheable-response - workbox-google-analytics - workbox-precaching - workbox-routing - workbox-runtime-caching - workbox-sw ---
The CacheExpirationPlugin
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limit on the responses cached.
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is used by the workbox-sw
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If you would like to use this functionality outside of the RequestWrapper
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const plugin = new workbox.cacheExpiration.CacheExpirationPlugin({
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Arpita and Aayush celebrate 5th wedding anniversary with Salman, Katrina, Karisma and others
Aayush Sharma and Arpita Khan Sharma hosted Bollywood stars Sonakshi Sinha, Katrina Kaif, Salman Khan, Arbaaz Khan, Shilpa Shetty and Genelia D'Souza among others at their residence.
19 Nov 2019, 06:43
Salman Khan's sister Arpita Khan Sharma and husband Aayush Sharma celebrated their fifth wedding anniversary on Monday. The couple hosted Bollywood stars Sonakshi Sinha, Katrina Kaif, Salman Khan, Arbaaz Khan, Shilpa Shetty and Genelia D'Souza among others at their residence.
Salim Khan, Salma Khan and Helen graced the party. (Express Photo: Varinder Chawla)
Salman Khan looked dapper in his casual avatar. (Express Photo: Varinder Chawla)
Arbaaz Khan was seen with girlfriend Giorgia at sister Arpita's anniversary celebrations. (Express Photo: Varinder Chawla)
Arpita's close friend Katrina Kaif clicked as she arrived for the party. (Express Photo: Varinder Chawla)
Iulia Vantur also celebrated Aayush and Arpita's anniversary with them. (Express Photo: Varinder Chawla)
Arpita and Aayush twinned in their black outfits at their wedding anniversary celebrations. (Express Photo: Varinder Chawla)
Karisma Kapoor joined Arpita and Aayush in their fifth wedding anniversary celebrations. (Express Photo: Varinder Chawla)
Jacqueline Fernandez at Arpita and Aayush wedding anniversary party. (Express Photo: Varinder Chawla)
Sonakshi Sinha at Arpita and Aayush's fifth wedding anniversary celebrations. (Express Photo: Varinder Chawla)
Shilpa Shetty and Raj Kundra also attended Arpita and Aayush's wedding anniversary party. (Express Photo: Varinder Chawla)
Riteish Deshmukh and Genelia D'Souza also came to congratulate Arpita and Aayush on their 5th wedding anniversary. (Express Photo: Varinder Chawla)
Divya Khosla and Bhushan Kumar at Arpita and Aayush's wedding anniversary party. (Express Photo: Varinder Chawla)


|
1. Club FAQ
|
||||
|
1.
What is an official club?
|
||||
| Official club is a community system, so players can have a closed environment to talk to and battle other clubs in the game. | ||||
|
2. What are the benefits of joining a club?
|
||||
| The player will be able to write on the club bulletin board, participate in a closed community, in game will have the club mark will be shown before nickname, chat in the club and be able to battle with other clubs. | ||||
| 3. What will happen to the club members? | ||||
|
Will be part of a closed community created by another player the "Club Master": ・ Chat in the club chat within the game; ・ Club mark will be shown before nickname and in tournaments; ・ And create or participate in club battles. |
||||


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Available at:
Web Server: Address (AP): ???.???.???.??? Address (WiFi): ???.???.???.??? Hostname: ???????????????????????????????? Port: ?????? URL: ??????
WiFi access point.
Configure WiFi access point (default).
Configure WiFi network connection.
The Knoblomat® uses WiFi Smart Configuration to connect to an existing network. A smartphone and an special app is required: - For iOS, this application is available under name "Espressif Esptouch". - For Android, an application is available under name "Espressif Esptouch" or "ESP8266 SmartConfig".
Clear the stored configuration data (after restart the Knoblomat® runs as an access point).
Restart the Knoblomat® (reboot).


clusteror package¶
clusteror.core module¶
This module contains Clusteror
class capsulating raw data to discover
clusters from, the cleaned data for a clusteror to run on.
The clustering model encompasses two parts:
Neural network: Pre-training (often encountered in Deep Learning context) is implemented to achieve a goal that the neural network maps the input data of higher dimension to a one dimensional representation. Ideally this mapping is one-to-one. A Denoising Autoencoder (DA) or Stacked Denoising Autoencoder (SDA) is implemented for this purpose.
One dimensional clustering model: A separate model segments the samples against the one dimensional representation. Two models are available in this class definition:
- K-Means
- Valley model
The pivot idea here is given the neural network is a good one-to-one mapper the separate clustering model on one dimensional representation is equivalent to a clustering model on the original high dimensional data.
Note
Valley model is explained in details in module clusteror.utils
.
-
class
clusteror.core.
Clusteror
(raw_data)[source]¶ Bases:
object
Clusteror
class can train neural networks DA or SDA, train taggers, or load saved models from files.Parameters: raw_data (Pandas DataFrame) – Dataframe read from data source. It can be original dataset without any preprocessing or with a certain level of manipulation for future analysis. -
_raw_data
¶ Pandas DataFrame – Stores the original dataset. It’s the dataset that later post-clustering performance analysis will be based on.
-
_cleaned_data
¶ Pandas DataFrame – Preprocessed data. Not necessarily has same number of columns with
_raw_data
as a categorical column can derive multiple columns. As thetanh
function is used as activation function for symmetric consideration. All columns should have values in range[-1, 1]
, otherwise anOutRangeError
will be raised.
-
_network
¶ str – da for DA; sda for SDA. Facilating functions called with one or the other algorithm.
-
_da_dim_reducer
¶ Theano function – Keeps the Theano function that is from trained DA model. Reduces the dimension of the cleaned data down to one.
-
_sda_dim_reducer
¶ Theano function – Keeps the Theano function that is from trained SDA model. Reduces the dimension of the cleaned data down to one.
-
_one_dim_data
¶ Numpy Array – The dimension reduced one dimensional data.
-
_valley
¶ Python function – Trained valley model tagging sample with their one dimensional representation.
-
_kmeans
¶ Scikit-Learn K-Means model – Trained K-Means model tagging samples with their one dimensional representation.
-
_tagger
¶ str – Keeps records of which tagger implemented.
-
_field_importance
¶ List – Keeps the list of coefficiences that influence the clustering emphasis.
-
add_cluster
()[source]¶ Tags each sample regarding their reduced one dimensional value. Adds an extra column ‘cluster’ to
raw_data
, seggesting a zero-based cluster ID.
-
cleaned_data
¶ Pandas DataFrame – For assgining cleaned dataframe to
_cleaned_dat
.
-
da_dim_reducer
¶ Theano function – Function that reduces dataset dimension. Attribute
_network
is given da to designate the method of the autoencoder asDA
.
-
field_importance
¶ List – Significance that given to fields when training of neural network is done. Fields with a large number will be given more attention.
Note
The importance is only meaningful relatively between fields. If no values are specified, all fields are treated equally.
Parameters: field_importance (List or Dict, default None (List of Ones)) – - If a list is designated, all fields should be assigned an
importance, viz, the length of the list should be equal to the length of the features training the neural network.
- It can also be given in a dict. In such a case, the fields can
be selectively given a value. Dict key is for field name and value is for the importance. Fields not included will be initiated with the default value one. A warning will be issued when a key is not on the list of field names, mostly because of a typo.
-
classmethod
from_csv
(filepath, **kwargs)[source]¶ Class method for directly reading CSV file.
Parameters: - filepath (str) – Path to the CSV file
- **kwargs (keyword arguments) – Other keyword arguments passed to
pandas.read_csv
-
kmeans
¶ Python function – Trained on the dimension reduced one dimensional data that segregates subjects into concentration of existence in a subset of
[-1, 1]
with K-Means algorithm._tagger
is given valley to facilitate follow-up usages.
-
load_dim_reducer
(filepath='dim_reducer.pk')[source]¶ Loads saved dimension reducer. Need to first name the network type.
Parameters: filepath (str) –
-
load_kmeans
(filepath)[source]¶ Loads a saved K-Means tagger from a file.
- filepath: str
- File path to the file saving the K-Means tagger.
-
load_valley
(filepath)[source]¶ Loads a saved valley tagger from a file. Create the valley function from the saved parameters.
- filepath: str
- File path to the file saving the valley tagger.
-
one_dim_data
¶ Numpy Array – Stores the output of neural network that has dimension one.
-
raw_data
¶ Pandas DataFrame – For assgining new values to
_raw_data
.
-
reduce_to_one_dim
()[source]¶ Reduces the dimension of input dataset to one before the tagging in the next step.
Input of the Theano function is the cleaned data and output is a one dimensional data stored in
_one_dim_data
.
-
save_dim_reducer
(filepath='dim_reducer.pk', include_network=False)[source]¶ Save dimension reducer from the neural network training.
Parameters: - filepath (str) – Filename to store the dimension reducer.
- include_network (boolean) – If true, prefix the filepath with the network type.
-
save_kmeans
(filepath, include_taggername=False)[source]¶ Saves K-Means model to the named file path. Can add a prefix to indicate this saves a K-Means model.
Parameters: - filepath (str) – File path for saving the model.
- include_taggername (boolean, default False) – Include the kmean_ prefix in filename if true.
-
save_valley
(filepath, include_taggername=False)[source]¶ Saves valley tagger.
Parameters: - filepath (str) – File path to save the tagger.
- include_taggername (boolean, default False) – Include the valley_ prefix in filename if true.
-
sda_dim_reducer
¶ Theano function – Function that reduces dataset dimension. Attribute
_network
is given sda to designate the method of the autoencoder asSDA
.
-
tagger
¶ str – Name the tagger if necessary to do so, which will facilitate, e.g. prefixing the filepath.
-
train_da_dim_reducer
(field_importance=None, batch_size=50, corruption_level=0.3, learning_rate=0.002, min_epochs=200, patience=60, patience_increase=2, improvement_threshold=0.98, verbose=False)[source]¶ Trains a
DA
neural network.Parameters: - field_importance (List or Dict, default None (List of Ones)) –
- If a list is designated, all fields should be assigned an
importance, viz, the length of the list should be equal to the length of the features training the neural network.
- It can also be given in a dict. In such a case, the fields can
be selectively given a value. Dict key is for field name and value is for the importance. Fields not included will be initiated with the default value one. A warning will be issued when a key is not on the list of field names, mostly because of a typo.
- batch_size (int) – Size of each training batch. Necessary to derive the number of batches.
- corruption_level (float, between 0 and 1) – Dropout rate in reading input, typical pratice in deep learning to avoid overfitting.
- learning_rate (float) – Propagating step size for gredient descent algorithm.
- min_epochs (int) – The mininum number of training epoch to run. It can be exceeded depending on the setup of patience and ad-hoc training progress.
- patience (int) – True number of training epochs to run if larger than
min_epochs
. Note it is potentially increased during the training if the cost is better than the expectation from current cost. - patience_increase (int) – Coefficient used to increase patience against epochs that have been run.
- improvement_threshold (float, between 0 and 1) – Minimum improvement considered as substantial improvement, i.e. new cost over existing lowest cost lower than this value.
- verbose (boolean, default False) – Prints out training at each epoch if true.
- field_importance (List or Dict, default None (List of Ones)) –
-
train_kmeans
(n_clusters=10, **kwargs)[source]¶ Trains K-Means model on top of the one dimensional data derived from dimension reducers.
Parameters: - n_clusters (int) – The number of clusters required to start a K-Means learning.
- **kwargs (keyword arguments) – Any other keyword arguments passed on to Scikit-Learn K-Means model.
-
train_sda_dim_reducer
(field_importance=None, batch_size=50, hidden_layers_sizes=[20], corruption_levels=[0.3], learning_rate=0.002, min_epochs=200, patience=60, patience_increase=2, improvement_threshold=0.98, verbose=False)[source]¶ Trains a
SDA
neural network.Parameters: - field_importance (List or Dict, default None (List of Ones)) –
- If a list is designated, all fields should be assigned an
importance, viz, the length of the list should be equal to the length of the features training the neural network.
- It can also be given in a dict. In such a case, the fields can
be selectively given a value. Dict key is for field name and value is for the importance. Fields not included will be initiated with the default value one. A warning will be issued when a key is not on the list of field names, mostly because of a typo.
- batch_size (int) – Size of each training batch. Necessary to derive the number of batches.
- hidden_layers_sizes (List of ints) – Number of neurons in the hidden layers (all but the input layer).
- corruption_levels (List of floats, between 0 and 1) – Dropout rate in reading input, typical pratice in deep learning to avoid overfitting.
- learning_rate (float) – Propagating step size for gredient descent algorithm.
- min_epochs (int) – The mininum number of training epoch to run. It can be exceeded depending on the setup of patience and ad-hoc training progress.
- patience (int) – True number of training epochs to run if larger than
min_epochs
. Note it is potentially increased during the training if the cost is better than the expectation from current cost. - patience_increase (int) – Coefficient used to increase patience against epochs that have been run.
- improvement_threshold (float, between 0 and 1) – Minimum improvement considered as substantial improvement, i.e. new cost over existing lowest cost lower than this value.
- verbose (boolean, default False) – Prints out training at each epoch if true.
- field_importance (List or Dict, default None (List of Ones)) –
-
train_valley
(bins=100, contrast=0.3)[source]¶ Trains the ability to cut the universe of samples into clusters based how the dimension reduced dataset assembles in a histogram. Unlike the K-Means, no need to preset the number of clusters.
Parameters: - bins (int) – Number of bins to aggregate the one dimensional data.
- contrast (float, between 0 and 1) – Threshold used to define local minima and local maxima. Detailed
explanation in
utils.find_local_extremes
.
Note
When getting only one cluster, check the distribution of
one_dim_data
. Likely the data points flock too close to each other. Try increasingbins
first. If not working, try different neural networks with more or less layers with more or less neurons.
-
valley
¶ Python function – Trained on the dimension reduced one dimensional data that segregates subjects into concentration of existence in a subset of
[-1, 1]
, by locating the “valley” in the distribution landscape._tagger
is given valley to facilitate follow-up usages.
-
clusteror.nn module¶
This module comprises of classes for neural networks.
-
class
clusteror.nn.
SdA
(n_ins, hidden_layers_sizes, np_rs=None, theano_rs=None, field_importance=None, input_data=None)[source]¶ Bases:
object
Stacked Denoising Autoencoder (SDA) class.
A SdA model is obtained by stacking several DAs. The hidden layer of the dA at layer i becomes the input of the dA at layer i+1. The first layer dA gets as input the input of the SdA, and the hidden layer of the last dA represents the output. Note that after pretraining, the SdA is dealt with as a normal MLP, the dAs are only used to initialize the weights.
Parameters: - n_ins (int) – Input dimension.
- hidden_layers_sizes (list of int) – Each int will be assgined to each hidden layer. Same number of hidden layers will be created.
- np_rs (Numpy function) – Numpy random state.
- theano_rs (Theano function) – Theano random generator that gives symbolic random values.
- field_importance (list or Numpy array) – Put on each field when calculating the cost. If not given, all fields given equal weight ones.
- input_data (Theano symbolic variable) – Variable for input data.
-
theano_rs
¶ Theano function – Theano random generator that gives symbolic random values.
-
field_importance
¶ list or Numpy array – Put on each field when calculating the cost. If not given, all fields given equal weight ones.
-
W
¶ Theano shared variable – Weight matrix. Dimension (n_visible, n_hidden).
-
W_prime
¶ Theano shared variable – Transposed weight matrix. Dimension (n_hidden, n_visible).
-
bhid
¶ Theano shared variable – Bias on output side. Dimension n_hidden.
-
bvis
¶ Theano shared variable – Bias on input side. Dimension n_visible.
-
x
¶ Theano symbolic variable – Used as input to build graph.
-
params
¶ list – List packs neural network paramters.
-
dA_layers
¶ list – List that keeps dA instances.
-
n_layers
¶ int – Number of hidden layers, len(dA_layers).
Computes the values of the last hidden layer.
Parameters: input_data (Theano symbolic variable) – Data input to neural network. Returns: A graph with output as the hidden layer values. Return type: Theano graph
-
get_first_reconstructed_input
(hidden)[source]¶ Computes the reconstructed input given the values of the last hidden layer.
Parameters: hidden (Theano symbolic variable) – Data input to neural network at the hidden layer side. Returns: A graph with output as the reconstructed data at the visible side. Return type: Theano graph
-
pretraining_functions
(train_set, batch_size)[source]¶ This function computes the cost and the updates for one trainng step of the dA.
Parameters: - train_set (Theano shared variable) – The complete training dataset.
- batch_size (int) – Number of rows for each mini-batch.
Returns: Theano functions that run one step training on each dA layers.
Return type: List
-
class
clusteror.nn.
dA
(n_visible, n_hidden, np_rs=None, theano_rs=None, field_importance=None, initial_W=None, initial_bvis=None, initial_bhid=None, input_data=None)[source]¶ Bases:
object
Denoising Autoencoder (DA) class.
Parameters: - n_visible (int) – Input dimension.
- n_hidden (int) – Output dimension.
- np_rs (Numpy function) – Numpy random state.
- theano_rs (Theano function) – Theano random generator that gives symbolic random values.
- field_importance (list or Numpy array) – Put on each field when calculating the cost. If not given, all fields given equal weight ones.
- initial_W (Numpy matrix) – Initial weight matrix. Dimension (n_visible, n_hidden).
- initial_bvis (Numpy array) – Initial bias on input side. Dimension n_visible.
- initial_bhid (Numpy arry) – Initial bias on output side. Dimension n_hidden.
- input_data (Theano symbolic variable) – Variable for input data.
-
theano_rs
¶ Theano function – Theano random generator that gives symbolic random values.
-
field_importance
¶ list or Numpy array – Put on each field when calculating the cost. If not given, all fields given equal weight ones.
-
W
¶ Theano shared variable – Weight matrix. Dimension (n_visible, n_hidden).
-
W_prime
¶ Theano shared variable – Transposed weight matrix. Dimension (n_hidden, n_visible).
-
bhid
¶ Theano shared variable – Bias on output side. Dimension n_hidden.
-
bvis
¶ Theano shared variable – Bias on input side. Dimension n_visible.
-
x
¶ Theano symbolic variable – Used as input to build graph.
-
params
¶ list – List packs neural network paramters.
-
get_corrupted_input
(input_data, corruption_level)[source]¶ Corrupts the input by multiplying input with an array of zeros and ones that is generated by binomial trials.
Parameters: - input_data (Theano symbolic variable) – Data input to neural network.
- corruption_level (float or Theano symbolic variable) – Probability to corrupt a bit in the input data. Between 0 and 1.
Returns: A graph with output as the corrupted input.
Return type: Theano graph
-
get_cost_updates
(corruption_level, learning_rate)[source]¶ This function computes the cost and the updates for one trainng step of the dA.
Parameters: - corruption_level (float or Theano symbolic variable) – Probability to corrupt a bit in the input data. Between 0 and 1.
- learning_rate (float or Theano symbolic variable) – Step size for Gradient Descent algorithm.
Returns: - cost (Theano graph) – A graph with output as the cost.
- updates (List of tuples) – Instructions of how to update parameters. Used in training stage to update parameters.
Computes the values of the hidden layer.
Parameters: input_data (Theano symbolic variable) – Data input to neural network. Returns: A graph with output as the hidden layer values. Return type: Theano graph
-
get_reconstructed_input
(hidden)[source]¶ Computes the reconstructed input given the values of the hidden layer.
Parameters: hidden (Theano symbolic variable) – Data input to neural network at the hidden layer side. Returns: A graph with output as the reconstructed data at the visible side. Return type: Theano graph
clusteror.plot module¶
Plotting tools relevant for illustrating and comparing clustering results can be found in this module.
-
clusteror.plot.
group_occurance_plot
(one_dim_data, cat_label, labels, group_label, colors=None, figsize=(10, 6), bbox_to_anchor=(1.01, 1), loc=2, grid=True, show=True, filepath=None, **kwargs)[source]¶ Plot the distribution of a one dimensional ordinal or categorical data in a bar chart. This tool is useful to check the clustering impact in this one-dimensional sub-space.
Parameters: - one_dim_data (list, Pandas Series, Numpy Array, or any iterable) – A sequence of data. Each element if for an instance.
- cat_label (str) – Field name will be used for the one dimensional data.
- labels (list, Pandas Series, Numpy Array, or any iterable) – The segment label for each sample in one_dim_data.
- group_label (str) – Field name will be used for the cluster ID.
- colors (list, default None) – Colours for each category existing in this one dimensional data. Default colour scheme used if not supplied.
- figsize (tuple) – Figure size (width, height).
- bbox_to_anchor (tuple) – Instruction to placing the legend box relative to the axes. Details
refer to
Matplotlib
document. - loc (int) – The corner of the legend box to anchor. Details refer to
Matplotlib
document. - grid (boolean, default True) – Show grid.
- show (boolean, default True) – Show figure in pop-up windows if true. Save to files if False.
- filepath (str) – File name to saving the plot. Must be assigned a valid filepath if
show
is False. - **kwargs (keyword arguments) – Other keyword arguemnts passed on to
matplotlib.pyplot.scatter
.
Note
Instances in a same cluster does not necessarily assemble together in all one dimensional sub-spaces. There can be possibly no clustering capaility for certain features. Additionally certain features play a secondary role in clustering as having less importance in
field_importance
inclusteror
module.
-
clusteror.plot.
hist_plot_one_dim_group_data
(one_dim_data, labels, bins=11, colors=None, figsize=(10, 6), xlabel='Dimension Reduced Data', ylabel='Occurance', bbox_to_anchor=(1.01, 1), loc=2, grid=True, show=True, filepath=None, **kwargs)[source]¶ Plot the distribution of a one dimensional numerical data in a histogram. This tool is useful to check the clustering impact in this one-dimensional sub-space.
Parameters: - one_dim_data (list, Pandas Series, Numpy Array, or any iterable) – A sequence of data. Each element if for an instance.
- labels (list, Pandas Series, Numpy Array, or any iterable) – The segment label for each sample in
one_dim_data
. - bins (int or iterable) – If an integer, bins - 1 bins created or a list of the delimiters.
- colors (list, default None) – Colours for each group. Use equally distanced colours on colour map if not supplied.
- figsize (tuple) – Figure size (width, height).
- xlabel (str) – Plot xlabel.
- ylabel (str) – Plot ylabel.
- bbox_to_anchor (tuple) – Instruction to placing the legend box relative to the axes. Details
refer to
Matplotlib
document. - loc (int) – The corner of the legend box to anchor. Details refer to
Matplotlib
document. - grid (boolean, default True) – Show grid.
- show (boolean, default True) – Show figure in pop-up windows if true. Save to files if False.
- filepath (str) – File name to saving the plot. Must be assigned a valid filepath if
show
is False. - **kwargs (keyword arguments) – Other keyword arguemnts passed on to
matplotlib.pyplot.scatter
.
Note
Instances in a same cluster does not necessarily assemble together in all one dimensional sub-spaces. There can be possibly no clustering capaility for certain features. Additionally certain features play a secondary role in clustering as having less importance in
field_importance
inclusteror
module.
-
clusteror.plot.
scatter_plot_two_dim_group_data
(two_dim_data, labels, markers=None, colors=None, figsize=(10, 6), xlim=None, ylim=None, alpha=0.8, bbox_to_anchor=(1.01, 1), loc=2, grid=True, show=True, filepath=None, **kwargs)[source]¶ Plot the distribution of a two dimensional data against clustering groups in a scatter plot.
A point represents an instance in the dataset. Points in a same cluster are painted with a same colour.
This tool is useful to check the clustering impact in this two-dimensional sub-space.
Parameters: - two_dim_data (Pandas DataFrame) – A dataframe with two columns. The first column goes to the x-axis, and the second column goes to the y-axis.
- labels (list, Pandas Series, Numpy Array, or any iterable) – The segment label for each sample in
two_dim_data
. - markers (list) – Marker names for each group.
- bbox_to_anchor (tuple) – Instruction to placing the legend box relative to the axes. Details
refer to
Matplotlib
document. - colors (list, default None) – Colours for each group. Use equally distanced colours on colour map if not supplied.
- figsize (tuple) – Figure size (width, height).
- xlim (tuple) – X-axis limits.
- ylim (tuple) – Y-axis limits.
- alpha (float, between 0 and 1) – Marker transparency. From 0 to 1: from transparent to opaque.
- loc (int) – The corner of the legend box to anchor. Details refer to
Matplotlib
document. - grid (boolean, default True) – Show grid.
- show (boolean, default True) – Show figure in pop-up windows if true. Save to files if False.
- filepath (str) – File name to saving the plot. Must be assigned a valid filepath if
show
is False. - **kwargs (keyword arguments) – Other keyword arguemnts passed on to
matplotlib.pyplot.scatter
.
Note
Instances in a same cluster does not necessarily assemble together in all two dimensional sub-spaces. There can be possibly no clustering capaility for certain features. Additionally certain features play a secondary role in clustering as having less importance in
field_importance
inclusteror
module.
clusteror.settings module¶
clusteror.utils module¶
This module works as a transient store of useful functions. New standalone functions will be first placed here. As they grow in number and can be consolidated into an independent class, module, or even a new package.
-
clusteror.utils.
find_local_extremes
(series, contrast)[source]¶ Finds local minima and maxima according to
contrast
. In theory, they can be determined by first derivative and second derivative. The result derived this way is of no value in dealing with a very noisy, zig-zag data as too many local extremes would be found for any turn-around. The method presented here compares the point currently looked at and the opposite potential extreme that is updated as scanning through the data sequence. For instance, a potential maximum is 10, then a data point of value smaller than 10 / (1 + contrast) is written down as a local minimum.Parameters: - series (Pandas Series) – One dimenional data to find local extremes in.
- contrast (float) – A value between 0 and 1 as a threshold between minimum and maximum.
Returns: - local_min_inds (list) – List of indices for local minima.
- local_mins (list) – List of minimum values.
- local_max_inds (list) – List of indices for local maxima.
- local_maxs (list) – List of maximum values.


Remote Slide let's you remotely control your presentations from your Smartphone (or any other device).
For example on Google Slides,
slides.com, Prezi
or PowerPoint.
You can start presenting in only 3 steps, without having to install any external applications!
Just scan the QR Code to get started!


Tablet of Vision:
Fiddling with this tablet further is futile.
Find the Priest Cerenas, Elven Tetrarch in Goddard Castle Town.


Photos of bread/toast by Rainer Zenz
Blackboxing is "the way scientific and technical work is made invisible by its own success.
When a machine runs efficiently, when a matter of fact is settled, one need focus only on its inputs and outputs and not on its internal complexity.
Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become."
-- Bruno Latour
Even if you don't completely understand how all these tools work yet, you can still use them to make a delicious breakfast a cool web app!
Delicious breakfast photo by Asimzb on Wikimedia
Languages:
Software Tools:
A runtime environment that lets you run JavaScript on the server, outside of the web browser. It comes with command line tools, including npm.
The default NodeJS package manager, a command line tool and online service that makes it easy to use JavaScript libraries and share your own.
The most popular NodeJS framework. Express provides functions that make it easier to create web applications.
A JavaScript library (both client-side and server-side) that makes it easier to use WebSocket for real-time communication.
The real star of today's show! The underlying protocol and web API that allows for fast, bidirectional communication between clients and servers.
Inside "socketio-paint-demo" folder:
Open up both of these files in your text editor:
app.js
public/local.js
var express = require('express'); // The require() function includes the code for Express
var app = express(); // Initialize the Express library
var http = require('http').Server(app); // Initialize an HTTP server
var io = require('socket.io')(http); // Include and initialize SocketIO
var port = process.env.PORT || 8000; // Set the default port number to 8000, or use Heroku's settings (process.env.PORT)
app.use(express.static('public')); // Tell Express to serve everything in the "public" folder as static files
http.listen(port, function() { // Activate the server and listen on our specified port number
console.log('Listening on port ' + port); // Display this message in the console once the server is active
});
io.on('connection', function(socket) { // When a user connects over websocket:
console.log('A user connected!'); // Display this message in the console
});
// Start a WebSocket connection with the server using SocketIO
var socket = io();
// Note that the SocketIO client-side library was imported on line 13 of index.html,
// and this file (local.js) was imported on line 14 of index.html
This tells NodeJS to run your "app.js" file, which starts the web server and puts everything into motion.
If it works, you'll see a message in the command line that it's listening on port 8000
To connect to your local web server, open a new tab in your web browser and go to the following URL:
http://localhost:8000
You'll see a blank page -- that's good!
Now check the command line:
If the connection is working, it will show the message "A user connected!"
In command line, press Ctrl + C and then run node app.js again.
Every time you change your code, you have to restart the server. And don't forget to refresh your browser after every change too!
// When a user connects over websocket,
io.on('connection', function(socket) {
// Display this message in the server console
console.log('A user connected!');
// Send an event named "test" to every client with io.sockets.emit() function (or just io.emit() for short)
// and with this event, send the string 'Hey everyone...' as the data
io.sockets.emit('test', 'Hello from the server!');
// When the server receives an event named "test",
socket.on('test', function(data) {
// Take whatever data was received and display it in the server console
console.log(data);
});
}); // End of SocketIO code
// Start a WebSocket connection with the server using SocketIO
var socket = io();
// Send an event named "test" to the server with socket.emit() function
// and with this event, send the string 'Hi, this is...' as the data
socket.emit('test', 'Hello from the client!');
// When the client receives an event named "test",
socket.on('test', function(data) {
// Take whatever data was received and display it in the client console
console.log(data);
});
Server Side Console:
Command Line
Client Side Console:
Web Browser
Remember to:
You should see something like this:
HTTP's request-response model is great for getting lots of information at once, but not so great for having a conversation.
WebSocket is better for conversations; both the client and the server can send and receive information all at the same time.
"Listen" icon by Rémy Médard, "Speak" icon by Eðvarð Atli Birgisson, from the Noun Project
Locate the second parameter of the socket.emit function in both JS files.
Replace those strings with objects containing two properties: who sent the message ("server" or "client") and a random number.
Hint: a JavaScript object looks like this:
{property: value, anotherProperty: anotherValue}
Previous code (change the highlighted section)
io.on('connection', function(socket) {
console.log('A user connected!');
io.sockets.emit('test', 'Hello from the server!');
socket.on('test', function(data) {
console.log(data);
});
});
Solution to challenge 1
io.on('connection', function(socket) {
console.log('A user connected!');
io.sockets.emit('test', {sender: 'server', randomNumber: 5});
socket.on('test', function(data) {
console.log(data);
});
});
Previous code (change the highlighted section)
var socket = io();
socket.emit('test', 'Hello from the client!');
socket.on('test', function(data) {
console.log(data);
});
Solution to challenge 1
var socket = io();
socket.emit('test', {sender: 'client', randomNumber: 99});
socket.on('test', function(data) {
console.log(data);
});
The event loop is literally an infinite loop running in the background, checking if any events have happened and running your at the right time.
There's one built into NodeJS and there's one built into your web browser!
See this great video for details on the event loop in JavaScript.
Anywhere you deal with user input, you'll see event-based programming.
It's big in NodeJS, SocketIO, and even in basic client-side JavaScript.
See the MDN event reference page for a gigantic list!
In fact, let's open that link right now and keep it handy for later!
We'll be making our own custom SocketIO events today.
You can also make custom events in plain JavaScript; see the MDN guide on creating events for more info.
You can create chain reactions in your code, not unlike a row of dominoes!
Event-based programming can be harder to learn and harder to debug,
so be prepared for some frustration.
document.addEventListener('click', myFunction); function myFunction(event) { console.log(event); }
There are five parts to a standard JavaScript event listener
Part 1: Where the events will happen
(in this case, the whole HTML document object)
Part 2: Start listening for events
(with JavaScript's built-in addEventListener method)
Part 3: The type of event
(see this reference list or make up your own)
Part 4: The name of the function to run when the event happens
(known as the "callback" function)
Part 5: This parameter receives an object with info about the event
(different events give you different information)
document.addEventListener('click', function(event) {
console.log(event);
});
Just note that if you ever need to remove an event listener later, it won't work with an anonymous function.
document.addEventListener('click', function(event) {
console.log(event);
});
You should see a MouseEvent object in the console. Click to expand the data:
socket.on('my event', myFunction); function myFunction(data) { console.log(data); }
The SocketIO event listener also has five parts, and it works the same way!
Part 1: Where the events will happen
(in this case, the socket object representing the WebSocket connection)
Part 2: Start listening for events
(with SocketIO's "on" method)
Part 3: The type of event
(here we made up our own and gave it a random name)
Part 4: The name of the function to run when the event happens
(known as the "callback" function)
Part 5: Data sent from the other side of the WebSocket connection
(it could be an object, a string, or whatever else)
socket.on('my event', function(data) {
console.log(data);
});
Just like with JavaScript's built-in listeners, if you ever need to remove a SocketIO listener later, it won't work with an anonymous function.
Change your code so that when the user clicks the screen, you use console.log to display the event info object and you also send it to the server.
Hint: you only need to change the code in one spot in local.js
var socket = io();
document.addEventListener('click', function(event) {
console.log(event);
socket.emit('test', event);
});
socket.on('test', function(data) {
console.log(data);
});
socket.emit('my event', myData);
The SocketIO event emitter sends events and data to the client or server.
It has three parts.
Part 1: The SocketIO object and emitter function
(there are three variations, which we'll look at in the next slide)
Part 2: The event to send
(a string value of our choosing)
Part 3: The data to send with the event
(as we've seen, it can be an object, a string, or whatever)
For the next challenge, you'll need to open at least two instances of your app, each with its own web console, to test your app with multiple clients.
You can use two browser tabs or make two separate windows side-by-side.
Change your server-side code so that when the server receives
the event named "test",
it does two more things:
1. It replies to the client who clicked their screen with an event called "test" containing the message "I saw you click!"
2. It also replies to every client except the one who clicked with an event called "test" containing the message "Off with their head!"
Hint: to test this code, you need to open up multiple browser windows/tabs,
with multiple browser consoles open!
io.on('connection', function(socket) {
console.log('A user connected!');
io.sockets.emit('test', 'Hello from the server!');
socket.on('test', function(data) {
console.log(data);
socket.emit('test', 'I saw you click!');
socket.broadcast.emit('test', 'Off with their head!');
});
});
With just those few SocketIO functions and some client-side JavaScript, you can create any and every type of real-time, multi-user interaction.
Let's learn the way real programmers do it -- with Google!
Choose one of the following missions:
document.addEventListener('mousedown', startDrawing); document.addEventListener('mousemove', drawStuff); document.addEventListener('mouseup', stopDrawing); function startDrawing(event) { // do cool stuff here } function drawStuff(event) { // do cool stuff here } function stopDrawing(event) { // do cool stuff here }
document.addEventListener('click', function(event) {
console.log(event);
socket.emit('test', {x: event.clientX, y: event.clientY});
});
Tinker with it this week, and when we meet again next time we'll look at what everyone came up with!


|
libomexmeta
1.2.13
Annotations for models in systems biology
|
Add or change annotations in xml. More...
#include <Editor.h>
Public Member Functions | |
| Editor (std::string xml_or_file, bool create_ids, LibrdfModel &model, NamespaceMap &ns_map, UriHandler &uriHandler, bool generate_new_metaids=false, bool sbml_semantic_extraction=true) | |
| constructor for Editor. More... | |
| ~Editor ()=default | |
| int | size () const |
| const NamespaceMap & | getNamespaces () const |
| returns a hashmap of namespaces to prefixes. More... | |
| LibrdfModel & | getModel () const |
| return the underlying librdf_model* pointer More... | |
| void | setNamespaces (const NamespaceMap &namespaces) |
| set the namespace map. | |
| const std::string & | getXml () const |
| return the xml More... | |
| const std::vector< std::string > & | getMetaids () const |
| returns a list of metaids both that existed previously and that was added during instantiation. More... | |
| void | addNamespace (const std::string &ns, std::string prefix) |
| add a namespace More... | |
| void | addSingleAnnotation (LibrdfNode subject, const PredicatePtr &predicate_ptr, const LibrdfNode &resource) |
| Add a SingleAnnotation (aka a Triple) to the model. More... | |
| void | addSingleAnnotation (SingularAnnotation &singularAnnotation) |
| Add a SingleAnnotation (aka Triple) to the rdf graph. More... | |
| void | removeSingleAnnotation (const SingularAnnotation &singularAnnotation) const |
| remove a singular annotation (aka Triple) from the rdf graph More... | |
| void | addCompositeAnnotation (PropertyBearer *phenomenonPtr) |
| add a composite annotation to the rdf graph. More... | |
| void | addPhysicalProperty (PhysicalProperty &physicalProperty) |
| add a physical process to the rdf graph | |
| void | addPhysicalEntity (PhysicalEntity &physicalEntity) |
| add a composite annotation of type PhysicalEntity to the rdf graph More... | |
| void | removePhysicalEntity (PhysicalEntity &physicalEntity) const |
| remove triples associated with a a PhysicalEntity object from the rdf graph More... | |
| void | removePersonalInformation (PersonalInformation *information) const |
| remove triples associated with a PersonalInformation object from the rdf graph More... | |
| void | addPhysicalProcess (PhysicalProcess &physicalProcess) |
| add a composite annotation of type PhysicalProcess to the rdf graph More... | |
| void | removePhysicalProcess (PhysicalProcess &physicalProcess) const |
| remove triples associated with a PhysicalProcess object from the rdf graph More... | |
| void | addEnergyDiff (EnergyDiff &ediff) |
| add a composite annotation of type EnergyDiff to the rdf graph More... | |
| void | addPersonalInformation (PersonalInformation *personalInformation) |
| add a PersonalInformation class to the rdf model More... | |
| void | removeEnergyDiff (EnergyDiff &physicalForce) const |
| remove triples associated with a a EnergyDiff object from the rdf graph More... | |
| void | checkValidMetaid (const std::string &metaid) |
| check that a metaid is valid by comparing with the output from Editor::getMetaIds() | |
| void | addNamespaceFromAnnotation (const std::string &predicate_string) |
| extract namespace part of uri from @parameter predicate_string and add it to namespace_ if we know it. | |
| const std::string & | getMetaidBase () const |
| get the string that is being used for the metaid base (default is OmexMetaID) More... | |
| void | setMetaidBase (const std::string &metaidBase) |
| set the base metaid string (default is OmexMetaId) More... | |
| OmexMetaXmlType | getType () const |
| getter for the current xml type identification variable. More... | |
| void | setType (OmexMetaXmlType type) |
| setter for the current xml type identification variable. More... | |
| PhysicalEntity | newPhysicalEntity () |
| create a new PhysicalEntity object. More... | |
| EnergyDiff | newEnergyDiff () |
| create a new EnergyDiff object. More... | |
| PhysicalProcess | newPhysicalProcess () |
| create a new PhysicalProcess object. More... | |
| PersonalInformation | newPersonalInformation () |
| create a new PersonalInformation object. More... | |
| void | addSingleAnnotationNoValidation (SingularAnnotation &singularAnnotation) |
| like addSingleAnnotation | |
| OMEXMETA_DEPRECATED void | addCompositeAnnotation2 (PropertyBearer *phenomenonPtr) |
| void | addTriples (Triples &triples) |
| void | removePropertyBearer (PropertyBearer *physicalPhenomenon) const |
| std::string | getArchiveUri () const |
| get the current value of archive_uri_ | |
| std::string | getLocalUri () const |
| get the current value of local_uri_ | |
| std::string | getModelUri () const |
| get the current value of model_uri_ | |
| std::string | getModelLevelAnnotationUri () const |
| get the current value of the uri used to annotate model level annotations | |
| std::string | getRepositoryUri () const |
| get the current value of archive_uri_ | |
| LibrdfNode | createNodeWithModelUri (const std::string &string) const |
| instantiate a LibrdfNode that is prefixed with the current local_uri | |
| Editor & | addCreator (std::string orcid_id) |
| add the "creator" model level annotation More... | |
| Editor & | addContributor (std::string orcid_id) |
| add the "curator" model level annotation More... | |
| Editor & | addTaxon (const std::string &taxon_id) |
| add the "taxon id" model level annotation More... | |
| Editor & | addPubmed (const std::string &pubmedid) |
| add the "pubmed id" model level annotation More... | |
| Editor & | addDescription (const std::string &date) |
| add the "description" model level annotation More... | |
| Editor & | addDateCreated (const std::string &date) |
| add the "date created" model level annotation More... | |
| SingularAnnotation | newSingularAnnotation (std::string metaid) const |
| create a new singular annotation object with metaid More... | |
| Editor & | addParentModel (const std::string &biomod_id) |
| add the "parent model" model level annotation More... | |
| SingularAnnotation | newSingularAnnotation () const |
| create a new SingularAnnotation object More... | |
| PhysicalProperty | newPhysicalProperty () |
| instantiate a new PhysicalProperty instance and return | |
| PhysicalProperty * | newPhysicalPropertyPtr () |
| instantiate a pointer to a new PhysicalProperty instance and return More... | |
| UriHandler & | getUriHandler () const |
| get a reference to the current UriHandler | |
| std::string | stripAnnotations (const std::string &annotationElementName="annotation") |
| remove all <annotation> elements from the xml associated with this Editor instance. More... | |
Add or change annotations in xml.
|
explicit |
constructor for Editor.
| xml_or_file | The valid xml content for annotation OR the path to the file on disk containing this content |
| generate_new_metaids. | Autogenerate metaids for xml element that do not already have them |
| sbml_semantic_extraction. | When |
| xml_or_file | is determined to be sbml, automatically extract some information regarding species and reactions. This option is ignored when |
| xml_or_file | is not sbml. Default is true. |
| model | a reference to the current model (owned by RDF). |
| nm_map | a set of namespaces for current xml |
The Editor is usually instantiated from the RDF::to_editor class which automatically takes care of the
| model | and |
| ns_map | arguments. When instantiated, the editor class automatically reads the XML and adds metaids to some or all of the elements, depending on the XML type parameter. If the user specifies that they are annotating an SBML model, the elements returned by SBMLAssistant::getValidElements() are given metaids (if not exist), while if the user chooses cellml the elements returned by CellMLAssistant::getValidElements() are used. If the type is unknown, then all elements are given metaids. |
|
default |
We no longer required to free the triples_ used by Editor since they are created and freed inplace - i.e. locally, not at the class scope.
| void omexmeta::Editor::addCompositeAnnotation | ( | PropertyBearer * | phenomenonPtr | ) |
add a composite annotation to the rdf graph.
| phenomenonPtr | A pointer to an object of type PhysicalPhenomenon, the superclass of the composite annotations. |
Composite annotations currently supported are PhysicalEntity, PhysicalProcess and EnergyDiff. The PhysicalDependency type will be supported in future releases.
For developers. Consider removing this function in favour of using the add* functions.
Implementation note: This method generates triples on the fly and then frees. This was implemented this way as it helped avoid memory issues but perhaps a better implementation would be similar to that in the PersonalInformation class.
| Editor & omexmeta::Editor::addContributor | ( | std::string | orcid_id | ) |
add the "curator" model level annotation
| an | orcid_id as string |
| Editor & omexmeta::Editor::addCreator | ( | std::string | orcid_id | ) |
add the "creator" model level annotation
| an | orcid_id as string |
| Editor & omexmeta::Editor::addDateCreated | ( | const std::string & | date | ) |
add the "date created" model level annotation
| The | date that the model was created |
| Editor & omexmeta::Editor::addDescription | ( | const std::string & | date | ) |
add the "description" model level annotation
| a | description of the model as string |
| void omexmeta::Editor::addEnergyDiff | ( | EnergyDiff & | ediff | ) |
add a composite annotation of type EnergyDiff to the rdf graph
| ediff | An instance of a EnergyDiff objec to add to the rdf graph. |
| void omexmeta::Editor::addNamespace | ( | const std::string & | ns, |
| std::string | prefix | ||
| ) |
add a namespace
| ns | the namespace |
| prefix | the prefix used in serialized annotations to refer to the namespace |
| Editor & omexmeta::Editor::addParentModel | ( | const std::string & | biomod_id | ) |
add the "parent model" model level annotation
| The | biomodels id for the model in which this model was derived from |
| void omexmeta::Editor::addPersonalInformation | ( | PersonalInformation * | personalInformation | ) |
add a PersonalInformation class to the rdf model
| personalInformation | An instance of a PersonalInformation object to add to the rdf graph. |
| void omexmeta::Editor::addPhysicalEntity | ( | PhysicalEntity & | physicalEntity | ) |
add a composite annotation of type PhysicalEntity to the rdf graph
| physicalEntity | An instance of a PhysicalEntity object to add to the rdf graph. |
| void omexmeta::Editor::addPhysicalProcess | ( | PhysicalProcess & | physicalProcess | ) |
add a composite annotation of type PhysicalProcess to the rdf graph
| physicalProcess | An instance of a PhysicalProcess object to add to the rdf graph. |
| Editor & omexmeta::Editor::addPubmed | ( | const std::string & | pubmedid | ) |
add the "pubmed id" model level annotation
| a | pubmed id as string |
| void omexmeta::Editor::addSingleAnnotation | ( | LibrdfNode | subject, |
| const PredicatePtr & | predicate_ptr, | ||
| const LibrdfNode & | resource | ||
| ) |
Add a SingleAnnotation (aka a Triple) to the model.
| subject | the subject portion of the triple |
| pointer | to the predicate the predicate portion of the triple. Ths is a pointer to support polymorphic calls. |
| resource | the resource portion of the triple |
| void omexmeta::Editor::addSingleAnnotation | ( | SingularAnnotation & | singularAnnotation | ) |
Add a SingleAnnotation (aka Triple) to the rdf graph.
| singularAnnotation | An instance of SingularAnnotation to add to the model |
| Editor & omexmeta::Editor::addTaxon | ( | const std::string & | taxon_id | ) |
add the "taxon id" model level annotation
| an | taxon_id as string |
| const std::string & omexmeta::Editor::getMetaidBase | ( | ) | const |
get the string that is being used for the metaid base (default is OmexMetaID)
this base will only be used when the generate_metaids boolean is set to true in the constructor.
| const std::vector< std::string > & omexmeta::Editor::getMetaids | ( | ) | const |
returns a list of metaids both that existed previously and that was added during instantiation.
| LibrdfModel & omexmeta::Editor::getModel | ( | ) | const |
return the underlying librdf_model* pointer
| const std::unordered_map< std::string, std::string > & omexmeta::Editor::getNamespaces | ( | ) | const |
returns a hashmap of namespaces to prefixes.
| OmexMetaXmlType omexmeta::Editor::getType | ( | ) | const |
| const std::string & omexmeta::Editor::getXml | ( | ) | const |
return the xml
| EnergyDiff omexmeta::Editor::newEnergyDiff | ( | ) |
create a new EnergyDiff object.
EnergyDiff objects should only be instantiated via the Editor because this enables the passing of necessary information behind the scenes, rather than needing to be provided by the user.
| PersonalInformation omexmeta::Editor::newPersonalInformation | ( | ) |
create a new PersonalInformation object.
PersonalInformation objects should only be instantiated via the Editor because this enables the passing of necessary information behind the scenes, rather than needing to be provided by the user.
| PhysicalEntity omexmeta::Editor::newPhysicalEntity | ( | ) |
create a new PhysicalEntity object.
PhysicalEntity objects should only be instantiated via the Editor because this enables the passing of necessary information behind the scenes, rather than needing to be provided by the user.
| PhysicalProcess omexmeta::Editor::newPhysicalProcess | ( | ) |
create a new PhysicalProcess object.
PhysicalProcess objects should only be instantiated via the Editor because this enables the passing of necessary information behind the scenes, rather than needing to be provided by the user.
| PhysicalProperty * omexmeta::Editor::newPhysicalPropertyPtr | ( | ) |
instantiate a pointer to a new PhysicalProperty instance and return
the caller is responsible for deleting the returned PhysicalProperty using deletePhysicalProperty
| SingularAnnotation omexmeta::Editor::newSingularAnnotation | ( | ) | const |
create a new SingularAnnotation object
a SingularAnnotation is a typedefed Triple object.
| SingularAnnotation omexmeta::Editor::newSingularAnnotation | ( | std::string | metaid | ) | const |
create a new singular annotation object with metaid
| metaid | the string to use in the "rdf:about" field or subject |
| void omexmeta::Editor::removeEnergyDiff | ( | EnergyDiff & | physicalForce | ) | const |
remove triples associated with a a EnergyDiff object from the rdf graph
| physicalForce | the EnergyDiff to remove |
| void omexmeta::Editor::removePersonalInformation | ( | PersonalInformation * | information | ) | const |
remove triples associated with a PersonalInformation object from the rdf graph
| information | the PersonalInformation object to remove. |
| void omexmeta::Editor::removePhysicalEntity | ( | PhysicalEntity & | physicalEntity | ) | const |
remove triples associated with a a PhysicalEntity object from the rdf graph
| physicalEntity | the PhysicalEntityto remove |
| void omexmeta::Editor::removePhysicalProcess | ( | PhysicalProcess & | physicalProcess | ) | const |
remove triples associated with a PhysicalProcess object from the rdf graph
| physicalProcess | the PhysicalProcessto remove |
| void omexmeta::Editor::removeSingleAnnotation | ( | const SingularAnnotation & | singularAnnotation | ) | const |
remove a singular annotation (aka Triple) from the rdf graph
| singularAnnotation | An instance of SingularAnnotation to remove from the model |
| void omexmeta::Editor::setMetaidBase | ( | const std::string & | metaidBase | ) |
set the base metaid string (default is OmexMetaId)
| metaidBase | the string you want to use for metaid base |
using editor.setMetaidBase("MyNewMetaid") will make metaids added by libomexmeta look like MyNewMetaid0001, MyNewMetaid0002, etc. It is assumed that it will not be necessary to have more than 9999 new metaids.
| void omexmeta::Editor::setType | ( | OmexMetaXmlType | type | ) |
| std::string omexmeta::Editor::stripAnnotations | ( | const std::string & | annotationElementName = "annotation" | ) |
remove all <annotation> elements from the xml associated with this Editor instance.
| annotationElementName | the name of the element that contains the annotations. The default is "annotation" since sbml embedded annotations are all contained within an <annotation> element. Other languages |
returns a copy of xml without any annotations (i.e. CellML) may not follow this convention and have the option of changing check element contains the annotations. Users should be warned that no verification is in place to prevent you from stripping away arbitrary elements with this method - all elements named
| annotationElementName | will be removed from this xml document, regardless of content. |