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FabLat Kids
Volunteer to be a professor to teach kids the fundamentals of
manufacturing, and electronics. We give lessons on how to 3D print,
laser cutting, and teach about circuits. The robot shaked with a motor,
and made a drawing in a paper on the floor.
Product designer.
Participate in the design and manufacturing process of the Dibujabot.
Tools and Technology
Software: Adobe Ilustrator, Cura.
Tools: 3D Printing, laser cut.


Yes. But please refer that your order may be subject to import duties and taxes, which are levied once a package reaches your country.
We have no control over these charges and cannot predict what the costs may be.
Customs policies vary from country to country; you should contact your local customs office for further information.
The pricing information for the fragrance oils and other products can be sent directly to your e-mail, just fill out the information Here.
Pricing on fragrances will vary depending on the fragrance and the size bottle you order.
The prices of the fragrances are based on the materials used to create them. Some of the raw materials are more expensive than others, so that is reflected in the price of the individual fragrance.
The fragrances that are more expensive cost more to produce.
While some of our fragrance oils contain natural ingredients, the fragrances that we carry are classified as synthetic or man made.
Essential oils are extracted from plant material and, for that reason, are usually much more expensive.
Yes. All the perfume oils for our generic perfume range are imported from Europe and are of the highest quality. In addition each of our generics goes through a rigorous in house “Nose” test to check that the smell is a perfect match to the original,
before it is introduced into our range. Yes we compare it against the original to ensure that the smell is a perfect match!
Deliveries can take up to 5-7 working days after the funds reflect in our account.
P.S. It can take up to 48 hours from the time you do the transfer for the funds to reflect in our account.
The answer is simple. Like the manufacturers of ‘generic’ products, all our “Inspired by” fine fragrances have the qualities and effectiveness of the originals but without us having to spend masses of money on branding, licensing, marketing, advertising,
sponsorships, etc. etc.
This means we can pass these cost savings onto you the consumer.
We offer samples to existing customers and prospective clients.
We receive many requests per week of prospective clients wanting samples, But we have to make sure that we prescreen our customers to evaluate their needs.
We have a 5 KG minimum per fragrance on all orders.
We don’t want to go lower than 5 KG because this becomes too labor intensive to keep our lower price.
The labor to make 5 KG to 500 KG is not that much different. The difference of 500 KG is at least 10% off the selling price compared to 5 KG of the same fragrance.


Business Managers are about to invade our town! Try Again! | HOME
| { } | | | |.| ____ __{ } {-} |B { } | |_|..| | | |/| _ |B $ | _____|_ ____| |_ | |.| |..| ¬¬¬¬|¬¬ | $ |B |B |B |/| ___ /~\ $$ $ $ | $ $$$ {_}_| . . | /\| / . . \ ___{_}_| |_|..| | . . | | $ ~~~~~~~~~ $ _ { }__{ }__{ } |/| |..|_____ /\ ___| |___ $ $$$$$$$$$ / \ $$$ | | | . . | /..\ | . . | _| | |_|.| |..| | . . | | $$$ | [] [] | $$$ / \ | o o o | |/| |..|....| __|| | . . . | $$$ | |$| / \ | | | | . = | | =| | . || | | | | || | | | | . = | | | | . . . | | | | | o o o | |/| |..|....| | | | A A A | | ##########
Use ← → to move and space to shoot


My project featured above is a quiz I made using CSS, HTML, and Javascript, with questions about Javascript! The quiz starts with a blank timer and a prompt to start the quiz. After initiating start the timer will commence and predetermined questions will appear. Answer the questions as quick as possible to achieve the highest score, but proceed with caution incorrect answers will decrement 10 seconds off the timer!
Github repository
Deployed application
My project featured below is an application which uses the OpenWeatherMap API to retrieve data and then displays weather stats related to a user input city. This application uses CSS and HTML to style and construct the page with Javascript running the process behind the scenes. Moment.js is used to display the current date as well.
Github repository
Deployed application


--- layout: speaker title: Robin Johnson date: "2013-05-06 02:12:52 +0200" author: ukmadlz version: 1.0.0 name: Robin Johnson image: speaker/robin_johnson.jpg job: Developer Evangelist company: SendGrid desc: Dev Evangelist @ Sendgrid github: rbin twitter: rbin website: http://rbin.co ---
A Polyglot Programmer and Developer Evangelist at SendGrid, currently working amongst the world-wide developer community to ease the adoption of Email API technology. Having spoken at some of the world's leading technical conferences, Robin is building his way to being a key player in the London tech scene; attending, speaking at, and even helping organise multiple large events. Mentoring on topics such as Distributed Systems, Golang, NoSQL Databases and Open Source values; Robin is as much hacker as he is hustler.
Talk contents not yet finalised.


My research background has honed a meticulous eye for solving problems.
Being creative and finding improvements is a thrill!
The code I write is short, sweet, and to the point. No fluff here!
I love setting goals and smashing them.
I worked closely with Ciara to understand the goal of her empathic business.  In the process, I set up a WooCommerce store and blog for her. Her website will officially launch in August of 2019!
Jill helps train women to be fit and fierce, through empowering workouts. She was so much fun to work with because she is passionate about helping women! She was ready to take her business to the next level by creating a website and I am so pleased that I was able to help her!
Wander A Little is my blog and passionate project where I share notes about my travels to help encourage people to be brave and explore. My main goal with my blog, is to let the pictures do the talking and keep a minimalism theme.
"Working with Katie has been an absolute dream. If you’re starting a business, or if you’ve been in business for awhile and need to put some magical new energy into your website, Katie is the woman for you! Not only is she super skilled, communicative, and prompt; she’s also immensely kind and intuitive. I’m a better business owner after working with her and I highly recommend you hire her without hesitation!"
Ciara Rubin, CiaraRubin.com
"Katie's communication skills are above and beyond. Her ability to find solutions, work efficiently and provide quality results make for a positive partnership."
Jill Allen, JillAllenCoaching.com
I am a passionate freelance web developer that loves to bring client's dreams to life. Let's talk!
Have a question? Send me a note!


Note: All pizzas come with tomato sauce and one standard layer of cheese. Only select cheese if you would like extra cheese
**Each topping: $0.50 each


|
Beautiful
Day
(U2) |
|
The
heart is a bloom
Shoots up through the stony ground There's no room No space to rent in this town You're out of luck And the reason that you had to care The traffic is stuck And you're not moving anywhere You thought you'd found a friend To take you out of this place Someone you could lend a hand In return for grace It's a beautiful day Sky falls, you feel like It's a beautiful day Don't let it get away You're on the road But you've got no destination You're in the mud In the maze of her imagination You love this town Even if that doesn't ring true You've been all over And it's been all over you It's a beautiful day Don't let it get away It's a beautiful day Touch me Take me to that other place Teach me I know I'm not a hopeless case See the world in green and blue See China right in front of you See the canyons broken by cloud See the tuna fleets clearing the sea out See the Bedouin fires at night See the oil fields at first light And see the bird with a leaf in her mouth After the flood all the colors came out It was a beautiful day Don't let it get away Beautiful day Touch me Take me to that other place Reach me I know I'm not a hopeless case What you don't have you don't need it now What you don't know you can feel it somehow What you don't have you don't need it now Don't need it now Was a beautiful day |


Home
/
About
/
Contact
This project is supported by the CFL, CTL, and Provost's Office OUCI Funding.


{% extends 'base.html' %} {% load staticfiles %} {% load crispy_forms_tags %} {% crispy postsearch_form postsearch_form.helper %} {% csrf_token %} {% block head_content %} {% endblock %} {% block content %}
These are the instructions for processing "the daily photo audit file":
The basic steps are as follows:
necesary software:
| Open the daily photo audit file in Excel. Save the file in the windows comma seperated values (csv) format; name it "universe_wcsv.csv", and save it to ADVERTISING/Inserts/MAGETRAC/csv (overwriting the existing one in this directory). |
| open the newly created "universe_wcsv.csv" in Sublime Text. Simply resave the file with UTF-8 encoding: | |
| from the terminal (shell) on the server workstion, run quantities.py |


a Multimedia journlist, I can do writing, photography, and web design.
Writing, photography, video shooding and editing, web design and front-end developing
More about me


Roland U-20 / U-220
Drum Patch Editor
This patch editor ought to work properly with both the U-20 and the U-220. However it has only been tested with the U-220. Please let me know of any discrepancies you discover.
Communicating with Edisyn
Set Edisyn's MIDI channel to the same as your rhythm part's Channel (typically 10). Set Edisyn's Synth ID to the same as the Roland Device ID (typically 17). Set the unit's Part Change to "On" or "Dir". On the U-220, set the Timbre Change, Rhythm Change, and Rhythm Ins. Assign all to "Dir". Turn on Sysex receive.
About the Menu
You can set up your synthesizer's Multimode patch to nicely audition the Rhythm part by selecting "Set up Test Patch for Drum Only".  Alternatively you can set up each Part to be on the same numbered channel (Part 2 is channel 2, etc.), and the Rhythm part to be Channel 10, and distribute voices among all of them, by selecting "Set Up Test Patch for All Parts".
You can also call up the current Multimode patch with "Show Current Patch".
It's often the case that you want to all drums to have the same value for a given parameter. Set the parameter in one drum, then call "Distribute Last Parameter to All Drums", which will, well, distribute the last parameter you set to all drums. If you want to distribute the Drum Tone, you should first distribute the Drum Media, then the Drum Tone.
Hints
- If you double-click on the Drum Source dial, it'll go to the lowest valid drum source.
- Some drum sources in the Internal Drum drumset are missing unless the Latin/FX card is installed (I believe). These have been labelled with a "[L]".
Gotchas
- Bulk downloads from the U-220 will not be understood by Edisyn. Here's why. Roland breaks its patch sysex into multiple messages, each 138 bytes long. But Roland stupidly made it very difficult to distinguish between sysex for a single patch and sysex for a message that's part of a bulk dump of patches. Edisyn must assume that the sysex is for a single patch. However while Edisyn can't figure out bulk downloads sent from the synthesizer, it can figure out bulk files loaded from disk (because it gets all of them at once and realizes what's happening).
- If you are presently editing a drum patch, the U-220 refuses to change patches, so Edisyn can load a new one but it can't get the U-220 to switch to it. Thus Edisyn is forced to send the patch back to the U-220 to change its parameters. This means that loading a patch will take twice as long as it needs to.
- In the manual (page 132), the Internal Drumset is listed as initialized to a number of Latin-2 sounds. These sounds do not exist in the U-220 and are not part of the Internal Drumset even if the Latin Percussion & FX card is inserted: it appears to simply be a mistake. Edisyn does not list these sounds.
Thanks
Thanks to Keith<EMAIL_ADDRESS>for his extraordinary service in loaning stuff, providing materials, and debugging.  Thanks also to Benjamin Wild<EMAIL_ADDRESS>for bug reports and suggestions.
| By
| Sean Luke
|
| Date
| October 2022
|


{% extends 'base.html' %} {% import 'bootstrap/wtf.html' as wtf %} {% block content %} Add Post You can add a blog post here.


Struct Rect
Stores a set of four integers that represent the location and size of a rectangle
Namespace: Terminal
Assembly: Terminal.dll
Syntax
public struct Rect
Constructors
Rect(Point, Size)
Rectangle Constructor
Declaration
public Rect (Terminal.Point location, Terminal.Size size);
Parameters
| Type | Name | Description |
|---|---|---|
| Point | location | To be added. |
| Size | size | To be added. |
Remarks
Creates a Rectangle from Point and Size values.
Rect(Int32, Int32, Int32, Int32)
Rectangle Constructor
Declaration
public Rect (int x, int y, int width, int height);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | x | To be added. |
| System.Int32 | y | To be added. |
| System.Int32 | width | To be added. |
| System.Int32 | height | To be added. |
Remarks
Creates a Rectangle from a specified x,y location and width and height values.
Fields
Empty
Empty Shared Field
Declaration
public static readonly Terminal.Rect Empty;
Field Value
| Type | Description |
|---|---|
| Rect | To be added. |
Remarks
An uninitialized Rectangle Structure.
Height
Gets or sets the height of this Rectangle structure.
Declaration
public int Height;
Field Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Width
Gets or sets the width of this Rect structure.
Declaration
public int Width;
Field Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
X
Gets or sets the x-coordinate of the upper-left corner of this Rectangle structure.
Declaration
public int X;
Field Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Y
Gets or sets the y-coordinate of the upper-left corner of this Rectangle structure.
Declaration
public int Y;
Field Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Properties
Bottom
Bottom Property
Declaration
public int Bottom { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Remarks
The Y coordinate of the bottom edge of the Rectangle. Read only.
IsEmpty
IsEmpty Property
Declaration
public bool IsEmpty { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Indicates if the width or height are zero. Read only.
Left
Left Property
Declaration
public int Left { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Remarks
The X coordinate of the left edge of the Rectangle. Read only.
Location
Location Property
Declaration
public Terminal.Point Location { get; set; }
Property Value
| Type | Description |
|---|---|
| Point | To be added. |
Remarks
The Location of the top-left corner of the Rectangle.
Right
Right Property
Declaration
public int Right { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Remarks
The X coordinate of the right edge of the Rectangle. Read only.
Size
Size Property
Declaration
public Terminal.Size Size { get; set; }
Property Value
| Type | Description |
|---|---|
| Size | To be added. |
Remarks
The Size of the Rectangle.
Top
Top Property
Declaration
public int Top { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Remarks
The Y coordinate of the top edge of the Rectangle. Read only.
Methods
Contains(Point)
Contains Method
Declaration
public bool Contains (Terminal.Point pt);
Parameters
| Type | Name | Description |
|---|---|---|
| Point | pt | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Checks if a Point lies within this Rectangle.
Contains(Rect)
Contains Method
Declaration
public bool Contains (Terminal.Rect rect);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | rect | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Checks if a Rectangle lies entirely within this Rectangle.
Contains(Int32, Int32)
Contains Method
Declaration
public bool Contains (int x, int y);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | x | To be added. |
| System.Int32 | y | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Checks if an x,y coordinate lies within this Rectangle.
Equals(Object)
Equals Method
Declaration
public override bool Equals (object obj);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Object | obj | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Checks equivalence of this Rectangle and another object.
FromLTRB(Int32, Int32, Int32, Int32)
FromLTRB Shared Method
Declaration
public static Terminal.Rect FromLTRB (int left, int top, int right, int bottom);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | left | To be added. |
| System.Int32 | top | To be added. |
| System.Int32 | right | To be added. |
| System.Int32 | bottom | To be added. |
Returns
| Type | Description |
|---|---|
| Rect | To be added. |
Remarks
Produces a Rectangle structure from left, top, right and bottom coordinates.
GetHashCode()
GetHashCode Method
Declaration
public override int GetHashCode ();
Returns
| Type | Description |
|---|---|
| System.Int32 | To be added. |
Remarks
Calculates a hashing value.
Inflate(Size)
Inflate Method
Declaration
public void Inflate (Terminal.Size size);
Parameters
| Type | Name | Description |
|---|---|---|
| Size | size | To be added. |
Remarks
Inflates the Rectangle by a specified Size.
Inflate(Int32, Int32)
Inflate Method
Declaration
public void Inflate (int width, int height);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | width | To be added. |
| System.Int32 | height | To be added. |
Remarks
Inflates the Rectangle by a specified width and height.
Inflate(Rect, Int32, Int32)
Inflate Shared Method
Declaration
public static Terminal.Rect Inflate (Terminal.Rect rect, int x, int y);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | rect | To be added. |
| System.Int32 | x | To be added. |
| System.Int32 | y | To be added. |
Returns
| Type | Description |
|---|---|
| Rect | To be added. |
Remarks
Produces a new Rectangle by inflating an existing Rectangle by the specified coordinate values.
Intersect(Rect, Rect)
Intersect Shared Method
Declaration
public static Terminal.Rect Intersect (Terminal.Rect a, Terminal.Rect b);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | a | To be added. |
| Rect | b | To be added. |
Returns
| Type | Description |
|---|---|
| Rect | To be added. |
Remarks
Produces a new Rectangle by intersecting 2 existing Rectangles. Returns null if there is no intersection.
Intersect(Rect)
Intersect Method
Declaration
public void Intersect (Terminal.Rect rect);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | rect | To be added. |
Remarks
Replaces the Rectangle with the intersection of itself and another Rectangle.
IntersectsWith(Rect)
IntersectsWith Method
Declaration
public bool IntersectsWith (Terminal.Rect rect);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | rect | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Checks if a Rectangle intersects with this one.
Offset(Point)
Offset Method
Declaration
public void Offset (Terminal.Point pos);
Parameters
| Type | Name | Description |
|---|---|---|
| Point | pos | To be added. |
Remarks
Moves the Rectangle a specified distance.
Offset(Int32, Int32)
Offset Method
Declaration
public void Offset (int x, int y);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | x | To be added. |
| System.Int32 | y | To be added. |
Remarks
Moves the Rectangle a specified distance.
ToString()
ToString Method
Declaration
public override string ToString ();
Returns
| Type | Description |
|---|---|
| System.String | To be added. |
Remarks
Formats the Rectangle as a string in (x,y,w,h) notation.
Union(Rect, Rect)
Union Shared Method
Declaration
public static Terminal.Rect Union (Terminal.Rect a, Terminal.Rect b);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | a | To be added. |
| Rect | b | To be added. |
Returns
| Type | Description |
|---|---|
| Rect | To be added. |
Remarks
Produces a new Rectangle from the union of 2 existing Rectangles.
Operators
op_Equality(Rect, Rect)
Equality Operator
Declaration
public static bool op_Equality (Terminal.Rect left, Terminal.Rect right);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | left | To be added. |
| Rect | right | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Compares two Rectangle objects. The return value is based on the equivalence of the Location and Size properties of the two Rectangles.
op_Inequality(Rect, Rect)
Inequality Operator
Declaration
public static bool op_Inequality (Terminal.Rect left, Terminal.Rect right);
Parameters
| Type | Name | Description |
|---|---|---|
| Rect | left | To be added. |
| Rect | right | To be added. |
Returns
| Type | Description |
|---|---|
| System.Boolean | To be added. |
Remarks
Compares two Rectangle objects. The return value is based on the equivalence of the Location and Size properties of the two Rectangles.


In Life...
You have to identify the good things
So that when something incredible comes along
You can make sure that it doesn't pass you by
This was another daily-made project by Steven O'Riley.


Pat's Global Sales Totals
Add New Store
Hours of Operation:
6:00am
7:00am
8:00am
9:00am
10:00am
11:00am
12:00pm
1:00pm
2:00pm
3:00pm
4:00pm
5:00pm
6:00pm
7:00pm
Return to Store Page


Trainer Card Buddy makes it easy to share and add new Pokemon Go friends online.
Create your own online Pokemon Go Trainer card, and get on your way to making new Best Friends today!
Trainer Card Buddy makes it easy to share and add new Pokemon Go friends online.
Create your own online Pokemon Go Trainer card, and get on your way to making new Best Friends today!


A suggested solution is seen below. It is important to add the components in the correct order.
At right is the display for the program. Observe how the components have been laid out in the frame.
FlowLayout is like a firehose that squirts out the components onto the frame in the same order in which they are added by the program. It starts in the top left, goes across the frame until it hits the right edge, then starts another left to right sweep far enough down to avoid the first row of components (this is called "raster order".) It also centers components within their row.
import java.awt.*; import java.awt.event.*; import javax.swing.*; public class TextEg2 extends JFrame { JTextField text; JLabel lbl ; public TextEg2() { text = new JTextField( 15 ) ; lbl = new JLabel ( "Enter Your Name:" ); getContentPane().setLayout( new FlowLayout() ); getContentPane().add( lbl ); getContentPane().add( text ); } public static void main ( String[] args ) { TextEg2 teg = new TextEg2() ; WindowQuitter wquit = new WindowQuitter(); teg.addWindowListener( wquit ); teg.setSize ( 300, 100 ); teg.setVisible( true ); } }
Run the program.
Type text into the field.
Correct it using the backspace and delete keys.
All of these functions comes along for free when you
construct a JTextField
object.
Click and drag on the left edge of the frame to widen it.
Observe what happens to the GUI components.


This is the clicking challenge
0
P1
In the lead:
0
P2
Play with:
Keyboard (Q&P)
Mouse
Change background color
Red
Blue
Yellow


pibcomp(1) Qualcomm Atheros Open Powerline Toolkit pibcomp(1) NAME pibcomp - Qualcomm Atheros Parameter Block Compare Utility SYNOPSIS pibcomp [options] file file [>stdout] DESCRIPTION Read and compare two Qualcomm Atheros powerline parameter block files and print only those objects that are different. It will detect and handle both old and new powerline parameter file formats. This program is similar to program odc but is designed specifically for Qualcomm Atheros powerline parameter files. Program odc is no longer supported and will be removed from future toolkit releases. Object driven comparison requires an object definition file that lists the size and name of data data objects (ie; inte‐ gers, arrays or structures) within the parameter file. Individual data members are presented as smaller, individual data dumps within a larger, overall data dump. See program pibdump for more information of object definition file format. OPTIONS -f file The object definition file that lists the size, name and description of individual individual objects in order of occurance. If this option is omitted then object definitions are read from stdin. The object definition file is not modified by this program. See the pibdump man page for an example of an object definition file. -?,--help Print program help summary on stdout. This option takes precedence over other options on the command line. -!,--version Print program version information on stdout. This option takes precedence over other options on the command line. Use this option when sending screen dumps to Atheros Technical Support so that they know exactly which version of the Linux Toolkit you are using. ARGUMENTS file The name of a binary file. Two binary files are needed for comparison and both should conform to the object defi‐ nition file for the comarison to be meaningful. EXAMPLES The following example compares file abc.bin and def.bin and prints objects that are different in hexdump format. Objects are defined in objects.txt and read using -f. # pibcomp -f objects.txt abc.bin def.bin SEE ALSO chkpib(7), chkpib2(7), getpib(7), modpib(1), pib2xml(1), pibdump(1), setpib(1), xml2pib(1) CREDITS Nathaniel Houghton<EMAIL_ADDRESS>Charles Maier<EMAIL_ADDRESS>open-plc-utils-0.0.3 November 2013 pibcomp(1)


subject
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Genre: Fantasy, Novel, Epic
A beautiful epic poem telling the heroic tale of the Trojan War. Filled with magic, jealously, blood, gore, raging gods and demigods, scheming goddesses and petty quarrels, "The Iliad" is a battle story. Events begin near the end of the decade long siege of Ilium (Troy), a battle sparked by the kidnapping of Helen. The men on both sides are exhausted and tensions between the leaders is rising and the gods are beginning to take sides and craft devious schemes. Who will win this battle?
Genre: Fantasy, Novel, Epic
The Trojan War has ended and all our heroes are heading home. For Oydesseus, the clever navigator, the journey home is about to become much more dangerous then he thought...Ten years have passed since the war ended, everyone is home...save Odysseus. At home, Odysseus's wife Penelope fends of suitors while their son, Telemachus, dispairs over his father's return. After a strange encounter, Telemachus sets off to find out what happened to Odysseus, unaware that elsewhere a god is taking an interest in his journey.
Genre: Play, Tragedy
What are you to do when your father dies, your uncle marries your mother, becomes the new king, and may be responsible for your Dad's death? Hamlet, the prince of Denmark, is faced with just that situation. Ghosts, revenge, and madness abound in this tale of the tragedy of the Prince of Denmark.
To be or not to be?
Genre: Play, Comedy, Gender-bender
Rosalind, left behind in Court when her Father flees after being disposed by his brother, lives under constant fear of banishment. When the sentence is suddenly passed, Rosalind, disguised as a young man, flees to the Forest of Arden in search of her father accompanied by her cousin, Celia, and the court jester, Touchstone. Unbeknownest to her, the young wrestler Orlando, with whom Rosalind has fallen in love, is also in the Forest. Hilarity insues.
Genre: Fiction, Novel
Told in language of great simplicity and power, it is the story of an old Cuban fisherman, down on his luck, and his supreme ordeal -- a relentless, agonizing battle with a giant marlin far out in the Gulf Stream.
Genre: Fiction, Novel, Thriller
Set largely on Dartmoor in Devon in England's West Country and tells the story of an attempted murder inspired by the legend of a fearsome, diabolical hound of supernatural origin. Sherlock Holmes and his companion Dr. Watson investigate the case.
Genre: Fiction, Novel
Set at a boys' boarding school in New England during the early years of World War II, A Separate Peace is a harrowing and luminous parable of the dark side of adolescence. Gene is a lonely, introverted intellectual. Phineas is a handsome, taunting, daredevil athlete. What happens between the two friends one summer, like the war itself, banishes the innocence of these boys and their world.


Fireworks factory blast kills five and injures two in Sicily
by Jez FielderItalian rescue services say a blast at a fireworks factory in Sicily has killed up to five people and seriously injured two others, according to Italian news agency ANSA.
Italian firefighters and Carabinieri police said that the 71-year-old wife of the owner of the family-run factory in the Sicilian town of Barcellona Pozzo di Gotto was among the victims.
The owner's son was hospitalised in serious condition.
Police say workers at the depot may have accidentally set off the explosion with welding equipment on Wednesday afternoon.
This is not a completely isolated incident. Indeed, safety at fireworks depots has been long debated in Italy, where the production of fireworks is often a family-run business.
Consumers association Codacons says 68 people have been killed in explosions at fireworks firms since 2000.


Overview
The purpose of this tool is to assist Administrators in creating
course schedules for future school semesters. The following
documentation page will help Admins to navigate and maximize
the tool’s capabilities. Admins will be able to gather student,
course, and teacher preference data. The collected data will
be used to create a course schedule to mitigate preference and
time constraints as well as conflicts. To get started, please
review the following overview of the sections to determine
where to start. Directions to navigate to required sections
will start from the home page. The following sections
will be displayed on the home screen for the Admin. To view the full documentation,
click here.
Data Sheets
- Admin will be able to upload CSV files pertaining to 3 categories: Courses,
Students, or Preferences. Uploading a preset CSV will prevent admin from having to manually
input respective data one by one.
- Admin will also be able to view previously created datasets within the tool
- Refer to this section to get started on uploading CSV data
Database
- Admin will be able to use data from uploaded CSVs or inputted data to create
courses, course sections, teacher preferences, and input new students and teachers.
- Admin will be able to create sets out of the section and preference data to
create
a schedule.
- Refer to this section to get started on working with data for the schedule.
Schedules
- Admin will be able to create a schedule view from uploaded CSVs or inputted
data.
- Admin will be able to check and edit schedules for constraint integrity.
- Refer to this section to get started on creating the schedule.
Student Data Collection
- Admin will be able to gather student data about the courses they are planning
on
taking for the respective semester.
- Refer to this section to collect student data preferences to be considered in
schedule creation.


#include <array_ops.h>
Returns a one-hot tensor.
The locations represented by indices in indices
take value on_value
, while all other locations take value off_value
.
If the input indices
is rank N
, the output will have rank N+1
, The new axis is created at dimension axis
(default: the new axis is appended at the end).
If indices
is a scalar the output shape will be a vector of length depth
.
If indices
is a vector of length features
, the output shape will be:
features x depth if axis == -1 depth x features if axis == 0
If indices
is a matrix (batch) with shape [batch, features]
, the output shape will be:
batch x features x depth if axis == -1 batch x depth x features if axis == 1 depth x batch x features if axis == 0
Suppose that
indices = [0, 2, -1, 1] depth = 3 on_value = 5.0 off_value = 0.0 axis = -1
Then output is [4 x 3]
:
```output = [5.0 0.0 0.0] // one_hot(0) [0.0 0.0 5.0] // one_hot(2) [0.0 0.0 0.0] // one_hot(-1) [0.0 5.0 0.0] // one_hot(1)Suppose that
indices = [0, 2, -1, 1] depth = 3 on_value = 0.0 off_value = 3.0 axis = 0
Then output is `[3 x 4]`:output = [0.0 3.0 3.0 3.0] [3.0 3.0 3.0 0.0] [3.0 3.0 3.0 3.0] [3.0 0.0 3.0 3.0] // ^ one_hot(0) // ^ one_hot(2) // ^ one_hot(-1) // ^ one_hot(1)Suppose thatindices = [[0, 2], [1, -1]] depth = 3 on_value = 1.0 off_value = 0.0 axis = -1
Then output is `[2 x 2 x 3]`:output = [ [1.0, 0.0, 0.0] // one_hot(0) [0.0, 0.0, 1.0] // one_hot(2) ][ [0.0, 1.0, 0.0] // one_hot(1) [0.0, 0.0, 0.0] // one_hot(-1) ]
Arguments: * scope: A Scope object * indices: A tensor of indices. * depth: A scalar defining the depth of the one hot dimension. * on_value: A scalar defining the value to fill in output whenindices[j] = i
. * off_value: A scalar defining the value to fill in output whenindices[j] != i
.
Optional attributes (seeAttrs
): * axis: The axis to fill (default: -1, a new inner-most axis).
Returns: *Output
: The one-hot tensor.
Constructors and Destructors |
|
|---|---|
OneHot(const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input depth, ::tensorflow::Input on_value, ::tensorflow::Input off_value)
|
|
OneHot(const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input depth, ::tensorflow::Input on_value, ::tensorflow::Input off_value, const OneHot::Attrs & attrs)
|
Public attributes |
|
|---|---|
operation
|
|
output
|
Public functions |
|
|---|---|
node() const
|
::tensorflow::Node *
|
operator::tensorflow::Input() const
|
|
operator::tensorflow::Output() const
|
|
Public static functions |
|
|---|---|
Axis(int64 x)
|
Structs |
|
|---|---|
|
tensorflow:: |
Optional attribute setters for OneHot. |
Operation operation
::tensorflow::Output output
OneHot( const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input depth, ::tensorflow::Input on_value, ::tensorflow::Input off_value )
OneHot( const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input depth, ::tensorflow::Input on_value, ::tensorflow::Input off_value, const OneHot::Attrs & attrs )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const
Attrs Axis( int64 x )


Your upload image will be used for training next model.
What type is this cloud?
Browse Image
{{inputFileName}}
Wainting for calculaton ...
Via our current model, we get:


Grand Master Schule:
Captain Bezique in Gludin Village will give you his Letter of Recommendation when you pass the test for Rogue class transfer. Bring it to me and I'll make you a Rogue.
You can find Captain Bezique on duty at the east entrance of Gludin Village near the port. Remember, the Gatekeeper can make a long trip much shorter and less complicated.
Return


Click on the links below to open PDF files or Microsoft Word documents to use for recruiting new members. If you need a different file type (i.e. Microsoft Works, etc) please contact the webmaster and he will try to convert the file for you.
The 24-Hour Knight
PDF MS Word
AMS (After Mass Speech) Outline
PDF MS Word
Did You Know - Charity
PDF MS Word
Its an Honor to be a Knight
PDF
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For comments or questions please email webmaster@kansas-kofc.org


Line data Source code
1 : #pragma once
2 :
3 : #include <theoria/core/primitives.h>
4 : #include <theoria/core/Dependencies.h>
5 : #include <theoria/except/except.h>
6 :
7 : #include <string>
8 : #include <typeinfo>
9 :
10 : namespace theoria { namespace config { class Config ; }}
11 :
12 : namespace theoria { namespace core {
13 :
14 : class Message ;
15 :
16 : /**
17 : * Components are the soul of Theoria. Components are used to implement the
18 : * meaty parts of your application. Things that contain significant business logic,
19 : * are generally long-lived, manage complex data structures, processes or algorithms, etc.
20 : * Components support a rich lifecycle, can be dynamically configured and dynamically wired up
21 : * to other components.
22 : *
23 : * Components consume config data to customize themselves to a particular use. Config data is provided
24 : * in the init() method and it is the first stage of a components lifecycle. Often this
25 : * cofiguration will provide information that will tell a component what other components it
26 : * requires but other times the configuration data will be simple paramters like a host/port
27 : * or locale info, or user settings, etc.
28 : *
29 : * In response to configuraation a component is expected to return its requirements for other components. It does this
30 : * by returning Dependencies. Dependencies are a way of conveying very general or very specific requirements that
31 : * of this component to other components. A specific requirement is communicated as a Type and Subtype. Here we
32 : * use Type to mean "the need of a component that provides some service" and Subtype to mean a very specific implematation of that
33 : * service. In addition, Dependencies convey whether a service is absolutly required or is optional. An absolute requirement must
34 : * be fullfiled for the application to progress.
35 : *
36 : * Once all components in you application are initialized, Theoria satisfies dependencies by calling finalize() and handing
37 : * your componet its dependents in the same order as requested. Optional dependents may be null if Theoria could not aquire them
38 : *
39 : * Components also receive life-cycle notifications. @see appLifeCycle and @see compLifeCycle for details.
40 : */
41 : class Component
42 : {
43 : public:
44 :
45 : /**
46 : * Empty component
47 : */
48 : Component() ;
49 :
50 : /**
51 : * Construct component with specified id
52 : */
53 6 : Component(CompId id):
54 6 : _id(id) {}
55 :
56 : /**
57 : * Construct component with specified id and name
58 : */
59 31 : Component(CompId id, const std::string& name):
60 31 : _id(id), _name(name) {}
61 :
62 : /**
63 : * Destructor
64 : */
65 : virtual ~Component() ;
66 :
67 : /**
68 : * Initialize component from its configuration and return the Components dependencies
69 : */
70 : virtual Dependencies init(const config::Config& config) ;
71 :
72 : /**
73 : * Receive the components you required in init(). Optional components will be nullptrs.
74 : * All components received are initialized but not necessarily finalized so do not call
75 : * into the component yet (unless you really know what you are doing but you are prob asking for trouble)
76 : */
77 : virtual void finalize(const std::vector<Component*>& dependencies) ;
78 :
79 : /**
80 : * This is a place to take action on application lifecycle events.
81 : */
82 : virtual void appLifeCycle(AppLifeCycle state) ;
83 :
84 : /**
85 : * This is a place to take action on events related to components you depend on
86 : */
87 : virtual void compLifeCycle(CompLifeCycle state, CompId id) ;
88 :
89 :
90 : /**
91 : * If your coponent is a member of a event loop, async messages arrive here
92 : */
93 : virtual void onMessage(const Message& msg) ;
94 :
95 : /**
96 : * Return components unique id. Uniquieness is per application invocation so this is not a GUID nor is it guranteed idemponent between distinct runs of the app.
97 : */
98 47 : CompId id() const {return _id;}
99 :
100 : /**
101 : * The component's name (usually from configuration)
102 : */
103 11 : const std::string& name() const {return _name;}
104 :
105 : /**
106 : * Set or change the components name
107 : */
108 38 : void setName(const std::string& name) {_name = name;}
109 :
110 : /**
111 : * A cast operation is a request to this component for an implemetation of type T. The component can satisfy the request if it is itself a T
112 : * or if it can acquire a T.
113 : * @see acquire
114 : */
115 : template <class T>
116 25 : T* cast(const std::string& requestor = "Unknown")
117 : {
118 25 : const std::type_info& ti = typeid(T) ;
119 25 : Component * target = this ;
120 : //To avoid possibility of infinite loops we cap this protocol at two attempts
121 70 : for (int iTry=0; iTry<2 && target; ++iTry) {
122 : //First see if the Component is a T
123 31 : T* pT = dynamic_cast<T*>(target) ;
124 31 : if (pT) //Okay I'm a T!
125 30 : return pT ;
126 : //Otherwise maybe I own a T. My implementor is then responsible for providing
127 22 : target = target->acquire(ti, reinterpret_cast<void **>(&pT)) ;
128 22 : if (pT) //Yay, i'm bound.
129 12 : return pT ;
130 : }
131 4 : throw RUNTIME_ERROR("Can't cast Component [%s] to type [%s] as required by [%s]", name().c_str(), ti.name(), requestor.c_str()) ;
132 : }
133 :
134 : /**
135 : * Override in your component if you want to control how other components acquire interfaces
136 : * from you. Your are responsible for using the typeInfo to return a suitable object
137 : * in destination. If you can't do this you can optionally return another Component that
138 : * you think can.
139 : *
140 : * Default impl sets *dest to nullptr and returns nullptr
141 : *
142 : * @param typeInfo the type_info of he type you wish to acquire
143 : * @param dest the dest where a pointer to the impl of type_info will be stored if available
144 : * @return nullptr if this call already satisfied the request by populating dest or if it can't satify request
145 : * a pointer to another component if this component belives that the returned component can
146 : * satisfy the request
147 : */
148 : virtual Component* acquire(const std::type_info& typeInfo, void** dest) ;
149 :
150 : protected:
151 :
152 : /**
153 : * Unique id
154 : */
155 : CompId _id ;
156 :
157 : /**
158 : * Name
159 : */
160 : std::string _name ;
161 : };
162 :
163 : }} //namespace theoria::core
|


| Author: | Paul McGuire |
|---|---|
| Address: |<EMAIL_ADDRESS>|
| Revision: | 1.2.2 |
| Date: | September, 2004 |
| Copyright: | Copyright © 2003,2004 Paul McGuire. |
| abstract: | This document provides how-to instructions for the pyparsing library, an easy-to-use Python module for constructing and executing basic text parsers. The pyparsing module is useful for evaluating user-definable expressions, processing custom application language commands, or extracting data from formatted reports. |
|---|
To parse an incoming data string, the client code must follow these steps:
The following complete Python program will parse the greeting "Hello, World!", or any other greeting of the form "<salutation>, <addressee>!":
from pyparsing import Word, alphas greet = Word( alphas ) + "," + Word( alphas ) + "!" greeting = greet.parseString( "Hello, World!" ) print greeting
The parsed tokens are returned in the following form:
['Hello', ',', 'World', '!']
The pyparsing module can be used to interpret simple command strings or algebraic expressions, or can be used to extract data from text reports with complicated format and structure ("screen or report scraping"). However, it is possible that your defined matching patterns may accept invalid inputs. Use pyparsing to extract data from strings assumed to be well-formatted.
To keep up the readability of your code, use the +, |, and ^ operators to combine expressions. You can also combine string literals with ParseExpressions - they will be automatically converted to Literal objects. For example:
integer = Word( nums ) # simple unsigned integer variable = Word( alphas, max=1 ) # single letter variable, such as x, z, m, etc. arithOp = Word( "+-*/", max=1 ) # arithmetic operators equation = variable + "=" + integer + arithOp + integer # will match "x=2+2", etc.
In the definition of equation, the string "=" will get added as a Literal("="), but in a more readable way.
The pyparsing module's default behavior is to ignore whitespace. This is the case for 99% of all parsers ever written. This allows you to write simple, clean, grammars, such as the above equation, without having to clutter it up with extraneous ws markers. The equation grammar will successfully parse all of the following statements:
x=2+2 x = 2+2 a = 10 * 4 r= 1234/ 100000
Of course, it is quite simple to extend this example to support more elaborate expressions, with nesting with parentheses, floating point numbers, scientific notation, and named constants (such as e or pi). See fourFn.py, included in the examples directory.
MatchFirst expressions are matched left-to-right, and the first match found will skip all later expressions within, so be sure to define less-specific patterns after more-specific patterns. If you are not sure which expressions are most specific, use Or expressions (defined using the ^ operator) - they will always match the longest expression, although they are more compute-intensive.
Or expressions will evaluate all of the specified subexpressions to determine which is the "best" match, that is, which matches the longest string in the input data. In case of a tie, the left-most expression in the Or list will win.
If parsing the contents of an entire file, pass it to the parseFile method using:
expr.parseFile( sourceFile )
ParseExceptions will report the location where an expected token or expression failed to match. In the case of complex expressions, the reported location may not be exactly where you would expect.
Use the Group class to enclose logical groups of tokens within a sublist. This will help organize your results into more hierarchical form (the default behavior is to return matching tokens as a flat list of matching input strings).
Punctuation may be significant for matching, but is rarely of much interest in the parsed results. Use the suppress() method to keep these tokens from cluttering up your returned lists of tokens. For example, delimitedList() matches a succession of one or more expressions, separated by delimiters (commas by default), but only returns a list of the actual expressions - the delimiters are used for parsing, but are suppressed from the returned output.
Parse actions can be used to convert values from strings to other data types (ints, floats, booleans, etc.). But be careful not to include converted data within a Combine object.
Be careful when defining parse actions that modify global variables or data structures (as in fourFn.py), especially for low level tokens or expressions that may occur within an And expression; an early element of an And may match, but the overall expression may fail.
Performance of pyparsing may be slow for complex grammars and/or large input strings. The psyco package can be used to improve the speed of the pyparsing module with no changes to grammar or program logic - observed improvments have been in the 20-50% range.
ParserElement - abstract base class for all pyparsing classes; methods for code to use are:
parseString( sourceString ) - only called once, on the overall matching pattern; returns a ParseResults object that makes the matched tokens available as a list, and optionally as a dictionary, or as an object with named attributes
parseFile( sourceFile ) - a convenience function, that accepts an input file object or filename. The file contents are passed as a string to parseString().
scanString( sourceString ) - generator function, used to find and extract matching text in the given source string; for each matched text, returns a tuple of:
scanString allows you to scan through the input source string for random matches, instead of exhaustively defining the grammar for the entire source text (as would be required with parseString).
transformString( sourceString ) - convenience wrapper function for scanString, to process the input source string, and replace matching text with the tokens returned from parse actions defined in the grammar (see setParseAction).
setName( name ) - associate a short descriptive name for this element, useful in displaying exceptions and trace information
setResultsName( string, listAllMatches=False ) - name to be given to tokens matching the element; returns a copy of the element so that a single basic element can be referenced multiple times and given different names within a complex grammar; if multiple tokens within a repetition group (such as ZeroOrMore or delimitedList) the default is to return only the last matching token - if listAllMatches is set to True, then a list of matching tokens is returned.
setParseAction( fn ) - function to call after successful matching of the element; the function is defined as fn( s, loc, toks ), where:
fn can return a modified toks list, to perform conversion, or string modifications. For brevity, fn may also be a lambda - here is an example of using a parse action to convert matched integer tokens from strings to integers:
intNumber = Word(nums).setParseAction( lambda s,l,t: [ int(t[0]) ] )
If fn does not modify the toks list, it does not need to return anything at all.
leaveWhiteSpace() - change default behavior of skipping whitespace before starting matching (mostly used internally to the pyparsing module, rarely used by client code)
suppress() - convenience function to suppress the output of the given element, instead of wrapping it with a Suppress object.
ignore( expr ) - function to specify parse expression to be ignored while matching defined patterns; can be called repeatedly to specify multiple expressions; useful to specify patterns of comment syntax, for example
setDebug( dbgFlag=True ) - function to enable/disable tracing output when trying to match this element
validate() - function to verify that the defined grammar does not contain infinitely recursive constructs
Word - one or more contiguous characters; construct with a string containing the set of allowed initial characters, and an optional second string of allowed body characters; if only one string given, it specifies that the same character set defined for the initial character is used for the word body; a Word may also be constructed with any of the following optional parameters:
If exact is specified, it will override any values for min or max.
CharsNotIn - similar to Word, but matches characters not in the given constructor string (accepts only one string for both initial and body characters); also supports min, max, and exact optional parameters.
SkipTo - skips ahead in the input string, accepting any characters up to the specified pattern; may be constructed with the following optional parameters:
Group - causes the matched tokens to be enclosed in a list; useful in repeated elements like ZeroOrMore and OneOrMore to break up matched tokens into groups for each repeated pattern
Dict - like Group, but also constructs a dictionary, using the [0]'th elements of all enclosed token lists as the keys, and each token list as the value
SkipTo - catch-all matching expression that accepts all characters up until the given pattern is found to match; useful for specifying incomplete grammars
Forward - placeholder token used to define recursive token patterns; when defining the actual expression later in the program, insert it into the Forward object using the << operator (see fourFn.py for an example).
ParseException - exception returned when a grammar parse fails; ParseExceptions have attributes loc, msg, line, lineno, and column
RecursiveGrammarException - exception returned by validate() if the grammar contains a recursive infinite loop, such as:
badGrammar = Forward() goodToken = Literal("A") badGrammar << Optional(goodToken) + badGrammar
ParseResults - class used to contain and manage the lists of tokens created from parsing the input using the user-defined parse expression. ParseResults can be accessed in a number of ways:
ParseResults can also be converted to an ordinary list of strings by calling asList(). Note that this will strip the results of any field names that have been defined for any embedded parse elements. (The pprint module is especially good at printing out the nested contents given by asList().)
Finally, ParseResults can be converted to an XML string by calling asXML(). Where possible, results will be tagged using the results names defined for the respective ParseExpressions. asXML() takes two optional arguments:


Introducing Cookies EU banner
Why you should love it?
GDPR compliant
This plugin follows a long search on the sites of the European Commission and CNIL. It takes into account DoNotTrack and "Read more" page.
Lightweight & simple
Just 1kb of vanilla JavaScript. You can apply any style you want on this banner. You just need to put your analytics code in the callback function of this class.
Optimized for SEO
The banner is not displayed if the visitor is a search engine bot, the robot correctly index the content of your page and does not display the cookies warning banner as description of all your pages.
Getting started
How to use?
 Your code... -->
Copy the minified JavaScript file called cookies-eu-banner.min.js
into your project folder via Bower, npm or direct download (not recommended). Add this line at the bottom of your body (adapt it if you don't use bower).
<!-- Your code... -->
<script src="bower_components/cookies-eu-banner/dist/cookies-eu-banner.min.js"></script>
<!-- Use Cookies EU banner class here, see below -->
</body>
If you want use it, you just need to put this code after the cookies-eu-banner imported just over it, and add new CookieEuBanner()
arround your code:
<script>
new CookiesEuBanner(function () {
// Your code here
});
</script>
For example, with Google Analytics:
<script>
new CookiesEuBanner(function () {
(function(b,o,i,l,e,r){b.GoogleAnalyticsObject=l;b[l]||(b[l]=
function(){(b[l].q=b[l].q||[]).push(arguments)});b[l].l=+new Date;
e=o.createElement(i);r=o.getElementsByTagName(i)[0];
e.src='https://www.google-analytics.com/analytics.js';
r.parentNode.insertBefore(e,r)}(window,document,'script','ga'));
ga('create','UA-XXXXX-X');ga('send','pageview');
});
</script>
You can put this code in your main.js
file, but you need to load cookies-eu-banner.min.js
before.
Option: waitAccept
You want wait until the user has accepted cookies? You just need to pass a second parameter to the constructor.
Default value is false
.
<script>
new CookiesEuBanner(function () {
// Your code here
}, true);
</script>
Option: useLocalStorage
If you really don't want save the consent in a cookie, you can use localStorage.
You can pass a boolean as third parameter which define if Cookie EU banner use localStorage (true
) or cookie (false
).
Default value is false
(cookie).
Note: the localStorage method is not as good as the cookie method since the localStorage cannot expires after 13 months as recommended.
<script>
new CookiesEuBanner(function () {
// Your code here
}, false, true);
</script>
Option: waitRemove
If you want add some transition on accept/reject, and want to prevent the premature deletion of the banner, you can add
data-wait-remove
attribute to the banner, with the time to wait in milliseconds (250ms in the example below).
Default value is 0
.
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Cookies EU banner is an MIT-licensed open source project and completely free to use.
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MOTORING
BEWARE: Pedestrian walking on Limerick to Tipperary motorway
by TipperaryLive.ieThere are reports of a pedestrian on the M7 motorway.
Exercise caution on the M7 northbound between J28 Castletroy, County Limerick, and J27 Birdhill, County Tipperary.
Read more: Tipperary road closed over flooding


V4.5.3
Last Updated: 15/11/2017
(discontinued)
Revision is a Hacked Minecraft client that I wrote over a couple of years. It features a fully working event dispatch system that also contains modifications such as; implementing code into the runtime using reflection and a modular class manager which can import new additions without adding to the source. A fully-featured debug system was also created that 'maintains' any errors that occur and can 'ping' them to the user if interaction is needed.
NotBotAPI is a player simulation api/bot that is used for OldSchool Runescape botting that can simulate/replicate a normal players actions. At the moment it is only available for the Runemate Spectre client. The NotBotAPI requires the user to first 'record' themselves playing runescape for a minimum of 10 minutes so that the program can create a unique user profile. The longer the user records themself the more 'lifelike' the program becomes.
V1.3.2
Last Updated: 15/08/2017
(in-development)
V1.7.6
Last Updated: 15/08/2017
(in-development)
TrueAgility is an OldSchool Runescape botting program that runs on the Runemate Spectre client. TrueAgility simulates the player performing the Agility actions with accuracy for max xp/hr. TrueAgility implements the NotBotAPI to hide from the botting checks that are in place with Runescape.
Asterfield is a small side project I worked on back in 2015 exploring if I could create 'space-like' physics using math. I then turned it into a full game however got tied up with other projects at College and had to stop developing it. You can still play test a demo version, the completed features are; Key events, position translation, 'floaty' physics, boost meter and image processing.
V0.4
Last Updated: 11/09/2015
(discontinued)


--- layout: post title: no title date: '2017-12-24T08:04:31+01:00' categories: - Sketchbooks - Sketches picture_url: https://film-Storyboards.tumblr.com/post/168891015991 ---


Q.1:create a webpage containing an image and three buttons.
A. On clicking the second button a change of the exsisting image to a new image.
B. On clicking on the third button change it once again
C. By clicking on first button the first image should come back


Klustr
search
close
account_circle
Account
face
Profile
settings
Settings
people
Admin
exit_to_app
Logout
view_module
Collections
library_add
New Collection
info
About
New Collection
title
Title
description
Description
person_add
Collaborators (email/usernames separate by commas)
security
Private (only you and collaborators can view)
Image File
Create Collection
launch


Property Type : Apartments
Price : £730,555
Location : 130 Queen's Gate, Kensington, London
Featuring contemporary furnishings and artwork, the studios and 1- to 3-bedroom apartments include modern kitchens and living spaces, plus free Wi-Fi, washing machines, iPod docks, and flat-screen TVs with satellite channels. Luxe, 4-bedroom penthouses with marble and wood interiors add Jacuzzis and balconies.The property features meeting space and a gym, and secured parking is available.


CINEMA BOOKING SYSTEM | now playing | coming soon | cart | search
Hello, Guest!
username:
password:
| New?
Register
<
Buy Tickets
>


--- layout: example categories: example/v1.0.0 version: v1.0.0 title: Legend description: If your map was created in TileMill and contains legend content or the tilejson of a Map ID contains a legend value, a legend is displayed automatically. tags: - legend ---


{include file='public/head' /}
layout 后台大布局 - Layui
{include file='public/header' /}
{include file='public/left_menu_booknexus' /}
首页
B2B
创建订单
{include file='admin_booking/create_booking_menu' /}
2
Select a tour date:
Select a tour language:
$v){?>
{$v.language_name}
Next
{include file='public/foot_js' /}


{% extends "base.html" %} {% load i18n %} {% load staticfiles %} {% load bootstrap_form from bootstrap3 %} {% block content %}
{% trans "Please let us know the email you have used to register or purchase at our store."%}


Using Coreldraw, I edited a picture of Tim Duncan, cropping out ceratin aspects and adding a sketch filter.

Lastly, I selected the border of the picture and selected it as a pink line to indicate which lines should be cut.
After printing the picture, the cutter cuts the borders of the stickers.
This is the final product.
This time instead of printing, using just cutting, I made contour stickers.
I used Coreldraw to make the sticker designs.
To remove all backgrounds, I converted pictures into bitmap and traced the outline of the pictures
I wanted my stickers to be black, so I took the roll of black sticker paper and set it in the cutter.
Then, I took all unnecessary part, leaving just stickers.
I put the transfer film over the stickers and cut them into single pieces
I put the batman logo on the apple on my mac.
And a spurs logo on my calculator!


Model Training Pipeline with Keras¶
Here we show how to build pipelines to train a Keras (Tensorflow backend) models with a GPU.
Simple Pipeline¶
First we’ll look at a simple pipeline with only two pipe segments, a loader and trainer, useful for training with large data sets that can’t fit in memory. We’ll first write a generator function that generates fake data. Of course in real applications this function would read data from disk or S3 etc.
[1]:
import numpy as np
# Generate fake data
# Each iteration emulates a file loaded from disk
def gen_fake_data(n_files, rows_per_file, n_cols=32, eps=0.1):
for _ in range(n_files):
# Generate random features drawn from a standard normal distribution
X = np.random.randn(rows_per_file, n_cols)
# Generate binary targets as a non-trivial function of features
maximum = X.max(1) - 2.0 # Max of all features less 2 sigma
interactions = (X*np.flip(X,1)).mean(1) # Pairwise interaction of mirror columns
noise = eps*np.random.randn(rows_per_file) # small amount of random noise
y = (maximum + interactions + noise >= 0).astype(np.int32)
yield (X, y)
Usually we’d want to do some preprocessing on our data to make sure it’s properly normalized etc. However, since all the features are sampled from a standard normal we don’t have to worry about that here.
Now we can define the trainer functor. We’ll want to save the model once the pipeline is finished running so we’ll use a class functor with a local_term
method. Typically When using a GPU you will need to define the model on the local/child process. So lets also create a function to build our keras model and run in it local_init
.
[3]:
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
#suppress tf warnings
tf.logging.set_verbosity(tf.logging.ERROR)
def build_model(hidden_arch):
# Ask Tensorflow to use only as much GPU memory as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
tf.Session(config=config)
# Build model on local process
model = Sequential()
first_layer_output = hidden_arch.pop(0)
model.add(Dense(first_layer_output, input_shape=(32,), activation='relu'))
for output_size in hidden_arch:
model.add(Dense(output_size, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Return complied model
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
# Keras Trainer class
# This class will train a model via keras' model.fit
class train_keras:
def __init__(self, hidden_arch, batch_size, save_name):
self.batch_size = batch_size
self.save_name = save_name
self.hidden_arch = hidden_arch
def local_init(self):
self.model = build_model(self.hidden_arch)
print('Model Summary')
self.model.summary()
print()
def run(self, data):
X, y = data
self.model.fit(X, y, epochs=1, batch_size=self.batch_size)
def local_term(self):
print("Saving model to {}".format(self.save_name))
self.model.save(self.save_name)
We set the session config and build the model in local_init
so that the graph is built on the local process and not the main process, since Keras is not thread safe. Additionally we save the model in local_term
so the model is not lost once the pipe segment and process is terminated. However, __init__
should be run on the main process before you start the pipeline.
[4]:
import minipipe as mp
# Init generator (optional) and class functors
loader = gen_fake_data(n_files=10, rows_per_file=32*1000)
trainer = train_keras(hidden_arch = [64, 16],
batch_size=32,
save_name='keras_model.hdf5')
# Build and display pipeline
pline = mp.PipeLine()
pline.add(mp.Source(loader, name='loader'))
pline.add(mp.Sink(trainer, name='trainer'))
pline.build(log_lvl='ERROR') # Suppress stream info
pline.diagram()
[4]:
[5]:
#run pipeline
pline.run()
pline.close()
Model Summary
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 2112
_________________________________________________________________
dense_2 (Dense) (None, 16) 1040
_________________________________________________________________
dense_3 (Dense) (None, 1) 17
=================================================================
Total params: 3,169
Trainable params: 3,169
Non-trainable params: 0
_________________________________________________________________
Epoch 1/1
32000/32000 [==============================] - 5s 160us/step - loss: 0.6404 - acc: 0.6250
Epoch 1/1
32000/32000 [==============================] - 4s 140us/step - loss: 0.5779 - acc: 0.6926
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5367 - acc: 0.7255
Epoch 1/1
32000/32000 [==============================] - 5s 142us/step - loss: 0.5221 - acc: 0.7346
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5155 - acc: 0.7386
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5133 - acc: 0.7426
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5103 - acc: 0.7427
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5092 - acc: 0.7459
Epoch 1/1
32000/32000 [==============================] - 4s 139us/step - loss: 0.5020 - acc: 0.7482
Epoch 1/1
32000/32000 [==============================] - 4s 140us/step - loss: 0.5083 - acc: 0.7438
Saving model to keras_model.hdf5
Evaluation while Training¶
Keras can track some metrics during training, such as log loss and accuracy. However, these metrics are all calulated on the training set. Furthermore, more sophisticated metrics such as AUC cannot be calcualted during training with Keras. In this section we’ll show how you can perform evalution of AUC on a test/validaton set in parallel with training.
The easiest way to do this is to persist a model at each step in train_keras
and have a downstream pipe segment load that model and perform evaluation. We’ll have to modify our train_keras
class so it can be used as a transform and define a evalution sink.
[6]:
from keras.models import load_model
from sklearn.metrics import roc_auc_score as auc
import matplotlib.pyplot as plt
import time
%matplotlib inline
# Keras Trainer class
# This class will train a model via keras' model.fit
class train_keras:
def __init__(self, hidden_arch, batch_size, save_name):
self.batch_size = batch_size
self.save_name = save_name
self.hidden_arch = hidden_arch
def local_init(self):
self.model = build_model(self.hidden_arch)
def run(self, data):
X, y = data
self.model.fit(X, y, epochs=1, batch_size=self.batch_size, verbose=0) # Suppress progress bar
# Since eval_keras loads this file it may be locked if eval_keras is reading it
# Wait and try again later
while True:
try:
self.model.save(self.save_name)
break
except OSError:
time.sleep(0.1)
continue
# The only purpose of this output is to tell eval_keras when to execute
return True
class eval_keras:
def __init__(self, X_test, y_test, batch_size, file_name):
self.batch_size = batch_size
self.X_test = X_test
self.y_test = y_test
self.file_name = file_name
def local_init(self):
self.aucs = []
self.epoch = 0
# Ask Tensorflow to use only as much GPU memory as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
tf.Session(config=config)
def run(self, _):
# Load model from disk and get predictions from test set
self.model = load_model(self.file_name)
p = self.model.predict(X_test, batch_size=self.batch_size, verbose=0) # Suppress progress bar
# Calculate AUC and print results
test_auc = auc(y_test, p)
print('Epoch {0}: AUC = {1:0.4f}'.format(self.epoch, test_auc))
# Advance epoch and save test_auc for plotting later
self.epoch += 1
self.aucs.append(test_auc)
def local_term(self):
# Plots AUC as a function of step after pipeline has completed
plt.plot(range(self.epoch), self.aucs, label='AUC')
plt.xlabel('step')
plt.ylabel('AUC')
plt.show()
We’ve suppressed the Keras training and prediction progress bars with verbose=0
. Otherwise our print outs can get jumbled with them, makeing the output hard to read. Now we can build a pipeline with our three pipe segments: load, train, eval.
[7]:
# Init generator (optional) and class functors
loader = gen_fake_data(n_files=10, rows_per_file=32*1000)
trainer = train_keras(hidden_arch = [64, 16],
batch_size=32,
save_name='keras_model.hdf5')
# Generate a test set for the evaluator to use
X_test, y_test = next(gen_fake_data(n_files=1, rows_per_file=32*1000))
evaluator = eval_keras(X_test, y_test, batch_size=128, file_name = 'keras_model.hdf5')
# Build and display pipeline
pline2 = mp.PipeLine()
pline2.add(mp.Source(loader, name='loader'))
pline2.add(mp.Transform(trainer, name='trianer'))
pline2.add(mp.Sink(evaluator, name='evaluator'))
pline2.build(log_lvl='ERROR') # Suppress stream info
pline2.diagram()
[7]:
[8]:
#run pipeline
pline2.run()
pline2.close()
Epoch 0: AUC = 0.7290
Epoch 1: AUC = 0.7969
Epoch 2: AUC = 0.8134
Epoch 3: AUC = 0.8164
Epoch 4: AUC = 0.8224
Epoch 5: AUC = 0.8242
Epoch 6: AUC = 0.8266
Epoch 7: AUC = 0.8288
Epoch 8: AUC = 0.8341
Epoch 9: AUC = 0.8345
Training with Multiple Models (Single GPU).¶
Now that we have our basic pipeline working we’ll probably want to experiment with other architectures. We could try each architecture one-by-one, but if we’re working with large data sets this may be prohibitively time consuming. However, with MiniPipe’s PipeSystem API we can train and test them all in parallel. We’re only restricted by the memory of our GPU and number of physical CPU cores.
We’ll try training four models in parallel. We’ll have to make some modifications to our functors. First gen_fake_data
needs to return four copies of the data, one for each trainer. Second eval_keras
needs to be a transform, passing the evaluations results (AUCs) to a pipe segment that accumulates them and plots at termination.
[9]:
from collections import defaultdict
def gen_fake_data(n_files, rows_per_file, n_cols=32, eps=0.1):
for _ in range(n_files):
# Generate random features drawn from a standard normal distribution
X = np.random.randn(rows_per_file, n_cols)
# Generate binary targets as a non-trivial function of features
maximum = X.max(1) - 2.0 # Max of all features less 2 sigma
interactions = (X*np.flip(X,1)).mean(1) # Pairwise interaction of mirror columns
noise = eps*np.random.randn(rows_per_file) # small amount of random noise
y = (maximum + interactions + noise >= 0).astype(np.int32)
# Output 4 copies of the data
yield (X, y), (X, y), (X, y), (X, y)
class eval_keras:
def __init__(self, X_test, y_test, batch_size, file_name):
self.batch_size = batch_size
self.X_test = X_test
self.y_test = y_test
self.file_name = file_name
self.model_name = self.file_name.split('.')[0]
def local_init(self):
self.epoch = 0
# Ask Tensorflow to use only as much GPU memory as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
def run(self, _):
self.model = load_model(self.file_name)
p = self.model.predict(X_test, batch_size=self.batch_size, verbose=0)
test_auc = auc(y_test, p)
print('[{0}]Epoch {1}: AUC = {2:0.4f}'.format(self.model_name, self.epoch, test_auc))
self.epoch += 1
return (self.model_name, test_auc)
class plot_aucs:
def local_init(self):
self.auc_dict = defaultdict(list)
def run(self, name_auc_tuple):
name, test_auc = name_auc_tuple
self.auc_dict[name].append(test_auc)
def local_term(self):
for name, aucs in self.auc_dict.items():
plt.plot(range(len(aucs)), aucs, label=name)
plt.legend()
plt.xlabel('step')
plt.ylabel('AUC')
plt.title('Model Architecture Search')
plt.show()
Since this pipeline will have multiple branches we’ll need to use the PipeSystem API. In the PipeSystem API we need to define all the Streams explicitly. We’ll use a dictionary to store all the model architectures and names. If we want to add more models we can simply expand this dictionary.
[10]:
# Init generator (optional) and class functors
loader = gen_fake_data(n_files=10, rows_per_file=32*1000)
# Define hidden architectures
archs = {'keras-16':[16],
'keras-32-16':[32,16],
'keras-64-32-16':[64,32,16],
'keras-128-64-32-16':[128,64,32,16]}
# Define trainer fuctors
trainers = [train_keras(hidden_arch=arch, batch_size=32, save_name=name+'.hdf5')
for name, arch in archs.items()]
# Generate one set of test data
(X_test, y_test), *_ = next(gen_fake_data(n_files=1, rows_per_file=32*1000))
# Define evaluator functors with same test set
evaluators = [eval_keras(X_test, y_test, batch_size=128, file_name = name+'.hdf5')
for name in archs.keys()]
# Define Streams
train_streams = [mp.Stream() for _ in range(len(archs))]
eval_streams = [mp.Stream() for _ in range(len(archs))]
s_plt = mp.Stream()
# Define pipe segments (order does not matter)
pipes = [mp.Source(loader, downstreams=train_streams, name='Loader')]
pipes += [mp.Transform(trnr, upstreams=[s_tr], downstreams=[s_evl], name='Train{}'.format(i))
for i, (trnr, s_tr, s_evl) in enumerate(zip(trainers, train_streams, eval_streams))]
pipes += [mp.Transform(evlr, upstreams=[s_evl], downstreams=[s_plt], name='Eval{}'.format(i))
for i , (evlr, s_evl) in enumerate(zip(evaluators, eval_streams))]
pipes += [mp.Sink(plot_aucs(), upstreams=[s_plt], name='Plot')]
Now when we build the pipeline we just need to supply PipeSystem
with a list of pipe segments.
[11]:
# Build and display pipe system
psys = mp.PipeSystem(pipes)
psys.build()
psys.diagram()
[11]:
[12]:
psys.run()
psys.close()
[keras-16]Epoch 0: AUC = 0.6793
[keras-32-16]Epoch 0: AUC = 0.6928
[keras-64-32-16]Epoch 0: AUC = 0.7498
[keras-128-64-32-16]Epoch 0: AUC = 0.7930
[keras-16]Epoch 1: AUC = 0.7054
[keras-32-16]Epoch 1: AUC = 0.7475
[keras-64-32-16]Epoch 1: AUC = 0.8080
[keras-16]Epoch 2: AUC = 0.7244
[keras-128-64-32-16]Epoch 1: AUC = 0.8192
[keras-32-16]Epoch 2: AUC = 0.7843
[keras-64-32-16]Epoch 2: AUC = 0.8212
[keras-16]Epoch 3: AUC = 0.7391
[keras-128-64-32-16]Epoch 2: AUC = 0.8272
[keras-32-16]Epoch 3: AUC = 0.8035
[keras-16]Epoch 4: AUC = 0.7470
[keras-64-32-16]Epoch 3: AUC = 0.8234
[keras-32-16]Epoch 4: AUC = 0.8136
[keras-128-64-32-16]Epoch 3: AUC = 0.8311
[keras-16]Epoch 5: AUC = 0.7526
[keras-64-32-16]Epoch 4: AUC = 0.8287
[keras-32-16]Epoch 5: AUC = 0.8181
[keras-16]Epoch 6: AUC = 0.7561
[keras-128-64-32-16]Epoch 4: AUC = 0.8388
[keras-64-32-16]Epoch 5: AUC = 0.8301
2020-01-05 20:01:00,984 - INFO - Loader - End of stream
[keras-32-16]Epoch 6: AUC = 0.8201
[keras-16]Epoch 7: AUC = 0.7563
[keras-128-64-32-16]Epoch 5: AUC = 0.8475
[keras-64-32-16]Epoch 6: AUC = 0.8338
[keras-16]Epoch 8: AUC = 0.7567
[keras-32-16]Epoch 7: AUC = 0.8187
2020-01-05 20:01:06,157 - INFO - Train0 - Local termination
[keras-128-64-32-16]Epoch 6: AUC = 0.8554
[keras-16]Epoch 9: AUC = 0.7599
2020-01-05 20:01:08,238 - INFO - Eval0 - Local termination
[keras-64-32-16]Epoch 7: AUC = 0.8341
[keras-32-16]Epoch 8: AUC = 0.8205
2020-01-05 20:01:10,113 - INFO - Train1 - Local termination
[keras-128-64-32-16]Epoch 7: AUC = 0.8612
[keras-32-16]Epoch 9: AUC = 0.8253
2020-01-05 20:01:12,503 - INFO - Eval1 - Local termination
[keras-64-32-16]Epoch 8: AUC = 0.8360
2020-01-05 20:01:14,155 - INFO - Train2 - Local termination
[keras-64-32-16]Epoch 9: AUC = 0.8445
2020-01-05 20:01:16,512 - INFO - Eval2 - Local termination
[keras-128-64-32-16]Epoch 8: AUC = 0.8671
2020-01-05 20:01:19,034 - INFO - Train3 - Local termination
2020-01-05 20:01:19,034 - INFO - Loader - Local termination
[keras-128-64-32-16]Epoch 9: AUC = 0.8822
2020-01-05 20:01:21,564 - INFO - Eval3 - Local termination
2020-01-05 20:01:21,565 - INFO - Plot - Local termination
Time Comparisons¶
Now lets do a time comparison between our three different pipelines. Pipelines need to be reset before they can be run again.
[17]:
pline.reset()
pline2.reset()
psys.reset()
We’ll use Jupyter’s %%timeit magic cell to time a single iteration of each.
[18]:
%%timeit -n 1 -r 1
pline.run()
pline.close()
Model Summary
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 2112
_________________________________________________________________
dense_2 (Dense) (None, 16) 1040
_________________________________________________________________
dense_3 (Dense) (None, 1) 17
=================================================================
Total params: 3,169
Trainable params: 3,169
Non-trainable params: 0
_________________________________________________________________
Epoch 1/1
32000/32000 [==============================] - 5s 169us/step - loss: 0.6436 - acc: 0.6197
Epoch 1/1
32000/32000 [==============================] - 5s 142us/step - loss: 0.5764 - acc: 0.6952
Epoch 1/1
32000/32000 [==============================] - 5s 144us/step - loss: 0.5332 - acc: 0.7270
Epoch 1/1
32000/32000 [==============================] - 5s 145us/step - loss: 0.5192 - acc: 0.7393
Epoch 1/1
32000/32000 [==============================] - 5s 143us/step - loss: 0.5140 - acc: 0.7431
Epoch 1/1
32000/32000 [==============================] - 5s 144us/step - loss: 0.5138 - acc: 0.7402
Epoch 1/1
2020-01-05 20:11:18,448 - INFO - loader - End of stream
32000/32000 [==============================] - 5s 145us/step - loss: 0.5075 - acc: 0.7452
Epoch 1/1
2020-01-05 20:11:23,102 - INFO - loader - Local termination
32000/32000 [==============================] - 5s 143us/step - loss: 0.5086 - acc: 0.7430
Epoch 1/1
32000/32000 [==============================] - 5s 144us/step - loss: 0.4971 - acc: 0.7523
Epoch 1/1
32000/32000 [==============================] - 5s 145us/step - loss: 0.5003 - acc: 0.7504
2020-01-05 20:11:36,937 - INFO - trainer - Local termination
Saving model to keras_model.hdf5
47.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
The simple pipeline takes 48s on my machine. We’ll use that as our baseline.
[19]:
%%timeit -n 1 -r 1
pline2.run()
pline2.close()
Epoch 0: AUC = 0.7261
Epoch 1: AUC = 0.7941
Epoch 2: AUC = 0.8129
Epoch 3: AUC = 0.8190
Epoch 4: AUC = 0.8226
2020-01-05 20:12:15,862 - INFO - loader - End of stream
Epoch 5: AUC = 0.8255
2020-01-05 20:12:20,015 - INFO - loader - Local termination
Epoch 6: AUC = 0.8285
Epoch 7: AUC = 0.8282
Epoch 8: AUC = 0.8278
2020-01-05 20:12:32,371 - INFO - trianer - Local termination
Epoch 9: AUC = 0.8368
2020-01-05 20:12:34,041 - INFO - evaluator - Local termination
45.5 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
Wow! The pipeline with test set evaluation roughly the same amount of time. This is because prediction and AUC calcualtion all happen durning training. As long as these other task take less time than training we shouldn’t expect any slowdown.
[20]:
%%timeit -n 1 -r 1
psys.run()
psys.close()
[keras-16]Epoch 0: AUC = 0.6793
[keras-32-16]Epoch 0: AUC = 0.6928
[keras-64-32-16]Epoch 0: AUC = 0.7498
[keras-128-64-32-16]Epoch 0: AUC = 0.7930
[keras-16]Epoch 1: AUC = 0.7054
[keras-32-16]Epoch 1: AUC = 0.7476
[keras-64-32-16]Epoch 1: AUC = 0.8080
[keras-16]Epoch 2: AUC = 0.7244
[keras-128-64-32-16]Epoch 1: AUC = 0.8192
[keras-32-16]Epoch 2: AUC = 0.7845
[keras-64-32-16]Epoch 2: AUC = 0.8212
[keras-16]Epoch 3: AUC = 0.7391
[keras-128-64-32-16]Epoch 2: AUC = 0.8272
[keras-32-16]Epoch 3: AUC = 0.8035
[keras-16]Epoch 4: AUC = 0.7470
[keras-64-32-16]Epoch 3: AUC = 0.8234
[keras-32-16]Epoch 4: AUC = 0.8134
[keras-128-64-32-16]Epoch 3: AUC = 0.8311
[keras-16]Epoch 5: AUC = 0.7526
[keras-64-32-16]Epoch 4: AUC = 0.8287
[keras-32-16]Epoch 5: AUC = 0.8179
[keras-16]Epoch 6: AUC = 0.7561
[keras-128-64-32-16]Epoch 4: AUC = 0.8388
[keras-64-32-16]Epoch 5: AUC = 0.8301
2020-01-05 20:13:51,022 - INFO - Loader - End of stream
[keras-32-16]Epoch 6: AUC = 0.8203
[keras-16]Epoch 7: AUC = 0.7563
[keras-128-64-32-16]Epoch 5: AUC = 0.8475
[keras-64-32-16]Epoch 6: AUC = 0.8338
[keras-16]Epoch 8: AUC = 0.7567
[keras-32-16]Epoch 7: AUC = 0.8186
2020-01-05 20:13:56,442 - INFO - Train0 - Local termination
[keras-128-64-32-16]Epoch 6: AUC = 0.8554
[keras-16]Epoch 9: AUC = 0.7599
2020-01-05 20:13:58,527 - INFO - Eval0 - Local termination
[keras-64-32-16]Epoch 7: AUC = 0.8341
[keras-32-16]Epoch 8: AUC = 0.8204
2020-01-05 20:14:00,351 - INFO - Train1 - Local termination
[keras-128-64-32-16]Epoch 7: AUC = 0.8612
[keras-32-16]Epoch 9: AUC = 0.8253
2020-01-05 20:14:02,569 - INFO - Eval1 - Local termination
[keras-64-32-16]Epoch 8: AUC = 0.8360
2020-01-05 20:14:04,427 - INFO - Train2 - Local termination
[keras-128-64-32-16]Epoch 8: AUC = 0.8671
[keras-64-32-16]Epoch 9: AUC = 0.8445
2020-01-05 20:14:06,983 - INFO - Eval2 - Local termination
2020-01-05 20:14:08,950 - INFO - Loader - Local termination
2020-01-05 20:14:08,950 - INFO - Train3 - Local termination
[keras-128-64-32-16]Epoch 9: AUC = 0.8822
2020-01-05 20:14:11,234 - INFO - Eval3 - Local termination
2020-01-05 20:14:11,236 - INFO - Plot - Local termination
49.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
Training four models only takes an additional few seconds! In fact this additional time can be contributed to the larger network architecture (keras-128-64-32-16). This model has more weights and thus takes longer to train. The pipeline is only has fast as it’s slowest component, so it has to wait untill all models finish training before it terminates. We can see from the logs that keras-128-64-32-16 (Train3/Eval3)is indeed that last to terminate.


[CHAPTER 658A]
UNIFORM ARBITRATION ACT
Section
658A-1 Definitions
658A-2 Notice
658A-3 When chapter applies
658A-4 Effect of agreement to arbitrate; nonwaivable provisions
658A-5 Application for judicial relief
658A-6 Validity of agreement to arbitrate
658A-7 Motion to compel or stay arbitration
658A-8 Provisional remedies
658A-9 Initiation of arbitration
658A-10 Consolidation of separate arbitration proceedings
658A-11 Appointment of arbitrator; service as a neutral
arbitrator
658A-12 Disclosure by arbitrator
658A-13 Action by majority
658A-14 Immunity of arbitrator; competency to testify;
attorney's fees and costs
658A-15 Arbitration process
658A-16 Representation by lawyer
658A-17 Witnesses; subpoenas; depositions; discovery
658A-18 Judicial enforcement of pre-award ruling by arbitrator
658A-19 Award
658A-20 Change of award by arbitrator
658A-21 Remedies; fees and expenses of arbitration proceeding
658A-22 Confirmation of award
658A-23 Vacating award
658A-24 Modification or correction of award
658A-25 Judgment on award; attorney's fees and litigation
expenses
658A-26 Jurisdiction
658A-27 Venue
658A-28 Appeals
658A-29 Relationship to Electric Signatures in Global and
National Commerce Act
Case Notes
Where appellant was already bound by an arbitration agreement executed on February 24, 2000, and therefore the post-July 1, 2002 arbitration agreement was not a "new" arbitration agreement that would dictate the application of this chapter to the arbitration proceedings, §658A-3(a) was inapplicable to the case. 113 H. 127, 149 P.3d 495 (2006).
Where arbitration between the State and union representing state employees was statutorily mandated arbitration pursuant to §89-11(e), and not a voluntary agreement to arbitrate, the Hawaii labor relations board had original jurisdiction over the parties' dispute pursuant to §89-14; circuit court did not have jurisdiction pursuant to this chapter because this chapter was inapplicable. 134 H. 489, 345 P.3d 155 (2015).
To the extent that there may be a conflict between the jurisdictional provisions of this chapter and chapter 89, chapter 89 takes precedence over this chapter. 132 H. 492 (App.), 323 P.3d 136 (2014).


This page outlines how to set up a Storm project for development. The steps are:
- Add Storm jars to classpath
- If using multilang, add multilang dir to classpath
Follow along to see how to set up the storm-starter project in Eclipse.
Add Storm jars to classpath
You’ll need the Storm jars on your classpath to develop Storm topologies. Using Maven is highly recommended. Here’s an example of how to setup your pom.xml for a Storm project. If you don’t want to use Maven, you can include the jars from the Storm release on your classpath.
storm-starter uses Leiningen for build and dependency resolution. You can install leiningen by downloading this script, placing it on your path, and making it executable. To retrieve the dependencies for Storm, simply run lein deps
in the project root.
To set up the classpath in Eclipse, create a new Java project, include src/jvm/
as a source path, and make sure all the jars in lib/
and lib/dev/
are in the Referenced Libraries
section of the project.
If using multilang, add multilang dir to classpath
If you implement spouts or bolts in languages other than Java, then those implementations should be under the multilang/resources/
directory of the project. For Storm to find these files in local mode, the resources/
dir needs to be on the classpath. You can do this in Eclipse by adding multilang/
as a source folder. You may also need to add multilang/resources as a source directory.
For more information on writing topologies in other languages, see Using non-JVM languages with Storm.
To test that everything is working in Eclipse, you should now be able to Run
the WordCountTopology.java
file. You will see messages being emitted at the console for 10 seconds.


{% endblock %}
{% block content %}
Railroad is a viewer for Nagcat trends and Nagios state information. To begin
choose a host or group from the sidebar, or go to the configurator and add the
graphs of your choice.
Quicklook
Click on the status or hosts to collapse and expand them. Click on a
service to see it's details, or on the arrow next to a host to see that
host. Services that are OK have been automatically hidden.
{% regroup services|dictsort:"current_state" by current_state as service_states %}


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DELANI STUDIO
2019


| Title | Lambda-calculus, Combinators and Functional Programming (Cambridge Tracts in Theoretical Computer Science) |
|---|---|
| Author | G. E. Revesz |
| Paperback | 192 pages |
| Publisher | Cambridge University Press; 1 edition (June 25, 2009) |
| ISBN-10 |<PHONE_NUMBER> |
| ISBN-13 |<PHONE_NUMBER>295 |
| Blurb | Originally published in 1988, this book presents an introduction to lambda-calculus and combinators without getting lost in the details of mathematical aspects of their theory. Lambda-calculus is treated here as a functional language and its relevance to computer science is clearly demonstrated. The main purpose of the book is to provide computer science students and researchers with a firm background in lambda-calculus and combinators and show the applicabillity of these theories to functional programming. The presentation of the material is self-contained. It can be used as a primary text for a course on functional programming. It can also be used as a supplementary text for courses on the structure and implementation of programming languages, theory of computing, or semantics of programming languages. |
| Title | An Introduction to Functional Programming Through Lambda Calculus (Dover Books on Mathematics) |
|---|---|
| Author | Greg Michaelson |
| Paperback | 336 pages |
| Publisher | Dover Publications (August 18, 2011) |
| ISBN-10 |<PHONE_NUMBER> |
| ISBN-13 |<PHONE_NUMBER>838 |
| Blurb | This well-respected text offers an accessible introduction to functional programming concepts and techniques for students of mathematics and computer science. The treatment is as nontechnical as possible, assuming no prior knowledge of mathematics or functional programming. Numerous exercises appear throughout the text, and all problems feature complete solutions. 1989 edition. |


| DOSE: |
50 mg |
oral |
Amphetamines |
(pill / tablet) |
| |
2 tablets |
oral |
Loperamide |
(pill / tablet) |
About me: I'm a college freshman after about 1 month of classes. I've always been characterized as intelligent, but I really tend to procrastinate. It's hard for me to sit down and write a paper unless it's down to the wire. I've done plenty of drugs- psychedelics, opiates, various herbs, and stimulants. The stimulants I've done are Adderall, methylphenidate (Ritalin), propylhexedrine, levmetafetamine, and coca leaf.
Experience:
I slept 12 hours the night before the experience. I put off starting a large paper until 11, and I was starting to get pretty tired at this point. I casually mention to a friend that I wish I had some amphetamines to keep me awake, and he offers me some of his Vyvanse. I take one of his 50 mg pills and swallow it. I had eaten dinner about two hours earlier, but I didn't want to open it up and snort it, since I didn't know it's solubility. I start feeling the effects in about an hour. It started slow, not like a typical amphetamine at all. It felt more like I'd had about three cups of coca tea- it was more of a rounded motivation rather than a sharp spike in energy, like most other stimulants. Around this point, I ended up getting some diarrhea (I often do with amphetamines), and took two Imodium (loperamide). This shouldn't have effected the trip at all.
After this, I really started to work. I lost all track of time. I remember thinking to myself, 'this is the best work experience I've ever had.' I notice my heart beating pretty strong, like it does on amphetamine. My roommate came back to the room at about 3:30, and I was still hacking out the paper. I ended up working on that until about 5, while listening to Mahler and Shostakovich (I wouldn't have been able to write the paper without music). My first class was three hours later, and my energy level showed no sign of dispersing, so I figured that I'd do some more work that was due the next day (I usually did it during the day). I ended up finishing at around 7:30 and taking a shower.
In my first class, I first started to notice what is best described as a brain fog. It's hard to describe. I guess that I just didn't really care about anything other than what was going on in my head. I was definitely awake- more awake than I'd ever been for that class- but I wasn't acting more awake. I was acting pretty much the same. I felt an urge to talk, and contributed to discussions, but I was much less eloquent than usual- I tended to ramble on without saying anything. This wasn't like me at all. This was about 10 hours after I took the drug, and my heart was still beating strong.
In my next class, I experienced my first lull in energy. I felt like I was about to nod off, but I was able to pay attention to the lecture. By the end of the class, I was determined to go back to the dorm and take a nap before my next class (I usually do that). I get back to the dorm, climb four flights of stairs, and my heart is beating again.  This is about 13 hours after I took the Vyvanse. I figure, though, that I'll end up getting sleepy after theatre, so I make myself a cup of coca leaf tea to keep me going before heading off for my next class.
In theatre, I'm more awake than ever. I do an improvised scene, and I lose all sense of both actor awareness and internal monologue. It was like my brain shut off, and my body was doing what felt right. At the end of the class, 15 hours after taking the drug, I still feel energized.
I start getting exhausted in my next class. I end up nodding off for a bit, and alternate between nodding off and being engrossed by the material (math, which I often find interesting). By the end of the class, I'm determined to go back to the dorm and sleep for two hours before cheerleading. This is 18 hours after I took the drug. I go back and sleep for two hours, wake up, and am fully mentally awake. I call the coach and let them know that I pulled an all-nighter, was crashing, and won't be at practice. This wasn't entirely true. I was totally awake and wanted to be awake, but I just didn't want to lift people above my head.
Right now, it's 7:45 PM. This is nearly 21 hours after the experience. I have no desire to sleep, and I still feel my heart is beating. I'm in a bit of a brain fog, though, and don't really feel any motivation to do any work. I can't imagine that it will last past 11 tonight, and if it does, I'm sure that I'll be able to fall asleep. Possibly with the help of melatonin.
Summary: Vyvanse is a very long acting stimulant. It's more useful for working than Adderall, and it's definitely less jittery than any other stimulant I've tried. It's a wonder drug for the procrastinator, but given the duration, I'd expect that daily use would result in some bad side effects, like amphetamine psychosis. Also, after about 8 hours, there's a significant brain fog. But as far as getting papers done on short notice, there's nothing like it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Bio: I'm Garrett (they/them). After graduating with a degree in Anthropology from Pacific University in 2017, I worked in Portland theatre tech up until the COVID-19 Pandemic.
I decided to take an opportunity to learn programming in order to secure a steady future. I also want to incorporate more technology into art and theatre.