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Detective_Conan
Kudo Shinichi is a seventeen year-old high school detective whom people call the "Modern Sherlock Holmes." However, one night after a date with his childhood sweetheart, Ran, Shinichi witnessed an illegal trade and, caught off his guard, was knocked unconscious and fed a drug that was supposed to kill him... but he woke up and found himself shrunken to a seven year-old. In order to track down the men who did this to him, Shinichi hid his identity and lived with Ran, whose father happened to be a hopeless detective, and with that came a series of murders and mysteries that he must solve.


Perfect weekend camping at Oregon coast: 70 degrees, followed by uncomfortably strong wind, and rain the next morning.
6/13/2010, 7:51:38 PM
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link--- layout: tweet ---
Perfect weekend camping at Oregon coast: 70 degrees, followed by uncomfortably strong wind, and rain the next morning.
6/13/2010, 7:51:38 PM
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A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
washparkdlt06-Aug-19 09:26 PM
Atm it appears the killer app is money
nocturnal06-Aug-19 09:27 PM
yup
washparkdlt06-Aug-19 09:27 PM
Hence partnerships and usability
dan.06-Aug-19 09:27 PM
theres a big difference for btc at 100k and btc at 10k. If alts stay put USD wise and btc happens to take off, i am not so sure that many alts will catch up in satoshis
nocturnal06-Aug-19 09:27 PM
i think in the more distant future Iot and IoV will run on blockchain or whatever supercedes it
so i do think it will have been a fundamental tech developement
in hindsight
dan.06-Aug-19 09:28 PM
the problem with dapps is there funnily enough needs to be a killer dapp
cause the performance of dapps is inherently awful
nocturnal06-Aug-19 09:29 PM
yea really bad
dan.06-Aug-19 09:29 PM
so there needs to be something with a lot of utility to make the shit performance worth it
which- i think there is. its not out there yet
mrtzmp306-Aug-19 09:29 PM
There is btc dump
washparkdlt06-Aug-19 09:29 PM
Global borderless transfer of value and banking the unbanked is pretty good
mrtzmp306-Aug-19 09:29 PM
Kmd is a nice indicator
Always dumps before
washparkdlt06-Aug-19 09:30 PM
Money is the app
nocturnal06-Aug-19 09:30 PM
yea money is the killer app, but also the robots will need to agree with each other
and i think it will be useful for that
dan.06-Aug-19 09:31 PM
kinda funny how that sentiment has come full circle
washparkdlt06-Aug-19 09:31 PM
Right?
nocturnal06-Aug-19 09:32 PM
woot i want a nice btc dump rn
i shorted at 11.9k
finger crossed
dan.06-Aug-19 09:33 PM
nocturnal06-Aug-19 09:33 PM
will be really interesting to see where the next support is
could be 9k or could stay higher which would be quite bullish
mrtzmp306-Aug-19 09:34 PM
We going 7 k
But not in one candle
:)
They will milk it every step of the way
nocturnal06-Aug-19 09:35 PM
haha i dont reckon for a while
dan.06-Aug-19 09:35 PM
if theres any sense to be made, i think btc is going to be range bound for a while, but i dont know
nocturnal06-Aug-19 09:35 PM
yea exactly descending triangle around 9k base for a while
Lumbi06-Aug-19 09:35 PM
USD is tanking vs. EUR.. interest rates vs. BTC hedge
nocturnal06-Aug-19 09:36 PM
then drop down for peeps to fill their bags again
dan.06-Aug-19 09:39 PM
i would pay a pretty decent fee if there was a managed portfolio available for retail. just buy a weighted crypto 100 and forget it(edited)
There is one risk we all share that it can help with too potentially
dan.06-Aug-19 09:49 PM
in terms of crypto a big problem is the single stock risk, cause people are so emotionally involved with 1 project
a spread out fund would help solve the single stock risk
washparkdlt06-Aug-19 09:49 PM
Sure. But I mean another risk. Longevity risk
Crypto might be able to help
Lumbi06-Aug-19 09:50 PM
what metrics would be best for an index? market cap?
washparkdlt06-Aug-19 09:50 PM
Market cap or equal weight
I’d say market cap with btc
Equal weight without as completion
nocturnal06-Aug-19 09:50 PM
you mean giving your money for someone else to trade?
dan.06-Aug-19 09:50 PM
good question, i would choose weighted by market caps
na basically an ETF, but itll never happen
at least not anytime soon
washparkdlt06-Aug-19 09:51 PM
It’s a good idea tho
I think
nocturnal06-Aug-19 09:51 PM
ah i dont know how that works
washparkdlt06-Aug-19 09:52 PM
It would have to be custody or smart contract I guess
dan.06-Aug-19 09:52 PM
the Spy 500 is the most common ETF for stocks. It basically just automatically buys the largest 500 corporations at any one time.
something similar to that
and i believe it does it by weight
washparkdlt06-Aug-19 09:53 PM
They do both
dan.06-Aug-19 09:53 PM
Ah alright
washparkdlt06-Aug-19 09:53 PM
Market weighted and equal
nocturnal06-Aug-19 09:53 PM
thing is in crypto that would be whatever was pumping and about to dump
Lumbi06-Aug-19 09:53 PM
perhaps a AI or machine learning component?
dan.06-Aug-19 09:54 PM
that would be ok tho. thats the point of an ETF is to subside that single stock risk. it would definitely still hurt though
it would definitely be very volatile
but, if the overall market grows, it would do good
nocturnal06-Aug-19 09:54 PM
yeah i feel you may as well just buy bitcoin
washparkdlt06-Aug-19 09:54 PM
Crypto could replicate a pension fund also
Replace insurance company with a DAO
dan.06-Aug-19 09:55 PM
thats true, people say btc = spy500
nocturnal06-Aug-19 09:55 PM
yeah for now
dan.06-Aug-19 09:55 PM
btc is what they call the market rate of return
anything beating btc outperforms
nocturnal06-Aug-19 09:55 PM
maybe in the future etf type thing will work
dan.06-Aug-19 09:56 PM
but, i would be interested in it, because i can cost average in and check it in 20 years.
nocturnal06-Aug-19 09:56 PM
sure
dan.06-Aug-19 09:57 PM
theres too many issues with custodianship and stuff for it to happen anytime soon
nocturnal06-Aug-19 09:57 PM
yeah big problems
dan.06-Aug-19 09:57 PM
and for a company to manage this as an ETF, wont happen anytime soon
SEC wont even let a BTC ETF happen(edited)
nocturnal06-Aug-19 09:57 PM
and to perform well in crypto is a lot more complicated and evolving at the moment
you would have to swing between btc and top alts at the right times
get in and out of crypto to fiat
but then all these dynamics will change as the market develops
youd be better off paying good traders to do it for you but then they can just say "sorry i effed up and lost it all!"(edited)
dan.06-Aug-19 09:59 PM
yeah thats risky too
mrtzmp306-Aug-19 10:05 PM
Cordier — who is believed to have managed $150 million, according to a Bloomberg report — went on to address some of his 290 clients personally.
He thanked one client in Kansas City for barbecue sauce and told another that he owes them a Cuban sandwich
A Florida-based hedge fund manager is tearfully apologizing to his 290 clients after losing their money. In a video made public earlier this week, James Cordier of Optionsellers.com blamed recent v…
@nocturnal
Hard to watch video really
nocturnal06-Aug-19 10:07 PM
ouch!
yeah a dude in china was 100x leveraging and lost like 200btc in an hour or something crazy. he didnt make it!(edited)


WITNESS LIST Appropriations Committee (Subcommittee on Welfare and Workforce Reform) July 1, 1998-9:30A Welfare and Workforce Reform On: Adams, Joe (TWC) Bates, Janie (Texoma Workforce Dev Bd) Cigainero, Barbara (TWC) Ellison, Burt (TWC) Fuller, John H. (TWC) Kamas, Susan (Central TX WDB) Martinez, Juan R. (Middle Rio Grande WDB) McDonald, Judy (Tarrant Co WDB) Rath, Diane (TWC) Sheridan, Mike (TWC) Temple, Larry (TWC) Valdez, Nickie G. (TWC) Wilkinson Jr., Tom (Brazos Valley WDB)


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FriendlyId
FriendlyId is the "Swiss Army bulldozer" of slugging and permalink plugins for Ruby on Rails. It allows you to create pretty URL's and work with human-friendly strings as if they were numeric ids for Active Record models.
Using FriendlyId, it's easy to make your application use URL's like:
http://example.com/states/washington
instead of:
http://example.com/states/4323454
FriendlyId Features
FriendlyId offers many advanced features, including: slug history and versioning, scoped slugs, reserved words, custom slug generators, and excellent Unicode support. For complete information on using FriendlyId, please see the FriendlyId Guide
Compatibility
FriendlyId 3.3.0 is compatible with Active Record 3.0, 3.1 and 3.2.
If you are still on Rails 2.3, please use FriendlyId 3.2.x.
Roadmap
FriendlyId 3.3 is now in long term maintenance mode. It will continue to be supported and maintained indefinitely, but no new features will be added to it.
FriendlyId 4.0 is a ground-up rewrite of FriendlyId, and is the project's future, and will be released by September, 2011.
Docs, Info and Support
Rails Quickstart
gem install friendly_id
rails new my_app
cd my_app
# add to Gemfile
gem "friendly_id", "~> 3.3.0"
rails generate friendly_id
rails generate scaffold user name:string cached_slug:string
rake db:migrate
# edit app/models/user.rb
class User < ActiveRecord::Base
has_friendly_id :name, :use_slug => true
end
User.create! :name => "Joe Schmoe"
rails server
GET http://<IP_ADDRESS>:3000/users/joe-schmoe
Sequel and DataMapper, too
Alex Coles maintains an implemntation of FriendlyId for DataMapper that supports almost all the features of the Active Record version.
Norman Clarke maintains an implementation of FriendlyId forSequel with some of the features of the Active Record version.
Bugs
Please report them on the Github issue tracker for this project.
If you have a bug to report, please include the following information:
- Version information for FriendlyId, Rails and Ruby.
- Stack trace and error message.
- Any snippets of relevant model, view or controller code that shows how your are using FriendlyId.
If you are able to, it helps even more if you can fork FriendlyId on Github, and add a test that reproduces the error you are experiencing.
Credits
FriendlyId was created by Norman Clarke, Adrian Mugnolo, and Emilio Tagua.
If you like FriendlyId, please recommend us on Working With Rails:
Thanks!
Copyright (c) 2008-2010, released under the MIT license.


--- layout: post title: My first SoCraTes description: "Unconference" modified: 2015-06-08 tags: [] image: feature: make-your-own-path.jpeg comments: false ---
I was first told about how amazing SoCraTes unconference is by Chris about a year ago. Ever since I have been wanting to go. I finally made it! The whole of last weekend I spent in the middle of Cotswolds learning hard and playing hard with developers from allover Europe.
For those, who don't know what unconference is, let me quickly explain. Another name fot it is open space. Each day any of the attendees propose topics, set time and place and add it to the schedule. You end up with a full schedule and get to pick which of those sessions you want to attend. Some sessions are hands-on workshops, some are demos, others can be more of discussion format. Schedule gets full very quickly, most of the time you are torn because there are some many interesting sessions to go to and some happen at the same time.
Unconference tends to be smaller than regular conferences. It is usually under a hundred people, which makes it more intimate and this is one of the reasons why it is so amazing. You get to know people closer, because you have more time to spend with everyone. Plus all attendees stay at the same hotel in the middle of nowhere, and there are no distractions. You have meals together, you go to sessions/talks, and in the evenings everyone huddles together around the bar.
In case you are wondering that there probably aren't enough sessions at the event like that, here's a list of some of the sessions we had:
Mateu, Sam and myself ran a session on Extreme Learning. It was based around the same exercise I mention in this post. We had a really good feedback from all the participants, and some said they will start using this practice.
Some of the sessions that really stood out for me were on Docker and Impact mapping. There was also a session on music modelling which I didn't get to attend due to clash but it sounded like a lot of fun.
Completely new to me terms were: event sourcing, currying, microservices vs Service Oriented Architecture. I have learnt loads but I also met a lot of people from London Software Craftsmanship Community and planning to stay in touch with them.
It is very common for SoCraTes attendees to organise other activities. This year some attendees were going for a run in the mornings, someone held a Tai Chi session. I did yoga in the mornings and some people joined me, which was great! Couple of people brought their guitars and played them in the evening.
After a long day of learning people would have dinner and gather in the bar area. But very few were just having drinks, most people were either playing various board games, engaging in discussions at the bar or hacking away at something. I have been introduced to the game of Set, Warewolf and Avalon. I actually got completely hooked on Set. On the last day after breakfast we all went for a long walk across fields and through the local town to get the taste of local ice cream. The whole event felt more like a summer camp, to be honest, and it was really sad to say goodbye to all these lovely people.
Overall, I had a blast and I am really proud that 8th Light were among the sponsors. I am already looking forward to the next one.


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Cracking India Earth
By Ross Anthony
"Earth" tells (in Hindi mixed with English) the story of the 1947 partitioning of India as seen through the eyes of a little girl named Lenny.
Painting in a yellowish stroke the passion of life, love and death; the film is accented with romance and courtship amidst tragic religion-based massacring.
Though this particular dispute between Eastern religious factions occurred half a century ago on the other side of the planet, it certainly colors an all too familiar picture. Not short on cute proverbs and parables, the film itself, teaches the saddest of lessons.
Slightly on the over-dramatic side (acting and camera direction), much of the spoken English may be difficult to decipher. But, the pace is strong, images warm and deep, and story powerful.
"Earth" is the second film in director Mehta's trilogy: Fire, Earth and Water.
Earth. Based on "Cracking India" by Bapsi Sidhwa. Not Rated. Starring Aamir Khan, Nandita Das, Maia Sethna, Rahul Kjanna, Kitu Gidwani. Director/Screenwriter Deepa Mehta Produced by Deepa Mehta at Cracking Earth. Released by Zeitgeist.
Grade..........................B+
-- Copyright © 1999 Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
Little Heroes
|
replacement for the Unity Rect class that is TouchKit and resolution-aware. More...
Public Member Functions | |
| TKRect (float x, float y, float width, float height) | |
| TKRect (float width, float height, Vector2 center) | |
| TKRect | copyWithExpansion (float allSidesExpansion) |
| TKRect | copyWithExpansion (float xExpansion, float yExpansion) |
| bool | contains (Vector2 point) |
| override string | ToString () |
Public Attributes | |
| float | x |
| float | y |
| float | width |
| float | height |
Properties | |
| float xMin | get |
| float | xMax [get] |
| float | yMin [get] |
| float | yMax [get] |
| Vector2 | center [get] |
replacement for the Unity Rect class that is TouchKit and resolution-aware.
Creating one will automatically scale all values if TouchKit autoScaleRectsAndDistances is true based on your designTimeResolution.
Note the TKRects use the bottom-left as the origin.
| TKRect.TKRect | ( | float | x, |
| float | y, | ||
| float | width, | ||
| float | height | ||
| ) |
| TKRect.TKRect | ( | float | width, |
| float | height, | ||
| Vector2 | center | ||
| ) |
| bool TKRect.contains | ( | Vector2 | point | ) |
| TKRect TKRect.copyWithExpansion | ( | float | allSidesExpansion | ) |
| TKRect TKRect.copyWithExpansion | ( | float | xExpansion, |
| float | yExpansion | ||
| ) |
| override string TKRect.ToString | ( | ) |
| float TKRect.height |
| float TKRect.width |
| float TKRect.x |
| float TKRect.y |
|
get |
| float xMin TKRect.get |
|
get |
|
get |
|
get |


TEST MODE - Use test card numbers.
No dogs.
Full terms, cancellation policy, etc.
By clicking next you accept these terms.


dtdobject
Class DtdElement
java.lang.Object
|
+--dtdobject.DtdElement
- public class DtdElement
- extends java.lang.Object
|
Constructor Summary |
DtdElement(java.lang.String name,
int type)
DtdElement()
This is the only constructor for DtdElement. |
|
Method Summary |
java.lang.String |
dtdToText()
dtdToText()
Convert a virtual DTD specfication given by this object into a String
that can be printed in a window, file, or other such output gizmo. |
DtdGroupingElement[] |
getGroupingElements()
getGroupingElements()
Returns an array of DtdGroupingElements for analysis by another object. |
java.lang.String |
getName()
getName()
Returns the name of the DTD element. |
int |
getType()
getType()
Returns the type of the DtdElement, which is either a CONTAINER_ELEMENT
or a VALUE_ELEMENT. |
void |
insertGroup(java.lang.String name,
int occurrence)
insertGroup(String name, int occurrence)
Inserts a subgroup into a container element. |
| Methods inherited from class java.lang.Object |
, clone, equals, finalize, getClass, hashCode, notify, notifyAll, registerNatives, toString, wait, wait, wait |
elementName
protected java.lang.String elementName
elementType
protected int elementType
elementGroup
protected BasicArray elementGroup
DtdElement
public DtdElement(java.lang.String name,
int type)
- DtdElement()
This is the only constructor for DtdElement. The constructor sets up
the BasicAray in which the elements are defined, sets the name of the
DTD element, and specify the type of the DTD element.
#parameter: name
The name of the element to be created in the DTD virtual
representation
#parameter: type
The kind of DTD element being created. Takes the constants
(CONTAINER_ELEMENT | VALUE_ELEMENT) as defined in DtdObject.
getName
public java.lang.String getName()
- getName()
Returns the name of the DTD element.
#returns: String
getType
public int getType()
- getType()
Returns the type of the DtdElement, which is either a CONTAINER_ELEMENT
or a VALUE_ELEMENT.
#returns: int
insertGroup
public void insertGroup(java.lang.String name,
int occurrence)
- insertGroup(String name, int occurrence)
Inserts a subgroup into a container element. Each subgroup also has some
occurrence specification given by the constants MAY and MUST which indicates
if an object may appear 0 or more times or must appear at least once.
#parameter: name
the name of the new subgroup to be inserted.
#parameter: occurrence
MAY -> the subgroup appears 0 to many times.
MUST -> the subgroup appears at least once.
#returns: void
getGroupingElements
public DtdGroupingElement[] getGroupingElements()
- getGroupingElements()
Returns an array of DtdGroupingElements for analysis by another object.
#returns: DtdGroupingElement[]
dtdToText
public java.lang.String dtdToText()
- dtdToText()
Convert a virtual DTD specfication given by this object into a String
that can be printed in a window, file, or other such output gizmo.
#returns: String


Baiturrahman Great Mosque is a mosque of Sultanate of Aceh built by Sultan Iskandar Muda Mahkota Alam in 1022 H / 1612 AD Beautiful and magnificent buildings similar to the Taj Mahal in India is located right in the heart of Banda Aceh and the center of all activities in Aceh Darussalam.
When the Kingdom of the Netherlands attacked the Sultanate of Aceh on the second Dutch army aggression on Shafar Moon 1290 Hijri / 10 April 1873 AD, Baiturrahman Great Mosque was burned. Then, in 1877 the Dutch rebuilt the Baiturrahman Great Mosque to attract the attention and dampen the anger of the Acehnese. At that time the Sultanate of Aceh was still under the rule of Sultan Muhammad Daud Syah Johan Sovereign who was the last Sultan of Aceh.
As a historic place that has high artistic value, the Baiturrahman Grand Mosque becomes a religious tourism object that is able to make every tourist who come chuckle awe at the history and beauty of architecture, where Baiturrahman Great Mosque is one of the most beautiful mosque in Indonesia that has stunning architecture, carving an attractive, spacious courtyard with a Turkish-Ottoman Ottoman Turkish bath pool and will be very cool when inside this mosque.


Wiki ▸ [[API Reference]] ▸ [[Scales]] ▸ Quantitative Scales
| Scales are functions that map from an input domain to an output range. Quantitative scales have a continuous domain, such as the set of real numbers, or dates. There are also [[ordinal scales | Ordinal-Scales]], which have a discrete domain, such as a set of names or categories.  However, using scales can greatly simplify the code needed to map a dimension of data to a visual representation. |
A scale object, such as that returned by d3.scale.linear, is both an object and a function. That is: you can call the scale like any other function, and the scale has additional methods that change its behavior. Like other classes in D3, scales follow the method chaining pattern where setter methods return the scale itself, allowing multiple setters to be invoked in a concise statement.
Linear scales are the most common scale, and a good default choice to map a continuous input domain to a continuous output range. The mapping is linear in that the output range value y can be expressed as a linear function of the input domain value x: y = mx + b. The input domain is typically a dimension of the data that you want to visualize, such as the height of students (measured in meters) in a sample population. The output range is typically a dimension of the desired output visualization, such as the height of bars (measured in pixels) in a histogram.
# d3.scale.linear()
Constructs a new linear scale with the default domain [0,1] and the default range [0,1]. Thus, the default linear scale is equivalent to the identity function for numbers; for example linear(0.5) returns 0.5.
# linear(x)
Given a value x in the input domain, returns the corresponding value in the output range.
Note: some interpolators reuse return values. For example, if the domain values are arbitrary objects, then d3.interpolateObject is automatically applied and the scale reuses the returned object. Often, the return value of a scale is immediately used to set an attribute or style, and you don’t have to worry about this; however, if you need to store the scale’s return value, use string coercion or create a copy as appropriate.
# linear.invert(y)
Returns the value in the input domain x for the corresponding value in the output range y. This represents the inverse mapping from range to domain. For a valid value y in the output range, linear(linear.invert(y)) equals y; similarly, for a valid value x in the input domain, linear.invert(linear(x)) equals x. Equivalently, you can construct the invert operator by building a new scale while swapping the domain and range. The invert operator is particularly useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
| Note: the invert operator is only supported if the output range is numeric! D3 allows the output range to be any type; under the hood, [[d3.interpolate | Transitions#d3_interpolate]] or a custom interpolator of your choice is used to map the normalized parameter t to a value in the output range. Thus, the output range may be colors, strings, or even arbitrary objects. As there is no facility to “uninterpolate” arbitrary types, the invert operator is currently supported only on numeric ranges. |
# linear.domain([numbers])
| If numbers is specified, sets the scale’s input domain to the specified array of numbers. The array must contain two or more numbers. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. Thus, a linear scale can be used to encode types such as [[date objects | https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Date]] that can be converted to numbers; however, it is often more convenient to use d3.time.scale for dates. (You can implement your own convertible number objects using [[valueOf | https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Object/valueOf]].) If numbers is not specified, returns the scale’s current input domain. |
Although linear scales typically have just two numeric values in their domain, you can specify more than two values for a polylinear scale. In this case, there must be an equivalent number of values in the output range. A polylinear scale represents multiple piecewise linear scales that divide a continuous domain and range. This is particularly useful for defining diverging quantitative scales. For example, to interpolate between white and red for negative values, and white and green for positive values, say:
javascript
var color = d3.scale.linear()
.domain([-1, 0, 1])
.range(["red", "white", "green"]);
The resulting value of color(-.5) is rgb(255, 128, 128), and the value of color(.5) is rgb(128, 192, 128). Internally, polylinear scales perform a binary search for the output interpolator corresponding to the given domain value. By repeating values in both the domain and range, you can also force a chunk of the input domain to map to a constant in the output range.
# linear.range([values])
If values is specified, sets the scale’s output range to the specified array of values. The array must contain two or more values, to match the cardinality of the input domain, otherwise the longer of the two is truncated to match the other. The elements in the given array need not be numbers; any value that is supported by the underlying interpolator will work. However, numeric ranges are required for the invert operator. If values is not specified, returns the scale’s current output range.
# linear.rangeRound(values)
| Sets the scale’s output range to the specified array of values, while also setting the scale’s interpolator to [[d3.interpolateRound | Transitions#d3_interpolateRound]]. This is a convenience routine for when the values output by the scale should be exact integers, such as to avoid antialiasing artifacts. It is also possible to round the output values manually after the scale is applied. |
# linear.interpolate([factory])
| If factory is specified, sets the scale’s output interpolator using the specified factory. The interpolator factory defaults to [[d3.interpolate | Transitions#d3_interpolate]], and is used to map the normalized domain parameter t in [0,1] to the corresponding value in the output range. The interpolator factory will be used to construct interpolators for each adjacent pair of values from the output range. If factory is not specified, returns the scale’s interpolator factory. |
# linear.clamp([boolean])
If boolean is specified, enables or disables clamping accordingly. By default, clamping is disabled, such that if a value outside the input domain is passed to the scale, the scale may return a value outside the output range through linear extrapolation. For example, with the default domain and range of [0,1], an input value of 2 will return an output value of 2. If clamping is enabled, the normalized domain parameter t is clamped to the range [0,1], such that the return value of the scale is always within the scale’s output range. If boolean is not specified, returns whether or not the scale currently clamps values to within the output range.
# linear.nice([count])
Extends the domain so that it starts and ends on nice round values. This method typically modifies the scale’s domain, and may only extend the bounds to the nearest round value. The precision of the round value is dependent on the extent of the domain dx according to the following formula: exp(round(log(dx)) - 1). Nicing is useful if the domain is computed from data and may be irregular. For example, for a domain of [0.20147987687960267, 0.996679553296417], the nice domain is [0.2, 1]. If the domain has more than two values, nicing the domain only affects the first and last value.
The optional tick count argument allows greater control over the step size used to extend the bounds, guaranteeing that the returned ticks will exactly cover the domain.
# linear.ticks([count])
Returns approximately count representative values from the scale’s input domain. If count is not specified, it defaults to 10. The returned tick values are uniformly spaced, have human-readable values (such as multiples of powers of 10), and are guaranteed to be within the extent of the input domain. Ticks are often used to display reference lines, or tick marks, in conjunction with the visualized data. The specified count is only a hint; the scale may return more or fewer values depending on the input domain.
# linear.tickFormat(count, [format])
| Returns a [[number format | Formatting#d3_format]] function suitable for displaying a tick value. The specified count should have the same value as the count that is used to generate the tick values. You don’t have to use the scale’s built-in tick format, but it automatically computes the appropriate precision based on the fixed interval between tick values. |
| The optional format argument allows a [[format specifier | Formatting#d3_format]] to be specified, where the precision of the format is automatically substituted by the scale to be appropriate for the tick interval. For example, to format percentage change, you might say: |
js
var x = d3.scale.linear().domain([-1, 1]);
console.log(x.ticks(5).map(x.tickFormat(5, "+%"))); // ["-100%", "-50%", "+0%", "+50%", "+100%"]
Likewise, if format uses the format type s
, the scale will compute a SI-prefix based on the largest value in the domain, and use that SI-prefix for all tick values. If the format already specifies a precision, this method is equivalent to d3.format.
Note that when using a log scale in conjunction with an axis, you typically want to use axis.ticks rather than axis.tickFormat to take advantage of the log scale’s custom tick format, as in bl.ocks.org/5537697.
# linear.copy()
Returns an exact copy of this linear scale. Changes to this scale will not affect the returned scale, and vice versa.
Identity scales are a special case of linear scales where the domain and range are identical; the scale and its invert method are both the identity function. These scales are occasionally useful when working with pixel coordinates, say in conjunction with the axis and brush components.
# d3.scale.identity()
Constructs a new identity scale with the default domain [0, 1] and the default range [0, 1]. An identity scale is always equivalent to the identity function.
# identity(x)
# identity.invert(x)
Returns the given value x.
# identity.domain([numbers])
# identity.range([numbers])
If numbers is specified, sets the scale’s input domain and output range to the specified array of numbers. The array must contain two or more numbers. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. If numbers is not specified, returns the scale’s current input domain (or equivalently, output range).
# identity.ticks([count])
Returns approximately count representative values from the scale’s input domain (or equivalently, output range). If count is not specified, it defaults to 10. The returned tick values are uniformly spaced, have human-readable values (such as multiples of powers of 10), and are guaranteed to be within the extent of the input domain. Ticks are often used to display reference lines, or tick marks, in conjunction with the visualized data. The specified count is only a hint; the scale may return more or fewer values depending on the input domain.
# identity.tickFormat(count, [format])
Returns a number format function suitable for displaying a tick value. The specified count should have the same value as the count that is used to generate the tick values. You don’t have to use the scale’s built-in tick format, but it automatically computes the appropriate precision based on the fixed interval between tick values.
The optional format argument allows a [[format specifier|Formatting#d3_format]] to be specified. If the format specifier doesn’t have a defined precision, the precision will be set automatically by the scale, returning the appropriate format. This provides a convenient, declarative way of specifying a format whose precision will be automatically set by the scale.
# identity.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.
Power scales are similar to linear scales, except there’s an exponential transform that is applied to the input domain value before the output range value is computed. The mapping to the output range value y can be expressed as a function of the input domain value x: y = mx^k + b, where k is the exponent value. Power scales also support negative values, in which case the input value is multiplied by -1, and the resulting output value is also multiplied by -1.
# d3.scale.sqrt()
Constructs a new power scale with the default domain [0,1], the default range [0,1], and the exponent .5. This method is shorthand for:
javascript
d3.scale.pow().exponent(.5)
| The returned scale is a function that takes a single argument x representing a value in the input domain; the return value is the corresponding value in the output range. Thus, the returned scale is equivalent to the [[sqrt | https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Math/sqrt]] function for numbers; for example sqrt(0.25) returns 0.5. |
# d3.scale.pow()
Constructs a new power scale with the default domain [0,1], the default range [0,1], and the default exponent 1. Thus, the default power scale is equivalent to the identity function for numbers; for example pow(0.5) returns 0.5.
# pow(x)
Given a value x in the input domain, returns the corresponding value in the output range.
Note: some interpolators reuse return values. For example, if the domain values are arbitrary objects, then d3.interpolateObject is automatically applied and the scale reuses the returned object. Often, the return value of a scale is immediately used to set an attribute or style, and you don’t have to worry about this; however, if you need to store the scale’s return value, use string coercion or create a copy as appropriate.
# pow.invert(y)
Returns the value in the input domain x for the corresponding value in the output range y. This represents the inverse mapping from range to domain. For a valid value y in the output range, pow(pow.invert(y)) equals y; similarly, for a valid value x in the input domain, pow.invert(pow(x)) equals x. Equivalently, you can construct the invert operator by building a new scale while swapping the domain and range. The invert operator is particularly useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
| Note: the invert operator is only supported if the output range is numeric! D3 allows the output range to be any type; under the hood, [[d3.interpolate | Transitions#d3_interpolate]] or a custom interpolator of your choice is used to map the normalized parameter t to a value in the output range. Thus, the output range may be colors, strings, or even arbitrary objects. As there is no facility to “uninterpolate” arbitrary types, the invert operator is currently supported only on numeric ranges. |
# pow.domain([numbers])
If numbers is specified, sets the scale’s input domain to the specified array of numbers. The array must contain two or more numbers. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. Thus, a power scale can be used to encode any type that can be converted to numbers. If numbers is not specified, returns the scale’s current input domain.
As with linear scales (see linear.domain), power scales can also accept more than two values for the domain and range, thus resulting in polypower scale.
# pow.range([values])
If values is specified, sets the scale’s output range to the specified array of values. The array must contain two or more values, to match the cardinality of the input domain, otherwise the longer of the two is truncated to match the other. The elements in the given array need not be numbers; any value that is supported by the underlying interpolator will work. However, numeric ranges are required for the invert operator. If values is not specified, returns the scale’s current output range.
# pow.rangeRound(values)
| Sets the scale’s output range to the specified array of values, while also setting the scale’s interpolator to [[d3.interpolateRound | Transitions#d3_interpolateRound]]. This is a convenience routine for when the values output by the scale should be exact integers, such as to avoid antialiasing artifacts. It is also possible to round the output values manually after the scale is applied. |
# pow.exponent([k])
If k is specified, sets the current exponent to the given numeric value. If k is not specified, returns the current exponent. The default value is 1.
# pow.interpolate([factory])
| If factory is specified, sets the scale’s output interpolator using the specified factory. The interpolator factory defaults to [[d3.interpolate | Transitions#d3_interpolate]], and is used to map the normalized domain parameter t in [0,1] to the corresponding value in the output range. The interpolator factory will be used to construct interpolators for each adjacent pair of values from the output range. If factory is not specified, returns the scale’s interpolator factory. |
# pow.clamp([boolean])
If boolean is specified, enables or disables clamping accordingly. By default, clamping is disabled, such that if a value outside the input domain is passed to the scale, the scale may return a value outside the output range through linear extrapolation. For example, with the default domain and range of [0,1], an input value of 2 will return an output value of 2. If clamping is enabled, the normalized domain parameter t is clamped to the range [0,1], such that the return value of the scale is always within the scale’s output range. If boolean is not specified, returns whether or not the scale currently clamps values to within the output range.
# pow.nice([m])
Extends the domain so that it starts and ends on nice round values. This method typically modifies the scale’s domain, and may only extend the bounds to the nearest round value. The precision of the round value is dependent on the extent of the domain dx according to the following formula: exp(round(log(dx)) - 1). Nicing is useful if the domain is computed from data and may be irregular. For example, for a domain of [0.20147987687960267, 0.996679553296417], the nice domain is [0.2, 1]. If the domain has more than two values, nicing the domain only affects the first and last value.
The optional m argument allows a tick count to be specified to control the step size used prior to extending the bounds.
# pow.ticks([count])
Returns approximately count representative values from the scale’s input domain. If count is not specified, it defaults to 10. The returned tick values are uniformly spaced, have human-readable values (such as multiples of powers of 10), and are guaranteed to be within the extent of the input domain. Ticks are often used to display reference lines, or tick marks, in conjunction with the visualized data. The specified count is only a hint; the scale may return more or fewer values depending on the input domain.
# pow.tickFormat([count, [format]])
| Returns a [[number format | Formatting#d3_format]] function suitable for displaying a tick value. The specified count should have the same value as the count that is used to generate the tick values. You don’t have to use the scale’s built-in tick format, but it automatically computes the appropriate precision based on the fixed interval between tick values. |
The optional format argument allows a [[format specifier|Formatting#d3_format]] to be specified. If the format specifier doesn’t have a defined precision, the precision will be set automatically by the scale, returning the appropriate format. This provides a convenient, declarative way of specifying a format whose precision will be automatically set by the scale.
# pow.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.
Log scales are similar to linear scales, except there’s a logarithmic transform that is applied to the input domain value before the output range value is computed. The mapping to the output range value y can be expressed as a function of the input domain value x: y = m log(x) + b.
As log(0) is negative infinity, a log scale must have either an exclusively-positive or exclusively-negative domain; the domain must not include or cross zero. A log scale with a positive domain has a well-defined behavior for positive values, and a log scale with a negative domain has a well-defined behavior for negative values (the input value is multiplied by -1, and the resulting output value is also multiplied by -1). The behavior of the scale is undefined if you pass a negative value to a log scale with a positive domain or vice versa.
# d3.scale.log()
Constructs a new log scale with the default domain [1,10], the default range [0,1], and the base 10.
# log(x)
Given a value x in the input domain, returns the corresponding value in the output range.
Note: some interpolators reuse return values. For example, if the domain values are arbitrary objects, then d3.interpolateObject is automatically applied and the scale reuses the returned object. Often, the return value of a scale is immediately used to set an attribute or style, and you don’t have to worry about this; however, if you need to store the scale’s return value, use string coercion or create a copy as appropriate.
# log.invert(y)
Returns the value in the input domain x for the corresponding value in the output range y. This represents the inverse mapping from range to domain. For a valid value y in the output range, log(log.invert(y)) equals y; similarly, for a valid value x in the input domain, log.invert(log(x)) equals x. Equivalently, you can construct the invert operator by building a new scale while swapping the domain and range. The invert operator is particularly useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
| Note: the invert operator is only supported if the output range is numeric! D3 allows the output range to be any type; under the hood, [[d3.interpolate | Transitions#d3_interpolate]] or a custom interpolator of your choice is used to map the normalized parameter t to a value in the output range. Thus, the output range may be colors, strings, or even arbitrary objects. As there is no facility to “uninterpolate” arbitrary types, the invert operator is currently supported only on numeric ranges. |
# log.domain([numbers])
If numbers is specified, sets the scale’s input domain to the specified array of numbers. The array must contain two or more numbers. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. Thus, a log scale can be used to encode any type that can be converted to numbers. If numbers is not specified, returns the scale’s current input domain.
As with linear scales (see linear.domain), log scales can also accept more than two values for the domain and range, thus resulting in polylog scale.
# log.range([values])
If values is specified, sets the scale’s output range to the specified array of values. The array must contain two or more values, to match the cardinality of the input domain, otherwise the longer of the two is truncated to match the other. The elements in the given array need not be numbers; any value that is supported by the underlying interpolator will work. However, numeric ranges are required for the invert operator. If values is not specified, returns the scale’s current output range.
# log.rangeRound(values)
| Sets the scale’s output range to the specified array of values, while also setting the scale’s interpolator to [[d3.interpolateRound | Transitions#d3_interpolateRound]]. This is a convenience routine for when the values output by the scale should be exact integers, such as to avoid antialiasing artifacts. It is also possible to round the output values manually after the scale is applied. |
# log.base([base])
If base is specified, sets the base for this logarithmic scale. If base is not specified, returns the current base, which defaults to 10.
# log.interpolate([factory])
| If factory is specified, sets the scale’s output interpolator using the specified factory. The interpolator factory defaults to [[d3.interpolate | Transitions#d3_interpolate]], and is used to map the normalized domain parameter t in [0,1] to the corresponding value in the output range. The interpolator factory will be used to construct interpolators for each adjacent pair of values from the output range. If factory is not specified, returns the scale’s interpolator factory. |
# log.clamp([boolean])
If boolean is specified, enables or disables clamping accordingly. By default, clamping is disabled, such that if a value outside the input domain is passed to the scale, the scale may return a value outside the output range through linear extrapolation. For example, with the default domain and range of [0,1], an input value of 2 will return an output value of 2. If clamping is enabled, the normalized domain parameter t is clamped to the range [0,1], such that the return value of the scale is always within the scale’s output range. If boolean is not specified, returns whether or not the scale currently clamps values to within the output range.
# log.nice()
Extends the domain so that it starts and ends on nice round values. This method typically modifies the scale’s domain, and may only extend the bounds to the nearest round value. The nearest round value is based on integer powers of the scale’s base, which defaults to 10. Nicing is useful if the domain is computed from data and may be irregular. For example, for a domain of [0.20147987687960267, 0.996679553296417], the nice domain is [0.1, 1]. If the domain has more than two values, nicing the domain only affects the first and last value.
# log.ticks()
Returns representative values from the scale’s input domain. The returned tick values are uniformly spaced within each power of ten, and are guaranteed to be within the extent of the input domain. Ticks are often used to display reference lines, or tick marks, in conjunction with the visualized data. Note that the number of ticks cannot be customized (due to the nature of log scales); however, you can filter the returned array of values if you want to reduce the number of ticks.
# log.tickFormat([count, [format]])
| Returns a [[number format | Formatting#d3_format]] function suitable for displaying a tick value. The returned tick format is implemented as d.toPrecision(1) . If a count is specified, then some of the tick labels may not be displayed; this is useful if there is not enough room to fit all of the tick labels. However, note that the tick marks will still be displayed (so that the log scale distortion remains visible). When specifying a count, you may also override the format function; you can also specify a format specifier as a string, and it will automatically be wrapped with d3.format. For example, to get a tick formatter that will display 20 ticks of a currency: |
js
scale.tickFormat(20, "$,.2f");
If the format specifier doesn’t have a defined precision, the precision will be set automatically by the scale, returning the appropriate format. This provides a convenient, declarative way of specifying a format whose precision will be automatically set by the scale.
# log.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.
Quantize scales are a variant of linear scales with a discrete rather than continuous range. The input domain is still continuous, and divided into uniform segments based on the number of values in (the cardinality of) the output range. The mapping is linear in that the output range value y can be expressed as a linear function of the input domain value x: y = mx + b. The input domain is typically a dimension of the data that you want to visualize, such as the height of students (measured in meters) in a sample population. The output range is typically a dimension of the desired output visualization, such as the height of bars (measured in pixels) in a histogram.
# d3.scale.quantize()
| Constructs a new quantize scale with the default domain [0,1] and the default range [0,1]. Thus, the default quantize scale is equivalent to the [[round | https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Math/round]] function for numbers; for example quantize(0.49) returns 0, and quantize(0.51) returns 1. |
javascript
var q = d3.scale.quantize().domain([0, 1]).range(['a', 'b', 'c']);
//q(0.3) === 'a', q(0.4) === 'b', q(0.6) === 'b', q(0.7) ==='c';
//q.invertExtent('a') returns [0, 0.3333333333333333]
# quantize(x)
Given a value x in the input domain, returns the corresponding value in the output range.
# quantize.invertExtent(y)
Returns the extent of values in the input domain [x0, x1] for the corresponding value in the output range y, representing the inverse mapping from range to domain. This method is useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
# quantize.domain([numbers])
If numbers is specified, sets the scale’s input domain to the specified two-element array of numbers. If the array contains more than two numbers, only the first and last number are used. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. Thus, a quantize scale can be used to encode any type that can be converted to numbers. If numbers is not specified, returns the scale’s current input domain.
# quantize.range([values])
If values is specified, sets the scale’s output range to the specified array of values. The array may contain any number of discrete values. The elements in the given array need not be numbers; any value or type will work. If values is not specified, returns the scale’s current output range.
# quantize.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.
| Quantile scales map an input domain to a discrete range. Although the input domain is continuous and the scale will accept any reasonable input value, the input domain is specified as a discrete set of values. The number of values in (the cardinality of) the output range determines the number of quantiles that will be computed from the input domain. To compute the quantiles, the input domain is sorted, and treated as a [[population of discrete values | http://en.wikipedia.org/wiki/Quantile#Quantiles_of_a_population]]. The input domain is typically a dimension of the data that you want to visualize, such as the daily change of the stock market. The output range is typically a dimension of the desired output visualization, such as a diverging color scale. |
# d3.scale.quantile()
Constructs a new quantile scale with an empty domain and an empty range. The quantile scale is invalid until both a domain and range are specified.
# quantile(x)
Given a value x in the input domain, returns the corresponding value in the output range.
# quantile.invertExtent(y)
Returns the extent of values in the input domain [x0, x1] for the corresponding value in the output range y, representing the inverse mapping from range to domain. This method is useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
# quantile.domain([numbers])
If numbers is specified, sets the input domain of the quantile scale to the specified set of discrete numeric values. The array must not be empty, and must contain at least one numeric value; NaN, null and undefined values are ignored and not considered part of the sample population. If the elements in the given array are not numbers, they will be coerced to numbers; this coercion happens similarly when the scale is called. A copy of the input array is sorted and stored internally. Thus, a quantile scale can be used to encode any type that can be converted to numbers. If numbers is not specified, returns the scale’s current input domain.
# quantile.range([values])
If values is specified, sets the discrete values in the output range. The array must not be empty, and may contain any type of value. The number of values in (the cardinality, or length, of) the values array determines the number of quantiles that are computed. For example, to compute quartiles, values must be an array of four elements such as [0, 1, 2, 3]. If values is not specified, returns the current output range.
# quantile.quantiles()
| Returns the quantile thresholds. If the output range contains n discrete values, the returned threshold array will contain n - 1 values. Values less than the first element in the thresholds array, quantiles()[0], are considered in the first quantile; greater values less than the second threshold are in the second quantile, and so on. Internally, the thresholds array is used with [[d3.bisect | Arrays#d3_bisect]] to find the output quantile associated with the given input value. |
# quantile.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.
Threshold scales are similar to quantize scales, except they allow you to map arbitrary subsets of the domain to discrete values in the range. The input domain is still continuous, and divided into slices based on a set of threshold values. The input domain is typically a dimension of the data that you want to visualize, such as the height of students (measured in meters) in a sample population. The output range is typically a dimension of the desired output visualization, such as a set of colors (represented as strings).
# d3.scale.threshold()
| Constructs a new threshold scale with the default domain [.5] and the default range [0,1]. Thus, the default threshold scale is equivalent to the [[round | https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Math/round]] function for numbers; for example threshold(0.49) returns 0, and threshold(0.51) returns 1. |
javascript
var t = d3.scale.threshold().domain([0, 1]).range(['a', 'b', 'c']);
t(-1) === 'a';
t(0) === 'b';
t(0.5) === 'b';
t(1) === 'c';
t(1000) === 'c';
t.invertExtent('a'); //returns [undefined, 0]
t.invertExtent('b'); //returns [0, 1]
t.invertExtent('c'); //returns [1, undefined]
# threshold(x)
Given a value x in the input domain, returns the corresponding value in the output range.
# threshold.invertExtent(y)
Returns the extent of values in the input domain [x0, x1] for the corresponding value in the output range y, representing the inverse mapping from range to domain. This method is useful for interaction, say to determine the value in the input domain that corresponds to the pixel location under the mouse.
# threshold.domain([domain])
If domain is specified, sets the scale’s input domain to the specified array of values. The values must be in sorted ascending order, or the behavior of the scale is undefined. The values are typically numbers, but any naturally ordered values (such as strings) will work. Thus, a threshold scale can be used to encode any type that is ordered. If the number of values in the scale’s range is N + 1, the number of values in the scale’s domain must be N. If there are fewer than N elements in the domain, the additional values in the range are ignored. If there are more than N elements in the domain, the scale may return undefined for some inputs. If domain is not specified, returns the scale’s current input domain.
# threshold.range([values])
If values is specified, sets the scale’s output range to the specified array of values. If the number of values in the scale’s domain is N, the number of values in the scale’s range must be N + 1. If there are fewer than N+1 elements in the range, the scale may return undefined for some inputs. If there are more than N + 1 elements in the range, the additional values are ignored. The elements in the given array need not be numbers; any value or type will work. If values is not specified, returns the scale’s current output range.
# threshold.copy()
Returns an exact copy of this scale. Changes to this scale will not affect the returned scale, and vice versa.