Margins, Borders and Padding are important when it comes to laying out a website. It gives each item that you see on this page space around each other and makes the content on a website more readable, (and in my opinion, more attractive - I definitely do not like a cramped webpage!) Let's breakdown what Margin, Border and Padding mean:
(if you didn't notice, I used a double border around that list!)
The uses for margins, padding and borders come in handy when we want to create space between different elements, section elements away from others or make sure that what you see on screen is in line with each other.


Anegada is the northernmost of the British Virgin Islands. It lies approximately 15 miles north of Virgin Gorda.
It is a flat island, formed from coral and limestone, its highest point being only 28 feet above sea level. It is best known for its miles of white sand beaches, but it also has large salt ponds which cover much of the west end of the island.
A totally unspoiled island and well worth a visit, even if it is just for their famous lobster dinner.
Position 18° 43'21"N, 064° 23'08"W (moorings)
The Anchorage lies between Setting Point and Pomato Point and the entrance through the reef is now well marked and lit. It is however difficult to see the buoys in rough conditions and concentration and care is needed in order to enter safely.
There are overnight mooring buoys available here. Contact the Anegada Reef Hotel or Neptune's Treasure for availability. Alternatively yachts can anchor in hard sand with good holding.
There are several dinghy docks at the various hotels and restaurants.
The Settlement is the main inhabited area on the island and is primarily a fishing village with little in the way of supplies. There is a small Post Office, Dotsy's Bakery, launderette, Government Administration Building, and a medical clinic here.
A walk to the isolated beach at the west end comes recommended, as does a trip to Loblolly Bay, by bike or taxi.
Last updated February 2014.


Why do all lightboxes out there use a gazillion images and options when all I want is to show a larger image when I click a small one?
data-lightbox
to a linked image.Click an image:
git clone git@github.com:Wolfr/jquery.bootstrap.simple.lightbox.git
npm install
bower install jquery
bower install sass-bootstrap
.hide
class)jquery.simple.lightbox.css
includes CSS to make the modal look nice, but this CSS is not required. You can ignore the file and roll your ownjquery.simple.lightbox.js
: the script itself
data-lightbox
to the

for the caption// Invoke our plugin
$('[data-lightbox]').simpleLightbox();
This code was tested in:
| IE7-IE10 | Windows 7 | Issue with portrait image (due to no max-height support) |
| Opera | Mac | Broken - issue with portrait and landscape images |
| Mobile Safari | Mac (simulated) | ✓ |
| Safari | Mac | ✓ |
| Chrome 26 | Mac and Linux | ✓ |
| Firefox 20 | Mac | ✓ |
©2013, Wolf's Little Store - Free to use under the MIT license.


Youth Incarceration in Milwaukee
March 2017
Despite declines in juvenile arrests and incarceration, Wisconsin still holds a considerable number of youth in juvenile correctional facilities, most of whom come from Milwaukee County.[1] In February 2017, well over half (60 percent) of the youth committed to facilities in Wisconsin were from Milwaukee County.[2]
Juvenile Correctional Facilities Have High Costs with Poor Returns
The cost of incarceration for youth in Wisconsin is high; in fiscal year 2014–15, the state spent over $30 million on the operation of its juvenile correctional facilities.[3] The return on that investment is poor: more than half of youth (61 percent) who were released from incarceration in Wisconsin committed a new criminal offense within three years of release, according to the most recent data (DJC 2015, 5).
Three-Year Recidivism Outcomes
DJC youth released from a juvenile correctional facility in 2010
Source: Division of Juvenile Corrections, 2014 Annual Report (Madison: State of Wisconsin Department of Corrections, 2015).
Incarceration Disproportionately Affects Black Youth
The majority of youth committed to Wisconsin state facilities are Black. According to the most recently available data (2013), Black youth comprise only 10 percent of the overall youth population in Milwaukee but 92 percent of those incarcerated.[4]
Milwaukee County Total Youth by Race, 2013
Milwaukee County Incarcerated Youth by Race, 2013
Source: “Unbalanced Juvenile Justice,” W. Haywood Burns Institute for Juvenile Justice Fairness and Equity, http://data.burnsinstitute.org.
In 2013, compared to White youth, Black youth in Milwaukee County were[5]
- 3 times more likely to be arrested,
- 2 times more likely to be detained,
- 9 times more likely to be found delinquent, and
- 3 times more likely to be incarcerated out of home.
While juvenile arrest and detention rates have declined significantly across Milwaukee County, the racial disparity in these measures appears to be widening. Between 2006 and 2013 the arrest and detention rates for Black youth dropped 40.6 percent and 22.3 percent; the arrest and detention rates for their White peers dropped 54.4 percent and 47.6 percent (Lecoanet et al. 2014). From 2006 to 2012, Black youth went from twice as likely as White youth to be arrested for a violent or property crime to three times more likely to be arrested for a violent crime, and four times more likely to be arrested for a property crime (Lecoanet et al. 2014).
Recent Lawsuit
In January 2017, the ACLU filed a federal lawsuit against the two primary juvenile correctional facilities in Wisconsin, Lincoln Hills School for Boys and Copper Lake School for Girls, on behalf of four currently and formerly incarcerated youth. The lawsuit raises major concerns surrounding the safety of these facilities, alleging that guards in the prisons physically abused incarcerated youth, routinely used pepper spray as a punishment tactic, and held as many as 20 percent of the population in solitary confinement for 23 hours a day. This follows an FBI investigation into allegations of abuse at Lincoln Hills that began in 2015 and has yet to result in charges. The ACLU has requested class action status for this suit in order to continue representation on behalf of all 170 currently incarcerated teens in the facility.[6]
Alternative Models Have Shown Success
Between 1999 and 2013, admissions to Wisconsin’s juvenile correctional facilities dropped 75 percent, and that dramatic trend appears to be continuing.[7] Following that decline, there has been a general trend toward alternatives to incarceration for youth. Since 2011, two juvenile correctional facilities have closed, and an alternative education facility (the Grow Academy) has opened (Carmichael 2015, 19, 27). Other states across the country have improved recidivism outcomes while reducing costs and reallocating resources through incarceration alternatives. For example, Missouri has had great success with its growth-focused model, which deviates from the large prisonlike institution model to offer services to youth in smaller settings closer to their homes. New York City has also seen success with its Close-to-Home program, and states and counties across the nation are increasingly moving toward these models as alternatives to juvenile prisons. As incarceration continues to decline in Milwaukee, there is a unique opportunity to invest in alternative models that show promise, working to improve outcomes for all Milwaukee County residents.
#Polls
Poll Results on Youth Justice Reform in Milwaukee County
At a time when partisan polarization dominates the political landscape, a new poll shows Milwaukee County residents of all political stripes support shifting the youth justice system from a focus on incarceration and punishment to prevention and rehabilitation. A strong 65% majority favors this shift, with broad support across partisan affiliations in the state (65% of Democrats, 71% of Independents, and 57% of Republicans). The following are key findings from the survey of 400 adults in Milwaukee County in the state of Wisconsin age 18+: Strong Support for Proposals to Reform Youth Justice System in Milwaukee County The broad support for this shift to reform by Milwaukee County residents is reinforced by support for a number of specific policy proposals aimed at addressing problems with the youth justice system, including the misguided focus on incarceration, racial and ethnic disparities in the system and the shortage of public defenders.
Option 1: Design treatment and rehabilitation plans that include a youth’s family in planning and services.
Option 2: Provide financial incentives for states and municipalities to invest in alternatives to youth incarceration, such as intensive rehabilitation, education, job training, community services, and programs that provide youth the opportunity to repair harm to victims and communities.
Option 3: Require states to reduce racial and ethnic disparities in the youth justice system.
Option 4: Increase funding to provide more public defenders who represent children in court.
References
Carmichael, Christina D. 2015. “Juvenile Justice and Youth Aids Program.” Informational Paper 56. Madison: Wisconsin Legislative Fiscal Bureau.
DJC (Division of Juvenile Corrections). 2015. 2014 Annual Report. Madison: State of Wisconsin Department of Corrections.
Lecoanet, Robin, Daphne Kuo, Stephanie Lindsley, and Sarah Seibold. 2014. “Disproportionate Minority Contact in Wisconsin’s Juvenile Justice System.” Madison: University of Wisconsin Population Health Institute.
[1]Wisconsin has four juvenile correctional facilities: Lincoln Hill School (boys), Copper Lake School (Girls), the Mendota Juvenile Treatment Center (29 bed secure mental health unit for boys), and the Grow Academy (12 bed, 120-day program residential program for boys).
[2] Wisconsin Division of Juvenile Corrections, Corrections at a Glance February 2017, http://doc.wi.gov/Documents/WEB/ABOUT/DATARESEARCH/NOTABLESTATISTICS/At_A_Glance_DJC_Current.pdf
[3] Another $35.6 million was allocated to Milwaukee County through the Youth Aids program, which provides state funding to counties for juvenile justice services, including out-of-home placement (Carmichael 2015, 20).
[4] “Unbalanced Juvenile Justice,” W. Haywood Burns Institute for Juvenile Justice Fairness and Equity, accessed March 13, 2017, http://data.burnsinstitute.org.
[5] “Unbalanced Juvenile Justice,” http://data.burnsinstitute.org.
[6] Todd Richmond, “Inmates Sue, Alleging Inhumane Conditions at Wisconsin’s Youth Prison,” Madison, com, January 24, 2017, http://host.madison.com/news/local/govt-and-politics/inmates-sue-alleging-inhumane-conditions-at-wisconsin-s-youth-prison/article_37c93e2c-b928-598b-9c58-99bd8b1db7d2.html.
[7] “Easy Access to the Census of Juveniles in Residential Placement: 1997–2013,” US Department of Justice, Office of Juvenile Justice and Delinquency Prevention, accessed March 13, 2017, http://www.ojjdp.gov/ojstatbb/ezacjrp/.


These control the amount that the decay time of each {_envelope} generator is modulated by its own instantaneous value. This varies the shape of the decay curve from the usual simple exponential shape. Setting this to a negative value is particularly useful, because it can yield a much faster initial decay, and then only on those higher amplitude envelopes.
These are contained in the {Envelope_decay_cluster Envelope decay clusters} within the {Envelope_sections}.
These are contained in the ENVELOPE 1 and ENVELOPE 2 sections of the right panel, under the following parameter numbers:
| Env 1 | Env 2 | |
| {sw-set-split.png height=48} {sw-16.png height=48} | {sw-set-split.png height=48} {sw-23.png height=48} |
These appear in the display as Env1/2 Dec Self Mod.
These are accessed via the following {_NRPN NRPNs}:
| Env 1 | Env 2 | ||
| A | 1,18 | 1,31 | |
| B | 2,18 | 2,31 |
Since these are {_bipolar} parameters, the values are offset by 64.
| MIDI | display | ||
| 0 | \-100% | ||
| \b\n\b\n\b | \b\n\b\n\b | ||
| 64 | off | (default) | |
| \b\n\b\n\b | \b\n\b\n\b | ||
| 127 | +100% |


App was meticulously designed, and was thourougly thought upon for months together.
It sure as hell will be feature rich!
Fill it a page a day or at a go, all on your convinience
You can also set Reminders to remind you to fill
Your data is encrypted by Cutting edge encryption tech.
I myself, am a big fan of the clean and sleek Material Design.
The App's design has been made in compliance with Google's Material Design guidelines


--- ---
Thanks, we really appreciate your feedback.
If you'd like to get in touch with us, we're always happy to chat: {{ site.contact_email }}


OUTSIDE THE CONTAINER
This paragraph is OUTSIDE the nested-rule
This is an anchor OUTSIDE the nested rule
INSIDE THE CONTAINER
This paragraph is within the nested-rule
This is an anchor within the nested rule


Endangered Aloe simii in jeopardy due to planned development
A critically endangered aloe species is in jeopardy due to the planned development of large portions of a protected nature reserve in Colt’s Hill.
by Landé WillemseAloe simii grows only in a very small natural geographic range; from Sabie southwards to White River and around Mbombela.
Populations are also declining due to the drying out of the aloe’s wetland habitat. This is caused by adjacent plantations and water extraction, alien plant invasion, urban expansion and rural development.
White River Nature Reserve (WRNR) NGO, White River Ratepayers Association (WRRPA) and various other outraged community members raised concerns over the proposed development.
Tiger Business Enterprise has allegedly already been appointed by the Mpumalanga Department of Human Settlements, and members of WRRPA claim that the contractor has already begun with soil samples.
ALSO READ: Ten suspects arrested for tender fraud of more than R600 million
Claims were also made that “they are moving through the grounds with heavy machinery with very little regard to our endangered and endemic aloe species, Aloe simii”.
Simon Evered of WRRPA said they were hoping that they had more time to deal with this at ground level, but that the municipality seemed to have gone ahead regardless.
“It is illegal until impact studies have been done, as they are required by law to do. They must also notify the Mpumalanga Tourism and Parks Agency (MTPA), public participation is required and signs must go up stating what they are planning.”
Tiffany Briggs, WRNR chairman, said, “This is also very destructive to all other small fauna, such as the marsh mongoose, porcupine, white-tailed mongoose, and duiker that are seeking sanctuary from the ongoing development and agricultural land use shift that is currently surrounding White River.”
Briggs added that not only is this a threat to the little bit of wild space left in town and to the the reserve’s re-wilding projects, but it will also put even more stress on the already constrained and struggling water supply and sanitation plants.
ALSO READ: Mozambican mother missing since October
“At this time White River cannot sustain its current population on a high- functioning level, never mind increasing this by a further 400-plus houses and flats.”
The suggested plans range from low- to more high-income housing, including but not limited to four- to eight-storey buildings, to increase high-density housing.
A source at MTPA confirmed that an independent ecological control officer must be appointed to ensure that plants are handled correctly and a suitable habitat found. He added that the MTPA received no documents with regards to development applications for that portion of White River.
Joseph Ngala, spokesman for the City of Mbombela Local Municipality, was unavailable to confirm or comment on the allegations, despite various attempts to reach him.
The planned Mbombela P166 link road to the R40 south, between White River and Mbombela, was recently put on hold because of this scarce aloe.
The MTPA allegedly required the South African National Roads Agency to put in place measures that include seed harvesting and propagation as conservation tactics while it constructs the road.
The harvesting and propagation has been undertaken successfully in the South African National Biodiversity Institute Lowveld National Botanical Garden, with germination rates in excess of 90 per cent.


| Home | Trees | Indices | Help |
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object --+ | ClansEntry
Class holding the data of one CLANS sequence entry.
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coords entry coordinates in 3D space |
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groups ClansSeqgroups that contain the entry |
| dict |
hsp connections between this and another ClansEntry |
| string |
name name of the entry |
| Clans |
parent Clans instance that parents this ClansEntry |
| string |
seq protein sequence of the entry |
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Inherited from |
| Method Details |
x.__init__(...) initializes x; see help(type(x)) for signature
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repr(x)
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Creates an HSP from self to other with the given value.
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Returns the id of the current entry. Note: the first call to this method triggers Clans._update_index, which will make it appear slower than successive calls.
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Creates the CLANS <hsp> block format representation of the entry.
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Create the CLANS <pos> block format representation of the entry.
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Creates the CLANS <seq> block format representation of the entry.
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Removes the HSP between
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coordsentry coordinates in 3D space raises ValueError if
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groupsClansSeqgroups that contain the entry
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hspconnections between this and another ClansEntry
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namename of the entry raises ValueError if
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parentClans instance that parents this ClansEntry
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seqprotein sequence of the entry raises ValueError if
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| Home | Trees | Indices | Help |
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| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:04 2017 | http://epydoc.sourceforge.net |


Create sub-grids (darker background) on the fly, by dragging items completely over others (nest) vs partially (push) using
the new v7 API GridStackOptions.subGridDynamic=true
This will use the new delay drag&drop option DDDragOpt.pause
to tell the gesture difference
Note: gridstack-extra.min.css
is required for [2-11] column of sub-grids


{% extends "base/base.html" %} {% load bootstrap3 %} {% block content %}
You can start PreSelect by clicking on "Overview" in the menu. A table such as below will be presented, with the progress per language.
| Language | Annotated | Actions |
|---|---|---|
| German |
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|
| English |
|


Welcome to Attack of the Fundead!
User
Score
Your High Score
You don't have one yet!
Your browser does not support this canvas element.


Senior Editor, Culture Jen Trolio
Associate Editor Allegra Frank
Critic at Large Emily Todd Vanderwerff
Senior Correspondent Alex Abad-Santos
Reporter Constance Grady
Reporter Aja Romano
Critic Alissa Wilkinson
← SEE DESIGN
← LEFT
← SLIDE →
RIGHT →
SEE IMAGE →


How Do You Like it Now? American Rapper Lil Pump Gets Bitten by Snake - Video
Gazzy Garcia, 19, known as American rapper and songwriter Lil Pump, is often portrayed using drugs, especially marijuana, lean, and Xanax. The rapper is also frequently criticised for his behaviour on social media.
Lil Pump was filming an official music video when he was bitten by a snake that he was holding in his hands. The rapper has published a video of the moment on his Instagram account.
The video shows the rapper sitting on the stairs when one of his assistants brings the snake to him. Suddenly, the reptile bites the artist's hand.
"Look at my hand, bro. I think it bit f*** out of me. Yo, I just got bitten by a snake", he says in the video.
Social media users have gone crazy over the clip, as it has already gained two million views after just six hours, along with hundreds of comments.
Canadian singer Justin Bieber commented on the video, saying "Bro can’t f*** with snakes".
The American rapper rose to popularity in 2017 after he released his single "Gucci Gang", which peaked at number three on the US Billboard Hot 100 and is accredited triple platinum by the Recording Industry Association of America.


Lives completely in your browser. There is no network involved, so it opens and shows previews instantly.
Be it Markdown, Jade, SCSS, LESS, Atomic CSS, JSX, CoffeeScript or TypeScript - you get it all.
Drop in any library from the available list or put a URL of any other library you wish to add.
Get exact same layout in front of you, that you last saved the creation in. Plus, a full screen layout to see your work in actual browser window.
Feel like taking a screenshot of your awesome creation, just hit the Capture button to get a beautiful screenshot of your result.
Done with your creation and want to share it with the world? One click and have your work open in CodePen.
If you're looking for a CodePen-like offline editor, and use Chrome, check out Web-Maker by Kushagra Gour.
Really enjoying using ZenUML - super fast offline web playground.
Finding awesome pens on @CodePen and recreating them in @webmakerApp is my new hobby
This is something I will be using alot. Thank you! :D
It's fantastic! I'm glad I've discovered it! Thanks!
This is like having your own copy of Codepen that also works offline.
@webmakerApp looks like a good offline alternative to @CodePen.
Meet ZenUML, A real-time tool for generating sequence diagrams.
Great Chrome extension via @chinchang457. I need offline CodePen from time to time.
Thanks for creating this. What a great tool for productivity!
Decided to use it to work on my latest project, it's pretty awesome! Thank you for making this!
ZenUML needs the following permissions to work with full capabilities. In words of Chrome extensions:
Read & change all your data on the websites that you visit - Worry not. This is just required for the new tab replacement feature where ZenUML shows up only if the new tab url is chrome://newtab/. Nothing is read, stored or changed.
ZenUML does not track any user specific data. It uses Google Analytics to track aggregated events to improve user experience based on what features are used more. If still you want to opt-out of Google Analytics tracking, please visit https://tools.google.com/dlpage/gaoptout or you can set up a filter in Adblock Plus or similar ad blocker tools like AdBlock, uBlock or Adblock Pro.


The class will be divided into two portions. The main portion will be conducted from 1-3 pm on Fridays. The second portion will be conducted from 3pm to four. The difference between the portions will be explained below.
Students should endeavor to be punctual. Excessive lateness will be viewed harshly. I am told that two full and unexplained absences constitute a failure.
The substance of this class is a combination of three elements:
Guidance exists in the form of the weekly exercise, its description and in meetings between the professor and the student to discuss the student’s progress. Performance exists in the exhibitions that the students will put on. Camaraderie exists in the business of the dream associates, described elsewhere
Students will be divided into 3 groups of 5. Each week, one group will conduct an exhibition based upon their dreamwork. The exhibition need not be a group-effort. It can be merely a matter of consecutive appearances.
The class will be structured in the following manner:
1-2 --- Group exhibition of works created.
2-2.30 --- Meet with your two associates to discuss issues/methods
2.30-3 --- Discussion of the following week’s objectives and methods
3-4 --- During this time I will meet individually with the members of the class. These meetings will last 10 minutes. I will meet with one group at each class session, and that group will be the group that has just presented. At these meetings the individual student‘s progress will be discussed. Obstacles, such as there may be, may be brought to light. Each student will bring the completed R5 forms from the previous weeks for me to examine. The R5 form is mandatory. It is the record and proof of the work being attempted.
In group exhibitions, the material you are accruing from your dreams will be employed in the creation of some kind of performance. A person can read from a story, can sing, can act, can do a false-interview, can mime, can exhibit drawings, film, etc. The work must be original, and generated from the students dreams specifically for this dream class. No prior work is acceptable. Students can use dream material to create a collaboration. All 5 students could collaborate on a single piece for instance.
The exhibitions are not a matter of obtaining people’s judgment on work. No one in the class will or should criticize the student for his/her work. It is merely a matter of using dream material fruitfully and expeditiously.
You could, for instance dress as a character from a dream and sit in a chair facing away from the class while reading aloud. Eye contact need not be involved. One could go so far as to specify that their performance will be given in the dark, or blindfolded.
The nature of this dream work involves the application of particular methods to the student’s life. In individual meetings, students will speak to me about how they are integrating the methods into their lives. Also, they will speak to me about the particulars of their attempts to use dream-generated imagery and content in literature, art, film, etc.
During this time I will outline for the class the particular method that we will be be attempting to apply during that week, and answering any questions that people might have about that technique. Questions about the previous week’s technique can be brought up on a person-by-person basis during individual meetings.
The intent of the course is that the student will, at courses end, be recalling their dreams every night, be possessed of a book filled with such recorded dreams, and furthermore be able to become conscious or lucid during dreaming.
Ideally, the person will gain the ability to lucid dream at will. This requires a great deal of dedication, however, and it is more likely that by the end of the semester, the students will have obtained for themselves the experience of having lucid-dreamed several times, and will continue to do so every so often as long as the intent remains.
Concerns on the part of students should be addressed to: [censored]
Note: Readings will not be discussed in class. These books are included not because I favor the particular thoughts or methods in them, but because I believe the reading of them will be useful and suggestive to those working with dreams. Students should read these books during the semester. They need not read them in any particular order.
The two books principally crucial to the method we will utilize are:
Do not buy these books!
I do not encourage the student to purchase these books during the class. When the class is finished, most certainly I would advocate that. They will be useful in order to cement and continue your dream-practice.
However, I don’t want students to get ahead of themselves. We will proceed step by step through methods espoused by LaBerge, methods found in the book named above. I will provide the materials as they become necessary. Were the student to be on their own. They might not give the early exercises the time and intention they require.
So, yes, buy these last two books, but not until December of 2014. Or, buy them now, but try not to get ahead of what we are attempting in class.
[ed. Note -- this page was from previous iteration of class -- when the class was indeed in a classroom. Our situation this semester (Ballroom) is different. I preserve this page for your amusement.]
I had wanted for the class to be in a theatre, and that we would therefore be able to have our exhibitions properly, with fanfare and aplomb. As it happens, it seems this may not be possible. Therefore, we will attempt to do our best to make our classroom into a theatre of some sort. All the chairs will point in the same direction, save one.
But what is the manner of interaction? What behaviour is expected?
THIS IS HOW:
If you don’t feel you can commit to working hard, please choose another course. I mean to actually attempt to have the members of this class:
Individuals who are not interested in working hard to obtain those goals should excuse themselves.
+
The works that we will be doing here is largely invisible. It is work that you will do for yourself and by yourself. No one else can know, for instance, how often you are stopping yourself during the day to see if you are awake or dreaming. If you are diligent, and do this often, you will begin to see results. If you are not, you won’t.
In the matter of our exhibitions, you can use the other members of your section as you go about preparing. The time during the week, between classes, is to be spent performing that week’s exercise, recording dreams, and mining your dream-content for creative purposes.
THE UTILITY OF THE DREAM ASSOCIATES
IS THREE FOLD.
In the first place, it creates an added forum for interntionality, thereby aiding dream-recall, and the creation of dream-momentum.
In the second place, it creates a location wherein associates may support each other in this difficult work.
In the third place, it allows the intuition of one associate to come to the aid of another.
The ringleader will introduce the members of his/her group during the exhibition. He/she will ease the audience and see that all is taken care of to ensure an appropriately wondrous performance.
The handbill maker will prepare the handbill for the exhibition and hand it out to the members of the audience at the exhibition's start. He or she will be emailed the information necessary to the creation of the handbill, or be brought that information by the class prior to the exhibition, at the very least by the class preceding the exhibition. Do not put your handmill maker in a bad spot by failing to email him/her the title of your performance.
The stage manager will see to it that the seating is appropriately done. He/she will arrange for any odd or unorthodox seating that a particular performance may require. He/she will see to the lighting, etc.
The theater critic will write a brief (three sentence) description of each performance, which will be entered into the log via emial.
The remaining individuals will constitute a group of lackeys who will perhaps replace the above Ringleader, Handbill Maker, Stage Manager or Theater Critic, should it prove necessary.
Notes: All the above individuals will be, in addition to their roles, giving a performance. Each group will, as is mentioned elsewhere, making an exhibition once every three weeks.


Who We Are
Alp Can Canöz
I have always devoted myself to voluntary youth work since I was young. The decisions I make on this path and the people who support me have always broadened my horizons.
At the core of our foundation’s work is the idea that every person deserves the chance to live a healthy and productive life. There is no question that this new decade is beginning at a time of tremendous unrest and uncertainty around the world. Pandemics, global warming, economic crises. That’s why because we are at a time when education is the only solution.
I have always felt enthusiastic about education. After graduate my bachelor degree in Turkey, I studied my master degree in international tourism and hospitality management in Poland.
I have participated in various activities as a young volunteer at Adana Youth Centre since 2008, During this period, I gained great experience in youth work. The experiences I have gained during my volunteer work are the building blocks of my current business.
However, during my undergraduate education, I have been involved in many Erasmus projects, in the management of the Erasmus community, and in project writing. During my master education, I have conducted various tourism and youth organizations in Europe, and we still continue these studies with my partner Marcin.
First of all my family and my dear teachers who always support me; I would like to thank Ms. Senem Açıkgöz, Dr.Mevlüt Aydoğmuş and all my teachers whose name I could not write.
"Full happiness and well-being in life can only be found in working for the honor, existence and well-being of future generations."
~ M. Kemal Ataturk
Marcin Małkiewicz
I am a graduate of Tourism and Recreation with a specialisation in Organisation of Tourist Enterprises at the Academy of Physical Education in Wrocław. From childhood, I was reading mythologies and books about ancient religions, I was interested in many cultures and beliefs, especially the ancient ones.
At the same time, I was interested in the countries of the Near and Far East. Traveling around them was one of my first dreams that I managed to make a few years ago. On a daily basis, I run a company that deals with all types of work at height.
Moreover, I am a Snowboard Instructor (SITS) and an international Kitesurfing Instructor (IKO). I have been practicing many other sports for over a decade: skiing, squash, yoga, frisbee ultimate, cycling and swimming. I also love cooking, which is also related to the journeys that I go on to look for and learn new flavours.
It gives me great pleasure to take on new challenges and face them. As a result, each day is a new step forward.
The moments spent on the road and learning about new world views give me a lot of energy and inspiration to act and implement new ideas.
Seeing the world around me outside the box and noticing connections that many would not have thought of is one of my unconditional behaviours.
That is why, together with my partner Alp, I decided to set up our company Galileo Lifelong Learning Center.
The contacts I have made throughout my life can eventually create a larger structure. This network of mutual connections, created by ambitious, innovative entrepreneurs who want to broaden their horizons and gain more experience, inspires me to act. The idea arose as a result of the growing interest of Turkish students and teachers in our country.
As a new entity on the market, created by young, ambitious and freshly perceived entrepreneurs, our task is to offer services that are innovative and at the highest level.
“No pain, no gain”


Public sector loans increase to QR286.5bn
Albayraq Center analysis of the figures showed that Public Sector loans rose to the level of QR 286.5bn, while its deposits fell to the level of QR 259.5bn, as follows:
Public Sector
After rising in September, Public Sector deposits returned to decline in October – by about QR 7bn to QR259.5bn. These deposits were distributed between QR 80.6bn for the government, QR154.4bn for the government institutions, and QR 24.6bn for Semi Gov Institution where the government share is 50 percent or more and less than 100 percent. On the other hand, the total public sector loans from local banks rose by about QR2bn to QR286.5bn distributed as follows:
• Government: QR116.5bn, down QR2.4bn from September.
• Government institutions: QR157bn, an increase of QR5.6bn.
• Semi Gov. Institutions: QR13bn, down QR1.2bn.
In addition, the balance of the government bonds and bills rose by QR0.4bn to QR163.6bn. Thus, the total credit of the local public sector (governmental, governmental and semi-governmental institutions), in addition to Bills, Bonds and sukuk, increased by about QR 2.4bn to the level of QR 450.1bn.
Private Sector
Total Private Sector Deposits with banks increased by the end of October by about QR0.2bn from the end of September to the level of QR 355.8bn. Total loans and credit facilities provided by banks to the local private sector rose by QR7.7bn to reach the level of QR628.3bn; including:
QR150.8bn for the Real estate Sector (down QR 1.5bn),
QR132.9bn for Consumer loans to Individuals (up QR 1.5bn),
QR151.7bn. For the Services sector (up QR4.7bn),
QR129.2bn for the Trade sector (up QR1.1bn), and
QR17.6bn for the Industrial sector (up QR0.6bn).
In addition, there were loans and facilities worth QR11.3bn for the non-banking financial sector (down QR2.7bn from September).
External Sector
On the asset side, Commercial Banks’ investment in securities outside Qatar stood at QR20bn. Its assets with banks outside Qatar declined by QR7.8bn to QR73.8bn. Local banks’ loans to third parties rose by about QR1bn to the level of QR77.2bn, Investments of local banks in foreign companies increased by about QR10.1bn to 39.4bn, and other assets abroad rose by about QR0.1bn to QR3.9bn, and thus the total Assets increased by QR3.2bn to QR214.3bn .
On the liabilities side, deposits of foreign banks with banks inside Qatar increased by QR1bn to QR247.6bn, while the indebtedness of local banks abroad in the form of bonds and certificates of deposit increased by about QR 3.2bn to the level of QR60.3bn. The balance of holders of foreign deposits with Qatari banks increased by QR1.6bn to QR198.8bn, and thus, the total liabilities increased by QR5.8bn to QR506.5bn.
By matching the Assets of the Banking Sector abroad with those of liabilities, the net Liabilities of the banking sector in Qatar to the outside world increased by the end of October by about QR2.6bn to the level of QR292.5bn.
(The views published in this column are those of the author and do not necessarily represent or reflect the views of The Peninsula)


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Your boy Cy started Disconnect to learn coding but ended up turning into something absolutely
incredible.
He released it to the public, and learned from the steady growth that he was in the right direction
and that the public had pretty good taste hehe.
And so he continued to rock a service people loved and adored which turned into this bot of
awesomeness before you!
So far it's been a really fun solo project for Cy and he wishes to share that fun by allowing everyone
to invite the bot to your server!
To make it even squeaky clean, the Disconnect bot has been rewritten for you to have the smoothest time generating pictures of cute dogs, memeing out, and staking your lives on a legit game of coin flip or rock, paper, scissors with your buddies! And yeah, it'll always stay up to date on those silly API switches!
Did my annual self code review
Cleaned up many files and updated EMCA script and added rating
Added Twitter notifications
Fixed guild info channels not working properly
Added YouTube Channel and Twitter Account Lookup command in Social Category
Have an idea or feature in mind you want to see on Disconnect? Request it on the Disconnect Support Server Here!


Research and analysis
A summary of responses to questions asked in the post-16 institutions omnibus survey.
Ref: ISBN 978-1-78105-537-3 , DFE-RB494 PDF , 166KB , 12 pages
MS Excel Spreadsheet , 289KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
MS Excel Spreadsheet , 312KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Topics covered include:
Published: 17 December 2015


Contributing to the Documentation
This section explains the process for contributing specifically to SweetPea’s documentation.
Installing Dependencies
First, you must have installed SweetPea as explained in Configuring Your Development Environment. After that’s done, you’ll need to install Sphinx and the Read the Docs theme to be able to build the documentation:
$ pip install sphinx sphinx-rtd-theme
Building the Documentation
To build the documentation locally, navigate to the docs/ directory in the repository and start the build process:
$ make html
(Sphinx supports alternate build targets, but we only use the HTML target.)
This will generate the documentation website locally, which will be located in docs/_build/ with the root page of the site at docs/_build/index.html. You can open that file in your web browser of choice to browse your local copy of the documentation.
Note
You should never be directly manipulating code in docs/_build/! These files are the result of the building process. Instead, edit the source code in docs/_source and run the build process.
Sphinx will output helpful errors and messages during the build process, with errors written to the terminal in red text. We only consider builds with no red text to be fully successful, so please strive to always ensure your build process works flawlessly!
Automatic vs Manual Documentation
Some of SweetPea’s documentation pages are written manually, while many are generated automatically from in-code documentation.
The automatically generated code will be put in docs/_source/api/ by the sphinx-apidoc tool as part of the build process.
Important
Do not contribute code to docs/_source/api/! Its contents are automatically deleted and regenerated by the build process.
The manual documentation is located in the rest of the docs/_source/ directory, with most of the content in the docs/_source/guide/ subdirectory.
Contributing to the Automatic Documentation
The automatic documentation is generated from docstrings in the Python code in the sweetpea/ directory. To modify the automatic documentation, you need only modify the comments in the source code. Currently, we recommend starting with the in-code documentation of the [sweetpea/primitives.py](https://github.com/pdarragh/sweetpea-py/blob/master/sweetpea/primitives.py) file.


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Want to be part of the team that makes products like this? Penn Labs is recruiting.
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{% extends "base.html" %} {% from "macros.html" import build_link, fmtdate %} {% set page_title = "Dashboard" %} {% block body %}
This is a list of all registered repositories. You can view the state of the most recently added build for a repository and follow the link to view all information, the logs and download build artifacts.
| {{ repo.name }} | {% if repo.builds %} {% set build = repo.builds[0] %}{{ build_link(build) }} | {% else %}{% endif %} |
| {{ auser.name }} |
This is the public key of the Flux CI server that needs to be added to the Git server from which repositories are cloned.
{{ public_key }}
{% else %}
The server has no SSH public key!
{% endif %}This is the Webhook URL. You should use the appropriate name of
the Git server for the ?api=
url parameter.
{{ flux.utils.strip_url_path(config.app_url) }}{{ url_for('hook_push') }}?api=(gogs|github)


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meme [options] <primary sequence file>
The name of a file containing the primary sequences in FASTA format, or the word stdin to indicate that the primary sequences should be read from standard input. Note that MEME does not attempt to detect the alphabet from the primary sequences so you should specify it with the -dna, -rna, -protein or -alph options. MEME also supports a modification to the FASTA format for weighting the primary sequences.
MEME writes its output to files in a directory named
meme_out
, which it creates if necessary. You can change the
output directory using the -o or -oc options.
The directory will contain:
meme.html
-
an HTML file that provides the results in an interactive, human-readable format
meme.txt
-
a plain text file of the results for backwards compatibility with earlier versions of MEME
meme.xml
-
an XML file that provides the results in a format designed for machine processing
logoN.png,.eps
-
PNG and EPS images files containing sequence logos for each of the
motifs found by MEME (where N is the motif number)
logo_rcN.png,.eps
-
(complementable alphabets only)
PNG and EPS images files containing reverse-complement sequence logos for each of the
motifs found by MEME (where N is the motif number)
MEME gives each motif it discovers a name, which is a consensus sequence that approximately describes the motif. Motif names are intended to be mnemonic only, and are not intended to be used for searching sequences for matches; use the position-specific probability matrix (PSPM) contained in each of the MEME output file formats for that purpose via programs such as FIMO and MAST.
| Option | Parameter | Description | Default Behavior | ||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Output | |||||||||||||||||||||||||||||||||
| -text | Output in text format only to standard output. | The program behaves as if -oc meme_out had
been specified. |
|||||||||||||||||||||||||||||||
| -brief | nbrief | If there are more than nbrief (primary) sequences, the size of MEME's output will be reduced by suppressing the inclusion of the sequence names, motif sites and scanned sites in MEME's HTML and XML outputs, and by suppressing the tables of sequence lengths, sites and block diagrams in MEME's text output. | A value of 1000 is used for nbrief. | ||||||||||||||||||||||||||||||
| Objective Function | |||||||||||||||||||||||||||||||||
| -objfun | classic| de| se| cd| ce| nc | This option is used to select the objective function that
MEME uses to select the width and number of sites in a motif,
to calculate the motif's statistical significance,
and to choose the best motif in the current iteration.
| MEME uses the Classic objective function. | ||||||||||||||||||||||||||||||
| -test | mhg| mbn| mrs | The statistical test used for computing motif enrichment when the objective function is specified as Differential Enrichment (-objfun de) or Selective Differential Enrichment (-objfun se). The three options cause MEME to use the the multiple hypergeometric test (mhg), the multiple binomial test (mbn), or the multiple rank-sum test (mrs), respectively. | MEME uses the multiple hypergeometric test with the de se objective functions. | ||||||||||||||||||||||||||||||
| -use_llr | Use the log-likelihood ratio method rather than the original E-value method for evaluating EM starting points when the objective function is set to classic. (See "Search for starting points" below for more details.) | The classic objective function uses the original E-value method for evaluating EM starting points. | |||||||||||||||||||||||||||||||
| Control Sequences, Hold-out Set and Efficiency Control | |||||||||||||||||||||||||||||||||
| -neg | control sequences | The name of a file containing control sequences in FASTA format. The control sequences must be in the same sequence alphabet as the primary sequences. | If the objective function requires control sequences and none are provided, MEME creates a set of control sequences by shuffling a copy of each primary sequence while preserving the frequencies of k-mers (see next option.) | ||||||||||||||||||||||||||||||
| -shuf | kmer | Preserve the frequencies of words of this size when shuffling primary sequences
to create control sequences. kmer
must be in the range [1,..,6].
Note: Using kmer = 1 is not advised if your sequences have been masked (e.g., using RepeatMasker) since the runs of "N"s in the masked sequence will be broken up by shuffling, resulting in shuffled sequences with very different statistical properties than the primary sequences. | MEME preserves the frequencies of words of length 2. | ||||||||||||||||||||||||||||||
| -hsfrac | hsfrac | The minimum fraction of the primary sequences that will be held out for
estimating the width, number of sites and significance of motifs
for certain objective functions (see above). A larger fraction of the
primary sequences may be held out if the size of the primary
sequences exceeds the value specified using the -searchsize option
(see below).
Note: The letter frequencies in the final motifs reported by MEME are based on all primary sequences, including those in the holdout set(s). |
MEME reserves 0.5 (50%) of the primary sequences for estimating the width, number of sites and significance of motifs with objective functions that require holdout set(s). | ||||||||||||||||||||||||||||||
| -cefrac | cefrac | The size of the central region (as a fraction of sequence length) that
MEME uses for computing central enrichment of motifs when the
Central Enrichment objective function
(-objfun ce)
is specified.
Note: Candidate starting points and EM are also restricted to this portion of the sequences with both the Central Enrichment (-objfun ce) and Central Distance (-objfun cd) objective functions. |
MEME uses a central region consisting of the middle 0.25 (25%) of each sequence with the Central Enrichment objective function. | ||||||||||||||||||||||||||||||
| -searchsize | searchsize | The maximum portion of the primary sequences (in letters) used by MEME
in searching for motifs.
To reduce running time, if the total size of the primary sequences exceeds
searchsize letters,
then MEME performs the search for motifs on a random
sample of the primary sequences of (approximately)
searchsize letters.
To ensure that the order of the primary sequences has no effect,
MEME orders the sequences alphabetically (by sequence, not sequence ID)
before choosing the sample (unless you specify
option -norand, see below).
Note 1: This option only affects the search for motifs; the motif letter frequencies and sites reported by MEME are based on all primary sequences. Note 2: MEME's running time will increase roughly as the square of searchsize. Note 3: If you specify searchsize as 0, MEME will use all of the sequences (no sampling). This will cause the classic objective function to mostly closely emulate previous versions of MEME, and MEME will run very slowly on large datasets. |
MEME performs sampling if the primary sequences contain more than 100,000 letters. (This is done to improve MEME's running time.) | ||||||||||||||||||||||||||||||
| -norand | If your (primary) sequences are sorted in order of confidence (best to worst)
then you should select this option. It causes MEME not to randomize the order of the input sequences
if there are more than searchsize letters in the (primary) sequences
(see the -searchsize option, above).
Note: Using this option may make the results of MEME depend on the order of the (primary) sequences. |
The order of the input sequences is randomized before applying the -searchsize option. (This is done to ensure that MEME's results, by default, do not depend on the order of the input sequences.) | |||||||||||||||||||||||||||||||
| -csites | csites | Maximum number of sites used for computing the E-value
with the Classic objective function
(-objfun classic).
To reduce running time, if the total number of primary sequences exceeds
this value with site distribution models oops and
zoops,
then the search for motifs is performed on a random sample of the primary sequences
of (approximately) csites sequences.
If the site distribution model is anr, sequence sampling is not done,
but the maximum number of sites a motif may have is limited to csites
(unless this is overridden by the -maxsites
or -nsites options).
Note 1: This only affects the search for motifs; the motif letter frequencies and sites reported by MEME are based on all sequences. Note 2: MEME's running time will increase roughly as the cube of csites when it is larger than 10,000. Note 3: This option has no effect if the objective function is not Classic. |
With the Classic objective function, MEME performs sequence sampling if
there are more than 1000 primary sequences (site distribution
models oops and
zoops), or limits the total number of sites per motif to
1000 (site distribution model anr).
Note: You can suppress sequence sampling by setting searchsize to 0 using the -searchsize option, above. |
||||||||||||||||||||||||||||||
| -seed | seed | Random seed for shuffling and sampling sequences (see above). | MEME uses a random seed of 0. | ||||||||||||||||||||||||||||||
| Alphabet | |||||||||||||||||||||||||||||||||
| Strands & Palindromes | |||||||||||||||||||||||||||||||||
| -revcomp | Consider both the given strand and the reverse complement strand when searching for motifs in a complementable alphabet (ie DNA). | MEME searches for motifs of complementable alphabets on the given strand only. | |||||||||||||||||||||||||||||||
| -pal | This causes MEME to only look for palindromes in complementable datasets. During EM, MEME will average the letter frequencies in complementary columns of the motif (PSPM) together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. For DNA the averaging would combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other. | MEME does not average the letter frequencies in complementary columns of motifs. | |||||||||||||||||||||||||||||||
| Contributing Site Distribution | |||||||||||||||||||||||||||||||||
| -mod | oops|zoops|anr | This option is used to describe the distribution of motif sites.
|
MEME assumes the Zero or One Occurrence Per Sequence model. | ||||||||||||||||||||||||||||||
| Number of Motifs | |||||||||||||||||||||||||||||||||
| -nmotifs | nmotifs | MEME will stop searching for motifs after finding nmotifs motifs. | MEME will find 1 motif. | ||||||||||||||||||||||||||||||
| -evt | ev | MEME will stop searching for motifs if the last motif found has an E-value > ev. | MEME will rely on other limits to decide when to stop searching for motifs. | ||||||||||||||||||||||||||||||
| -time | t | MEME will stop searching for motifs if it has found at least 1 motif and it estimates that finding any more will cause the total running time to exceed t CPU seconds. | MEME will rely on other limits to decide when to stop searching for motifs. | ||||||||||||||||||||||||||||||
| Number of Motif Occurrences | |||||||||||||||||||||||||||||||||
| -nsites | nsites | When the motif site distribution model allows, MEME will only attempt to find motifs with nsites sites. Specifying this option is equivalent to setting the -minsites and -maxsites options to the same value. When the site distribution model is oops this is ignored and the number of sites is set to the number of primary sequences. | See the -minsites and -maxsites options for information on the default behavior. | ||||||||||||||||||||||||||||||
| -minsites | minsites | When the motif site distribution model allows, MEME will attempt to find motifs with at least minsites sites. When the site distribution model is oops this is ignored and the number of sites is set to the number of primary sequences. | The minimum number of sites is set to 2 when it is not otherwise defined by the use of the oops model or the -nsites option. | ||||||||||||||||||||||||||||||
| -maxsites | maxsites | When the motif site distribution model allows, MEME will attempt to find motifs with at most maxsites sites. When the site distribution model is oops this is ignored and the number of sites is set to the number of primary sequences. | When the site distribution model is zoops maxsites is set to the number of primary sequences. When the site distribution model is anr maxsites is set to 5 times the number of primary sequences. | ||||||||||||||||||||||||||||||
| -wnsites | weight | This controls the strength of the bias towards motifs with exactly the expected number of sites as defined by the -nsites, -minsites and -maxsites options. It is a number in the range [0..1). The closer to one it is, the stronger the bias towards motifs with exactly the expected number of sites. | The value of weight is set to 0.8. | ||||||||||||||||||||||||||||||
| Motif Width | |||||||||||||||||||||||||||||||||
| -w | w | Search for motifs with a width of w. | Search for motifs with widths between the range set by -minw and -maxw. | ||||||||||||||||||||||||||||||
| -minw | minw | Search for motifs with a width ≥ minw. | Searches for motifs with a minimum width of 8. | ||||||||||||||||||||||||||||||
| -maxw | maxw | Search for motifs with a width ≤ maxw. | Searches for motifs with a maximum width of 50. | ||||||||||||||||||||||||||||||
| -allw | Find starting points for EM for each possible motif width
from minw to maxw,
inclusive.
Note: Using this option with large motif width ranges will slow MEME substantially. |
MEME finds EM starting points only for widths in a geometric progression from minw to maxw and then refines the width of the motif after running EM. | |||||||||||||||||||||||||||||||
| -nomatrim | Do not adjust motif width using multiple alignments. | With the Classic objective function,
the motif is trimmed to avoid insertions and deletions.
Note: This is not done with the other objective functions, so this option has no effect with them. |
|||||||||||||||||||||||||||||||
| -wg | wg | The gap opening cost for creating the alignments used to trim the motif. | The opening cost for a gap is 11. | ||||||||||||||||||||||||||||||
| -ws | ws | The gap extension cost for creating the alignments used to trim the motif. | The extension cost for a gap is 1. | ||||||||||||||||||||||||||||||
| -noendgaps | Do not count end gaps in the alignments used to trim the motif. | End gaps are penalized like any other gap. | |||||||||||||||||||||||||||||||
| Background Sequence Model | |||||||||||||||||||||||||||||||||
| -bfile | bfile | The name of Markov background model file.
You can use a background model with MEME in order
to normalize for biased distribution of letters and groups of letters in your sequences.
A 0-order model adjusts for single letter biases, a 1-order model adjusts for
dimer biases (e.g., GC content in DNA sequences), etc.
The background model is used by MEME for four purposes:
Note that MEME uses only the 0-order portion (single letter frequencies) of the background model for purpose 4 for all objective functions other than classic, and for purposes 3 and 4 when the objective function is classic.
Note that if both strands are being considered, MEME will create a background model that averages the frequencies of letters (0-order) or words (higher order) and their reverse complements. |
A background model of the order requested (see -markov_order) is estimated from the primary sequences. | ||||||||||||||||||||||||||||||
| -markov_order | order | The (maximum) order of the Markov model to read from the background file
or to create from the primary sequences.
If the -bfile option is not specified,
MEME uses the fasta-get-markov program with a total
pseudocount of 0.1 to create a Markov model from the primary sequences.
|
MEME uses the 0-order Markov model from the background file, or creates a 0-order Markov model using an add-one prior from the primary sequences. | ||||||||||||||||||||||||||||||
| Position-Specific Priors | |||||||||||||||||||||||||||||||||
| -psp | PSP file | The name of a MEME position-specific priors file. This can be used to bias the search for motifs in MEME. The PSP file supplies a position-specific prior distribution on the location of motif sites in sequence(s) in the input dataset. | All motif sites are considered equally likely. | ||||||||||||||||||||||||||||||
| EM Algorithm | |||||||||||||||||||||||||||||||||
| -maxiter | maxiter | The number of iterations of EM to run from any starting point. EM is run for maxiter iterations or until convergence (see -distance, below) from each starting point. | MEME will use a maximum of 50 EM iterations from any starting point. | ||||||||||||||||||||||||||||||
| -distance | distance | MEME stops iterating EM when the change in the motif frequency matrix is less than distance. Change is defined as the euclidean distance between two successive frequency matrices. | The distance used for measuring convergence is 0.001. | ||||||||||||||||||||||||||||||
| -prior | dirichlet|dmix|mega|megap|addone | The type of prior to use.
|
When the prior is not selected:
|
||||||||||||||||||||||||||||||
| -b | b | The strength of the prior on model parameters: b = 0 means use intrinsic strength of prior for prior = dmix. |
|
||||||||||||||||||||||||||||||
| -plib | plib | The name of the file containing the Dirichlet mixtures prior library. |
The default value of plib depends on
the sequence alphabet.
|
||||||||||||||||||||||||||||||
| Selecting Starts for EM | |||||||||||||||||||||||||||||||||
| -spfuzz | fuzz | The fuzziness of sequence to theta mapping. The meaning of this parameter depends on the choice of mapping function as set by the -spmap option. |
The default value of fuzz depends on
the mapping function (see -spmap).
|
||||||||||||||||||||||||||||||
| -spmap | uni|pam | The mapping function to use for estimating theta.
|
The default mapping function depends on the sequence alphabet.
|
||||||||||||||||||||||||||||||
| -cons | consensus | (May be repeated.) Override the sampling of starting points and just use a
starting point derived from consensus.
This is useful when an actual occurrence of a motif is known and
can be used as the starting point for finding the motif. If this
option is specified N times, starting point sampling will be
suppressed for the first N motifs.
Note: With DNA and RNA sequences, motifs shorter than 6 can be difficult for MEME to find, so you may want to pad the consensus sequence with "N"s to width 6, e.g. -cons NGATAA rather than -cons GATAA. |
Refer to the -spmap option for the default behavior. | ||||||||||||||||||||||||||||||
| Misc | -p | np | Use faster, parallel version of MEME with np processors.
The parameter np may be a number or it
may be a quoted string starting with a number and followed by arguments
to the particular MPI run command for your installation (e.g., mpirun ). |
Use a single processor. | |||||||||||||||||||||||||||||
| -maxsize | maxsize | Maximum allowed dataset size (in letters). You can set this in scripts to prevent very
large running times. A maxsize value of 0 means "no limit".
Note: Unlike previous versions of MEME, there is no limit on dataset size by default, so this option does not normally need to be used. |
No limit on dataset size. | ||||||||||||||||||||||||||||||
| -maxtime | maxtime | Maximum allowed running time (in CPU seconds). You can set this in scripts to prevent very large running times. A maxtime value of 0 means "no limit". | No limit on running time. | ||||||||||||||||||||||||||||||
| -nostatus | Print no status messages to terminal. | Print minimal status messages to terminal. | |||||||||||||||||||||||||||||||
| -sf | sf | Print sf as name of sequence file. | Print actual file name. | ||||||||||||||||||||||||||||||
| -V | Print extensive status messages to terminal. | Print minimal status messages to terminal. |
MEME searches for motifs in a four step process:
First, MEME searches for words in the primary sequences that are likely to be good starting points for EM. Second, MEME runs EM to convergence from each such starting point. Third, after EM converges, MEME refines the width and depth (number of sites) of the motif. Finally, MEME performs a "soft" erase procedure that essentially erases all the sites of the best motif found proportional to the certainty that the site is real. MEME repeats the above four steps to find subsequent motifs until a stopping criterion is met, as specified in the "Number of Motifs" section above.Search for starting points. The search for starting points is over words that occur in the primary sequences. The search tests words in a geometric progression of widths from minw to maxw, inclusive. Each candidate word is tested for its likelihood as a good starting point for EM for finding a motif with (approximately) a given number of sites, testing separately for each value of nsites in a geometric progression from minsites to maxsites, inclusive. Starting points are scored by using them for one iteration of EM to produce an initial motif, and then scoring the resulting motif using an objective function. When the objective function is Classic or Numerically Correct, the score is an estimate of the E-value of the information content of the initial motif. When the objective function is Central Enrichment, the score is the number of sites of the initial motif in the central regions of the sequences. When the objective function is Central Distance, the score is the mean distance of the sites of the initial motif from the centers of the sequences. When the objective function is Differential Enrichment and Selective Enrichment, starting points are scored by optimizing the p-value of the motif objective function over the number of sites. With all objective functions, MEME uses the highest scoring candidate word for each combination of width and number of sites tested (or the optimal number of sites for Differential Enrichment and Selective Enrichment) as a starting point from which to initialize EM.
Expectation Maximization. The EM algorithm is used to generate motif models that are optimal with respect to the primary sequences in a maximum likelihood sense. EM is prone to getting stuck in local optima, so it is important to start it from several good, initial motif models. Thus, MEME converts each candidate starting point to an initial motif model and runs EM to convergence.
Motif Refinement.
MEME subjects each motif found by EM to further refinement using the selected objective
function, selecting the as the final motif the one with the best value of the objective function.
This refinement is over both the width and depth (number of sites) of the motif.
To compute the objective function on a motif, MEME predicts all possible sites using
the motif, sorts them, and then applies the objective function to the sorted sites.
With the Classic and Numerically Correct objective functions,
MEME tests all possible sub-motifs consisting of consecutive columns of the motif in this way.
With the SE objective function, trims the motif to a single width by
selecting the highest nkeep
columns and masking
the remainder by setting them to the background frequencies,
where nkeep = max(6, 6 + floor(width-6/3)
.
MEME uses a greedy search over sub-motifs with high information content.
First, it sorts the columns of the motif by information content.
Then MEME tests the sub-motifs spanned
by the first 2, 3, ... etc. columns. Consequently, if the first and last motif
columns have the highest information content, only the original motif is tested.
With the Classic and Numerically Correct objective
functions, MEME also further shortens each candidate submotif using a procedure
that minimizes the number of gaps in a multiple alignment of the motifs sites.
Motif Erasing. To enable EM to find additional motifs, MEME "erases" the predicted sites of the last motif found. This is done in a "soft" fashion by adjusting a weight associated each position in the primary sequences, reducing it in proportion to its likelihood as the the start of a site for the motif. These weights are used when computing the scores of both starting points and final motifs, and a low weight causes a sequence position to be less likely to be chosen as a starting point and reduces its contribution to the motif model during EM.
MEME uses an objective function on motifs when refining a motif, and to select the "best" motif from among all the refined motifs obtained from different starting points for EM. MEME supports several choices of objective function, which are described above under the -objfun option. Each of the objective functions is based on the significance (E-value) of the motif according to a statistical test.
The Classic objective function is based on the statistical significance of the log likelihood ratio (LLR) of the occurrences of the motif. The E-value of the motif is an estimate of the number of motifs (with the same width and number of occurrences) that would have equal or higher log likelihood ratio if the input sequences had been generated randomly according to the (0-order portion of the) background model. An approximate algorithm is used to estimate the probability of the observed (or better) LLR, and this p-value is then multiplied by an estimate of the number of possible motifs to give the E-value.
The Differential Enrichment and Selective Enrichment objective functions are based on the ability of the motif to discriminate predicted sites in the primary sequences from those in the control sequences. The motif is used to predict sites in a held-out subset of the primary sequences, and in the control sequences. The sites are sorted by their likelihood ratio scores. A statistical test is then applied to the sites in the sorted list above and below each possible score threshold. With the Selective Enrichment objective function, the best p-value among the tests is then multiplied by the total number of motifs and thresholds tested to give the E-value of the final motif. With the Discriminative Enrichment objective function, the best motif is selected as above, and then a second holdout set of primary sequences is used for computing the final E-value. The statistical validity of the E-value is ensured by basing it on held-out primary sequences that are not used in finding the motif.
The Central Enrichment and Central Distance objective functions are based on the central enrichment of motif in the central region of the (equal-length) primary sequences. The motif is used to predict sites in each of the primary sequences, and the sites are sorted by their likelihood ratio scores. With the Central Enrichment objective function, the binomial test is then applied to the counts of the number of times the highest-scoring site occurs in the central vs. the flanking regions of the sequences. With the Central Distance objective function, the average distance of the best site in each sequence to the center of the sequence is computed, and its significance is estimated using the Bates distribution.
Once a candidate motif has been found the multiple alignment method performs a separate pairwise alignment of the site with the highest probability and each other possible site (the alignment includes width/2 positions on either side of the sites).
The pairwise alignments are then combined and the method determines the widest section of the motif with no insertions or deletions. If this alignment is shorter than minw, it tries to find an alignment allowing up to one insertion/deletion per motif column. This continues (allowing up to 2, 3 ... insertions/deletions per motif column) until an alignment of width at least minw is found.
The switches -nomatrim, -wg, -ws and -noendgaps control trimming of motifs using the multiple alignment method. Specifying the -nomatrim option causes MEME to skip trimming altogether. The -wg and -ws options set the costs of including gaps in the alignments and the -noendgaps allows gaps at the beginning and end to be treated specially.
After trimming, the number of occurrences is then adjusted to maximize the motif E-value, and then the motif width is further shortened to optimize the E-value.
MEME's running time depends on the type of model you have chosen: oops < zoops < anr. Running time also depends on the objective function you have chosen: ce,cd < de, se < classic « nc, for small datasets; the order reverses (except for nc) for very large datasets. Running time also is affected by the options -csites, -searchsize, -hsfrac, or -cefrac. Running time is shorter with smaller alphabets (e.g., DNA) compared to with larger alphabets (e.g., Protein). Running time increases with the number of motif widths that MEME considers (see the options -w, -maxw, -minw, and -allw). The table below gives more details on the relationship between sequence dataset size and MEME's running time with large datasets.
| objective function | running time per motif (for large datasets) |
|---|---|
| classic, nc | approximately nlog(n) for motifs with up to csites sites, where n is the size of the sequence file; thereafter, the running time is essentially constant |
| de, se | approximately nlog(n) for sequence files with up to searchsize divided by hsfrac letters, where n is the size of the (primary) sequence file; thereafter, the running time is essentially constant |
| ce, cd | approximately nlog(n) for sequence files with up to searchsize divided by cefrac letters, where n is the size of the (primary) sequence file; thereafter, the running time is essentially constant |
The following table shows actual running times for MEME on DNA datasets containing different numbers of 100bp sequences on a 4 GHz Intel Core i7 processor with 16GB of memory. MEME was run using either one or 6 processors (option -p), different models and objective functions (options -mod and -objfun), and the option -revcomp. (The parallel version of MEME scales up to about 128 processors. Please see https://academic.oup.com/bioinformatics/article/12/4/303/183600 for a discussion of the parallel version of MEME.)
| processors | OOPS model | ZOOPS model | ANR model |
|---|---|---|---|
| 1 | |||
| 6 |
The following examples use data files provided in this release of the MEME Suite (in the tests directory).
A simple DNA example:
MEME looks for a single motif in the file crp0.s
which
contains DNA sequences in FASTA format.
The oops site distribution model is used so MEME
assumes that every sequence contains exactly one occurrence of the
motif. The palindrome switch is given so the motif model (PSPM) is
converted into a palindrome by combining corresponding frequency
columns. MEME automatically chooses the best width for the motif in
this example since no width was specified.
Searching for motifs on both DNA strands:
This is like the previous example except that the -revcomp switch tells MEME to consider both DNA strands, and the -pal switch is absent so the palindrome conversion is omitted. When DNA uses both DNA strands, motif occurrences on the two strands may not overlap. That is, any position in the sequence given in the input sequences may be contained in an occurrence of a motif on the positive strand or the negative strand, but not both.
A fast DNA example:
This example differs from example 1) in that MEME is told to only
consider motifs of width 20. This causes MEME to execute about 10
times faster. The -w
switch can also be used with protein
datasets if the width of the motifs is known in advance.
Using a higher-order background model:
In this example we use -mod anr
and -bfile
yeast.nc.6.freq
. This specifies that
Using a higher order background model can often result in more sensitive detection of motifs. This is because the background model more accurately models non-motif sequence, allowing MEME to discriminate against it and find the true motifs.
A simple protein example:
The -dna
switch is absent, so MEME assumes the file
lipocalin.s
contains protein sequences. MEME searches for
two motifs each of width less than or equal to 20. (Specifying
-maxw 20
makes MEME run faster since it does not have to
consider motifs longer than 20.) Each motif is assumed to occur in
each of the primary sequences because the oops distribution
model is specified.
Another simple protein example:
MEME searches for a motif of width up to 40 with up to 50 occurrences in the entire set of input sequences. The anr site distribution model is specified, which allows each motif to have any number of occurrences in each sequence. This dataset contains motifs with multiple repeats of motifs in each sequence. This example is fairly time consuming due to the fact that the time required to initialize the motif probability tables is proportional to maxw × maxsites.
A much faster protein example:
This time MEME is constrained to search for three motifs of width exactly ten. The effect is to break up the long motif found in the previous example. The -w switch forces motifs to be exactly ten letters wide. This example is much faster because, since only one width is considered, the time to build the motif probability tables is only proportional to maxsites.
Splitting the sites into three:
This forces each motif to have 24 occurrences, exactly, and be up to 12 letters wide.
A larger protein example with E-value cutoff:
In this example, MEME looks for up to 20 motifs, but stops when a motif is found with E-value greater than 0.01. Motifs with large E-values are likely to be statistical artifacts rather than biologically significant.


For the 29th time the Tour de France will feature its most iconic Alpine ascent when the peloton arrives at the base of Alpe d’Huez for the climax of stage 20. We take a closer look at what it takes to tackle the 14km climb to the summit, and some of the Tour’s most memorable moments.


Token economies rely on sinks and faucets. My rule of thumb is that faucets are 10% of the value of a token. 90% is in the sinks. This adds a new worker faucet, which is useful, and it tweaks the rewards pool which isn’t really adding much value to the chain right now. They are both good problems to solve, but they aren’t really hitting the area where we need to focus yet, which is finding good sinks for Steem. That said if the SPS is up and we can use it to fund good projects that lead to sinks then it can help us.
Summary
I’m a yes to the SPS. I’m a “no” if it isn’t funded, but it looks like it will draw 10% of the rewards pool. That doesn’t mean you’ll get less. It means you’ll get less for posting and if you contribute in other ways that add value you’ll have different opportunities to earn. If you add value you’ll be able to earn more. So, it’s a start… I don’t think it’s enough, but it’s better than zero for sure.
I’m a yes to EIP. When things aren’t really working I like to ask the question”How’s that working out for you?” When the change to linear rewards came out I put out posts and funded them into trending with words like “clusterfuckening.” Hate to say it but I kinda called this, but I didn’t call that there can be some amazing parts to linear rewards too. The thing that’s missing to me in linear rewards is down voting. I know that the user base is a little terrified of downvotes because of Steem distribution and half-crazed whales, but I think it can really changes how we reward and spend on content on the platform.
I’m also cool with higher curation rewards. I think it’ll get more whales piggy backing on content the platform seems to like.  Ultimately, I think the authors that can show more value to the chain and to the whales will begin to earn more.
Tweaking the curve from true linear to a little bit higher is good. It’s not a drastic tweak and don’t think it will really impact much.
Downvotes, economic violence and economic protection
I’ve stated an unpopular opinion that I hold that downvotes are a form of economic violence. People put their stake towards rewarding something. Others removed it. I think it’s natural and ok, but usually it’s done with the intention to silence people or cause them harm. Adding free downvotes sounds scary, but it should really be scary to the shitposters of the world rather than normal authors.
You likely think all violence is bad and that aggroed guy as the leader of a peace group must also think violence is bad. As it turns out I’m a peaceful guy, but not a pacifist.  Other’s can act violently towards the rewards pool. The correct response is downvoting and protecting a communal resource.
Right now it’s broken because it costs money to downvote so the whales don’t want to do it.  However good flagging is one of the noblest things you can do because it spends your money to help the common pool. That’s honorable.
If the whales or any users really has the ability to flag than this place can actually determine good content from bad. That’s important. The internet is filled with content. If you’re famous or extra special people care about your content, but failing that what the internet wants is highly curated content. That’s a very valuable service. Right now we’re not really supporting either of those. We’re supporting paid advertising through bidbots. I think it’s good, but the pendulum is way too far towards bidbots and there’s not really content or curation happening. The down voting pool can help.
Take from posts that blow and allow posts that don’t blow to go up in value.  Give to authors that do. This sounds like a better system than anyone willing to spend money can milk rewards. It might also mean that bidbots start looking for better authors to protect the votes that go out.
Maybe then inflation going to post rewards can add value to steem again.
How’s the current system working for ya?
Blogging didn’t moon this place even after three years. So, we need new ways to reach new users. Steemit has a lot of Steem but thus far they have been unwilling to spend it to market this place. So, we need funding to get the word out. I’ve been publicly in favor of this before it was cool. I strongly support a funded SPS
Every blogpost is like a mini funding request, but distribution and the current rewards system isn’t really functioning to spread the steem blockchain. It’s a good time to be a bidbot owner, and not so great for everyone else. But frankly, because the price of steem is so low they are getting a large portion of a pie that’s a much smaller pie than it could be. We need something different.
I also happen to think it’s not enough money to the sps. At current prices it’s about $1M usd that’ll go into it for the year. That sounds like a drop in the bucket to me. I’ve been advocating a witness parameter so witnesses can easily change things. If the sps is working and we want to get more funding for projects we should be able to easily increase it. If it isn’t we should be able to decrease it.  This part of the plan sounds very poorly thought out, but whatever…
The arguments against that idea have mostly focused on “what about investors… how will they react to knowing it could change?” I think most people with money haven’t even heard of steem, and the first question they ask won’t be “can witnesses change inflation rewards to redirect more to a worker proposal system or away from it depending on efficacy of the system?” If they hear about steem and it seems to be a cool project they’ll invest, but they need to hear about us and that means we need to reward people for spreading the word beyond our own little bubble- thus a bigger sps.
We are up against tron with a multi million dollar fund and hype. The chain needs a bigger pool to draw from to market what we do and if spent wisely we will raise this place up more than a bidbotted post about some random topic will be able to do.
Steem Alliance
At the end of the day people will have to do the work. Not just machines and code, but people. The steem alliance to me represents the best chance we have to take money that stakeholders are willing to put into efforts to mainstream the chain and actually have something happen. When the SPS is alive I’ll be working with the Steem Alliance to request funding and get to work recruiting businesses and growing the Steem ecosystem.
Steem-Engine thoughts
All of this is really the start of the conversation to be having though… The value of a currency is in the sinks not the faucet. While we’re sitting here debating how to tweak rewards going out what we should be focused on is how to incentivize systems that sink the token. We need more businesses that need Steem. I guess that’s an argument for the DAO, but since it’s got so little money going into it that it feels a little ho-hum.
We need more use cases for steem.
That’s a major part of what I’m trying to solve with Steem Engine.  That’s a super powerful concept to me. We can also add features so much faster than any other group out there, so watch as what steem-engine can do completely outpaces other approaches to this.
One size fits all usually fits no one all that well. The ability to tweak everything to make your community function exactly as you see fit, to be able to build it exactly as you envision it without having a different party in charge sounds awesome to me.
So, I’ll build it. And I’ll keep building new functionality, new products, and grinding out new deals. It’ll grow, new apps will come, and more people will need steem.
Summary
Yes to SPS (but it doesn’t go far enough and should have a witness parameter), yes to EIP.
Then let’s get to work on the thing that will actually drive this place; more sinks.
This page is synchronized from the post: ‘Steem Hardfork thoughts- Do this, stop tweaking the faucets, and let’’s work on sinks.’


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 2
For the court bouillion, place the vegetables in a large pan (big enough to fit your lobster in) and fill with water. Bring to the boil and gently simmer for 5-10 minutes. Add the wine, parsley and chervil stalks and the crushed peppercorns and pimento seeds. Simmer for a further 10 minutes and then leave to cool. Pass the bouillion through a fine sieve and return to the cleaned pan.
Bring the prepared bouillion to a rolling boil (about 80C) and cook the lobster for 4 minutes. Remove to a plate and cover with cling film until cool. Split the cooled lobster in half lengthways and carefully remove the main body meat in whole pieces. Use lobster crackers and pickers to remove the meat from the claws.
Meanwhile, cook the potatoes until tender, drain and mash with the butter and seasoning. Pass the mash through a sieve or ricer and beat in a large bowl with a spatula until completely smooth. Stir the crème fraiche through and check for seasoning. Spoon the mash into a piping bag with a 2.5cm/1in plain nozzle and pipe into the main body of the lobster shells.
Sit the filled lobster shells side by side on a large serving platter. Arrange the body meat on top of the piped mash and the claw meat where it would naturally be. Pile salad leaves between the claws. To make the vierge, simply stir the tomatoes, basil, chives and olive oil together to combine. Spoon the vierge over the lobster and dress with chives. Serve at once.


Tutorial Page
The Det.Belt server allows you to have a broad idea of the detergent belt around your membrane protein. The detergent is represented as a transparent hollow cylinder around the hydrophobic region of the protein. This approximation is particularly fitted to biochemical studies to give an overall idea of what is expected around your membrane protein of interest.
The server performs the following steps to reach the creation of the detergent belt, as described in
Quantification of Detergents Complexed with Membrane Proteins. Chaptal V, Delolme F, Kilburg A, Magnard S, Montigny C, Picard M, Prier C, Monticelli L, Bornert O, Agez M, Ravaud S, Orelle C, Wagner R, Jawhari A, Broutin I, Pebay-Peyroula E, Jault JM, Kaback HR, le Maire M, Falson P. Sci Rep. 2017 Feb 8;7:41751.
2 Using the oriented PDB, we calculate the Accessible Hydrophobic Surface (AHS) using the program naccess and a sphere of 1.5 Å radius. This metric is useful in two ways; first it allows us to draw the graph volume_occupied_by_the_detergent = ƒ(hydrophobic area(AHS)). Second, this graph can be used to predict how many detergent molecules are around the membrane protein of interest. This is being implemented and should be available shortly.
3 Calculation of the detergent belt as a cylinder. This approximation is based on the neutron diffraction of crystals showing the detergent as a taurus surrounding membrane proteins, and more recent Cryo-EM images also showing the detergent belt. A hollow cylinder is a good approximation of the detergent belt, with the ease and speed of calculations.
- We estimate the membranous part of the protein as a cylinder. The radius of this (inner) cylinder is calculated by averaging all the distances from the center of the protein to the lipid facing parts of the protein, across the whole membrane thickness.
- The volume occupied by the detergent belt is calculated as the sum of the volumes occupied by single detergents. For example, 400 DDM represent a volume of 400 x 453 Å 3 (volume of DDM) = 181,200 Å 3.
- The cylinder is represented using all the information gathered in the steps described above and shown in the joint figure.
Representation of the server side detergent belt generation process
Finally, the detergent belt is represented as a hollow cylinder around the membrane protein, with the Web GL viewer ngl (
AS Rose, AR Bradley, Y Valasatava, JM Duarte, A Prlić and PW Rose. Web-based molecular graphics for large complexes. ACM Proceedings of the 21st International Conference on Web3D Technology (Web3D '16): 185-186, 2016. ) allowing users to move the protein, and to represent it in cartoon mode or as electrostatic potential surface.
The metrics used in the calculation are output in a table bellow the viewer.
The user has access to a graph representing the
volume_occupied_by_the_detergent=f(hydrophobic area(AHS)), with the user value highlighted, useful to gage how the request distributes among other data available in the literature.
High quality figures can be generated via the program Pymol using premade scripts that can be downloaded using the download buttons on the top-right of the page.


public class SoundManager
extends java.lang.Object
| Modifier and Type | Class and Description |
|---|---|
(package private) class |
SoundManager.SoundSystemStarterThread |
| Modifier and Type | Field and Description |
|---|---|
private com.google.common.collect.Multimap<SoundCategory,java.lang.String> |
categorySounds |
private java.util.Map<ISound,java.lang.Integer> |
delayedSounds |
private java.util.Map<ISound,java.lang.String> |
invPlayingSounds |
private boolean |
loaded
Set to true when the SoundManager has been initialised.
|
private static org.apache.logging.log4j.Marker |
LOG_MARKER
The marker used for logging
|
private static org.apache.logging.log4j.Logger |
logger |
private GameSettings |
options
Reference to the GameSettings object.
|
private java.util.Map<ISound,SoundPoolEntry> |
playingSoundPoolEntries |
private java.util.Map<java.lang.String,ISound> |
playingSounds |
private java.util.Map<java.lang.String,java.lang.Integer> |
playingSoundsStopTime |
private int |
playTime
A counter for how long the sound manager has been running
|
private SoundHandler |
sndHandler
A reference to the sound handler.
|
private SoundManager.SoundSystemStarterThread |
sndSystem
A reference to the sound system.
|
private java.util.List<ITickableSound> |
tickableSounds |
| Constructor and Description |
|---|
SoundManager(SoundHandler p_i45119_1_,
GameSettings p_i45119_2_) |
| Modifier and Type | Method and Description |
|---|---|
private float |
getNormalizedPitch(ISound sound,
SoundPoolEntry entry)
Normalizes pitch from parameters and clamps to [0.5, 2.0]
|
private float |
getNormalizedVolume(ISound sound,
SoundPoolEntry entry,
SoundCategory category)
Normalizes volume level from parameters.
|
private float |
getSoundCategoryVolume(SoundCategory category)
Returns the sound level (between 0.0 and 1.0) for a category, but 1.0 for the master sound category
|
private static java.net.URL |
getURLForSoundResource(ResourceLocation p_148612_0_) |
boolean |
isSoundPlaying(ISound sound)
Returns true if the sound is playing or still within time
|
private void |
loadSoundSystem()
Tries to add the paulscode library and the relevant codecs.
|
void |
pauseAllSounds()
Pauses all currently playing sounds
|
void |
playDelayedSound(ISound sound,
int delay)
Adds a sound to play in n tick
|
void |
playSound(ISound sound) |
void |
reloadSoundSystem() |
void |
resumeAllSounds()
Resumes playing all currently playing sounds (after pauseAllSounds)
|
void |
setListener(EntityPlayer player,
float p_148615_2_)
Sets the listener of sounds
|
void |
setSoundCategoryVolume(SoundCategory category,
float volume)
Adjusts volume for currently playing sounds in this category
|
void |
stopAllSounds()
Stops all currently playing sounds
|
void |
stopSound(ISound sound) |
void |
unloadSoundSystem()
Cleans up the Sound System
|
void |
updateAllSounds() |
private static final org.apache.logging.log4j.Marker LOG_MARKER
private static final org.apache.logging.log4j.Logger logger
private final SoundHandler sndHandler
private final GameSettings options
private SoundManager.SoundSystemStarterThread sndSystem
private boolean loaded
private int playTime
private final java.util.Map<java.lang.String,ISound> playingSounds
private final java.util.Map<ISound,java.lang.String> invPlayingSounds
private java.util.Map<ISound,SoundPoolEntry> playingSoundPoolEntries
private final com.google.common.collect.Multimap<SoundCategory,java.lang.String> categorySounds
private final java.util.List<ITickableSound> tickableSounds
private final java.util.Map<ISound,java.lang.Integer> delayedSounds
private final java.util.Map<java.lang.String,java.lang.Integer> playingSoundsStopTime
public SoundManager(SoundHandler p_i45119_1_, GameSettings p_i45119_2_)
public void reloadSoundSystem()
private void loadSoundSystem()
private float getSoundCategoryVolume(SoundCategory category)
public void setSoundCategoryVolume(SoundCategory category, float volume)
public void unloadSoundSystem()
public void stopAllSounds()
public void updateAllSounds()
public boolean isSoundPlaying(ISound sound)
public void stopSound(ISound sound)
public void playSound(ISound sound)
private float getNormalizedPitch(ISound sound, SoundPoolEntry entry)
private float getNormalizedVolume(ISound sound, SoundPoolEntry entry, SoundCategory category)
public void pauseAllSounds()
public void resumeAllSounds()
public void playDelayedSound(ISound sound, int delay)
private static java.net.URL getURLForSoundResource(ResourceLocation p_148612_0_)
public void setListener(EntityPlayer player, float p_148615_2_)