Season One explores the case of alleged Canadian serial killer Bruce McArthur. This season explores
background information on the ongoing investigation as well as examining criminological theories, and
a variety of different lenses on the cases. Some topics will include the media coverage of the case,
how police conduct forensic investigations, and speaking with LGBTQA+ community advocacy members on details
regarding the case. This season containing six episodes will thrill and educate simultaneously.
Written and edited by: Sukhman Jassal, Jacksis Kodia, Ayman Mobin, Gresha Shah, and
Manroop Virk.
This episode explores the background of the life of alleged serial killer Bruce McArthur as well
as giving an in-depth analysis of the ongoing investigation which at the time was six missing
persons from Toronto’s LGBQT community over a 5-year period. Project Houston and Project
Prism become focal points of the police investigation. Not to be forgotten are all of alleged
serial murder McArthur’s victims: MAJEED KAYHAN, SOROUSH MAHMUDI, DEAN
LISOWICK, SELIM ESEN, ANDREW KINSMAN, SKANDARAJ NAVARATNAM
Citations
Adrian C, Natasha M, Kate M, Shanifa N. (Jan 23, 2018). Man missing since 2010 was romantically involved with accused killer Bruce McArthur, says friend. CBC News. Retrieved May 10 2018 from: http://www.cbc.ca/news/canada/toronto/bruce-mcarthur-press-conference-1.4547969
Andrew, R. (March 6, 2018). Bruce McArthur had me in a ‘kill position,’ says Toronto man haunted by date with alleged serial killer. Global News. Retrieved May 12 2018 from https://globalnews.ca/news/4063145/bruce-mcarthur-sean-cribbin-date-with-alleged-serial-killer/
Arshy, M. (Aug 09, 2017). Toronto man Selim Esen was getting his life on track before he went missing. Daily Xtra. Retrieved May 12 2018 from https://www.dailyxtra.com/toronto-man-selim-esen-was-getting-his-life-on-track-before-he-went-missing-77274
Canadian Press. (April 11, 2018 ). A look at Bruce McArthur's alleged victims. CP24 News. Retrieved May 10 2018 from https://www.cp24.com/news/a-look-at-bruce-mcarthur-s-alleged-victims-1.3880979.
CBC News. (Apr 17, 2018). Police 'always had that feeling' a killer lurked in the Gay Village, McArthur investigator says. CBC News. Retrieved May 28 2018 from: http://www.cbc.ca/news/canada/toronto/hank-idsinga-bruce-mcarthur-project-houston-prism-1.4622869
CBC News. (Feb 08, 2018). Remains of Andrew Kinsman found on property where Bruce McArthur worked. CBC News. Retrieved May 10 2018 from http://www.cbc.ca/news/canada/toronto/mcarthur-mallory-excavation-1.4526240
Codi, W. (January 30 2018). Man found tied to McArthur's bed when police made arrest: source.
CP24 News. Retrieved May 21 2018 from https://www.cp24.com/news/man-found-tied-to-mcarthur-s-bed-when-police-made-arrest-source-1.3781338Gallant, J., Hunter, P., and Isai, V. (16 March 2018). How alleged serial killer Bruce McArthur hid in plain sight for years. The Star. Retrieved 18 May 2018 from https://www.thestar.com/news/gta/2018/03/16/how-alleged-serial-killer-bruce-mcarthur-hid-in-plain-sight-for-years.html
Geoffrey, V. (February 12, 2018) Sketching serial killers: PhD student creates database to help understand why people kill. University of Toronto. Retrieved March 13 2018 from https://www.utoronto.ca/news/sketching-serial-killers-phd-student-creates-database-help-understand-why-people-kill
Jacques, G. (Feb. 23, 2018). Bruce McArthur in 2003 assault case: ‘I’m sorry for all the pain and anger I’ve caused’. Toronto Star. Retrieved June 2 2018 from https://www.thestar.com/news/crime/2018/02/23/bruce-mcarthur-in-2003-assault-case-im-sorry-for-all-the-pain-and-anger-ive-caused.html
Jeff, L. (February 26, 2018). Researcher says she tried to warn Toronto police about a serial killer. CTV News. Retrieved April 26 2018 from https://www.ctvnews.ca/canada/researcher-says-she-tried-to-warn-toronto-police-about-a-serial-killer-1.3819658
Justin, L., Tu Thanh, H. (January 26, 2018). Suspect in killings of gay men had history of violence. The Globe and Mail. Retrieved May 10 2018 from https://www.theglobeandmail.com/news/toronto/suspect-in-killings-of-toronto-gay-men-had-history-ofviolence/article37759243/
Kate, M. (Feb 20, 2018). Head of Bruce McArthur investigation hints at evidence that led to arrest of alleged serial killer. CBC News. Retrieved April 26 2018 from http://www.cbc.ca/news/canada/toronto/hank-idsinga-interview-1.4544098
Mark G, Melissa M. (Mar 16, 2018). Man who disappeared from Toronto's Gay Village 'led double life,' wife learned. CBC News. Retrieved May 12 2018 from http://www.cbc.ca/news/canada/toronto/abdulbasir-faizi-project-houston-missing-bruce-mcarthur-1.4579635
Nicole, B. (April 17, 2018). Family of Bruce McArthur's latest alleged victim thought he was in hiding, after refugee status rejected. CBC News. Retrieved May 20 2018 from http://www.cbc.ca/news/canada/toronto/family-of-bruce-mcarthur-s-latest-alleged-victim-thought-he-was-in-hiding-after-refugee-status-rejected-1.4623195
Rob, G. (Feb 26, 2018). Andrew Kinsman’s death may have led police to alleged Toronto serial killer Bruce McArthur: friend. Global News. Retrieved May 10 from https://globalnews.ca/news/4047193/bruce-mcarthur-serial-killer-andrew-kinsman/
Tamar, H. (Jan. 22, 2018). Friend remembers Selim Esen as a kind, loving man. Toronto Star.Retrieved May 10 2018 from https://www.thestar.com/news/gta/2018/01/22/friend-remembers-selim-esen-as-a-kind-loving-man.html
Victoria, G. (Feb. 23, 2018). Murder victim’s wife tells of love story turned wrenching tragedy.
Toronto Star. Retrieved May 12 2018 from https://www.thestar.com/news/crime/2018/02/23/in-the-apartment-they-once-shared-soroush-mahmudis-wife-fareena-is-surrounded-by-memories-of-a-man-with-a-good-heart.html
Victoria, G. (April 11, 2018). Charge in Abdulbasir Faizi killing brings sorrow, new questions. Toronto Star. Retrieved May 12 2018 from https://www.thestar.com/news/gta/2018/04/11/charge-in-abdulbasir-faizi-killing-brings-sorrow-new-questions.html
Vjosa, I. (Feb 27, 2018). Ph.D candidate profiled potential serial killer in gay village in July 2017. Toronto Star. Retrieved April 24 2018 from https://www.thestar.com/news/crime/2018/02/27/police-chief-isnt-blaming-victims-of-alleged-serial-killer-mayor-john-tory-says.html
Vjosa, I. (January 26 2018). Missing men from Toronto’s Gay Village led ‘double lives,’ friends say. Toronto Star. Retrieved May 10 2018 from https://www.thestar.com/news/gta/2018/01/26/missing-men-from-torontos-gay-village-led-double-lives-friends-say.html
Warnica, R., and Edmiston, J. (2 February 2018). How Bruce McArthur went from small-town sock salesmen to accused serial killer. National Post. Retrieved 18 May 2018 from http://nationalpost.com/feature/bruce-mcarthur-small-town-sock-salesman-to-accused-serial-killer
Written and edited by: Noni Brown, Thurkka Jeyassanthan, Aarthi Nathan, Rani Panchal, and
Lily Wight
Interviews are conducted with members of Toronto Police Service regarding some details of the
McArthur case, but the episode delves more into the specific detailed process of conducting
forensics during a crime event. Lead Detective Hank Idsinga from the Bruce McArthur case as
well as Detective Thomas Greer provide a fascinating view into how police use forensics.
Citations
CBC. (2018). Alleged serial killer Bruce McArthur charged with 8th murder count | CBC News. [online] Available at: http://www.cbc.ca/news/canada/toronto/toronto-police-release-identity-of-8th-alleged-bruce-mcarthur-victim-1.4620848 [Accessed 18 Apr. 2018].
CBC. (2018). Investigators begin piecing together 'puzzle' in Toronto serial killer case | CBC News. [online] Available at: http://www.cbc.ca/news/canada/toronto/bruce-mcarthur-forensic-investigators-search-1.4510889 [Accessed 15 May. 2018].
Kassam, A. (2018). Bruce McArthur: Toronto police review decades of cold cases after seventh murder charge. [online] the Guardian. Available at: https://www.theguardian.com/world/2018/apr/11/bruce-mcarthur-toronto-serial-killer-allegations-charges [Accessed 20 May. 2018].
The Government of Ontario. (2018). [online] Available at: https://www.attorneygeneral.jus.gov.on.ca/inquiries/cornwall/en/hearings/exhibits/OPC/pdf/56_MCM_Manual.pdf [Accessed 10 April. 2018].
The Toronto Star. (2018). 1,800 exhibits and 18,000 photos: Toronto police complete intensive search of Bruce McArthur’s apartment.[online] Available at: https://www.thestar.com/news/gta/2018/05/15/police-end-forensic-investigation-into-bruce-mcarthurs-apartment.html [Accessed 2 Jun. 2018].
Written and edited by: Sara Acosta, Elizabeth Acquino, Maria Butt, Nathaniel Browne-Walker,
Victoria Moniz
Comparisons are made between historical Canadian serial killers such as Robert Pikton and Paul
Bernardo to alleged serial killer Bruce McArthur. An extensive interview with James Dubrow
provides understanding of how the allegations against Bruce McArthur made be true and how
he was able to stay under the radar of the police
Citations
Baum K. B., & McClearn, M. (2015). The Taken: Who qualifies as a serial killer and more on the data behind the project. The Globe and Mail. Retrieved from https://www.theglobeandmail.com/news/national/the-taken-who-qualifies-as-a-serial-killer-and-more-on-the-data-behind-the-project/article27443307/
Brockbank, N. (2018). From Project Houston to Bruce McArthur’s arrest: a timeline of what we know so far. CBC News. Retrieved from http://www.cbc.ca/news/canada/toronto/mcarthur-investigation-timeline-1.4697727
Brown, B. (1996). Sex killer case probe faults police. United Press International. Retrieved from https://www.upi.com/Archives/1996/07/11/Sex-killer-case-probe-faults-police/4476837057600/
Butts, E. (2016). Paul Bernardo and Karla Homolka Case. In The Canadian Encyclopedia. Retrieved from http://www.thecanadianencyclopedia.ca/en/article/paul-bernardo-and-karla-homolka-case/
CBC News. (2010). Key events in the Bernardo/Homolka case. CBC News. Retrieved from http://www.cbc.ca/news/canada/key-events-in-the-bernardo-homolka-case-1.933128
Gallant, J., Hunter, P., Isai, V. (2018). How alleged serial killer Bruce McArthur hid in plain sight for years. The Star. Retrieved from https://www.thestar.com/news/gta/2018/03/16/how-alleged-serial-killer-bruce-mcarthur-hid-in-plain-sight-for-years.html
Global News. (2009). Backgrounder: the “faint hope clause”. Global News. Retrieved from https://globalnews.ca/news/66435/backgrounder-the-faint-hope-clause/
Gollom, M. (2018). How the Robert Pickton case sparked changes to B.C. missing persons investigations. CBC News. Retrieved from http://www.cbc.ca/news/canada/toronto/bruce-mcarthur-robert-pickton-missing-persons-1.4517472
Hall, N. (2012). Failures of Pickton investigation similar to Bernardo probe, inquiry told.
Vancouver Sun. Retrieved from http://www.vancouversun.com/sunrun/event/Failures+Pickton+investigation+similar+Bernardo+probe+inquiry+told/6003237/story.html
Jones, R. (2014). Allan Legere, notorious serial killer, captured 25 years ago. CBC News. Retrieved from http://www.cbc.ca/news/canada/new-brunswick/allan-legere-notorious-serial-killer-captured-25-years-ago-1.2844917
Larue, J. (2009). Gilbert Paul Jordan: The Boozing Barber. First Nations Drum. Retrieved from http://www.firstnationsdrum.com/2009/04/gilbert-paul-jordan-the-boozing-barber/
Martin, S. (2011). The life and death of Clifford Olson. The Globe and Mail. Retrieved from https://www.theglobeandmail.com/news/national/the-life-and-death-of-clifford-olson/article4197011/
Theodore, T. (2010). Changes needed to prevent another B.C. serial killer: profiler. CTV News Vancouver. Retrieved from https://bc.ctvnews.ca/changes-needed-to-prevent-another-b-c-serial-killer-profiler-1.544998
Snagy. (2014). Human remains from Pickton farm may have reached food supply. The Globe and Mail. Retrieved from https://www.theglobeandmail.com/news/national/human-remains-from-pickton-farm-may-have-reached-food-supply/article1129161/
Written and edited by: Alisha Ilyas, Taylor McIntyre, Noor Mubashir, and Urvi Patel
Toronto’s LGBQT community has felt a lack of effort of the Toronto Police Service and
inefficient investigations led to six community members disappearing and eventually been
found murdered by alleged serial killer Bruce McArthur. Interviews are conducted with Haran
Vijayanathan and Monica Forrester who represent LGBQT advocacy groups in the
Greater Toronto Area, give opinions on the McArthur investigation and provides insight
into the community’s relationship with Toronto Police.
Citations
Canadian encyclopedia. Lesbian, Gay, Bisexual and Transgender Rights In Canada. (2014). Retrieved March 28 2018 from www.thecanadianencyclopedia.ca/en/article/lesbian-gay-bisexual-and-transgender-rights-in-canada/
Maggie’s Toronto Sex Worker Action Project (2011). Retrieved June 9 2018 from http://maggiestoronto.ca
Written and edited by: Najat Ahmed, Arnavi Patel, Srushti Patel, and Humera Syed
Toronto media outlets are one the largest and most extensive in the world, let alone in the
Canadian landscape. “The medium is the message” by Marshall McLuhan is the theme for this
episode as a panel discussion of journalists explore how stories become news items and once a
story become news, why and how does it stay in the public discourse. Commentary on their
entry into journalism and the ongoing Bruce McArthur investigation is also provided. Interviews
are conducted with Jeff Gray, Kenyon Wallace, and Vjosa Isai. This episode is dedicated to Najat
Ahmed who lost her life in a tragic bus accident during the summer of 2018.
Citations
Gallant, J., Hunter, P., and Isai, V. (2018). How alleged serial killer Bruce McArthur hid in plain sight for years. The Star. Retrieved May 2, 2018 from: https://www.thestar.com/news/gta/2018/03/16/how-alleged-serial-killer-bruce-mcarthur-hid-in-plain-sight-for-years.html
Isai, V. (2018). Who is Bruce McArthur, the man charged with the murders of two missing men? Toronto Star. Retrieved May 2, 2018 from: https://www.thestar.com/news/gta/2018/01/18/who-is-bruce-mcarthur.html
Kassam, A. (2018). Bruce McArthur: Toronto police review decades of cold cases after seventh murder charge. The Guardian. Retrieved May 9, 2018 from: https://www.theguardian.com/world/2018/apr/11/bruce-mcarthur-toronto-serial-killer-allegations-charges
Ling, J., and Gray,. (2018). Toronto police drop bid to join Pride march amid push to improve LGBTQ relations. The Globe and Mail. Retrieved May 9, 2018 from: https://www.theglobeandmail.com/canada/toronto/article-toronto-police-agree-not-to-march-in-pride-parade/
McGillivray, K. (2018). What we know about the life of accused serial killer Bruce McArthur. Canadian Broadcasting Corporation. Retrieved May 9, 2018 from: http://www.cbc.ca/news/canada/toronto/bruce-mcarthur-what-we-know-1.4509436
Shum, D. (2018). Toronto’s LGBTQ community calling for public inquiry into missing persons, murder cases. Global News. Retrieved May 2, 2018 from: https://globalnews.ca/news/4098627/lgbtq-bruce-mcarthur-independent-inquiry/
Shum, D. (2018). Toronto police identify John Doe, alleged victim of Bruce McArthur. Global News.Retrieved May 9, 2018 from: https://globalnews.ca/news/4142460/bruce-mcarthur-john-doe-identified/
Szklarski, C. (2018). Alleged serial killer Bruce McArthur ‘likely a psychopath’: expert. Global News. Retrieved May 2, 2018 from: https://globalnews.ca/news/3998432/alleged-serial-killer-bruce-mcarthur-psychopath/
Wallace, K. (2018). The red tape behind the Bruce Mcarthur tapes. Toronto Star. Retrieved May 2, 2018 from: https://www.thestar.com/trust/2018/03/02/the-red-tape-behind-the-bruce-mcarthur-tapes.html
Warnica, R., and Edmiston, J. (2018). HOW BRUCE MCARTHUR WENT FROM SMALL-TOWN
SOCK SALESMAN TO ACCUSED SERIAL KILLER. National Post. Retrieved May 2, 2018 from: http://nationalpost.com/feature/bruce-mcarthur-small-town-sock-salesman-to-accuse d-serial-killer
Winter, J., Hayes, M., and Gray, J. (2018). Accused in murder of Toronto gay men was on police radar since last fall, businessman says. The Globe and Mail. Retrieved May 9, 2018 from: https://www.theglobeandmail.com/news/toronto/suspect-in-murder-of-toronto-gay-men-was-on-police-radar-since-last-fall-businessman-says/article37679202/
Written and edited by: Navneel Agnhotri, Austin Barreiro, Jacob Coulson, and Kiratpal Hothi
For over a hundred years, criminologists are always trying to determine why people commit
crime, especially of the most horrendous nature. There is no one tell-all answer, but several
theories are explored in this episode. Interviews are conducted with current law school and
justice student Ally Lawrence and educator Justin Schikschneit, both who offer opinions on
why/how alleged serial killer Bruce McArthur committed these horrible crimes.
Citations
Brockbank, N. (2018). “From Project Houston to Bruce McArthur's arrest: a timeline of what we know so far”. CBC. Retrieved from: http://www.cbc.ca/news/canada/toronto/mcarthur-investigation-timeline-1.4697727
CTV News. (2018). “Police investigated alleged serial killer for months before arrest”. Retrieved from: https://www.ctvnews.ca/canada/police-investigated-alleged-serial-killer-for-months-before-arrest-1.3959649


ASSISTANT-QT4(1) ASSISTANT-QT4(1) NAME assistant-qt4 - on-line documentation browser for Qt4 SYNOPSIS assistant [options] DESCRIPTION This page documents the Qt Assistant from the Qt application framework, which is typically used to display the Qt Reference Documentation. OPTIONS -collectionFile file Uses the specified collection file instead of the default one. -showUrl url Shows the document with the url. -enableRemoteControl Enables Assistant to be remotely controlled. -show widget Shows the specified dockwidget which can be "contents", "index", "bookmarks" or "search". -activate widget Activates the specified dockwidget which can be "contents", "index", "bookmarks" or "search". -hide widget Hides the specified dockwidget which can be "contents", "index" "bookmarks" or "search". -register helpFile Registers the specified help file (.qch) in the given collection file. -unregister helpFile Unregisters the specified help file (.qch) from the give collec‐ tion file. -setCurrentFilter filter Set the filter as the active filter. -quiet Does not display any error or status message. -help Displays a help message. SEE ALSO /usr/share/qt4/doc/html/assistant-manual.html, http://doc.trolltech.com AUTHOR This manual page was written by Brian Nelson<EMAIL_ADDRESS>based on the output of assistant -help . Frederik Schwarzer <schwarz‐<EMAIL_ADDRESS>updated it to Qt 4.5.2. Nokia Corporation and/or its subsidiar4.5.2s) ASSISTANT-QT4(1)


5 Easy Exercises
Regular physical activity, combined with a healthy diet, can help prevent and manage Type 2 Diabetes & about Only 10% of people are successful at losing weight through diet alone.
FOR BEGINNERS. 8 Easy Workouts for You.
In fact, easy workouts for beginners at home or outdoors are better choices when you're just starting out. Easy, beginning-level exercise is essential for good health and weight loss. Beginners who start to work out can expect to enjoy a wide range of benefits. If you are completely new to exercise, low-intensity activity sessions will help you burn more calories. And if you don't compensate by eating more, the pounds will start to fall off. But these workouts have a more important function, as well.
FOR BUSY WOMEN. Guide to Looking Fit.
It would be nice if you had an hour to spend at the gym right now. It would also be nice if calorie-free pies existed. Until then, what you need is a quick and easy workout plan that gets even the busiest woman through the holiday season without weight gain. A San Diego-based exercise physiologist and personal trainer, a mission: Design a workout that you can whip through in about 10 minutes, anywhere, with zero equipment. Do each move here as many times as you can in 60 seconds; rest 30 seconds, then do the next move.
THE SIMPLE. No-Equipment Workout You Can Do at Home.
You don't need a gym membership to melt that unwanted flab and look fit. In fact, you don't even need equipment. Use this super-simple body-weight workout from The Women's Health Big Book of Exercises to burn fat, tone every muscle and improve your fitness.
7 MINUTE WORKOUT. Helps You Make Every Minute Count!
In 12 exercises deploying only body weight, a chair and a wall, it fulfills the latest mandates for high-intensity effort, which essentially combines a long run and a visit to the weight room into about seven minutes of steady discomfort — all of it based on science.
TABATA WORKOUT. The 4-Minute Fat-Burning Miracle.
The trick to getting all these benefits is the level of intensity. To do a Tabata, an exercise developed in the '70's for Japanese Olympians, all you have to do is pick a cardio activity such as running, jumping rope, or biking and go as hard as you can for 20 seconds.


Recipe 6.6. Delegating Special Methods in ProxiesCredit: Gonçalo Rodrigues ProblemIn the new-style object model, Python operations perform implicit lookups for special methods on the class (rather than on the instance, as they do in the classic object model). Nevertheless, you need to wrap new-style instances in proxies that can also delegate a selected set of special methods to the object they're wrapping. SolutionYou need to generate each proxy's class on the fly. For example: class Proxy(object): """ base class for all proxies """ def _ _init_ _(self, obj): super(Proxy, self)._ _init_ _(obj) self._obj = obj def _ _getattr_ _(self, attrib): return getattr(self._obj, attrib) def make_binder(unbound_method): def f(self, *a, **k): return unbound_method(self._obj, *a, **k) # in 2.4, only: f._ _name_ _ = unbound_method._ _name_ _ return f known_proxy_classes = { } def proxy(obj, *specials): ''' factory-function for a proxy able to delegate special methods ''' # do we already have a suitable customized class around? obj_cls = obj._ _class_ _ key = obj_cls, specials cls = known_proxy_classes.get(key) if cls is None: # we don't have a suitable class around, so let's make it cls = type("%sProxy" % obj_cls._ _name_ _, (Proxy,), { }) for name in specials: name = '_ _%s_ _' % name unbound_method = getattr(obj_cls, name) setattr(cls, name, make_binder(unbound_method)) # also cache it for the future known_proxy_classes[key] = cls # instantiate and return the needed proxy return cls(obj) DiscussionProxying and automatic delegation are a joy in Python, thanks to the _ _getattr_ _ hook. Python calls it automatically when a lookup for any attribute (including a methodPython draws no distinction there) has not otherwise succeeded. In the old-style (classic) object model, _ _getattr_ _ also applied to special methods that were looked up as part of a Python operation. This required some care to avoid mistakenly supplying a special method one didn't really want to supply but was otherwise handy. Nowadays, the new-style object model is recommended for all new code: it is faster, more regular, and richer in features. You get new-style classes when you subclass object or any other built-in type. One day, some years from now, Python 3.0 will eliminate the classic object model, as well as other features that are still around only for backwards-compatibility. (See http://www.python.org/peps/pep-3000.html for details about plans for Python 3.0almost all changes will be language simplifications, rather than new features.) In the new-style object model, Python operations don't look up special methods at runtime: they rely on "slots" held in class objects. Such slots are updated when a class object is built or modified. Therefore, a proxy object that wants to delegate some special methods to an object it's wrapping needs to belong to a specially made and tailored class. Fortunately, as this recipe shows, making and instantiating classes on the fly is quite an easy job in Python. In this recipe, we don't use any advanced Python concepts such as custom metaclasses and custom descriptors. Rather, each proxy is built by a factory function proxy, which takes as arguments the object to wrap and the names of special methods to delegate (shorn of leading and trailing double underscores). If you've saved the "Solution"'s code in a file named proxy.py somewhere along your Python sys.path, here is how you could use it from an interactive Python interpreter session: >>> import proxy >>> a = proxy.proxy([ ], 'len', 'iter') # only delegate _ _len_ _ & _ _iter_ _ >>> a # _ _repr_ _ is not delegated <proxy.listProxy object at 0x0113C370> >>> a._ _class_ _ <class 'proxy.listProxy'> >>> a._obj [ ] >>> a.append # all non-specials are delegated <built-in method append of list object at 0x010F1A10> Since _ _len_ _ is delegated, len(a) works as expected: >>> len(a) 0 >>> a.append(23) >>> len(a) 1 Since _ _iter_ _ is delegated, for loops work as expected, as does intrinsic looping performed by built-ins such as list, sum, max, . . . : >>> for x in a: print x ... 23 >>> list(a) [23] >>> sum(a) 23 >>> max(a) 23 However, since _ _getitem_ _ is not delegated, a cannot be indexed nor sliced: >>> a._ _getitem_ _ <method-wrapper object at 0x010F1AF0> >>> a[1] Traceback (most recent call last): File "<interactive input>", line 1, in ? TypeError: unindexable object Function proxy uses a "cache" of classes it has previously generated, the global dictionary known_proxy_classes, keyed by the class of the object being wrapped and the tuple of special methods' names being delegated. To make a new class, proxy calls the built-in type, passing as arguments the name of the new class (made by appending 'Proxy' to the name of the class being wrapped), class Proxy as the only base, and an "empty" class dictionary (since it's adding no class attributes yet). Base class Proxy deals with initialization and delegation of ordinary attribute lookups. Then, factory function proxy loops over the names of specials to be delegated: for each of them, it gets the unbound method from the class of the object being wrapped, and sets it as an attribute of the new class within a make_binder closure. make_binder deals with calling the unbound method with the appropriate first argument (i.e., the object being wrapped, self._obj). Once it's done preparing a new class, proxy saves it in known_proxy_classes under the appropriate key. Finally, whether the class was just built or recovered from known_proxy_classes, proxy instantiates it, with the object being wrapped as the only argument, and returns the resulting proxy instance. See AlsoRecipe 6.5 for more information about automatic delegation; Recipe 6.9 for another example of generating classes on the fly (using a class statement rather than a call to type). |


tracklib¶
tracklib.taggedset¶
-
class
tracklib.taggedset.
TaggedSet
(iterable=[], hasTags=True)¶ Bases:
object
A set with tags for each object.
This class can be used as an iterator in constructs such as
for datum in myset: ...
. The subset to be iterated over can be adjusted usingmakeSelection
. If you also need the tags for each datum, usemyset(giveTags=True)
.For processing data (in the sense of “applying a function to all of them”) it is often useful to simply use the built-in
map
. If the function in question has to overwrite the data, you can useapply
. Note that in-place modification can be achieved withmap()
.- Parameters
iterable (iterable of data or (datum, tags) pairs, optional) – most useful things to use here: an actual list, or a generator. Remember to set
hasTags
accordingly.hasTags (bool, optional) – whether the iterable in the first argument gives data only or (datum, tags) pairs.
Notes
For a TaggedSet
myset
, the following operations are defined:len(myset)
return number of data in current selection
myset(giveTags)
return a generator yielding single data or (datum, tags) pairs, depending on the value of
giveTags
(False by default).iter(myset)
shortcut for
myset(giveTags=False)
. This enables the constructionfor datum in myset: ...
myset[i]
element access within the current selection. You can use indices from
0
tolen(myset)-1
.myset |= otherset
add the data in
otherset
intomyset
. This is a shortcut formyset.mergein(otherset)
.
-
static
makeTagsSet
(tags)¶ Input reformatting, mostly internal use.
Make sure that the frequently used
tags
argument is a set of strings.- Parameters
tags (str, list of str, set of str, or None) –
- Returns
set of str
-
add
(datum, tags=None)¶ Append the given datum with the given tags.
- Parameters
datum (object) – the datum to append
tags (str, list of str, or set of str, optional) – the tags to attach to this new datum
Notes
The newly added datum will be part of the current selection.
-
__call__
(giveTags=False, randomize=False)¶ Iterate through the current selection of the set.
- Parameters
giveTags (bool, optional) – whether to yield only data or (datum, tags) pairs.
randomize (bool, optional) – set to
True
to randomize the order in which data are yielded.
- Yields
datum – the data in the current selection
set of str, optional – the tags associated with the datum
See also
-
makeSelection
(**kwargs)¶ Mark a subset of the current set as ‘current selection’. For most purposes, the set will behave as if it contained only these data.
There are multiple ways to select data. Which one is used depends on the kwargs given. With increasing preference, these methods are
select randomly: use the kwargs
nrand
orprand
select by tag: use the kwargs
tags
andlogic
select with a user-specified function: use kwarg
selector
Call this without arguments to reset the selection to the whole dataset.
- Parameters
nrand (int) – number of trajectories to select at random
prand (float, in [0, 1]) – fraction of trajectories to select at random
random_seed (int, str, or bytes, optional) – seed for random selection. Note that this can be a string.
tags (str, list of str, or set of str) – the tags to select. How these go together will be determined by
logic
.logic (callable with signature bool = logic(<list of bool>)) – the logic for handling multiple tags. Set this to (the built-in)
all
to select the data being tagged with all the given tags, or toany
(the default) to select the data having any of the given tags.selector (callable with signature bool = selector(datum, tags)) – should expect the datum and a set of tags as input and return True if the datum is to be selected, False otherwise.
- Other Parameters
refining (bool, optional) – set to True to apply the current selection scheme only to those data that are already part of the selection. This can be used to successively refine a selection by using different selection methods (e.g. first by tag, then by some other criterion).
See also
-
refineSelection
(*args, **kwargs)¶ A wrapper for
makeSelection(..., refining=True)
.See also
-
saveSelection
()¶ Return a copy of the current selection for reference.
This is useful when planning to undo subsequent selections without having to redo the whole selection from scratch.
- Returns
list of bool – the current selection.
See also
-
restoreSelection
(selection)¶ Restore a previously saved selection.
- Parameters
selection (list of bool) – the selection that was saved using
saveSelection
. Will be copied.
See also
-
copySelection
()¶ Generate a new (copied) set from the current selection.
- Returns
The new set
See also
-
deleteSelection
()¶ Remove the selected entries from the set
See also
Notes
Since everything in the current selection is deleted, the selection will be reset.
-
mergein
(other, additionalTags=None)¶ Add the contents of the TaggedSet
other
to the caller.- Parameters
other (TaggedSet) – the set whose data to add
additionalTags (str, list of str, or set of str, optional) – additional tag(s) to add to all of the new data.
Notes
This can also be invoked as
self |= other
. In that case no additional tags can be added.
-
addTags
(tags)¶ Add new tag(s) to all data in the current selection
- Parameters
tags (str, list of str, or set of str) – the tag(s) to add
Return the set of all tags in the current selection.
- Returns
set of str – set of all tags in the current selection
-
apply
(fun)¶ Apply the given function to all data.
- Parameters
fun (callable of signature
datum = fun(datum)
) –
See also
process()
,builtins.map()
Notes
This function works in-place. If you need the original data to remain unchanged, use
process
.
-
process
(fun)¶ Generate a new
TaggedSet
with processed data.Same as
apply
, except that a new set with the processed data is returned, while the original one remains unchanged.- Parameters
fun (callable of signature
datum = fun(datum)
) –- Returns
TaggedSet – a new set containing the processed data
See also
apply()
,builtins.map()
Notes
The new set will contain only the processed data, i.e. data that are not in the current selection will not be copied.
-
map_unique
(fun)¶ Apply a function to all data and check that the result is unique.
- Parameters
fun (callable with signature
ret = fun(datum)
) – the function to apply. The return value can be any object for whichret1 == ret2
is defined.- Returns
ret (object) – if
fun
returns the same value on all data, that value.- Raises
RuntimeError – if the return value of
fun
is not the same on all data.
See also
builtins.map()
Notes
Returns
None
iflen(self) == 0
(i.e. the caller / its current selection does not contain any data)
tracklib.trajectory¶
-
class
tracklib.trajectory.
Trajectory
(**kwargs)¶ Bases:
abc.ABC
Represents all kinds of trajectories.
This class represents trajectories with 1 or 2 loci in 1, 2, or 3 spatial dimensions. Consequently, the internal
np.ndarray
has shape (N
,T
,d
). Besides the actual trajectory data, this class also contains a dict for meta data. This can be used by the end-user, is also intended for use within the library though. For reference, see Trajectory.meta-info fields used by tracklibFor creation of actual
Trajectory
objects, usefromArray
. This will select and instantiate the appropriate subclass based on the shape of the input array. Any keyword arguments given to that function (or the constructor) will simply be written tometa
-
data
¶ the actual data in the trajectory. Try to avoid direct access.
- Type
(N, T, d) np.ndarray
-
meta
¶ a dict for (mostly user-specified) meta data. Also occasionally used by the library
- Type
dict
Notes
For a
Trajectory
traj
, the following operations are defined:len(traj)
equivalent to
traj.T
traj[ind]
accesss the time (stretch) specified by ind. The
N
andT
dimensions might be squeezed (removed if they have length 1), while thed
dimension is guaranteed to be present.
This class implements the Sequence interface, i.e. it can be used much like a list.
-
classmethod
fromArray
(array, t=None, **kwargs)¶ Create a new
Trajectory
from an array.Any keyword arguments are simply written into
self.meta
.- Parameters
array ((N, T, d) array-like) – the data for the new trajectory. We expect
N in {1, 2}
,d in {1, 2, 3}
. Arrays with less than three dimensions will be interpreted as(T, d)
or(T,)
, respectively.t ((T,) array-like, optional) – the frame number for each data point in the array. Use this if your array has missing data points; they will be patched with
np.nan
.
- Returns
Trajectory_?N_?d – a new
Trajectory
object with the specified data
Notes
The input data is copied.
-
property
N
¶ Number of loci
-
property
T
¶ Length in frames
-
property
d
¶ Number of dimensions
-
valid_frames
()¶ Return the number of frames that have data.
We regard a frame as unusable as soon as any data is missing. So this function counts the number of frames in the trajectory where none of the data is
np.nan
.- Returns
int
-
__getitem__
(key)¶ Element-access
- Parameters
key (index or slice into the
T
dimension) –- Returns
np.ndarray – the corresponding part of the trajectory.
Notes
The
N
dimension will be squeezed, i.e. for single locus trajectories that first dimension will be removed. TheT
dimension of the output follows the numpy conventions, whiled
is guaranteed to be present.
-
abs
()¶ Modifier: 2-norm
- Returns
Trajectory
Notes
For multi-locus trajectories, this will take the norm of each locus individually. To get a
Trajectory
of relative distance, useTrajectory.relative().abs()
.See also
-
diff
(dt=1)¶ Modifier: displacements
Calculate the displacements over
dt
frames.- Parameters
dt (integer) – the time lag to use for displacement calculation default: 1, i.e. frame to frame displacements
- Returns
Trajectory
See also
-
dims
(key)¶ Modifier: select dimensions
- Parameters
key (list of int, or slice) – which dimensions to use. Attention: this cannot be a single
int
. To get thei
-th spatial component, usetraj.dims([i])
.- Returns
Trajectory
See also
-
rescale
(factor)¶ Modifier: rescale all data by a constant factor
- Parameters
factor (float) –
- Returns
Trajectory
-
offset
(off)¶ Modifier: shift the trajectory by some offset
- Parameters
off (float or array of shape
(d,)
,(N, d)
, or(N, T, d)
) – the offset to add- Returns
Trajectory
-
relative
()¶ Modifier: distance vector between two loci
- Returns
Trajectory
Notes
Applies only to multi-locus trajectories and should thus be implemented in subclasses.
-
plot_vstime
(ax=None, maskNaNs=True, **kwargs)¶ Plot the trajectory / spatial components versus time.
See the implementations in the
Trajectory_?N
subclasses for more detail.Keyword arguments are forwarded to
ax.plot()
- Parameters
ax (axes, optional) – the axes in which to plot. Can be
None
, in which case we plot toplt.gca()
maskNaNs (bool, optional) – if
True
, remove invalid frames before plotting. This means that the plot will be one continuous line, as opposed to breaking off at missing frames. The latter might be useful to draw attention to a few missing frames in otherwise mostly dense trajectories, but for trajectories with many missing frames we would not see any frames that lie isolated between two missing ones.
- Returns
list of matplotlib.lines.Line2D – the output of
ax.plot()
.
See also
-
abstract
plot_spatial
(ax=None, dims=(0, 1), **kwargs)¶ Plot the trajectory in space.
For more detail see the implementation in the subclasses.
Keyword arguments are forwarded to
ax.plot()
- Parameters
ax (axes, optional) – the axes in which to plot. Can be
None
, in which case we plot toplt.gca()
dims (2-tuple of int, optional) – the dimensions to plot. Only relevant for
d >= 3
.
- Returns
list of matplotlib.lines.Line2D – the output of
ax.plot()
.
See also
-
-
exception
tracklib.trajectory.
N12Error
¶ Bases:
ValueError
For indicating that you confused
N=1
andN=2
trajectories


 The day started normally I phoned my friend E and we bought some cigarettes and a 10s of skunk, went back to his house and smoked a joint. I was really stoned after that felt fairly anxious and nauseous something which has got gradually worse over the time Iíve been smoking puff.  I was totally out of it.
Iíve been really stoned before and have never been sick, Iíve smoked a half ounce to myself in a night and felt fine, but this buzz was different. I became gradually more disorientated, nauseated and paranoid.  I couldnít breathe and had a panic attack. This is when I knew my relationship with cannabis was very much over. The buzz became very psychedelic and as I was feeling sick and dizzy this just made it worse I felt very trapped. I looked at a window and the world turned upside down, and stayed that way.  I return to Eís bedroom and continued to feel very sick and disorientated for another 2 hours or so and had some mad visuals, I thought I was going mad.
When I woke up this morning I felt fine and thought I had just smoked too much too quick so stupidly decided to smoke another joint, I felt those feelings of insanity return and decided that this was it, Iíve had good times and bad with puff but why bother getting high and spending money just to feel insane!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


optimModels.optimization package¶
Submodules¶
optimModels.optimization.decoders module¶
-
class
optimModels.optimization.decoders.
Decoder
¶ Bases:
object
Abstract class with the abstract methods that must be implemented by all decoders.
-
decode_candidate
(candidate)¶
-
get_override_simul_problem
(candidate, simulProblem)¶
-
-
class
optimModels.optimization.decoders.
DecoderMedium
(ids)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the list of indexes into a list of identifiers.
Parameters: candidate (list) – indexes of parameters. Returns: list of parameters identifiers.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of identifiers into a list of integers (indexes).
Parameters: identifiers (list) – Ids of parameters Returns: List of integers (parameters indexes)
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the drains that will be open for uptake. :param candidate: indexes of reactions that will be open (drains). :type candidate: list :param simulProblem: all information required to perform a model simulation. :type simulProblem: SimulationProblem
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
-
class
optimModels.optimization.decoders.
DecoderMediumLevels
(ids, levels)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the map of type {parameterIndex : levelIndex} to a map of type {parameterId: levelOfExpression}
Parameters: candidate (dict) – The key is the parameter index and the value is the level of expression index. - Returns: A dictionary where the key is the parameter id and the value is the level of expression with values between
- 0 and 1 to represent under expression or higher that 1 to represent the over expression.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of tupples of identifiers into a list of tuples of integers (indexes).
Parameters: identifiers (list) – List of tuples whit the parameters and levels ids Returns: List of tuples indexes of parameters.
-
get_override_simul_problem
(candidate, simulProblem)¶ Function to create a instance of OverrideSimulationProblem based on the candidate given by argument. :param candidate: candidate to decode :type candidate: list :param simulProblem: Simulation problem instance.
Returns: OverrideStoicSimulProblem instance
-
-
class
optimModels.optimization.decoders.
DecoderMediumReacKO
(idsDrains, idsReactions)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the list of index into a list of identifiers. :param candidate: list of indexes of parameters :type candidate: list
Returns: list of parameters ids.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of identifiers into a list of integers (indexes). :param identifiers: parameters identifiers :type identifiers: list
Returns: List of parameters indexes.
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the drains that will be open for uptake and KO reactions. :param candidate: indexes of reactions that will be open (drains) or the flux will be 0 (internal reactions). :type candidate: list :param simulProblem: all information required to perform a model simulation. :type simulProblem: SimulationProblem
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
-
class
optimModels.optimization.decoders.
DecoderProtKnockouts
(ids)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the list of indexes into a list of identifiers.
Parameters: candidate (list) – indexes of parameters. Returns: list of parameters identifiers.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of identifiers into a list of integers (indexes).
Parameters: identifiers (list) – Ids of proteins Returns: List of integers (proteins indexes)
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the protein knockouts.
Parameters: - candidate (list) – indexes of proteins.
- simulProblem (SimulationProblem) – all information required to perform a model simulation.
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
-
class
optimModels.optimization.decoders.
DecoderProtUnderOverExpression
(ids, levels)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the map of type {proteinIndex : levelIndex} to a map of type {proteinId: levelOfExpression}
Parameters: candidate (dict) – The key is the parameter index and the value is the level of expression index. - Returns: A dictionary where the key is the protein id and the value is the level of expression with values between
- 0 and 1 to represent under expression or higher that 1 to represent the over expression.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of tupples of identifiers into a list of tuples of integers (indexes).
Parameters: identifiers (list) – List of tuples whit the proteins and levels ids Returns: List of tuples indexes.
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the under/over proteins expression. :param candidate: candidate represented using proteins and levels indexes. :type candidate: dict :param simulProblem: all information required to perform a model simulation. :type simulProblem: SimulationProblem
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
-
class
optimModels.optimization.decoders.
DecoderReacKnockouts
(ids)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the list of indexes into a list of identifiers.
Parameters: candidate (list) – indexes of parameters/reactions. Returns: list of parameters/reactions identifiers.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of identifiers into a list of integers (indexes).
Parameters: identifiers (list) – Ids of parameters/reactions Returns: List of integers (parameters indexes)
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the reactions knockouts.
Parameters: - candidate (list) – indexes of reactions.
- simulProblem (SimulationProblem) – all information required to perform a model simulation.
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
-
class
optimModels.optimization.decoders.
DecoderReacUnderOverExpression
(ids, levels)¶ Bases:
optimModels.optimization.decoders.Decoder
-
decode_candidate
(candidate)¶ Convert the map of type {parameterIndex : levelIndex} to a map of type {parameterId: levelOfExpression}
Parameters: candidate (dict) – The key is the parameter index and the value is the level of expression index. - Returns: A dictionary where the key is the parameter id and the value is the level of expression with values between
- 0 and 1 to represent under expression or higher that 1 to represent the over expression.
-
decode_candidate_ids_to_index
(identifiers)¶ Convert the list of tuples of identifiers into a list of tuples of integers (indexes).
Parameters: identifiers (list) – List of tuples whit the parameters and levels ids Returns: List of tuples indexes of reactions.
-
get_override_simul_problem
(candidate, simulProblem)¶ Build the override simulation problem which will contains the modifications that must be applied to the model in order to simulate the under/over enzymes expression. :param candidate: candidate represented using reactions and levels indexes. :type candidate: dict :param simulProblem: all information required to perform a model simulation. :type simulProblem: SimulationProblem
Returns: OverrideSimulProblem instance with the modifications to be applied over the simulation Problem.
-
optimModels.optimization.evaluation_functions module¶
-
class
optimModels.optimization.evaluation_functions.
BPCY
(biomassId, productId, uptakeId)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
This class implements the “Biomass-Product Coupled Yield” objective function. The fitness is given by the equation: (biomass_flux * product_flux)/ uptake_flux
Parameters: - biomassId (str) – Biomass reaction identifier
- productId (str) – Target product reaction identifier
- uptakeId (str) – Reaction of uptake
-
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
class
optimModels.optimization.evaluation_functions.
BP_MinModifications
(biomassId, productId)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
This class is based the “Biomass-Product Coupled Yield” objective function but considering the candidate size. The fitness is given by the equation: (biomass_flux * product_flux)/ candidate_size)
Parameters: - biomassId (str) – biomass reaction identifier
- productId (str) – target product reaction identifier
-
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
class
optimModels.optimization.evaluation_functions.
EvaluationFunction
¶ Bases:
object
This abstract class should be extended by all evaluation functions classes.
-
get_fitness
(simulationResult, candidate)¶
-
method_str
()¶
-
-
class
optimModels.optimization.evaluation_functions.
MinCandSize
(maxCandidateSize, minFluxes)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
This class implements the “minimization of number of reactions” objective function. The fitness is given by 1 - size(candidate)/ max_candidate_size, where the max_candidate_size is the maximum size that a candidate can have during optimization.
Parameters: - maxCandidateSize (int) – Maximum size allowed for candidate
- minFluxes (dict) – Minimal value for fluxes to consider fitness different of 0 (key: reaction id, value: minimum of flux).
-
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
class
optimModels.optimization.evaluation_functions.
MinCandSizeAndMaxTarget
(maxCandidateSize, maxTargetFlux)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
This evaluation function finds the solution with the minimum candidate size and levels considering the maximization of the target flux.
Parameters: - maxCandidateSize (int) – maximum of candidate size
- maxTargetFlux (str) – reaction id to maximize
-
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
class
optimModels.optimization.evaluation_functions.
MinCandSizeWithLevelsAndMaxTarget
(maxCandidateSize, levels, maxTargetFlux)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
-
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
-
class
optimModels.optimization.evaluation_functions.
TargetFlux
(targetReactionId)¶ Bases:
optimModels.optimization.evaluation_functions.EvaluationFunction
This class implements the “target flux” objective function. The fitness is given by the flux value of the target reaction.
Parameters: targetReactionId (str) – Reaction identifier of the target compound production. -
get_fitness
(simulResult, candidate)¶
-
static
get_id
()¶
-
static
get_name
()¶
-
static
get_parameters_ids
()¶
-
method_str
()¶
-
-
optimModels.optimization.evaluation_functions.
build_evaluation_function
(id, *args)¶ Function to return an evaluation function instance.
Parameters: - id (str) – Name of the objective function. The implemented objective functions should be registed in constants.objFunctions class
- *args (list of str) – The number of arguments depends of the objective function chosen by user.
Returns: return an evaluation function instance.
Return type:
optimModels.optimization.evaluators module¶
-
optimModels.optimization.evaluators.
evaluator
(candidates, args)¶ This function allows the evaluation of candidate solutions.
Parameters: - candidates (list) – A list of candidate solutions
- args (dict) – A dictionary of keyword arguments
Returns: a list of fitness values
Return type: list of floats
-
optimModels.optimization.evaluators.
parallel_evaluation_mp
(candidates, args)¶ Evaluate the candidates in parallel using
multiprocessing
.This function allows parallel evaluation of candidate solutions. It uses the standard multiprocessing library to accomplish the parallelization. The function assigns the evaluation of each candidate to its own job, all of which are then distributed to the available processing units.
Parameters: - candidates – list the candidate solutions
- args – a dictionary of keyword arguments
Returns:
Notes: All arguments to the evaluation function must be pickleable. Those that are not will not be sent through the
args
variable and will be unavailable to your function. Required keyword arguments in args: - mp_evaluator – actual evaluation function to be used (This functionshould have the same signature as any other inspyred evaluation function.)Optional keyword arguments in args:
- mp_nprocs – number of processors that will be used (default machine cpu count)
optimModels.optimization.evolutionary_computation module¶
-
class
optimModels.optimization.evolutionary_computation.
EAConfigurations
¶ Bases:
object
Basic configurations to Evolutionary Algorithm.
-
get_default_config
()¶
-
-
class
optimModels.optimization.evolutionary_computation.
OptimProblemConfiguration
(simulationProblem=None, type=None, decoder=None, evaluationFunc=None, EAConfig=None, scaleProblem=1)¶ Bases:
object
This class contains all information to perform a strain optimization
-
get_decoder
()¶
-
get_ea_configurations
()¶
-
get_evaluation_function
()¶
-
get_number_reactions
()¶
-
get_simulation_problem
()¶
-
-
optimModels.optimization.evolutionary_computation.
run_optimization
(optimProbConf, resultFile=None, isMultiProc=False, population=None)¶ Function to perform the optimization using the integer set representation to the candidates solutions.
Args: optimProbConf (OptimProblemConfiguration): This object contains all information to perform the strain optimization task. resultFile (str): The path file to store all the results obtained during the optimization (default results are not saved into a file) isMultiProc (bool): True, if the user wants parallelize the population evaluation. (default False)
- Returns
- list: the individuals of the last population.
optimModels.optimization.generators module¶
-
optimModels.optimization.generators.
generator_single_int_set
(random, args)¶ Function to generate a new individual using the integer set representation. The function returns a set of integer values with a maximum of candidate_max_size elements.
Parameters: - random – the random number generator object
- args (dict) – a dictionary of keyword arguments
Returns: a new individual
Return type: set
Notes
Required arguments in args: - candidate_max_size : number of integer values which compose a individual. - _ec : configuration of evolutionary computation. The argument bounder is required to get the maximum value allowed for the individual values.
-
optimModels.optimization.generators.
generator_single_int_tuple
(random, args)¶ Function to generate a new individual using the integer tuple representation. The function returns a set of tuples values with a maximum of candidate_max_size elements.
Parameters: - random – the random number generator object
- args (dict) – keyword arguments
Returns: a new individual where each element is composed by a tuple.
Return type: set
Notes
Required arguments in args: - candidate_max_size : number of integer values which compose a individual. - _ec : configuration of evolutionary computation. The argument bounder is required to get the maximum value allowed for the individual values.
-
optimModels.optimization.generators.
generator_tuple_int_set
(random, args)¶ Function to generate a new individual using the integer representation. The function returns two integer sets.
Parameters: - random – the random number generator object
- args (dict) – keyword arguments
Returns: returns a tuple with two integer sets.
Return type: tuple
optimModels.optimization.observers module¶
-
optimModels.optimization.observers.
load_population
(initPopFile=None, decoder=None)¶ Parameters: - initPopFile (str) – file name with the population to be loaded.
- decoder (Decoder) – decoder to decode the candidates.
Returns: number of the generation population (list): list of candidates fitness (list): fitness values for each candidate
Return type: num_generations(str)
-
optimModels.optimization.observers.
save_all_results
(population, num_generations, num_evaluations, args)¶ Print the output of the evolutionary computation to a file with the follow fields: - number of generation - fitness of candidate - the solution candidates - the solution encoded candidates
Parameters: - population (list) – the population of Individuals
- num_generations (int) – the number of elapsed generations
- num_evaluations (int) – the number of evaluations already performed
- args (dict) – a dictionary of keyword arguments
Notes
Optional keyword arguments in args: - results_file – the file path of the result file - configuration – the configuration of the EA algorithm
optimModels.optimization.replacers module¶
-
optimModels.optimization.replacers.
new_candidates_no_duplicates_replacement
(random, population, parents, offspring, args)¶ Performs replacement for the offspring.
This function performs a new candidates replacement, which means that the offspring replace the least fit individuals in the existing population, even if those offspring are less fit than the individuals that they replace.Moreover a new set of candidates random generated will be added to the population. The duplicated candidates are replaced by new ones randomly generated.
Parameters: - random – the random number generator object
- population – the population of individuals
- parents – the list of parent individuals
- offspring – the list of offspring individuals
- args – a dictionary of keyword arguments
Returns: Returns the new population that will be used to next generation.
Return type: list
-
optimModels.optimization.replacers.
new_candidates_replacement
(random, population, parents, offspring, args)¶ Performs replacement for the offspring.
This function performs a new candidates replacement, which means that the offspring replace the least fit individuals in the existing population, even if those offspring are less fit than the individuals that they replace. Moreover a new set of candidates random generated will be added to the population
Parameters: - random – the random number generator object
- population – the population of individuals
- parents – the list of parent individuals
- offspring – the list of offspring individuals
- args – a dictionary of keyword arguments
Returns: Returns the new population that will be used to next generation.
Return type: list
optimModels.optimization.run module¶
-
optimModels.optimization.run.
cbm_strain_optim
(simulProblem, evaluationFunc, levels, type=1, criticalReacs=[], isMultiProc=False, candidateSize=None, resultFile=None, initPopFile=None)¶ Function to run the optimization process :param simulProblem: Simulation problem :type simulProblem: StoicSimulationProblem :param evaluationFunc: evaluation function used in EA to calculate the fitness value of each candidate :param levels: List for under/expression of proteins (the levels should be set when type is *_UO :type levels: list :param type: type of optimization ( by default Reaction knockouts) :param criticalReacs: list of reactions that should not be considered in the optimization :type criticalReacs: list :param isMultiProc: use multiprocessing :type isMultiProc: bool :param candidateSize: (by default use the size defined in EAConfigurations.MAX_CANDIDATE_SIZE) :type candidateSize: int :param resultFile: File to store the solutions of each generation :type resultFile: string :param initPopFile: File with the initial population :type initPopFile: string
Returns (list): Returns the list of the best modifications.
-
optimModels.optimization.run.
findBestSolutions
(population, eaConfig)¶ Function to get the best individuals of a populations according to their fitness value. The number of individuals to return is given by EAConfigurations.NUM_BEST_SOLUTIONS parameter.
Parameters: - population (list) – list of individuals returned by EA
- eaConfig (EAConfigurations) – EA configuration object
Returns: list of best individuals
-
optimModels.optimization.run.
gecko_strain_optim
(simulProblem, evaluationFunc, levels, type=10, criticalProteins=[], isMultiProc=False, candidateSize=None, resultFile=None, initPopFile=None)¶ Function to run the optimization process. :param simulProblem: Simulation problem :type simulProblem: GeckoSimulationProblem :param evaluationFunc: evaluation function used in EA to calculate the fitness value of each candidate :param levels: List for under/expression of proteins (the levels should be set when type is PROTEIN_UO :type levels: list :param type: type of optimization ( by default Protein knockouts) :param criticalProteins: list of proteins that should not be considered in the optimization :type criticalProteins: list :param isMultiProc: use multiprocessing :type isMultiProc: bool :param candidateSize: (by default use the size defined in EAConfigurations.MAX_CANDIDATE_SIZE) :type candidateSize: int :param resultFile: File to store the solutions of each generation :type resultFile: string :param initPopFile: File with the initial population :type initPopFile: string
Returns (list): Returns the list of the best modifications.
-
optimModels.optimization.run.
kinetic_strain_optim
(simulProblem, objFunc=None, levels=None, type=1, criticalParameters=[], isMultiProc=False, candidateSize=None, resultFile=None, initPopFile=None)¶ Function to run the optimization process :param simulProblem: Simulation problem :type simulProblem: KineticSimulationProblem :param evaluationFunc: evaluation function used in EA to calculate the fitness value of each candidate :param levels: List for under/expression of proteins (the levels should be set when type is *_UO :type levels: list :param type: type of optimization ( by default Reaction knockouts) :param criticalReacs: list of reactions that should not be considered in the optimization :type criticalReacs: list :param isMultiProc: use multiprocessing :type isMultiProc: bool :param candidateSize: (by default use the size defined in EAConfigurations.MAX_CANDIDATE_SIZE) :type candidateSize: int :param resultFile: File to store the solutions of each generation :type resultFile: string :param initPopFile: File with the initial population :type initPopFile: string
- Returns (list): Returns the list of the best modifications.
- The function returns the best solutions found in strain optimization. The kineticSimulationResults object has the flux distribution and metabolites concentration at steady-state, and the modifications made over the original model.
-
optimModels.optimization.run.
print_results
(fileName, population, optimConfig)¶ - Save the results of a generation in the selected file.
- Args:
- fileName (string): file where the results are stored. If the file already exist append the new information. population: list of candidates optimConfig: optimization configuration object with the decoder of solutions
Returns:
-
optimModels.optimization.run.
simplifySolutions
(optimProbConf, population)¶ Simplify the solution by removing the modification that not affect the final fitness value. :param optimProbConf: The configuration problem. :type optimProbConf: optimProblemConfiguration :param population: List of individuals returned by EA algorithm. :type population: list
- Returns: list of SimulationResults
- The function returns the best solutions found in strain optimization. The SimulationResults object has the flux distribution, and the modifications made over the original model.
optimModels.optimization.variators module¶
-
optimModels.optimization.variators.
grow_mutation_intSetRep
(random, candidate, args)¶ Returns the mutant produced by a grow mutation on the candidate (when the representation is a set of integers). If a candidate solution has the maximum size candidate allowed, this function leaves it unchanged.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: new candidate
Return type: set
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
grow_mutation_intTupleRep
(random, candidate, args)¶ Returns the mutant produced by a grow mutation on the candidate (when the representation is a set of tuples). If a candidate solution has the maximum size candidate allowed, this function leaves it unchanged.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: Returns a new candidate
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
grow_mutation_tuple_intSetRep
(random, candidate, args)¶ Returns the mutant produced by a grow mutation on the candidate (when the representation is a set of integers). If a candidate solution has the maximum size candidate allowed, this function leaves it unchanged.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: Returns a new candidate
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
shrink_mutation
(random, candidate, args)¶ Returns the mutant produced by shrink mutation on the candidate. If a candidate solution has length of 1, this function leaves it unchanged.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: new candidate
Return type: set
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
shrink_mutation_tuple
(random, candidate, args)¶ Returns the mutant produced by shrink mutation on the candidate. If a candidate solution has length of 1, this function leaves it unchanged.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: Returns a new candidate
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
single_mutation_intSetRep
(random, candidate, args)¶ Returns the mutant produced by a single mutation on the candidate (when the representation is a set of integers). The candidate size is maintained.
Parameters: - random (the random number generator object) –
- candidate (the candidate solution) –
- args (a dictionary of keyword arguments) –
Returns: - out (new candidate)
- Optional keyword arguments in args
- - *mutation_rate – the rate at which mutation is performed (default 0.1)*
-
optimModels.optimization.variators.
single_mutation_intTupleRep
(random, candidate, args)¶ Returns the mutant produced by a single mutation on the candidate (when the representation is a set of tuples). The candidate size is maintained.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: Returns a new candidate
Optional keyword arguments in args:
- mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
single_mutation_tuple_intSetRep
(random, candidate, args)¶ Returns the mutant produced by a single mutation on the candidate (when the representation is a set of integers). The candidate size is maintained.
Parameters: - random – the random number generator object
- candidate – the candidate solution
- args – a dictionary of keyword arguments
Returns: Returns a new candidate
Notes
Optional keyword arguments in args: - mutation_rate – the rate at which mutation is performed (default 0.1)
-
optimModels.optimization.variators.
uniform_crossover
(random, mom, dad, args)¶ Return the offspring of the uniform crossover on the candidate. Based on two candidates (parents) build 2 children: - elements present in both parents will be present in both children; - both children have at least one element; - elements present in only one parent have equal probability to be present in child 1 or child 2 (after each child has at least one element).
Parameters: - random – the random number generator object
- mom – the first parent candidate
- dad – the second parent candidate
- args – a dictionary of keyword arguments
Returns: Return the offspring of the candidates given as argument.
Notes
Optional keyword arguments in args: - crossover_rate – the rate at which crossover is performed (default 1.0)
-
optimModels.optimization.variators.
uniform_crossover_intTupleRep
(random, mom, dad, args)¶ Return the offspring of the uniform crossover on the candidate. Based on two candidates (parents) build 2 children: - elements present in both parents will be present in both children; - both children have at least one element; - elements present in only one parent have equal probability to be present in child 1 or child 2 (after each child has at least one element).
Parameters: - random – the random number generator object
- mom – the first parent candidate
- dad – the second parent candidate
- args – a dictionary of keyword arguments
Returns: Returns the offspring of the candidates given as argument.
Notes
Optional keyword arguments in args: - crossover_rate – the rate at which crossover is performed (default 1.0)
-
optimModels.optimization.variators.
uniform_crossover_tuple
(random, mom, dad, args)¶ Return the offspring of the uniform crossover on the candidate. Based on two candidates (parents) build 2 children: - elements present in both parents will be present in both children; - both children have at least one element; - elements present in only one parent have equal probability to be present in child 1 or child 2 (after each child has at least one element).
Parameters: - random – the random number generator object
- mom – the first parent candidate
- dad – the second parent candidate
- args – a dictionary of keyword arguments
Returns: Return the offspring of the candidates given as argument.
Notes
Optional keyword arguments in args: - crossover_rate – the rate at which crossover is performed (default 1.0)


Choose a CSV file to randomly pull names from. The file should have one name per line.
Review the names below, if any are incorrect or not a name:
1. Correct the file
2. Save it
3. Chose the file again to refresh the names
When ready, press the Pull all button to randomly select 25 names.
Version: 1.0.0


| 7:30 PM | President Calls Meeting to Order | Ger O'Sullivan |
| 7:40 PM | Toastmaster Introduces:- | |
| 8:00 PM | Toastmaster Introduces Evaluators & Speakers |
| Timer, Ah-Counter, Grammarian, General Evaluator provides meeting & leader evaluations | ||
| 9:00 PM | President Makes Closing Remarks. Welcomes Guests, and Meeting Adjourns | Ger O'Sullivan |


|
ActiveTcl User Guide
|
||
selection - Manipulate the X selection
selection option ?arg arg ...?
This command provides a Tcl interface to the X selection mechanism and implements the full selection functionality described in the X Inter-Client Communication Conventions Manual (ICCCM).
Note that for management of the CLIPBOARD selection (see below), the clipboard command may also be used.
The first argument to selection determines the format of the rest of the arguments and the behavior of the command. The following forms are currently supported:
When selection is requested, window is the selection owner, and type is the requested type, command will be executed as a Tcl command with two additional numbers appended to it (with space separators). The two additional numbers are offset and maxChars: offset specifies a starting character position in the selection and maxChars gives the maximum number of characters to retrieve. The command should return a value consisting of at most maxChars of the selection, starting at position offset. For very large selections (larger than maxChars) the selection will be retrieved using several invocations of command with increasing offset values. If command returns a string whose length is less than maxChars, the return value is assumed to include all of the remainder of the selection; if the length of command's result is equal to maxChars then command will be invoked again, until it eventually returns a result shorter than maxChars. The value of maxChars will always be relatively large (thousands of characters).
If command returns an error then the selection retrieval is rejected just as if the selection didn't exist at all.
The format argument specifies the representation that should be used to transmit the selection to the requester (the second column of Table 2 of the ICCCM), and defaults to STRING. If format is STRING, the selection is transmitted as 8-bit ASCII characters (i.e. just in the form returned by command). If format is ATOM, then the return value from command is divided into fields separated by white space; each field is converted to its atom value, and the 32-bit atom value is transmitted instead of the atom name. For any other format, the return value from command is divided into fields separated by white space and each field is converted to a 32-bit integer; an array of integers is transmitted to the selection requester.
The format argument is needed only for compatibility with selection requesters that don't use Tk. If Tk is being used to retrieve the selection then the value is converted back to a string at the requesting end, so format is irrelevant.
The second form of selection own causes window to become the new owner of selection on window's display, returning an empty string as result. The existing owner, if any, is notified that it has lost the selection. If command is specified, it is a Tcl script to execute when some other window claims ownership of the selection away from window. Selection defaults to PRIMARY.
clear, format, handler, ICCCM, own, selection, target, type
Copyright © 1990-1994 The Regents of the University of California. Copyright © 1994-1996 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


§431:1-213 State defined. State means any state of the United States and the governments of Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, the United States Virgin Islands, and the District of Columbia. [L 1987, c 347, pt of §2; am L 1997, c 233, §2; am L 2011, c 68, §2]


Class: Services_Trackback
Source Location: /library/pear/Services/Trackback.php
Class Overview
Author(s):
- Tobias Schlitt<EMAIL_ADDRESS>Version:
- Release: 0.5.0
Copyright:
- 1997-2005 The PHP Group
Class Details
Trackback A generic class to send/receive trackbacks.
Tags:
| author: | Tobias Schlitt<EMAIL_ADDRESS>|
| version: | Release: 0.5.0 |
| copyright: | 1997-2005 The PHP Group |
| link: | http://pear.php.net/package/Services_Trackback |
| since: | 0.1.0 |
| access: | public |
| license: | PHP License 3.0 |
[ Top ]
Class Variables
$_data = array(
'id'=>'',
'title'=>'',
'excerpt'=>'',
'blog_name'=>'',
'url'=>'',
'trackback_url'=>'',
'host'=>'',
)
[line 93]
The necessary trackback data.
Tags:
| since: | 0.1.0 |
| access: | protected |
| Type: | array |
$_options = array(
//OptionsforServices_Trackbackdirectly
'strictness'=>SERVICES_TRACKBACK_STRICTNESS_LOW,
'timeout'=>30,//seconds
'fetchlines'=>30,
//OptionsforHTTP_Requestclass
'httpRequest'=>array(
'allowRedirects'=>true,
'maxRedirects'=>2,
'useragent'=>'PEAR::Services_Trackbackv0.5.0'
),)
[line 115]
Options to influence Services_Trackback.
Tags:
| see: | Services_Trackback::create() |
| since: | 0.4.0 |
| access: | protected |
| Type: | array |
$_spamChecks = array()
[line 131]
| Type: | mixed |
Class Methods
constructor Services_Trackback [line 144]
void Services_Trackback(
)
Constructor Creates a new Trackback object. Private because of factory use.
Tags:
| since: | 0.1.0 |
| access: | protected |
[ Top ]
method addSpamCheck [line 539]
mixed &addSpamCheck(
&$spamCheck, [int
$priority = 0], object(Services_Trackback_SpamCheck)
$spamCheck)
addSpamCheck Add a spam check module to the trackback.
Tags:
| return: | Added SpamCheck module instance on success, otherwise PEAR_Error. |
| see: | Services_Trackback::checkSpam() |
| see: | Services_Trackback::removeSpamCheck() |
| since: | 0.5.0 |
| access: | public |
Parameters:
| object(Services_Trackback_SpamCheck) | $spamCheck | The spam check module to add. |
| int | $priority | A priority value for the spam check. Lower priority indices are processed earlier. If no priority level is set, 0 is assumed. |
| &$spamCheck |
[ Top ]
method autodiscover [line 273]
bool autodiscover(
)
autodiscover Checks a given URL for trackback autodiscovery code.
Tags:
| return: | True on success, otherwise PEAR_Error. |
| since: | 0.2.0 |
| access: | public |
[ Top ]
method checkSpam [line 631]
bool checkSpam(
[
$continouse = false], bool
$continouseCheck)
checkSpam Checks the given trackback against several spam protection sources such as DNSBL, SURBL, Word BL,... The sources to check are defined using Services_Trackback_SpamCheck modules.
Tags:
| return: | True, if one of the sources |
| see: | Services_Trackback::removeSpamCheck() |
| see: | Services_Trackback::addSpamCheck() |
| since: | 0.5.0 |
| access: | public |
Parameters:
| bool | $continouseCheck | Wether to check all spam protection modules or quit checking if one modules returns a positive result. |
| $continouse |
[ Top ]
method create [line 187]
object(Services_Trackback) &create(
array
$data, [array
$options = null])
Factory
This static method is used to create a trackback object. (Services_Trackback::create($data)) The factory requires a data array as described below for creation. The 'id' key is obligatory for this method. Every other data is not quite necessary for the creation, but might be necessary for calling other methods afterwards. See the specific methods for further info on which data is required.Tags:
| return: | The newly created Trackback. |
| see: | HTTP_Request::HTTP_Request() for more detailed documentation. Some options for HTTP_Request are overwritten through the global settings of Services_Trackback (such as timeout). 'timeout': float THE TIMEOUT SETTING IS OVERWRITTEN BY THE GLOBAL Services_Trackback SETTING. 'allowRedirects': bool Wether to follow HTTP redirects or not. 'maxRedirects': int Maximum number of redirects. 'useragent': string The user agent to use for HTTP requests. |
| since: | 0.2.0 |
| access: | public |
Parameters:
| array | $data | Data for the trackback, which is obligatory: 'id' The ID of the trackback target. Data which is optional (for construction, not for specific methods): 'title' string Title of the trackback sending/receiving blog entry. 'excerpt' string Abstract of the trackback sending/receiving blog entry. 'blog_name' string Name of the trackback sending/receiving weblog. 'url' string URL of the trackback sending/receiving blog entry. 'trackback_url' string URL to send trackbacks to. |
| array | $options | Options to set for this trackback. Valid options: 'strictness': int The default strictness to use in @see Services_Trackback::autodiscover(). 'timeout': int The default timeout for network operations in seconds. 'fetchlines': int The max number of lines to fetch over the network. 'httpRequest' array The options utilized by HTTP_Request are stored here. The following options are the most commonly used for HTTP_Request in Services_Trackback. All other options are supported too, |
[ Top ]
method createSpamCheck [line 566]
mixed &createSpamCheck(
string
$spamCheckType, [array
$options = array()], [int
$priority = 0])
createSpamCheck Create and add a spam check module to the trackback.
Tags:
| return: | Instance of the created SpamCheck module or PEAR_Error. |
| see: | Services_Trackback::checkSpam() |
| see: | Services_Trackback::removeSpamCheck() |
| see: | Services_Trackback::addSpamCheck() |
| since: | 0.5.0 |
| access: | public |
Parameters:
| string | $spamCheckType | Name of the spamcheck module to create and add. |
| array | $options | Options for the spamcheckmodule. |
| int | $priority | A priority value for the spam check. Lower priority indices are processed earlier. If no priority level is set, 0 is assumed. |
[ Top ]
method get [line 662]
mixed get(
string
$key)
get Get data from the trackback. Returns the value of a given key or PEAR_Error.
Tags:
| return: | A string value or a PEAR_Error on failure. |
| since: | 0.2.0 |
| access: | public |
Parameters:
| string | $key | The key to fetch a value for. |
[ Top ]
method getAutodiscoveryCode [line 417]
string getAutodiscoveryCode(
[bool
$comments = true])
getAutodiscoverCode Returns the RDF Code for a given website to let weblogs autodiscover the possibility of tracking it back.
The following data has to be set to call this method: 'id' 'title' 'url' 'trackback_url'Tags:
| return: | RDF code |
| since: | 0.1.0 |
| access: | public |
Parameters:
| bool | $comments | Whether to include HTML comments around the RDF using <!-- --> |
[ Top ]
method getOptions [line 257]
array getOptions(
)
getOptions Get the currently set option set.
Tags:
| return: | The currently active options. |
| see: | Services_Trackback::create() |
| see: | Services_Trackback::setOptions() |
| since: | 0.4.0 |
| access: | public |
[ Top ]
method getResponseError [line 511]
void getResponseError(
string
$message, int
$code)
getResponseError Returns an XML response for a trackback error.
Tags:
| since: | 0.1.0 |
| access: | public |
Parameters:
| int | $code | The error code |
| string | $message | The error message |
[ Top ]
method getResponseSuccess [line 489]
string getResponseSuccess(
)
getResponseSuccess Returns an XML response for a successful trackback.
Tags:
| return: | The XML code |
| since: | 0.1.0 |
| access: | public |
[ Top ]
method receive [line 456]
object Services_Trackback receive(
[
$data = null])
receive
Receives a trackback. The following data has to be set in the data array to fulfill this operation: 'id'Tags:
| since: | 0.1.0 |
| access: | public |
Parameters:
| $data |
[ Top ]
method removeSpamCheck [line 601]
bool removeSpamCheck(
object(Services_Trackback_SpamCheck)
&$spamCheck)
removeSpamCheck Remove a spam check module from the trackback.
Tags:
| return: | True on success, otherwise PEAR_Error. |
| see: | Services_Trackback::checkSpam() |
| see: | Services_Trackback::addSpamCheck() |
| since: | 0.5.0 |
| access: | public |
Parameters:
| object(Services_Trackback_SpamCheck) | &$spamCheck | The spam check module to remove. |
[ Top ]
method send [line 346]
mixed send(
[string
$data = null])
send This method sends a trackback to the trackback_url saved in it. The data array of the trackback object can be completed by submitting the necessary data through the $data parameter of this method.
The following data has to be set to call this method: 'title' Title of the weblog entry sending the trackback. 'url' URL of the weblog entry sending the trackback. 'excerpt' Excerpt of the weblog entry sending the trackback. 'blog_name' Name of the weblog sending the trackback. 'trackback_url' URL to send the trackback to. Services_Trackback::send() requires PEAR::HTTP_Request. The options for the HTTP_Request object are stored in the global options array using the key 'http_request'.Tags:
| return: | True on success, otherwise PEAR_Error. |
| since: | 0.3.0 |
| access: | public |
Parameters:
| string | $data | Additional data to complete the trackback. |
[ Top ]
method set [line 681]
mixed set(
string
$key, string
$val)
set Set data of the trackback. Saves the value of a given key , returning true on success, PEAR_Error on faulure.
Tags:
| return: | Boolean true on success or a PEAR_Error on failure. |
| since: | 0.2.0 |
| access: | public |
Parameters:
| string | $key | The key to set a value for. |
| string | $val | The value for the key. |
[ Top ]
method setOptions [line 217]
mixed setOptions(
array
$options)
setOptions Set options for the trackback.
Tags:
| return: | Bool true on success, otherwise PEAR_Error. |
| see: | Services_Trackback::getOptions() |
| see: | Services_Trackback::create() |
| since: | 0.4.0 |
| access: | public |
Parameters:
| array | $options | Pairs of 'option' => 'value' as described at @see Services_Trackback::create(). |
[ Top ]
method _checkData [line 801]
void _checkData(
array
$keys, [array
$data = null])
_checkData Checks a given array of keys for the validity of their data.
Tags:
| since: | 0.1.0 |
| access: | protected |
Parameters:
| array | $keys | Data keys to check. |
| array | $data | Optionally the data to check (default is the object data). |
[ Top ]
method _checkURLs [line 831]
mixed _checkURLs(
string
$url1, string
$url2, constant
$strictness)
_checkURLs This little method checks if 2 URLs (the URL to trackback against the trackback identifier found in the autodiscovery code) are equal.
Tags:
| return: | True on success, otherwise PEAR_Error. |
| see: | Services_Trackback::autodiscover() |
| since: | 0.2.0 |
| access: | protected |
Parameters:
| string | $url1 | The first URL. |
| string | $url2 | The second URL. |
| constant | $strictness | How strict to check URLs. Use one of SERVICES_TRACKBACK_STRICTNESS_* constants. |
[ Top ]
method _fromArray [line 698]
mixed _fromArray(
array
$data)
Create a Trackback from a $data array.
Tags:
| return: | True on success, otherwise PEAR_Error. |
| since: | 0.2.0 |
| access: | protected |
Parameters:
| array | $data | The data array (@see Services_Trackback::create()). |
[ Top ]
method _getContent [line 720]
string _getContent(
string
$url)
_getContent Receive the content from a specific URL.
Tags:
| return: | The content. |
| since: | 0.4.0 |
| access: | protected |
Parameters:
| string | $url | The URL to download data from. |
[ Top ]
method _getDecodedData [line 775]
void _getDecodedData(
array
$keys, [array
$data = null])
_getDecodedData Receives a number of data from the internal data store.
Tags:
| since: | 0.1.0 |
| access: | protected |
Parameters:
| array | $keys | Data keys to receive |
| array | $data | Optionally the data to check (default is the object data). |
[ Top ]
method _getEncodedData [line 749]
void _getEncodedData(
array
$keys, [array
$data = null])
_getEncodedData Receives a number of data from the internal data store, encoded for XML usage.
Tags:
| since: | 0.1.0 |
| access: | protected |
Parameters:
| array | $keys | Data keys to receive |
| array | $data | Optionally the data to check (default is the object data). |
[ Top ]
method _interpretTrackbackResponse [line 876]
void _interpretTrackbackResponse(
$response)
Interpret the returned XML code, when sending a trackback.
Tags:
| return: | Mixed true on success, otherwise PEAR_Error. |
| see: | Services_Trackback::send() |
| since: | 0.3.0 |
| access: | protected |
Parameters:
| $response |
[ Top ]


iterami
/
Docs.htm
/
Repositories
/
Platform-2D.sh
Disable infinite terminal scrollback.
Use "bash platform-2d.sh" to play.
"ENTER" "a" or "d" to move.


--- title: DC Taxi Public Notice layout: default ---
Thank you for contacting the District of Columbia Taxicab Industry Co-op. Membership in the District of Columbia Taxicab Industry Co-op is limited to those persons, companies and associations included at 31 DCMR § 1613.15(8)(j). To register as a member please complete the membership form below
The first meeting of the District of Columbia Taxicab Industry Co-op is scheduled for December 21, 2016 at 10:00 am at 3399 Benning Road NE, Rear Training Facility, Washington, DC 20019.


Burushãski Girminas keyboard
2018 © Quwat K. Sunny
Version 1.0
Description
Burushãski Girminãs is a Latin-base keyboard developed by Quwat K. Sunny for Burushãski of yasin while he was working on a large Burushãski (Yasin) project. It can be used flawlessly in other variants of Burushaski as well as shina and khowar.
Links
Homepage: https://keyman.com/keyboards/burushaski_girminas
Sponsored by: http://www.dardistantimes.com
Supported Platforms
* Windows
* macOS
* Linux
* Web
* iPhone
* iPad
* Android phone
* Android tablet
* Mobile devices
* Desktop devices
* Tablet devices


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.collections4.iterators; | |
| 18 | ||
| 19 | import org.apache.commons.collections4.OrderedMapIterator; | |
| 20 | import org.apache.commons.collections4.ResettableIterator; | |
| 21 | ||
| 22 | /** | |
| 23 | * Provides an implementation of an empty ordered map iterator. | |
| 24 | * | |
| 25 | * @since 3.1 | |
| 26 | * @version $Id: EmptyOrderedMapIterator.java 1543955 2013-11-20 21:23:53Z tn $ | |
| 27 | */ | |
| 28 | public class EmptyOrderedMapIterator<K, V> extends AbstractEmptyMapIterator<K, V> | |
| 29 | implements OrderedMapIterator<K, V>, ResettableIterator<K> { | |
| 30 | ||
| 31 | /** | |
| 32 | * Singleton instance of the iterator. | |
| 33 | * @since 3.1 | |
| 34 | */ | |
| 35 | @SuppressWarnings("rawtypes") | |
| 36 | public static final OrderedMapIterator INSTANCE = new EmptyOrderedMapIterator<Object, Object>(); | |
| 37 | ||
| 38 | /** | |
| 39 | * Get a typed instance of the iterator. | |
| 40 | * @param <K> the key type | |
| 41 | * @param <V> the value type | |
| 42 | * @return {@link OrderedMapIterator}<K, V> | |
| 43 | */ | |
| 44 | @SuppressWarnings("unchecked") | |
| 45 | public static <K, V> OrderedMapIterator<K, V> emptyOrderedMapIterator() { | |
| 46 |
1
1. emptyOrderedMapIterator : mutated return of Object value for org/apache/commons/collections4/iterators/EmptyOrderedMapIterator::emptyOrderedMapIterator to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return (OrderedMapIterator<K, V>) INSTANCE; |
| 47 | } | |
| 48 | ||
| 49 | /** | |
| 50 | * Constructor. | |
| 51 | */ | |
| 52 | protected EmptyOrderedMapIterator() { | |
| 53 | super(); | |
| 54 | } | |
| 55 | ||
| 56 | } | |
Mutations | ||
| 46 |
1.1 |


From<EMAIL_ADDRESS>Date: 12/23/02
Phil, you are now subscribed to the list. does the mail reach you?
- Suman
This archive was generated by hypermail 2.1.5 : 12/23/02 EST


Various processing tools such as data cleaning and preprocessing for further analysis are provided here. Visit https://github.com/paulozk/Airbnb for documentation on this API.


| Interface | Description |
|---|---|
| CacheInterface | |
| CCFileInterface | |
| ConfidentialModeInterface | |
| MultiAreaInterface |
Interface specific to Tags supporting several memory areas
|
| RandomNumberInterface | |
| RegisterInterface |
Register interface
|
| RFReaderInterface | |
| SectorInterface |
Interface specific to Tags supporting several memory areas
|
| STRegister.RegisterListener | |
| SysFileInterface |
| Class | Description |
|---|---|
| Crc |
Helper to compute CRC from
The aim of an error detection technique is to enable the receiver of a
message transmitted through a noisy (error-introducing) channel to
determine whether the message has been corrupted.
|
| CrcModel |
Helper to handler CRC
|
| Helper | |
| NFCTag | |
| STDynamicRegister | |
| STException | |
| STLog |
Static class used for logging in STSDK
|
| STRegister | |
| TagCache | |
| TagHelper |
COPYRIGHT 2017 STMicroelectronics. All rights reserved.


{% extends 'layout-2-1-bleedbar.html' %} {# HEAD items #} {% block title -%} Page Title | Consumer Financial Protection Bureau {%- endblock title %} {% block desc -%} Our vision is a consumer finance marketplace that works for American consumers, responsible providers, and the economy as a whole. {%- endblock desc %} {% block og_image -%} {# Optional property if you want to use Twitter's large card format https://developer.twitter.com/en/docs/tweets/optimize-with-cards/overview/summary-card-with-large-image #} {%- endblock og_image %} {% block og_desc -%} {{ self.desc() }} {%- endblock og_desc %} {% block css -%} {{ super() }} {%- endblock css %} {# BODY items #} {% block content_main scoped %}