-
How do I register GCT Challenge Workshop taking place on September 29-30?
-
Do I need to come to the kick off meeting with a finished project?
What is the Global City Teams Challenge
The Global City Teams Challenge is a collaboration between US Ignite and National Institute of Standards and Technology (NIST) designed to advance the deployment of Internet of Things (IoT) and Cyber-Physical Systems (CPS) technologies within a smart city/smart community environment. Current partners include National Science Foundation (NSF), Department of Transportation (DoT), and Department of Health and Human Services (HHS), as well as private sector corporations such as IBM, Intel, Qualcomm, Cisco, Juniper Networks, Extreme Networks, and ARM Holdings. Voluntary participants work collaboratively over the coming year to build, deploy and test transformative IoT/CPS applications within communities across the U.S. The Global City Teams Challenge builds upon the success of NIST’s 2013-2014 SmartAmerica initiative in which more than 100 organizations formed 24 teams.-
Who can participate?
The Challenge is open to any organization or individual willing to work in collaborative teams on IoT projects on a voluntary basis. We welcome participation from communities of all sizes around the world, working in partnership with industry, academia, not-for-profit and government to identify and share best practices and resources. -
How do I register my interest in The GCT Challenge?
To register your interest in the Global City Teams Challenge visit http://us-ignite.org/globalcityteams. -
How do I register GCT Challenge Workshop taking place on September 29-30?
To register and attend the the Global City Teams Challenge workshop to be held September 29th-30th on the NIST campus in Gaithersburg, MD go to http://www.nist.gov/cps/global-city-teams-challenge-workshop.cfm.PLEASE NOTE: NIST is a controlled-access facility. In order to gain access to the grounds you must fill out the registration on the NIST site. http://www.nist.gov/cps/global-city-teams-challenge-workshop.cfm
Can’t attend in person? The workshop will also be webcast for interested participants unable to attend the event. As the date of the workshop approaches, please visit http://us-ignite.org/globalcityteams or NIST registration site for more information.
What are key dates for the initiative?
NIST and US Ignite will launch the Challenge with a workshop on September 29-30, 2014. Viable teams will be formed at this event. In winter 2015, these teams will present their progress and discuss challenges. In June 2015, teams will present their work at the Global City Teams Festival.What types of projects are you looking for?
We anticipate that Global City Teams will be formed around projects connecting smart devices and systems that provide tangible benefits in areas including disaster response, economic development, energy, healthcare, homeland security, energy efficiency environment, transportation and many other sectors.-
What are the goals of the Global City Teams Challenge?
Through the GCT Challenge NIST and US Ignite seek to encourage the development of public-private partnerships that will accelerate IoT deployments within smart city/smart community environments. Additionally, project outcomes reported by the Challenge’s participants will help inform the efforts of NIST's Cyber-Physical Systems Program Office and its initiatives to develop technology-neutral standards that will facilitate IoT R&D and catalyze IoT adoption in the U.S. and around the world. What is the Internet of Things?
The Internet of Things (IoT) has many definitions. For the conveners of the Global City Teams Challenge, IoT is a concept that describes a future where devices, systems, and services around the world are interconnected across a common network (typically the Internet) and are able to interact in ways that fundamentally enhance they way we live our lives. Far beyond current concepts of machine-to-machine communications, the IoT is expected to offer advanced connectivity of devices, systems, and services that span all industry sectors and communities. From heart monitoring implants, to smart energy grids, to automobiles with built-in sensors, and emergency response services that assist firefighters and police officers in search and rescue, the IoT has incredible potential.-
How will participation benefit my community?
Participation provides forward-looking communities with opportunities to: 1) Showcase your community as a ready partner to public and private sector organizations seeking to fund research related to IoT implementations; 2) Collaborate with other innovative local governments and non-profits, thereby expanding and enhancing the value of your community’s current investments in advanced networking and IoT 3) Develop partnerships with leading corporations conducting research in the IoT sector -
Why should my company or not-for-profit participate?
Participation provides organizations the ability to showcase, deploy and/or assess IoT solutions. There will be opportunities to: 1) Showcase the value of your technologies/solutions to large number of potential customers; 2) Collaborate with leading developers of IoT technologies and create interconnected IoT solutions with greater utility to potential customers; and 3) Contribute to NIST’s effort to develop technical standards related to IoT. -
Do I need to come to the kick off meeting with a finished project?
Interested parties do not need to have fully formed projects before the kickoff workshop. However we encourage interested parties to develop "building blocks" (technologies or products in the case of innovators, and project concepts or requirements in the case of city planners) before the workshop and submit a one-page overview of these building blocks. The goal of the kickoff meeting is to match technology partners with municipal needs in order to form complete projects. Interested parties that have already identified fully-formed project proposals before the workshop are welcome and encouraged to attend. How are the Global City Teams formed?
Using the information participants share about their current or desired “smart” project, as well as the type of partner that would help them advance their project goals, NIST and US Ignite will organize a number of "action clusters," composed of representatives from innovative local governments, nonprofits and private companies. Working collaboratively over the next year, these volunteer teams will build, deploy and/or test transformative IoT applications.Is there a cost to participation?
No. Participation in the Global City Team Program has no cost and is completely voluntary.-
Is there funding for the projects?
While there is no guarantee for funding of the projects we will attempt to connect on-going projects with potential sponsors/funders. Where can I go for more information?
For more information please visit https://us-ignite.org/globalcityteams or http://nist.gov/cps/sagc.cfm.


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Piano Genie allows anyone to improvise on the piano! Example videos.
Controls: Press keys 1-8 to improvise.


Dynamic visualizations — which we define as interactive, computer-based animations of scientific phenomena — provide an alternative pathway for students to learn science concepts through exploration. Visualizations make unseen processes like chemical reactions or planetary motion visible. They allow students to conduct virtual experiments about complex situations. Visualizing to Integrate Science Understanding for All Learners (VISUAL) investigated promising visualizations to determine how and when they improve science learning.
Explore what happens when a force is exerted on a rubber tire. There are many different types of materials. Each material has a particular molecular structure, which is responsible for the material's mechanical properties.
Explore what happens when a force is exerted on a polymeric plastic material. There are many different types of materials. Each material has a particular molecular structure, which is responsible for the material's mechanical properties.
Explore what happens when a force is exerted on a metallic material. There are many different types of materials. Each material has a particular molecular structure, which is responsible for the material's mechanical properties.
Explore what happens when a force is exerted on a ceramic material. There are many different types of materials. Each material has a particular molecular structure, which is responsible for the material's mechanical properties.


|
|
|
DOCKET NUMBER |
PLAINTIFF TRIBE |
PAGE NUMBER |
|
350-A
|
The Three Affiliated Tribes of the Fort Berthold Reservation, et al.
Findings of Fact
Opinion of the Commission
Interlocutory Award to Petitioners
|
1
37
43-a
|
|
274
|
The Creek Nation
Findings of Fact
Opinion of the Commission
Final Order
|
44
48
61-a
|
|
65
|
The Peoria Tribe of Indians of Oklahoma, et al.
Opinion of the Commission
Order Substituting Finding of Fact, Vacating Final Award of August 4, 1965, and Final Award
|
62
69-a
|
|
343
|
The Cayuga Nation of Indians, et al.
Opinion of the Commission
Order Granting Defendant's "Amended (or Supplemental) Motion for Partial Summary Judgment"
|
70
75-a
|
|
47, 147, 160, 164
|
The Yakima Tribe of Indians
Statement and Additional Findings of Fact on Compromise Settlement of Claims of the Yakima Tribe of the State of Washington
Final Judgment
|
76
96-a
|
|
255, (Consolidated with), 124-C
|
The Miami Tribe of Oklahoma, et al.; Ira S. Godfroy, et al.; the Miami Indians of Indiana
Findings of Fact on Compromise Settlement
Opinion of the Commission
Final Judgment
|
97
110
112-a
|
|
256, (Consolidated with), 124-D, 124-E, 124-F
|
The Miami Tribe of Oklahoma, et al; Ira S. Godfroy, et al.; the Miami Indians of Indiana
Findings of Fact on Compromise Settlement
Opinion of the Commission
Final Judgment
|
113
127
129-a
|
|
170
|
The Pascagoula, Biloxi and Mobilian Consolidated Band of Indians
Order Dismissing Case
|
130
|
|
236-K, L, M
|
Gila River Pima-Maricopa Indian Community, et al.
Opinion
Order Dismissing Petitions
|
131
136-a
|
|
18-E • 58
|
Red Lake Band, et al., including Bay Mils Indian Community • Ottawa and Chippewa Indians of Michigan, et al.
Opinion of the Commission
Additional Findings of Fact
Second Interlocutory Order
|
137
141
176-a
|
|
342-B, 342-C, 342-D • 368
|
The Seneca Nation of Indians • Tonawanda Band of Seneca Indians
Opinion of the Commission
Findings of Fact
Interlocutory Order
|
177
186
209-a
|
|
91 • 229
|
The Havasupai Tribe of the Havasupai Reservation, Arizona • The Navajo Tribe of Indians
Opinion of the Commission
Findings of Fact
Interlocutory Order
|
210
222
235-a
|
|
251-A
|
The Miami Tribe of Oklahoma, et al.
Opinion of the Commission
Findings of Fact on Compromise Settlement
Final Judgment
|
236
239
246-a
|
|
270
|
The Chickasaw Nation
Opinion of the Commission
Order Dismissing Case with Prejudice
|
247
251-a
|
|
332-A
|
Yankton Sioux Tribe
Opinion of the Commission
Findings of Fact on Compromise Settlement
Final Judgment
|
252
255
271-a
|
|
322
|
The Ponca Tribe of Oklahoma
William Overland, Metha Collins, and John Williams, as representatives of the Ponca Tribe and all the members thereof
Opinion of the Commission as to Value
Additional Findings of Fact
Second Interlocutory Order
|
272
281
307-a
|
|
79-A
|
The Iowa Tribe of the Iowa Reservation in Kansas and Nebraska, the Iowa Tribe of the Iowa Reservation in Oklahoma, et al.
Opinion of the Commission
Findings of Fact
Interlocutory Order
|
308
320
535
|
|
301
|
The Oneida Nation of New York, the Oneida Tribe of Indians of Wisconsin, the Oneida Nation by Julius Danforth, Oscar Archiquette, Sherman Skenandore, Mamie Smith, Milton Babcock, Beryl Smith and Amanda Pierce
On Defendant's Motion for Partial Summary Judgment
Order Denying Defendant's "Amended (or Supplemental) Motion for Partial Summary Judgment"
|
337
360
|
|
22-E, 22-F
|
The Yavapai and the groups and bands thereof, ex rel. Calloway Bonnaha, Harry Jones, Fred Beauty and Warren Gazzam
The Yavapai-Apache Indian Community
The Fort McDowell Mohave-Apache Community
Additional Findings of Fact on Compromise Settlement in Docket Nos. 22-E and 22-F
Final Judgment
|
361
377
|
|
271
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The Cherokee Nation
Order Dismissing Petition
|
379
|
|
297
|
The Cherokee Nation
Order Dismissing Petition
|
380
|
|
92
|
The Upper Skagit Tribe of Indians
Findings of Fact on Attorneys' Fees
Order Allowing Attorneys' Fees
|
381
388
|
|
93
|
The Snoqualmie Tribe of Indians, on its own behalf, and on relation of the Skykomish Tribe of Indians
Findings of Fact on Attorneys' Fees
Order Allowing Attorneys' Fees
|
389
397
|
|
142
|
The Sisseton and Wahpeton Bands or Tribes, etc.
Opinion of the Commission
Additional Findings of Fact Attorneys' Fees
Order Awarding Balance of Attorneys' Fees
|
398
403
409
|
|
13-N
|
James Strong, Elmer B. Simonds, William Robert Warren, Margaret Arvold, Julia Potter, Betty Ann Nordwall, Edwin Carl Lerke, Jr., et al., as the representatives and on behalf of all members by blood of the Chippewa Tribe of Indians
Order Dismissing Petition with Prejudice
|
411
|
|
75
|
Emigrant New York Indians
Order Allowing Attorney's Reimbursable Expenses
|
413
|
|
87
|
The Northern Paiute Nation and the Bands thereof, et al.
Findings of Fact on Award of Attorneys' Fees
Order Allowing Attorneys' Fees
|
414
432
|
|
65
|
The Peoria Tribe of Indians of Oklahoma, et al.
Order Allowing Attorneys' Fees and Reimbursable Expenses
|
433
|
|
279-A • (Intervenors)
|
The Blackfeet and Gros Ventre Tribes of Indians, residing upon the Blackfeet and Fort Belknap Reservations in the State of Montana • The Assiniboine Tribe of Indians, residing upon the Fort Belknap and Fort Peck Reservations, Montana, and the Sioux Tribe of the Fort Peck Reservation, Montana
Findings of Fact on Attorneys' Fees for Services to the Sioux Tribe of the Fort Peck Reservation, Intervenor
Order Allowing Attorney's Fees
|
435
438
|
|
158 • 209 • 231
|
The Sac and Fox Tribe of Indians of Oklahoma, et al. • The Iowa Tribe of the Iowa Reservation in Kansas and Nebraska, et al. • The Sac and Fox Tribe of Indians of Oklahoma, et al.
Opinion of the Commission
Findings of Fact
Final Award
|
439
458
505
|
|
278-A
|
The Tlingit and Haida Indians of Alaska, in its own right and as the representatives of, or successor to, the tribes, clans and groups of Tlingit and Haida Indians of Alaska and
The tribes, clans and groups of Tlingit and Haida Indians of Alaska, in their own right, jointly and severally
Opinion of the Commission
Order Denying Motion to Dismiss
Order Denying Motion for an Interlocutory Order
|
508
520
521
|
|
100-A
|
The Klamath and Modoc Tribes and Yahooskin Band of Snake Indians
Opinion of the Commission
Findings of Fact
Conclusions of Law and First Interlocutory Order
|
522
528
543
|


Help
Please copy and paste since Google Apps do not allow links outside sheets.
Project on github
https://github.com/glondono/twilio-sms
Twilio Console
https://www.twilio.com/console


[gecode-users] Citing Gecode
Christian Schulte
cschulte at kth.se
Fri Feb 1 11:09:33 CET 2008
Here we go, providing even BibTeX advice ;-)
@Misc{gecode,
author = "{Gecode Team}",
title = "Gecode: Generic Constraint Development Environment",
year = {2006},
note = {Available from \texttt{http://www.gecode.org}},
}
All the best
Christian
On 2/1/08 1:39 AM, "Malcolm Ryan" <malcolmr at cse.unsw.edu.au> wrote:
>
> On 01/02/2008, at 9:35 AM, Christian Schulte wrote:
>
>> If you check one of the papers on the Gecode webpage, you'll see
>> how we do
>> it: we refer to the Gecode Team as authors.
>
> Any idea how I do that in BibTex? I keep getting a reference to "G.
> Team".
>
> Malcolm
>
> --
> "If there is one thing worse than the modern weakening of major morals
> it is the modern strengthening of minor morals.
> Thus it is considered more withering to accuse a man
> of bad taste than of bad ethics." -- G.K.Chesterton, On Lying
> in Bed
>
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
--
Christian Schulte, web.ict.kth.se/~cschulte/
More information about the gecode-users
mailing list


Ryu always wins!
― I do not compete with others, the only competition that challenges me is myself. As a fighter, friend and an employee I am known as contemplative, tortured and driven.
EXPERIENCE
2018
Amongst many fighters of the past was featured in the movie "Ready Player One". Ryu ready always!
2017
United with unknown until then to me Power Rangers to defeat resurrected M.Bison.Damn good fighters they are, even not knowing karate ways!
2016
In "Street Fighter V" once and for all erased M.Bison from earth and tamed my inner Evil Ryu. Also spent some time on a side-project, mini-series "Street Fighter: Resurrection".
2014
Filmed in a martial arts tutorial "Street Fighter: Assassin's Fist", revealing to audience forgotten art of Ansatsuken.
2012
The gods of intenet concluded me in the animated movie "Wreck-It Ralph", but rewarded me with a good drink for it!
2008
My evil alter ego sneaked out of control and became a boss in "Street Fighter IV".
2006
Borrowed my famous ripped gi to Soki in "Onimusha: Dawn of Dreams", gods informed me he needed that for the battle.
1998
Defeated M. Bison in "Street Fighter Alpha 2", bastard wanted to use my heroic body for his evil intentions, but retreated with shame. Also this year I encountered Marvel universe in "Marvel vs. Capcom: Clash of Super Heroes" fighter,loyal to my employer, Capcom, of course.
1997
Reunited with Ken, my friend and opponent in "Street Fighter III". Men's friendship works mysteriously. Also helped with Arika project "Street Fighter EX"
1996
Took care of villainous Akuma in "Street Fighter Alpha 2". As a freelance on "Super Puzzle Fighter II Turbo" tried myself in a new genre, puzzle games. Let the wrath of seven heavens come upon these malicious enigmas, never again!
1995
Kept fighting in "Street Fighter Alpha: Warriors' Dreams".
1994
Screened in a movie "Street Fighter", so that the batllesof past would not be forgotten by millenial generation.
1991
Was promoted to "Street Fighter II". Didn't stay there for long, celebrations and office dwelling is not for me!
1987
Started working for Capcom project "Street Fighter". My partner was Ken Masters, the boss'name was Sagat.
PUBLICATIONS
2011
The true and humble story of me under pseudonim Tyler Wilde in an article "The evolution of Ken and Ryu"
2003-2008
For 15 years I have published comic books with Udon Entertainment depicting my adventures, bitter lessons and great victories.
ACHIEVEMENTS
- #1 in "Top 25 Street Fighter Characters" by IGN, who also called me "fancy", "icon" and "a testament to the virtue of simplicity" ❤
- #2 in "Top 20 Street Fighter Characters of All Time" by GameDaily, same council found me worth of #6 place in "Top 25 Capcom Characters of All Time", which I proudly share with my partner Ken
I was named "5th Most Powerful Street Fighter Character" by Screen Rant-san - #71 in "Top 100 Heroes of All Time" by UGO Networks, also recognized #2 in their list of "Top 50 Street Fighter Characters"
In a survey of 4000 online matches for Super Street Fighter IV, I got to be the most popular character, with 16.6% of players choosing my side
LIFE & EDUCATION
To live is to fight and to fight is to live. I started learning fighting at my master Gouken's castle, I was beaten at head way too often to remember when exactly.
LANGUAGES
- 🇺🇸 ENGLISH
- 🇨🇳 MANDARIN
- 🇯🇵 JAPANESE
LIFE LESSON
You must defeat Sheng Long to stand a chance.
SKILLS
- KARATE
- ENERGY ATTACK
- SHORYUKEN
- WALKING
- PLAYING VIDEOGAMES
- DOING BURGERS
- MASHING POTATOES
- COPYWRITING
- ACTING
- VOICEOVERS
CHARACTER
- NOBLE
- DIRECT
- BOLD
- CONFIDENT
- DARING
- SCHIZOPHRENIC
- DRAMATIC
- PERSUASIVE
- IMMEDIATE
- WISE
LIKES 😃
- GI
- VEGETABLES
- BLOOD
- SMALL MARSHMELLOWS
- RED COLOR
- KILLED ENEMY
DISLIKES 😞
- BISONS
- GHOSTS
- SMARTPHONES
- SMELL OF SEAFOOD
- BARBER SHOPS
- SLEEVES
WANTS 🥺
- BE FIT
- WRITE MORE BOOKS
- FIND A GIRLFRIEND TO GO TO CINEMA WITH
- KILL ALL OF AKUMA FAMILY


{% extends "wagtailadmin/base.html" %} {% load i18n wagtailflags_admin %} {% block titletag %}{{ flag.name }}{% endblock %} {% block content %} {% include "wagtailflags/includes/flag_breadcrumbs.html" with title=flag.name %} {% include "wagtailflags/includes/flag_header.html" with title=flag.name icon="flag" %}


Spatial Resolution refers to the scale you are working at. Scale can be indicated with a descriptive term such as local, regional, national or global.
The spatial resolution of both raster and vector data is important.
In raster data the resolution is the pixel size or cell of the data (e.g. 250 m) or, in another sense, it is the size of the smallest object that can be detected, or ‘minimum mapping unit’. For example a raster data set representing ‘wetlands’ might have been generated using a technique that could only detect wetlands greater than 1 ha in extent. Therefore, no matter what the pixel resolution of the data, it has a 'resolution' of one hectare.
With vector data the term ‘minimum mapping unit’ is usually used instead, since in a GIS the scale of vector data can be changed easily. The minimum mapping unit or the smallest addressable unit is the smallest object that is mapped, in the same sense as with rasters above. For example, in a land cover map with a minimum mapping unit of 1 ha, only distinct land cover patches larger than 1 ha will be represented. Smaller ones will be lost inside bigger ones
Higher resolution data uses up more memory than lower resolution data and also uses more processing power. If you are serving spatial data or maps over the Internet, lower resolution data will consume less bandwidth and be transmitted faster. Therefore one usually has to find a compromise between resolution and available computer resources.


From:Concklin Elaine To:Wiederholt Patricia ; Phan Kim Sent:2017-01-11T04:36:21.0000000Z Subject:Forecast for MGT
At Third Current Estimate, we added a full year forecast for MGT for Operations and added the MGT and Border amounts together on the Border page in the ETS Functional Income Statement as instructed.
We are trying to confirm what we should be reflecting. Jason is being instructed that ETS should not pick up any actuals for January through May, and that we should use Actuals received from El Paso for June forward, and not our numbers. The El Paso amounts for June through September are close to what we have, but not exact.
Please advise us as to what we should be reflecting and we will revise our schedules. We did not pick up MGT at all until 3rd CE, so I'm OK with restating 3rd CE to reflect the appropriate amounts for MGT and rolling forward from there.
Thanks, Elaine
Elaine Concklin
Enron Transportation Services
Director Financial Planning
Operations Support Services
1400 Smith St. Suite 3AC-3311
Houston, Texas 77002
(713) 853-5521
<EMAIL_ADDRESS>***********
EDRM Enron Email Data Set has been produced in EML, PST and NSF format by ZL Technologies, Inc. This Data Set is licensed under a Creative Commons Attribution 3.0 United States License <http://creativecommons.org/licenses/by/3.0/us/> . To provide attribution, please cite to "ZL Technologies, Inc. (http://www.zlti.com)."
***********
California


Jeremiah 37:9
Thus saith the LORD; Deceive not yourselves, saying, The Chaldeans shall surely depart from us: for they shall not depart.


Content-type: text/html
#include <libtecla.h> PathCache *new_PathCache(void); PathCache *del_PathCache(PathCache *pc); int pca_scan_path(PathCache *pc, const char *path); void pca_set_check_fn(PathCache *pc, CplCheckFn *check_fn, void *data); char *pca_lookup_file(PathCache *pc, const char *name, int name_len, int literal); const char *pca_last_error(PathCache *pc); CPL_MATCH_FN(pca_path_completions);
The PathCache object is part of the tecla library (see the libtecla(3) man page).
PathCache objects allow an application to search for files in any colon separated list of directories, such as the unix execution PATH environment variable. Files in absolute directories are cached in a PathCache object, whereas relative directories are scanned as needed. Using a PathCache object, you can look up the full pathname of a simple filename, or you can obtain a list of the possible completions of a given filename prefix. By default all files in the list of directories are targets for lookup and completion, but a versatile mechanism is provided for only selecting specific types of files. The obvious application of this facility is to provide Tab-completion and lookup of executable commands in the unix PATH, so an optional callback which rejects all but executable files, is provided.
Under UNIX, the following example program looks up and displays the full pathnames of each of the command names on the command line.
#include <stdio.h> #include <stdlib.h> #include <libtecla.h> int main(int argc, char *argv[]) { int i; /* * Create a cache for executable files. */ PathCache *pc = new_PathCache(); if(!pc) exit(1); /* * Scan the user's PATH for executables. */ if(pca_scan_path(pc, getenv("PATH"))) { fprintf(stderr, "%s\n", pca_last_error(pc)); exit(1); } /* * Arrange to only report executable files. */ pca_set_check_fn(pc, cpl_check_exe, NULL); /* * Lookup and display the full pathname of each of the * commands listed on the command line. */ for(i=1; i<argc; i++) { char *cmd = pca_lookup_file(pc, argv[i], -1, 0); printf("The full pathname of '%s' is %s\n", argv[i], cmd ? cmd : "unknown"); } pc = del_PathCache(pc); /* Clean up */ return 0; }
The following is an example of what this does on my laptop under linux:
$ ./example less more blob The full pathname of 'less' is /usr/bin/less The full pathname of 'more' is /bin/more The full pathname of 'blob' is unknown $
In order to use the facilities of this module, you must first allocate a PathCache object by calling the new_PathCache() constructor function.
PathCache *new_PathCache(void)
This function creates the resources needed to cache and lookup files in a list of directories. It returns NULL on error.
int pca_scan_path(PathCache *pc, const char *path);
Whenever this function is called, it discards the current contents of the cache, then scans the list of directories specified in its path argument for files. The path argument must be a string containing a colon-separated list of directories, such as "/usr/bin:/home/mcs/bin:.". This can include directories specified by absolute pathnames such as "/usr/bin", as well as sub-directories specified by relative pathnames such as "." or "bin". Files in the absolute directories are immediately cached in the specified PathCache object, whereas sub-directories, whose identities obviously change whenever the current working directory is changed, are marked to be scanned on the fly whenever a file is looked up.
On success this function return 0. On error it returns 1, and a description of the error can be obtained by calling pca_last_error(pc).
Once the cache has been populated with files, you can look up the full pathname of a file, simply by specifying its filename to pca_lookup_file().
char *pca_lookup_file(PathCache *pc, const char *name, int name_len, int literal);
To make it possible to pass this function a filename which is actually part of a longer string, the name_len argument can be used to specify the length of the filename at the start of the name[] argument. If you pass -1 for this length, the length of the string will be determined with strlen(). If the name[] string might contain backslashes that escape the special meanings of spaces and tabs within the filename, give the literal argument, the value 0. Otherwise, if backslashes should be treated as normal characters, pass 1 for the value of the literal argument.
Looking up the potential completions of a filename-prefix in the filename cache, is achieved by passing the provided pca_path_completions() callback function to the cpl_complete_word() function (see the cpl_complete_word(3) man page).
CPL_MATCH_FN(pca_path_completions);
This callback requires that its data argument be a pointer to a PcaPathConf object. Configuration objects of this type are allocated by calling new_PcaPathConf().
PcaPathConf *new_PcaPathConf(PathCache *pc);
This function returns an object initialized with default configuration parameters, which determine how the cpl_path_completions() callback function behaves. The functions which allow you to individually change these parameters are discussed below.
By default, the pca_path_completions() callback function searches backwards for the start of the filename being completed, looking for the first un-escaped space or the start of the input line. If you wish to specify a different location, call ppc_file_start() with the index at which the filename starts in the input line. Passing start_index=-1 re-enables the default behavior.
void ppc_file_start(PcaPathConf *ppc, int start_index);
By default, when pca_path_completions() looks at a filename in the input line, each lone backslash in the input line is interpreted as being a special character which removes any special significance of the character which follows it, such as a space which should be taken as part of the filename rather than delimiting the start of the filename. These backslashes are thus ignored while looking for completions, and subsequently added before spaces, tabs and literal backslashes in the list of completions. To have unescaped backslashes treated as normal characters, call ppc_literal_escapes() with a non-zero value in its literal argument.
void ppc_literal_escapes(PcaPathConf *ppc, int literal);
When you have finished with a PcaPathConf variable, you can pass it to the del_PcaPathConf() destructor function to reclaim its memory.
PcaPathConf *del_PcaPathConf(PcaPathConf *ppc);
#define CPL_CHECK_FN(fn) int (fn)(void *data, \ const char *pathname) typedef CPL_CHECK_FN(CplCheckFn);
Registering one of these functions involves calling the pca_set_check_fn() function. In addition to the callback function, passed via the check_fn argument, you can pass a pointer to anything via the data argument. This pointer will be passed on to your callback function, via its own data argument, whenever it is called, so this provides a way to pass appplication specific data to your callback.
void pca_set_check_fn(PathCache *pc, CplCheckFn *check_fn, void *data);
Note that these callbacks are passed the full pathname of each matching file, so the decision about whether a file is of interest can be based on any property of the file, not just its filename. As an example, the provided cpl_check_exe() callback function looks at the executable permissions of the file and the permissions of its parent directories, and only returns 1 if the user has execute permission to the file. This callback function can thus be used to lookup or complete command names found in the directories listed in the user's PATH environment variable. The example program given earlier in this man page provides a demonstration of this.
Beware that if somebody tries to complete an empty string, your callback will get called once for every file in the cache, which could number in the thousands. If your callback does anything time consuming, this could result in an unacceptable delay for the user, so callbacks should be kept short.
To improve performance, whenever one of these callbacks is called, the choice that it makes is cached, and the next time the corresponding file is looked up, instead of calling the callback again, the cached record of whether it was accepted or rejected is used. Thus if somebody tries to complete an empty string, and hits tab a second time when nothing appears to happen, there will only be one long delay, since the second pass will operate entirely from the cached dispositions of the files. These cached dipositions are discarded whenever pca_scan_path() is called, and whenever pca_set_check_fn() is called with changed callback function or data arguments.
If pca_scan_path() reports that an error occurred by returning 1, you can obtain a terse description of the error by calling pca_last_error(pc). This returns an internal string containing an error message.
const char *pca_last_error(PathCache *pc);
Once you have finished using a PathCache object, you can reclaim its resources by passing it to the del_PathCache() destructor function. This takes a pointer to one of these objects, and always returns NULL.
PathCache *del_PathCache(PathCache *pc);
In multi-threaded programs, you should use the libtecla_r.a version of the library. This uses POSIX reentrant functions where available (hence the _r suffix), and disables features that rely on non-reentrant system functions. In the case of this module, the only disabled feature is username completion in ~username/ expressions, in cpl_path_completions().
Using the libtecla_r.a version of the library, it is safe to use the facilities of this module in multiple threads, provided that each thread uses a separately allocated PathCache object. In other words, if two threads want to do path searching, they should each call new_PathCache() to allocate their own caches.
libtecla.a - The tecla library libtecla.h - The tecla header file.
libtecla(3), gl_get_line(3), ef_expand_file(3), cpl_complete_word(3)


source of all good bits
`Prev `__
S
`Next `__
--------------
**source of all good bits**: n.
A person from whom (or a place from which) useful information may be
obtained. If you need to know about a program, a
`*guru* <../G/guru.html>`__ might be the source of all good bits. The
title is often applied to a particularly competent secretary.
--------------
+---------------------------+----------------------------+-----------------------------------------+
| `Prev `__ | `Up <../S.html>`__ | `Next `__ |
+---------------------------+----------------------------+-----------------------------------------+
| source | `Home <../index.html>`__ | space-cadet keyboard |
+---------------------------+----------------------------+-----------------------------------------+


# -*- coding: utf-8 -*-
"""
Discord API Wrapper
~~~~~~~~~~~~~~~~~~~
A basic wrapper for the Discord API.
:copyright: (c) 2015-2020 Rapptz
:license: MIT, see LICENSE for more details.
"""
__title__ = 'discord'
__author__ = 'Rapptz'
__license__ = 'MIT'
__copyright__ = 'Copyright 2015-2020 Rapptz'
__version__ = '1.5.1'
__path__ = __import__('pkgutil').extend_path(__path__, __name__)
from collections import namedtuple
import logging
from .client import Client
from .appinfo import AppInfo
from .user import User, ClientUser, Profile
from .emoji import Emoji
from .partial_emoji import PartialEmoji
from .activity import *
from .channel import *
from .guild import Guild
from .flags import *
from .relationship import Relationship
from .member import Member, VoiceState
from .message import Message, MessageReference, Attachment
from .asset import Asset
from .errors import *
from .calls import CallMessage, GroupCall
from .permissions import Permissions, PermissionOverwrite
from .role import Role
from .file import File
from .colour import Color, Colour
from .integrations import Integration, IntegrationAccount
from .invite import Invite, PartialInviteChannel, PartialInviteGuild
from .template import Template
from .widget import Widget, WidgetMember, WidgetChannel
from .object import Object
from .reaction import Reaction
from . import utils, opus, abc
from .enums import *
from .embeds import Embed
from .mentions import AllowedMentions
from .shard import AutoShardedClient, ShardInfo
from .player import *
from .webhook import *
from .voice_client import VoiceClient, VoiceProtocol
from .audit_logs import AuditLogChanges, AuditLogEntry, AuditLogDiff
from .raw_models import *
from .team import *
VersionInfo = namedtuple('VersionInfo', 'major minor micro releaselevel serial')
version_info = VersionInfo(major=1, minor=5, micro=1, releaselevel='final', serial=0)
try:
from logging import NullHandler
except ImportError:
class NullHandler(logging.Handler):
def emit(self, record):
pass
logging.getLogger(__name__).addHandler(NullHandler())


A luxurious escape for people of all ages,with homes starting at prices as low as R50 000 000 it's affordable and fits into everyones budget


Scamming Minorities...with this Title XD - Human Fall Flat
IT'S ME! YOKI!
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
Scamming Minorities...with this Title XD - Human Fall Flat
Let's Fail [1] : Super's How To D-Duel Tutorial
Time To Shake & Bake Boys! - Project GTA Cancer w/Yoki & Friends


Instructions
In this experiment, you will see a list of words presented one at a time. Try to remember as many as possible: you will be tested on these words later.
You will now either be directed to a waiting room, or the words could begin appearing immediately!


Delete nested properties from an object using dot notation.
Install with npm:
$ npm install --save unset-value
var unset = require('unset-value');
var obj = {a: {b: {c: 'd', e: 'f'}}};
unset(obj, 'a.b.c');
console.log(obj);
//=> {a: {b: {e: 'f'}}};
var obj = {a: 'b'};
unset(obj, 'a');
console.log(obj);
//=> {}
unset({a: 'b'}, 'a') // true
true
when a property does not existThis is consistent with delete
behavior in that it does not
throw when a property does not exist.
unset({a: {b: {c: 'd'}}}, 'd') // true
var one = {a: {b: {c: 'd'}}};
unset(one, 'a.b');
console.log(one);
//=> {a: {}}
var two = {a: {b: {c: 'd'}}};
unset(two, 'a.b.c');
console.log(two);
//=> {a: {b: {}}}
var three = {a: {b: {c: 'd', e: 'f'}}};
unset(three, 'a.b.c');
console.log(three);
//=> {a: {b: {e: 'f'}}}
unset();
// 'expected an object.'
a.b.c
) to get a nested value from an object. | homepagea.b.c
and… more | homepage'a.b.c'
) paths. | homepage'a.b.c'
) paths. | homepagePull requests and stars are always welcome. For bugs and feature requests, please create an issue.
| Commits | Contributor |
|---|---|
| 6 | jonschlinkert |
| 2 | wtgtybhertgeghgtwtg |
(This project's readme.md is generated by verb, please don't edit the readme directly. Any changes to the readme must be made in the .verb.md readme template.)
To generate the readme, run the following command:
$ npm install -g verbose/verb#dev verb-generate-readme && verb
Running and reviewing unit tests is a great way to get familiarized with a library and its API. You can install dependencies and run tests with the following command:
$ npm install && npm test
Jon Schlinkert
Copyright © 2017, Jon Schlinkert. Released under the MIT License.
This file was generated by verb-generate-readme, v0.4.2, on February 25, 2017.


I came to the UK from Brazil back in 2016. Having completed Master’s degree in Graphic Design at The University of Nottingham I’ve decided to pursue a career in UX design by taking at design course at The School of UX.


|
mpi_cmake_modules
|
Namespaces | |
| mpi_cmake_modules.clang_format.py | |
| Utility functions for creating formatting script based on clang. | |
Functions | |
| def | mpi_cmake_modules.clang_format.find_clang_format (name_list=['clang-format']) |
| Find the full path to the clang-format executable. More... | |
| def | mpi_cmake_modules.clang_format.load_clang_format_config () |
| Look for the clang-formt parameter file in this package. More... | |
| def | mpi_cmake_modules.clang_format.test_valid_file (filename, extensions) |
| Test if the input file exists and is of one of the provided extension. More... | |
| def | mpi_cmake_modules.clang_format.get_absolute_path (file_or_directory) |
| Get the absolute path of a given path and check its existance. More... | |
| def | mpi_cmake_modules.clang_format.list_of_files_to_format (files_or_directories, extensions) |
| Get the list of files to format exploring the input arguments. More... | |
| def | mpi_cmake_modules.clang_format.execute_clang_format (clang_format_bin, clang_format_config, clang_format_arg) |
| Execute the formatting of C/C++ files using clang-format. More... | |
Variables | |
| mpi_cmake_modules.clang_format.which = distutils.spawn.find_executable | |
| def mpi_cmake_modules.clang_format.execute_clang_format | ( | clang_format_bin, | |
| clang_format_config, | |||
| clang_format_arg | |||
| ) |
Execute the formatting of C/C++ files using clang-format.
Get the path to the executable, and run it using the clang-format insput parameter on the list of files to format.
| clang_format_bin | Path to the clang-format binary. |
| clang_format_config | One line dictionnary string containing the clang-format parameters. |
| clang_format_arg | List of source files to parse. |
| def mpi_cmake_modules.clang_format.find_clang_format | ( | name_list = ['clang-format'] | ) |
Find the full path to the clang-format executable.
Look by default for the clang-format
executable in the PATH environment variable.
| name_list | list(str) -- Potential executable names which might differ according to the clang-format version available. |
| def mpi_cmake_modules.clang_format.get_absolute_path | ( | file_or_directory | ) |
Get the absolute path of a given path and check its existance.
Check if file_or_directory is an existing file or directory, if so, returns it. Otherwise, assumes it is a relative path, which it upgrades to an absolute path, this it returns. If the upgraded path does not correspond to an existing file or directory, returns None
| file_or_directory | str -- a relative or an absolute path |
| def mpi_cmake_modules.clang_format.list_of_files_to_format | ( | files_or_directories, | |
| extensions | |||
| ) |
Get the list of files to format exploring the input arguments.
Explore recursively the directories given in the input list and create a list of all files. Add to this list the files given in the input list. Sort out the files that are source-files or not and return the list of source files.
| files_or_directories | list(str) -- List of files or directories. |
| extensions | list(str) -- List of extensions to check against |
| def mpi_cmake_modules.clang_format.load_clang_format_config | ( | ) |
Look for the clang-formt parameter file in this package.
Look for the _clang-format file located in this package and convert it in a one line dictionnary string.
| def mpi_cmake_modules.clang_format.test_valid_file | ( | filename, | |
| extensions | |||
| ) |
Test if the input file exists and is of one of the provided extension.
| filename | Path to the file to test. |
| extensions | iterable of accepted extensions. |
False otherwise.


Fractal Trees with JavaScript.
Move MOUSE while LEFT BUTTON is DOWN to change the spread and the bias.
Type 1 to 9 to change the template.
Hit SPACE to randomize seed.
Hit + or - to change branch count.


- r - Variable in class cip.render.utilColour.RGBf
-
- RADIANS - Static variable in class cip.render.util.AngleF
-
The radians type.
- RANDOM_ARRAY_LENGTH - Static variable in class
cip.render.raytrace.RenderXmlOSJ
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign1.ImplicitPolyhedra
-
- RayIntersection - Class in cip.CSE581.assign1
-
- RayIntersection(RGBf) - Constructor for class cip.CSE581.assign1.RayIntersection
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign1.Sphere3f
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign2.ImplicitPolyhedra
-
- RayIntersection - Class in cip.CSE581.assign2
-
- RayIntersection(RGBf) - Constructor for class cip.CSE581.assign2.RayIntersection
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign2.Sphere3f
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign2c.ImplicitPolyhedra
-
- rayIntersection(Line3f, RayIntersection) - Method in interface cip.CSE581.assign2c.IRtGeometry
-
Test for a ray intersection closer than intersection.m_fDistance and if there is a closer intersection
save in information about that intersection in the intersection
- RayIntersection - Class in cip.CSE581.assign2c
-
- RayIntersection() - Constructor for class cip.CSE581.assign2c.RayIntersection
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign2c.Sphere3f
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign3.ImplicitPolyhedra
-
- rayIntersection(Line3f, RayIntersection) - Method in interface cip.CSE581.assign3.IRtGeometry
-
Test for a ray intersection closer than intersection.m_fDistance and if there is a closer intersection
save in information about that intersection in the intersection
- RayIntersection - Class in cip.CSE581.assign3
-
- RayIntersection() - Constructor for class cip.CSE581.assign3.RayIntersection
-
- rayIntersection(Line3f, RayIntersection) - Method in class cip.CSE581.assign3.Sphere3f
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in class cip.CSE581.assign3b.ImplicitPolyhedra
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in interface cip.CSE581.assign3b.IRtGeometry
-
Test for a ray intersection closer than intersection.m_fDistance and if there is a closer intersection
save in information about that intersection in the intersection
- RayIntersection - Class in cip.CSE581.assign3b
-
- RayIntersection() - Constructor for class cip.CSE581.assign3b.RayIntersection
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in class cip.CSE581.assign3b.Sphere3f
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in class cip.CSE581.assign4.ImplicitPolyhedra
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in interface cip.CSE581.assign4.IRtGeometry
-
Test for a ray intersection closer than intersection.m_fDistance and if there is a closer intersection
save in information about that intersection in the intersection
- RayIntersection - Class in cip.CSE581.assign4
-
- RayIntersection() - Constructor for class cip.CSE581.assign4.RayIntersection
-
- rayIntersection(Line3f, boolean, RayIntersection) - Method in class cip.CSE581.assign4.Sphere3f
-
- RayIntersection - Class in cip.render.raytrace
-
This is the description of the surface at the intersection of a ray with a geometric object.
- RayIntersection() - Constructor for class cip.render.raytrace.RayIntersection
-
Creates a new uninitialized instance of RayIntersection
- RenderCanvas - Class in cip.render
-
- RenderCanvas(GraphicsConfiguration, IRenderScene) - Constructor for class cip.render.RenderCanvas
-
- renderImage(Image) - Method in class cip.CSE581.assign1.Assignment1
-
- renderImage(Image) - Method in class cip.CSE581.assign2.Assignment2a
-
- renderImage(Image) - Method in class cip.CSE581.assign2c.Assignment2c
-
- renderImage(Image) - Method in class cip.CSE581.assign3.Assignment3a
-
- renderImage(Image) - Method in class cip.CSE581.assign3b.Assignment3b
-
- renderImage(Image) - Method in class cip.CSE581.assign4.Assignment4
-
- renderImage(Image) - Method in interface cip.render.IRenderScene
-
Renders the scene, or a part of the scene, to the an Image
.
- renderImage(Image) - Method in class cip.render.raytrace.RenderXml
-
- renderImage(Image) - Method in class cip.render.TestRenderScene
-
- RenderPixel(Assignment4) - Constructor for class cip.CSE581.assign4.Assignment4.RenderPixel
-
- RenderPixel(RenderXml) - Constructor for class cip.render.raytrace.RenderXml.RenderPixel
-
Instantiate a pixel render to be run in a rendering thread.
- RenderSample(RenderXmlOS)
- Constructor for class cip.render.raytrace.RenderXmlOS.RenderSample
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign1.Assignment1
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign2.Assignment2a
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign2c.Assignment2c
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign3.Assignment3a
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign3b.Assignment3b
-
- renderScene(Component, Graphics) - Method in class cip.CSE581.assign4.Assignment4
-
- renderScene(Component, Graphics) - Method in interface cip.render.IRenderScene
-
Renders the scene, or a part of the scene, to the Graphics
context of a window.
- renderScene(Component, Graphics) - Method in class cip.render.raytrace.RenderXml
-
- renderScene(Component, Graphics) - Method in class cip.render.TestRenderScene
-
- RenderWindow - Class in cip.render
-
This is the main for window-centric rendering.
- RenderXml - Class in cip.render.raytrace
-
This is a ray tracer that loads the scene to be rendered from an XML scene description file, and renders that scene using a
single sample per pixel.
- RenderXml() - Constructor for class cip.render.raytrace.RenderXml
-
Creates a new instance of RenderXml
- RenderXml.RenderPixel - Class in cip.render.raytrace
-
This is the implementation of a pixel renderer intended to be run in a rendering thread.
- RenderXmlOS - Class in cip.render.raytrace
-
This is the
RenderXml
implementation
with the infrastructure added for oversampling and filtering.
- RenderXmlOS() -
Constructor for class cip.render.raytrace.RenderXmlOS
-
Creates a new instance of RenderXmlHierarchy
- RenderXmlOS.RenderSample
- Class in cip.render.raytrace
-
- RenderXmlOSJ - Class in cip.render.raytrace
-
This implementation of the
IRenderScene
interface extends
RenderXmlOS
to include a 'jitter' array for preturbing samples in either 1, 2, or 3 dimensions.
- RenderXmlOSJ() -
Constructor for class cip.render.raytrace.RenderXmlOSJ
-
Creates a new instance of RenXmlHierOSJ
- resetCamVectors() - Method in class cip.render.raytrace.camera.ACamera
-
- resolveGeometryRef(String, LinkedList) - Method in class cip.render.raytrace.geometry.AGeometry
-
Resolve a geometry reference by finding the geometry in the reference object list.
- resolveLightRef(String, LinkedList) - Method in class cip.render.raytrace.light.ALight
-
- resolveMaterialRef(String, LinkedList) - Static method in class cip.render.raytrace.material.texture.ATexture
-
- resolveMaterialRef(String, LinkedList) - Method in class cip.render.raytrace.material.texture.Ridges
-
- returnAngle(AngleF) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed angle.
- returnBvInt(Bv3fIntersection) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed bounding volume intersection.
- returnIntersection(RayIntersection) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed ray intersection object.
- returnLightInfo(LightInfo) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed light information object.
- returnLine(Line3f) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed line.
- returnPlane(Plane3f) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed plane.
- returnPlaneInt(Plane3fIntersection) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed plane intersection.
- returnPoint(Point3f) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed pt.
- returnQuadricInt(Quadric3fIntersection) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed quadric intersection.
- returnRGB(RGBf) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed colour.
- returnVector(Vector3f) - Method in class cip.render.raytrace.material.D.Phong
-
Returns a borrowed vector.
- returnVector(Vector3f) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed vector.
- returnXfm(Xfm4x4f) - Method in class cip.render.raytrace.RayIntersection
-
Returns a borrowed transformation.
- reverse() - Method in class cip.render.util3d.Vector3f
-
Reverse the direction of this vector.
- RGBf - Class in cip.render.utilColour
-
A class representing an RGB colour of single precision (the components are represented by float values),
hence the name RGBf.
- RGBf() - Constructor for class cip.render.utilColour.RGBf
-
Creates a new instance of RGBf initialized to 0,0,0.
- RGBf(float, float, float) - Constructor for class cip.render.utilColour.RGBf
-
Creates a new instance of RGBf initialized to a specified colour.
- RGBf(RGBf) - Constructor for class cip.render.utilColour.RGBf
-
Creates a new instance of RGBf initialized to a specified colour.
- Ridges - Class in cip.render.raytrace.material.texture
-
- Ridges() - Constructor for class cip.render.raytrace.material.texture.Ridges
-
Creates a new instance of Checkerboard
- rotate(int, AngleF) - Method in class cip.render.util3d.Xfm4x4f
-
Premultiply this transformation by a rotation transformation for the specified rotation about the specified axis.
- rotate(int, float, float) - Method in class cip.render.util3d.Xfm4x4f
-
- rotate(Vector3f, AngleF) - Method in class cip.render.util3d.Xfm4x4f
-
- run() - Method in class cip.CSE581.assign4.Assignment4.RenderPixel
-
- run() - Method in class cip.render.raytrace.RenderXml.RenderPixel
-
- run() - Method in
class cip.render.raytrace.RenderXmlOS.RenderSample
-


{% capture note %}
All measurements to be performed are given in international units (mostly millimeters, abbreviated mm) however all purchased parts are measured in imperial units (mostly inches, abbreviated "). All quantities are written in English with the number also included in parenthesis. Always double-check measurements and units before doing any work.
Example: Take one (1) of the 12” long aluminum plates, mark a line 200 mm from one end of the plate.
See the Standards and Conventions page for details.
{% endcapture %}
{% include note.html text=note %}


참고
Click here to download the full example code
PyTorch Profiler With TensorBoard¶
This tutorial demonstrates how to use TensorBoard plugin with PyTorch Profiler to detect performance bottlenecks of the model.
Introduction¶
PyTorch 1.8 includes an updated profiler API capable of recording the CPU side operations as well as the CUDA kernel launches on the GPU side. The profiler can visualize this information in TensorBoard Plugin and provide analysis of the performance bottlenecks.
In this tutorial, we will use a simple Resnet model to demonstrate how to use TensorBoard plugin to analyze model performance.
Steps¶
Prepare the data and model
Use profiler to record execution events
Run the profiler
Use TensorBoard to view results and analyze model performance
Improve performance with the help of profiler
Analyze performance with other advanced features
1. Prepare the data and model¶
First, import all necessary libraries:
import torch
import torch.nn
import torch.optim
import torch.profiler
import torch.utils.data
import torchvision.datasets
import torchvision.models
import torchvision.transforms as T
Then prepare the input data. For this tutorial, we use the CIFAR10 dataset.
Transform it to the desired format and use DataLoader
to load each batch.
transform = T.Compose(
[T.Resize(224),
T.ToTensor(),
T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True)
Next, create Resnet model, loss function, and optimizer objects. To run on GPU, move model and loss to GPU device.
device = torch.device("cuda:0")
model = torchvision.models.resnet18(weights='IMAGENET1K_V1').cuda(device)
criterion = torch.nn.CrossEntropyLoss().cuda(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
model.train()
Define the training step for each batch of input data.
def train(data):
inputs, labels = data[0].to(device=device), data[1].to(device=device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
2. Use profiler to record execution events¶
The profiler is enabled through the context manager and accepts several parameters, some of the most useful are:
schedule
- callable that takes step (int) as a single parameter and returns the profiler action to perform at each step.In this example with
wait=1, warmup=1, active=3, repeat=2
, profiler will skip the first step/iteration, start warming up on the second, record the following three iterations, after which the trace will become available and on_trace_ready (when set) is called. In total, the cycle repeats twice. Each cycle is called a 《span》 in TensorBoard plugin.During
wait
steps, the profiler is disabled. Duringwarmup
steps, the profiler starts tracing but the results are discarded. This is for reducing the profiling overhead. The overhead at the beginning of profiling is high and easy to bring skew to the profiling result. Duringactive
steps, the profiler works and records events.on_trace_ready
- callable that is called at the end of each cycle; In this example we usetorch.profiler.tensorboard_trace_handler
to generate result files for TensorBoard. After profiling, result files will be saved into the./log/resnet18
directory. Specify this directory as alogdir
parameter to analyze profile in TensorBoard.record_shapes
- whether to record shapes of the operator inputs.profile_memory
- Track tensor memory allocation/deallocation. Note, for old version of pytorch with version before 1.10, if you suffer long profiling time, please disable it or upgrade to new version.with_stack
- Record source information (file and line number) for the ops. If the TensorBoard is launched in VS Code (reference), clicking a stack frame will navigate to the specific code line.
with torch.profiler.profile(
schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/resnet18'),
record_shapes=True,
profile_memory=True,
with_stack=True
) as prof:
for step, batch_data in enumerate(train_loader):
if step >= (1 + 1 + 3) * 2:
break
train(batch_data)
prof.step() # Need to call this at the end of each step to notify profiler of steps' boundary.
Alternatively, the following non-context manager start/stop is supported as well.
prof = torch.profiler.profile(
schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/resnet18'),
record_shapes=True,
with_stack=True)
prof.start()
for step, batch_data in enumerate(train_loader):
if step >= (1 + 1 + 3) * 2:
break
train(batch_data)
prof.step()
prof.stop()
3. Run the profiler¶
Run the above code. The profiling result will be saved under ./log/resnet18
directory.
4. Use TensorBoard to view results and analyze model performance¶
Install PyTorch Profiler TensorBoard Plugin.
pip install torch_tb_profiler
Launch the TensorBoard.
tensorboard --logdir=./log
Open the TensorBoard profile URL in Google Chrome browser or Microsoft Edge browser.
http://localhost:6006/#pytorch_profiler
You could see Profiler plugin page as shown below.
Overview
The overview shows a high-level summary of model performance.
The 《GPU Summary》 panel shows the GPU configuration, GPU usage and Tensor Cores usage. In this example, the GPU Utilization is low. The details of these metrics are here.
The 《Step Time Breakdown》 shows distribution of time spent in each step over different categories of execution.
In this example, you can see the DataLoader
overhead is significant.
The bottom 《Performance Recommendation》 uses the profiling data to automatically highlight likely bottlenecks, and gives you actionable optimization suggestions.
You can change the view page in left 《Views》 dropdown list.
Operator view
The operator view displays the performance of every PyTorch operator that is executed either on the host or device.
The 《Self》 duration does not include its child operators’ time. The 《Total》 duration includes its child operators’ time.
View call stack
Click the View Callstack
of an operator, the operators with same name but different call stacks will be shown.
Then click a View Callstack
in this sub-table, the call stack frames will be shown.
If the TensorBoard is launched inside VS Code (Launch Guide), clicking a call stack frame will navigate to the specific code line.
Kernel view
The GPU kernel view shows all kernels’ time spent on GPU.
Tensor Cores Used: Whether this kernel uses Tensor Cores.
Mean Blocks per SM: Blocks per SM = Blocks of this kernel / SM number of this GPU. If this number is less than 1, it indicates the GPU multiprocessors are not fully utilized. 《Mean Blocks per SM》 is weighted average of all runs of this kernel name, using each run’s duration as weight.
Mean Est. Achieved Occupancy: Est. Achieved Occupancy is defined in this column’s tooltip. For most cases such as memory bandwidth bounded kernels, the higher the better. 《Mean Est. Achieved Occupancy》 is weighted average of all runs of this kernel name, using each run’s duration as weight.
Trace view
The trace view shows timeline of profiled operators and GPU kernels. You can select it to see details as below.
You can move the graph and zoom in/out with the help of right side toolbar. And keyboard can also be used to zoom and move around inside the timeline. The ‘w’ and ‘s’ keys zoom in centered around the mouse, and the ‘a’ and ‘d’ keys move the timeline left and right. You can hit these keys multiple times until you see a readable representation.
If a backward operator’s 《Incoming Flow》 field is with value 《forward correspond to backward》, you can click the text to get its launching forward operator.
In this example, we can see the event prefixed with enumerate(DataLoader)
costs a lot of time.
And during most of this period, the GPU is idle.
Because this function is loading data and transforming data on host side,
during which the GPU resource is wasted.
5. Improve performance with the help of profiler¶
At the bottom of 《Overview》 page, the suggestion in 《Performance Recommendation》 hints the bottleneck is DataLoader
.
The PyTorch DataLoader
uses single process by default.
User could enable multi-process data loading by setting the parameter num_workers
.
Here is more details.
In this example, we follow the 《Performance Recommendation》 and set num_workers
as below,
pass a different name such as ./log/resnet18_4workers
to tensorboard_trace_handler
, and run it again.
train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True, num_workers=4)
Then let’s choose the recently profiled run in left 《Runs》 dropdown list.
From the above view, we can find the step time is reduced to about 76ms comparing with previous run’s 132ms,
and the time reduction of DataLoader
mainly contributes.
From the above view, we can see that the runtime of enumerate(DataLoader)
is reduced,
and the GPU utilization is increased.
6. Analyze performance with other advanced features¶
Memory view
To profile memory, profile_memory
must be set to True
in arguments of torch.profiler.profile
.
You can try it by using existing example on Azure
pip install azure-storage-blob
tensorboard --logdir=https://torchtbprofiler.blob.core.windows.net/torchtbprofiler/demo/memory_demo_1_10
The profiler records all memory allocation/release events and allocator’s internal state during profiling. The memory view consists of three components as shown in the following.
The components are memory curve graph, memory events table and memory statistics table, from top to bottom, respectively.
The memory type could be selected in 《Device》 selection box. For example, 《GPU0》 means the following table only shows each operator’s memory usage on GPU 0, not including CPU or other GPUs.
The memory curve shows the trends of memory consumption. The 《Allocated》 curve shows the total memory that is actually in use, e.g., tensors. In PyTorch, caching mechanism is employed in CUDA allocator and some other allocators. The 《Reserved》 curve shows the total memory that is reserved by the allocator. You can left click and drag on the graph to select events in the desired range:
After selection, the three components will be updated for the restricted time range, so that you can gain more information about it. By repeating this process, you can zoom into a very fine-grained detail. Right click on the graph will reset the graph to the initial state.
In the memory events table, the allocation and release events are paired into one entry. The 《operator》 column shows
the immediate ATen operator that is causing the allocation. Notice that in PyTorch, ATen operators commonly use
aten::empty
to allocate memory. For example, aten::ones
is implemented as aten::empty
followed by an
aten::fill_
. Solely display the operator name as aten::empty
is of little help. It will be shown as
aten::ones (aten::empty)
in this special case. The 《Allocation Time》, 《Release Time》 and 《Duration》
columns〉 data might be missing if the event occurs outside of the time range.
In the memory statistics table, the 《Size Increase》 column sums up all allocation size and minus all the memory release size, that is, the net increase of memory usage after this operator. The 《Self Size Increase》 column is similar to 《Size Increase》, but it does not count children operators〉 allocation. With regards to ATen operators〉 implementation detail, some operators might call other operators, so memory allocations can happen at any level of the call stack. That says, 《Self Size Increase》 only count the memory usage increase at current level of call stack. Finally, the 《Allocation Size》 column sums up all allocation without considering the memory release.
Distributed view
The plugin now supports distributed view on profiling DDP with NCCL/GLOO as backend.
You can try it by using existing example on Azure:
pip install azure-storage-blob
tensorboard --logdir=https://torchtbprofiler.blob.core.windows.net/torchtbprofiler/demo/distributed_bert
The 《Computation/Communication Overview》 shows computation/communication ratio and their overlapping degree. From this view, User can figure out load balance issue among workers. For example, if the computation + overlapping time of one worker is much larger than others, there may be a problem of load balance or this worker may be a straggler.
The 《Synchronizing/Communication Overview》 shows the efficiency of communication. 《Data Transfer Time》 is the time for actual data exchanging. 《Synchronizing Time》 is the time for waiting and synchronizing with other workers.
If one worker’s 《Synchronizing Time》 is much shorter than that of other workers’, this worker may be a straggler which may have more computation workload than other workers’.
The 《Communication Operations Stats》 summarizes the detailed statistics of all communication ops in each worker.


Microsoft Teams passes 20 million daily users, up more than half in 4 months
by Reuters(Reuters) — Microsoft said on Tuesday its workplace messaging app, Teams, has more than 20 million daily active users, up from 13 million in July.
The software maker offers the app as part of some Office365 business packages, as well as a free version. Teams allows users to chat, share files, make calls, and hold web video conferences.
Microsoft Teams, used by companies such as General Electric and SAP, competes with Slack.
Slack, whose customers include Electronic Arts and Nordstrom, reported more than 10 million daily active users in the second quarter ended July 31.
Slack’s shares fell 8.4% following the news, to $21.18. They are down 45% from the close of their first day of trade in June.
Microsoft Teams also competes with Workplace by Facebook and Cisco’s Webex Teams.
(Reporting by Ambhini Aishwarya in Bengaluru, editing by Rashmi Aich and Clarence Fernandez.)


We visualize users!
- empower plug-and-play infrastructures
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- maximize compelling deliverables
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Syntax:
declaim declaration-specifier* => implementation-dependent
Arguments and Values:
declaration-specifier---a declaration specifier; not evaluated.
Description:
Establishes the declarations specified by the declaration-specifiers.
If a use of this macro appears as a top level form in a file being processed by the file compiler, the proclamations are also made at compile-time. As with other defining macros, it is unspecified whether or not the compile-time side-effects of a declaim persist after the file has been compiled.
Examples:
Side Effects: None.
Affected By: None.
Exceptional Situations: None.
See Also:
Notes: None.


Run the query in the right panel.
In this step, base tables will be created that will be used in a join.
The Parts table contains some part numbers and a quantity number. For instance, there may be part #1 ("wood" screw) and a total of 10 of these in stock. This table will be used to join with a description table, but using a Fetch first command instead of other SQL techniques.
The Parts Description table contains some information on the part numbers. Note that there are multiple descriptions of some part numbers.
What you should see after running is two result sets underneath the query panel to verify the tables have been created and filled with data correctly.


Uses of Class
imagingbook.common.geometry.fd.FourierDescriptor
Package
Description
Fourier descriptors for 2D shape representation and matching.
-
Uses of FourierDescriptor in imagingbook.common.geometry.fd
Modifier and TypeMethodDescriptionstatic FourierDescriptor
Creates aFourierDescriptor
directly from the vertices of a closed polygon (without interpolation).static FourierDescriptor
Creates a new Fourier descriptor from a uniformly sampled polygon V with the maximum number of Fourier coefficient pairs.static FourierDescriptor
Creates a new Fourier descriptor from a uniformly sampled polygon V with Mp coefficient pairs.static FourierDescriptor
Creates a new Fourier descriptor from a uniformly sampled polygon V with the maximum number of Fourier coefficient pairs.static FourierDescriptor
Creates a new Fourier descriptor from a uniformly sampled polygon V with Mp coefficient pairs.FourierDescriptor.makeInvariant()
Makes this Fourier descriptor invariant to scale, start-point and rotation.FourierDescriptor.makeRotationInvariant()
Returns a new rotation invariant Fourier descriptor by applying complex rotation to all coefficients (except coefficient 0).FourierDescriptor.makeScaleInvariant()
Returns a new scale invariant Fourier descriptor by normalizing the L2 norm of the sub-vector {G[-mp], ..., G[-1], G[1], ..., G[mp]}.FourierDescriptor.makeStartPointInvariant()
Returns a pair of start-point normalized Fourier descriptors.FourierDescriptor.makeTranslationInvariant()
Returns a new translation invariant Fourier descriptor by setting coefficient 0 to zero.Modifier and TypeMethodDescriptiondouble
FourierDescriptor.distanceComplex
(FourierDescriptor fd2) Returns a L2-type distance between this and anotherFourierDescriptor
instance comparing the real and imaginary parts of all coefficient pairs.double
FourierDescriptor.distanceComplex
(FourierDescriptor fd2, int p) Returns a L2-type distance between this and anotherFourierDescriptor
instance comparing the real and imaginary parts of a limited range of (low-frequency) coefficient pairs.double
FourierDescriptor.distanceMagnitude
(FourierDescriptor fd2) Returns a L2-type distance between this and anotherFourierDescriptor
instance comparing the magnitudes of all coefficient pairs.double
FourierDescriptor.distanceMagnitude
(FourierDescriptor fd2, int p) Returns a L2-type distance between this and anotherFourierDescriptor
instance comparing the magnitudes of a limited range of (low-frequency) coefficient pairs.ModifierConstructorDescriptionConstructor for cloning Fourier descriptors.


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The copy to clipboard component creates a button that copies content to the clipboard.
Specifies the content to copy. This property binds data, so you must use brackets to evaluate the template expression: [copyTarget]. Without the brackets, Angular treats the value as a string and initializes the target property with the string value instead of evaluating the data value.
Specifies a label to display on the copy to clipboard button after users select it. This property accepts string values.
Specifies a label for the copy to clipboard button. This property accepts string values.