If you are following the handbook on configuring Xorg, ignore what the handbook says about running Xorg -configure. Don't run Xorg -configure If you already followed the handbook and created an xorg.conf make sure to remove /etc/X11/xorg.conf or /usr/local/etc/X11/xorg.conf before proceeding: rm -f /etc/X11/xorg.conf /usr/local/etc/X11/xorg.conf Install x11/nvidia-driver: pkg install x11/nvidia-driver For some older cards you need to use x11/nvidia-driver-340 or x11/nvidia-driver-304 instead. Consult NVIDIA's download page to see which driver version you need. Note that there is no need (and it's even counterproductive) to download the driver from that page. Run sysrc kld_list+="nvidia-modeset" to add an entry to /etc/rc.conf to load the kernel modules at boot. sysrc(8) is a nice utility that safely edits /etc/rc.conf for you. nvidia-modeset is only available for driver versions >= 358.009, if you to use an older version use sysrc kld_list+="nvidia" instead. Either reboot with shutdown -r now or load the required kernel modules now with kldload nvidia-modeset or kldload nvidia Create the /usr/local/etc/X11/xorg.conf.d directory: mkdir -p /usr/local/etc/X11/xorg.conf.d Use your favourite editor to create /usr/local/etc/X11/xorg.conf.d/driver-nvidia.conf with the following contents: Code: Section "Device" Identifier "NVIDIA Card" VendorName "NVIDIA Corporation" Driver "nvidia" EndSection At this point you can continue following the rest of the handbook on setting up a display manager and desktop environment.
Thanks for this! Just want to add that the Linux compatibility option is on by default in the port so the linux(4) kernel module needs to be loaded by adding linux_load="YES" to /boot/loader.conf. I'm not sure however if this is loaded at boot automatically as a dependent module when the Nvidia kernel driver is loaded or not. I always set up my environment before installing ports when installing FreeBSD. Edit: Just checked and linux.ko is loaded automatically as a dependent module at boot but if installing x11/nvidia-driver from ports it will need to be loaded as a prerequisite to installing the port unless the Linux compatibility option is turned off.
For users having tearing problems when using compositing with Nvidia Kepler and newer based cards: From what I understand, starting with Nvidia's Kepler based GPUs, Nvidia removed the hardware support for tearing/Vsync in the GPU and added additions to control this in the driver instead. For many users of FreeBSD, Linux and even Windows in some cases, this does not prevent tearing very well if at all. Nvidia's answer to this is it's newer G-Sync technology built into some monitors and supported by some cards. If you do not have a card and monitor that supports G-Sync and have tearing issues, there is a workaround. Enabling the options "ForceFullCompositionPipeline" and "TripleBuffer" in the /usr/local/etc/X11/xorg.conf/10-nvidia.conf file referenced above should fix the tearing issues. The drawback is a slight penalty in performance but for every day desktop use, this shouldn't really be noticed at all. It may be noticed more when playing games under FreeBSD, though YMMV. I didn't notice much of a difference at all myself in my limited testing. If you are following tobik's instructions above, your 10-nvidia.conf file would look similar to the following with these changes included: Code:Section "Device" Identifier "NVIDIA Card" VendorName "NVIDIA Corporation" Driver "nvidia" Option "AccelMethod" "none" Option "TripleBuffer" "True" Option "MetaModes" "nvidia-auto-select +0+0 { ForceFullCompositionPipeline = On }" EndSection I tested this using both compositing under KDE as well as x11-wm/compton.
Hi, Thank you very much, I was looking for that trick for a long time ! My video adapter is an Nvidia GT610 and tearing disappears with your instructions. But another problem appears : my mouse cursor disappears when it approach the top of the screen. I am using FreeBSD 10.2-RELEASE-p9 and xorg-7.7_2 with fvwm-2.6.5_7.
This issue is fixed in newer versions of the Nvidia driver which have not been pulled into the ports tree yet.
I tried going to this page to find out which version of the driver I should use. The only information I was able to find is that I would need version 361.28. Is there a way to translate this version number to whether I need the 330, 304 or regular nvidia-driver? Thank you
There's no port for that version yet AFAICT The version numbers should correspond with the port versions. What GPU do you have?
In that case you should be fine with just using x11/nvidia-driver (see http://us.download.nvidia.com/XFree86/FreeBSD-x86_64/346.96/README/supportedchips.html)
For users that are having problems or have questions about Xorg and Nvidia GPUs, please create a new thread for your question in a support forum that would be more appropriate. If you have a question directly related to this Howto/FAQ, or have something to add to it, you are always still welcome to post it to this thread. Thanks!
A quick note for those who want to build x11/nvidia-driver or related ports themselves. Make sure you have the correct source tree in /usr/src. Errors like Code: make[2]: "/usr/share/mk/bsd.kmod.mk" line 12: Unable to locate the kernel source tree. Set SYSDIR to override *** Error code 1 during building or Code: KLD nvidia:ko: depends on kernel - not available or version mismatch linker_load_file: Unsupported file type when loading the driver are strong hints that you have either no sources or the wrong sources installed. For FreeBSD 10.3-RELEASE you can fetch the correct tree via one of these two methods: svnlite checkout https://svn.freebsd.org/base/releng/10.3 /usr/src Or grab and extract the src.txz tarball (it's the one you can select in the installer as well): Code: fetch https://download.freebsd.org/ftp/releases/amd64/10.3-RELEASE/src.txz tar -C / -xf src.txz Starting an update with freebsd-update fetch and freebsd-update install is recommend even if you've already updated previously. freebsd-update will make sure that the sources match the most recent patch level.
"ForceCompositionPipeline" works better for me (GeForce 9 Series). Code: Option "metamodes" "1280x1024 +0+0 { ForceCompositionPipeline = On }" I'm using it in "Screen" section in /etc/X11/xorg.conf Code: Section "Screen" Identifier "Screen0" Device "Device0" Monitor "Monitor0" DefaultDepth 24 Option "metamodes" "1280x1024 +0+0 { ForceCompositionPipeline = On }" SubSection "Display" Depth 24 EndSubSection EndSection Also it is possible to start your WM (window manager) with right resolution with this fix via ~/.xinitrc: Code: #!/bin/sh nvidia-settings --assign CurrentMetaMode="1280x1024 +0+0 { ForceCompositionPipeline = On }" exec /usr/local/bin/wmaker (x11/nvidia-settings should be installed).
Interesting! I have two computers with (older) NVIDIA cards, both run 11.0-CURRENT, and neither ever needed ANY /usr/local/etc/X11/xorg.conf.d/*.conf file to explicitly inform my system to use NVIDIA driver. However, what I did need was to instruct Xorg how to handle my dual monitors the way I needed it. The problem was, while using one screen for 2 monitors it would always put them in the "wrong" order -- and me not being able to physically swap them for certain reasons. So, instead of physically swapping them I figured out the far more gracious way to do that. DON'T use any nvidia-settings or nvidia-xconfig or any such stuff. They are WAY too complicated for this simple task! Both will create huge and overly complicated xorg.conf file stuffed with specific nvidia-related options and settings you don't really need for a simple task of using 2 monitors with default driver-decided settings in the needed order. It just took me to put a simple twin.conf file into /usr/local/etc/X11/xorg.conf.d: Code:Section "ServerLayout" Identifier "myLayout" Screen 0 "myScreen" 0 0 EndSection Section "Device" Identifier "Card0" Driver "nvidia" VendorName "NVIDIA Corporation" EndSection Section "Screen" Identifier "myScreen" Device "Card0" Option "MetaModes" "DFP-1:nvidia-auto-select,DFP-0:nvidia-auto-select" Option "MetaModeOrientation" "DFP-1 LeftOf DFP-0" EndSection I've taken the exact data (DFP-1 and such) from /var/log/Xorg.0.log, BTW. It clearly reports there whether it is using configuration from /usr/local/etc/X11/xorg.conf.d or not, and why not. So it wouldn't use anything defined in "Screen" section alone unless a "Device" section be present as well. But with monitors and input devices it seems quite safe to stay with defaults. I'm putting this here partly because this is just as simple as they come, while whatever I've found on the web was far more complicated (even at the very helpful Arch Wiki page).
I was struggling to get Xorg working on a late-2013 iMac and this thread did the trick. Many thanks!!


Id: 199
Name: Larva Alien
Status: Alive
Species: Alien
Type: Larva alien
Gender: unknown
OriginName: Larva Alien's Planet
OriginUrl: https://rickandmortyapi.com/api/location/51
LocationName: Planet Squanch
LocationUrl: https://rickandmortyapi.com/api/location/35
Image: https://rickandmortyapi.com/api/character/avatar/199.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/16', 'https://rickandmortyapi.com/api/episode/19', 'https://rickandmortyapi.com/api/episode/21']
Url: https://rickandmortyapi.com/api/character/199
Created: 2017-12-30T12:48:44.677Z


My name is Linh Phung and I just graduated as a .NET developer focused on Microsoft Azure. I'm taking this frontend developer education paralell with Business Intellegence Architect and I'm glad I'm here since I really love to make beatiful websites, coding and so on.
Something that I'm good at is eating hahah... I love food and I can discuss food all day long... So if you want to know anything about food you can ask me :)
From the very beginning the web was just plain text and links, already then people thought it was amazing that we could communicate to the world through writing and reading text and it was something that actually stayed there and we could re-visit again and again. From then until now the web has developed tremendous to the better and the internet also play a role in here. The internet access to people has grown significantly and almost everyone in the world who have access to a cellphone/ computer/ tablet and so on have access to the internet. I believe that in five years even more people have access to the internet and the web.
Except from plain text and links the web today have pictures, videos, sounds that is interactive with the end user. You can for example click on something and then scroll down automatically to the content for the link. I think in five years people will come up with cooler and more crazy UI designs than there already is since new techniques are coming up and the web are constantly changing and evolving.
Something else that wasn’t possible before is online shopping. Today you can order anything from the internet for example food, clothes, tickets etc. In the future people will expand their business to the web and make smart stuff for their business that is connected to internet.
Already now we can check our bank account in our cellphones. What if we could check other stuff in apps or websites that is connected to the internet? For example, I have a water turtle that needs to be fed once a day. Maybe one day I’m not at home but I have invented something that feeds my water turtle through a web or mobile application. Through that application I only need internet to connect to my device at home and I can feed my water turtle even though I’m not there. What I mean is more products will go through the web since we people are smart but lazy ones that loves to make life more comfortable.
With that said I think the future will be more online than it already is.
Written by Linh Phung 2015.09.06


Your daily horoscope: November 21
by Sally BromptonIF TODAY IS YOUR BIRTHDAY
You will find yourself torn between contrasting needs and aims over the coming year and somehow you will have to find a way to satisfy them both. You could start by getting rid of your black-and-white view of the world. Nothing is all “good” or all “bad”.
ARIES (March 21 - April 20):
If you are hoping to be on the move over the next few days you can expect to have a really fun time. If, however, your responsibilities keep you stuck close to home you can still make plans for your next vacation. Make it the best one ever.
TAURUS (April 21 - May 21):
Have the confidence to back a hunch with time, energy and, if it comes to it, hard cash as well. Opportunities to get ahead in your career or in business will come thick and fast over the next few weeks and you must trust yourself to do well.
GEMINI (May 22 - June 21):
The sun is about to move into your opposite sign, which can be seen as the midpoint of your solar year. Make some time over the next few days to look back over the past six months. You’ll be amazed when you realize how far you have come.
CANCER (June 22 - July 23):
Don’t worry if you have to take a back seat to someone whom you rate as inferior in terms of both intelligence and ability. Let them have their moment in the spotlight while you keep working away behind the scenes at your own long-term success.
 Don’t wonder too long – just enjoy.
VIRGO (Aug. 24 - Sept. 23):
What happens over the next few days may not be entirely to your liking but the planets indicate that it is entirely necessary, so find ways to work with events rather than against them. It might help too if you let go of unrealistic expectations. Be ruthlessly rational.
LIBRA (Sept. 24 - Oct. 23):
You have been under a lot of pressure recently but you dealt with it well and now you can start enjoying the benefits. The sun’s move into the most open and outgoing area of your chart tomorrow will lighten your load and get you laughing again.
SCORPIO (Oct. 24 - Nov. 22):
You have been very much in control of events in recent weeks, but now the sun is leaving your sign certain things won’t come quite so easy to you. That’s fine. In fact it’s good. You do best when you are challenged, not when you are coasting.
SAGITTARIUS (Nov. 23 - Dec. 21):
No matter how many delays and setbacks you have endured of late the good times are about to return in spectacular fashion. Be ready to take advantage of new opportunities and don’t be afraid to rock the boat – it’s what you do better than anything else.
CAPRICORN (Dec. 22 - Jan. 20):
The sun enters the most sensitive area of your chart tomorrow, so maybe now is a good time to retreat from the limelight a bit and ponder the whys and wherefores of what you are doing. It’s not just about action Capricorn, it’s about thinking ahead too.
AQUARIUS (Jan. 21 - Feb. 19):
You won’t be able to persuade someone to change their beliefs, so don’t waste time and energy trying. What you need to understand is that they are every bit as wedded to their view of the world as you are to your own. There is no middle ground.
PISCES (Feb. 20 - Mar. 20):
The sun is about to move into the area of your chart that governs your social and professional standing, so even if you are the sort of Pisces who is not particularly ambitious you will want to make your mark on the world. Make it a big mark!
Discover more about yourself at sallybrompton.com


MusicLab 2016!
Music Controls!
Play On or Off
Which song do you want to play?
JingleBells
Starcraft
MegaMan
Airplane


Susan Granger's review of 'WITHOUT LIMITS" (Warner Bros.)
"Running is not about winning, it's about guts...To give anything less than your best is to sacrifice the gift," said Steve Prefontaine. In an era where heroes are hard to find, it's no wonder that this popular track star's story has inspired so much interest. Prefontaine had it all: talent, charm, grace, beauty, self-confidence, and - above all - tremendous drive. He broke every American record between 2,000 and 10,000 meters before he tragically died at the age of 24 in 1975 in a car crash. Written by Robert Towne and Kenny Moore (a champion runner and one of Pre's closest friends) and directed by Towne, this biopic examines the boy behind the man, the coach behind the athlete. A mill worker's son who grew up in a German-speaking household in Coos Bay, Oregon, Steve Prefontaine (Billy Crudup) learned early how to run. As a kid, he ran from kids who taunted him. As a teenager, he ran for fun and glory. As an athlete, he ran for himself. He was a rebel, a perpetual front-runner who refused to win a race with a mediocre effort, saying: "When you set the pace, you control the race." At the University of Oregon, he met the two most pivotal people in his life: his legendary coach, Bill Bowerman (Donald Sutherland), and his devoutly Catholic girl-friend, Mary Marckx (Monica Potter), who both cooperated in the making of this movie. The story doesn't idealize Prefontaine as a role model. On the contrary, it details his blunt, cocky attitude, self-obsession, and anti-authoritarian outbursts. Robert Towne has been this route before with "Personal Best," his story about women runners training for the Olympics. On the Granger Movie Gauge of 1 to 10, "Without Limits" is an inspiring 7, examining the cool, fearless legacy of the long-distance runner. It's a great American story.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


const assert = require("assert");
const readline = require("readline");
const fs = require("fs");
/**
* This function would read file from a path line by line and populate the filecontent
* object for further processing
* @param {string} filePath
* @param {Readable} testStream : sole purpose of this stream is for testing
* @returns {Promise} this would resolve when all of the content is read from the file
*/
function fileReader(filePath, testStream) {
return new Promise((resolve, reject) => {
try {
if (!testStream) {
assert(filePath, "input file path is empty");
}
const fileContent = {
"ships": []
};
let lineCount = 0;
const lineReader = readline.createInterface({
"input": testStream || fs.createReadStream(filePath)
});
lineReader.on("line", function (line) {
lineCount++;
switch (true) {
case (lineCount === 1):
fileContent.AreaDimensions = line.trim();
break;
case (lineCount === 2):
fileContent.shipCount = parseInt(line.trim(), 10);
break;
case (lineCount > 2 && lineCount <= (fileContent.shipCount + 2)):
fileContent.ships.push(line.trim());
break;
case (lineCount === (fileContent.shipCount + 3)):
fileContent.player1Targets = line.trim();
break;
case (lineCount === (fileContent.shipCount + 4)):
fileContent.player2Targets = line.trim();
break;
default:
break;
}
});
lineReader.on("close", function () {
return resolve(fileContent);
});
} catch (error) {
console.log("Error reading file, Please check the file path", error);
reject(error);
}
});
}
module.exports = fileReader;


I was prescribed Lamictal for manic episodes caused by Effexor XR. I do not believe I am bipolar, I just think the high dose of Effexor, 225mg, put me in the episodes. During the episodes, I spoke very fast, had little sleep, major energy, irritability, walked out of school, and spent a lot of money on unnecessary things. I also stole from stores, which was nothing like me. The Lamictal stabilized my mood. However, I had no filter in what I said. I called my uncle on the phone and screamed at him for not being in my life.  I got into a fight over a petty thing with my co-worker, almost costing me my job. On Lamictal, I was extremely aggressive. I know another person that takes Lamictal who is very aggressive, swears frequently, and has an extremely short temper. Two weeks later, I had to stop Lamictal due to a rash associated with the drug.
Now I am on Seroquel XR as well as Effexor XR. Seroquel XR helps with my unstable mood and has little side effects compared to Lamictal. The only major side effect of Seroquel is drowsiness, which went away in a month.
[Reported Dose of Lamictal: 50mg Twice Daily]
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.


Minna Halme is professor of management at Aalto University School of Business. Her research focuses on inclusive business models for poverty alleviation and sustainability management in organizations. She has worked with a number of European research projects and headed several national ones. She teaches Business & Sustainability in Aalto's master's programs and executive MBA courses in Finland and internationally. She cooperates with companies in several research projects.
Sara Lindeman is an expert on inclusive business. Her research focuses on market creation in low-income world majority contexts. Sara has worked closely with Finnish companies on inclusive innovation in the fields of renewable energy, forestry and ICT.
Teija Lehtonen, M.Sc. (Econ) works as a Director of the Aalto Global Impact strategic initiative at the Aalto University. Her professional career of over 25 years covers leadership, business development, human resource and financial management in private sector and international organizations. Her long-term field experience covers India, Sri Lanka, Nepal, Afghanistan, Abkhazia/Georgia, Sierra Leone and Ivory Coast. She is currently promoting inclusive innovations and business at the Base of the Pyramid (BOP) market through various networks.
Petri Allekotte is responsible of corporate engagement into Aalto’s business development innovation projects in developing high potential countries. Petri is also responsible of developing Aalto’s coordination of its strategic corporate partnership program and fostering international social entrepreneurship through Aalto Global Impact’s projects. He has extensive career in senior strategic business development leadership positions in globally operating technology, wellbeing and service sectors corporates.
Tatu Lyytinen is PhD candidate at Aalto University School of Business Department of Management and International Business. His research interest includes international business, inclusive business models, and business ecosystems. He is interested in connecting scientific and practical knowledge in management of business in emerging markets. Tatu has worked with international politics, international business and applied research especially related to Latin America and Africa.
Susu Nousala (PhD) Currently a project-based learning researcher/teacher in Aalto University’s Creative Sustainability program, she is a Research Fellow at GAMUT research group, University of Melbourne, and a co-director of Kororoit Institute (KI) Australia. Previously a Research Fellow at RMIT University College of Design and Social Context, and an Australian Research Fellow/Project Manager for an EU FP5 for process improvement for SMEs, the Cost-Worth project. Also invited scientist for the UNEP foresight Process on Emerging Environmental Issues 2012. Research interests include: social complex adaptive systems. To date - chaired international symposiums/conferences, international keynote, authored over 50 refereed journal, conference papers, book chapters.
M.Sc. Helena Sandman’s research is on affordable housing for rapidly growing communities using architecture as an empowering tool. She is founding member of Ukumbi NGO providing architecture as a means of development and a partner in Hollmén Reuter Sandman Architects Ltd. Helena has been teaching multidisciplinary student teams in project based slum upgrading since 2009 and is lecturing regularly world wide.
Antti Hellgren is an aspiring professional in the field of international business at the Base of Pyramid. In Aalto University Antti is presently finalizing his master’s thesis on corporate-NGO partnership opportunities at the Chinese BoP. Moreover, Antti has extensively explored the developing world in Asia with his camera, and also spent 3 years studying and working in South Korea and China.


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Build your own legends in this masterfully crafted cabin. Perfect for families or large parties
The Legend Room
Build your own legends in this masterfully crafted cabin. Perfect for families or large parties
The Legend Room
Build your own legends in this masterfully crafted cabin. Perfect for families or large parties.
The Legend Room
Build your own legends in this masterfully crafted cabin. Perfect for families or large parties
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Build your own legends in this masterfully crafted cabin. Perfect for families or large partie
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The excitement of not knowing where you are
The vivid colours of the
wild flowers
The cacaphony of the bird calls


{% extends "base.html" %} {% block content %}
{% trans "Your username and pin do not match. Please try again." %}


This was once a tourist resort, but now is rarely visited by land tourists. Visiting yachts are warmly welcomed by the locals. The bay lies on the north end of Palawan's west coast, just south of Boayan Island.
It is not a port, just a large, peaceful, sheltered bay, protected by several off lying islands.
Position 10°25'30"N, 119°09'E (Port Barton Bay)
The nearest port for clearance, is Puerto Princesa, which can be reached by regular van service which runs several times a day, both ways. Leave on the 6am van and return on the 4pm one.
The van drops you at San Jose terminal just north of Puerto Princesa. You will then need to take a tricycle ride into city Immigration or to the airport. The journey takes about 3 hrs and costs aproximately 300 peso.
Imigration in Puerto Princesa is free.
Customs is 115 peso.
Only the captain needs to go.
Port Barton is accepted as a valid port to leave the boat while completing clearance.
Last updated January 2016.
This is reported to be a safe place to stay and anchor. The bottom is gently shelving sand with good holding. In the north east monsoon a swell comes into the bay mainly on the south side, so in these conditions anchor at the north end of the beach to avoid the swell. The dinghy can be left anywhere on the beach.
Ashore there is fuel, water and supplies, also several restaurants.
A good contact is Brian Tennant who runs a lodge and accommodation here.
Last updated January 2016.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/casts.html ---
What is the canonical solution to:
import data.polynomial open polynomial universe u variables {α : Type u} [integral_domain α] [decidable_eq α] example (n : ℕ) : degree (X^n : polynomial α) = n := begin simp, -- We're left with `↑n = ↑n` sorry end
Are we missing some lemmas? Should norm_cast
be able to help here (it currently doesn't).
is it not refl
?
Unfortunately not
There is clearly a missing lemma. What are the coe instances involved?
adding the lemma
@[simp] lemma nat.cast_with_bot_cast {α} [add_monoid α] [has_one α] : ∀ n : ℕ, (n : with_bot α) = (n : α) | 0 := rfl | (n+1) := by simp [nat.cast_with_bot_cast n] example (n : ℕ) : degree (X^n : polynomial α) = n := by simp
works
But they don't feel like the right lemmas!
That looks right to me... I recall Johan mentioning this lemma
Oh, actually the second one isn't necessary. (removed it)
I'm not sure about the direction of the simp though. Isn't the RHS two up arrows?
Can it be generalised in some useful way?
that looks already maximally generalized
Okay. If I reverse the direction of the simp lemma, it no longer fixes my problem.
Eh, I guess it's fine the way you wrote it... There are more coes but they are simpler
and more likely to trigger with other stuff
Okay. And this belongs in src/order/bounded_lattice.lean
, right after the coercion is introduced?
is nat.cast in scope there?
no...
what about the other way around, putting it in data.nat.cast
Seems to work.
am I meant to mark this with squash_cast
?
even though it's actually unsquashing a cast? :-)
(Going through abandoned pieces of students' work salvaging useful bits. :-)
I remember having talked about this before...
link or it didn't happen
Thanks
Thanks Kenny, I've switched over to using your faster proof.
did you and Mario come up with the exact same lemma and proof across 4 months :o
that's when you know it's a good interface
@Kevin Buzzard someone verified your canonical criterion!
Yeah, that's a proof that it's the canonical interface.
No, I found that in a student's old repo that I was looking at for other reasons; I suspect he got it from Mario to begin with.
:-)
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Angélique Weger
angeliqueweger.com | @messypixels
Senior Front End Developer | Detroit Trading
Chapter Leader | Baltimore Girl Develop It | @gdibaltimore
$ gem install middleman
$ middleman init project
Where "project" is whatever you're calling your site/project folder
$ cd project
$ middleman server
gem "middleman-blog"
$ middleman init --template=blog
$ middleman build
Eventually this walkthrough sorted me out.
$ gem install jekyll
$ jekyll new project
Where "project" is whatever you're calling your site/project folder
$ cd project
$ jekyll serve
$ jekyll build
Should have been a warning sign when the site noted Windows wasn't officially supported.
Creating & using a Rakefile was my golden ticket.
GH Pages Site: http://angeliquejw.github.io/mm-test/
GH Pages Site: http://angeliquejw.github.io/j-test/


This chapter describes two similar expert systems which were developed at Cullinet Software, a large software vendor for IBM mainframes, VAXes, and PCs. The systems illustrate some of the difficulties in knowledge base design and show the different features needed in two seemingly very similar systems.
Both expert systems were designed to set parameters for the mainframe database, IDMS/R, at a new user site. The parameters varied from installation to installation, and it was necessary to have an experienced field support person set them at the site. Since field support people are expensive, the expert systems were written to allow the customer to set the parameters, thus freeing the support person for more demanding tasks.
The first, CVGEN, set the system generation (sysgen) parameters for the run time behavior of the system. This included such parameters as storage pool sizes, logging behavior, and restart procedures. These parameters had a serious effect on the performance of the system, and needed to be set correctly based on each site's machine configuration and application mix.
The second, AIJMP, set all of the parameters which ran an automated installation procedure. This included parameters which determined which modules to include and how to build installation libraries. These parameters determined how the software would reside at the customer's site.
The systems were built using a variation of the pure Prolog approach described earlier in the book. The inferencing parts of the system were separated from the knowledge base. It was surprising to find that even with two systems as similar as these, they both set parameters, the shell for one was not completely adequate for the other.
Various shells available on the PC were examined when CVGEN was built, yet none seemed particularly well suited for this application. The main difficulty centered around the nature of the dialog with the user. To a large degree, the expertise a field support person brought to a site was the ability to ask the right questions to get information from the systems programmers at the site, and the ability to judge whether the answers were realistic.
To capture this expertise, the knowledge base had to be rich in its ability to represent the dialog with the user. In particular:
• The system was designed to be used by systems programmers who were technically sophisticated, but not necessarily familiar with the parameters for IDMS/R. This meant fairly lengthy prompts were needed in the dialog with the user.
• The input data had to be subjected to fairly complex validation criteria, which was often best expressed in additional sets of rules. A large portion of the field person's expertise was knowing what values made sense in a particular situation.
• The output of the system had to be statements which were syntactically correct for IDMS/R. This meant the rules not only found values for parameters but built the statements as well.
The first objective of the system was to gather the data necessary to set the parameters by asking meaningful questions of the systems programmer. This meant providing prompts with a fair amount of text.
The next objective of the system was to validate the user's input data. The answers to the questions needed to be checked for realistic values. For example when asking for the desired number of simultaneous batch users, the answer had to be checked for reasonableness based on the size of machine.
A similar objective was to provide reasonable default answers for most of the questions. As were the edit checks, the defaults were often based on the particular situation and required calculation using rules.
Given these objectives, the questioning facility needs to have the ability to call rule sets to compute the default before asking a question, and another rule set to validate the user's response. It also needs to be able to store questions which are up to a full paragraph of text.
The knowledge base needs to be designed to make it easy for the experts to view the dialog, and the edit and default rules. The knowledge base also needs some pure factual information.
The actual rules for inferencing were relatively simple. The system had a large number of shallow rules (the inference chains were not very deep) which were best expressed in backward chaining rules. The backward chaining was natural since the experts also tackled the problem by working backward from the goals of setting individual parameter values.
Also, since the system was setting parameters, uncertainty was not an issue. The parameter was either set to a value or it wasn't. For this reason pure Prolog was used for the main rule base.
Pure Prolog had the additional advantage of making it easy for the rules to generate IDMS/R syntax. The arguments to the parameter setting rules were lists of words in the correct syntax, with variables in the positions where the actual value of the parameter was placed. The rules then sought those values and plugged them into the correct syntax.
The knowledge base is divided into six parts, designed to make it easy for the expert to examine and maintain it. These are:
• main rules for the parameters;
• rules for derived information;
• questions for the user;
• rules for complex validation;
• rules for complex default calculations;
• static information.
The rules for each parameter are stored in the knowledge base with the parameter name as the functor. Thus each parameter is represented by a predicate. The argument to the predicate is a list with the actual IDMS/R syntax used to set the parameter. Variables in the list are set by the body of the predicate. A separate predicate, parm, is used to hold the predicate names which represent parameters.
Most knowledge bases are designed with askable information listed separately from the rules, as in the earlier examples in the book. In this case however, the experts wanted the relationship between user dialog and rules to be more explicit. Therefor the ask predicate is embedded in the body of a rule whenever it is appropriate.
In the following example the parameter is ina which when set will result in a text string of the form INACTIVE INTERVAL IS X, where X is some time value. Some of the sub-goals which are derived from other rules are online_components and small_shop, whereas int_time_out_problems is obtained from the user.
parm(ina).
ina( ['INACTIVE
INTERVAL IS', 60]):-
online_components,
small_shop.
ina( ['INACTIVE
INTERVAL IS', 60]):-
online_components,
heavily_loaded.
ina( ['INACTIVE
INTERVAL IS', 60]):-
ask(initial_install, no),
online_components,
ask(int_time_out_problems, yes).
ina( ['INACTIVE
INTERVAL IS', 30]):-
online_components.
Some parameters also have subparameters which must be set. The structure of the knowledge base reflects this situation:
parm(sys).
sys( ['SYSCTL
IS', 'NO']):-
never.
sys( ['SYSCTL
IS', 'SYSCTL']):-
os_class(os).
subprm(sys,
dbn, [' DBNAME IS', 'NULL']):-
ask(initial_install, no),
ask(multiple_dictionaries, yes),
ask(db_name, null).
subprm(sys,
dbn, [' DBNAME IS', V1]):-
ask(initial_install, no),
ask(multiple_dictionaries, yes),
ask(db_name, V1),
V1 \== null.
The next part of the knowledge base contains the level of rules below the parameter / subparameter level. These rules represent derived information. They read as standard Prolog. Here are a few examples:
heavily_loaded:-
ask(heavy_cpu_utilization, yes), !.
heavily_loaded:-
ask(heavy_channel_utilization, yes), !.
mvs_xa:-
ask(operating_system, mvs),
ask(xa_installed, yes), !.
online_components:-
dc_ucf, !.
online_components:-
ask(cv_online_components, yes), !.
The next portion of the knowledge base describes the user interaction. Standard Prolog rules do not cover this case, so special structures are used to hold the information. Operator definitions are used to make it easy to work with the structure.
The first two examples show some of the default and edit rules which are simple enough to keep directly in the question definition.
quest abend_storage_size
default 200
edit between( 0, 32767)
prompt
['Enter the amount of storage, in fullwords, available',
'to the system for processing abends in the event',
'of a task control element (TCE) stack overflow.',
'Note that this resource is single threaded.'].
quest abru_value
default no
edit member( [yes, no])
prompt
['Do you want the system to write a snap dump to the',
'log file when an external run unit terminates',
'abnormally?'].
The next two rules require more complex edit and default rule sets to be called. The square brackets in the default field indicate there is a rule set to be consulted. In these examples, ed_batch_user will be called to check the answer to allowed_batch_users, and def_storage_cushion is used to calculate a default value for storage_cushion_size.
quest allowed_batch_users
default 0
edit ed_batch_user
prompt
['How many concurrent batch jobs may access',
'the CV at one time?'].
quest storage_cushion_size
default [def_storage_cushion]
edit between( 0, 16384)
prompt
['How many bytes of storage cushion would',
'you like? When available storage is less than the',
'cushion no new tasks are started. A recommended',
'value has been calculated for you.'].
The next two sections contain the rules which are used for edit and default calculations. For example, the following rules are used to calculate a default value for the storage cushion parameter. Notice that it in turn asks other questions and refers to the settings of another parameter, in this case the storage pool (stopoo).
def_storage_cushion(CUS):-
ask(initial_install, yes),
stopoo([_, SP]),
PSP is SP / 10,
min(PSP, 100, CUS), !.
def_storage_cushion(V1):-
ask(total_buffer_pools, V2),
stopoo([_, V3]),
ask(maximum_tasks, V4),
V1 is (V2 + V3 + 3) / (3 * V4), !.
Here are the rules which are used to edit the response to the number of batch users. The user's response is passed as the argument and rules succeed or fail in standard Prolog fashion depending on the user's response.
ed_batch_user(V1):-
V1 =< 2, !.
ed_batch_user(V1):-
machine_size(large),
V1 =< 10, !.
ed_batch_user(V1):-
machine_size(medium),
V1 =< 5, !.
ed_batch_user(V1):-
machine_size(small),
V1 =< 3, !.
The final section contains factual information. For example, here is a table of the MIPS ratings for various machines, and the rules used to broadly classify machines into sizes.
mac_mips('4381-1', 1.7).
mac_mips('4381-2', 2.3).
mac_mips('3083EX', 3.7).
mac_mips('3083BX', 6.0).
mac_mips('3081GX', 12.2).
mac_mips('3081KX', 15.5).
mac_mips('3084QX', 28.5).
mips_size(M,
tiny):-
M < 0.5, !.
mips_size(M,
small):-
M >= 0.5,
M < 1.5, !.
mips_size(M,
medium):-
M >= 1.5,
M < 10, !.
mips_size(M,
large):-
M >= 10.
The knowledge base is designed to reduce the semantic gap between it and the way in which the experts view the knowledge. The main parameter setting rules are organized by parameter and subparameter as the expert expects. The secondary rules for deriving information, and the queries to the user are kept in separate sections.
The dialog with the user is defined by data structures which act as specialized frames with slots for default routines and edit routines. Their definition is relatively simple since the frames are not general purpose, but designed specifically to represent knowledge as the expert describes it.
The standard Prolog rule format is used to define the edit and default rules. In the knowledge base the rules are simple, so Prolog's native syntax is not unreasonable to use. It would of course be possible to utilize a different syntax, but the Prolog syntax captures the semantics of these rules exactly. The experts working with the knowledge base are technically oriented and easily understand the Prolog syntax. Finally, supporting data used by the system is stored directly in the knowledge base.
It is up to the inference engine to make sense of this knowledge base.
The inference is organized around the specialized knowledge base. The highest level predicates are set up to look for values for all of the parameters. The basic predicate set_parms accomplishes this. It uses the parm predicate to get parameter names and then uses the univ (=..) built-in function to build a call to a parameter setting predicate.
set_parms:-
parm(Parm),
set_parm(Parm),
fail.
set_parms:-
write('no more parms'), nl.
set_parm(Parm):-
get_parm(Parm, Syntax),
write(Parm), write(': '),
print_line(Syntax), nl,
subs(Parm).
get_parm(Parm,
Syntax):-
PS =.. [Parm, Syntax],
call(PS), !.
subs(Parm):-
subprm(Parm, Sub, Syntax),
write(Parm), write('/'), write(Sub), write(':'),
print_line(Syntax), nl,
subs(Sub),
fail.
subs(Parm):-true.
The next portion of the inference engine deals with the questions to the user. The following operator definitions are used to define the data structure for questions.
:-op(250, fx, quest).
:-op(240, yfx, default).
:-op(240, yfx, edit).
:-op(240, yfx, prompt).
The basic ask predicate follows the patterns used earlier, but is more complex due to the fact that it handles both attribute-value pairs and object-attribute-value triples. The implementation of triples is relatively straight-forward and not worth repeating. The interesting portions of ask have to do with handling defaults and edits.
The following code is used by the ask predicate to perform edits on a user response. It is called after the user enters a value. If the edit fails, the user is presented with an explanation for why the edit failed, and is reprompted for the answer.
The third argument to edit is the edit criterion. It could be a simple edit such as member or less_than, or one of the more complex edit rules. The built-in univ (=..) is used to construct the goal which is called for the edit process. The actual code is slightly more complex due to additional arguments holding trace information for explanations.
edit(X, X, none):-!. % passes, no edit criteria.
edit(X, X,
Ed) :-
Ed =.. [Pred | Args],
Edx =.. [Pred, X | Args],
call(Edx),
!.
edit(X, X,
not(Ed)):-
Ed =.. [Pred | Args],
Edx =.. [Pred, X | Args],
notcall(Edx),
!.
The default is handled in a similar fashion. It is calculated before the prompt to the user, and is displayed in the answer window. Just hitting enter allows the user to take the default rather than entering a new value.
default([], []):-!.
default(D,
D):-
atomic(D), !.
default([D],
X):-
P =.. [D, X],
call(P).
Explanations become a bit more difficult with the ask predicate. The how questions are handled pretty much as in the Clam and Native systems described earlier in the book. Since why traces require overhead during the inference process, and performance is a key issue for a system with a long dialog such as this one, the why trace implementation is different from that in Native. The basic strategy is to use pure Prolog as indicated for most of the inferencing, but to redo the inference using a Prolog in Prolog inference engine to answer why questions.
In order to do this the system must in fact restart the inference, but since the parameters are all basically independent, the why trace need only restart from the last call to set a parameter. For this reason, the set_parm predicate writes a record to the database indicating which parameter is currently being set.
Once the why trace gets into ask, the Prolog in Prolog must stop. In fact, the question might have arisen from setting a parameter, or calculating a default value, or specifying an edit criteria. Again, for these cases a flag is kept in the database so that trace knows the current situation.
The why trace then starts at the beginning, traces pure Prolog inferencing until it encounters ask. The why explanation then notes that it is in ask, and finds out from the database if ask has gone into either default or edit. If so it proceeds to trace the default or edit code. The final explanation to the user has the Prolog traces interspersed with the various junctions caused by edit and default in ask.
This system is a perfect example of one in which the explanations are of more use in diagnosing the system than in shedding light on an answer for the user. Many of the rules are based solely on empirical evidence, and reflect no understanding of underlying principles. For this reason a separate explanation facility was added to the knowledge base that would explain in English the rationale behind the setting of a particular parameter.
For example, the setting of the maxeru parameter is relatively complex. The rule, while correct in figuring a value for the parameter, does not give much insight into it. The separate exp predicate in the knowledge base is displayed in addition to the rule if the user asks how a value of maxeru was derived.
parm(maxeru).
maxeru( ['MAXIMUM
ERUS IS', MAXERU]):-
maxeru_potential(PMERU),
max_eru_tas(F),
MAXERUF is PMERU * F,
MAXERU is integer(MAXERUF),
explain(maxerutas01).
exp(maxerutas01,
['MAXERUS and MAXTASKS are set together. They are ',
'both potentially set to values which are dictated by the size ',
'of the terminal network. The total tasks for both is then ',
'compared to the maximum realistic number for the ',
'machine size. If the total tasks is too high, both ',
'MAXERUS and MAXTASKS are scaled down ',
'accordingly.']).
CVGEN is also designed to handle many of the details necessary in a commercially deployed system. These details include the ability to change an answer to a question, save a consultation session and restore it, build and save test runs of the system, and the ability to list and examine the cache and the knowledge base from within a consultation. The system also includes a tutorial which teaches how to use the system.
Most of these features are straight-forward to implement, however changing a response is a bit tricky. When the user changes an answer to a question, it is almost impossible to predict what effects that will have on the results. Whole new chains of inferencing might be triggered. The safest way to incorporate the change is to rerun the inference. By saving the user's responses to questions, the system avoids asking any questions previously asked. New questions might be asked due to the new sequence of rules fired after the change.
The facts which are stored are not necessarily the same as the user's response. In particular, the user response of "take the default" is different from the actual answer which is the default value itself. For this reason, both the facts and the user responses to questions are cached. Thus when the user asks to change a response, the response can be edited and the inference rerun without reprompting for all of the answers.
This list of responses can also be used for building test cases which are rerun as the knowledge base is modified.
The AIJMP system seemed on the surface to be identical to the CVGEN system. Both set parameters. It was initially assumed that the shell used for CVGEN could be applied to AIJMP as well. While this was in general true, there were still key areas which needed to be changed.
The differences have much to do with the nature of the user interaction. The CVGEN system fits very nicely into the classic expert system dialog as first defined in the MYCIN system. The system tries to reach goals and asks questions as it goes. However for AIJMP there is often the need for large amounts of tabular data on various pieces of hardware and software. For these cases a question and answer format becomes very tedious for the user and a form-based front end to gather information is much more appropriate.
AIJMP uses forms to capture some data, and dialogs to ask for other data as needed. This led to the need to expand the basic inferencing to handle these cases.
Another difficulty became evident in the nature of the expertise.  For example, part of the system uses formulas to compute library sizes based on different storage media. Many of the parameters required both rules of thumb and algorithmic calculations.
The best solution to the problem, for the knowledge engineer, was to build into the inference engine the various predicates which performed calculations. This way they could be referred to easily from within the rules.
Some of the declarative knowledge required for AIJMP could not be easily represented in rules. For example, many products depend on the existence of co-requisite products. When the user enters a list of products to be installed, it must be checked to make sure all product dependencies are satisfied. The clearest way to represent this knowledge was with specialized data structures. Operators are used to make the structures easy to work with.
product 'ads
batch 10.1'
psw [adsb]
coreqs ['idms db', 'i data dict'].
product 'ads
batch 10.2'
psw [adsb]
coreqs ['idms db', 'i data dict'].
product 'ads
online'
psw [adso, nlin]
coreqs ['idms db', 'idms cv', 'i data dict', 'idms dc' / 'idms ucf'].
product auditor
psw [audi, culp]
coreqs [].
product autofile
psw [auto]
coreqs [].
The inference engine was enhanced to use this structure for co-requisite checking. The design goal is to make the knowledge base look as familiar as possible to the experts. With Prolog, it is not difficult to define specialized structures that minimize semantic gap and to modify the inference engine to use them.
One simple example of how the custom approach makes life easier for the expert and knowledge engineer is in the syntax for default specifications in the questions for the user. The manual on setting these parameters used the "@" symbol to indicate that a parameter had as its default the value of another parameter. This was a shorthand syntax well understood by the experts. In many cases the same value (for example a volume id on a disk) would be used for many parameters by default. Only a slight modification to the code allowed the knowledge to be expressed using this familiar syntax:
quest loadunit
default @ diskunit
edit none
prompt
['What is the unit for the load library?'].
One of the major bottlenecks in expert system development is knowledge engineering. By customizing the knowledge base so it more closely matches the expert's view of the knowledge domain, the task becomes that much simpler. A simple change such as this one makes it easier for the expert and the knowledge base to interact.
These two systems show how some of the techniques in this book can be used to build real systems. The examples also show some of the difficulties with shells, and the advantages of customized systems in reducing semantic gap.
10.1 - Incorporate data structures for user queries with edits and defaults for the Clam shell.
10.2 - The CVGEN user query behavior can be built into Foops when a value is sought from the frame instances. If there is no other way to get the value, the user should be queried. Additional facets can be used for prompt, default, and edit criteria which the inference engine uses just like in CVGEN.
10.3 - Add features of CVGEN to the shells which are needed for real world applications. These include the ability to save user responses, allow editting of responses, saving a consultation, and rerunning a consultation. The last feature is essential for testing and debugging systems. Old test runs can be saved and rerun as the knowledge base changes. Hopefully the changes will not adversely affect the old runs.
Copyright ©1989,2000 Amzi! inc. All Rights Reserved.


I am a recent graduate from the University of Illinois at Chicago (UIC) with a passion for coding, learning, and sharing the knowledge I've gained. Having been in a position of teaching others as a tutor and teaching assistant, I've enhanced my listening and analyzation skills to break down the problem at hand and create a process to achieve a solution. I am experienced in software engineering and working in teams. Very excited about and looking to contribute to projects that will make a difference!
GPA: 3.12
Summa Cum Laude
Tutored students, helping them understand computing concepts in their classes and complete their homework
Mentored freshmen students majoring in computer science on how to become successful professionally and academically within the field
Designed and developed video games using Unity engine and C# language. Prepared and gave tutorials and demonstrations on the tools and features necessary to create games in Unity.
Provided assistance to professors and speakers with classroom instruction and distributing materials. Directed and organized students to make class time and space efficient. Coordinated with fellow TAs, so tasks were done properly and in a timely manner. Logged attendance records. Graded and provided feedback on students’ work. Answered questions and addressed concerns through email or during office hours. Lectured and led class discussion and lab activities.
Mentored and guided Freshman PAP scholars to set them on the path to a successful first year of college. Provided mentees with information about the school and resources available for academic success.
User is able to compare COVID-19 data from each state. Data is fetched from an API to allow the user to select criteria of the data that they wish to see on a single page application. The selected data is then shown on a table and drawn on a Google Chart.
Processed, extracted features, and classified tweets from politicians to predict the political inclination of a user using a binary classifier with training and test sets
Created a 3D environment interactable in virtual reality using VRTK and enhanced immersion with 3D sound. Designed a dream office space collaboratively with teammates with each cubicle having its own central theme.
A list of various smart phone devices along with their product images and information are displayed in a listview by using custom adapters. Different activities are used to change views and open web pages.
Apart from coding, I enjoy most of my time being active. I like to workout at home, but I take advantage of the warmer months to enjoy running around town with my dogs or biking along forest trails.
Also love to explore. Whether it's going off the literal beaten path in the forest or a new restaurant in a new town, I'm always interested in seeing and experiencing new things.
When tired out, I like to play piano and guitar or try new hobbies. Of course, there's always Netflix and catching up with the news too.


| protocols |
To evaluate an existing Classification And Regression Trees (CART) classifier, run the CART module with the classifier (model) file and a test data set.
Evaluating an existing classifier requires:
| learn more: file formats |
To test a previously built CART classifier, run the CART module. Specify the classifier (*.cart.model) and the test data set. The module creates a prediction results file (*.pred.odf) that assesses the accuracy of the predictor.
| learn more: CART |
To view the prediction results file (*.pred.odf), use the PredictionResultsViewer module. The viewer lists each sample with its actual and predicted class.
| learn more: PredictionResultsViewer |


| EzyFit Release Notes |
See also the EzyFit known software problems section.
| V2.41c | 2012/07/08 |
New function:
Bug fixes
Compatibility issue:
|
| V2.40 | 2010/07/20 |
New function:
Minor changes:
Compatibility issue:
|
| V2.30 | 2009/02/06 |
Major changes:
New function:
New feature:
|
| V2.20 | 2008/03/08 |
Major changes:
New features:
Bug fixes:
Minor changes:
Compatibility issue: Since Matlab 7.4 (R2007a), the behavior of the command doc has changed: the command doc ezyfit does not display the Ezyfit toolbox start page in the help browser any more. If you use Matlab >=7.4, type docezyfit to display the start page. Type docezyfit function_name or showdemo function_name to display the html documention of function_name in the help browser. If you use Matlab 7.3 or before, doc and docezyfit will produce the same result. |
| V2.10 | 2006/10/30 |
Major upgrade: Fit properties and display settings can now be passed to showfit and selectfit, such as fit colors, linewidth etc. For example: showfit('power','fitlinewidth',2,'fitcolor','green')The default properties are defined in fitparam. See the Settings page for details. New options in fitparam: 'selectfitloopmode' and 'dispeqmode'; option 'eqdisplaymode' renamed 'eqreplacemode'. By default, the polygon procedure in selectfit is done once; set the option selectfitloopmode to 'on' to repeat the procedure until an empty polygon is drawn (as in the previous versions). fit now accepts row and column vectors for x and y. Minor bug fixed: showfit and selectfit now accept a fit structure F even if the original curve has been deleted. Minor bug fixed: when fitting histograms, the X points are now taken at the middle of the bins. |
| V2.04 | 2006/09/07 |
New Frequently Asked Questions section. Bug fixed: Selectfit now works when a legend box is present. New options in fitparam: automakevarfit and whichpickdata. fit, showfit and selectfit now also accept '!' and '$' instead of ';' to separate the list of options in the string FUN: this allows to use the syntax showfit power!log instead of showfit('power;log'). Minor bug fixed: showfit and selectfit now accept a fit structure F even if the original curve has been deleted. New options in the item 'plotsample' of the Ezyfit menu. |
| V2.03 | 2006/04/12 |
The 'EasyFit' toolbox has been renamed 'EzyFit', in order to avoid confusion with other products. No change with EasyFit V2.02. |
| V2.02 | 2006/03/09 |
Minor bugs fixed (the menus disappeared after a second call to plotsample; polynomial fits with unspecified orders was bugged). 'efmenu install' improved. |
| V2.01 | 2006/02/27 |
Major upgrade: Left-hand side now accepted for the fitting equation: fit('e(k)=c/k^n')specifies that 'k' stands for the X-Data. If not specified, 'x' is taken by default. The fitting equation is now case sensitive ('x' and 'X' are different). New functions evalfit, makevarfit and showresidual. Bug fixed: Matlab's standard legends are now correctly supported. Capital greek letters, and greek letters as subscripts, are now correctly interpreted in the equation boxes. |
| V2.00 | 2006/02/10 |
Major change: free parameter names may now be used with fit, showfit and selectfit, e.g., fit('a0+cste/x^n'), instead of fixed parameter names m1, m2, m3... The parameter names are automatically recognized. New fit structure F. New mat-files for predefined fits and user-defined fits. New functions loadfit, editcoeff, showeqbox, dispeqfit. Greek letters (lower case) are interpreted in the equation boxes (latex syntax). Additional settings in fitparam. Easyfit menu improved (Edit Fit Coefficients and Swap of the X- and Y- axis). Better compatibility of the documentation with the Matlab help browser. Function getfit retired (fit now does what getfit did). |
| V1.24 | 2006/01/30 |
Bugs fixed in selectfit, drawpolygon and selectdata (better mouse button inquiries). Bug fixed in efmenu (selectfit was wrong). Showslope improved. |
| V1.23 | 2006/01/25 |
User defined fits improved again (no need to type the fit number). Check when fitting negative data in log mode. |
| V1.22 | 2006/01/12 |
User defined fits in the Easyfit menu improved. |
| V1.21 | 2005/12/21 |
Documentation improved. New 'Known bugs' section. File 'Readme.txt' retired. selectfit improved (message boxes). |
| V1.20 | 2005/12/15 |
EasyFit menu and dialog boxes included. Standard items for the toolbox in the Matlab 'Start' menu. New about, checkupdate. New home page (www.fast.u-psud.fr/ezyfit). drawpolygon improved. |
| V1.14 | 2005/12/03 |
drawpolygon improved: interactive properties of the window suspended during the drawing. |
| V1.13 | 2005/11/25 |
Bug fixed in myginput for Matlab >=7.0.0. |
| V1.12 | 2005/11/03 |
New parameter file 'paramfit.m'. New drawpolygon, and selectdata improved. getslope and showslope improved. Now displays equations in Kaleidagraph or ABC mode. |
| V1.11 | 2005/10/31 |
fit now computes the Pearson's correlation coefficient R. n-th order polynomial fits added. Initial guess may be defined within the fitting function string (as in Kaleidagraph). New functions pickdata, undofit, rmfit, swx... |
| V1.10 | 2005/10/18 |
Bug fixed for showfit. New function selectfit, to select data by mouse in a polygon. New function plotsample. |
| V1.05 | 2005/10/07 |
New predefined fits (gaussian fits). showfit now displays the equation in the plot. New file 'readme.txt'. |
| v1.04 | 2005/07/27 | |
| v1.00 | 2005/05/20 |
First version. Originally called 'EasyFit toolbox'. |
|   |


--- layout: documentation title: ProcessStatusType ---
The ProcessStatusType is used for specifying the status of a running or terminated process. Since this property is platform-specific, it is created here as an abstract type and then used in the platform-specific process CybOX objects.
| Field Name | Type | Description |
|---|


https://komodoelection.com/1-about/komodo-elections/
Every year, the Komodo community holds an election to choose who will operate notary nodes.
- The 28 best-performing notary node operators are automatically re-elected.
- Performance is measured according to the number of completed notarizations
- The top 7 nodes from each of the four regions are re-elected
- The other 32 positions are opened up and the community elects new operators.
- Anyone can run in the election. You don’t need to own any KMD or be on the team.
- VOTE tokens are airdropped to KMD holders at a 1:1 ratio.
- Community members simply send VOTE tokens to their preferred candidate(s).
- At the end of the voting period, the top 8 addresses with most VOTE tokens within each four regions win. Thus, in total, 32 new operators are elected.
(edited)


Irish, Keith Alan
| Birth Name | Irish, Keith Alan |
| Gender | male |
| Age at Death | 61 years, 5 months, 16 days |
Narrative
SMH - IRISH, KEITH ALLEN - 1 Dec 1997, at home, at Westleigh, loving husband of Jo, dearly loved father & father-in-law of Sally & Simon, Phillip & Fiona & Debbie, loving pop of Ben, Loving son of Allen & Zena, son-in-law Joe. Aged 61 years. Loved & always remembered.
FUNERAL - IRISH - The family & friends of KEITH ALLAN IRISH are kindly invited to attend his funeral service, to be held at Northern Suburbs Crematorium, on Friday (5/12/1997) at 1.40 pm. By request, no flowers. Donations to Instution of Oncology, Cancer Research.
SMH - IRISH, KEITH, A man's contributionis measured by the quality of those he leaves behind. Walk Tall, Sally Phillip, Debbie & Jo. Thinking of you. Keith, Lori, Jane & Jock
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | 1936-06-15 | |||
|
|
||||
| Death | 1997-12-01 | late of Westleigh NSW | ||
|
|
||||
| Burial | 1997-12-05 | Northern Suburbs Crematorium. | ||
|
|
Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Father | Irish, Allan James | 1913-03-18 | 2006-04-28 | |
| Mother | Margerum, Zenna Jean | 1913-10-24 | 2011-07-16 | |
| Irish, Keith Alan | 1936-06-15 | 1997-12-01 |
Families
Family of Irish, Keith Alan and Campbell, [Living] |
||||||||||||||
| Married | Wife | Campbell, [Living] ( * + ... ) | ||||||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Irish, [Living] | ||
| Irish, [Living] | ||
| Irish, [Living] |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | CCBE78476F2BD9119522CBFB42EE7377649B |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | C9BE78476F2BD9119522CBFB42EE7377616B |


|
JMovies.IMDb
|
Classes | |
| class | ActingCredit |
| Class definition of an Acting Credit. More... | |
| class | AKA |
| Class definition for other names of the title. More... | |
| class | Budget |
| Class definition of Budget of a Title. More... | |
| class | Character |
| Class definition of a character in a title. More... | |
| class | Company |
| Class definition of a company related to the industry. More... | |
| class | Country |
| Class definition of a country. More... | |
| class | Credit |
| Class definition of a credit in a title. More... | |
| class | Genre |
| Class definition of a genre. More... | |
| class | Keyword |
| Class definition of a keyword related to a title. More... | |
| class | Language |
| Class definition of a language. More... | |
| class | Movie |
| Class definition of a movie. More... | |
| class | OfficialSite |
| Class definition of an Official Site. More... | |
| class | Production |
| Base class of any production. More... | |
| class | ProductionCountry |
| Class definition that handles country to production mapping. More... | |
| class | ProductionLanguage |
| Class definition that handles language to production mapping. More... | |
| class | Rating |
| Class definition of Ratings. More... | |
| class | ReleaseDate |
| Class definition of release dates. More... | |
| class | TagLine |
| Tag line of the production. More... | |
| class | TVCharacter |
| Class definition of a TV Character. More... | |
| class | TVSeries |
| Class definition of TV Series. More... | |
Enumerations | |
| enum | CharacterTypeEnum { Character , TVCharacter } |
| Class definition of a character type. More... | |
| enum | CreditRoleType { Undefined , Director , Acting , Writer , Creator , Producer , Composer , MusicDepartment , Actor , Actress } |
| Enumeration of Role Types in a credit. More... | |
| enum | ProductionTypeEnum { Undefined , Movie , TVSeries } |
| Enumeration of Production Types. More... | |
| enum JMovies.IMDb.Entities.Movies.CharacterTypeEnum |
| enum JMovies.IMDb.Entities.Movies.CreditRoleType |
Enumeration of Role Types in a credit.


There are different types of simulation experiments. In the simplest case, a simulation scenario is run repeatedly for being able to compute aggregate statistics.
An experiment type is defined for a given simulation model and an experiment of that type is run on top of a given simulation scenario for that model.
A simple experiment type is
defined with a sim.experimentType
record on top of a model by
defining (1) the number of replications and (2) possibly a list of
seed values, one for each replication. The following code shows an
example of a simple experiment type definition:
sim.experimentType = { title: "Simple Experiment with 10 replications, each running for 1000 time units (days)", nmrOfReplications: 10 };
Running this simple experiment means running the underlying scenario 10 times. The resulting statistics are composed of the statistics for each replication complemented with summary statistics listing averages, standard deviations, min/max values and confidence intervals, as shown in the following table:
| Experiment Results | |||
|---|---|---|---|
| Replication | Statistics | ||
| arrivedCustomers | departedCustomers | maxQueueLength | |
| 1 | 285 | 283 | 7 |
| 2 | 274 | 274 | 6 |
| 3 | 285 | 285 | 4 |
| 4 | 287 | 286 | 5 |
| 5 | 284 | 284 | 6 |
| 6 | 300 | 299 | 4 |
| 7 | 288 | 286 | 5 |
| 8 | 286 | 284 | 4 |
| 9 | 286 | 285 | 4 |
| 10 | 295 | 293 | 6 |
| Average | 287 | 285.9 | 5.1 |
| Std.dev. | 6.848 | 6.506 | 1.101 |
| Minimum | 274 | 274 | 4 |
| Maximum | 300 | 299 | 7 |
| CI Lower | 282.9 | 281.9 | 4.4 |
| CI Upper | 291 | 289.6 | 5.7 |


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ds_grid_delete_column
Deletes from a grid the column at a given column index. The grid is reduced in width by one.
$$\mathbf{G} = \begin{array}{|c|c|c|c|} \hline 00&10&\color{#F80}{20}&30 \\\hline 01&11&\color{#F80}{21}&31 \\\hline 02&12&\color{#F80}{22}&32 \\\hline 03&13&\color{#F80}{23}&33 \\\hline \end{array} \qquad f(\mathbf{G},2) = \begin{array}{|c|c|c|c|} \hline 00&10&30 \\\hline 01&11&31 \\\hline 02&12&32 \\\hline 03&13&33 \\\hline \end{array}$$
WARNING: Attempting to delete a column from a grid with only one column will generate an error.
- ds_grid_delete_column(grid,col)
- Deletes from a grid the column at a given column index.
COPY/// ds_grid_delete_column(grid,col)
//
// Deletes from a grid the column at a given column
// index. The grid is reduced in width by one.
//
// grid grid data structure, id
// col column index, integer
//
// Warning: Attempting to delete a column from a grid
// with only one column will generate an error.
//
/// GMLscripts.com/license
{
var grid = argument0;
var col = argument1;
var w = ds_grid_width(grid);
var h = ds_grid_height(grid);
ds_grid_set_grid_region(grid, grid, col+1, 0, w-1, h-1, col, 0);
ds_grid_resize(grid, w-1, h);
}
Contributors: xot
GitHub: View · Commits · Blame · Raw


Hi, I'm Michael, some just call me Jamie! I am a Software Developer based in Fort Bragg, North Carolina. Reports
Reports
Website 10% Forums(AMA - Ask me anything + open developments and support) 5%


RT @Fighting4Nippon: Drift and cut through our first episode review of Drifters, the new anime by the same mangaka as Hellsing! 🗡
Read: ht…


Bradlee Speice, Fri 26 February 2016, Blog
I've previously talked about crafting an Investment Formula that would guarantee making money if you could predict which direction the stock market was going to go. This is going to be the first in a series of posts trying to flesh out what an actual investment strategy based on this formula would look like.
But first, the formula doesn't take into account two very important things: leverage, and the number of days invested. That's why I want to set up what I'm going to call the Profitability Score.
The definition is going to be very simple:
$s = \frac{1000 p}{i(l + d)} = \frac{1000 p}{m + i\cdot d}$
Crazy, right? The score is simply the (normalized) profit you made divided by the leverage plus days invested. The $\cdot 1000$ is just to turn the number into something more reasonable - people don't like hearing something with a profitability score of .001 for example.
The formula itself is designed to be simple in principle: I like making a profit, and I want to penalize the leverage you incur and days you have to invest. Ideally, we want to have a stock that goes up all the time. However, the investment formula takes advantage of a different case: trying to profit from highly volatile assets. If we can make money when the investment only has one day up, let's do it!
Even so, there are two potential issues: First, stocks that trend upward will have a higher profitability score - both leverage and days invested will be 1. To protect against only investing in this trend, I can do things like taking $\log(d)$. I don't want to start biasing the scoring function until I have a practical reason to do so, so right now I'll leave it standing.
The second issue is how to penalize leverage and days invested relative to each other. As it currently stands, a leverage of 6x with only 1 day invested is the same as leveraging 2x with 3 days invested. In the future, I'd again want to look at making the impact of days invested smaller - I can get over an extra 3 days in the market if it means that I don't have to incur a highly leveraged position.
So there could be things about the scoring function we change in the future, but I want to run some actual tests before we start worrying about things like that!
This won't be an incredibly rigorous backtest, I just want to see some results from the work so far. Let's set up the simulation code again, and start looking into some random stocks. If you've read the last blog post, you can skip over the code. The only difference is that it's been ported to python to make the data-wrangling easier. Julia doesn't yet support some of the multi-index things I'm trying to do.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from Quandl import get as qget
%matplotlib inline
api_key = ''
profitability = lambda p, i, m, d: 1000*p / (m + i*d)
def is_profitable(current_price, purchase_history, open_history):
shares = (purchase_history / open_history).sum()
return current_price * shares > sum(purchase_history)
def daily_investment(current_open, current_close, purchase_history, open_history):
t1 = current_close / current_open - 1
t2 = (purchase_history - purchase_history * current_close / open_history).sum()
return t2 / t1
def simulate_day(open_vals, close_vals, init, expected, bias):
invested = np.array([init])
day = 1
profitable = is_profitable(close_vals[day-1], invested, open_vals[0:len(invested)]) \
or is_profitable(open_vals[day], invested, open_vals[0:len(invested)])
while not profitable:
expected_close = open_vals[day] * expected
todays_purchase = daily_investment(open_vals[day], expected_close, invested, open_vals[0:day])
invested = np.append(invested, todays_purchase + bias)
# expected_profit = expected_close * (invested / open_vals[0:len(invested)]).sum() - invested.sum()
day += 1
profitable = is_profitable(close_vals[day-1], invested, open_vals[0:len(invested)]) \
or is_profitable(open_vals[day], invested, open_vals[0:len(invested)])
shares = (invested / open_vals[0:len(invested)]).sum()
# Make sure we can't see into the future - we know either today's close or tomorrow's open
# will be profitable, but we need to check which one.
if is_profitable(close_vals[day-1], invested, open_vals[0:len(invested)]):
ending_price = close_vals[day-1]
else:
ending_price = open_vals[day]
profit = shares * ending_price - sum(invested)
return invested, profit
def simulate_ts(name, start, end, initial, expected, bias):
ticker_info = qget(name, trim_start=start, api_key=api_key)
evaluation_times = ticker_info[:end].index
# Handle Google vs. YFinance data
if "Adjusted Close" in ticker_info.columns:
close_column = "Adjusted Close"
else:
close_column = "Close"
sim = {d: simulate_day(ticker_info[d:]["Open"], ticker_info[d:][close_column],
100, 1.02, 10) for d in evaluation_times}
sim_series = pd.Series(sim)
result = pd.DataFrame()
result["profit"] = sim_series.apply(lambda x: x[1])
result["max"] = sim_series.apply(lambda x: max(x[0]))
result["days"] = sim_series.apply(lambda x: len(x[0]))
result["score"] = sim_series.apply(lambda x: profitability(x[1], x[0][0], max(x[0]), len(x[0])))
result["investments"] = sim_series.apply(lambda x: x[0])
return result
def simulate_tickers(tickers):
from datetime import datetime
results = {}
for ticker in tickers:
start = datetime(2015, 1, 1)
results_df = simulate_ts(ticker, start, datetime(2016, 1, 1), 100, 1.01, 10)
results[ticker] = results_df
return pd.concat(list(results.values()), keys=list(results.keys()), axis=1)
Let's start looking into the data! FANG stocks have been big over the past year, let's see how they look:
fang_df = simulate_tickers(["YAHOO/FB", "YAHOO/AAPL", "YAHOO/NFLX", "YAHOO/GOOG"])
fang_df.xs('days', axis=1, level=1).hist()
plt.gcf().set_size_inches(18, 8);
plt.gcf().suptitle("Distribution of Days Until Profitability", fontsize=18);
fang_df.xs('score', axis=1, level=1).plot()
plt.gcf().set_size_inches(18, 6)
plt.gcf().suptitle("Profitability score over time", fontsize=18);
Let's think about these graphs. First, the histogram. What we like seeing is a lot of 1's - that means there were a lot of days that the stock went up and we didn't have to worry about actually implementing the strategy - we were able to close the trade at a profit.
Looking at the profitability score over time though is a bit more interesting. First off, stocks that are more volatile will tend to have a higher profitability score, no two ways about that. However, Netflix consistently outperformed on this metric. We know that 2015 was a good year for Netflix, so that's a (small) sign the strategy is performing as expected.
The final interesting note happens around the end of August 2015. Around this period, the markets were selling off in a big way due to issues in China (not unlike what's happening now). Even so, all of the FANG stocks saw an uptick in profitability around this time. This is another sign that the strategy being developed performs better during periods of volatility, rather than from riding markets up or down.
What about FANG vs. some cyclicals?
cyclic_df = simulate_tickers(["YAHOO/X", "YAHOO/CAT", "YAHOO/NFLX", "YAHOO/GOOG"])
cyclic_df.xs('days', axis=1, level=1).hist()
plt.gcf().set_size_inches(18, 8);
plt.gcf().suptitle("Distribution of Days Until Profitability", fontsize=18);
cyclic_df.xs('score', axis=1, level=1).plot()
plt.gcf().set_size_inches(18, 6)
plt.gcf().suptitle("Profitability score over time", fontsize=18);
Some more interesting results come from this as well. First off, US Steel (X) has a much smoother distribution of days until profitability - it doesn't have a huge number of values at 1 and then drop off. Intuitively, we're not terribly large fans of this, we want a stock to go up! However, on the profitability score it is the only serious contender to Netflix.
Second, we see the same trend around August - the algorithm performs well in volatile markets.
For a final test, let's try some biotech and ETFs!
biotech_df = simulate_tickers(['YAHOO/REGN', 'YAHOO/CELG', 'GOOG/NASDAQ_BIB', 'GOOG/NASDAQ_IBB'])
biotech_df.xs('days', axis=1, level=1).hist()
plt.gcf().set_size_inches(18, 8);
plt.gcf().suptitle("Distribution of Days Until Profitability", fontsize=18);
biotech_df.xs('score', axis=1, level=1).plot()
plt.gcf().set_size_inches(18, 6)
plt.gcf().suptitle("Profitability score over time", fontsize=18);
In this example, we don't see a whole lot of interesting things: the scores are all fairly close together with notable exceptions in late August, and mid-October.
What is interesting is that during the volatile period, the ETF's performed significantly better than the stocks did in terms of profitability. The leveraged ETF (BIB) performed far above anyone else, and it appears that indeed, it is most profitable during volatile periods. Even so, it was far more likely to take multiple days to give a return. Its count of 1-day investments trails the other ETF and both stocks by a decent margin.
And consider me an OCD freak, but I just really like Celgene's distribution - it looks nice and smooth.
So far I'm really enjoying playing with this strategy - there's a lot of depth here to understand, though the preliminary results seem to indicate that it profits mostly from taking the other side of a volatile trade. I'd be interested to run results later on data from January - It's been a particularly volatile start to the year so it would be neat to see whether this strategy would work then.
For the next post, I want to start playing with some of the parameters: How do the bias and expected close influence the process? The values have been fairly conservative so far, it will be interesting to see how the simulations respond afterward.


Hike5280
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Reports loops whose second and the subsequent iterations do not produce any additional side effects other than produced by the first iteration,
which could indicate a programming error. Such loops may iterate only zero, one or infinite number of times.
If infinite number of times case is unreachable, such loop could be replaced with if
statement. Otherwise there's a danger that
the program could stuck.
Example:
public void foo(String baseName, String names) {
int suffix = 1;
String name = baseName;
while (names.contains(name)) {
// error: suffix is not updated making loop body idempotent
name = baseName + suffix;
}
}
New in 2018.1


Hey Jamie, thanks for booking with us - your reservation is now confirmed ✔

|
|
|
|
Customer Receipt
Your reservation is now confirmed
|
|
| Guest |
Jamie Doe |
| Nights |
6 |
| Address |
45 Park avenue, Apt. 303 |
|
Check-in
29 NOV 2018
|
>>> |
Check-out
4 DEC 2018
|
| Price per night |
$144.99 |
| Cleaning fee |
$19.99 |
| Total |
$889.93 |
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Brahman Samaj of North America (BSNA) is the largest Brahman organization with sole objective of bringing Brahmans together and helps them seek their own cultural and social identity. It endeavors to create an international community of Brahmans. BSNA has won the acceptance and respect of a large number of highly distinguished people from all over the world. Our network and membership includes scientists, engineers, doctors, lawyers, industrialists, and professionals from all walks of life. BSNA strongly disapproves the evils of the Hindu caste system.
Brahman Samaj of North America (BSNA) announces its 22nd Annual Convention to be held in Auburn Hills Marriott, Detroit from July 28th to July 30th, 2017.
Detroit Metropolitan Area; July 28, 29 and 30, 2017
Theme: Role of Women in Shaping Brahmanic Values
Be part of BSNA- Become a Member
Register for Upcoming Event (Convention)
Donate
Oppotunities: volunteering, mentoring and networking, cultural protection, matrimonial networking


SHIVA BANIHASHEM
UI Developer
LEO TORRES
CECIL JOHN TANTAY
Backend Developer
KYLE BARNETT
Frontend Developer
LIAM EDLINGER
MAHIYA HOQUE
Experience Designer


Smart Search
Explore Your Option, Find Here What Your Money Can actually Buy
Read MoreLong established fact that a reader will be undo ut distracted by the readable sed content off a page when seds looking und at its layouts The sesd points using Lorem Ipsum is that it has a more-or-less than normal distributionof letter james opposed using content here content here will be making look like readable property developers will be ut distracted by the readable real estate.
Long established fact that a reader will be undo ut distracted by the readable sed content off a page when seds looking und at its layouts The sesd points using Lorem Ipsum is that it has a more-or-less than normal distributionof letter james opposed using content here content here will be making look like readable property developers will be ut distracted by the readable real estate.
Long established fact that a reader will be undo ut distracted by the readable sed content off a page when seds looking und at its layouts The sesd points using Lorem Ipsum is that it has a more-or-less than normal distributionof letter james opposed using content here content here will be making look like readable property developers will be ut distracted by the readable real estate.


{% extends '../layouts/template.html' %} {% block title %}Check my State Pension - {{mvpversion}}{% endblock %} {% block serviceName %}{% endblock %} {% block main %}
This is to help us confirm who you are.
For example, 31 3 1980


Item
Description
Quantity
Tires
Tires of all kinds. We serve all the kinds of tires with varied threading as per request.
300


Verify Scratch accounts as genuine, for use in authorization or identification.
You've found the documentation for ScratchVerifier's API! These pages show you how to use ScratchVerifier for anything you might be making that needs to know who somebody is on Scratch.
This documentation is on GitHub so feel free to submit an issue for any corrections or improvements!
If you find something that you think is a bug, report it on GitHub and we'll be sure to check it out.
See the usage walkthrough to get started!


| TrackId | Name | AlbumId | Media-TypeId | GenreId | Composer | Milliseconds | Bytes | UnitPrice |
|---|---|---|---|---|---|---|---|---|
| 1 | For Those About To Rock (We Salute You) | 1 | 1 | 1 | Angus Young, Malcolm Young, Brian Johnson | 343719 | 11170334 | 0.99 |
| 2 | Jacob | Thornton | @fat | Jacob | Thornton | @fat | Jacob | Thornton |
| 3 | Larry | the Bird | Larry | the Bird | Larry | the Bird |


--- post_id: /internetnoll-i-mexico-del-tva-motorvagen date: 2010-05-17 01:24 return_url: '2010-05-15-internetnoll-i-mexico-del-tva-motorvagen.html' name: 'Geraldine' email<EMAIL_ADDRESS>comment: '
You could probably make another full text only about OXXO to describe Mexico right? it is kind of sad.. if topes work and the little community selling food and offering talachas in the side of the highway, does well.. probably a gas station and a OXXO will replace them as a sign of progress :/
' ---


Copyright © 2010 Martin Kirchgessner Copyright © 2015 John Ko
Cloudbox is a cloudy jukebox (hence the name). Originally written for CouchApp by Martin Kirchgessner, then ported to Kanso by John Ko, it is released under the MIT License and powered by CouchDB, Kanso, a CouchApp shim that includes the Evently + Mustache framework (couchapp.js), Bootstrap, jQuery, jPlayer... and a whole lot of other open source projects.
https://github.com/johnko/kanso-music