Under-fire Heyneke receives backing from Stade owner
Paris - Stade Francais's billionaire owner Hans-Peter Wild on Saturday defended his South African coach Heyneke Meyer despite the club sitting bottom of the French Top 14 table with only two wins from their opening eight matches of the season.
Meyer took charge of the Parisians in 2018 after having led the Springboks to a Rugby World Cup semi-final four years ago.
But poor results in the early part of this season, when his World Cup players were all in Japan, have prompted a range of local media reports claiming he is close to being sacked.
"We have an excellent coach. We don't need to discuss the quality of our coach," said Wild ahead of Sunday's eagerly-awaited Paris derby at home to Racing 92.
"There are clubs who change their coach every two years. It's easy.
"A company is the same thing. If you have a problem, what do you do? Fire the CEO. New CEO comes in, if you're lucky he turns it around. In two years the problem is back, fire the CEO again.
"Or you have a long-term programme. I'm one who loves long-term success with the same team," he added.
Wild denied he had considered replacing 52-year-old Meyer.
"I have never planned to hire another coach. I haven't talked to Gonzalo Quesada and I haven't talked to Vern Cotter. I haven't talked to anybody," he said.
"The coach is the coach. Period," he added.
German-born Wild, who bought the club in 2017, defended Meyer's inability to converse fluently in French but light-heartedly criticised the former Bulls boss' English.
"It's as big a problem as we make it a problem. I run companies with 15-20 nationalities, what do we do? We speak English," he said.
"There are some people who can learn languages and there are some that will never learn a language.
"There are other people like at Paris Saint-Germain, Thomas Tuchel he learnt French in two weeks. Heyneke will never learn it.
"He's not a great communicator, not even in English. I need a translator to talk to him sometimes.
"His South African accent is tough to understand. But he's a very professional and excellent coach," he added.
France centre Gael Fickou returns to the Stade Francais starting line-up for the first time since the World Cup on Sunday while former Springbok flyhalf Morne Steyn is on the bench for his last game for the club before returning to Pretoria to play for the Bulls.


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Class Hierarchy
object
:
The most base type
yipit.Api
:
A python interface into the Yipit API
exceptions.BaseException
:
Common base class for all exceptions
exceptions.Exception
:
Common base class for all non-exit exceptions.
yipit.YipitError
:
Base class for Yipit errors
yipit.YipitObject
:
An abstract class that implements methods several classes the Yipit API will require.
yipit.Business
:
A class representing the business structure used by the Yipit API
yipit.Deal
:
A class representing the deal structure used by the Yipit API
yipit.Division
:
A class representing the division structure used by the Yipit API
yipit.Source
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A class representing the source structure used by the Yipit API
yipit.Tag
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A class representing the tag structure used by the Yipit API
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leaf Figure leaves marginal growth expression meristem Arabidopsis —figure margins S:miRa/S:amiR-NGA cell supplement wild-type development activity WOX genes Plant shoot NGA John {border-style plant plants Alvarez distal indeterminate lamina px;} #ffffff;} CIN-TCP abaxial cells gene factors seed Cell Eshed Research TCP adaxial cloned SAM activities primordia Bowman Nakata PRS data early http://dx.doi.org/./ meristems upstream using Efroni GUS Paul http://dx.doi.org/./eLife.. miRa mm; polarity tissue ./tpc.. Development developing downstream ectopic fragment kan older S:miRa S:miRa-amiR-NGA apical differentiation expressing initiating line meristematic organs results transcription Biology cotyledons floral organ plants, promoter px; thaliana {font-family Analysis Australian Council Foster, Israel KNOX S:amiR-NGA Science ancestral continued data, division font-size grow including interpretation leaves, maintained observed patterns program prs radialized restricted ./eLife.. Botany Furumizu Goldshmidt Hagemann LINK Laux Sciences University WUSCHEL analysis auxin blade cotyledon cuc- described divisions domains double; epidermal formation juxtaposition lack later leaves. marker meristem. mitotic patterning primordium proliferation provided reduced regions regulation shown supplements wox (Atg) ./pnas. American CIN-TCPs Chihiro Floyd However, KANADI Leaf Maksymowych Online PCR PNAS Wang Yuval adaxial/abaxial annotated as- border-color border-width consistent derived des design, eLife evolved exhibits genetic lateral lines loss maintenance mutants nga,,, ongoing proximal region regulatory required sequence set solid; species suggesting suggests vascular {font-style {font-weight —source (Atg), (excitation ./BF ./science. Alvarez, Amsellem Bulletin CUC Conception Des Drafting FIL:WOX Friml Genetics Hibara Idan Lee NGATHA Nature PIN:PIN-GFP PRS:GUS Poethig Regeneration SAM, Scale Snow States Sussex, Tsukaya United [], active analyses approximately article. background bars: beam bold central class coding collab: combined common corresponding courier; create cuc der development, different elaboration em; emission entire evidence expansion expressed features filter functional genes, knockdown lineage maturation meristem, miR miRa-amiR-NGA mutant outgrowths phb-d/ plate potential presence previously proliferative prolonged proximo-distal pt;} requires restriction revising samples sections sequences shape showing specific splitter start supported surface target used young zones


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Convicted of a Crime:
{{ $_SESSION['LongAnswer']->getConvict() }}
{{ $_SESSION['LongAnswer']->getConvictText() }}
Education – last degree completed:
{{ $_SESSION['LongAnswer']->getDegree() }}
Work/volunteer experience:
{{ $_SESSION['LongAnswer']->getVolunteerExperience() }}
When Diagnosed:
{{ $_SESSION['LongAnswer']->getDiagnose() }}
How old When Diagnosed:
{{ $_SESSION['LongAnswer']->getDiagnoseTime() }}
Current Diagnosis:
{{ $_SESSION['LongAnswer']->getCurrentDiagnosis() }}
Hospitalized:
{{ $_SESSION['LongAnswer']->getHospitalized() }}
{{ $_SESSION['LongAnswer']->getRecently() }}
Public Speaking Experience:
{{ $_SESSION['LongAnswer']->getSpeakingExperience() }}
Comfortable Speaking in Front of Groups:
{{ $_SESSION['LongAnswer']->getComfortable() }}
transportation:
{{ $_SESSION['LongAnswer']->getTransportation() }}
Groups you do not want to present:
{{ $_SESSION['LongAnswer']->getNotWantPresent() }}
Why a NAMI IOOV Presenter:
{{ $_SESSION['LongAnswer']->getWhyPresenter() }}
Stayed in and Maintained Recovery:
{{ $_SESSION['LongAnswer']->getStayedRecover() }}
Views About Medicine and Recovery:
{{ $_SESSION['LongAnswer']->getRecovery() }}


Part 1
- more practical information in 3.class-part1.js file from line 7.Class - What is it in JS??
Classes appeared in JavaScript together with ES6 version in 2015th. Class we can say that is a box/container for constructor and all its methodes and properties which lets us keep code more eye/user/programmer ;) friendly. Class is a such an element which lets us use such a behaviours like inheritance, encapsulation or polymorphism
How to create Class.
Creating class we are starting with key word class lowercase written.
Class - Prototype.
As same as in constructor we can add method to the constructor of class and than by creating object every of them will have the same methodes.
OR
// Or we can add the method as a prototype in class under the constructor and than the instance which will really need it will have the prototype, beside the queue of executing the methodes is the same like in constructor so first is executed from class construftor and if it is not there than the prototype will be searched for the methode so the coverage will appeare as same as levels of embedded methodes.
Constructor function and Class.
The difference between the two constructors is only that that the class is a box for constructors ant its methodes, but the construcror itself cane contain the properties and methodes being created separately.
Class - Expression.
he class expression is jus different type of class declaration
const ClassName = class {...}
Class - Static Methodes.
That methodes are assigned directly to the class. Satic methodes are availible only from the level of class/constructor.
Class - it is worth to remember about them.
// 1. Are not subject to hoisting/ nie podlegają hoistingowi. // 2. Smaller possibilities of making mistakes eg there is no possible to make instance without word new/ mniejsze mozliwości popełnienia błędu choćby nie da się wywołać bez new // 3. Are clearlier than construcrors itself / są czytelniejsze // 4. Theere is more simple to code some things like inheritance for example by prefix extends / uproszczenie w napisaniu wielu innych rzeczy jako np. dziedziczenie (extends). // 5. What can be coced thanks to class may be coded in ES6 pure as well but with classes it is easier / co da się napisać za pomocą klasy, mozna też napisać w ES6, ale jest to bardziej (lub zdecydowanie bardziej) zawiłe. Tu jest proste.
Part 2- more practical information in 3.a.class-part2.js file.
Classes and Inheritance of child class/subclass prototype.
The parent class/super class is a class from which the child class/sub class inherits properties and methodes. The superclass contains the properties and methodes which can be 'borrowed' to the child classes/subclasses.
Creating of inherit class prototype.
During the inheritance there is a prototype chain appeared - it means that every subclasses has connection to each other by the superclass prototypes.
Calling of constructor of superclass.
The constructor of superclass is called in the subclass/es that is why the subclass/es have the access to every properties and methodes from superclass, moreover we can add any method by creating the prototypes inside the subclasses - remember about line of inheritance (always from parent class).
Extends.
The keyword which shows the parent class from which the subclass will be inherited any elements. Creating object from subclass is equal to calling the constructor of superclass as well.
Super.
The keyword is used for extend the constructor of subclass/es and all other 'higher' subclasses by constructor of superclass.


You will be given a random number at the star of the game.
There are four crystals below. By clicking on a crystal you will add a specific amount of points to your total score.
You win the game by matching your total score to random number, you lose the game if your total score goes above the random number.
The value of each crystal is hidden from you until you click on it.
Each time when the game starts, the game will change the values of each crystal.


All forms of membership attract benefits such as reduced price tickets, after concert meet and greets with artists and other social events.


+++ title = "Candelaria hostel, Valladolid" slug = "candelaria-hostel-valladolid" date = 2007-03-01T18:00:00+10:00 tags = ["accommodation"] locations = ["mexico/valladolid"] draft = false summary = """
Nice and cheap, but I think that this will be one of the less memorable hostels on my journey. It has the same outdoorish setup as Nomadas in Mérida, but somehow, it doesn't seem as well done. Anyway, it was a good place to crash for two nights.
""" +++Nice and cheap, but I think that this will be one of the less memorable hostels on my journey. It has the same outdoorish setup as Nomadas in Mérida, but somehow, it doesn't seem as well done. Anyway, it was a good place to crash for two nights.
Rooms: basic, not much privacy (window looks out onto town square).
People: great, but perennial.
Staff: some are a bit unfriendly and obnoxious.
Location: nice 'n' central.
Food: nice fine breakfast.
Internet: none (great café across the road, though).


About
ES6+ Feature Tests as a service.
It's easy! Load the "rs.js"
library, request feature test results for the current browser, use those results to determine what code to deliver for your application!
 Why would you load dozens of kb of polyfills/shims if all (or most of) those APIs are already built-in?
Just use the original authored ES6+ code in newest browsers! Only use transpiled code and polyfills/shims for browsers which are missing the features/syntax you need.
Don't rely on stale caniuse data baked into build tools that matches a browser based on brittle UserAgent
string parsing. Feature Test! That's the most reliable and sensible approach.
Privacy
There is absolutely no data collection of any kind performed by this service (not even cookies). The only persistent information is the LocalStorage
caching of test results in each user's browser, which can be disabled via the library's API. These test results are not collected or monitored by the service in any way.
License & Copyright
The code and all the documentation are released under the MIT license.


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{{ self.title() }}
The bulk of the work here is RichFilemanager, available here: RichFilemanager on GitHub
{% extends 'base.html' %} {% block title %}Flask Filemanager Test App{% endblock %} {% block h1 %}{% endblock %} {% block content %}
The bulk of the work here is RichFilemanager, available here: RichFilemanager on GitHub


What is it?
The Sun Labs Lively Kernel is a web programming environment and computing kernel built on the JavaScript language and graphics capabilities available in ordinary web browsers. JavaScript brings the graphics to life, and the graphics are then composed into an interactive application platform. In addition to its application execution capabilities, the platform can also function as an IDE (integrated development environment), making the whole system self-contained and able to improve and extend itself on the fly. The Lively Kernel includes the ability to save on web pages the applications built with it, and even new versions of the system itself.
How did it come to be?
It began as a study of web programming. A group of us at Sun Labs had surveyed the field and we felt that web programming was much more complicated than it needed to be. When we looked at the lash-ups of HTML, DOM, CSS, PHP and all the AJAX web toolkits, they seemed unnecessarily complex and cumbersome compared to the desktop programming environments that we were familiar with already years ago before the web.
It seemed to us that if, instead of starting with a text markup language, you began with a dynamic language and structured graphics like desktops systems of the 1980's, then even web-based applications could be just as lively and interactive as the best desktop software.
Beyond just rethinking web programming, the Lively Kernel has become a rethinking of the web itself. It offers a world of lively objects, all active and distributed throughout the universe of the web.
Can anybody use it?
Anyone can probably have some fun with it. The Lively Kernel includes a simple graphics editor, and many ingredients of a UI builder. There is an online tutorial, and most features of the system can be tried out while you are reading.
That said, it must be understood that the Lively Kernel is an experiment and a research environment in its early stages, and at this point it is probably more appropriate for students, computing enthusiasts and even children than for, e.g., commercial web site designers.
How do I get it?
The Sun Labs Lively Kernel is alive on the web, meaning that you do not download it and install it. If your browser supports the Lively Kernel (see the list of supported browsers below), then merely clicking on the “Enter Lively Kernel” link causes the system to come to life in your computer. You may see some static items on the screen but, by default, everything there is alive and changeable.
I don't understand. Where is the source code or binaries?
The entirety of the Lively Kernel is written in JavaScript, a dynamic language that uses no binary files for execution. The Lively Kernel does not require any installation or plug-ins, since the necessary JavaScript engine is already part of your web browser. As soon as you click on “Enter Lively Kernel” link, all the Lively Kernel code is loaded into your browser and running. At some point we may compress the files to make for faster startup but, if so, we will make the full uncompressed sources available on this site.
For your convenience, we have made the source code available here as a ZIP file.
For technical documentation, refer to the Lively Kernel Technical Overview document.
Is the Sun Labs Lively Kernel open source?
Yes, the Sun Labs Lively Kernel is made available from Sun Microsystems Laboratories as open source software under the GPL license, version 2.
What is the GPL license?
In layman's terms, the GPL license says that individuals may use the software for their own use without charge, companies may use it for their own use without charge, and individuals and companies may even build products based on the Lively Kernel for sale without charge. The only requirement (the so-called "copy left" condition) is that if an individual or company wishes to distribute the software with changes or enhancements, then those changes or enhancements must be made available back to the Open Source community. For further information, refer to http://www.gnu.org/licenses/.
What if a company wants to build a product around the Lively Kernel, but does not want to reveal proprietary enhancements?
Sun Microsystems, as licensor of the Lively Kernel, may negotiate alternative proprietary licenses with such developers. Interested parties should contact Sun Microsystems for further information.
How is the Sun Labs Lively Kernel related to JavaScript?
The Lively Kernel is written entirely in the JavaScript programming language. Lively Kernel applications are also written entirely in JavaScript. We call it "Lively" because the system makes the browser far more interactive and alive. The applications and even those scripts that comprise the Lively Kernel system itself can be examined and changed at will by the user.
Why do you call it a “Kernel”?
We call it a kernel for three reasons: First, the system is still reasonably small – less than ten thousand lines of JavaScript code. Second, the system is intended to be self-contained and extensible, meaning that it is possible to enhance the capabilities of the system dramatically using only the system itself. Third, the system exhibits various operating system like qualities, such as the ability to run multiple applications simultaneously and to serve as a host for entirely new types of applications, widgets and tools that were not pre-built into the system itself. In general, one of the central characteristics of the Lively Kernel is its malleability: An imaginative user can take the Lively Kernel in directions that were entirely unforeseen by its designers. In that sense, the current system is only a “kernel” of its full potential.
What is the graphics model in the Lively Kernel?
The design is inspired by the Morphic graphics framework originally built in the Self programming system (http://research.sun.com/self/), and later incorporated into the Squeak Smalltalk environment (http://www.squeak.org/). It is a desktop-style graphics architecture supporting superposition, transformation, clipping and other useful graphical traits and operations, as well as a multi-user event model.
Whoa! What do you mean, multi-user?
The hand metaphor used for the cursor in the Lively Kernel allows for several users to actively inhabit a single world at the same time. We had this working in an earlier version, and plan to make it work in the release version as soon as possible. The objects are serialized at times of change, very much the way they are serialized to store a web page. The performance is not optimized, and the synchronization is not as rigorous as systems like Croquet, and yet it has shown itself to be practical for simple applications like a multi-user whiteboard and simple shared games.
Where does the Lively Kernel run?
The Lively Kernel is intended to run in every commercial web browser client with no plug-ins or installation whatsoever. However, the system uses advanced features that are only now appearing in web browsers. Because of differences in the underlying graphics capabilities of the various web browsers, currently the system runs best on Apple's Safari browser. It also runs on Firefox, although with less performance and with some remaining bugs. Microsoft Internet Explorer is not supported currently, but we are hoping to support it in the near future.
Why doesn't the Lively Kernel run on Microsoft Internet Explorer?
Unfortunately, the graphics libraries supported by different web browsers are still incompatible. Safari, Firefox and Opera have support for a graphics standard called SVG (Scalable Vector Graphics), while Microsoft Internet Explorer supports VML (Vector Markup Language). Currently, we have a public Lively Kernel implementation available only for those browsers that support SVG. We are hoping to have support for Microsoft Internet Explorer available soon.
What is SVG?
SVG stands for Scalable Vector Graphics. It is a declarative graphics language and specification designed by the World Wide Web Consortium (W3C). It is supported by most web browsers, although the implementations are still somewhat incompatible. While SVG is commonly used via its HTML-like declarative syntax, the SVG functionality of a web browser can also be accessed programmatically from JavaScript.
How dependent is the Lively Kernel on SVG?
To us, SVG is an implementation vehicle. We have chosen to use SVG because of its functionality and widespread availability in commercial web browsers. However, to end users and developers, the Lively Kernel is an environment that is based purely on JavaScript. At that level, the use of SVG or any other implementation technology is not intended to be visible at all. Future releases of the Lively Kernel may include back-ends also for other graphics standards than SVG.
What exact browser versions currently support the Lively Kernel?
Safari – 3.0.2 and later (RECOMMENDED)
Firefox – 3.0 beta and <IP_ADDRESS>
Note: On Firefox and Opera our system still has various bugs. Performance is also rather poor on Firefox 2.0.
For best performance and quality of experience, we highly recommend Safari.
Why is the performance of the system poor sometimes?
The performance of the Lively Kernel is heavily dependent on the performance of the JavaScript virtual machine and the SVG engine inside your web browser. Neither JavaScript virtual machines nor SVG engines have been optimized for executing significant, long-running web applications yet. However, based on our earlier experience with other dynamic languages, these problems should go away soon. For instance, there is no reason for a JavaScript application to run any slower than a comparable Smalltalk application (Smalltalk is an equally dynamic programming language), or significantly slower than a comparable Java application. It is just that JavaScript virtual machines have not been optimized for high performance yet. In the near future, web browsers will likely have JavaScript engines and graphics libraries with dramatically better performance.
What future releases are planned for the Lively Kernel?
The
development of the Lively Kernel is still very much in
progress.
In the future, you can expect changes in areas such as:
* Saving of applications directly from within the Lively Kernel
* Dynamic access to source code base
* Graphics enhancements: curves, filters, fancy borders, etc.
* Multi-user collaboration capabilities
* Programming enhancements (more advanced development tools, debugging facilities, etc.)
* Music and sound
* Internationalization
You can also expect additional ports of the Lively Kernel to make it available in additional browsers and other environments.
What have people done with the Lively Kernel?
See our developer and user forum for interesting links. Note that by posting there, you are giving Sun permission to publish your application (with credit) in the archive. The archive is a repository maintained by Sun Microsystems showcasing various applications that we consider to be fun or useful.
What Lively Kernel projects are currently going on?
The best way to find out what is going on is to visit and read the forums.
How do I become a Lively Kernel developer?
Visit the forum and our web site for latest information. Currently, we do not have much developer information available yet. We will post developer information on our web site as soon as such material becomes available.
How can I contribute to the Lively Kernel?
Visit the forums to find out what people want and are working on. If you produce a bug fix or enhancement, then post it on a forum to share it, and submit it to us to proffer it for inclusion in future releases.
How can I share something built with the Lively Kernel?
Post a link to it in the forum. Please include your name and email address if desired, and a line or two of description – ideally enough for a search engine to find it.


User Sign On
If you select a sign-on mode here, authentication will be requested after the completion of the marking-words and
the results will only be submitted if the authentication was successful.
The various sign-on modes are described below.
Guest Mode
The user can authenticate with any username and without password. This mode is mostly used for demo scenarios.
H-BRS FB02
In this mode the user has to authenticate access with a valid account from the Department of Computer Sciences
at Hochschule Bonn-Rhein-Sieg University of Applied Sciences.
Submit Button
Only select if the result should be saved. In this case a user login should be set.
Submit Button Label
Here you can specify the label of the "Submit"-button, for example "Save", "Submit", "Sent" etc..
Retry
Select if several attempts can be made.
Check
Choose whether user can check his solution. This means that the words marked by user are displayed
green (correct) or red (wrong). It will not reveal what he should have marked as in "Show Solution".
Show Solution
Choose if the solution can be seen. It will reveal what user should have marked.


|
|
Dojo Samples |
Zazl is a Dynamic Javascript Optimizer that enables the loading of AMD modules and all their dependencies in a single response stream without depending on static build tools. It also leverages JavaScript compression tools such as Google Closure, Uglify-js and Dojo Shrinksafe.
The samples use the Zazl AMD loader to load their modules.The Zazl backend component that performs all the dependency analysis and optimization is running on a Node.js based server. Take a look at the javascript network traffic in a browsers developers tools to see the optimization in action.
The samples source code can be found here


WITNESS LIST Finance Committee May 15, 1997-9:00A HB 99 On: Jones, Tom A. (SPRINT), Austin Millwee, Tom (DPS), Austin Registering, but not testifying: On: Flores, Henry G. (TX Telephone Assc), Austin HB 733 On: Caldarola, Corie (Comptroller's Office), Austin HB 767 Registering, but not testifying: On: Papick, Joe (DPRS), Austin HB 846 Registering, but not testifying: For: Igo, Shanna (TX Municipal League), Austin On: Wendel, Susan (TX Assc of Counties), Austin HB 1209 Registering, but not testifying: On: Cowan, Andrea (Comptroller's Office), Austin HB 2157 For: Reynolds, Jim (TX Ticket Brokers Assc), Austin Registering, but not testifying: On: Barton, Karey (Comptroller's Office), Austin HB 2252 On: Alexander, Cindy (Comptroller's office), Austin Black, Kermit (TX A&M University), College Station Cedicco, Ruth (TDHCA), Austin Registering, but not testifying: For: Contreras, Marcy (Self), Progreso Galvan, Mario (Self), Progreso On: Ayala, Yesenia (UTPA CEED), Edinburg Hernandez, Victor (UT - Pan American), Edinburg Olivares, Leonardo (UT - Pan American), Edinburg Wendler, Walter (TX A&M University), College Station HB 2747 Registering, but not testifying: For: Garcia, Ray (TX Assc of Comm College), Austin HB 2778 Registering, but not testifying: On: Reynolds, Carl (TX Bd Criminal Justice), Austin HB 2798 Registering, but not testifying: On: Talerico, Jeanne (TX Bond Review Board), Austin HB 2799 Registering, but not testifying: For: Anderson, Susan (Govt Treasurers Org TX), Arlington Hynson, Kathy (Co Treasurers Assc TX), Richmond On: Wendel, Susan (TX Assc of Counties), Austin HB 3027 For: Thompson, Jay (Medical Protective Co), Austin On: Durden, David (TX Dept. of Insurance), Austin HB 3189 Registering, but not testifying: On: Burgdorf, Jayna (TX Parks/Wildlife), Austin Sansom, Andrew (TX Parks/Wildlife), Austin HB 3203 For: Craven, Eric (TX Electric Coop), Austin HB 3250 Registering, but not testifying: For: Trimble, Robert L. (Humane Soc of Dallas), Dallas HB 3271 On: Furgason, Robert R. (TX A&M - Corpus Christi), Corpus Christi SCR 71 Registering, but not testifying: On: Cuellar, Robert (TXDOT), Austin


Big Nose Kate's was named in honor of one of our "Ladies of the Night" who was frequently seen with Doc Holliday. While she didn't actually own a bar, she was well known while she was in town. Today's saloon is located where the Tombstone Grand Hotel originally stood and most of the paranormal activity reported here seems to be related to that earlier business.
To understand the paranormal activity reported at Kate's it is important to understand the Tombstone Grand Hotel. By all accounts, it was indeed grand. The lobby was on the ground floor, where Kate's is located today, and there were two floors of guest rooms upstairs (which no longer exists), along with a bar in the basement. The lobby had chandeliers and oak-paneled walls with expensive paintings. The guest rooms were luxurious for the day and included touches like soft beds with clean linen (not always found in Victorian-era hotels). The basement in Kate's is a gift shop today, but it seems to be the source of the paranormal activity in the building since it has a link to the mines below.
A few of the people who work at Big Nose Kate's say that some nights after they've closed and are cleaning up they can hear the sounds of boots walking on a wooden floor above them. There is no second floor today but back in the day that was the location of the guest rooms. It is possible that cowboys, or maybe ladies of the night, are still walking the halls upstairs.
One other legend is that of the Swamper, who is said to still live in the basement. The Swamper was the hotel's custodian and he had a small room off of the bar in the basement. We believe that he managed to drill through the floor in his bedroom into a mine located just a few feet below and find (or maybe steal) enough silver to fill a bag that is still hidden there. Sometimes when guests are looking through a barred door into his room they report feeling something push them out of the way, as if the Swamper is trying to tell them to quit looking for his silver.
Guests who visit Kate's should look for a photograph that shows an apparition that visited Kate's several years ago. It can be found hanging on the wall behind the right end of the bar.
Tour Information: Because Big Nose Kate's lies in the middle of a block that is closed to vehicular traffic, the Tombstone After Dark tour does not pass this location. I included it on my website because there are occasional reports of paranormal activity here.


Provides function to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points.
| Version: | 2.2.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | tcltk, stringr, assertthat |
| Suggests: | lattice, testthat, pdftools |
| Published: | 2018-05-09 |
| Author: | Priyanga Dilini Talagala [aut, cre], Ogan Mancarci [aut], Daniel Padfield [aut] |
| Maintainer: | Priyanga Dilini Talagala <dilini.talagala at monash.edu> |
| BugReports: | https://github.com/pridiltal/staplr/issues |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | staplr results |
| Reference manual: | staplr.pdf |
| Package source: | staplr_2.2.0.tar.gz |
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API¶
The two main objects that constitue the scraps framework are the Resonator
and ResonatorSweep
objects. Additionally, several helper functions exist
for building or interacting with these objects. There is also a default function
for loading in data from a text file provided, althogh you will likely need to write
your own.
Fitting has been semantically separated, and you will likely want to supply your own functions for initializing parameters and fitting data, but some default fit functions are also provided. See the fit functions section of this page.
Plotting is handled by a separate set of plotting functions that take as an argument
either a Resonator
or ResonatorSweep
object.
Resonator class¶
-
class
scraps.
Resonator
(name, temp, pwr, freq, I, Q, sigmaI=None, sigmaQ=None)¶ Fit an S21 measurement of a hanger (or notch) type resonator.
Parameters: - name (string) – The resonator name. Does not have to be unique, but each physical resonator in the experiment should have a unique name to avoid confusion when using some of the other tools in scraps.
- temp (float) – The temperature in (K) that the S21 measurement was taken at.
- pwr (float) – The power (in dBm) at which the resonator was measured.
- freq (array-like[nDataPoints]) – The frequency points at which the S21 scan was measured.
- I (array-like[nDataPoints]) – The in-phase (or real part) of the complex S21 measurement. Units are typically volts, and I should be specified in linear units (as opposed to dB).
- Q (array-like[nDataPoints]) – The out-of-phase (or imaginary part) of the complex S21 measurement. Units are typically volts, and I should be specified in linear units (as opposed to dB).
- sigmaI (array-like[nDataPoints] (optional)) – An array of uncertaintly values for each data point in I. Default
is
None
. - sigmaQ (array-like[nDataPoints] (optional)) – An array of uncertaintly values for each data point in Q. Default
is
None
. - following attributes are automatically calculated and added during (The) –
- initialization. –
-
name
¶ string – The resonator name passed at initialization.
-
temp
¶ float – The temperature passed at initialization.
-
pwr
¶ float – The power passed at initialization.
-
freq
¶ array-like[nDataPoints] – The frequency points passed at initialization.
-
I
¶ array-like[nDataPoints] – The I data points passed at initialization.
-
Q
¶ array-like[nDataPoints] – The Q data points passed at initialization.
-
sigmaI
¶ array-like[nDataPoints] – The sigmaI values passed at initialization.
-
sigmaQ
¶ array-like[nDataPoints] – The sigmaQ values passed at initialization.
-
S21
¶ array-like[nDataPoints] – The complex transmission
S21 = I + 1j*Q
.
-
phase
¶ array-like[nDataPoints] – The raw phase
phase = np.arctan2(Q, I)
.
-
uphase
¶ array-like[nDataPoints] – The unwrapped phase is equivalent to the phase, but with jumps of 2 Pi removed.
-
mag
¶ array-like[nDataPoints] – The magnitude
mag = np.abs(S21)
or, equivalentlymag = np.sqrt(I**2 + Q**2)
.
-
hasFit
¶ bool – Indicates whether or not
Resonator.do_lmfit
method has been called.
-
lmfit_result
¶ lmfit.Result
object – The result object created bylmfit
containing all the fit information. Some of the fit information is futher extracted for convenience in the following Attributes. For an exhaustive list of the attributes of lmfit_result see the docs forlmfit
. The most useful attribute of this object islmfit_result.params
, which contains the best-fit parameter values.
-
residualI
¶ array-like[nDataPoints] – The residual of the fit model against the I data, wieghted by the uncertainties.
-
residualQ
¶ array-like[nDataPoints] – The residual of the fit model against the Q data, wieghted by the uncertainties.
-
resultI
¶ array-like[nDataPoints] – The best
lmfit
fit result to the fit model for I.
-
resultQ
¶ array-like[nDataPoints] – The best
lmfit
fit result to the fit model for Q.
-
resultMag
¶ array-like[nDataPoints] –
resultMag = np.abs(resultI + 1j*resultQ)
-
resultPhase
¶ array-like[nDataPoints] –
resultPhase = np.arctan2(resultQ/resultI)
-
emcee_result
¶ lmfit.Result
object – This object is nearly identical to the lmfit_result object, but also contains the maximum-liklihood values for the varying parameters of the fit model as well as the chains returned byemcee
. The most important attribute is probablyemcee_result.flatchain
, which can be passed directly topygtc
orcorner
to make a really nice GTC/Triangle/Corner plot. For an exhaustive list of the attributes of emcee_result see the docs forlmfit
, specifically the section involving thelmfit
implementation ofemcee
.
-
mle_vals
¶ list of float – The maximum-liklihood estimate values of the varying parameter in the fit model as calculated by
emcee
. Unpacked here for convenience fromemcee_result.params
.
-
mle_labels
¶ list of string – The parameter names of the values in mle_vals. Provided here for easy passing to
pygtc
orcorner
.
-
magBaseLine
¶ array-like[nDataPoints] – The best initial guess of the baseline of the magnitude. Calculated by fitting a quadratic polynomial to the beginning and end of the magnitdue vs frequency curve.
-
phaseBaseLine
¶ array-like[nDataPoints] – The best initial guess of the baseline of the phase. Calculated by fitting a line to the beginning and end of the phase vs frequency curve. This is equivalent to calculating the electrical delay in the measurement lines.
-
params
¶ lmfit.Parameters
object – The initial parameter guesses for fitting the S21 data. Seelmfit
documentation for a complete overview. To get the parameter names, callparams.keys()
. Default isNone
. Initialize params by callingResonator.load_params
. Delete params withResonator.torch_params
.
Resonator methods¶
.load_params()¶
-
Resonator.
load_params
(paramsFn, **kwargs)¶ Load up a lmfit Parameters object for a custom fit function.
Parameters: - paramsFn (method) – The paramsFn method should return a
lmfit.Paramters
object. This object will be passed to the fit method whendo_lmfit
ordo_emcee
is called. - kwargs (dict) – A dictionary of keyword arguments to pass to paramsFn.
- paramsFn (method) – The paramsFn method should return a
.do_lmfit()¶
-
Resonator.
do_lmfit
(fitFn, label='default', fit_type='IQ', **kwargs)¶ Run lmfit on an existing resonator object and update the results.
Parameters: - fitFn (function) – fitFn must have the signature fitFn(params, res, residual, **kwargs). If residual == True, fitFn must return a 1D list-like object of residuals with form [I residual, Q residual] where [A, B] means concatenate. Otherwise it must return the model data in the same form.
- label (string) – A label to use as a key when storing results from the fit to the lmfit_results dict.
- fit_type (string) – Indicates the type of fit to be run. For some types of fits, certain quantities will automatically be calculated and added to the resonator object. For instance, ‘IQ’ will cause the magnitude, phase, I, and Q as well as associated residuals to be calculated.
- kwargs (optional keywords) – Use this to override any of the lmfit parameter initial guesses or
toggle whether the paramter varys. Example:
qi=1e6
is equivalent to callingResonator.params['qi'].value = 1e6
. Example:qi_vary=False
will setResonator.params['qi'].vary = False
. Any parameter name can be used in this way.
.torch_lmfit()¶
-
Resonator.
torch_lmfit
(label='default')¶ Reset all the lmfit attributes to
None
and sethasFit = False
.Parameters: label (string (optional)) – Choose which fit to kill off.
.do_emcee()¶
-
Resonator.
do_emcee
(fitFn, label='default', **kwargs)¶ Run the Monte-Carlo Markov Chain routine to generate samples for each parameter given a model.
Parameters: - fitFn (function) – fitFn must have the signature fitFn(params, res, residual, **kwargs). If residual == True, fitFn must return a 1D list-like object of residuals with form [I residual, Q residual] where [A, B] means concatenate. Otherwise it must return the model data in the same form.
- label (string (optional)) – A label to assign to the fit results. This will be the dict key they are stored under in the emcee_results dict. Also, if label matches a label in lmfit_results, then that params object will be used to seed the emcee fit.
- kwargs (optional keyword arguments) – These are passed through to the
lmfit.Minimizer.emcee
method. See thelmfit
documentation for more information.
Resonator helper functions¶
These functions are designed to make it easy to use the Resonator
class.
process_file()¶
-
scraps.
process_file
(fileName, mask=None, **loadtxt_kwargs)¶ Load Keysight PNA file data into dict.
Parameters: - fileName (string) –
Path to data. Actual name of the file should be in the format:
name + '_' + pwr + '_DBM_TEMP_' + temp + '.S2P'
where:- name: Must be ‘RES-N’ where N is any single character you like.
- pwr: The power the resonator sees in dBm, three characters Example: ‘-25’
- temp: The temperature of the resonator in K, five characters Example: ‘0.150’
- mask (slice (optional)) – You can pass a slice to cut out some region of data. Example 1: mask = slice(10,-10) will cut out the first 10 and last 10 datapoints. Example 2: maks = slice(None,None,10) will resample your data and take every 10th point. Example 3: mask = slice(None, -50) will cut the last 50 points off your data.
- loadtxt_kwargs (dict (optional)) – This is a pass-through to numpy.loadtxt
Returns: dataDict – Dictionary contains the following keys: ‘name’, ‘temp’, ‘pwr’, ‘freq’, ‘I’, ‘Q’. If fileName does not exist, then returns
None
.Return type: dict or
None
Note
This assumes the data in the file is in three columns in the order frequency, I, Q.
This is also a terribly written function, and you really should write your own!
- fileName (string) –
makeResFromData()¶
-
scraps.
makeResFromData
(dataDict, paramsFn=None, fitFn=None, fitFn_kwargs=None, paramsFn_kwargs=None)¶ Create a Resonator object from a data dictionary.
Parameters: - dataDict (dict) – Must have the following keys: ‘I’, ‘Q’, ‘temp’, ‘pwr’, ‘freq’, ‘name’. Optional keys are: ‘sigmaI’, ‘sigmaQ’
- paramsFn (function (optional)) – A function that initializes and returns an lmfit parameters object for passing to fitFn.
- fitFn (function (optional)) – If a fit function is passed, an lmfit minimization will be done automatically.
- fitFn_kwargs (dict (optional)) – A dict of keyword arguments passed to fitFn.
- paramsFn_kwargs (dict (optional)) – A dict of keyword arguments passed to paramsFn.
Returns: res – A Resonator object or
None
if there is an error loading the data.Return type: Resonator
object orNone
makeResList()¶
-
scraps.
makeResList
(fileFunc, dataPath, resName, **fileFunc_kwargs)¶ Create a list of resonator objects from a directory of dataDict
Parameters: - fileFunc (function) – A function that converts a single data file into a dictionary. The resulting dictionary must have the following keys: ‘I’, ‘Q’, ‘temp’, ‘pwr’, ‘freq’, ‘name’, and may have the following ptional keys: ‘sigmaI’, ‘sigmaQ’
- dataPath (string) – Path to the directory containing the data files that will be processed by fileFunc.
- resName (string) – The name of your resonator. This can be anything, but it is useful to use the same name for every data file that comes from the same physical resonator.
- fileFunc_kwargs (dict) – Keyword arguments to pass through to the fileFunc
indexResList()¶
-
scraps.
indexResList
(resList, temp=None, pwr=None, **kwargs)¶ Index resList by temp and pwr.
Parameters: - resList (list-like) – resList is a list of
scraps.Resonator
objects - temp (numeric) – The temperature of a single Resonator object.
- pwr (int) – The power of a single Resonator object
- itemp (boolean (optional)) – Switch to determine whether lookup uses temp or itemp (rounded value of
temp). Default is
False
.
Returns: index – Index is the index of the Resonator in resList or a list of indices of all matches if only pwr or only temp is specified.
Return type: int or list
Notes
indexResList does not check for duplicates and will return the first match.
- resList (list-like) – resList is a list of
ResonatorSweep class¶
The ResonatorSweep class inherits python’s dict
class. Each key of the dict
corresponds to a pandas.DataFrame
of data. Because of this, you can add your
own custom derived data sets by simply assigning them to a dict entry. As an
example:
#Start with a ResonatorSweep object called resSweep that contains a
#key called 'f0', which is the resonant frequency of many different
#resonators. Maybe what you want to plot though, is the reduced
#frequency df = (f-fr)/fr where fr is some reference frequency
#Choose a reference frequency from the existing data
fr = resSweep['f0'].iloc[0,0]
#Pick a descriptive key to describe the data
key = 'df_over_fr'
#Calculate the derived quantity and add it to the ResonatorSweep
resSweep[key] = (resSweep['f0']-fr)/fr
#And now you can plot it by just passing that key when you use the
#plotting tools. If you want errorbars, you'll need to calculate those
#for the derived quantity as well, and store them in: key + '_sigma'.
-
class
scraps.
ResonatorSweep
(resList, **kwargs)¶ Dictionary object with custom
__init__
method.Parameters: resList (list-like) – A list of
scraps.Resonator
objects. Each object must have the attributehasFit == True
.Keyword Arguments: - label (string (optional)) – Selects which set of fit data to build the ResonatorSweep from. Default is ‘default’.
- index (string{'raw', 'round', 'block'} (optional)) – Selects which method to use for indexing.
- roundto (int (optional)) – Number to round temperature index to in mK. Default is 5.
-
tvec
¶ array-like[nUniqeTemps] – Index of temperature values, one for each unique temperature.
-
pvec
¶ array-like[nUniqePowers] – Index of powers values, one for each unique power.
-
smartindex
¶ string – Indicates the method of formatting temperature values in tvec. ‘raw’ means to take temperature values as they are, ‘round’ means to round to the neareast X mK, where X is set by roundTo, ‘block’ means to figure out which temperature values are nominally the same and set each block of those temperatures to the same value.
-
rountTo
¶ float – The number of mK to round to when
smartIndex == 'round'
.
-
lmfit_results
¶ dict – A dictionary containing the fit results for different data by key. Initially empty, results are added by calling
ResonatorSweep.do_lmfit
.
-
emcee_results
¶ dict – A dictionary containing the MCMC results for different data by key. Initially empty, results are added by calling
ResonatorSweep.do_emcee
.
-
lmfit_joint_results
¶ dict – If multiple data sets are fit simultaneously via
ResonatorSweep.do_lmfit
, the results will appear in this attribute by key, where ``key = ‘key1+key2’ == ‘key2+key1’.
-
emcee_joint_results
¶ dict – If multiple data sets are fit simultaneously via
ResonatorSweep.do_emcee
, the results will appear in this attribute by key, where ``key = ‘key1+key2’.
Note
The following keys are added to the self dict:
- ‘temps’ :
pandas.DataFrame
- The temperature of each resonator in the sweep.
- ‘fmin’ :
pandas.DataFrame
- The minimum value of the magnitude vs frequency curve after subtraction of the best-guess baseline.
- ‘listIndex’ :
pandas.DataFrame
- The sweep objects are built from a list of
pyres.Resonator
objects; this is the index of the original list for each data value in the sweep.
If the
do_lmfit
method has been run, these keys are also added:- ‘chisq’ :
pandas.DataFrame
- The Chi-squared value of each fit in the sweep.
- ‘redchi’ :
pandas.DataFrame
- The reduced Chi-squared value of each fit in the sweep.
- ‘feval’ :
pandas.DataFrame
- The number of function evaluations for each fit in the sweep.
- paramNames :
pandas.DataFrame
- There is a key for each parameter value in the
Resonator.params
attribute. - paramNames + ‘_sigma’ :
pandas.DataFrame
- There is a key for the uncertainty on each paramter value returned by the fit. Key name is paramName + ‘_sigma’
If the
do_emcee
method has been run, these keys are also added:- paramNames + ‘_mle’ :
pandas.DataFrame
- The maximum-liklihood estimate from the MCMC sampling.
- paramNames + ‘_mc’ :
pandas.DataFrame
- The 50th percentile value of the posterior distribution from MCMC sampling.
- paramNames + ‘_sigma_plus_mc’ :
pandas.DataFrame
- The plus errorbar on the MCMC data. Calculated as the difference between the 84th percentile value and the 50th percentile value.
- paramNames + ‘_sigma_minus_mc’ :
pandas.DataFrame
- The minus errorbar on the MCMC data. Calculated as the difference between the 50th percentile value and the 16th percentile value.
ResonatorSweep methods¶
.do_lmfit()¶
-
ResonatorSweep.
do_lmfit
(fit_keys, models_list, params_list, model_kwargs=None, param_kwargs=None, **kwargs)¶ Run simulatneous fits on the temp/pwr data for several parameters. Results are stored in either the
lmfit_results
orlmfit_joint_results
attribute depending on whether one or multiple keys are passed to fit_keys.Parameters: - fit_keys (list-like) – A list of keys that correspond to existing data. Any combination of keys from self.keys()` is acceptable, but duplicates are not permitted.
- models_list (list-like) – A list of fit functions, one per key in fit_keys. Function must
return a residual of the form:
residual = (model-data)/sigma
whereresidual
,model
, anddata
are allnumpy
arrays. Function signature ismodel_func(params, temps, powers, data=None, sigmas=None)
. Ifdata==None
the functions must return the model calculated attemps
andpowers
. The model functions should also gracefully handlenp.NaN
orNone
values. - params_list (list-like) – A list of
lmfit.Parameters
objects, one for each key in fit_keys. Parameters sharing the same name will be merged so that the fit is truly joint. Alternately, a list of functions that returnlmfit.Parameters
objects may be passed. In this case, one should use param_kwargs to pass any needed options to the functions. - model_kwargs (list-like (optional)) – A list of
dict
objects to pass to the individual model functions as kwargs.None
is also an acceptable entry if there are no kwargs to pass to a model function. Default isNone.
- param_kwargs (list-like (optional)) – A list of
dict
objects to pass to the individual params functions as kwargs.None
is also an acceptable entry if there are no kwargs to pass to a model function. Default isNone.
- lmfit_kwargs (dict (optional)) – Keyword arguments to pass options to the fitter
- kwargs (dict (optional)) – Supported keyword arugments are ‘min_temp’, ‘max_temp’, ‘min_pwr’, and ‘max_pwr’. These set limits on which data to fit.
Keyword Arguments: - min_temp (numeric) – Lower limit of temperature to fit. Default is 0.
- max_temp (numeric) – Upper limit of temerature to fit. Default is infinity.
- min_pwr (numeric) – Lower limit of temperature to fit. Default is -infinity.
- max_pwr (numeric) – Upper limit of temperature to fit. Default is infinity.
- raw_data (string {'lmfit', 'emcee', 'mle'}) – Whether to use the values returned by lmfit, or the values returned by the emcee fitter (either the 50th percentile or the maximum liklihood). This also chooses which set of errorbars to use: either those from the lmfit covariance matrix, or those from the 16th and 84th percentiles of the posterior probablility distribution. Default is ‘lmfit’.
Note
If the fits are succesful, the resulting fit data (ie the best fit surface) will be added to the self dict in the form of a
pandas.DataFrame
under the following keys:For a joint fit (
len(fit_keys) > 1
):'lmfit_joint_'+joint_key+'_'+key for each key in fit_keys
For a single fit (
len(fit_keys) == 1
):'lmfit_'+key
.do_emcee()¶
-
ResonatorSweep.
do_emcee
(fit_keys, models_list, params_list=None, model_kwargs=None, param_kwargs=None, emcee_kwargs=None, **kwargs)¶ Run simulatneous MCMC sampling on the temp/pwr data for several parameters. Results are stored in either the
emcee_results
oremcee_joint_results
attribute depending on whether one or multiple keys are passed to fit_keys.Parameters: - fit_keys (list-like) – A list of keys that correspond to existing data. Any combination of keys from self.keys()` is acceptable, but duplicates are not permitted.
- models_list (list-like) – A list of fit functions, one per key in fit_keys. Function must
return a residual of the form:
residual = (model-data)/sigma
whereresidual
,model
, anddata
are allnumpy
arrays. Function signature ismodel_func(params, temps, powers, data=None, sigmas=None)
. Ifdata==None
the functions must return the model calculated attemps
andpowers
. The model functions should also gracefully handlenp.NaN
orNone
values. - params_list (list-like) – A list of
lmfit.Parameters
objects, one for each key in fit_keys. Parameters sharing the same name will be merged so that the fit is truly joint. Alternately, a list of functions that returnlmfit.Parameters
objects may be passed. In this case, one should use param_kwargs to pass any needed options to the functions. Default isNone
and is equivalent to settinguse_lmfit_params = True
. - model_kwargs (list-like (optional)) – A list of
dict
objects to pass to the individual model functions as kwargs.None
is also an acceptable entry if there are no kwargs to pass to a model function. Default isNone.
- param_kwargs (list-like (optional)) – A list of
dict
objects to pass to the individual params functions as kwargs.None
is also an acceptable entry if there are no kwargs to pass to a model function. Default isNone.
- emcee_kwargs (dict (optional)) – Keyword arguments to pass options to the fitter
Keyword Arguments: - min_temp (numeric) – Lower limit of temperature to fit. Default is 0.
- max_temp (numeric) – Upper limit of temerature to fit. Default is infinity.
- min_pwr (numeric) – Lower limit of temperature to fit. Default is -infinity.
- max_pwr (numeric) – Upper limit of temperature to fit. Default is infinity.
- use_lmfit_params (bool) – Whether or not to use the resulting best-fit
lmfit.Paramters
object that resulted from callingResonatorSweep.do_lmfit()
as the starting value for the MCMC sampler. Default is True. - raw_data (string {'lmfit', 'emcee', 'mle'}) – Whether to use the values returned by lmfit, or the values returned by the emcee fitter (either the 50th percentile or the maximum liklihood). This also chooses which set of errorbars to use: either those from the lmfit covariance matrix, or those from the 16th and 84th percentiles of the posterior probablility distribution. Default is ‘lmfit’.
Note
If the fits are succesful, the resulting fit data (ie the best fit surface) will be added to the self dict in the form of a
pandas.DataFrame
under the following keys:For a joint fit (
len(fit_keys) > 1
):'emcee_joint_'+joint_key+'_'+key for each key in fit_keys
For a single fit (
len(fit_keys) == 1
):'emcee_'+key
Plotting tools¶
plotResListData()¶
-
scraps.
plotResListData
(resList, plot_types=['IQ'], **kwargs)¶ Plot resonator data and fits.
Parameters: - resList (list-like) – A list of
pyres.Resonator
objects. A singleResonator
object can be passed, as long as it is in a list. - plot_types (list, optional) –
A list of plots to create, each one specified by a string. Possible plot types are:
- ’IQ’: Plots the real part of the transmission (I) vs the imaginary
- part (Q). This is the default plot.
- ’rIQ’: Plots the residual of I vs the residual of Q. This plot is
- only available if the
do_lmfit
method of eachResonator
object has been called. The I and Q residuals are normalized by the uncertainty of the I and Q data respectively. If this is not explicitly supplied, it is calculated by taking the standard deviation of the first 10 data points.
- ’LinMag’: Plots the magnitude of the tranmission in Volts vs
- frequency.
- ’LogMag’: Plots the magnitude of the transmission in dB vs frequency.
LogMag = 20*np.log10(LinMag)
.
- ’rMag’: Plots the difference of LinMag and the best-fit magnitude vs
- frequency. This plot is only available if the
do_lmfit
method of eachResonator
object has been called.
- ’Phase’: Plots the phase of the transmision vs frequency.
Phase = np.arctan2(Q, I)
.
- ’rPhase’: Plots the difference of Phase and the best-fit phase vs
- frequency. This plot is only available if the
do_lmfit
method of eachResonator
object has been called.
- ’uPhase’: Plots the unwrapped phase vs frequency.
uPhase = np.unwrap(Phase)
.
- ’ruPhase’: Plots the difference of uPhase and the unwrapped best-fit
- phase vs frequency. This plot is only available if the
do_lmfit
method of eachResonator
object has been called.
- ’I’: Plots the real part of the transmission vs frequency.
- ’rI’: Plots the residual of I vs frequency. The residual is weighted
- by the uncertainty in I. This plot is only available if the
do_lmfit
method of eachResonator
object has been called.
- ’Q’: Plots the imaginary part of the transmission vs frequency.
- ’rQ’: Plots the residual of Q vs frequency. The residual is weighted
- by the uncertainty in Q. This plot is only available if the
do_lmfit
method of eachResonator
object has been called.
Keyword Arguments: - plot_fits (list-like, optional) – A list of boolean flags, one for each plot type specified. Determines
whether or not to overplot the best fit on the data. This is only
effective if the
do_lmfit
method of eachResonator
object has been called. Default is all False. - powers (list-like, optional) – A list of power values to plot. Default is to plot all of the unique
powers that exist in the list of
Resonator
objects. - max_pwr (float) – An upper bound on the powers to plot. Applies to values passed to powers also.
- min_pwr (float) – A lower bound on the powers to plot. Applies to values passed to powers also.
- temps (list-like, optional) – A list of temperature values to plot. Default is to plot all of the
unique temperatures that exist in the list of
Resonator
obejcts. - max_temp (float) – An upper bound on the temperatures to plot. Applies to values passed to temps also.
- min_temp (float) – A lower bound on the temperatures to plot. Applies to values passed to temps also.
- use_itemps ({False, True}, optional) – If a
ResonatorSweep
object has been generated from the resList it may have added theitemp
attrubute to eachResonatorObject
in the list. Specifyinguse_itemps = True
will force the plotting routine to use those tempeartures. - freq_units ({'GHz', 'Hz', 'kHz', 'MHz', 'THz'}, optional) – The units for the frequency axis, if it exists. Defaul is ‘GHz’.
- detrend_phase ({False, True}, optional) – Whether or not to remove a linear trend from the Phase data. A typical reason for a steep linear offset in the phase is an uncorrected electrical delay due to long transmission lines.
- num_cols (int, optional) – The number of columns to include in the grid of subplots. Default is 1.
- fig_size (int, optional) – The size of an individual subplot in inches. Default is 3.
- force_square ({False, True}, optional) – Whether or not to force each subplot axis to be perfectly square.
- show_colorbar ({True, False}, optional) – Whether or not to add a colorbar to the right edge of the figure. The colorbar will correspond to the limits of the colored data. Default is True.
- color_by ({'temps', 'pwrs'}, optional) – If multiple temperatures and multiple powers are passed, this selects which variable will set the color of the plots. Default is ‘temps’.
- color_map (str, optional) – The name of any colormap returned by calling
matplotlib.pyplot.colormaps()
is a valid option. Default is ‘coolwarm’. - waterfall (numeric, optional) – The value used to space out LogMag vs frequency plots. Currently only available for LogMag. Default is 0.
- plot_kwargs (dict, optional) – A dict of keyword arguments to pass through to the individual plots. Attempting to set ‘color’ will result in an error.
- fit_kwargs (dict, optional) – A dict of keyword arguments to pass through to the fit plots. Default is a dashed black line.
Returns: figS – A
matplotlib.pyplot
figure object.Return type: matplotlib.pyplot.figure
- resList (list-like) – A list of
plotResSweepParamsVsX()¶
-
scraps.
plotResSweepParamsVsX
(resSweep, plot_keys=None, ignore_keys=None, xvals='temperature', **kwargs)¶ Plot parameter data vs temperature from a ResonatorSweep object.
Parameters: - resSweep (
scraps.ResonatorSweep
object or list of objects) – The object containing the data you want to look at. It is also possible to pass a list of these objects and it will combine them into one plot. - plot_keys (list-like (optional)) – A list of strings corresponding to avaiable plot data. The available
keys depend on your parameter definitions and may be found by executing
print(resSweep.keys())
. Some keys may point to empty (NaN) objects. Default is to plot all of the keys that exist. If you pass plot_keys you may not pass ignore_ignore keys. - ignore_keys (list-like (optional)) – A list of strings corresponding to plots that should not be made. This
is useful if you want to plot most of the avaialble data, but ignore one
or two sets of data. Default is
None
. If you pass ignore_keys you may not pass plot_keys. - xvals (string) – What axis you want to plot by. Deafult is ‘temperature’. Could also be ‘power’. Future releases may include other options.
- color_by (string) – Set this to ‘index’ if you pass multiple res sweeps and want to color them by index.
Keyword Arguments: - plot_labels (list-like) – A list of strings to use to label the y-axes of the plots. There must be
one for each plot requested.
None
is acceptable for any position in the list and will default to using the key as the label. Default is to use the key as the label. - unit_multipliers (list-like) – A list of numbers to multiply against the y-axes data. There must be one
for each plot requested.
None
is acceptable for any position in the list and will default to 1. Default is 1. - fitter (string {'lmfit', 'emcee'}) – Which fit data to use when overlaying best fits. Default is ‘lmfit’.
- num_cols (int) – The number of columns to create in the plot grid. Default is 1. The number of rows will be calculated based on num_cols and the number of requested plots.
- powers (list) – List of powers to plot. Default is to plot all available. If xvals is ‘power’ this kwarg is ignored.
- temperatures (list) – List of temperatures to plot. Default is to plot all available. If xvals is ‘temperature’ this kwarg is ignored.
- xmax (numeric) – Don’t plot any xvals above this value. Default is infinity.
- xmin (numeric) – Don’t plot any xvals below this value. Default is 0 for temperature and -infinity for power.
- errorbars ({None, 'lmfit', 'emcee'}) – Add error bars to the data. Pulls from either the least-squares or the MCMC fits. Default is None.
- fig_size (numeric) – Size in inches for each plot in the figure.
- color_map (string) – Specifies the colormap to use. Any value in
matplotlib.pyplot.colormaps()
is a valid option. - show_colorbar ({True, False}, optional) – Whether or not to add a colorbar to the right edge of the figure. The colorbar will correspond to the limits of the colored data. Default is True.
- force_square (bool) – Whether or not to force each subplot to have perfectly square axes.
- plot_kwargs (dict or list of dicts) – Dict of keyword args to pass through to the plotting function. Default is {‘linestyle’:’–’, label=’Power X dB’}. If errorbars is not None, then default linestyle is {‘linestyle’:’o’}. Attempting to set ‘color’ or ‘yerr’ will result in an exception. Use the color_map and errorbars keywords to set those. If you passed in a list of objects to resSweep, then you can also pass a list of plot_kwargs, one for each sweep object.
- resSweep (
plotResSweepParamsVsTemp()¶
Note: this has been deprecated in favor of plotResSweepParamsVsX .. autofunction :: scraps.plotResSweepParamsVsTemp
plotResSweepParamsVsPwr()¶
Note: this has been deprecated in favor of plotResSweepParamsVsX .. autofunction :: scraps.plotResSweepParamsVsPwr
plotResSweep3D()¶
-
scraps.
plotResSweep3D
(resSweep, plot_keys, **kwargs)¶ Make 3D surface or mesh plots of any key in the
ResonatorSweep
object as functions of temperature and power.Parameters: - resSweep (
ResonatorSweep
object) – Apyres.ResonatorSweep
object containing all of the data to be plotted. - plot_keys (list-like) – List of strings where each string is a key corresponding to a plot that
should be made. For a list of acccetable keys, run
print resSweep.keys()
.
Keyword Arguments: - min_temp (numeric (optional)) – The minimum temperature to plot. Defaults to
min(temperatures)
. - max_temp (numeric (optional)) – The maximum temperature to plot. Defaults to
max(temperatres)
. - min_pwr (numeric (optional)) – The minimum power to plot. Defaults to
min(powers)
. - max_pwr (numeric (optional)) – The maximum power to plot. Defauts to
max(powers)
. - unit_multipliers (list (optional)) – Values to scale the z-axis by. Default is 1.
len(unit_multipliers) == len(plot_keys)
. - plot_labels (list (optional)) – Labels for the z-axis. Default is the plot key.
len(plot_labels) == len(plot_keys)
. - num_cols (int (optional)) – The number of columns in the resulting plot grid. Default is 1.
- fig_size (numeric (optional)) – The size of an individual subplot in inches. Default is 3.
- plot_lmfits (bool (optional)) – Whether or not to show a fit. The fit must exist! Default is False. Deprecated! Use plot_fits.
- plot_fits (list (optional)) – A list of fit keys to plot. Fits must exist for all parameters specified in plot_keys. Example: plot_fits = [‘lmfit’, ‘lmfit_joint_f0+qi’] will plot the lmfit results for each key in plot_keys, and the joint fit results from the ‘f0+qi’ fit for each key in plot_keys. If only one fit result is desired, a single string may be passed instead of a list.
- plot_kwargs (dict (optional)) – A dictionary of keyword arguments to pass the plotting function.
Default is
None
.
Note
This function is currently a little buggy, because I can’t figure out how to intelligently adjust the label positions, sizes, etc to deal with large numbers in the ticks. The current workaround is to pick a large fig_size (so far anything larger than 5 seems ok) and then scale the plot as needed in some other application.
You can also use something like the following to adjust the ticks on a specific axis:
figX.axes[0].tick_params(axis='z', pad=8) figX.axes[0].zaxis.labelpad = 13
- resSweep (
Fit models¶
Each fit model consists of two functions. One that returns a lmfit.Parameters
object,
and one that takes parameters and data and returns a residual.
I and Q vs frequency¶
The built-in fit model is called complx_IQ.py and is located in the fitsS21 folder. It has two functions, one that calculates best guess values for each of the ten fit parameters, and one that applies those guesses to the data and calculates the residual.
cmplxIQ_params()¶
-
scraps.
cmplxIQ_params
(res, **kwargs)¶ Initialize fitting parameters used by the cmplxIQ_fit function.
Parameters: res (
scraps.Resonator
object) – The object you want to calculate parameter guesses for.Keyword Arguments: - fit_quadratic_phase (bool) – This determines whether the phase baseline is fit by a line or a quadratic function. Default is False for fitting only a line.
- hardware (string {'VNA', 'mixer'}) – This determines whether or not the Ioffset and Qoffset parameters are allowed to vary by default.
- use_filter (bool) – Whether or not to use a smoothing filter on the data before calculating parameter guesses. This is especially useful for very noisy data where the noise spikes might be lower than the resonance minimum.
- filter_win_length (int) – The length of the window used in the Savitsky-Golay filter that smoothes
the data when
use_filter == True
. Default is0.1 * len(data)
or 3, whichever is larger.
Returns: params
Return type: lmfit.Parameters
object
cmplxIQ_fit()¶
-
scraps.
cmplxIQ_fit
(paramsVec, res, residual=True, **kwargs)¶ Return complex S21 resonance model or, if data is specified, a residual.
Parameters: - params (list-like) – A an
lmfit.Parameters
object containing (df, f0, qc, qi, gain0, gain1, gain2, pgain0, pgain1, pgain2) - res (scraps.Resonator object) – A Resonator object.
- residual (bool) – Whether to return a residual (True) or to return the model calcuated at the frequencies present in res (False).
Keyword Arguments: - freqs (list-like) – A list of frequency points at which to calculate the model. Only used if residual=False
- remove_baseline (bool) – Whether or not to remove the baseline during calculation (i.e. ignore pgain and gain polynomials). Default is False.
- only_baseline (bool) – Whether or not to calculate and return only the baseline. Default is False.
Returns: model or (model-data)/eps – If residual=True is specified, the return is the residuals weighted by the uncertainties. If residual=False, the return is the model values calculated at the frequency points. The returned array is in the form
I + Q
orresidualI + residualQ
.Return type: numpy.array
- params (list-like) – A an
Two-level system (TLS) and Mattis-Bardeen effect (BMD)¶
This model is for fitting frequency shifts and internal quality factors as functions of temperature and input power. It is a very simple model, employs a lot of simplifying assumptions, and should be regarded with extreme skepticism. However, it qualitatively describes the dominant behavior of most resonators and so is useful as an example.
There is no accompanying parameter-generation function. See Example 3 for usage.
qi_tlsAndMBT()¶
-
scraps.fitsSweep.
qi_tlsAndMBT
(params, temps, powers, data=None, eps=None, **kwargs)¶ A model of internal quality factor vs temperature and power, weighted by uncertainties.
Parameters: - params (
lmfit.Parameters
object) – Parameters must include['Fd', 'q0', 'f0', 'alpha', 'delta0']
. - temps (
numpy.Array
) – Array of temperature values to evaluate model at. May be 2D. - powers (
numpy.Array
) – Array of power values to evaluate model at. May be 2D. - data (
numpy.Array
) – Data values to compare to model. May also beNone
, in which case function returns model. - eps (
numpy.Array
) – Uncertianties with which to weight residual. May also beNone
, in which case residual is unwieghted.
Returns: residual – The weighted or unweighted vector of residuals if
data
is passed. Otherwise, it returns the model.Return type: numpy.Array
Note
The following constraint must be satisfied:
all(numpy.shape(x) == numpy.shape(data) for x in [temps, powers, eps])
It is almost certain that this model does NOT apply to your device as the assumptions it makes are highly constraining and ignore several material parameters. It is included here more as an example for how to write a model than anything else, and it does at least qualitatively describe the behavior of most superconducting resonators.
This model is taken from J. Gao’s Caltech dissertation (2008) and the below equations are from that work.
(2.54) gives for MBD:
1/Q(T)-1/Q(0) = alpha * R(T)/X(0)
(5.72) and (5.65) give for TLS:
1/Q(T)-1/Q(0) = Fd*tanh(hf/2kT)/sqrt(1+P/P0)
R(T)/X(0) calculated from (2.80), (2.89), and (2.90), using the
deltaBCS
function in this module for returning gap as a function of temperature.- params (
f0_tlsAndMBT()¶
-
scraps.fitsSweep.
f0_tlsAndMBT
(params, temps, powers, data=None, eps=None, **kwargs)¶ A model of frequency shift vs temperature and power, weighted by uncertainties.
Parameters: - params (
lmfit.Parameters
object) – Parameters must include['Fd', 'df', 'fRef', 'alpha', 'delta0']
. - temps (
numpy.Array
) – Array of temperature values to evaluate model at. May be 2D. - powers (
numpy.Array
) – Array of power values to evaluate model at. May be 2D. - data (
numpy.Array
) – Data values to compare to model. May also beNone
, in which case function returns model. - eps (
numpy.Array
) – Uncertianties with which to weight residual. May also beNone
, in which case residual is unwieghted.
Returns: residual – The weighted or unweighted vector of residuals if
data
is passed. Otherwise, it returns the model.Return type: numpy.Array
Note
The following constraint must be satisfied:
all(numpy.shape(x) == numpy.shape(data) for x in [temps, powers, eps])
It is almost certain that this model does NOT apply to your device as the assumptions it makes are highly constraining and ignore several material parameters. It is included here more as an example for how to write a model than anything else, and it does at least qualitatively describe the behavior of most superconducting resonators.
This model is taken from J. Gao’s Caltech dissertation (2008) and the below equations are from that work.
(2.54) gives for MBD (f(T)-f(0))/f(0) = -alpha*0.5*(X(T)-X(0))/X(0)
(5.71) gives for TLS “” = Fd/pi * (usual TLS expression from Phillips)
(X(T)-X(0))/X(0) calculated from (2.80), (2.89), and (2.90), using the
deltaBCS
function in this module for returning gap as a function of temperature.- params (


Before Change
// NumPy has an np.argparition() method, however log(1000) is so small that
// sorting the whole array is simpler and fast enough.
top_indicies = np.argsort(predicted_scores_by_user, axis=1)[:, -_TOP_K:]
top_indicies = np.flip(top_indicies, axis=1)
// Both HR and NDCG vectorized computation takes advantage of the fact that if
// the positive example for a user is not in the top k, that index does not
// appear. That is to say: hit_ind.shape[0] <= num_users
hit_ind = np.argwhere(np.equal(top_indicies, 0))
hr = hit_ind.shape[0] / ncf_dataset.num_users
ndcg = np.sum(np.log(2) / np.log(hit_ind[:, 1] + 2)) / ncf_dataset.num_users
global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)
eval_results = {
_HR_KEY: hr,
After Change
tf.logging.info("Computing metrics...")
hr, ndcg = get_hit_rate_and_ndcg(predicted_scores_by_user, items_by_user,
match_mlperf=FLAGS.ml_perf)
global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)


Buffalo Plains [au67], plain, in Imperia, peasants: 195
Last updated Turn #135
Buffalo Plains [au67], plain, in Imperia, peasants: 195
------------------------------------------------------------------------
30: The population grows by one peasant.
Routes leaving Buffalo Plains:
North, plain, to Buffalo Plains [at67], 7 days
East, desert, to Painted Desert [au68], 8 days
South, desert, to Painted Desert [av67], 8 days
West, plain, to Aztec Empire [au66], 7 days
Garrison [ii6r], garrison, uncontrolled, with two soldiers
Seen here:
Stack of nazgul [cs6s], nazgul, number: 5


Search
Preparing search index...


The entirety of the religio of the Roman people is divided into rites and auspices, to which is added a third thing, namely whatever warnings the interpreters of the Sibylline books or haruspices issue for the sake of foreknowledge on the basis of portents and omens. I hold that none of these religiones should ever be neglected, and I have persuaded myself that Romulus and Numa laid the foundations of our state by establishing the auspices and rites, respectively, and that our state could never have become so great without the greatest appeasement of the immortal gods.
Thus that which is the most awful of evils, death, is nothing to us, since when we exist there is no death, and when there is death we do not exist.
Homer and Hesiod have attributed to the gods everything that is a shame and reproach among men, stealing and committing adultery and deceiving each other.
But mortals consider that the gods are born, and that they have clothes and speech and bodies like their own. The Ethiopians say that their gods are snub-nosed and black, the Thracians that theirs have light blue eyes and red hair.
But if cattle and horses or lions had hands, or were able to draw with their hands and do the works that men can do, horses would draw the forms of the gods like horses, and cattle like cattle, and they would make their bodies such as they each had themselves.


Tasker: launch app when you plug in headphones
Another Tasker post, I love how you can automate almost everything you usually have to do manually. This is a very easy task, but I find it very useful. When I am at work, I listen to Noisli. I find this helps me keep my focus, but you can also do this with Spotify or any other app. I listen to this on my phone, but when I have a meeting, I do take my phone with me.
Therefore I made a task that will launch Noisli when I plug in my headphones, and kill the app when I unplug them:
- Go to the tab
TASKS
and add a new one, calledLaunch Noisli
. - Add an action
App -> Launch App
, and select the app you want to open. - Press back button to confirm.
- Add another task called
Kill Noisli
. - Add the action
App -> Kill App
, and select the app you want to close when you unplug your headphones. - In tab
PROFILES
, add a new profileState -> Hardware -> Headset Plugged
. Press back button to confirm. - Link task
Launch Noisli
. This will launch the app when your headset is plugged in. - Long press
Launch Noisli
in the profile and pressAdd Exit Task
.


We are Millenial Monarchs! As teenagers, we have a lot of new ideas about how to tackle pressing problems in society. This website was created to facilitate communication between other individuals who are just as passionate about bringing change to our world!


Currently Amanda is the Lead Designer at Albert, an app that acts as your financial genius and helps you save effortlessly. Previously she was an Imagineer at Walt Disney Imagineering—the birthplace of Disneyland (and all Disney theme parks)—where she visualized business data and contributed as a type designer
In her free time Amanda moonlights as a freelance designer, helping small businesses with visual branding and web development. When she's not working, she enjoys spending time with her dog, Captain.
As humans, we have an underlying “blueprint” for how we perceive and process the world around us, and the study of psychology helps us define this blueprint. As designers, we can leverage psychology to build more intuitive, human-centered products and experiences.
As humans, we have an underlying “blueprint” for how we perceive and process the world around us, and the study of psychology helps us define this blueprint. As designers, we can leverage psychology to build more intuitive, human-centered products and experiences.
As humans, we have an underlying “blueprint” for how we perceive and process the world around us, and the study of psychology helps us define this blueprint. As designers, we can leverage psychology to build more intuitive, human-centered products and experiences.
As humans, we have an underlying “blueprint” for how we perceive and process the world around us, and the study of psychology helps us define this blueprint. As designers, we can leverage psychology to build more intuitive, human-centered products and experiences.
As humans, we have an underlying “blueprint” for how we perceive and process the world around us, and the study of psychology helps us define this blueprint. As designers, we can leverage psychology to build more intuitive, human-centered products and experiences.


Fresh VC is a micro VC fund started by Shri Ganeshram (founder @ FlightCar) and Brian Sheng (formerly @ Shenzen Capital Group, China's largest VC). Fresh VC raised its inaugural fund in 2014.
We're proud to be early investors in several of the most exciting early stage companies.
Our portfolio and our own companies have raised later rounds from top firms and investors such as Andreessen Horowitz, First Round Capital, NEA, SV Angel, Sherpa Ventures, Jerry Yang, Ryan Seacrest, Brian Chesky, Tim Ferriss, and Ashton Kutcher, among others.


PLANES, TRAINS, AND AUTOMOBILES A film review by Hal Render Copyright 1987 Hal Render
I saw PLANES, TRAINS, AND AUTOMOBILES last night and left with mixed feelings about it. On the surface it is one thing, in reality something else, and the dichotomy is not handled as well as it could be. I think writer/director John Hughes has made another attempt at a comedy with a serious underside, but this time the two goals do not mesh as well as in past films.
Basically the plot concerns marketing exec Neil Page (Steve Martin) and his attempts to return home from a business trip just before Thanksgiving. The trip starts awful and gets worse, due to a combination of incredibly bad luck and the presence of Del Griffith (John Candy). Del is a sales rep and one of the single most boorish people on the face of the earth. For the purpose of this trip he also seems to be an instrument of fate, sent to accompany Neil through his trials and to have him examine his life and his relations with other people.
This is noble stuff, but the movie doesn't always reach the level Hughes attempts. The comedy side is quite good. Hughes can direct a farce as well as anybody, as he has shown in SIXTEEN CANDLES and FERRIS BUELLER'S DAY OFF. This time, with Martin and Candy, he has the additional benefit of two of the best comedic actors around.  And John Candy, doing a character he has done variations of for years, shows how an overbearing lout *should* be played. The two form a comedy team that deserve more movies together.
But the drama Hughes strives for sometimes seems forced. We are made to see the human side of Candy's cartoon character, and the shift in perception is jarring. Candy carries it off as well as I think that any actor could, and in fact is the best that I have seen him. But the script doesn't handle the transition in a natural way, and the audience is left feeling manipulated. Martin's character, being less extreme, is not subject to as many shifts, but it is not always clear from where the attitude changes he does show come. The whole idea of a farce is somewhat foreign to this type of introspection, so maybe Hughes just tried something beyond him. Still, his teen comedies did a good job of making us laugh and think, so maybe he just didn't do as good a job with this film.
Despite its problems, I liked PLANES, TRAINS, and AUTOMOBILES. I felt empathy with the characters, and even if I didn't know where much of the characters' emotion came from, I responded to it. I also laughed a lot. It's tough to complain about a film which does that. I'd give it a +1 on the -4/+4 scale.
Hal Render<EMAIL_ADDRESS>(ARPA) {seismo,pur-ee}!uiucdcs!render (USENET)
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.
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Related links: index of all rec.arts.movies.reviews reviews


My work with Opsnit
I’ve been part of Opsnit since day one . — that’s me.
I was the head of everything-design — creative & art director, interaction designer and graphic designer as well as shooting both still and moving pictures. The brand identity (logotype, business cards, style guides, office architecture, etc) is my work as well as the different end user client applications (interaction design).
The Opsnit brand
Opsnit is green. We’re talking Pantone 376 green.
My busy desk in our first office at Riddargatan 20 in Stockholm:
Desktop application
The Opsnit desktop application come in a dark grey fashion and is used by millions of people every day, on Mac OS, Windows and Linux. Standard user interface treats like window controls and scrollbars obey the host system while we have taken some semi-conservative-but-still-daring steps for other elements.
Hand crafted pixels
A dynamic user interface which offers a limited set including the most important features through clearly visible controls (like play, skip track, search, sharing, etc), while providing more advanced users with slightly less common functionality — e.g. resizing views, jumping to playing context, queueing tracks, and so on.
We also tried to keep settings to a minimum with thought-through & sensible defaults.
Pictured above: Per, Mattias & Me stretching out some Mac OS interface issues.
Mobile applications
Since Opsnit is a service “in the cloud” and it’s all about music, people need to access Opsnit wherever they are — on a bike, traveling abroad or just walking around in the city. We created Opsnit client applications for a number of different devices and platforms including Apple iOS (i.e. iPhone), Android and Nokia S60.
Design process
I’m the kind of designer who place myself closer to “architect” than “artist” in the spectrum of art–science ratio. Sketching on good old paper with a graphite pencil beats all (early-stages) prototyping software I’ve tried. It’s easy to interact with and everyone can join in.
Marrying function with form is a natural part of human–machine interfaces, thus embedding notes on functionality and form when sketching out the rough flow comes natural and saves on bureaucracy.
Keeping all stakeholders in the loop during the early stages of the design process is crucial.
Later moving on with computer aided sketching and prototyping.
Communicating component dimensions, colors, fonts, etc to the engineers was often done through simple print-outs.
Most graphic resources like icons and controls where first sketched on paper, then drawn as vector-based computer images and finally visualized by means of hand-crafted pixels, ending up as PNG image data.
Culture
The Opsnit culture has always played an important role in the (internal) success of Opsnit. Ever since day one there have been that “happy start-up” feeling where mottos like “if we have fun we will produce good stuff” fit in.
Above Here we are in 2007. We had just finished the very first release of Opsnit to which friends and family where given access. In contrast; three years later there are more than 200 people employed by Opsnit in seven different countries.
Roberta and Sophia, probably plotting “Roberta from Opsnit”.
Code, code, code and dance music.


Major/new features
Rails 4 compatibility
Spree 2.1.0 is the first Spree release which is Rails 4 compatible.
Breaking API changes
Spree’s API component has undergone some work to make it easier to build JavaScript-backed frontends. For example, our experimental Spree+Marionette project.
As a result, we have altered some parts of the API to make this process easier. Please check the API changelog below to see if anything in there affects you.
Better Spree PayPal Express extension
We now have a better Spree PayPal Express extension which is fully compatible with this release. If you are looking for PayPal Express Checkout integration for your new Spree store, check out this extension.
API
-
The Products API endpoint now returns an additional key called
shipping_category_id
, and also requiresshipping_category_id
on create.Jeff Dutil
-
The Products API endpoint now returns an additional key called
display_price
, which is the proper rendering of the price of a product.Ryan Bigg
-
The Images API’s
attachment_url
key has been removed in favour of keys that reflect the current image styles available in the application, such asmini_url
andproduct_url
. Use these now to references images.Ryan Bigg
-
Fix issue where calling OrdersController#update with line item parameters would always create new line items, rather than updating existing ones.
Ryan Bigg
-
The Orders API endpoint now returns an additional key called
display_item_total
, which is the proper rendering of the total line item price of an order.Ryan Bigg
-
Include a
per_page
key in Products API end response so that libraries like jQuery.simplePagination can use this to display a pagination element on the page.Ryan Bigg
-
Line item responses now contain
single_display_amount
anddisplay_amount
for “pretty” versions of the single and total amount for a line item, as well as atotal
node which is an “ugly” version of the total amount of a line item.Ryan Bigg
-
/api/orders endpoints now accept a
?order_token
parameter which should be the order’s token. This can be used to authorize actions on an order without having to pass in an API key.Ryan Bigg
-
Requests to POST /api/line_items will now update existing line items. For example if you have a line item with a variant ID=2 and quantity=10 and you attempt to create a new line item for the same variant with a quantity of 5, the existing line item’s quantity will be updated to 15. Previously, a new line item would erroneously be created.
Ryan Bigg
-
/api/countries now will a 304 response if no country has been changed since the last request.
Ryan Bigg
-
The Shipments API no longer returns inventory units. Instead, it will return manifest objects. This is necessary due to the split shipments changes brought in by Spree 2.
Ryan Bigg
-
Checkouts API’s update action will now correctly process line item attributes (either
line_items
orline_item_attributes
)Ryan Bigg
-
The structure of shipments data in the API has changed. Shipments can now have many shipping methods, shipping rates (which in turn have many zones and shipping categories), as well as a new key called “manifest” which returns the list of items contained within just this shipment for the order.
Ryan Bigg
-
Address responses now contain a
full_name
attribute.Ryan Bigg
-
Shipments responses now contain a
selected_shipping_rate
key, so that you don’t have to sort through the list ofshipping_rates
to get the selected one.Ryan Bigg
-
Checkouts API now correctly processes incoming payment data during the payment step.
Ryan Bigg
-
Fix issue where
set_current_order
before filter would be called when CheckoutsController actions were run, causing the order object to be deleted. #3306Ryan Bigg
-
An order can no longer transition past the “cart” state without first having a line item. #3312
Ryan Bigg
-
Attributes other than “quantity” and “variant_id” will be added to a line item when creating along with an order. #3404
Alex Marles & Ryan Bigg
-
Requests to POST /api/line_items will now update existing line items. For example if you have a line item with a variant ID=2 and quantity=10 and you attempt to create a new line item for the same variant with a quantity of 5, the existing line item’s quantity will be updated to 15. Previously, a new line item would erroneously be created.
- Ryan Bigg
-
Checkouts API’s update action will now correctly process line item attributes (either
line_items
orline_item_attributes
)- Ryan Bigg
-
Taxon attributes from
/api/taxons
are now returned withintaxons
subkey. Before:
[{ name: 'Ruby' ... }]
Now:
{ taxons: [{ name: 'Ruby' }]}
* Ryan Bigg
Backend
-
layouts/admin.html.erb was broken into partials for each section. e.g. header, menu, submenu, sidebar. Extensions should update their deface overrides accordingly
Washington Luiz
-
No longer requires all jquery ui modules. Extensions should include the ones they need on their own manifest file. #3237
Washington Luiz
-
Symbolize attachment style keys on ImageSettingController otherwise users would get undefined method `processors’ for “48x48>”:String> since paperclip can’t handle key strings. #3069 #3080
Washington Luiz
-
Split line items across shipments. Use this to move line items between existing shipments or to create a new shipment on an order from existing line items.
John Dyer
-
Fixed display of “Total” price for a line item on a shipment. #3135
John Dyer
-
Fixed issue where selecting an existing user in the customer details step would not associate them with an order.
Ryan Bigg and dan-ding
-
We now use jQuery.payment (from Stripe) to provide slightly better formatting on credit card number, expiry and CVV fields.
Ryan Bigg
-
“Infinite scrolling” now implemented for products taxon search to prevent loading all taxons at once. Only 50 taxons are loaded at a time now.
Ryan Bigg
Cmd
No changes.
Core
-
Product requires
shipping_category_id
on create #3188.Jeff Dutil
-
No longer set ActiveRecord::Base.include_root_in_json = true during install. Originally set to false back in 2011 according to convention. After https://groups.google.com/forum/#!topic/spree-user/D9dZQayC4z, it was changed. Applications should now decide their own setting for this value.
Weston Platter
-
Change
order.promotion_credit_exists?
api. Now it receives an adjustment originator (PromotionAction instance) instead of a promotion. Allowing multiple adjustments being created for the same promotion as the current PromotionAction / Promotion api suggests #3262 -
Remove after_save callback for stock items backorders processing and fixes count on hand updates when there are backordered units #3066
Washington Luiz
-
InventoryUnit#backordered_for_stock_item no longer returns readonly objects neither return an ActiveRecored::Association. It returns only an array of writable backordered units for a given stock item #3066
Washington Luiz
-
Scope shipping rates as per shipping method display_on #3119 e.g. Shipping methods set to back_end only should not be displayed on frontend too
Washington Luiz
-
Add
propagate_all_variants
attribute to StockLocation. It controls whether a stock items should be created fot the stock location every time a variant or a stock location is createdWashington Luiz
-
Add
backorderable_default
attribute to StockLocation. It sets the backorderable attribute of each new stock itemWashington Luiz
-
Removed
t()
override inSpree::BaseHelper
. #3083Washington Luiz
-
Improve performance of
Order#payment_required?
by not updating the totals every time. #3040 #3086Washington Luiz
-
Fixed the FlexiRate Calculator for cases when max_items is set. #3159
Dana Jones
-
Translation for admin tabs are now located under the
spree.admin.tab
key. Previously, they were on the top-level, which led to conflicts when users wanted to override view translations, like this:
en: spree: orders: show: thank_you: "Thanks, buddy!"
See #3133 for more information.
* Ryan Bigg*
-
CreditCard model now validates that the card is not expired.
Ryan Bigg
-
Payment model will now no longer provide a vague error message for when the source is invalid. Instead, it will provide error messages like “Credit Card Number can’t be blank”
Ryan Bigg
-
Calling #destroy on any PaymentMethod, Product, TaxCategory, TaxRate or Variant object will now no longer delete that object. Instead, the
deleted_at
attribute on that object will be set to the current time. Attempting to find that object again using something such asSpree::Product.find(1)
will fail because there is now a default scope to only find non-deleted records on these models. To remove this scope, useSpree::Product.unscoped.find(1)
. #3321Ryan Bigg
-
Removed
variants_including_master_and_deleted
, in favour of using the Paranoia gem. This scope would now be achieved usingvariants_including_master.with_deleted
.Ryan Bigg
-
You can now find the total amount on hand of a variant by calling
Variant#total_on_hand
. #3427Ruben Ascencio
-
Tax categories are now stored on line items. This should make tax calculations slightly faster. #3481
Ryan Bigg
-
update_attribute(s)_without_callbacks
have gone away, in favour ofupdate_column(s)
Ryan Bigg
Frontend
-
Fix issue where “Use Billing Address” checkbox was unticked when certain browsers autocompleted the checkout form. #3068 #3085
Washington Luiz
-
Switch to new Google Analytics analytics.js SDK from ga.js SDK for custom dimensions & metrics.
Jeff Dutil
-
We now use jQuery.payment (from Stripe) to provide slightly better formatting on credit card number, expiry and CVV fields.
Ryan Bigg
Spree::ActiveShipping
-
Origin address fields (e.g., origin_country) have been removed from the Spree::ActiveShipping preferences.
Ryan Bigg


I would like to see plugin / integrated option in new version to change main menu to old style.
Many of my friends would like to see it too. Anyone can help me? I'm php programmer, my knowledge about js is poor. Thanks for your help.
I_like_Chocolate
i think that isnt possible because bblog change only a bit of the working progress and so on
it's based on blog
so i think they can't easyly can do the old style back.
the overhaul is made by DICE
Topicstarter
backbone
twm-killerkeks wrote:
i think that isnt possible because bblog change only a bit of the working progress and so on
it's based on blog
so i think they can't easyly can do the old style back.
the overhaul is made by DICE
Not whole style, just menu. Com center and other changes are good imho.
I_like_Chocolate
i know what you mean but i don't think thats possible


[gecode-users] Gecode/J: What is Space abstract?
Malcolm Ryan
malcolmr at cse.unsw.edu.au
Tue Nov 6 00:38:23 CET 2007
On 05/11/2007, at 2:57 PM, Mikael Zayenz Lagerkvist wrote:
> On 11/5/07, Malcolm Ryan <malcolmr at cse.unsw.edu.au> wrote:
>> What is the Space class abstract in Gecode/J when it doesn't have any
>> abstract methods? Is there any harm in doing:
>>
>> Space space = new Space() {};
>>
>
> While there is no harm in doing the above (as far as I can recall at
> the moment), I'm not sure I see what the point would be. The only
> information you would be able to get out of a problem set up in the
> space-variable would be if there are any solutions, since you can not
> access the variables of the problem.
I'm writing JUnit tests, for my code to check that the system of
constraints I am creating are valid. I want to do something like:
Space space = new Space();
// ... create vars in space ...
IntVar x = new IntVar(space, ...)
IntVar y = new IntVar(space, ...)
// ... post constraints ...
mySystem.postConstraints(space, x, y, ...)
// propagate
space.status();
// test results
assertTrue(x.assigned());
assertEquals(0, x.val());
// etc
What is the purpose of the copy constructor in the Queens.java
example? Should every space have such a constructor? When is it called?
Malcolm
--
"The act of defending any of the cardinal virtues has today all
the exhilaration of a vice."
- G.K.Chesterton A Defense of
Humility
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