***
Home
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Day 1+2
Day 3
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All projects
Epicodus Day 1 + 2
Block Elements practice:
Cats
Block Elements practice:
Dog Blog
Block Elements practice:
Favorite Things
Styling Text practice:
Favorite Places
Branching + Merging practice:
Private Island Resort
Epicodus Day 3
HTML CSS practice:
Boring Lecture
Classes practice:
Cupcake
Cascading practice:
Animal Shelter
Column practice:
Interior Decorator
Float practice:
Favorite Band
Div Span practice:
Blog
Epicodus Day 4
Bootstrap practice:
Vacation


EBD - Easton Bible DIctionary
Tablet
TABLET
"TABLET "probably a string of beads worn round the neck (
Exodus 35:22
;
Numbers 31:50
). In
Isaiah 3:20
the Hebrew word means a perfume-box, as it is rendered in the Revised Version.
"TABLET "probably a string of beads worn round the neck (Exodus 35:22; Numbers 31:50). In Isaiah 3:20 the Hebrew word means a perfume-box, as it is rendered in the Revised Version.


63 It is also possible to get the special menus used in some primitive blocks, by choosing from the menu submenu: broadcast messages, sprites and stage, costumes, sounds, variables that can be set in this scope, the play note piano keyboard, or the point in direction 360° dial. Finally, you can make the input box accept more than one line of text (that is, text including a newline character) from the special submenu, either “multi-line” for regular text or “code” for monospace-font computer code. If the input type is something other than text, then clicking the button will instead show this menu: As an example, we want to make this block: The second input must be a read-only object menu: The “Multiple inputs” option: The list block introduced earlier accepts any number of inputs to specify the items of the new list. To allow this, Snap ! introduces the arrowhead notation (34 ) that expands and contracts the block, adding and removing input slots. (Shift-clicking on an arrowhead adds or removes three input slots at once.) Custom blocks made by the Snap! user have that capability, too. If you choose the “Multiple inputs” button, then arrowheads will appear after the input slot in the block. More or fewer slots (as few as zero) may be used. When the block runs, all of the values in all of the slots for this input name are collected into a list, and the value of the input as seen inside the script is that list of values: The ellipsis (…) in the orange input slot name box in the prototype indicates a multiple or variadic input. The third category, “Upvar - make internal variable visible to caller,” isn’t really an input at all, but rather a sort of output from the block to its user. It appears as an orange variable oval in the block, rather than as an input slot. Here’s an example; the uparrow (↑) in the prototype indicates this kind of internal variable name: Input variants We now turn to the three mutually exclusive options that come below the type array. The “single input” option: In Scratch, all inputs are in this category. There is one input slot in the block as it appears in its palette. If a single input is of type Any, Number, Text, or Boolean, then you can specify a default value that will be shown in that slot in the palette, like the “10” in the move (10) steps block. In the prototype block input at the top of the script in the Block Editor, an input with name “size” and default value 10 looks like this:


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Phone number:+27 13 751 1038
Email:reservations@royalchundu.com


| (A) | ---- no text ---- | |
| 362 | (C) | That were shamed & shent . on suche a fowle wyse |
| 363 | (D) | þat were shamefully shent in sucħ a foul wyse |
| 364 | (E) | þat were yschaue & ystent on such a foul wyse . |
| (Ex) | ---- no text ---- | |
| 372 | (L) | þat were scorned & schende vpon schame wyse |
| (P) | ---- no text ---- | |
| 356 | (U) | That were shamed and shent / on swiche a foule wise |
| (V) | ---- no text ---- |


--- --- {% include head.html %} {% include nav.html %} {% include components/header-large.html %} {{site.data.settings.contact.phone_number}} {{site.data.settings.contact.email}} download vCard {{ content }} {% include footer.html %} {% include foot.html %}


#assign Encrypted = "true">
Annotate properties that will be store in encrypted form in the database.
@Entity @Table(name="patient") public class Patient { ... // database side encryption @Encrypted String name; // database side encryption @Encrypted LocalDate dob; // client side encryption @Lob @Encrypted(dbEncryption=false) String description; }
Goto docs / features / encryption for more details.


Ph.D in Mechanical Engineering, McGill University (2014-2019)
M.S in Aerospace Engineering, Northwestern Polytechnical University (2011-2014)
B.S in Manufacturing Engineering, Northwestern Polytechnical University (2007-2011)
Awards & Honors
ACU Blue Charter Fellowship (2019, 1/35 globally)
ASME IDETC/CIE Best Paper Award (2018)
Chinese Government Award for Outstanding Self-Financed Students Abroad (2017)
McGill Engineering Doctoral Award (2014)


| DOSE: |
6 nuts |
oral |
Nutmeg |
(ground / crushed) |
Continuing my experimentation with household spices, I took an oral dose of 6 nuts, grated into slivers with a normal knife (same method of preparation as my nutmeg-smoking venture) and took it orally by the method of taking 'shots' of it, followed by long gulps of various liquids (I tried milk, orange juice, and water -- all of them were fairly satisfactory). The problem was 'backlog' of small slivers of nutmeg getting into the liquid I was washing it down with -- which made it fairly disgusting, but much better in the whole than any other method of ingestion I had taken.
I settled down to watching some television (a Nature Channel programme about sharks, and then one about lions) -- when the lions chased an animal
or made a kill, or slept in the shade, or anything else, I felt as if I was *PART* of the scene. I then retired to bed. When I closed my eyes, I had fairly vivid, although non-concrete, closed eyed visuals, which would persist for some time after I opened my eyes.
Sometime during this venture, I fell asleep, and had very vivid dreams. Waking up, I felt very disoriented, and walked like I was very, very drunk.  I masturbated, along with a mental fantasy which was lived out in my body (I felt tactile sensations in other parts of my body). I intend to try intercourse on nutmeg, with my partner also under the influence (consensually, of course!) sometime soon. Nutmeg, apparently, can be strong sexual enhancer. Notify Pfizer at once!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


me.edwards.des.net.packet
Classes
Packet
PacketAddr
PacketBallot
PacketBlock
PacketGetAddr
PacketGetBlocks
PacketGetData
PacketInv
PacketInv.InvVector
PacketNotFound
PacketPing
PacketPong
PacketVerack
PacketVersion
Enums
Packet.PacketTypes


Trader Ralford:
In the Sea of Spores, there are spiders called Trisalim Spiders and Trisalim Tarantulas. Please bring me 10 Spinneret from them and I'll make spidersilk for you.


--- layout: post date: 2012-09-16 time: "09:00:00" authors: ["Greg Wilson"] title: Systematic Curriculum Design tags: ["Content", "Education"] ---
Executive summary: we'd appreciate your help organizing and motivating our material better.
One of the good things about traveling is that it gives me time to think. One of the bad things about thinking is that every time I do, I wind up with more work than I had when I started. For example, to organize and motivate our content, I'm using eight questions that scientists frequently ask:
On the other side of the equation I have a syllabus for the core Software Carpentry material, which includes:
In order to figure out how well we're helping scientists, we need to map their needs onto our content. Here's what I've come up with:
| Question | Subject | Answer |
| How can I manage this data? | The Shell | Use directories and sub-directories with meaningful names. |
| Use filenames that can easily be matched with wildcards. | ||
| Use filename extensions that indicate the type of data in the file. | ||
| Use text unless there's a powerful reason to use something else. | ||
| Version Control | If it's megabytes or less, put it under version control. | |
| Basic Programming | Create and use data formats that are easy for programs to parse. | |
| Functions and Libraries | — | |
| Databases | Store it in a relational database. | |
| Store each atom of information in its own field. | ||
| Make sure each record has a unique key. | ||
| Make sure that information is never duplicated. | ||
| Use foreign keys and joins to combine information from different tables. | ||
| Number Crunching | Represent it as a matrix, because that's easy to process. | |
| Quality | — | |
| Sets and Dictionaries | Store it in a set or dictionary so that elements can be looked up by value rather than by position. | |
| Development | — | |
| Web Programming | Format it as HTML (or XML, or some other widely-used format). | |
| Separate content from presentation (e.g., use CSS for styling). | ||
| Question | Subject | Answer |
| How can I process it? | The Shell | Use Unix commands that manipulate lines of text. |
| Combine those commands using pipes and redirection. | ||
| Use loops to perform the same operations on many files. | ||
| Version Control | — | |
| Basic Programming | Write programs that use loops, file I/O, and string splitting to read data. | |
| Use floating-point numbers unless you are sure all values (including calculated values) will always be integers. | ||
| Functions and Libraries | Define functions to do simple operations, then combine those for more complicated effects. | |
| Equivalently, describe what you would do in a language customized to your problem, then fill in the missing bits of code by creating functions. | ||
| Databases | Write SQL queries to select, filter, aggregate, and sort data. | |
| Use a general-purpose programming language for everything else. | ||
| Number Crunching | Use a linear algebra package like NumPy. | |
| Quality | — | |
| Sets and Dictionaries | Use algorithms that don't depend on the order of items. | |
| Development | Use the right data structures. | |
| Web Programming | Use an HTTP library to fetch it. | |
| Use an XML or JSON library to parse it. | ||
| Question | Subject | Answer |
| How can I tell if I've processed it correctly? | The Shell | — |
| Version Control | — | |
| Basic Programming | Test your programs with small data sets whose results can be checked by hand. | |
| Functions and Libraries | — | |
| Databases | Build queries in small steps. | |
| Run queries against small data sets whose output can be checked manually. | ||
| Number Crunching | Compare a program's output to analytic results, experimental results, simplified test cases, and previous programs. | |
| Use tolerances when comparing results. | ||
| Quality | Create simple data sets for which the right answer can be calculated by hand. | |
| Compare the results produced by the new program to results produced by older programs. | ||
| Sets and Dictionaries | — | |
| Development | Make code testable by dividing it into functions, and then replacing some functions with others for testing purposes. | |
| Web Programming | — | |
| Question | Subject | Answer |
| How can I find and fix bugs when I haven't? | The Shell | — |
| Version Control | — | |
| Basic Programming | — | |
| Functions and Libraries | — | |
| Databases | — | |
| Number Crunching | — | |
| Quality | Write test cases that fail when the bug is present, but pass when the bug is fixed. | |
| Add assertions to programs to check its internal consistency. | ||
| Use a debugger. | ||
| Sets and Dictionaries | — | |
| Development | Write tests. | |
| Web Programming | — | |
| Question | Subject | Answer |
| How can I keep track of what I've done? | The Shell | — |
| Version Control | Keep your work under version control. | |
| Check in whenever you've completed a significant change. | ||
| Write meaningful check-in comments. | ||
| Basic Programming | Put version control IDs in programs (and data files), and copy them forward to results. | |
| Functions and Libraries | Give functions meaningful names. | |
| Group related functions and related definitions into modules. | ||
| Write docstrings to explain what functions and modules do and how to use them. | ||
| Databases | Store queries in files (just like programs). | |
| Number Crunching | — | |
| Quality | Turn bug fixes into assertions and test cases. | |
| Use a coverage analyzer to see what code is and isn't being tested. | ||
| Sets and Dictionaries | — | |
| Development | — | |
| Web Programming | Use meta headers in your HTML/XML data files. |
|
| Question | Subject | Answer |
| How can I find and use other people's work? | The Shell | — |
| Version Control | Get it from their version control repositories. | |
| Basic Programming | — | |
| Functions and Libraries | Use the help function to read their documentation. |
|
| Databases | — | |
| Number Crunching | — | |
| Quality | — | |
| Sets and Dictionaries | — | |
| Development | — | |
| Web Programming | Ask them to use well-formed URLs. | |
| And to format it according to well-defined machine-readable standards (e.g., XML or JSON). | ||
| Question | Subject | Answer |
| How can other people find and use mine? | The Shell | — |
| Version Control | Put your work in a publicly-accessible version control repository. | |
| Basic Programming | — | |
| Functions and Libraries | Write docstrings to explain what functions and modules do and how to use them. | |
| Databases | Raise exceptions to signal errors so that other people can handle them as they think best. | |
| Number Crunching | — | |
| Quality | — | |
| Sets and Dictionaries | — | |
| Development | — | |
| Web Programming | Put it on the web at a stable URL. | |
| Format it according to well-defined machine-readable standards (e.g., XML or JSON). | ||
| Include meta-data. | ||
| Question | Subject | Answer |
| How can I do all these things faster? | The Shell | Put commands in shell scripts so that they can be re-used. |
| Version Control | — | |
| Basic Programming | Use appropriate variable names so that people will waste less time trying to read programs. | |
| Functions and Libraries | Learn to recognize and use common design patterns. | |
| Databases | — | |
| Number Crunching | Use a linear algebra package like NumPy. | |
| Quality | Design code for testing. | |
| Write test cases before writing new code. | ||
| Sets and Dictionaries | Use sets and dictionaries for sparse, irregular, or unordered data. | |
| Development | Use a profiler to figure out why code is slow before trying to optimize it. | |
| Build code so that parts can be replaced easily. | ||
| Web Programming | — |
In parallel with this, a group of us have been working on a paper describing best practices for computational science. The list we've converged on is:
As you can see, this list only partially overlaps the "Answers" column in the table above. That makes me nervous: when two independent attacks on a problem yield two different answers, the odds are good that neither of them is right. I trust the "best practices" list more than I do the breakdown of our existing material, which leaves me with some awkward choices. Changing the motivating questions would feel like moving the goalposts so that I can declare victory with the content I have, but on the other hand, maybe there is a better way to carve up the space of things scientists want to do that will give a better mapping. Or are there connections between our content and those motivating questions that I'm just missing? Or do we really have the wrong content, i.e., are we teaching what we know, rather than what would actually be most useful to scientists?
Stepping back for a moment, the real point of this exercise is to ensure that:
What we should really do is go one step further and figure out how to tell whether our learners can actually do the things embodied in our eight questions. We should then work backward from that assessment to figure out what demonstrable skills they need to acquire, then what understanding they need in order to become proficient with those skills, and then see how that maps onto our best practices. We've made a start toward this with the "driver's license" exam described in an earlier post; if you'd like to help us follow through, please get in touch.


So far my impression of Shereef is that he seems to be a very compassionate person. His ability to understand that all of us come from different backgrounds and have similar fears, leads me to believe I made the right decision in joining the DBC family. That being said, it's my belief that family or team based environment creates solidarity and feeling of dedication that would otherwise not be as strong or apparent. And Shereef being the base of DBC, compassion can only be a natural progression for each team member.
I actually chose DBC instead of a different bootcamp because of its focus on community and family type ideals. I feel there is a lack of integrity in the world. I believe an entity such as DBC that accepts the responsibility to teach its students to engage in and practice empathy, integrity, and kindness can only produce a better human for tomorrow.
Just like everyone else, I have my fears of how I'll fit in. That includes questions like: Will I learn fast enough? Will I be kind and empathetic enough? Do I really understand what kindness is? Will I get hired into a job that feels right for my life? Or will I follow the path of the working stiffs? However, I don't focus on those fears, I immediatly look at the opposite of them and find a spot that feels comfortable enough to continue on this path. I am excited to get to the end of Phase 0. I'll be the one walking in with the apron and a wrench :-) ready to go.


WebRTC (web, real-time communications) is – a browser-based, application programming interface (API) – leveraging JavaScript & HTML5, it enables developers to build apps capable of delivering – simultaneous voice/video/data sharing capability – using a browser with no plug-ins for peer-to – peer communications..


http://battlelog.battlefield.com/bf3/platoon/2832655391313956444/
^^ It's our platoon.
In the platoon presentation will be added that HTML code I mentioned when the Minor Imp. are activated:
">>>
http://www.germanunitednetwork.de/joinus/index.php"
target="_blank">http://www.germanunitednetwork.de/joinus/index.php "
When Minor Imp. are deactivated all works fine.
greets
Skeeter


WITNESS LIST
Appropriations - S/C on Article III Committee
February 19, 2019 - 7:30 AM
Health Related Institutions
On:
Bailey, Guy (UTRGV)
Byington, Carrie (Texas A&M)
Calhoun, MD, Kirk (UTHSC Tyler)
Callender, David (The University of Texas Medical Branch at Galveston)
Colasurdo, Giuseppe (Self; The University of Texas Health Science Center at Houston)
Klotman, Paul (Baylor College of Medicine)
Lange, Richard (TTUHSC El Paso)
Mitchell, Tedd (Ttuhsc)
Pisters, M.D., Peter (UT MD Anderson Cancer Center)
Pliszka md, Steven (Uthscsa)
Podolsky, Daniel (UT southwestern Medical Center)
Simmons, Trevor (Legislative Budget Board)
Williams, Michael (University of North Texas Health Science Center)
Registering, but not testifying:
On:
Burton, Jeff (Texas A&M University Health Science Center)
Dontes, Arnim (Ut southwestern Mdical Center)
Hernandez, Demetrio (LBB)
Higher Education Special Provisions
On:
Hernandez, Demetrio (LBB)
Junior Colleges/ Community Colleges
On:
Fraire, Jacob (Texas Association of Commmunity Colleges)
Hellyer, Brenda (San Jacinto College)
Hernandez, Demetrio (LBB)
Overmyer, Andrew (Legislative Budget Board)
Phillips, Mike (Self; COPS/Metro)
University of North Texas System (GAIs)
On:
Mong, Bob (University of North Texas at Dallas)
Roe, Lesa (University of North Texas System)
Smatresk, Neal (University of North Texas)
1


We meet Fridays 5-7PM @Murray Hall Room 113!
Not restricted to the Mason Gross School of Art!
Pursuing all forms of art and design:
drawing, painting, graphic design, and more!
For yourself, your business, or anything else!
We have a recurring mural with the Rutgers Cinema, and have about 2+ more in the works!
We are partnered with Adobe, and have a very talented design team!
Anything else you're interested in? Shoot us a message!


We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks.
In traditional deep learning architectures, the prediction of the model is obtained after a single forward pass. In this paper (CVPR 2017), authors propose to obtain the prediction after several passes. The first pass will be a 'rough' prediction of the model. The second pass will be a refined prediction, based on the first pass. And so on, until the last pass which will be the ultimate refined prediction of the model. The webpage provides a Youtube video of about 5 minutes long. It does a great job at summarising the method and the results obtained in the paper.
Imagine the wonders of being able to synthetise an infinite amount of data... Well, we could leverage existing physics engine to do this. That is exactly what is tackled in this very interesting blog post. It is not a tutorial on how to generate data with Unreal Engine 4, but rather a story of the different steps the author went through. He shares code, resources and training materials he used to generate landscapes in the engine. I think it would make a fine starting point if you wanted to get started in this sort of project.
In this work, we develop a multi-stage progressive structure with integrated confidence analysis and decision policy learning for deep neural networks. This new framework consists of a set of network units to be activated in a sequential manner with progressively increased complexity and visual recognition power.
Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action category from all other categories. [...] We specify a new ConvNet framework, consisting of a summarizer and discriminator. The summarizer is a two-stream ConvNet aimed at, first, capturing the appearance and motion features of video frames, and then encoding the obtained appearance and motion features for video representation. The discriminator is a fitting function aimed at distinguishing between the key frames and others in the video.
 It could sound boring but they actually made it very interesting and even entertaining. It's maybe not as slick as the OpenAI webpages, but it certaintly catches the attention! Their finding is quite intriguing as well. Instead of optimizing a network by searchinig in the whole parameter space, they limit the search to a sub-space. For instance, they will train a network by searching solutions on a line. They'll train another one by searching solutions within a plane. Then another network with solutions in a 3d space, etc. Likely, those networks won't really learn anything. But they observe that there is a critical dimension in which a good minima can be obtained. They call this the 'Intrinsic Dimension'. As an example, they trained a small CNN on MNIST and found that its intrinsic dimension was only 290! From there, they make other interesting observations.
This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training.
The collected experimental results for the CIFAR-10, CIFAR-100 and ImageNet datasets show that increasing the mini-batch size progressively reduces the range of learning rates that provide stable convergence and acceptable test performance. On the other hand, small mini-batch sizes provide more up-to-date gradient calculations, which yields more stable and reliable training. The best performance has been consistently obtained for mini-batch sizes between m=2 and m=32, which contrasts with recent work advocating the use of mini-batch sizes in the thousands.
I feel like we're going back and forth on this...
When Uber did its research on Deep Neuroevolution, researchers needed monstruous machines of around 720 CPU cores. In this blog post, they show how they managed to reduce this number of cores to about 48, enabling more people to use and apply Deep Neuroevolution algorithms. In the original implementation, a lot of time was wasted alternating between GPU processing time (for forward/backward pass) and CPU processing time (for rendering the physics engine). The speedup therefore consists in making the CPU and GPU work simultaneously. They also managed to process minibatches through the GPU, which is not so obvious to do with Deep Neuroevolution. I also liked how by recoding some Python operations as Tensorflow operations they obtained significant speedups. This demonstrates again the versatility of Tensorflow.
The current dominant paradigm of imitation learning relies on strong supervision of expert actions for learning both what to and how to imitate. We propose an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its own experience into a goal-conditioned skill policy using a novel forward consistency loss formulation.
Collaborative intelligence is a new paradigm for efficient deployment of deep neural networks across the mobile-cloud infrastructure. By dividing the network between the mobile and the cloud, it is possible to distribute the computational workload such that the overall energy and/or latency of the system is minimized. However, this necessitates sending deep feature data from the mobile to the cloud in order to perform inference. In this work, we examine the differences between the deep feature data and natural image data, and propose a simple and effective near-lossless deep feature compressor.
Keras provides a handy model.summary() method that prints the architecture of your network, the shape at each layers and the number of parameters it has. It can be useful to have this as a sanity check; for instance to check that all the layers are connected within the network, or to verify the shapes of feature maps. Well, this project offers a Python package that does exactly what model.summary() does, the difference being that you can use it with Pytorch.
Pytorch keeps getting more amazing... Oh, that sounded biased... Well, nobody will be surprised... The changes are somewhat important and could potentially break existing code. Some time will be required to update the code. Variables objects and Tensors objects are now the same thing. As a result, using something like 'x.data' is useless. Other thing: scalars are now possible (before they had to be a vector of length 1). 'torch.FloatTensor', 'torch.LongTensor', 'torch.DoubleTensor' are deprecated and a Numpy-style notation is now available (torch.float32).


|
The Chevrolet Express and its mechanically identical twin GMC Savana are Full-size vans from General Motors. They replaced the Chevrolet Van and GMC Vandura in 1995. The Express and Savana currently hold 44.8% of the full-size van market in the United States, just behind rival Ford E-Series. The Express outsells the Savana by more than 3 to 1 in the USA, but the Savana outsells the Express 2 to 1 in Canada. |
|||||
Manufacturer |
Class |
Base MSRP$30,745 |
Average MPGUp to 11 city / 17 highway |
Drive DescriptionFront or All-Wheel Drive |
Production Run1995-present |


{% extends "layout.html" %} {% block body %}
{{ grant.client.client_name }} is requesting with a scope of: {{ grant.request.scope }}


Handsontable
Static content
Instance 1
Dump object to console
Selection: {{currentSelection}}
Selection:
{{option.Description}}
Instance 2
Filter:
|
Dynamically add column


Nearly all applications will in some way handle user input. User input can come from a keyboard, a mouse, or a stylus. (Other types of input are generally considered data input.) For the most part, GEOS hides the actual mechanics of input from the application; at the same time, it allows extreme flexibility of input handling.
Input to generic UI objects is handled by these objects--unless you have very specific needs and will subclass a generic gadget, you can leave its input handling up to GEOS. The same is true with most specialized system-provided objects such as the GrObj, the VisText, the Spreadsheet, and others. Other objects (such as basic visible objects you subclass yourself) will need more attention depending on the behavior you want them to have.
Read this chapter if your application will handle user input. Before reading this chapter, however, you should be relatively familiar with the GEOS system architecture, Goc syntax, and the GEOS object model and message passing. It is also suggested that you spend some time programming with generic and visible objects and getting used to the rest of the system before handling input events.
1 Input Flow
1.1 Devices and Drivers
1.2 Input Manager and GenSystem
1.3 Input Events
1.4 Input Hierarchies
2 Mouse Input
2.1 Mouse Events
2.2 Gaining the Mouse Grab
2.3 Large Mouse Events
2.4 Setting the Pointer Image
3 Keyboard Input
3.1 Keyboard Input Flow
3.2 Keyboard Events
4 Pen Input and Ink
4.1 Ink Data Structures
4.2 Ink Input Flow
5 Input Hierarchies
5.1 The Three Hierarchies
5.2 Common Hierarchy Basics
5.3 Using Focus
5.4 Using Target
5.5 Using Model
5.6 Extending the Hierarchies


Lapack documentation
Purpose
=======
DGEEV computes for an N-by-N real nonsymmetric matrix A, the
eigenvalues and, optionally, the left and/or right eigenvectors.
The right eigenvector v(j) of A satisfies
A * v(j) = lambda(j) * v(j)
where lambda(j) is its eigenvalue.
The left eigenvector u(j) of A satisfies
u(j)**H * A = lambda(j) * u(j)**H
where u(j)**H denotes the conjugate transpose of u(j).
The computed eigenvectors are normalized to have Euclidean norm
equal to 1 and largest component real.
Arguments
=========
JOBVL (input) CHARACTER*1
= 'N': left eigenvectors of A are not computed;
= 'V': left eigenvectors of A are computed.
JOBVR (input) CHARACTER*1
= 'N': right eigenvectors of A are not computed;
= 'V': right eigenvectors of A are computed.
N (input) INTEGER
The order of the matrix A. N >= 0.
A (input/output) DOUBLE PRECISION array, dimension (LDA,N)
On entry, the N-by-N matrix A.
On exit, A has been overwritten.
LDA (input) INTEGER
The leading dimension of the array A. LDA >= max(1,N).
WR (output) DOUBLE PRECISION array, dimension (N)
WI (output) DOUBLE PRECISION array, dimension (N)
WR and WI contain the real and imaginary parts,
respectively, of the computed eigenvalues. Complex
conjugate pairs of eigenvalues appear consecutively
with the eigenvalue having the positive imaginary part
first.
VL (output) DOUBLE PRECISION array, dimension (LDVL,N)
If JOBVL = 'V', the left eigenvectors u(j) are stored one
after another in the columns of VL, in the same order
as their eigenvalues.
If JOBVL = 'N', VL is not referenced.
If the j-th eigenvalue is real, then u(j) = VL(:,j),
the j-th column of VL.
If the j-th and (j+1)-st eigenvalues form a complex
conjugate pair, then u(j) = VL(:,j) + i*VL(:,j+1) and
u(j+1) = VL(:,j) - i*VL(:,j+1).
LDVL (input) INTEGER
The leading dimension of the array VL. LDVL >= 1; if
JOBVL = 'V', LDVL >= N.
VR (output) DOUBLE PRECISION array, dimension (LDVR,N)
If JOBVR = 'V', the right eigenvectors v(j) are stored one
after another in the columns of VR, in the same order
as their eigenvalues.
If JOBVR = 'N', VR is not referenced.
If the j-th eigenvalue is real, then v(j) = VR(:,j),
the j-th column of VR.
If the j-th and (j+1)-st eigenvalues form a complex
conjugate pair, then v(j) = VR(:,j) + i*VR(:,j+1) and
v(j+1) = VR(:,j) - i*VR(:,j+1).
LDVR (input) INTEGER
The leading dimension of the array VR. LDVR >= 1; if
JOBVR = 'V', LDVR >= N.
WORK (workspace/output) DOUBLE PRECISION array, dimension (MAX(1,LWORK))
On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
LWORK (input) INTEGER
The dimension of the array WORK. LWORK >= max(1,3*N), and
if JOBVL = 'V' or JOBVR = 'V', LWORK >= 4*N. For good
performance, LWORK must generally be larger.
If LWORK = -1, then a workspace query is assumed; the routine
only calculates the optimal size of the WORK array, returns
this value as the first entry of the WORK array, and no error
message related to LWORK is issued by XERBLA.
INFO (output) INTEGER
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value.
> 0: if INFO = i, the QR algorithm failed to compute all the
eigenvalues, and no eigenvectors have been computed;
elements i+1:N of WR and WI contain eigenvalues which
have converged.
function dgeev_eigenValues
extends Modelica.Icons.Function;
input Real A[:, size(A, 1)];
output Real EigenReal[size(A, 1)];
output Real EigenImag[size(A, 1)];
output Integer info;
end dgeev_eigenValues;
Generated at 2019-01-25T16:44:03Z by OpenModelica 1.14.0~dev-130-gefa44fb


X-Card
X-Card is made of a polycarbonate material that can be printed using digital imaging technology. You can improve relevance, easy identification and tracking by personalising each card, including numbering and barcoding. Personal URLs can be added, allowing customers to access private information online.
X-Card is durable and waterproof, perfect for membership, loyalty and ID cards as well as applications where paper-based cards don’t last very long! And you can order small quantities as you need them. Minimum order is just 300 cards.

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First look at Sir Michael Caine and Rita Ora in new Oliver Twist film
Sir Michael Caine is joined by Rita Ora, Lena Headey and Raff Law in the first picture from a new film version of Oliver Twist.
The forthcoming Sky Cinema film, called Twist, is a modern take on Charles Dickens’ classic novel.
The titular character, played by Law, has been reinvented as a “streetwise artist” who lives on the streets of modern-day London.
It is the acting debut for 23-year-old Law, the son of Jude Law and Sadie Frost.
Two-time Oscar-winner Sir Michael plays master thief Fagin, while singer Ora portrays Dodge, a charismatic character based on the Artful Dodger.
Game Of Thrones star Headey is Fagin’s psychopathic business partner Sikes.
This new interpretation of the much-loved Dickens novel will see Twist have a chance encounter with a gang of grifters led by Dodge.
Twist is caught up in a high-stakes heist to steal a priceless painting for Fagin and Sikes.
The cast also includes David Walliams, Legends Of Tomorrow star Franz Drameh and Daybreak actress Sophie Simnett.
Walliams plays Losberne, Drameh is Batesey and Simnett plays Red.
Twist, which is currently being filmed in London, will be available on Sky Cinema the same day as its theatrical release.


the and {background margin {border-style that px;} #ffffff;} for vaccine are COVID- this with approval clinical px; they {font-family font-size phase safety was FDA The about double; regulatory science would been border-color border-width data only public scientific solid; than vaccines were {font-style {font-weight However, after already bold can courier; em; emergency has have human medicines not process pt;} such testing through trials use when


| TEST_URL |
= |
'https://www.cardstream.com/merchantsecure/Cardstream/VPDirect.cfm' |
| LIVE_URL |
= |
'https://www.cardstream.com/merchantsecure/Cardstream/VPDirect.cfm' |
| CURRENCY_CODES |
= |
{ "AUD"=> '036', "CAD"=> '124', "CZK"=> '203', "DKK"=> '208', "HKD"=> '344', "ICK"=> '352', "JPY"=> '392', "NOK"=> '578', "SGD"=> '702', "SEK"=> '752', "CHF"=> '756', "GBP"=> '826', "USD"=> '840', "EUR"=> '978' |
| TRANSACTIONS |
= |
{ :purchase => 'ESALE_KEYED', :refund => 'EREFUND_KEYED', :authorization => 'ESALE_KEYED' |
| CVV_CODE |
= |
{ '0' => 'U', '1' => 'P', '2' => 'M', '4' => 'N' |
| AVS_POSTAL_MATCH |
= |
{ "0" => nil, "1" => nil, "2" => "Y", "4" => "N", "8" => "N" |
|
0 - No additional information available. 1 - Postcode not checked. 2 -
Postcode matched. 4 - Postcode not matched. 8 - Postcode partially matched.
|
| AVS_STREET_MATCH |
= |
{ "0" => nil, "1" => nil, "2" => "Y", "4" => "N", "8" => "N" |
|
0 - No additional information available. 1 - Address numeric not checked. 2
- Address numeric matched. 4 - Address numeric not matched. 8 - Address
numeric partially matched.
|


Canvas Life #636
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Paragraph of text beneath the heading to explain the heading. We'll add onto it with another sentence and probably just keep going until we run out of words.
Featured title
Paragraph of text beneath the heading to explain the heading. We'll add onto it with another sentence and probably just keep going until we run out of words.


Irish trucker hopes to be home this weekend after 15 stowaways found in lorry he was driving
by By Digital Desk staffThe Irish truck driver arrested in the UK after 15 men were found in the back of a lorry, is hoping to come home this weekend.
The 55-year-old man was released without charge last night after he was arrested following the discovery in Wiltshire, England yesterday.
The truck belongs to a Cork-based company, Delaney Transport. The men found inside the container – believed to be aged between 16 and 30 – are from Iran and Iraq.
The Irish truck driver was travelling from Calais to Dover and on his way to Pembroke to get the ferry back to Cork, when he heard what he described as a banging noise from inside the truck, and called the police.
When they arrived, they opened the back of the truck and found the stowaways.
Ambulance service staff carried out initial medical checks on the group and all but one were deemed fit and well and were taken into custody. One person was taken to hospital as a precaution but was later released into custody.
The Irish truck driver was also taken into custody, and was released late last night. The truck remains in police custody, and is expected to be released back to Delaney Transport today or tomorrow.
READ MORE
Update: Police release Irish man arrested in UK after 15 people found in back of Cork-owned lorry


REBUILD-JAR-REPOSITORY
Section: User Commands (1)Updated: March 2006
Index Return to Main Contents
NAME
rebuild-jar-repository - rebuild a JAR repository based on the current JVMSYNOPSIS
rebuild-jar-repository [OPTION]... DIRECTORYDESCRIPTION
Rebuild a jar repository (built by build-jar-repository ) based on the current JVM settingsOPTIONS
If no option is specified the default action will be to rebuild with symbolic links
- -c, --copy
- copy files
- -h, --hard
- create hard links
- -s, --soft, --symbolic
- create symbolic links (default)
EXAMPLES
- rebuild-jar-repository .
- Will update all links created by build-jar-repository in the current working directory. Updated links will be symbolic links.
- rebuild-jar-repository -h /tmp
- Will update all links created by build-jar-repository in the /tmp. Updated links will be hard links.
AUTHOR
Written by the JPackage ProjectREPORTING BUGS
Report bugs using JPackage Bugzilla (http://www.jpackage.org/bugzilla/)
Index
This document was created by man2html, using the manual pages.
Time: 05:29:10 GMT, December 24, 2015


#{msgs['supergluu.scanQRCode']}
#{msgs['supergluu.enroll.scanqrcode']}
#{msgs['supergluu.enroll.supergluuisfreesecure']}
Gluu, Inc, | Use subject to MIT LICENSE
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I'm a web developer living in Boston, Massachusetts. I have a background in applied math and a passion for art; I believe that good design is the place where math and art intersect.
I'm a web developer living in Boston, Massachusetts. I have a background in applied math and a passion for art; I believe that good design is the place where math and art intersect.
Here are some things I recently built:
Want to chat? Feel free to contact me!


{{>loginNavBar}}
Toggle navigation
PMBA
Home
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Settings
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Logout
Old Password
New Password
Confirm New Password
Change Password
Admin
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Statistics
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{% extends "base2.html" %} {% block title %}Password changed{% endblock %} {% block content %}
Password changed
Your password has been successfully changed.


Put each row of data on a new line. 1 column of data is shown as a histogram. 2 columns are shown as a scatter plot. 3 columns are shown as a scatter plot with labels.
Example 1 - Example 2 - Example 3


NUnit 2.5 supports parameterized tests. Test methods may have parameters and various attributes are available to indicate what arguments should be supplied by NUnit.
Multiple sets of arguments cause the creation of multiple tests. All arguments are created at the point of loading the tests, so the individual test cases are available for display and selection in the Gui, if desired.
Some attributes allow you to specify arguments inline - directly on the attribute - while others use a separate method, property or field to hold the arguments. In addition, some attributes identify complete test cases, including all the necessary arguments, while others only provide data for a single argument. This gives rise to four groups of attributes, as shown in the following table.
| Complete Test Cases | Data for One Argument | |
|---|---|---|
| Inline | ||
| Separate |
In addition, when data is specified for individual arguments, special attributes may be added to the test method itself in order to tell NUnit how to go about combining the arguments. Currently, the following attributes are provided:


Demonstrates TagTagger.js with default settings
➨ Default look and feel comes with:
width
min-height
taggerStyles.css
Your browser does not support iframes.


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Internal Server Error - Read
The server encountered an internal error or misconfiguration and was unable to complete your request.
Reference #3.7524db17.1586181285.5650d62d


People
Tomkins was recruited to UCSF in 1969, influenced by William Rutter’s decision to come to San Francisco to revitalize UCSF’s Department of Biochemistry. As Dr. Rutter’s first recruit, he welcomed the opportunity to develop a central research theme around human genetics. Together, they recruited a number of young investigators who formed an active community of interdisciplinary scholars developing the new field of molecular biology.
Although he was only at UCSF for six years before his untimely death at age forty-nine from a brain tumor, Dr. Tomkin’s influence extended far beyond his own scientific eminence—he was a key person in the development of the UCSF academic community in the early 1970s, a highly successful investigative environment that generated many important discoveries. William Rutter emphasized that “he contributed to the spirit and overall attitude of the institution…he gathered good people like the Pied Piper.” Other colleagues remembered Tomkins for his “agility of mind…sympathetic interest and eclectic curiosity [which] facilitated the diffusion of ideas across barriers of departments and disciplines.”
Currently many senior scientists who encountered Dr. Tomkins at his NIH laboratory or at UCSF testify readily on his influence in their early careers. See for example, B. N. Ames, “Gordon M. Tomkins (1926-1975)”, Biochemical Actions of Hormones (Litwack, G., ed) vol. IV, New York, N.Y.: Academic Press, 1977). His peers recall that he “tackled fundamental questions….by temperament and conscious esthetic choice, Gordon sought an underlying simplicity and unity in biology…the holistic bent of Gordon’s imagination led naturally to exploration of analogies between prokaryotic systems and more complex animal cells.”


Welcome to IoT-498!
List of Device Simulations
Add a Device Simulation
Device ID
Frequency (Hz)
Min Value
Max Value
Steps
Add


Ctrl-Enter creates a new slide
Pressing delete or backspace in an empty slide deletes it
When you're done, close the editor and save this page


Events Demo
The main program emits an event every 10s which will be displayed in the section below. To send an event from this window, click here:
Send Event


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WHO WE ARE
Maestranza AceroMas's mission is to be a relevant supplier, providing steel structure manufacturing services for the national and international market, delivering high-quality technical solutions, using state-of-the-art technology in manufacturing, with qualified personnel and with the support of tools as TEKLA, for the correct development of manufacturing plans. This vision allows us to become a provider that is committed to delivering its services responsibly and professionally to its clients, with the aim of fully meeting their objectives.
AceroMas is a factory belonging to the Collins Group, with companies in different fields and a leader in the national and international metalworking industry, with a history of more than 80 years.


Please, be informed, that up-to-dated demo files are still on preparation process.
Versions described below are very old.
Please, check demo pages:
Simple demo with .contains (true / false) tests
[window output]
Simple demo with .contains (true / false) tests
[console output]
Press
CTRL + ALT + U
after tests are finished and you will see your results in 'window' output.


Congratulations!
You are currently running an app built for the IBM Cloud.
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See: Description
| Package | Description |
|---|---|
| mondrian.calc |
Provides compiled expressions.
|
| mondrian.calc.impl |
Provides implementation classes for compiled expressions.
|
| mondrian.gui |
Contains a workbench written in Swing for designing Mondrian schemas.
|
| mondrian.gui.validate | |
| mondrian.gui.validate.impl | |
| mondrian.i18n |
Utilities for internationalization and localization.
|
| mondrian.mdx |
Defines a parse tree for MDX expressions.
|
| mondrian.olap |
Mondrian's core package, this defines connections and the catalog metamodel,
and allows you to execute queries.
|
| mondrian.olap.fun |
Defines the set of MDX built-in functions.
|
| mondrian.olap.fun.extra |
Defines MDX extension functions.
|
| mondrian.olap.fun.vba |
Implements the set of functions defined by the Visual Basic for Applications
(VBA) specification.
|
| mondrian.olap.type |
Type system for MDX expessions.
|
| mondrian.olap4j |
olap4j driver for the Mondrian OLAP engine.
|
| mondrian.parser | |
| mondrian.recorder |
Provides a set a classes for logging the process of a task.
|
| mondrian.resource | |
| mondrian.rolap |
Implements the data access layer for the olap package.
|
| mondrian.rolap.agg |
Manages a cache of aggregates containing cell values.
|
| mondrian.rolap.aggmatcher |
Defines a 'matcher' which scans the schema to find candidate aggregate tables.
|
| mondrian.rolap.cache |
Provides primitives for policy-based caching.
|
| mondrian.rolap.format | |
| mondrian.rolap.sql |
Database-independent library for generating SQL.
|
| mondrian.server |
Augments the Mondrian engine with server functionality.
|
| mondrian.server.monitor | |
| mondrian.spi |
Contains the server-provider interfaces (SPIs) which user-defined
extensions to Mondrian should implement.
|
| mondrian.spi.impl |
Provides implementations of SPIs defined in the
mondrian.spi package. |
| mondrian.test |
Suite of regression tests.
|
| mondrian.test.build |
Regression tests relating to the build process.
|
| mondrian.test.clearview | |
| mondrian.test.comp |
Utilities for comparison-based testing.
|
| mondrian.test.loader |
Utilities to load test data.
|
| mondrian.tui |
Text user interface for Mondrian.
|
| mondrian.udf | |
| mondrian.util |
Mondrian utilities.
|
| mondrian.web.servlet | |
| mondrian.web.taglib |
Provides a tag library for embedding MDX queries in JSP pages.
|
| mondrian.xmla |
Implements the XML for Analysis API.
|
| mondrian.xmla.impl | |
| mondrian.xmla.test |


|
STM8S Standard Peripheral Libary
2.2.0
|
Functions | |
| void | EXTI_DeInit (void) |
| Deinitializes the external interrupt control registers to their default reset value. More... | |
| void | EXTI_SetExtIntSensitivity (EXTI_Port_TypeDef Port, EXTI_Sensitivity_TypeDef SensitivityValue) |
| Set the external interrupt sensitivity of the selected port. More... | |
| void | EXTI_SetTLISensitivity (EXTI_TLISensitivity_TypeDef SensitivityValue) |
| Set the TLI interrupt sensitivity. More... | |
| EXTI_Sensitivity_TypeDef | EXTI_GetExtIntSensitivity (EXTI_Port_TypeDef Port) |
| Get the external interrupt sensitivity of the selected port. More... | |
| EXTI_TLISensitivity_TypeDef | EXTI_GetTLISensitivity (void) |
| Get the TLI interrupt sensitivity. More... | |
|
inline |
Deinitializes the external interrupt control registers to their default reset value.
| None |
| None |
Definition at line 157 of file stm8s_exti.h.
|
inline |
Get the external interrupt sensitivity of the selected port.
| Port | The port number to access. |
| EXTI_Sensitivity_TypeDef | The external interrupt sensitivity of the selected port. |
Definition at line 229 of file stm8s_exti.h.
|
inline |
Get the TLI interrupt sensitivity.
| None |
| EXTI_TLISensitivity_TypeDef | The TLI interrupt sensitivity read. |
Definition at line 264 of file stm8s_exti.h.
|
inline |
Set the external interrupt sensitivity of the selected port.
| Port | The port number to access. |
| SensitivityValue | The external interrupt sensitivity value to set. |
| None |
Definition at line 174 of file stm8s_exti.h.
|
inline |
Set the TLI interrupt sensitivity.
| SensitivityValue | The TLI interrupt sensitivity value. |
| None |
Definition at line 214 of file stm8s_exti.h.


Postgres.types is a psuedo-module used to facilite import statements so that data types in the pg_catalog schema can be easily referenced from Python code:
from Postgres.types import bytea, circle
This chapter discusses some of the interfaces provided on those data types.
The Postgres.Type for the pg_catalog.date data type:
from Postgres.types import date
d = date('2000-01-01')
assert d.year == 2000
assert d.month == 1
assert d.day == 1
Properties:
- date.millennium
- The ‘millennium’ part as a Python int.
- date.century
- The ‘century’ part as a Python int.
- date.decade
- The ‘decade’ part as a Python int.
- date.year
- The ‘year’ part as a Python int.
- date.quarter
- The ‘quarter’ part as a Python int.
- date.week
- The ‘week’ part as a Python int.
- date.month
- The ‘month’ part as a Python int.
- date.day
- The ‘day’ part as a Python int.
- date.dayofyear
- The ‘doy’ part as a Python int.
- date.dayofweek
- The ‘dow’ part as a Python int.
- date.epoch
- The ‘epoch’ part as a Python float.
The Postgres.Type for the pg_catalog.interval data type:
from Postgres.types import interval
i = interval('10 years 1 months 2 days 3 hours 4 minutes 5 seconds')
assert i.year == 10
assert i.month == 1
assert i.day == 2
assert i.hour == 3
assert i.minute == 4
assert i.second == 5
Properties:
- millennium
- The ‘millennium’ part as a Python int.
- century
- The ‘century’ part as a Python int.
- decade
- The ‘decade’ part as a Python int.
- year
- The ‘year’ part as a Python int.
- quarter
- The ‘quarter’ part as a Python int.
- week
- The ‘week’ part as a Python int.
- month
- The ‘month’ part as a Python int.
- day
- The ‘day’ part as a Python int.
- hour
- The ‘hour’ part as a Python int.
- minute
- The ‘minute’ part as a Python int.
- second
- The ‘second’ part as a Python int.
- millisecond
- The ‘millisecond’ part as a Python int.
- microsecond
- The ‘microsecond’ part as a Python int.
- timezone
- The ‘timezone’ part as a Python int.
- epoch
- The ‘epoch’ part as a Python float.
The Postgres.Type instance for the pg_catalog.record data type. This type is used as the abstract base type for all composite types.
Subclasses of this type are used to represent table types. These types are used by Trigger Returning Functions in order to represent the data given to the row-level entry points.
Constructors:
- record(sequence)
- Create an instance of the record using the given sequence. The length of the sequence must be equal to the number of attributes in the record.
- record(dictionary)
- Create an instance of the record using the given dictionary. The keys of the dictionary must map to an attribute name. Any unspecified attributes will be initialized to NULL.
- record(str)
- Create an instance of the record using a string. This string is given to the record’s type input function.
Only subclasses Postgres.types.record can be directly instantiated.
Properties:
- record.column_types
A tuple of Postgres.Type instances. The index of each item corresponds to the column type.
This property is available on record subclasses and instances thereof.
- record.column_names
A tuple of strings naming the columns of the composite.
This property is available on record subclasses and instances thereof.
- record.pg_column_types
A tuple of type Oid’s specifying the column types.
This property is available on record subclasses and instances thereof.
- record.descriptor
A Postgres.TupleDesc object describing the composite.
This property is available on record subclasses and instances thereof.
Methods:
- record.replace(*args, **kw)
Replace the attributes of the record instance using the arguments. A new instance will be returned.
If positional arguments are given, the index of each argument will correspond with the attribute that will be replaced using the argument.
If keyword arguments are given, each keyword corresponds to the attribute that will be replaced using the attribute’s name:
BEGIN; CREATE TYPE py_test_ctyp AS (i int, t text, f float); CREATE FUNCTION modify_fields(py_test_ctyp) RETURNS py_test_ctyp LANGUAGE python AS $python$ def main(x): return x.replace(None, 'text', f = 1.423) $python$; SELECT modify_fields(ROW(100, 'not-this-text', NULL)::py_test_ctyp); -- modify_fields ----------------- -- (,text,1.423) ABORT;- record.transform(*args, **kw)
Transform the attributes of the record instance using the callable arguments. A new instance will be returned. Similar to replace, but arguments must be callables that can take a single parameter.
If positional arguments are given, the index of each argument will correspond with the attribute that will be transformed using the callable argument. If the argument is None, the attribute will not be changed.
If keyword arguments are given, each keyword corresponds to the attribute that will be transformed using the callable:
BEGIN; CREATE TYPE py_test_ctyp2 AS (i int, t text, f float); CREATE FUNCTION transform_fields(py_test_ctyp2) RETURNS py_test_ctyp2 LANGUAGE python AS $python$ def main(x): return x.transform(None, lambda x: x + ' ! appended text', f = lambda x: x / 2.0) $python$; SELECT transform_fields(ROW(200, 'text', 27)::py_test_ctyp2); -- transform_fields ------------------------------------- -- (200,"text ! appended text",13.5) ABORT;- record.keys()
- Returns the column_names tuple.
- record.values()
- Returns the record object.
- record.items()
Returns iterator producing tuples whose first item is the attribute name, and whose second item the associated value.
The iterator produces the pairs using the index order of the attributes.
- record.__getitem__(idx_or_slice), record[idx_or_slice]
Return the attributes in the record associated with index or slice. If a slice is requested, a Python tuple of Postgres.Object instances will be returned.
Attributes can be referenced by name or index as records are both a sequence and a mapping.
- record.__len__(), len(record)
- Return the number of attributes in the record.
The Postgres.Type for the pg_catalog.time data type:
from Postgres.types import time
t = time('12:00:00')
assert t.hour == 12
assert t.minute == 0
assert t.second == 0
The Postgres.Type for the pg_catalog.timestamp data type:
from Postgres.types import timestamp
ts = timestamp('2000-01-01 12:00:00')
assert ts.year == 2000
assert ts.month == 1
assert ts.day == 1
assert ts.hour == 12
assert ts.minute == 0
assert ts.second == 0
Properties:
- millennium
- The ‘millennium’ part as a Python int.
- century
- The ‘century’ part as a Python int.
- decade
- The ‘decade’ part as a Python int.
- year
- The ‘year’ part as a Python int.
- quarter
- The ‘quarter’ part as a Python int.
- week
- The ‘week’ part as a Python int.
- month
- The ‘month’ part as a Python int.
- day
- The ‘day’ part as a Python int.
- dayofyear
- The ‘doy’ part as a Python int.
- dayofweek
- The ‘dow’ part as a Python int.
- hour
- The ‘hour’ part as a Python int.
- minute
- The ‘minute’ part as a Python int.
- second
- The ‘second’ part as a Python int.
- millisecond
- The ‘millisecond’ part as a Python int.
- microsecond
- The ‘microsecond’ part as a Python int.
- epoch
- The ‘epoch’ part as a Python float.
The Postgres.Type for the pg_catalog.timetz data type:
from Postgres.types import timetz
t = time('12:00:00+00')
assert t.hour == 12
assert t.minute == 0
assert t.second == 0
assert t.timezone == 0
Properties:
- hour
- The ‘hour’ part as a Python int.
- minute
- The ‘minute’ part as a Python int.
- second
- The ‘second’ part as a Python int.
- millisecond
- The ‘millisecond’ part as a Python int.
- microsecond
- The ‘microsecond’ part as a Python int.
- epoch
- The ‘epoch’ part as a Python float.
- timezone
- The ‘timezone’ part as a Python int.
The Postgres.Type for the pg_catalog.timestamptz data type:
from Postgres.types import timestamptz
ts = timestamptz('2000-01-01 12:00:00')
assert ts.year == 2000
assert ts.month == 1
assert ts.day == 1
assert ts.hour == 12
assert ts.minute == 0
assert ts.second == 0
Properties:
- timestamptz.millennium
- The ‘millennium’ part as a Python int.
- timestamptz.century
- The ‘century’ part as a Python int.
- timestamptz.decade
- The ‘decade’ part as a Python int.
- timestamptz.year
- The ‘year’ part as a Python int.
- timestamptz.quarter
- The ‘quarter’ part as a Python int.
- timestamptz.week
- The ‘week’ part as a Python int.
- timestamptz.month
- The ‘month’ part as a Python int.
- timestamptz.day
- The ‘day’ part as a Python int.
- timestamptz.dayofyear
- The ‘doy’ part as a Python int.
- timestamptz.dayofweek
- The ‘dow’ part as a Python int.
- timestamptz.hour
- The ‘hour’ part as a Python int.
- timestamptz.minute
- The ‘minute’ part as a Python int.
- timestamptz.second
- The ‘second’ part as a Python int.
- timestamptz.millisecond
- The ‘millisecond’ part as a Python int.
- timestamptz.microsecond
- The ‘microsecond’ part as a Python int.
- timestamptz.timezone
- The ‘timezone’ part as a Python int.
- timestamptz.epoch
- The ‘epoch’ part as a Python float.
The Postgres.Type for the pg_catalog.xid data type:
from Postgres.types import xid
x = xid.current()
Constructors:
- xid.current()
- Create a new instance using the current transaction identifier(GetCurrentTransactionId).
- xid.current_sub()
- Create a new instance using the current subtransaction identifier(GetCurrentSubTransactionId).


The first thing to consider is the shape of the wavelet. For decomposing the NINO3 SST data, we chose the Morlet wavelet because:
      For the Morlet wavelet transform, where the mother wavelet is:
we must first choose the wavenumber w0, which gives the number of oscillations within the wavelet itself. One condition of the wavelet transform is that the average of the wavelet itself must be zero. In practice, if we choose w0=6, then the errors due to non-zero mean are smaller than the typical computer round-off errors (Farge 1992).
      Our next choice is a set of scaling parameters s,
such that we adequately sample all the frequencies present in our
time series. We first choose the smallest resolvable scale,
s0, as some multiple of our time resolution, dt.
For the NINO3 SST data, we have seasonal data, thus dt = 0.25 years.
The smallest wavelet we could possibly resolve is 2dt, thus
we choose s0 = 2dt = 0.5 years.

The largest scale chosen should be less than 1/2 the length of the entire time series. The choice of scales for the NINO3 SST data is shown on the right-hand axis of Figure 3. In theory, if these scales are chosen wisely, then one can construct an orthogonal complete basis set. In reality, one usually over-samples the scales so as to provide information on freqencies in between the orthogonal scales.
To use this formula, the FT of the wavelet function should be known analytically.
Unlike the convolution, the FFT method allows the computation of all n points simultaneously, and can be efficiently coded using any standard FFT package.
      The steps to compute the wavelet transform for a time series are thus:
      One problem with performing the wavelet transform in Fourier space is that this assumes the time series is periodic. The result is that signals in the wavelet transform at one end of the time series will get wrapped around to the other end. This effect is more pronouced at larger scales as the influence of each wavelet extends further in time. One way to avoid this is to pad one end of the time series with zeroes. A clever method is to pad with enough zeroes to make the length of the time series equal to a power of two, and thereby speed up the FFT as well.